From 1fe3fc483721d6d63cd30fb42974f71339226412 Mon Sep 17 00:00:00 2001 From: perib Date: Fri, 20 Sep 2024 14:48:56 -0700 Subject: [PATCH 01/44] edits --- Tutorial/1_Estimators_Overview.ipynb | 1911 ----------------- Tutorial/1_Using_TPOT.ipynb | 599 ++++++ tpot2/config/classifiers.py | 3 +- tpot2/config/regressors.py | 4 +- tpot2/config/template_search_spaces.py | 83 +- tpot2/objectives/average_path_length.py | 9 + tpot2/objectives/complexity.py | 16 +- tpot2/objectives/number_of_leaves.py | 10 +- tpot2/objectives/number_of_nodes.py | 9 + tpot2/tpot_estimator/estimator.py | 2 +- .../tpot_estimator/templates/tpottemplates.py | 8 +- 11 files changed, 730 insertions(+), 1924 deletions(-) delete mode 100644 Tutorial/1_Estimators_Overview.ipynb create mode 100644 Tutorial/1_Using_TPOT.ipynb diff --git a/Tutorial/1_Estimators_Overview.ipynb b/Tutorial/1_Estimators_Overview.ipynb deleted file mode 100644 index cae78a96..00000000 --- a/Tutorial/1_Estimators_Overview.ipynb +++ /dev/null @@ -1,1911 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Overview\n", - "\n", - "There are two evolutionary algorithms built into TPOT2, which corresponds to two different estimator classes.\n", - "\n", - "1. The `tpot2.TPOTEstimator` uses a standard evolutionary algorithm that evaluates exactly population_size individuals each generation. This is similar to the algorithm in TPOT1. The next generation does not start until the previous is completely finished evaluating. This leads to underutilized CPU time as the cores are waiting for the last individuals to finish training, but may preserve diversity in the population. \n", - "\n", - "2. The `tpot2.TPOTEstimatorSteadyState` differs in that it will generate and evaluate the next individual as soon as an individual finishes evaluation. The number of individuals being evaluated is determined by the n_jobs parameter. There is no longer a concept of generations. The population_size parameter now refers to the size of the list of evaluated parents. When an individual is evaluated, the selection method updates the list of parents. This allows more efficient utilization when using multiple cores.\n", - "\n", - "\n", - "Additionally, two other simplified estimators are provided. These have a simplified set of hyperparameters with default values set for classification and regression problems. Currently, both of these use the standard evolutionary algorithm in the `tpot2.TPOTEstimator` class.\n", - "\n", - "1. `tpot2.TPOTClassifier` for classification tasks\n", - "2. `tpot2.TPOTRegressor` for regression tasks" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Scorers, Objective Functions, and multi objective optimization.\n", - "\n", - "There are two ways of passing objectives into TPOT2. \n", - "\n", - "1. `scorers`: Scorers are functions that have the signature (estimator, X, y). These can be produced with the [sklearn.metrics.make_scorer](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html) function. This function is used to evaluate the test folds during cross validation. These are passed into TPOT2 via the scorers parameter. This can take in the scorer itself or the string corresponding to a scoring function ([as listed here](https://scikit-learn.org/stable/modules/model_evaluation.html)). TPOT2 also supports passing in a list of several scorers for multiobjective optimization. \n", - "\n", - "2. `other_objective_functions` : Other objective functions in TPOT2 have the signature (estimator) and returns a float or list of floats. These get passed an unfitted estimator (in the case of TPOT2, a `tpot2.GraphPipeline`). \n", - "\n", - "\n", - "Each scorer and objective function must be accompanied by a list of weights corresponding to the list of objectives. By default, TPOT2 maximizes objective functions (this can be changed by `bigger_is_better=False`). Positive weights means that TPOT2 will seek to maximize that objective, and negative weights correspond to minimization.\n", - "\n", - "Here is an example of using two scorers\n", - "\n", - " scorers=['roc_auc_ovr',tpot2.objectives.complexity_scorer],\n", - " scorers_weights=[1,-1],\n", - "\n", - "\n", - "Here is an example with a scorer and a secondary objective function\n", - "\n", - " scorers=['roc_auc_ovr'],\n", - " scorers_weights=[1],\n", - " other_objective_functions=[tpot2.objectives.number_of_leaves_objective],\n", - " other_objective_functions_weights=[-1]," - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Generation: 0%| | 0/1 [00:00 \n", - " Pipeline has none of the following attributes: predict_proba. \n", - " Traceback (most recent call last):\n", - " File \"/home/ribeirop/common/Projects/TPOT_Dev/tpot2/tpot2/utils/eval_utils.py\", line 53, in objective_nan_wrapper\n", - " value = func_timeout.func_timeout(timeout, objective_function, args=[individual], kwargs=objective_kwargs)\n", - " File \"/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/func_timeout/dafunc.py\", line 108, in func_timeout\n", - " raise_exception(exception)\n", - " File \"/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/func_timeout/py3_raise.py\", line 7, in raise_exception\n", - " raise exception[0] from None\n", - " File \"/home/ribeirop/common/Projects/TPOT_Dev/tpot2/tpot2/tpot_estimator/estimator.py\", line 620, in objective_function\n", - " return objective_function_generator(\n", - " File \"/home/ribeirop/common/Projects/TPOT_Dev/tpot2/tpot2/tpot_estimator/estimator_utils.py\", line 55, in objective_function_generator\n", - " cv_obj_scores = cross_val_score_objective(sklearn.base.clone(pipeline),x,y,scorers=scorers, cv=cv , fold=step)\n", - " File \"/home/ribeirop/common/Projects/TPOT_Dev/tpot2/tpot2/tpot_estimator/cross_val_utils.py\", line 31, in cross_val_score_objective\n", - " this_fold_scores = [sklearn.metrics.get_scorer(scorer)(this_fold_pipeline, X_test, y_test) for scorer in scorers]\n", - " File \"/home/ribeirop/common/Projects/TPOT_Dev/tpot2/tpot2/tpot_estimator/cross_val_utils.py\", line 31, in \n", - " this_fold_scores = [sklearn.metrics.get_scorer(scorer)(this_fold_pipeline, X_test, y_test) for scorer in scorers]\n", - " File \"/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 253, in __call__\n", - " return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)\n", - " File \"/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 344, in _score\n", - " response_method = _check_response_method(estimator, self._response_method)\n", - " File \"/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/utils/validation.py\", line 2106, in _check_response_method\n", - " raise AttributeError(\n", - "AttributeError: Pipeline has none of the following attributes: predict_proba.\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Generation: 100%|██████████| 1/1 [00:07<00:00, 7.82s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Generation: 1\n", - "Best roc_auc_score score: 0.9938492063492064\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "2024-06-28 17:22:24,449 - distributed.scheduler - ERROR - Removing worker 'tcp://127.0.0.1:33053' caused the cluster to lose scattered data, which can't be recovered: {'ndarray-71df36028cf839ff98696c18d6668a27', 'ndarray-809a54d2fd885201030a189763e7bd92'} (stimulus_id='handle-worker-cleanup-1719620544.4491522')\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.0\n" - ] - } - ], - "source": [ - "import tpot2\n", - "import sklearn\n", - "import sklearn.datasets\n", - "\n", - "scorer = sklearn.metrics.get_scorer('roc_auc_ovo')\n", - "X, y = sklearn.datasets.load_iris(return_X_y=True)\n", - "X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, train_size=0.75, test_size=0.25)\n", - "\n", - "\n", - "est = tpot2.TPOTClassifier(n_jobs=40, max_time_mins=30, verbose=5, generations=1, population_size=5)\n", - "est.fit(X_train, y_train)\n", - "\n", - "\n", - "print(scorer(est, X_test, y_test))" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Pipeline(steps=[('robustscaler',\n",
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-       "                ('passthrough', Passthrough()),\n",
-       "                ('featureunion-1',\n",
-       "                 FeatureUnion(transformer_list=[('skiptransformer',\n",
-       "                                                 SkipTransformer()),\n",
-       "                                                ('passthrough',\n",
-       "                                                 Passthrough())])),\n",
-       "                ('featureunion-2',\n",
-       "                 FeatureUnion(transformer_list=[('skiptransformer',\n",
-       "                                                 SkipTransformer()),\n",
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In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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" - ], - "text/plain": [ - "Pipeline(steps=[('robustscaler',\n", - " RobustScaler(quantile_range=(0.16675428907107737,\n", - " 0.7012433303146526))),\n", - " ('passthrough', Passthrough()),\n", - " ('featureunion-1',\n", - " FeatureUnion(transformer_list=[('skiptransformer',\n", - " SkipTransformer()),\n", - " ('passthrough',\n", - " Passthrough())])),\n", - " ('featureunion-2',\n", - " FeatureUnion(transformer_list=[('skiptransformer',\n", - " SkipTransformer()),\n", - " ('passthrough',\n", - " Passthrough())])),\n", - " ('bernoullinb',\n", - " BernoulliNB(alpha=0.7637690262115946, fit_prior=False))])" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est._evolver_instance.population.evaluated_individuals.iloc[0]['Individual'].export_pipeline()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Generation: : 1it [00:35, 35.93s/it]\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py:102: UserWarning: Only one sample available. You may want to reshape your data array\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-5421.324324324324\n" - ] - } - ], - "source": [ - "import tpot2\n", - "import sklearn\n", - "import sklearn.metrics\n", - "import sklearn.datasets\n", - "\n", - "scorer = sklearn.metrics.get_scorer('neg_mean_squared_error')\n", - "X, y = sklearn.datasets.load_diabetes(return_X_y=True)\n", - "X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, train_size=0.75, test_size=0.25)\n", - "\n", - "est = tpot2.tpot_estimator.templates.TPOTRegressor(n_jobs=4, max_time_mins=30, verbose=2, cv=5)\n", - "est.fit(X_train, y_train)\n", - "\n", - "print(scorer(est, X_test, y_test))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Best Practices\n", - "\n", - "When running tpot from an .py script, it is important to protect code with `if __name__==\"__main__\":`" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Generation: : 1it [01:05, 65.90s/it]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.9994639357871052\n" - ] - } - ], - "source": [ - "#my_analysis.py\n", - "\n", - "from dask.distributed import Client, LocalCluster\n", - "import tpot2\n", - "import sklearn\n", - "import sklearn.datasets\n", - "import numpy as np\n", - "\n", - "if __name__==\"__main__\":\n", - " scorer = sklearn.metrics.get_scorer('roc_auc_ovo')\n", - " X, y = sklearn.datasets.load_digits(return_X_y=True)\n", - " X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, train_size=0.75, test_size=0.25)\n", - "\n", - "\n", - " est = tpot2.TPOTClassifier(n_jobs=4, max_time_mins=60, verbose=2)\n", - " est.fit(X_train, y_train)\n", - "\n", - "\n", - " print(scorer(est, X_test, y_test))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Common parameters\n", - "\n", - " scorers : (list, scorer)\n", - " A scorer or list of scorers to be used in the cross-validation process. \n", - " see https://scikit-learn.org/stable/modules/model_evaluation.html\n", - " \n", - " scorers_weights : list\n", - " A list of weights to be applied to the scorers during the optimization process.\n", - " \n", - " classification : bool\n", - " If True, the problem is treated as a classification problem. If False, the problem is treated as a regression problem.\n", - " Used to determine the CV strategy.\n", - " \n", - " cv : int, cross-validator\n", - " - (int): Number of folds to use in the cross-validation process. By uses the sklearn.model_selection.KFold cross-validator for regression and StratifiedKFold for classification. In both cases, shuffled is set to True.\n", - " - (sklearn.model_selection.BaseCrossValidator): A cross-validator to use in the cross-validation process.\n", - " - max_depth (int): The maximum depth from any node to the root of the pipelines to be generated.\n", - " \n", - " other_objective_functions : list, default=[tpot2.objectives.estimator_objective_functions.average_path_length_objective]\n", - " A list of other objective functions to apply to the pipeline.\n", - " \n", - " other_objective_functions_weights : list, default=[-1]\n", - " A list of weights to be applied to the other objective functions.\n", - " \n", - " objective_function_names : list, default=None\n", - " A list of names to be applied to the objective functions. If None, will use the names of the objective functions.\n", - " \n", - " bigger_is_better : bool, default=True\n", - " If True, the objective function is maximized. If False, the objective function is minimized. Use negative weights to reverse the direction.\n", - " \n", - " generations : int, default=50\n", - " Number of generations to run\n", - " \n", - " max_time_mins : float, default=float(\"inf\")\n", - " Maximum time to run the optimization. If none or inf, will run until the end of the generations.\n", - " \n", - " max_eval_time_mins : float, default=60*5\n", - " Maximum time to evaluate a single individual. If none or inf, there will be no time limit per evaluation.\n", - "\n", - " n_jobs : int, default=1\n", - " Number of processes to run in parallel.\n", - " \n", - " memory_limit : str, default=\"4GB\"\n", - " Memory limit for each job. See Dask [LocalCluster documentation](https://distributed.dask.org/en/stable/api.html#distributed.Client) for more information.\n", - "\n", - " \n", - " verbose : int, default=1 \n", - " How much information to print during the optimization process. Higher values include the information from lower values.\n", - " 0. nothing\n", - " 1. progress bar\n", - " \n", - " 3. best individual\n", - " 4. warnings\n", - " >=5. full warnings trace\n", - " 6. evaluations progress bar. (Temporary: This used to be 2. Currently, using evaluation progress bar may prevent some instances were we terminate a generation early due to it reaching max_time_mins in the middle of a generation OR a pipeline failed to be terminated normally and we need to manually terminate it.)\n", - " \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# TPOTEstimator and TPOTEstimatorSteadyState\n", - "\n", - "TPOTEstimator and TPOTEstimatorSteadyState expose more parameters for customizing search spaces and evolutionary algorithms. The next tutorial will cover customizing search spaces in more detail.\n", - "\n", - "The TPOTClassifier and TPOTRegressor set default parameters for the TPOTEstimator for Classification and Regression.\n", - "In the future, a metalearner will be used to predict the best values for a given dataset." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### tpot2.TPOTEstimatorSteadyState" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Evaluations: : 77it [00:30, 2.54it/s]\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.0\n" - ] - } - ], - "source": [ - "import tpot2\n", - "import sklearn\n", - "import sklearn.datasets\n", - "\n", - "\n", - "graph_search_space = tpot2.search_spaces.pipelines.GraphPipeline(\n", - " root_search_space= tpot2.config.get_search_space([\"KNeighborsClassifier\", \"LogisticRegression\", \"DecisionTreeClassifier\"]),\n", - " leaf_search_space = tpot2.config.get_search_space(\"selectors\"), \n", - " inner_search_space = tpot2.config.get_search_space([\"transformers\"]),\n", - " max_size = 10,\n", - ")\n", - "\n", - "est = tpot2.TPOTEstimatorSteadyState( \n", - " search_space = graph_search_space,\n", - " scorers=['roc_auc_ovr'], #scorers can be a list of strings or a list of scorers. These get evaluated during cross validation. \n", - " scorers_weights=[1],\n", - "\n", - " classification=True,\n", - "\n", - " max_eval_time_mins=15,\n", - " max_time_mins=30,\n", - " verbose=2)\n", - "\n", - "\n", - "scorer = sklearn.metrics.get_scorer('roc_auc_ovo')\n", - "X, y = sklearn.datasets.load_iris(return_X_y=True)\n", - "X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, train_size=0.75, test_size=0.25)\n", - "est.fit(X_train, y_train)\n", - "print(scorer(est, X_test, y_test))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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\n", - "" - ], - "text/plain": [ - " roc_auc_score complexity_scorer Parents Variation_Function \\\n", - "3 0.985615 4.0 NaN NaN \n", - "14 0.997540 8.0 NaN NaN \n", - "24 1.000000 23.0 NaN NaN \n", - "25 0.997619 17.4 NaN NaN \n", - "42 0.990556 6.0 NaN NaN \n", - "44 0.993313 7.4 NaN NaN \n", - "63 0.965675 1.0 (9, 42) ind_crossover \n", - "\n", - " Individual Submitted Timestamp \\\n", - "3 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pareto_front = est.pareto_front\n", - "\n", - "#plot the pareto front of number_of_leaves_objective vs roc_auc_score\n", - "\n", - "import matplotlib.pyplot as plt\n", - "plt.scatter(pareto_front['complexity_scorer'], pareto_front['roc_auc_score'])\n", - "plt.xlabel('complexity_scorer')\n", - "plt.ylabel('roc_auc_score')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### tpot2.TPOTEstimator" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Generation: 100%|██████████| 5/5 [01:12<00:00, 14.45s/it]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.9971509971509972\n" - ] - } - ], - "source": [ - "import tpot2\n", - "import sklearn\n", - "import sklearn.datasets\n", - "\n", - "est = tpot2.TPOTEstimator( \n", - " search_space = graph_search_space,\n", - " population_size=30,\n", - " generations=5,\n", - " scorers=['roc_auc_ovr'], #scorers can be a list of strings or a list of scorers. These get evaluated during cross validation. \n", - " scorers_weights=[1],\n", - " classification=True,\n", - " n_jobs=1, \n", - " early_stop=5, #how many generations with no improvement to stop after\n", - " \n", - " #List of other objective functions. All objective functions take in an untrained GraphPipeline and return a score or a list of scores\n", - " other_objective_functions= [ ],\n", - " \n", - " #List of weights for the other objective functions. Must be the same length as other_objective_functions. By default, bigger is better is set to True. \n", - " other_objective_functions_weights=[],\n", - " verbose=2)\n", - "\n", - "scorer = sklearn.metrics.get_scorer('roc_auc_ovo')\n", - "X, y = sklearn.datasets.load_iris(return_X_y=True)\n", - "X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, train_size=0.75, test_size=0.25)\n", - "est.fit(X_train, y_train)\n", - "print(scorer(est, X_test, y_test))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "tpot_dev", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.14" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "7fe1fe9ef32cd5efd76326a08046147513534f0dd2318301a1a96ae9071c1c4e" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Tutorial/1_Using_TPOT.ipynb b/Tutorial/1_Using_TPOT.ipynb new file mode 100644 index 00000000..b4f18627 --- /dev/null +++ b/Tutorial/1_Using_TPOT.ipynb @@ -0,0 +1,599 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# What to expect from AutoML software\n", + "Automated machine learning (AutoML) takes a higher-level approach to machine learning than most practitioners are used to, so we've gathered a handful of guidelines on what to expect when running AutoML software such as TPOT.\n", + "\n", + "#### AUTOML ALGORITHMS AREN'T INTENDED TO RUN FOR ONLY A FEW MINUTES\n", + "Of course, you can run TPOT for only a few minutes and it will find a reasonably good pipeline for your dataset. However, if you don't run TPOT for long enough, it may not find the best possible pipeline for your dataset. It may even not find any suitable pipeline at all, in which case a RuntimeError('A pipeline has not yet been optimized. Please call fit() first.') will be raised. Often it is worthwhile to run multiple instances of TPOT in parallel for a long time (hours to days) to allow TPOT to thoroughly search the pipeline space for your dataset.\n", + "\n", + "#### AUTOML ALGORITHMS CAN TAKE A LONG TIME TO FINISH THEIR SEARCH\n", + "AutoML algorithms aren't as simple as fitting one model on the dataset; they are considering multiple machine learning algorithms (random forests, linear models, SVMs, etc.) in a pipeline with multiple preprocessing steps (missing value imputation, scaling, PCA, feature selection, etc.), the hyperparameters for all of the models and preprocessing steps, as well as multiple ways to ensemble or stack the algorithms within the pipeline.\n", + "\n", + "As such, TPOT will take a while to run on larger datasets, but it's important to realize why. With the default TPOT settings (100 generations with 100 population size), TPOT will evaluate 10,000 pipeline configurations before finishing. To put this number into context, think about a grid search of 10,000 hyperparameter combinations for a machine learning algorithm and how long that grid search will take. That is 10,000 model configurations to evaluate with 10-fold cross-validation, which means that roughly 100,000 models are fit and evaluated on the training data in one grid search. That's a time-consuming procedure, even for simpler models like decision trees.\n", + "\n", + "Typical TPOT runs will take hours to days to finish (unless it's a small dataset), but you can always interrupt the run partway through and see the best results so far. TPOT also provides a warm_start parameter that lets you restart a TPOT run from where it left off.\n", + "\n", + "#### AUTOML ALGORITHMS CAN RECOMMEND DIFFERENT SOLUTIONS FOR THE SAME DATASET\n", + "If you're working with a reasonably complex dataset or run TPOT for a short amount of time, different TPOT runs may result in different pipeline recommendations. TPOT's optimization algorithm is stochastic in nature, which means that it uses randomness (in part) to search the possible pipeline space. When two TPOT runs recommend different pipelines, this means that the TPOT runs didn't converge due to lack of time or that multiple pipelines perform more-or-less the same on your dataset.\n", + "\n", + "This is actually an advantage over fixed grid search techniques: TPOT is meant to be an assistant that gives you ideas on how to solve a particular machine learning problem by exploring pipeline configurations that you might have never considered, then leaves the fine-tuning to more constrained parameter tuning techniques such as grid search." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TPOT with code\n", + "\n", + "We've taken care to design the TPOT interface to be as similar as possible to scikit-learn.\n", + "\n", + "TPOT can be imported just like any regular Python module. To import TPOT, type:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from tpot2 import TPOTClassifier" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "then create an instance of TPOT as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "classification_optimizer = TPOTClassifier()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It's also possible to use TPOT for regression problems with the TPOTRegressor class. Other than the class name, a TPOTRegressor is used the same way as a TPOTClassifier. You can read more about the TPOTClassifier and TPOTRegressor classes in the API documentation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from tpot2 import TPOTRegressor\n", + "regression_optimizer = TPOTRegressor()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fitting a TPOT model works exactly like any other sklearn estimator. Some example code with custom TPOT parameters might look like:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generation: : 3it [00:33, 11.04s/it]\n", + "/home/perib/miniconda3/envs/myenv/lib/python3.10/site-packages/sklearn/preprocessing/_data.py:2762: UserWarning: n_quantiles (1895) is greater than the total number of samples (455). n_quantiles is set to n_samples.\n", + " warnings.warn(\n", + "/home/perib/miniconda3/envs/myenv/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "auroc_score: 0.9904100529100529\n" + ] + } + ], + "source": [ + "import sklearn\n", + "import sklearn.datasets\n", + "import sklearn.metrics\n", + "\n", + "classification_optimizer = TPOTClassifier(search_space=\"light\", max_time_mins=30/60, n_jobs=30, cv=5)\n", + "\n", + "X, y = sklearn.datasets.load_breast_cancer(return_X_y=True)\n", + "X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, random_state=1, test_size=0.2)\n", + "\n", + "classification_optimizer.fit(X_train, y_train)\n", + "\n", + "auroc_score = sklearn.metrics.roc_auc_score(y_test, classification_optimizer.predict_proba(X_test)[:,1])\n", + "print(\"auroc_score: \", auroc_score)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Scorers, Objective Functions, and multi objective optimization.\n", + "\n", + "There are two ways of passing objectives into TPOT2. \n", + "\n", + "1. `scorers`: Scorers are functions that have the signature (estimator, X, y). These can be produced with the [sklearn.metrics.make_scorer](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html) function. This function is used to evaluate the test folds during cross validation. These are passed into TPOT2 via the scorers parameter. This can take in the scorer itself or the string corresponding to a scoring function ([as listed here](https://scikit-learn.org/stable/modules/model_evaluation.html)). TPOT2 also supports passing in a list of several scorers for multiobjective optimization. \n", + "\n", + "2. `other_objective_functions` : Other objective functions in TPOT2 have the signature (estimator) and returns a float or list of floats. These get passed an unfitted estimator (in the case of TPOT2, a `tpot2.GraphPipeline`). \n", + "\n", + "\n", + "\n", + "Each scorer and objective function must be accompanied by a list of weights corresponding to the list of objectives. By default, TPOT2 maximizes objective functions (this can be changed by `bigger_is_better=False`). Positive weights means that TPOT2 will seek to maximize that objective, and negative weights correspond to minimization.\n", + "\n", + "Here is an example of using two scorers\n", + "\n", + " scorers=['roc_auc_ovr',tpot2.objectives.complexity_scorer],\n", + " scorers_weights=[1,-1],\n", + "\n", + "\n", + "Here is an example with a scorer and a secondary objective function\n", + "\n", + " scorers=['roc_auc_ovr'],\n", + " scorers_weights=[1],\n", + " other_objective_functions=[tpot2.objectives.number_of_leaves_objective],\n", + " other_objective_functions_weights=[-1],\n", + "\n", + "\n", + "TPOT will automatically name the scores based on the function name for the columns in the final results dataframe. If you would like to specify custom function names, you can set the `objective_function_names` to be a list of names (str) for each score. The order of the names are scorers first, and other objective functions second. (e.g. `objective_function_names=['scorer1','scorer2', 'objective1','objective2'])`.\n", + "\n", + "It is possible to have either the scorer or other_objective_function to return multiple values. In that case, just make sure that the `scorer_weights` and `other_objective_function_weights` are the same length as the number of returned scores.\n", + "\n", + "\n", + "TPOT comes with a few additional built in objective functions you can use. The first table are objectives applied to fitted pipelines, and thus are passee into the `scorers` parameter. The second table are objective functions for the `other_objective_functions` param.\n", + "\n", + "Scorers:\n", + "| Function | Description |\n", + "| :--- | :----: |\n", + "| tpot2.objectives.complexity_scorer | Estimates the number of learned parameters across all classifiers and regressors in the pipelines. Additionally, currently transformers add 1 point and selectors add 0 points (since they don't affect the complexity of the \"final\" predictive pipeline.) |\n", + "\n", + "Other Objective Functions.\n", + "\n", + "| Function | Description |\n", + "| :--- | :----: |\n", + "| tpot2.objectives.average_path_length | Computes the average shortest path from all nodes to the root/final estimator (only supported for GraphPipeline) |\n", + "| tpot2.objectives.number_of_leaves_objective | Calculates the number of leaves (input nodes) in a GraphPipeline |\n", + "| tpot2.objectives.number_of_nodes_objective | Calculates the number of nodes in a pipeline (whether it is an scikit-learn Pipeline, GraphPipeline, Feature Union, or the previous nested within each other) |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Built In Configurations\n", + "TPOT can be used to optimize hyperparameters, select models, and optimize pipelines of models including determining the sequence of steps. Tutorial 2 goes into more detail on how to customize search spaces with custom hyperparameter ranges, model types, and possible pipeline configurations. TPOT also comes with a handful of default operators and parameter configurations that we believe work well for optimizing machine learning pipelines. Below is a list of the current built-in configurations that come with TPOT. These can be passed in as strings to the `search space` parameter of any of the TPOT estimators.\n", + "\n", + "| String | Description |\n", + "| :--- | :----: |\n", + "| linear | A linear pipeline with the structure of \"Selector->(transformers+Passthrough)->(classifiers/regressors+Passthrough)->final classifier/regressor.\" For both the transformer and inner estimator layers, TPOT may choose one or more transformers/classifiers, or it may choose none. The inner classifier/regressor layer is optional. |\n", + "| light | Same search space as linear, but without the inner classifier/regressor layer and with a reduced set of faster running estimators. |\n", + "| graph | TPOT will optimize a pipeline in the shape of a directed acyclic graph. The nodes of the graph can include selectors, scalers, transformers, or classifiers/regressors (inner classifiers/regressors can optionally be not included). This will return a custom GraphPipeline rather than an sklearn Pipeline. More details in Tutorial 6. |\n", + "| mdr |TPOT will search over a series of feature selectors and Multifactor Dimensionality Reduction models to find a series of operators that maximize prediction accuracy. The TPOT MDR configuration is specialized for genome-wide association studies (GWAS), and is described in detail online here.\n", + "\n", + "Note that TPOT MDR may be slow to run because the feature selection routines are computationally expensive, especially on large datasets. |\n", + "\n", + "Note: the `linear` and `graph` configurations by default allow for additional stacked classifiers/regressors within the pipeline in addition to the final classifier/regressor. If you would like to disable this, you can manually get the search space without inner classifier/regressors through the function `tpot2.config.template_search_spaces.get_template_search_spaces` with `inner_predictios=False`. You can pass the resulting search space into the `search space` param.\n", + "\n", + "The specific hyperparameter ranges used by TPOT can be found in files in the tpot2/config folder. The template search spaces listed above are defined in tpot2/config/template_search_spaces.py. Search spaces for individual models can be acquired in the tpot2/config/get_configspace.py file (`tpot2.config.get_search_space`). More details in Tutorial 2." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example analysis " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Best Practices\n", + "\n", + "When running tpot from an .py script, it is important to protect code with `if __name__==\"__main__\":` . This is because of how TPOT handles parallelization with Python and Dask." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#my_analysis.py\n", + "\n", + "from dask.distributed import Client, LocalCluster\n", + "import tpot2\n", + "import sklearn\n", + "import sklearn.datasets\n", + "import numpy as np\n", + "\n", + "if __name__==\"__main__\":\n", + " scorer = sklearn.metrics.get_scorer('roc_auc_ovo')\n", + " X, y = sklearn.datasets.load_digits(return_X_y=True)\n", + " X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, train_size=0.75, test_size=0.25)\n", + "\n", + "\n", + " est = tpot2.TPOTClassifier(n_jobs=4, max_time_mins=60, verbose=2)\n", + " est.fit(X_train, y_train)\n", + "\n", + "\n", + " print(scorer(est, X_test, y_test))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Common parameters\n", + "\n", + " scorers : (list, scorer)\n", + " A scorer or list of scorers to be used in the cross-validation process. \n", + " see https://scikit-learn.org/stable/modules/model_evaluation.html\n", + " \n", + " scorers_weights : list\n", + " A list of weights to be applied to the scorers during the optimization process.\n", + " \n", + " classification : bool\n", + " If True, the problem is treated as a classification problem. If False, the problem is treated as a regression problem.\n", + " Used to determine the CV strategy.\n", + " \n", + " cv : int, cross-validator\n", + " - (int): Number of folds to use in the cross-validation process. By uses the sklearn.model_selection.KFold cross-validator for regression and StratifiedKFold for classification. In both cases, shuffled is set to True.\n", + " - (sklearn.model_selection.BaseCrossValidator): A cross-validator to use in the cross-validation process.\n", + " - max_depth (int): The maximum depth from any node to the root of the pipelines to be generated.\n", + " \n", + " other_objective_functions : list, default=[tpot2.objectives.estimator_objective_functions.average_path_length_objective]\n", + " A list of other objective functions to apply to the pipeline.\n", + " \n", + " other_objective_functions_weights : list, default=[-1]\n", + " A list of weights to be applied to the other objective functions.\n", + " \n", + " objective_function_names : list, default=None\n", + " A list of names to be applied to the objective functions. If None, will use the names of the objective functions.\n", + " \n", + " bigger_is_better : bool, default=True\n", + " If True, the objective function is maximized. If False, the objective function is minimized. Use negative weights to reverse the direction.\n", + " \n", + " generations : int, default=50\n", + " Number of generations to run\n", + " \n", + " max_time_mins : float, default=float(\"inf\")\n", + " Maximum time to run the optimization. If none or inf, will run until the end of the generations.\n", + " \n", + " max_eval_time_mins : float, default=60*5\n", + " Maximum time to evaluate a single individual. If none or inf, there will be no time limit per evaluation.\n", + "\n", + " n_jobs : int, default=1\n", + " Number of processes to run in parallel.\n", + " \n", + " memory_limit : str, default=\"4GB\"\n", + " Memory limit for each job. See Dask [LocalCluster documentation](https://distributed.dask.org/en/stable/api.html#distributed.Client) for more information.\n", + "\n", + " \n", + " verbose : int, default=1 \n", + " How much information to print during the optimization process. Higher values include the information from lower values.\n", + " 0. nothing\n", + " 1. progress bar\n", + " \n", + " 3. best individual\n", + " 4. warnings\n", + " >=5. full warnings trace\n", + " 6. evaluations progress bar. (Temporary: This used to be 2. Currently, using evaluation progress bar may prevent some instances were we terminate a generation early due to it reaching max_time_mins in the middle of a generation OR a pipeline failed to be terminated normally and we need to manually terminate it.)\n", + " \n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# More Options\n", + "\n", + "`tpot2.TPOTClassifier` and `tpot2.TPOTRegressor` have a simplified set of hyperparameters with default values set for classification and regression problems. Currently, both of these use the standard evolutionary algorithm in the `tpot2.TPOTEstimator` class. If you want more control you can look into either the `tpot2.TPOTEstimator` or `tpot2.TPOTEstimatorSteadyState` class.\n", + "\n", + "There are two evolutionary algorithms built into TPOT2, which corresponds to two different estimator classes.\n", + "\n", + "1. The `tpot2.TPOTEstimator` uses a standard evolutionary algorithm that evaluates exactly population_size individuals each generation. This is similar to the algorithm in TPOT1. The next generation does not start until the previous is completely finished evaluating. This leads to underutilized CPU time as the cores are waiting for the last individuals to finish training, but may preserve diversity in the population. \n", + "\n", + "2. The `tpot2.TPOTEstimatorSteadyState` differs in that it will generate and evaluate the next individual as soon as an individual finishes evaluation. The number of individuals being evaluated is determined by the n_jobs parameter. There is no longer a concept of generations. The population_size parameter now refers to the size of the list of evaluated parents. When an individual is evaluated, the selection method updates the list of parents. This allows more efficient utilization when using multiple cores.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import tpot2\n", + "import sklearn\n", + "import sklearn.datasets\n", + "\n", + "scorer = sklearn.metrics.get_scorer('roc_auc_ovo')\n", + "X, y = sklearn.datasets.load_iris(return_X_y=True)\n", + "X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, train_size=0.75, test_size=0.25)\n", + "\n", + "\n", + "est = tpot2.TPOTClassifier(n_jobs=40, max_time_mins=30, verbose=5, generations=1, population_size=5)\n", + "est.fit(X_train, y_train)\n", + "\n", + "\n", + "print(scorer(est, X_test, y_test))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "est._evolver_instance.population.evaluated_individuals.iloc[0]['Individual'].export_pipeline()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import tpot2\n", + "import sklearn\n", + "import sklearn.metrics\n", + "import sklearn.datasets\n", + "\n", + "scorer = sklearn.metrics.get_scorer('neg_mean_squared_error')\n", + "X, y = sklearn.datasets.load_diabetes(return_X_y=True)\n", + "X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, train_size=0.75, test_size=0.25)\n", + "\n", + "est = tpot2.tpot_estimator.templates.TPOTRegressor(n_jobs=4, max_time_mins=30, verbose=2, cv=5)\n", + "est.fit(X_train, y_train)\n", + "\n", + "print(scorer(est, X_test, y_test))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### tpot2.TPOTEstimatorSteadyState" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import tpot2\n", + "import sklearn\n", + "import sklearn.datasets\n", + "\n", + "\n", + "graph_search_space = tpot2.search_spaces.pipelines.GraphPipeline(\n", + " root_search_space= tpot2.config.get_search_space([\"KNeighborsClassifier\", \"LogisticRegression\", \"DecisionTreeClassifier\"]),\n", + " leaf_search_space = tpot2.config.get_search_space(\"selectors\"), \n", + " inner_search_space = tpot2.config.get_search_space([\"transformers\"]),\n", + " max_size = 10,\n", + ")\n", + "\n", + "est = tpot2.TPOTEstimatorSteadyState( \n", + " search_space = graph_search_space,\n", + " scorers=['roc_auc_ovr'], #scorers can be a list of strings or a list of scorers. These get evaluated during cross validation. \n", + " scorers_weights=[1],\n", + "\n", + " classification=True,\n", + "\n", + " max_eval_time_mins=15,\n", + " max_time_mins=30,\n", + " verbose=2)\n", + "\n", + "\n", + "scorer = sklearn.metrics.get_scorer('roc_auc_ovo')\n", + "X, y = sklearn.datasets.load_iris(return_X_y=True)\n", + "X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, train_size=0.75, test_size=0.25)\n", + "est.fit(X_train, y_train)\n", + "print(scorer(est, X_test, y_test))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fitted_pipeline = est.fitted_pipeline_ # access best pipeline directly\n", + "fitted_pipeline.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#view the summary of all evaluated individuals as a pandas dataframe\n", + "est.evaluated_individuals" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import tpot2\n", + "import sklearn\n", + "import sklearn.datasets\n", + "\n", + "est = tpot2.TPOTEstimatorSteadyState( \n", + " search_space = graph_search_space,\n", + " scorers=['roc_auc_ovr',tpot2.objectives.complexity_scorer],\n", + " scorers_weights=[1,-1],\n", + "\n", + " classification=True,\n", + "\n", + " max_eval_time_mins=15,\n", + " max_time_mins=30,\n", + " verbose=2)\n", + "\n", + "\n", + "scorer = sklearn.metrics.get_scorer('roc_auc_ovo')\n", + "X, y = sklearn.datasets.load_iris(return_X_y=True)\n", + "X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, train_size=0.75, test_size=0.25)\n", + "est.fit(X_train, y_train)\n", + "print(scorer(est, X_test, y_test))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fitted_pipeline = est.fitted_pipeline_ # access best pipeline directly\n", + "fitted_pipeline.plot() #plot the best pipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "view the results of all evaluated individuals as a pandas dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "est.evaluated_individuals" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "view pareto front as a pandas dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "est.pareto_front" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pareto_front = est.pareto_front\n", + "\n", + "#plot the pareto front of number_of_leaves_objective vs roc_auc_score\n", + "\n", + "import matplotlib.pyplot as plt\n", + "plt.scatter(pareto_front['complexity_scorer'], pareto_front['roc_auc_score'])\n", + "plt.xlabel('complexity_scorer')\n", + "plt.ylabel('roc_auc_score')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### tpot2.TPOTEstimator" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import tpot2\n", + "import sklearn\n", + "import sklearn.datasets\n", + "\n", + "est = tpot2.TPOTEstimator( \n", + " search_space = graph_search_space,\n", + " population_size=30,\n", + " generations=5,\n", + " scorers=['roc_auc_ovr'], #scorers can be a list of strings or a list of scorers. These get evaluated during cross validation. \n", + " scorers_weights=[1],\n", + " classification=True,\n", + " n_jobs=1, \n", + " early_stop=5, #how many generations with no improvement to stop after\n", + " \n", + " #List of other objective functions. All objective functions take in an untrained GraphPipeline and return a score or a list of scores\n", + " other_objective_functions= [ ],\n", + " \n", + " #List of weights for the other objective functions. Must be the same length as other_objective_functions. By default, bigger is better is set to True. \n", + " other_objective_functions_weights=[],\n", + " verbose=2)\n", + "\n", + "scorer = sklearn.metrics.get_scorer('roc_auc_ovo')\n", + "X, y = sklearn.datasets.load_iris(return_X_y=True)\n", + "X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, train_size=0.75, test_size=0.25)\n", + "est.fit(X_train, y_train)\n", + "print(scorer(est, X_test, y_test))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tpot_dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "7fe1fe9ef32cd5efd76326a08046147513534f0dd2318301a1a96ae9071c1c4e" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tpot2/config/classifiers.py b/tpot2/config/classifiers.py index 2fb09e41..8ee26963 100644 --- a/tpot2/config/classifiers.py +++ b/tpot2/config/classifiers.py @@ -42,7 +42,6 @@ def get_KNeighborsClassifier_ConfigurationSpace(n_samples): 'n_neighbors': Integer("n_neighbors", bounds=(1, min(100,n_samples)), log=True), 'weights': Categorical("weights", ['uniform', 'distance']), 'p': Integer("p", bounds=(1, 3)), - 'metric': Categorical("metric", ['euclidean', 'minkowski']), 'n_jobs': 1, } ) @@ -79,7 +78,7 @@ def get_DecisionTreeClassifier_ConfigurationSpace(n_featues, random_state): space = { 'criterion': Categorical("criterion", ['gini', 'entropy']), 'max_depth': Integer("max_depth", bounds=(1, min(20,2*n_featues))), #max of 20? log scale? - 'min_samples_split': Integer("min_samples_split", bounds=(1, 20)), + 'min_samples_split': Integer("min_samples_split", bounds=(2, 20)), 'min_samples_leaf': Integer("min_samples_leaf", bounds=(1, 20)), 'max_features': Categorical("max_features", [NONE_SPECIAL_STRING, 'sqrt', 'log2']), 'min_weight_fraction_leaf': 0.0, diff --git a/tpot2/config/regressors.py b/tpot2/config/regressors.py index ab14a7ea..6348f5c2 100644 --- a/tpot2/config/regressors.py +++ b/tpot2/config/regressors.py @@ -240,9 +240,7 @@ def get_KNeighborsRegressor_ConfigurationSpace(n_samples): space = { 'n_neighbors': Integer("n_neighbors", bounds=(1, min(100,n_samples))), 'weights': Categorical("weights", ['uniform', 'distance']), - 'p': Integer("p", bounds=(1, 3)), - 'metric': Categorical("metric", ['minkowski', 'euclidean', 'manhattan']), - } + 'p': Integer("p", bounds=(1, 3)), } ) diff --git a/tpot2/config/template_search_spaces.py b/tpot2/config/template_search_spaces.py index 593f0499..23b7a324 100644 --- a/tpot2/config/template_search_spaces.py +++ b/tpot2/config/template_search_spaces.py @@ -85,12 +85,93 @@ def get_graph_search_space(classification=True, inner_predictors=True, **get_sea return search_space -def get_template_search_spaces(default_search_space, classification=True, inner_predictors=True, **get_search_space_params): + +def get_light_search_space(classification=True, inner_predictors=False, **get_search_space_params ): + + selectors = get_search_space(["SelectFwe", "SelectPercentile", "VarianceThreshold","Passthrough"], **get_search_space_params) + + if classification: + estimators = get_search_space(['BernoulliNB', 'DecisionTreeClassifier', 'GaussianNB', 'KNeighborsClassifier', 'LogisticRegression', 'MultinomialNB'], **get_search_space_params) + else: + estimators = get_search_space(["RidgeCV", "LinearSVR", "LassoLarsCV", "KNeighborsRegressor", "DecisionTreeRegressor", "ElasticNetCV"], **get_search_space_params) + + # this allows us to wrap the classifiers in the EstimatorTransformer + # this is necessary so that classifiers can be used inside of sklearn pipelines + wrapped_estimators = WrapperPipeline(tpot2.builtin_modules.EstimatorTransformer, {}, estimators) + + scalers = get_search_space(["scalers","Passthrough"], **get_search_space_params) + + transformers_layer =UnionPipeline([ + ChoicePipeline([ + DynamicUnionPipeline(get_search_space(["transformers"],**get_search_space_params)), + get_search_space("SkipTransformer", **get_search_space_params), + ]), + get_search_space("Passthrough", **get_search_space_params) + ] + ) + + inner_estimators_layer = UnionPipeline([ + ChoicePipeline([ + DynamicUnionPipeline(wrapped_estimators), + get_search_space("SkipTransformer", **get_search_space_params), + ]), + get_search_space("Passthrough", **get_search_space_params)] + ) + + if inner_predictors: + search_space = SequentialPipeline(search_spaces=[ + scalers, + selectors, + transformers_layer, + inner_estimators_layer, + estimators, + ]) + else: + search_space = SequentialPipeline(search_spaces=[ + scalers, + selectors, + transformers_layer, + estimators, + ]) + + return search_space + +def get_mdr_search_space(classification=True, **get_search_space_params ): + + mdr_sp = DynamicLinearPipeline(get_search_space(["ReliefF", "SURF", "SURFstar", "MultiSURF", "ContinuousMDR"], **get_search_space_params), max_length=10) + + if classification: + estimators = get_search_space(['LogisticRegression'], **get_search_space_params) + else: + estimators = get_search_space(["ElasticNetCV"], **get_search_space_params) + + search_space = SequentialPipeline(search_spaces=[ + mdr_sp, + estimators, + ]) + + return search_space + + + + +def get_template_search_spaces(default_search_space, classification=True, inner_predictors=None, **get_search_space_params): + + if inner_predictors is None: + if default_search_space == "light": + inner_predictors = False + else: + inner_predictors = True + if isinstance(default_search_space, str): if default_search_space == "linear": return get_linear_search_space(classification, inner_predictors, **get_search_space_params) elif default_search_space == "graph": return get_graph_search_space(classification, inner_predictors, **get_search_space_params) + elif default_search_space == "light": + return get_light_search_space(classification, inner_predictors, **get_search_space_params) + elif default_search_space == "mdr": + return get_mdr_search_space(classification, **get_search_space_params) else: raise ValueError("Invalid search space") else: diff --git a/tpot2/objectives/average_path_length.py b/tpot2/objectives/average_path_length.py index 407b918b..dd3fcb0d 100644 --- a/tpot2/objectives/average_path_length.py +++ b/tpot2/objectives/average_path_length.py @@ -2,6 +2,15 @@ import numpy as np def average_path_length_objective(graph_pipeline): + """ + Computes the average shortest path from all nodes to the root/final estimator (only supported for GraphPipeline) + + Parameters + ---------- + graph_pipeline: GraphPipeline + The pipeline to compute the average path length for + + """ path_lengths = nx.shortest_path_length(graph_pipeline.graph, source=graph_pipeline.root) return np.mean(np.array(list(path_lengths.values())))+1 \ No newline at end of file diff --git a/tpot2/objectives/complexity.py b/tpot2/objectives/complexity.py index f4d5112d..f3c305a1 100644 --- a/tpot2/objectives/complexity.py +++ b/tpot2/objectives/complexity.py @@ -228,6 +228,20 @@ def calculate_model_complexity(est): return 1 -def complexity_scorer(est, X, y): +def complexity_scorer(est, X=None, y=None): + """ + Estimates the number of learned parameters across all classifiers and regressors in the pipelines. + Additionally, currently transformers add 1 point and selectors add 0 points (since they don't affect the complexity of the "final" predictive pipeline. + + Parameters + ---------- + est: sklearn.base.BaseEstimator + The estimator or pipeline to compute the complexity for + X: array-like + The input samples (unused) + y: array-like + The target values (unused) + + """ return calculate_model_complexity(est) diff --git a/tpot2/objectives/number_of_leaves.py b/tpot2/objectives/number_of_leaves.py index 2ea34c62..f876caff 100644 --- a/tpot2/objectives/number_of_leaves.py +++ b/tpot2/objectives/number_of_leaves.py @@ -1,5 +1,13 @@ -def number_of_leaves_scorer(est,X,y): +def number_of_leaves_scorer(est,X=None, y=None): return len([v for v, d in est.graph.out_degree() if d == 0]) def number_of_leaves_objective(est): + """ + Calculates the number of leaves (input nodes) in a GraphPipeline + + Parameters + ---------- + est: GraphPipeline + The pipeline to compute the number of leaves for + """ return len([v for v, d in est.graph.out_degree() if d == 0]) \ No newline at end of file diff --git a/tpot2/objectives/number_of_nodes.py b/tpot2/objectives/number_of_nodes.py index a56368e2..a531851d 100644 --- a/tpot2/objectives/number_of_nodes.py +++ b/tpot2/objectives/number_of_nodes.py @@ -3,6 +3,15 @@ import sklearn def number_of_nodes_objective(est): + """ + Calculates the number of leaves (input nodes) in an sklearn pipeline + + Parameters + ---------- + est: GraphPipeline | Pipeline | FeatureUnion | BaseEstimator + The pipeline to compute the number of nodes from. + """ + if isinstance(est, GraphPipeline): return sum(number_of_nodes_objective(est.graph.nodes[node]["instance"]) for node in est.graph.nodes) if isinstance(est, Pipeline): diff --git a/tpot2/tpot_estimator/estimator.py b/tpot2/tpot_estimator/estimator.py index 4623b12f..fdaaae86 100644 --- a/tpot2/tpot_estimator/estimator.py +++ b/tpot2/tpot_estimator/estimator.py @@ -36,7 +36,7 @@ def __init__(self, scorers, scorers_weights, classification, - cv = 5, + cv = 10, other_objective_functions=[], other_objective_functions_weights = [], objective_function_names = None, diff --git a/tpot2/tpot_estimator/templates/tpottemplates.py b/tpot2/tpot_estimator/templates/tpottemplates.py index c91645eb..ce37d31b 100644 --- a/tpot2/tpot_estimator/templates/tpottemplates.py +++ b/tpot2/tpot_estimator/templates/tpottemplates.py @@ -29,11 +29,11 @@ def __init__( self, early_stop = None, warm_start = False, periodic_checkpoint_folder = None, - verbose = 0, + verbose = 2, memory_limit = "4GB", client = None, random_state=None, - allow_inner_regressors=True, + allow_inner_regressors=None, **tpotestimator_kwargs, ): ''' @@ -282,11 +282,11 @@ def __init__( self, early_stop = None, warm_start = False, periodic_checkpoint_folder = None, - verbose = 0, + verbose = 2, memory_limit = "4GB", client = None, random_state=None, - allow_inner_classifiers=True, + allow_inner_classifiers=None, **tpotestimator_kwargs, ): From 1314eb75849d3fd37654fdbbbd65f4c85d6da477 Mon Sep 17 00:00:00 2001 From: perib Date: Fri, 20 Sep 2024 15:27:26 -0700 Subject: [PATCH 02/44] edits --- Tutorial/1_Using_TPOT.ipynb | 355 +++++++++++++++++- tpot2/evolvers/base_evolver.py | 2 +- tpot2/tpot_estimator/estimator.py | 2 +- .../tpot_estimator/templates/tpottemplates.py | 4 +- 4 files changed, 348 insertions(+), 15 deletions(-) diff --git a/Tutorial/1_Using_TPOT.ipynb b/Tutorial/1_Using_TPOT.ipynb index b4f18627..9e707306 100644 --- a/Tutorial/1_Using_TPOT.ipynb +++ b/Tutorial/1_Using_TPOT.ipynb @@ -137,7 +137,7 @@ "\n", "\n", "\n", - "Each scorer and objective function must be accompanied by a list of weights corresponding to the list of objectives. By default, TPOT2 maximizes objective functions (this can be changed by `bigger_is_better=False`). Positive weights means that TPOT2 will seek to maximize that objective, and negative weights correspond to minimization.\n", + "Each scorer and objective function must be accompanied by a list of weights corresponding to the list of objectives, these are `scorer_weights` and `other_objective_function_weights`, respectively. By default, TPOT2 maximizes objective functions (this can be changed by `bigger_is_better=False`). Positive weights means that TPOT2 will seek to maximize that objective, and negative weights correspond to minimization. For most selectors (and the default), only the sign matters. The scale of the weight may matter if using a custom selection function for the optimization algorithm. A zero weight means that the score will not have an impact on the selection algorithm.\n", "\n", "Here is an example of using two scorers\n", "\n", @@ -199,23 +199,26 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Example analysis " + "## Terminating Optimization\n", + "\n", + "Note that we use a short time duration for a quick example, but in practice you may need to run TPOT for a longer duration. by default, TPOT sets a time limit of 1 hour with a max limit of 5 minutes per pipeline. In practice you may want to increase these values.\n", + "\n", + "There are three methods of terminating a TPOT run and ending the optimization process. TPOT will always terminate as soon as one of the conditions is met.\n", + "* `max_time_mins` : (Default, 1 hour) After this many minutes, TPOT will terminate and return the best pipeline it found so far.\n", + "* `early_stop` : An int causes TPOT to terminate early if it goes that number of generations without seeing an improvement in performance. Generally a value of around 5 to 20 is sufficient to be reasonably sure that performance has converged.\n", + "* `generations` : The total number of generations of the evolutionary algorithm to run.\n", + "\n", + "By default, TPOT will run until the time limit is up, with no generation or early stop limits." ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Best Practices\n", + "### Best Practices and tips:\n", "\n", - "When running tpot from an .py script, it is important to protect code with `if __name__==\"__main__\":` . This is because of how TPOT handles parallelization with Python and Dask." + "* When running tpot from an .py script, it is important to protect code with `if __name__==\"__main__\":` . This is because of how TPOT handles parallelization with Python and Dask.\n", + "* You can use the `early_stop` parameter to have TPOT terminate early. " ] }, { @@ -245,6 +248,336 @@ " print(scorer(est, X_test, y_test))" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Example analysis " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "TPOTEstimator.__init__() got an unexpected keyword argument 'scorer_weights'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[5], line 29\u001b[0m\n\u001b[1;32m 15\u001b[0m X_train, X_test, y_train, y_test \u001b[38;5;241m=\u001b[39m sklearn\u001b[38;5;241m.\u001b[39mmodel_selection\u001b[38;5;241m.\u001b[39mtrain_test_split(X, y, train_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.75\u001b[39m, test_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.25\u001b[39m)\n\u001b[1;32m 18\u001b[0m est \u001b[38;5;241m=\u001b[39m tpot2\u001b[38;5;241m.\u001b[39mTPOTClassifier(\n\u001b[1;32m 19\u001b[0m scorers\u001b[38;5;241m=\u001b[39m[scorer, tpot2\u001b[38;5;241m.\u001b[39mobjectives\u001b[38;5;241m.\u001b[39mcomplexity_scorer],\n\u001b[1;32m 20\u001b[0m scorer_weights\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m1.0\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1.0\u001b[39m],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 27\u001b[0m early_stop\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m,\n\u001b[1;32m 28\u001b[0m verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m,)\n\u001b[0;32m---> 29\u001b[0m \u001b[43mest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 32\u001b[0m \u001b[38;5;28mprint\u001b[39m(scorer(est, X_test, y_test))\n", + "File \u001b[0;32m~/Projects/common/Projects/TPOT_Dev/tpot2/tpot2/tpot_estimator/templates/tpottemplates.py:487\u001b[0m, in \u001b[0;36mTPOTClassifier.fit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 479\u001b[0m get_search_space_params \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn_classes\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mlen\u001b[39m(np\u001b[38;5;241m.\u001b[39munique(y)), \n\u001b[1;32m 480\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn_samples\u001b[39m\u001b[38;5;124m\"\u001b[39m:\u001b[38;5;28mlen\u001b[39m(y), \n\u001b[1;32m 481\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn_features\u001b[39m\u001b[38;5;124m\"\u001b[39m:X\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m], \n\u001b[1;32m 482\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrandom_state\u001b[39m\u001b[38;5;124m\"\u001b[39m:\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrandom_state}\n\u001b[1;32m 484\u001b[0m search_space 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tpot2.objectives.complexity_scorer],\n", + " scorer_weights=[1.0, -1.0],\n", + " objective_function_names=['roc_auc_ovr', 'complexity'],\n", + "\n", + " search_space=\"light\",\n", + " n_jobs=4, \n", + " max_time_mins=60, \n", + " max_eval_time_mins=5,\n", + " early_stop=2,\n", + " verbose=2,)\n", + "est.fit(X_train, y_train)\n", + "\n", + "print(scorer(est, X_test, y_test))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Further analysis of results\n", + "\n", + "The `evaluated_individuals` attribute of the tpot estimator object is a Pandas Dataframe containing information about a run. Each row corresponds to an individual pipeline explored by tpot. The dataframe contains the following columns:\n", + "\n", + "| Column | Description |\n", + "| :--- | :----: |\n", + "| | The first set of columns will correspond to each objective function. These can either be automatically named by TPOT, or passed in by the user. |\n", + "| Parents | This contains a tuple that contains the indexes of the 'parents' of the current pipeline. For example, (29, 42) means that the pipelines in indexes 29 and 42 were utilized to generate that pipeline. |\n", + "| | |\n", + "| | |\n", + "| | |\n", + "| | |\n", + "| | |" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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roc_auc_scoreParentsVariation_FunctionIndividualGenerationSubmitted TimestampCompleted TimestampEval ErrorPareto_FrontInstance
00.992125NaNNaN<tpot2.search_spaces.pipelines.sequential.Sequ...0.01.726870e+091.726870e+09NoneNaN(Passthrough(), SelectFwe(alpha=0.005516602107...
1NaNNaNNaN<tpot2.search_spaces.pipelines.sequential.Sequ...0.01.726870e+091.726870e+09TIMEOUTNaN(RobustScaler(quantile_range=(0.232938031941, ...
20.991613NaNNaN<tpot2.search_spaces.pipelines.sequential.Sequ...0.01.726870e+091.726870e+09NoneNaN(MinMaxScaler(), Passthrough(), FeatureUnion(t...
30.974053NaNNaN<tpot2.search_spaces.pipelines.sequential.Sequ...0.01.726870e+091.726870e+09NoneNaN(MaxAbsScaler(), VarianceThreshold(threshold=0...
4NaNNaNNaN<tpot2.search_spaces.pipelines.sequential.Sequ...0.01.726870e+091.726870e+09TIMEOUTNaN(RobustScaler(quantile_range=(0.0238159499352,...
.................................
1950.946552(108, 124)ind_crossover , ind_mutate<tpot2.search_spaces.pipelines.sequential.Sequ...3.01.726871e+091.726871e+09NoneNaN(StandardScaler(), SelectPercentile(percentile...
196NaN(94, 94)ind_mutate<tpot2.search_spaces.pipelines.sequential.Sequ...3.01.726871e+091.726871e+09TIMEOUTNaN(MinMaxScaler(), SelectPercentile(percentile=9...
1970.999038(34, 12)ind_crossover<tpot2.search_spaces.pipelines.sequential.Sequ...3.01.726871e+091.726871e+09NoneNaN(RobustScaler(quantile_range=(0.2919651866521,...
1980.998404(118, 118)ind_mutate<tpot2.search_spaces.pipelines.sequential.Sequ...3.01.726871e+091.726871e+09NoneNaN(RobustScaler(quantile_range=(0.2919651866521,...
199NaN(103, 124)ind_crossover<tpot2.search_spaces.pipelines.sequential.Sequ...3.01.726871e+091.726871e+09TIMEOUTNaN(StandardScaler(), SelectPercentile(percentile...
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" + ], + "text/plain": [ + " roc_auc_score Parents Variation_Function \\\n", + "0 0.992125 NaN NaN \n", + "1 NaN NaN NaN \n", + "2 0.991613 NaN NaN \n", + "3 0.974053 NaN NaN \n", + "4 NaN NaN NaN \n", + ".. ... ... ... \n", + "195 0.946552 (108, 124) ind_crossover , ind_mutate \n", + "196 NaN (94, 94) ind_mutate \n", + "197 0.999038 (34, 12) ind_crossover \n", + "198 0.998404 (118, 118) ind_mutate \n", + "199 NaN (103, 124) ind_crossover \n", + "\n", + " Individual Generation \\\n", + "0 Date: Fri, 20 Sep 2024 19:44:59 -0700 Subject: [PATCH 03/44] tutorial 1 --- Tutorial/1_Using_TPOT.ipynb | 1589 ++++++++++++++++++++++++++++++++--- 1 file changed, 1478 insertions(+), 111 deletions(-) diff --git a/Tutorial/1_Using_TPOT.ipynb b/Tutorial/1_Using_TPOT.ipynb index 9e707306..d3cf8307 100644 --- a/Tutorial/1_Using_TPOT.ipynb +++ b/Tutorial/1_Using_TPOT.ipynb @@ -131,13 +131,13 @@ "\n", "There are two ways of passing objectives into TPOT2. \n", "\n", - "1. `scorers`: Scorers are functions that have the signature (estimator, X, y). These can be produced with the [sklearn.metrics.make_scorer](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html) function. This function is used to evaluate the test folds during cross validation. These are passed into TPOT2 via the scorers parameter. This can take in the scorer itself or the string corresponding to a scoring function ([as listed here](https://scikit-learn.org/stable/modules/model_evaluation.html)). TPOT2 also supports passing in a list of several scorers for multiobjective optimization. \n", + "1. `scorers`: Scorers are functions that have the signature (estimator, X_test, y_test) and take in estimators that are expected to be fitted to training data. These can be produced with the [sklearn.metrics.make_scorer](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html) function. This function is used to evaluate the test folds during cross validation (defined in the `cv` parameter). These are passed into TPOT2 via the scorers parameter. This can take in the scorer itself or the string corresponding to a scoring function ([as listed here](https://scikit-learn.org/stable/modules/model_evaluation.html)). TPOT2 also supports passing in a list of several scorers for multi-objective optimization. For each fold of CV, TPOT only fits the estimator once, then evaluates all provided scorers in a loop.\n", "\n", - "2. `other_objective_functions` : Other objective functions in TPOT2 have the signature (estimator) and returns a float or list of floats. These get passed an unfitted estimator (in the case of TPOT2, a `tpot2.GraphPipeline`). \n", + "2. `other_objective_functions` : Other objective functions in TPOT2 have the signature (estimator) and returns a float or list of floats. These get passed a single unfitted estimator once, outside of cross validation. The user may choose to fit the pipeline within this objective function as well.\n", "\n", "\n", "\n", - "Each scorer and objective function must be accompanied by a list of weights corresponding to the list of objectives, these are `scorer_weights` and `other_objective_function_weights`, respectively. By default, TPOT2 maximizes objective functions (this can be changed by `bigger_is_better=False`). Positive weights means that TPOT2 will seek to maximize that objective, and negative weights correspond to minimization. For most selectors (and the default), only the sign matters. The scale of the weight may matter if using a custom selection function for the optimization algorithm. A zero weight means that the score will not have an impact on the selection algorithm.\n", + "Each scorer and objective function must be accompanied by a list of weights corresponding to the list of objectives, these are `scorers_weights` and `other_objective_function_weights`, respectively. By default, TPOT2 maximizes objective functions (this can be changed by `bigger_is_better=False`). Positive weights means that TPOT2 will seek to maximize that objective, and negative weights correspond to minimization. For most selectors (and the default), only the sign matters. The scale of the weight may matter if using a custom selection function for the optimization algorithm. A zero weight means that the score will not have an impact on the selection algorithm.\n", "\n", "Here is an example of using two scorers\n", "\n", @@ -153,9 +153,9 @@ " other_objective_functions_weights=[-1],\n", "\n", "\n", - "TPOT will automatically name the scores based on the function name for the columns in the final results dataframe. If you would like to specify custom function names, you can set the `objective_function_names` to be a list of names (str) for each score. The order of the names are scorers first, and other objective functions second. (e.g. `objective_function_names=['scorer1','scorer2', 'objective1','objective2'])`.\n", + "TPOT will always automatically name the scorers based on the function name for the columns in the final results dataframe. TPOT will use the function name as the column name for `other_objective_functions`. However, if you would like to specify custom column names, you can set the `objective_function_names` to be a list of names (str) for each value returned by the function in `other_objective_functions`. This can be useful if your additional functions return more than one value per function.\n", "\n", - "It is possible to have either the scorer or other_objective_function to return multiple values. In that case, just make sure that the `scorer_weights` and `other_objective_function_weights` are the same length as the number of returned scores.\n", + "It is possible to have either the scorer or other_objective_function to return multiple values. In that case, just make sure that the `scorers_weights` and `other_objective_function_weights` are the same length as the number of returned scores.\n", "\n", "\n", "TPOT comes with a few additional built in objective functions you can use. The first table are objectives applied to fitted pipelines, and thus are passee into the `scorers` parameter. The second table are objective functions for the `other_objective_functions` param.\n", @@ -174,6 +174,19 @@ "| tpot2.objectives.number_of_nodes_objective | Calculates the number of nodes in a pipeline (whether it is an scikit-learn Pipeline, GraphPipeline, Feature Union, or the previous nested within each other) |" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Measuring Model Complexity\n", + "\n", + "When running TPOT, it can sometimes be beneficial to include a secondary objective that measures model complexity. More complex models can yield higher performance but this comes at the cost of interpretability. Simpler models may be more interpretable, but often have lower predictive performance. Sometimes, however, vast increases in complexity only marginally improve predictive performance. There may be other simpler and more interpretable pipelines with marginal performance decreases that could be acceptable for the increased interpretability. However, these pipelines are often missed by optimizing purely for performance. By including both performance and complexity as objective functions, TPOT will attempt to optimize the best pipeline for all complexity levels simultaneously. After optimization, the user will be able to see the complexity vs performance tradeoff and make the decision of which pipeline best suits their needs. \n", + "\n", + "Two methods of measuring complexity to consider would be `tpot2.objectives.number_of_nodes_objective` or `tpot2.objectives.complexity_scorer`. The number of nodes objective simply calculates the number of steps within a pipeline. This is a simple metric, however it does not differentiate between the complexity of different model types. For example, a simple LogisticRegression counts the same as the much more complex XGBoost. The complexity scorer tries to estimate the number of learned parameters included in the classifiers and regressors of the pipeline. It is challenging and potentially subjective how to exactly quantify and compare complexity between different classes of models. However, this function provides a reasonable heuristic for the evolutionary algorithm that at least separates out qualitatively more or less complex algorithms from one another. While it may be hard to exactly compare the relative complexities of LogisticRegression and XGBoost, for example, both will always be on opposite ends of the complexity values returned by this function. This allows for pareto fronts with LogisticRegression on one side, and XGBoost on the other.\n", + "\n", + "An example of this analysis is demonstrated in a following section." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -252,24 +265,39 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Example analysis " + "# Example analysis and the Estimator class \n", + "\n", + "Here we use a toy example dataset included in scikit-learn. We will use the `light` configuration and the `complexity_scorer` to estimate complexity.\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "metadata": {}, "outputs": [ { - "ename": "TypeError", - "evalue": "TPOTEstimator.__init__() got an unexpected keyword argument 'scorer_weights'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[5], line 29\u001b[0m\n\u001b[1;32m 15\u001b[0m X_train, X_test, y_train, y_test \u001b[38;5;241m=\u001b[39m sklearn\u001b[38;5;241m.\u001b[39mmodel_selection\u001b[38;5;241m.\u001b[39mtrain_test_split(X, y, train_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.75\u001b[39m, test_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.25\u001b[39m)\n\u001b[1;32m 18\u001b[0m est \u001b[38;5;241m=\u001b[39m tpot2\u001b[38;5;241m.\u001b[39mTPOTClassifier(\n\u001b[1;32m 19\u001b[0m scorers\u001b[38;5;241m=\u001b[39m[scorer, tpot2\u001b[38;5;241m.\u001b[39mobjectives\u001b[38;5;241m.\u001b[39mcomplexity_scorer],\n\u001b[1;32m 20\u001b[0m scorer_weights\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m1.0\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1.0\u001b[39m],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 27\u001b[0m early_stop\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m,\n\u001b[1;32m 28\u001b[0m verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m,)\n\u001b[0;32m---> 29\u001b[0m \u001b[43mest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 32\u001b[0m \u001b[38;5;28mprint\u001b[39m(scorer(est, X_test, y_test))\n", - "File 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sklearn.metrics.get_scorer('roc_auc_ovr')\n", "\n", - "X, y = sklearn.datasets.load_digits(return_X_y=True)\n", + "X, y = sklearn.datasets.load_breast_cancer(return_X_y=True)\n", "X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, train_size=0.75, test_size=0.25)\n", "\n", "\n", "est = tpot2.TPOTClassifier(\n", " scorers=[scorer, tpot2.objectives.complexity_scorer],\n", - " scorer_weights=[1.0, -1.0],\n", - " objective_function_names=['roc_auc_ovr', 'complexity'],\n", + " scorers_weights=[1.0, -1.0],\n", "\n", " search_space=\"light\",\n", " n_jobs=4, \n", @@ -311,7 +338,538 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Further analysis of results\n", + "You can access the best pipeline selected by TPOT with the `fitted_pipeline_` attribute. This is the pipeline with the highest cross validation score (on the first scorer, or first objective function if no scorer is provided.)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Pipeline(steps=[('maxabsscaler', MaxAbsScaler()),\n",
+       "                ('variancethreshold',\n",
+       "                 VarianceThreshold(threshold=0.0056828922429)),\n",
+       "                ('featureunion',\n",
+       "                 FeatureUnion(transformer_list=[('skiptransformer',\n",
+       "                                                 SkipTransformer()),\n",
+       "                                                ('passthrough',\n",
+       "                                                 Passthrough())])),\n",
+       "                ('kneighborsclassifier',\n",
+       "                 KNeighborsClassifier(n_jobs=1, n_neighbors=28, p=1,\n",
+       "                                      weights='distance'))])
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" + ], + "text/plain": [ + "Pipeline(steps=[('maxabsscaler', MaxAbsScaler()),\n", + " ('variancethreshold',\n", + " VarianceThreshold(threshold=0.0056828922429)),\n", + " ('featureunion',\n", + " FeatureUnion(transformer_list=[('skiptransformer',\n", + " SkipTransformer()),\n", + " ('passthrough',\n", + " Passthrough())])),\n", + " ('kneighborsclassifier',\n", + " KNeighborsClassifier(n_jobs=1, n_neighbors=28, p=1,\n", + " weights='distance'))])" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "best_pipeline = est.fitted_pipeline_\n", + "best_pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 3, 2, 9, 1, 2, 5, 4, 2, 3, 2, 0, 0, 2, 0, 1, 8, 2, 8, 9, 5,\n", + " 7, 3, 9, 1, 8, 2, 9, 3, 4, 5, 6, 1, 0, 9, 8, 3, 1, 3, 3, 1, 1, 4,\n", + " 3, 0, 3, 2, 2, 5, 2, 4, 0, 8, 6, 3, 1, 3, 9, 8, 3, 4, 7, 8, 4, 3,\n", + " 0, 3, 1, 8, 7, 0, 2, 8, 2, 0, 4, 5, 8, 1, 1, 8, 4, 0, 1, 5, 9, 9,\n", + " 7, 8, 7, 3, 2, 3, 5, 2, 9, 2, 2, 5, 9, 8, 3, 1, 9, 2, 6, 5, 5, 8,\n", + " 2, 3, 7, 4, 0, 2, 7, 9, 6, 3, 8, 8, 8, 9, 4, 4, 7, 7, 8, 6, 1, 0,\n", + " 4, 3, 8, 4, 4, 2, 1, 0, 6, 7, 9, 0, 4, 7, 6, 0, 4, 5, 0, 0, 3, 4,\n", + " 5, 5, 9, 5, 7, 2, 9, 7, 4, 2, 3, 2, 5, 7, 4, 8, 4, 6, 6, 0, 1, 2,\n", + " 7, 0, 1, 9, 7, 5, 5, 8, 3, 7, 9, 5, 9, 5, 7, 7, 2, 2, 1, 6, 9, 3,\n", + " 8, 9, 8, 8, 8, 3, 9, 1, 1, 5, 3, 6, 9, 8, 6, 4, 7, 7, 2, 0, 7, 8,\n", + " 8, 0, 7, 9, 6, 9, 4, 2, 1, 8, 5, 6, 1, 2, 5, 8, 3, 9, 0, 6, 2, 7,\n", + " 5, 5, 3, 6, 7, 8, 6, 6, 5, 4, 1, 1, 6, 5, 9, 4, 9, 1, 1, 8, 8, 1,\n", + " 0, 5, 6, 9, 3, 4, 1, 5, 9, 7, 0, 2, 1, 9, 3, 6, 4, 3, 6, 0, 2, 6,\n", + " 1, 3, 1, 4, 7, 6, 2, 4, 2, 1, 9, 7, 6, 7, 4, 1, 8, 8, 7, 6, 1, 6,\n", + " 7, 5, 7, 7, 3, 8, 8, 0, 6, 4, 5, 2, 0, 4, 4, 4, 4, 2, 4, 2, 1, 0,\n", + " 5, 4, 1, 5, 9, 0, 6, 2, 4, 5, 7, 3, 1, 1, 5, 1, 9, 3, 2, 4, 7, 9,\n", + " 4, 4, 0, 1, 2, 2, 5, 9, 7, 1, 6, 7, 1, 1, 1, 7, 6, 1, 1, 9, 8, 3,\n", + " 0, 5, 4, 7, 7, 7, 5, 4, 8, 1, 3, 1, 5, 8, 1, 0, 0, 4, 8, 7, 2, 7,\n", + " 8, 0, 1, 5, 3, 5, 0, 6, 2, 0, 1, 5, 7, 5, 2, 5, 7, 7, 9, 0, 2, 9,\n", + " 9, 5, 0, 5, 6, 3, 5, 5, 0, 6, 5, 5, 3, 7, 9, 5, 9, 4, 6, 2, 3, 9,\n", + " 3, 7, 1, 3, 8, 0, 1, 2, 1, 5])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "best_pipeline.predict(X_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### saving the pipeline\n", + "\n", + "We recommend using dill or pickle to save the instance of the fitted_pipeline_. Note that we do not recommend pickling the TPOT object itself." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import dill as pickle\n", + "with open(\"best_pipeline.pkl\", \"wb\") as f:\n", + " pickle.dump(best_pipeline, f)\n", + "\n", + "#load the pipeline\n", + "import dill as pickle\n", + "with open(\"best_pipeline.pkl\", \"rb\") as f:\n", + " my_loaded_best_pipeline = pickle.load(f)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The evaluated_individuals Dataframe - Further analysis of results\n", "\n", "The `evaluated_individuals` attribute of the tpot estimator object is a Pandas Dataframe containing information about a run. Each row corresponds to an individual pipeline explored by tpot. The dataframe contains the following columns:\n", "\n", @@ -319,16 +877,39 @@ "| :--- | :----: |\n", "| | The first set of columns will correspond to each objective function. These can either be automatically named by TPOT, or passed in by the user. |\n", "| Parents | This contains a tuple that contains the indexes of the 'parents' of the current pipeline. For example, (29, 42) means that the pipelines in indexes 29 and 42 were utilized to generate that pipeline. |\n", - "| | |\n", - "| | |\n", - "| | |\n", - "| | |\n", - "| | |" + "| Variation_Function | The function applied to the parents to generate the new pipeline |\n", + "| Individual | The individual class that represents a specific pipeline and hyperparameter configuration. This class also contains functions for mutation and crossover. To get the sklearn estimator/pipeline object from the individual you can call the `export_pipeline()` function. (as in, `pipe = ind.export_pipeline()`) |\n", + "| Generation | The generation where the individual was created. (Note that the higher performing pipelines from previous generations may still be present in the current \"population\" of a given generation if selected.) |\n", + "| Submitted Timestamp | Timestamp, in seconds, at which the pipeline was sent to be evaluated. This is the output of time.time(), which is \"Return the time in seconds since the epoch as a floating-point number. \" |\n", + "| Completed Timestamp | Timestamp at which the pipeline evaluation completed in the same units as Submitted Timestamp |\n", + "| Pareto_Front\t | If you have multiple parameters, this column is True if the pipeline performance fall on the pareto front line. This is the set of pipelines with scores that are strictly better than pipelines not on the line, but not strictly better than one another. |\n", + "| Instance | This contains the unfitted pipeline evaluated for this row. (This is the pipeline returned by calling the export_pipeline() function of the individual class) |\n" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['roc_auc_score', 'complexity_scorer']" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#get the score/objective column names generated by TPOT\n", + "est.objective_names" + ] + }, + { + "cell_type": "code", + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -353,6 +934,7 @@ " \n", " \n", " roc_auc_score\n", + " complexity_scorer\n", " Parents\n", " Variation_Function\n", " Individual\n", @@ -367,68 +949,73 @@ " \n", " \n", " 0\n", - " 0.992125\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", " 0.0\n", - " 1.726870e+09\n", - " 1.726870e+09\n", - " None\n", + " 1.726883e+09\n", + " 1.726883e+09\n", + " INVALID\n", " NaN\n", - " (Passthrough(), SelectFwe(alpha=0.005516602107...\n", + " (Normalizer(norm='max'), SelectPercentile(perc...\n", " \n", " \n", " 1\n", - " NaN\n", + " 0.945922\n", + " 546.0\n", " NaN\n", " NaN\n", " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", " 0.0\n", - " 1.726870e+09\n", - " 1.726870e+09\n", - " TIMEOUT\n", + " 1.726883e+09\n", + " 1.726883e+09\n", + " None\n", " NaN\n", - " (RobustScaler(quantile_range=(0.232938031941, ...\n", + " (RobustScaler(quantile_range=(0.2886683384991,...\n", " \n", " \n", " 2\n", - " 0.991613\n", + " 0.991940\n", + " 23.0\n", " NaN\n", " NaN\n", " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", " 0.0\n", - " 1.726870e+09\n", - " 1.726870e+09\n", + " 1.726883e+09\n", + " 1.726883e+09\n", " None\n", " NaN\n", - " (MinMaxScaler(), Passthrough(), FeatureUnion(t...\n", + " (MaxAbsScaler(), SelectFwe(alpha=0.00027494990...\n", " \n", " \n", " 3\n", - " 0.974053\n", + " 0.975386\n", + " 34.0\n", " NaN\n", " NaN\n", " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", " 0.0\n", - " 1.726870e+09\n", - " 1.726870e+09\n", + " 1.726883e+09\n", + " 1.726883e+09\n", " None\n", " NaN\n", - " (MaxAbsScaler(), VarianceThreshold(threshold=0...\n", + " (Passthrough(), Passthrough(), FeatureUnion(tr...\n", " \n", " \n", " 4\n", - " NaN\n", + " 0.990177\n", + " 24.0\n", " NaN\n", " NaN\n", " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", " 0.0\n", - " 1.726870e+09\n", - " 1.726870e+09\n", - " TIMEOUT\n", + " 1.726883e+09\n", + " 1.726883e+09\n", + " None\n", " NaN\n", - " (RobustScaler(quantile_range=(0.0238159499352,...\n", + " (Normalizer(norm='l1'), Passthrough(), Feature...\n", " \n", " \n", " ...\n", @@ -442,90 +1029,109 @@ " ...\n", " ...\n", " ...\n", + " ...\n", " \n", " \n", " 195\n", - " 0.946552\n", - " (108, 124)\n", - " ind_crossover , ind_mutate\n", + " 0.964649\n", + " 4.0\n", + " (120, 120)\n", + " ind_mutate\n", " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", " 3.0\n", - " 1.726871e+09\n", - " 1.726871e+09\n", + " 1.726883e+09\n", + " 1.726883e+09\n", " None\n", " NaN\n", - " (StandardScaler(), SelectPercentile(percentile...\n", + " (MaxAbsScaler(), SelectFwe(alpha=0.00062804731...\n", " \n", " \n", " 196\n", - " NaN\n", - " (94, 94)\n", + " 0.994112\n", + " 4.0\n", + " (124, 124)\n", " ind_mutate\n", " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", " 3.0\n", - " 1.726871e+09\n", - " 1.726871e+09\n", - " TIMEOUT\n", + " 1.726883e+09\n", + " 1.726883e+09\n", + " None\n", " NaN\n", - " (MinMaxScaler(), SelectPercentile(percentile=9...\n", + " (Passthrough(), SelectFwe(alpha=0.011355913641...\n", " \n", " \n", " 197\n", - " 0.999038\n", - " (34, 12)\n", - " ind_crossover\n", + " 0.982564\n", + " 5.0\n", + " (34, 114)\n", + " ind_mutate , ind_mutate , ind_crossover\n", " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", " 3.0\n", - " 1.726871e+09\n", - " 1.726871e+09\n", + " 1.726883e+09\n", + " 1.726883e+09\n", " None\n", " NaN\n", - " (RobustScaler(quantile_range=(0.2919651866521,...\n", + " (MinMaxScaler(), SelectFwe(alpha=0.01135591364...\n", " \n", " \n", " 198\n", - " 0.998404\n", - " (118, 118)\n", - " ind_mutate\n", + " 0.683244\n", + " 19.0\n", + " (114, 37)\n", + " ind_crossover\n", " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", " 3.0\n", - " 1.726871e+09\n", - " 1.726871e+09\n", + " 1.726883e+09\n", + " 1.726883e+09\n", " None\n", " NaN\n", - " (RobustScaler(quantile_range=(0.2919651866521,...\n", + " (RobustScaler(quantile_range=(0.0249691582537,...\n", " \n", " \n", " 199\n", " NaN\n", - " (103, 124)\n", + " NaN\n", + " (133, 62)\n", " ind_crossover\n", " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", " 3.0\n", - " 1.726871e+09\n", - " 1.726871e+09\n", + " 1.726883e+09\n", + " 1.726883e+09\n", " TIMEOUT\n", " NaN\n", " (StandardScaler(), SelectPercentile(percentile...\n", " \n", " \n", "\n", - "

200 rows × 10 columns

\n", + "

200 rows × 11 columns

\n", "" ], "text/plain": [ - " roc_auc_score Parents Variation_Function \\\n", - "0 0.992125 NaN NaN \n", - "1 NaN NaN NaN \n", - "2 0.991613 NaN NaN \n", - "3 0.974053 NaN NaN \n", - "4 NaN NaN NaN \n", - ".. ... ... ... \n", - "195 0.946552 (108, 124) ind_crossover , ind_mutate \n", - "196 NaN (94, 94) ind_mutate \n", - "197 0.999038 (34, 12) ind_crossover \n", - "198 0.998404 (118, 118) ind_mutate \n", - "199 NaN (103, 124) ind_crossover \n", + " roc_auc_score complexity_scorer Parents \\\n", + "0 NaN NaN NaN \n", + "1 0.945922 546.0 NaN \n", + "2 0.991940 23.0 NaN \n", + "3 0.975386 34.0 NaN \n", + "4 0.990177 24.0 NaN \n", + ".. ... ... ... \n", + "195 0.964649 4.0 (120, 120) \n", + "196 0.994112 4.0 (124, 124) \n", + "197 0.982564 5.0 (34, 114) \n", + "198 0.683244 19.0 (114, 37) \n", + "199 NaN NaN (133, 62) \n", + "\n", + " Variation_Function \\\n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + ".. ... \n", + "195 ind_mutate \n", + "196 ind_mutate \n", + "197 ind_mutate , ind_mutate , ind_crossover \n", + "198 ind_crossover \n", + "199 ind_crossover \n", "\n", " Individual Generation \\\n", "0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "#replace nans in pareto front with 0\n", + "fig, ax = plt.subplots(figsize=(5,5))\n", + "sns.scatterplot(df[df['Pareto_Front']!=1], x='roc_auc_score', y='complexity_scorer', label='other', ax=ax)\n", + "sns.scatterplot(df[df['Pareto_Front']==1], x='roc_auc_score', y='complexity_scorer', label='Pareto Front', ax=ax)\n", + "ax.title.set_text('Performance of all pipelines')\n", + "#log scale y\n", + "ax.set_yscale('log')\n", + "plt.show()\n", + "\n", + "#replace nans in pareto front with 0\n", + "fig, ax = plt.subplots(figsize=(10,5))\n", + "sns.scatterplot(df[df['Pareto_Front']==1], x='roc_auc_score', y='complexity_scorer', label='Pareto Front', ax=ax)\n", + "ax.title.set_text('Performance of only the Pareto Front')\n", + "#log scale y\n", + "# ax.set_yscale('log')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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roc_auc_scorecomplexity_scorerParentsVariation_FunctionIndividualGenerationSubmitted TimestampCompleted TimestampEval ErrorPareto_FrontInstance
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1160.9949444.0(85, 39)ind_crossover<tpot2.search_spaces.pipelines.sequential.Sequ...2.01.726883e+091.726883e+09None1.0(RobustScaler(quantile_range=(0.2187724978734,...
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" + ], + "text/plain": [ + " roc_auc_score complexity_scorer Parents Variation_Function \\\n", + "183 0.998989 31.0 (5, 5) ind_mutate \n", + "146 0.998853 25.0 (43, 43) ind_mutate \n", + "62 0.998671 14.0 (43, 43) ind_mutate \n", + "175 0.998538 13.0 (86, 62) ind_crossover \n", + "187 0.997800 10.0 (144, 144) ind_mutate \n", + "77 0.997795 9.0 (18, 18) ind_mutate \n", + "148 0.997014 7.0 (43, 43) ind_mutate \n", + "116 0.994944 4.0 (85, 39) ind_crossover \n", + "\n", + " Individual Generation \\\n", + "183 #sk-container-id-3 {\n", + " /* Definition of color scheme common for light and dark mode */\n", + " --sklearn-color-text: black;\n", + " --sklearn-color-line: gray;\n", + " /* Definition of color scheme for unfitted estimators */\n", + " --sklearn-color-unfitted-level-0: #fff5e6;\n", + " --sklearn-color-unfitted-level-1: #f6e4d2;\n", + " --sklearn-color-unfitted-level-2: #ffe0b3;\n", + " --sklearn-color-unfitted-level-3: chocolate;\n", + " /* Definition of color scheme for fitted estimators */\n", + " --sklearn-color-fitted-level-0: #f0f8ff;\n", + " --sklearn-color-fitted-level-1: #d4ebff;\n", + " --sklearn-color-fitted-level-2: #b3dbfd;\n", + " --sklearn-color-fitted-level-3: cornflowerblue;\n", + "\n", + " /* Specific color for light theme */\n", + " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n", + " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n", + " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n", + " --sklearn-color-icon: #696969;\n", + "\n", + " @media (prefers-color-scheme: dark) {\n", + " /* Redefinition of color scheme for dark theme */\n", + " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n", + " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n", + " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n", + " --sklearn-color-icon: #878787;\n", + " }\n", + "}\n", + "\n", + "#sk-container-id-3 {\n", + " color: var(--sklearn-color-text);\n", + "}\n", + "\n", + "#sk-container-id-3 pre {\n", + " padding: 0;\n", + "}\n", + "\n", + "#sk-container-id-3 input.sk-hidden--visually {\n", + " border: 0;\n", + " clip: rect(1px 1px 1px 1px);\n", + " clip: rect(1px, 1px, 1px, 1px);\n", + " height: 1px;\n", + " margin: -1px;\n", + " overflow: hidden;\n", + " padding: 0;\n", + " position: absolute;\n", + " width: 1px;\n", + "}\n", + "\n", + "#sk-container-id-3 div.sk-dashed-wrapped {\n", + " border: 1px dashed var(--sklearn-color-line);\n", + " margin: 0 0.4em 0.5em 0.4em;\n", + " box-sizing: border-box;\n", + " padding-bottom: 0.4em;\n", + " background-color: var(--sklearn-color-background);\n", + "}\n", + "\n", + "#sk-container-id-3 div.sk-container {\n", + " /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n", + " but bootstrap.min.css set `[hidden] { display: none !important; }`\n", + " so we also need the `!important` here to be able to override the\n", + " default hidden behavior on the sphinx rendered scikit-learn.org.\n", + " See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n", + " display: inline-block !important;\n", + " position: relative;\n", + "}\n", + "\n", + "#sk-container-id-3 div.sk-text-repr-fallback {\n", + " display: none;\n", + "}\n", + "\n", + "div.sk-parallel-item,\n", + "div.sk-serial,\n", + "div.sk-item {\n", + " /* draw centered vertical line to link estimators */\n", + " background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n", + " background-size: 2px 100%;\n", + " background-repeat: no-repeat;\n", + " background-position: center center;\n", + "}\n", + "\n", + "/* Parallel-specific style estimator block */\n", + "\n", + "#sk-container-id-3 div.sk-parallel-item::after {\n", + " content: \"\";\n", + " width: 100%;\n", + " border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n", + " flex-grow: 1;\n", + "}\n", + "\n", + "#sk-container-id-3 div.sk-parallel {\n", + " display: flex;\n", + " align-items: stretch;\n", + " justify-content: center;\n", + " background-color: var(--sklearn-color-background);\n", + " position: relative;\n", + "}\n", + "\n", + "#sk-container-id-3 div.sk-parallel-item {\n", + " display: flex;\n", + " flex-direction: column;\n", + "}\n", + "\n", + "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n", + " align-self: flex-end;\n", + " width: 50%;\n", + "}\n", + "\n", + "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n", + " align-self: flex-start;\n", + " width: 50%;\n", + "}\n", + "\n", + "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n", + " width: 0;\n", + "}\n", + "\n", + "/* Serial-specific style estimator block */\n", + "\n", + "#sk-container-id-3 div.sk-serial {\n", + " display: flex;\n", + " flex-direction: column;\n", + " align-items: center;\n", + " background-color: var(--sklearn-color-background);\n", + " padding-right: 1em;\n", + " padding-left: 1em;\n", + "}\n", + "\n", + "\n", + "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n", + "clickable and can be expanded/collapsed.\n", + "- Pipeline and ColumnTransformer use this feature and define the default style\n", + "- Estimators will overwrite some part of the style using the `sk-estimator` class\n", + "*/\n", + "\n", + "/* Pipeline and ColumnTransformer style (default) */\n", + "\n", + "#sk-container-id-3 div.sk-toggleable {\n", + " /* Default theme specific background. It is overwritten whether we have a\n", + " specific estimator or a Pipeline/ColumnTransformer */\n", + " background-color: var(--sklearn-color-background);\n", + "}\n", + "\n", + "/* Toggleable label */\n", + "#sk-container-id-3 label.sk-toggleable__label {\n", + " cursor: pointer;\n", + " display: block;\n", + " width: 100%;\n", + " margin-bottom: 0;\n", + " padding: 0.5em;\n", + " box-sizing: border-box;\n", + " text-align: center;\n", + "}\n", + "\n", + "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n", + " /* Arrow on the left of the label */\n", + " content: \"▸\";\n", + " float: left;\n", + " margin-right: 0.25em;\n", + " color: var(--sklearn-color-icon);\n", + "}\n", + "\n", + "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n", + " color: var(--sklearn-color-text);\n", + "}\n", + "\n", + "/* Toggleable content - dropdown */\n", + "\n", + "#sk-container-id-3 div.sk-toggleable__content {\n", + " max-height: 0;\n", + " max-width: 0;\n", + " overflow: hidden;\n", + " text-align: left;\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-3 div.sk-toggleable__content.fitted {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-3 div.sk-toggleable__content pre {\n", + " margin: 0.2em;\n", + " border-radius: 0.25em;\n", + " color: var(--sklearn-color-text);\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-fitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n", + " /* Expand drop-down */\n", + " max-height: 200px;\n", + " max-width: 100%;\n", + " overflow: auto;\n", + "}\n", + "\n", + "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n", + " content: \"▾\";\n", + "}\n", + "\n", + "/* Pipeline/ColumnTransformer-specific style */\n", + "\n", + "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " color: var(--sklearn-color-text);\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "/* Estimator-specific style */\n", + "\n", + "/* Colorize estimator box */\n", + "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n", + "#sk-container-id-3 div.sk-label label {\n", + " /* The background is the default theme color */\n", + " color: var(--sklearn-color-text-on-default-background);\n", + "}\n", + "\n", + "/* On hover, darken the color of the background */\n", + "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n", + " color: var(--sklearn-color-text);\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "/* Label box, darken color on hover, fitted */\n", + "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n", + " color: var(--sklearn-color-text);\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "/* Estimator label */\n", + "\n", + "#sk-container-id-3 div.sk-label label {\n", + " font-family: monospace;\n", + " font-weight: bold;\n", + " display: inline-block;\n", + " line-height: 1.2em;\n", + "}\n", + "\n", + "#sk-container-id-3 div.sk-label-container {\n", + " text-align: center;\n", + "}\n", + "\n", + "/* Estimator-specific */\n", + "#sk-container-id-3 div.sk-estimator {\n", + " font-family: monospace;\n", + " border: 1px dotted var(--sklearn-color-border-box);\n", + " border-radius: 0.25em;\n", + " box-sizing: border-box;\n", + " margin-bottom: 0.5em;\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-3 div.sk-estimator.fitted {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-0);\n", + "}\n", + "\n", + "/* on hover */\n", + "#sk-container-id-3 div.sk-estimator:hover {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-3 div.sk-estimator.fitted:hover {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n", + "\n", + "/* Common style for \"i\" and \"?\" */\n", + "\n", + ".sk-estimator-doc-link,\n", + "a:link.sk-estimator-doc-link,\n", + "a:visited.sk-estimator-doc-link {\n", + " float: right;\n", + " font-size: smaller;\n", + " line-height: 1em;\n", + " font-family: monospace;\n", + " background-color: var(--sklearn-color-background);\n", + " border-radius: 1em;\n", + " height: 1em;\n", + " width: 1em;\n", + " text-decoration: none !important;\n", + " margin-left: 1ex;\n", + " /* unfitted */\n", + " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n", + " color: var(--sklearn-color-unfitted-level-1);\n", + "}\n", + "\n", + ".sk-estimator-doc-link.fitted,\n", + "a:link.sk-estimator-doc-link.fitted,\n", + "a:visited.sk-estimator-doc-link.fitted {\n", + " /* fitted */\n", + " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n", + " color: var(--sklearn-color-fitted-level-1);\n", + "}\n", + "\n", + "/* On hover */\n", + "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n", + ".sk-estimator-doc-link:hover,\n", + "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n", + ".sk-estimator-doc-link:hover {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-3);\n", + " color: var(--sklearn-color-background);\n", + " text-decoration: none;\n", + "}\n", + "\n", + "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n", + ".sk-estimator-doc-link.fitted:hover,\n", + "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n", + ".sk-estimator-doc-link.fitted:hover {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-3);\n", + " color: var(--sklearn-color-background);\n", + " text-decoration: none;\n", + "}\n", + "\n", + "/* Span, style for the box shown on hovering the info icon */\n", + ".sk-estimator-doc-link span {\n", + " display: none;\n", + " z-index: 9999;\n", + " position: relative;\n", + " font-weight: normal;\n", + " right: .2ex;\n", + " padding: .5ex;\n", + " margin: .5ex;\n", + " width: min-content;\n", + " min-width: 20ex;\n", + " max-width: 50ex;\n", + " color: var(--sklearn-color-text);\n", + " box-shadow: 2pt 2pt 4pt #999;\n", + " /* unfitted */\n", + " background: var(--sklearn-color-unfitted-level-0);\n", + " border: .5pt solid var(--sklearn-color-unfitted-level-3);\n", + "}\n", + "\n", + ".sk-estimator-doc-link.fitted span {\n", + " /* fitted */\n", + " background: var(--sklearn-color-fitted-level-0);\n", + " border: var(--sklearn-color-fitted-level-3);\n", + "}\n", + "\n", + ".sk-estimator-doc-link:hover span {\n", + " display: block;\n", + "}\n", + "\n", + "/* \"?\"-specific style due to the `` HTML tag */\n", + "\n", + "#sk-container-id-3 a.estimator_doc_link {\n", + " float: right;\n", + " font-size: 1rem;\n", + " line-height: 1em;\n", + " font-family: monospace;\n", + " background-color: var(--sklearn-color-background);\n", + " border-radius: 1rem;\n", + " height: 1rem;\n", + " width: 1rem;\n", + " text-decoration: none;\n", + " /* unfitted */\n", + " color: var(--sklearn-color-unfitted-level-1);\n", + " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n", + "}\n", + "\n", + "#sk-container-id-3 a.estimator_doc_link.fitted {\n", + " /* fitted */\n", + " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n", + " color: var(--sklearn-color-fitted-level-1);\n", + "}\n", + "\n", + "/* On hover */\n", + "#sk-container-id-3 a.estimator_doc_link:hover {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-3);\n", + " color: var(--sklearn-color-background);\n", + " text-decoration: none;\n", + "}\n", + "\n", + "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-3);\n", + "}\n", + "
Pipeline(steps=[('robustscaler',\n",
+       "                 RobustScaler(quantile_range=(0.2187724978734,\n",
+       "                                              0.7909007640608))),\n",
+       "                ('variancethreshold',\n",
+       "                 VarianceThreshold(threshold=0.0193318854527)),\n",
+       "                ('featureunion',\n",
+       "                 FeatureUnion(transformer_list=[('skiptransformer',\n",
+       "                                                 SkipTransformer()),\n",
+       "                                                ('passthrough',\n",
+       "                                                 Passthrough())])),\n",
+       "                ('kneighborsclassifier',\n",
+       "                 KNeighborsClassifier(n_jobs=1, n_neighbors=1))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "Pipeline(steps=[('robustscaler',\n", + " RobustScaler(quantile_range=(0.2187724978734,\n", + " 0.7909007640608))),\n", + " ('variancethreshold',\n", + " VarianceThreshold(threshold=0.0193318854527)),\n", + " ('featureunion',\n", + " FeatureUnion(transformer_list=[('skiptransformer',\n", + " SkipTransformer()),\n", + " ('passthrough',\n", + " Passthrough())])),\n", + " ('kneighborsclassifier',\n", + " KNeighborsClassifier(n_jobs=1, n_neighbors=1))])" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#access the best performing pipeline with the lowest complexity\n", + "\n", + "best_pipeline_lowest_complexity = sorted_pareto_front.iloc[-1]['Instance']\n", + "best_pipeline_lowest_complexity" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plot performance over time + Continuing a run from where it left off\n", + "\n", + "Plotting the performance over time is a good way of trying to access whether or not the TPOT model has converged. If performance seems to asymptote over time, there may not be much more performance to be gained by running for a longer period of time. If the plot looks like it is still actively improving, it may be worth running TPOT for a longer duration. \n", + "\n", + "There are two ways to resume TPOT. If the `warm_start` parameter is set to True, subsequent calls to `fit` will continue training where it left off (The conventional scikit-learn default is to retrain from scratch on subsequent calls to fit). Additionally, if `periodic_checkpoint_folder` is set, TPOT will periodically save its current state. If TPOT terminates normally, is interrupted (job canceled, PC shut off), or crashes (memory issues), it will be able to resume training from where it left off. ** NOTE: If the periodic_checkpoint_folder is set, TPOT will always resume from the **" ] }, { @@ -640,6 +1987,26 @@ " \n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Common mistakes or issues\n", + "\n", + "If you are experiencing issues with TPOT, here are some common issues and how to address them.\n", + "\n", + "* Performance is lower than expected.\n", + " * TPOT may have to be run for a longer duration, increase `max_time_mins`, `early_stop`, or `generations`.\n", + " * Individual pipelines may need more time to complete fitting, increase `max_eval_time_seconds`.\n", + " * The configuration may not include the optimal model types or hyperparameter ranges, explore other included templates or customize your own search space (see Tutorial 2!)\n", + "* TPOT is running forever and never terminating\n", + " * Check that at least one of the three termination conditions is set to a reasonable level. These are `max_time_mins`, `early_stop`, or `generations`. Additionally check that `max_eval_time_seconds` is giving enough time for most models to train without being overly long. (Some estimators may take an unreasonably long time to fit, this parameter is intended to prevent them from slowing everything to a halt. In my experience, SVC and SVR tend to be the culprits, so removing them from the search space may also improve run time).\n", + "* Many pipelines in the evaluated_individuals dataframe have crashed or turned up invalid!\n", + " * This may actually be normal and is expected behavior for TPOT. In some cases, TPOT may attempt an invalid hyperparameter combination which results in the pipeline not working. Other times, the pipeline configuration itself may be invalid. For example, a selector may not select any features due to its hyperparameter. Another common example is `MultinomialNB` throwing an error because it expects positive values, but a prior transformation yielded a negative value. \n", + " * If you used custom search spaces, you can use `ConfigSpace` conditionals to prevent invalid hyperparameters (this may still occur due to how TPOT uses crossover).\n", + " * Setting `verbose=5` will print out the full error message for all failed pipelines. This can be useful for debugging whether or not there is something misconfigured in your pipeline, custom search space modules, or something else." + ] + }, { "attachments": {}, "cell_type": "markdown", From 2ee9ac8ea5cf84858147b442b82a2ef3d2532bb9 Mon Sep 17 00:00:00 2001 From: perib Date: Fri, 20 Sep 2024 19:54:21 -0700 Subject: [PATCH 04/44] edits --- Tutorial/1_Using_TPOT.ipynb | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/Tutorial/1_Using_TPOT.ipynb b/Tutorial/1_Using_TPOT.ipynb index d3cf8307..6b74347e 100644 --- a/Tutorial/1_Using_TPOT.ipynb +++ b/Tutorial/1_Using_TPOT.ipynb @@ -1991,7 +1991,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Common mistakes or issues\n", + "### Common mistakes or issues - Debugging\n", "\n", "If you are experiencing issues with TPOT, here are some common issues and how to address them.\n", "\n", @@ -2004,7 +2004,16 @@ "* Many pipelines in the evaluated_individuals dataframe have crashed or turned up invalid!\n", " * This may actually be normal and is expected behavior for TPOT. In some cases, TPOT may attempt an invalid hyperparameter combination which results in the pipeline not working. Other times, the pipeline configuration itself may be invalid. For example, a selector may not select any features due to its hyperparameter. Another common example is `MultinomialNB` throwing an error because it expects positive values, but a prior transformation yielded a negative value. \n", " * If you used custom search spaces, you can use `ConfigSpace` conditionals to prevent invalid hyperparameters (this may still occur due to how TPOT uses crossover).\n", - " * Setting `verbose=5` will print out the full error message for all failed pipelines. This can be useful for debugging whether or not there is something misconfigured in your pipeline, custom search space modules, or something else." + " * Setting `verbose=5` will print out the full error message for all failed pipelines. This can be useful for debugging whether or not there is something misconfigured in your pipeline, custom search space modules, or something else.\n", + "\n", + "Other things to be aware of:\n", + "\n", + "* On small datasets, it is not impossible for TPOT to over fit the cross validation score itself. This can lead to lower than expected performance on held out datasets. TPOT will always return the model with the highest CV score as its final fitted_pipeline. However, if the highest performing model as evaluated by cross validation actually was just overfit to the CV score, it may actually be worse performing compared to other models on the pareto front.\n", + " * Using a secondary complexity objective and evaluating the entire pareto front may be beneficial. In some cases a lower performing pipeline with lower complexity can actually perform better on held out sets. These can either be evaluated and compared on a held out validation set, or sometimes, if very data limited, simply using a different seed of splitting the CV folds can work as well.\n", + " * TPOT can do this automatically. The `validation_strategy` parameter than select between re-testing the final pareto front on either a held out validation set (percent of data set by `validation_fraction`) or on a different seed for splitting the CV folds. These can be selected by setting `validation_strategy` to \"split\" or \"reshuffled\", respectively.\n", + " * Increasing the number of folds of cross validation can mitigate this. \n", + " * Nested cross validation can also be used to estimate the performance of the TPOT optimization algorithm itself.\n", + " * Removing more complex methods from the search space can reduce the changes of overfitting" ] }, { From 714b1e81f4e44ee107c1cd110b21ad67d375f6d9 Mon Sep 17 00:00:00 2001 From: perib Date: Fri, 20 Sep 2024 20:11:17 -0700 Subject: [PATCH 05/44] allowed memory to be passed through to linear pipelines (previously only supported graphpipelines) --- tpot2/search_spaces/base.py | 2 +- tpot2/search_spaces/nodes/fss_node.py | 2 +- tpot2/search_spaces/nodes/genetic_feature_selection.py | 2 +- tpot2/search_spaces/pipelines/choice.py | 4 ++-- tpot2/search_spaces/pipelines/dynamic_linear.py | 4 ++-- tpot2/search_spaces/pipelines/dynamicunion.py | 2 +- tpot2/search_spaces/pipelines/graph.py | 8 +++----- tpot2/search_spaces/pipelines/sequential.py | 9 ++++----- tpot2/search_spaces/pipelines/union.py | 2 +- tpot2/search_spaces/pipelines/wrapper.py | 4 ++-- tpot2/tpot_estimator/estimator.py | 2 +- tpot2/tpot_estimator/estimator_utils.py | 6 +++--- tpot2/tpot_estimator/steady_state_estimator.py | 2 +- 13 files changed, 23 insertions(+), 26 deletions(-) diff --git a/tpot2/search_spaces/base.py b/tpot2/search_spaces/base.py index 97bb2a57..ea3b751b 100644 --- a/tpot2/search_spaces/base.py +++ b/tpot2/search_spaces/base.py @@ -33,7 +33,7 @@ def wrapper(self, other, rng=None, **kwargs): return wrapper - def export_pipeline(self) -> BaseEstimator: + def export_pipeline(self, **kwargs) -> BaseEstimator: return def unique_id(self): diff --git a/tpot2/search_spaces/nodes/fss_node.py b/tpot2/search_spaces/nodes/fss_node.py index 4dda0d92..a5dfe2b8 100644 --- a/tpot2/search_spaces/nodes/fss_node.py +++ b/tpot2/search_spaces/nodes/fss_node.py @@ -55,7 +55,7 @@ def crossover(self, other, rng=None): self.selected_subset_name = other.selected_subset_name self.sel_subset = other.sel_subset - def export_pipeline(self): + def export_pipeline(self, **kwargs): return FeatureSetSelector(sel_subset=self.sel_subset, name=self.selected_subset_name) diff --git a/tpot2/search_spaces/nodes/genetic_feature_selection.py b/tpot2/search_spaces/nodes/genetic_feature_selection.py index 0bea039a..0af42aa1 100644 --- a/tpot2/search_spaces/nodes/genetic_feature_selection.py +++ b/tpot2/search_spaces/nodes/genetic_feature_selection.py @@ -160,7 +160,7 @@ def _crossover_swap(self, ss2, rng=None): self.mask = np.where(mask, self.mask, ss2.mask) - def export_pipeline(self): + def export_pipeline(self, **kwargs): return MaskSelector(mask=self.mask) diff --git a/tpot2/search_spaces/pipelines/choice.py b/tpot2/search_spaces/pipelines/choice.py index 694567db..da1fcfd0 100644 --- a/tpot2/search_spaces/pipelines/choice.py +++ b/tpot2/search_spaces/pipelines/choice.py @@ -33,8 +33,8 @@ def _mutate_node(self, rng=None): def crossover(self, other, rng=None): return self.node.crossover(other.node, rng) - def export_pipeline(self): - return self.node.export_pipeline() + def export_pipeline(self, **kwargs): + return self.node.export_pipeline(**kwargs) def unique_id(self): return self.node.unique_id() diff --git a/tpot2/search_spaces/pipelines/dynamic_linear.py b/tpot2/search_spaces/pipelines/dynamic_linear.py index 528ec7c4..e58005d3 100644 --- a/tpot2/search_spaces/pipelines/dynamic_linear.py +++ b/tpot2/search_spaces/pipelines/dynamic_linear.py @@ -127,8 +127,8 @@ def _crossover_node(self, other, rng): crossover_success = True return crossover_success - def export_pipeline(self): - return sklearn.pipeline.make_pipeline(*[step.export_pipeline() for step in self.pipeline]) + def export_pipeline(self, memory=None, **kwargs): + return sklearn.pipeline.make_pipeline(*[step.export_pipeline() for step in self.pipeline], memory=memory) def unique_id(self): l = [step.unique_id() for step in self.pipeline] diff --git a/tpot2/search_spaces/pipelines/dynamicunion.py b/tpot2/search_spaces/pipelines/dynamicunion.py index 8d8772eb..25a0147f 100644 --- a/tpot2/search_spaces/pipelines/dynamicunion.py +++ b/tpot2/search_spaces/pipelines/dynamicunion.py @@ -136,7 +136,7 @@ def _crossover_node(self, other, rng): return changed - def export_pipeline(self): + def export_pipeline(self, **kwargs): values = list(self.union_dict.values()) return sklearn.pipeline.make_union(*[step.export_pipeline() for step in values]) diff --git a/tpot2/search_spaces/pipelines/graph.py b/tpot2/search_spaces/pipelines/graph.py index fc769b1c..68a64441 100644 --- a/tpot2/search_spaces/pipelines/graph.py +++ b/tpot2/search_spaces/pipelines/graph.py @@ -85,7 +85,6 @@ def __init__( self.cross_val_predict_cv = cross_val_predict_cv self.method = method - self.memory = memory self.use_label_encoder = use_label_encoder self.root = self.root_search_space.generate(rng) @@ -597,7 +596,7 @@ def _merge_duplicated_nodes(self): return graph_changed - def export_pipeline(self): + def export_pipeline(self, memory=None, **kwargs): estimator_graph = self.graph.copy() #mapping = {node:node.method_class(**node.hyperparameters) for node in estimator_graph} @@ -623,7 +622,7 @@ def export_pipeline(self): for label, instance in label_to_instance.items(): estimator_graph.nodes[label]["instance"] = instance - return tpot2.GraphPipeline(graph=estimator_graph, memory=self.memory, use_label_encoder=self.use_label_encoder, method=self.method, cross_val_predict_cv=self.cross_val_predict_cv) + return tpot2.GraphPipeline(graph=estimator_graph, memory=memory, use_label_encoder=self.use_label_encoder, method=self.method, cross_val_predict_cv=self.cross_val_predict_cv) def plot(self): @@ -749,13 +748,12 @@ def __init__(self, self.cross_val_predict_cv = cross_val_predict_cv self.method = method - self.memory = memory self.use_label_encoder = use_label_encoder def generate(self, rng=None): rng = np.random.default_rng(rng) ind = GraphPipelineIndividual(self.root_search_space, self.leaf_search_space, self.inner_search_space, self.max_size, self.crossover_same_depth, - self.cross_val_predict_cv, self.method, self.memory, self.use_label_encoder, rng=rng) + self.cross_val_predict_cv, self.method, self.use_label_encoder, rng=rng) # if user specified limit, grab a random number between that limit if self.max_size is None or self.max_size == np.inf: diff --git a/tpot2/search_spaces/pipelines/sequential.py b/tpot2/search_spaces/pipelines/sequential.py index 8d7fc9ca..ab9f97da 100644 --- a/tpot2/search_spaces/pipelines/sequential.py +++ b/tpot2/search_spaces/pipelines/sequential.py @@ -126,8 +126,8 @@ def _crossover_node(self, other, rng): return crossover_success - def export_pipeline(self): - return sklearn.pipeline.make_pipeline(*[step.export_pipeline() for step in self.pipeline], memory=self.memory) + def export_pipeline(self, memory=None, **kwargs): + return sklearn.pipeline.make_pipeline(*[step.export_pipeline() for step in self.pipeline], memory=memory) def unique_id(self): l = [step.unique_id() for step in self.pipeline] @@ -138,13 +138,12 @@ def unique_id(self): class SequentialPipeline(SklearnIndividualGenerator): - def __init__(self, search_spaces : List[SklearnIndividualGenerator], memory=None ) -> None: + def __init__(self, search_spaces : List[SklearnIndividualGenerator] ) -> None: """ Takes in a list of search spaces. will produce a pipeline of Sequential length. Each step in the pipeline will correspond to the the search space provided in the same index. """ self.search_spaces = search_spaces - self.memory = memory def generate(self, rng=None): - return SequentialPipelineIndividual(self.search_spaces, memory=self.memory, rng=rng) \ No newline at end of file + return SequentialPipelineIndividual(self.search_spaces, rng=rng) \ No newline at end of file diff --git a/tpot2/search_spaces/pipelines/union.py b/tpot2/search_spaces/pipelines/union.py index 811ef38b..a8fd392b 100644 --- a/tpot2/search_spaces/pipelines/union.py +++ b/tpot2/search_spaces/pipelines/union.py @@ -60,7 +60,7 @@ def _crossover_node(self, other, rng): return crossover_success - def export_pipeline(self): + def export_pipeline(self, **kwargs): return sklearn.pipeline.make_union(*[step.export_pipeline() for step in self.pipeline]) def unique_id(self): diff --git a/tpot2/search_spaces/pipelines/wrapper.py b/tpot2/search_spaces/pipelines/wrapper.py index d61bc5f3..1b5807c8 100644 --- a/tpot2/search_spaces/pipelines/wrapper.py +++ b/tpot2/search_spaces/pipelines/wrapper.py @@ -100,14 +100,14 @@ def check_hyperparameters_for_None(self): self.hyperparameters[key] = False - def export_pipeline(self): + def export_pipeline(self, **kwargs): if self.hyperparameters_parser is not None: final_params = self.hyperparameters_parser(self.hyperparameters) else: final_params = self.hyperparameters - est = self.node.export_pipeline() + est = self.node.export_pipeline(**kwargs) wrapped_est = self.method(est, **final_params) return wrapped_est diff --git a/tpot2/tpot_estimator/estimator.py b/tpot2/tpot_estimator/estimator.py index ae1762d8..b8a14b75 100644 --- a/tpot2/tpot_estimator/estimator.py +++ b/tpot2/tpot_estimator/estimator.py @@ -881,7 +881,7 @@ def ind_generator(rng): if self.export_graphpipeline: best_individual_pipeline = best_individual.export_flattened_graphpipeline(memory=self.memory, cross_val_predict_cv=self.cross_val_predict_cv) else: - best_individual_pipeline = best_individual.export_pipeline() + best_individual_pipeline = best_individual.export_pipeline(memory=self.memory) if self.preprocessing: self.fitted_pipeline_ = sklearn.pipeline.make_pipeline(sklearn.base.clone(self._preprocessing_pipeline), best_individual_pipeline ) diff --git a/tpot2/tpot_estimator/estimator_utils.py b/tpot2/tpot_estimator/estimator_utils.py index 7be96e26..0eef127b 100644 --- a/tpot2/tpot_estimator/estimator_utils.py +++ b/tpot2/tpot_estimator/estimator_utils.py @@ -19,7 +19,7 @@ def apply_make_pipeline(graphindividual, preprocessing_pipeline=None, export_gra if export_graphpipeline: est = graphindividual.export_flattened_graphpipeline(**pipeline_kwargs) else: - est = graphindividual.export_pipeline() + est = graphindividual.export_pipeline(**pipeline_kwargs) if preprocessing_pipeline is None: @@ -38,7 +38,7 @@ def objective_function_generator(pipeline, x,y, scorers, cv, other_objective_fun if export_graphpipeline: pipeline = pipeline.export_flattened_graphpipeline(**pipeline_kwargs) else: - pipeline = pipeline.export_pipeline() + pipeline = pipeline.export_pipeline(**pipeline_kwargs) if budget is not None and budget < 1: if is_classification: @@ -70,7 +70,7 @@ def val_objective_function_generator(pipeline, X_train, y_train, X_test, y_test, if export_graphpipeline: pipeline = pipeline.export_flattened_graphpipeline(**pipeline_kwargs) else: - pipeline = pipeline.export_pipeline() + pipeline = pipeline.export_pipeline(**pipeline_kwargs) fitted_pipeline = sklearn.base.clone(pipeline) fitted_pipeline.fit(X_train, y_train) diff --git a/tpot2/tpot_estimator/steady_state_estimator.py b/tpot2/tpot_estimator/steady_state_estimator.py index e3440dd9..0a55c2c1 100644 --- a/tpot2/tpot_estimator/steady_state_estimator.py +++ b/tpot2/tpot_estimator/steady_state_estimator.py @@ -893,7 +893,7 @@ def ind_generator(rng): if self.export_graphpipeline: best_individual_pipeline = best_individual.export_flattened_graphpipeline(memory=self.memory, cross_val_predict_cv=self.cross_val_predict_cv) else: - best_individual_pipeline = best_individual.export_pipeline() + best_individual_pipeline = best_individual.export_pipeline(memory=self.memory) if self.preprocessing: self.fitted_pipeline_ = sklearn.pipeline.make_pipeline(sklearn.base.clone(self._preprocessing_pipeline), best_individual_pipeline ) From 37291373d94a7fec7035a57f2aac82ca0423fcb8 Mon Sep 17 00:00:00 2001 From: perib Date: Fri, 20 Sep 2024 20:13:51 -0700 Subject: [PATCH 06/44] edits --- Tutorial/1_Using_TPOT.ipynb | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/Tutorial/1_Using_TPOT.ipynb b/Tutorial/1_Using_TPOT.ipynb index 6b74347e..67abf7de 100644 --- a/Tutorial/1_Using_TPOT.ipynb +++ b/Tutorial/1_Using_TPOT.ipynb @@ -1999,12 +1999,15 @@ " * TPOT may have to be run for a longer duration, increase `max_time_mins`, `early_stop`, or `generations`.\n", " * Individual pipelines may need more time to complete fitting, increase `max_eval_time_seconds`.\n", " * The configuration may not include the optimal model types or hyperparameter ranges, explore other included templates or customize your own search space (see Tutorial 2!)\n", - "* TPOT is running forever and never terminating\n", + "* TPOT is too slow! It is running forever and never terminating\n", " * Check that at least one of the three termination conditions is set to a reasonable level. These are `max_time_mins`, `early_stop`, or `generations`. Additionally check that `max_eval_time_seconds` is giving enough time for most models to train without being overly long. (Some estimators may take an unreasonably long time to fit, this parameter is intended to prevent them from slowing everything to a halt. In my experience, SVC and SVR tend to be the culprits, so removing them from the search space may also improve run time).\n", + " * Set the `memory` parameter to allow TPOT to prevent repeated work when using either scikit-learn pipelines or TPOT GraphPipelines.\n", "* Many pipelines in the evaluated_individuals dataframe have crashed or turned up invalid!\n", " * This may actually be normal and is expected behavior for TPOT. In some cases, TPOT may attempt an invalid hyperparameter combination which results in the pipeline not working. Other times, the pipeline configuration itself may be invalid. For example, a selector may not select any features due to its hyperparameter. Another common example is `MultinomialNB` throwing an error because it expects positive values, but a prior transformation yielded a negative value. \n", " * If you used custom search spaces, you can use `ConfigSpace` conditionals to prevent invalid hyperparameters (this may still occur due to how TPOT uses crossover).\n", " * Setting `verbose=5` will print out the full error message for all failed pipelines. This can be useful for debugging whether or not there is something misconfigured in your pipeline, custom search space modules, or something else.\n", + "* TPOT is crashing due to memory issues\n", + " * Set the `memory_limit` parameter so that n_jobs*memorylimit is less than the available RAM on your machine plus some wiggle room. This should prevent crashing due to memory concerns.\n", "\n", "Other things to be aware of:\n", "\n", From fc4c4e554d3bd1e71ad20f4f95331260701e6874 Mon Sep 17 00:00:00 2001 From: perib Date: Fri, 20 Sep 2024 20:25:42 -0700 Subject: [PATCH 07/44] edit --- Tutorial/1_Using_TPOT.ipynb | 41 ++++++++++++++++++++++++++++++++++++- 1 file changed, 40 insertions(+), 1 deletion(-) diff --git a/Tutorial/1_Using_TPOT.ipynb b/Tutorial/1_Using_TPOT.ipynb index 67abf7de..64ef0153 100644 --- a/Tutorial/1_Using_TPOT.ipynb +++ b/Tutorial/1_Using_TPOT.ipynb @@ -1922,7 +1922,41 @@ "\n", "Plotting the performance over time is a good way of trying to access whether or not the TPOT model has converged. If performance seems to asymptote over time, there may not be much more performance to be gained by running for a longer period of time. If the plot looks like it is still actively improving, it may be worth running TPOT for a longer duration. \n", "\n", - "There are two ways to resume TPOT. If the `warm_start` parameter is set to True, subsequent calls to `fit` will continue training where it left off (The conventional scikit-learn default is to retrain from scratch on subsequent calls to fit). Additionally, if `periodic_checkpoint_folder` is set, TPOT will periodically save its current state. If TPOT terminates normally, is interrupted (job canceled, PC shut off), or crashes (memory issues), it will be able to resume training from where it left off. ** NOTE: If the periodic_checkpoint_folder is set, TPOT will always resume from the **" + "There are two ways to resume TPOT. If the `warm_start` parameter is set to True, subsequent calls to `fit` will continue training where it left off (The conventional scikit-learn default is to retrain from scratch on subsequent calls to fit). Additionally, if `periodic_checkpoint_folder` is set, TPOT will periodically save its current state. If TPOT terminates normally, is interrupted (job canceled, PC shut off), or crashes (memory issues), it will be able to resume training from where it left off. ** NOTE: If the periodic_checkpoint_folder is set, TPOT will always resume from the **\n", + "\n", + "In this case we can see that performance is near optimal and has slowed, so more time is likely unnecessary." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#get columns where roc_auc_score is not NaN\n", + "scores_and_times = df[df['roc_auc_score'].notna()][['roc_auc_score', 'Completed Timestamp']].sort_values('Completed Timestamp', ascending=True).to_numpy()\n", + "\n", + "#get best score at a given time\n", + "best_scores = np.maximum.accumulate(scores_and_times[:,0])\n", + "times = scores_and_times[:,1]\n", + "times = times - df['Submitted Timestamp'].min()\n", + "\n", + "fig, ax = plt.subplots(figsize=(10,5))\n", + "ax.plot(times, best_scores)\n", + "ax.set_xlabel('Time (seconds)')\n", + "ax.set_ylabel('Best Score')\n", + "plt.show()\n" ] }, { @@ -2002,12 +2036,17 @@ "* TPOT is too slow! It is running forever and never terminating\n", " * Check that at least one of the three termination conditions is set to a reasonable level. These are `max_time_mins`, `early_stop`, or `generations`. Additionally check that `max_eval_time_seconds` is giving enough time for most models to train without being overly long. (Some estimators may take an unreasonably long time to fit, this parameter is intended to prevent them from slowing everything to a halt. In my experience, SVC and SVR tend to be the culprits, so removing them from the search space may also improve run time).\n", " * Set the `memory` parameter to allow TPOT to prevent repeated work when using either scikit-learn pipelines or TPOT GraphPipelines.\n", + " * Increase n_jobs to use more processes/CPU power. See Tutorial 7 for advanced Dask usage, including parallelizing across multiple nodes on an HPC.\n", + " * Use feature selection, either the build in configuration of sklearn methods (see Tutorial 2), or genetic feature selection (see Tutorials 3 and 5 for two different strategies).\n", + " * Use successive halving to reduce computational load (See tutorial 8).\n", "* Many pipelines in the evaluated_individuals dataframe have crashed or turned up invalid!\n", " * This may actually be normal and is expected behavior for TPOT. In some cases, TPOT may attempt an invalid hyperparameter combination which results in the pipeline not working. Other times, the pipeline configuration itself may be invalid. For example, a selector may not select any features due to its hyperparameter. Another common example is `MultinomialNB` throwing an error because it expects positive values, but a prior transformation yielded a negative value. \n", " * If you used custom search spaces, you can use `ConfigSpace` conditionals to prevent invalid hyperparameters (this may still occur due to how TPOT uses crossover).\n", " * Setting `verbose=5` will print out the full error message for all failed pipelines. This can be useful for debugging whether or not there is something misconfigured in your pipeline, custom search space modules, or something else.\n", "* TPOT is crashing due to memory issues\n", " * Set the `memory_limit` parameter so that n_jobs*memorylimit is less than the available RAM on your machine plus some wiggle room. This should prevent crashing due to memory concerns.\n", + " * Using feature selection may also improve memory usage as described above.\n", + " * Remove modules that create high RAM usage (e.g. multiple PolynomialFeatures or one with high degree).\n", "\n", "Other things to be aware of:\n", "\n", From 66eaf7a788807e4537be292f35bbc406ce77b384 Mon Sep 17 00:00:00 2001 From: perib Date: Fri, 20 Sep 2024 20:57:42 -0700 Subject: [PATCH 08/44] edit --- Tutorial/1_Using_TPOT.ipynb | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/Tutorial/1_Using_TPOT.ipynb b/Tutorial/1_Using_TPOT.ipynb index 64ef0153..0e34e66d 100644 --- a/Tutorial/1_Using_TPOT.ipynb +++ b/Tutorial/1_Using_TPOT.ipynb @@ -1941,6 +1941,13 @@ }, "metadata": {}, "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generation: : 0it [1:47:46, ?it/s]\n" + ] } ], "source": [ From 308fef3c5ff1239635d3aa3ba8f1a6390c06d7e0 Mon Sep 17 00:00:00 2001 From: perib Date: Mon, 23 Sep 2024 09:02:43 -0700 Subject: [PATCH 09/44] more docs --- ISSUE_TEMPLATE.md | 2 +- Tutorial/1_Using_TPOT.ipynb | 156 +++++++++++------- tpot2/tpot_estimator/estimator.py | 2 +- .../tpot_estimator/steady_state_estimator.py | 2 +- .../tpot_estimator/templates/tpottemplates.py | 4 +- 5 files changed, 105 insertions(+), 61 deletions(-) diff --git a/ISSUE_TEMPLATE.md b/ISSUE_TEMPLATE.md index 1bf2b7b4..ae32ba3b 100644 --- a/ISSUE_TEMPLATE.md +++ b/ISSUE_TEMPLATE.md @@ -8,7 +8,7 @@ ## Process to reproduce the issue -[ordered list the process to finding and recreating the issue, example below] +[ordered list the process to finding and recreating the issue, example below. A minimally reproducible example would be ideal. This refers to the minimum amount of code necessary to reproduce the issue.] 1. User creates TPOT instance 2. User calls TPOT `fit()` function with training data diff --git a/Tutorial/1_Using_TPOT.ipynb b/Tutorial/1_Using_TPOT.ipynb index 0e34e66d..182710da 100644 --- a/Tutorial/1_Using_TPOT.ipynb +++ b/Tutorial/1_Using_TPOT.ipynb @@ -1972,59 +1972,27 @@ "source": [ "### Common parameters\n", "\n", - " scorers : (list, scorer)\n", - " A scorer or list of scorers to be used in the cross-validation process. \n", - " see https://scikit-learn.org/stable/modules/model_evaluation.html\n", - " \n", - " scorers_weights : list\n", - " A list of weights to be applied to the scorers during the optimization process.\n", - " \n", - " classification : bool\n", - " If True, the problem is treated as a classification problem. If False, the problem is treated as a regression problem.\n", - " Used to determine the CV strategy.\n", - " \n", - " cv : int, cross-validator\n", - " - (int): Number of folds to use in the cross-validation process. By uses the sklearn.model_selection.KFold cross-validator for regression and StratifiedKFold for classification. In both cases, shuffled is set to True.\n", - " - (sklearn.model_selection.BaseCrossValidator): A cross-validator to use in the cross-validation process.\n", - " - max_depth (int): The maximum depth from any node to the root of the pipelines to be generated.\n", - " \n", - " other_objective_functions : list, default=[tpot2.objectives.estimator_objective_functions.average_path_length_objective]\n", - " A list of other objective functions to apply to the pipeline.\n", - " \n", - " other_objective_functions_weights : list, default=[-1]\n", - " A list of weights to be applied to the other objective functions.\n", - " \n", - " objective_function_names : list, default=None\n", - " A list of names to be applied to the objective functions. If None, will use the names of the objective functions.\n", - " \n", - " bigger_is_better : bool, default=True\n", - " If True, the objective function is maximized. If False, the objective function is minimized. Use negative weights to reverse the direction.\n", - " \n", - " generations : int, default=50\n", - " Number of generations to run\n", - " \n", - " max_time_mins : float, default=float(\"inf\")\n", - " Maximum time to run the optimization. If none or inf, will run until the end of the generations.\n", - " \n", - " max_eval_time_mins : float, default=60*5\n", - " Maximum time to evaluate a single individual. If none or inf, there will be no time limit per evaluation.\n", - "\n", - " n_jobs : int, default=1\n", - " Number of processes to run in parallel.\n", - " \n", - " memory_limit : str, default=\"4GB\"\n", - " Memory limit for each job. See Dask [LocalCluster documentation](https://distributed.dask.org/en/stable/api.html#distributed.Client) for more information.\n", - "\n", - " \n", - " verbose : int, default=1 \n", - " How much information to print during the optimization process. Higher values include the information from lower values.\n", - " 0. nothing\n", - " 1. progress bar\n", - " \n", - " 3. best individual\n", - " 4. warnings\n", - " >=5. full warnings trace\n", - " 6. evaluations progress bar. (Temporary: This used to be 2. Currently, using evaluation progress bar may prevent some instances were we terminate a generation early due to it reaching max_time_mins in the middle of a generation OR a pipeline failed to be terminated normally and we need to manually terminate it.)\n", + "Here is a subset of the most common parameters to customize and what they do. See the docs for `TPOTEstimator` or `TPOTEstimatorSteadyState` full documentation of all parameters. \n", + "\n", + "| Parameter | Type | Description |\n", + "|--------------------------------|-----------------------|-----------------------------------------------------------------------------|\n", + "| scorers | list, scorer | List of scorers for cross-validation; see |\n", + "| scorers_weights | list | Weights applied to scorers during optimization |\n", + "| classification | bool | Problem type: True for classification, False for regression |\n", + "| cv | int, cross-validator | Cross-validation strategy: int for folds or custom cross-validator |\n", + "| max_depth | int | Maximum pipeline depth |\n", + "| other_objective_functions | list | Additional objective functions; default: [average_path_length_objective] |\n", + "| other_objective_functions_weights | list | Weights for additional objective functions; default: [-1] |\n", + "| objective_function_names | list | Names for objective functions; default: None (uses function names) |\n", + "| bigger_is_better | bool | Optimization direction: True for maximize, False for minimize |\n", + "| generations | int | Number of optimization generations; default: 50 |\n", + "| max_time_mins | float | Maximum optimization time (minutes); default: infinite |\n", + "| max_eval_time_mins | float | Maximum evaluation time per individual (minutes); default: 300 |\n", + "| n_jobs | int | Number of parallel processes; default: 1 |\n", + "| memory_limit | str | Memory limit per job; default: \"4GB\" |\n", + "| verbose | int | Optimization process verbosity: 0 (none), 1 (progress), 3 (best individual), 4 (warnings), 5+ (full warnings) |\n", + "| memory | str, memory object | If supplied, pipeline will cache each transformer after calling fit with joblib.Memory. |\n", + "| periodic_checkpoint_folder | str | Folder to save the population to periodically. If None, no periodic saving will be done. If provided, training will resume from this checkpoint.|\n", " \n" ] }, @@ -2032,14 +2000,76 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Common mistakes or issues - Debugging\n", + "# Pipeline caching in TPOT (joblib.Memory)\n", + "\n", + "With the memory parameter, pipelines can cache the results of each transformer after fitting them. This feature is used to avoid repeated computation by transformers within a pipeline if the parameters and input data are identical to another fitted pipeline during optimization process. TPOT allows users to specify a custom directory path or joblib.Memory in case they want to re-use the memory cache in future TPOT runs (or a warm_start run).\n", + "\n", + "There are three methods for enabling memory caching in TPOT:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from tpot2 import TPOTClassifier\n", + "from tempfile import mkdtemp\n", + "from joblib import Memory\n", + "from shutil import rmtree\n", + "\n", + "# Method 1, auto mode: TPOT uses memory caching with a temporary directory and cleans it up upon shutdown\n", + "est = TPOTClassifier(memory='auto')\n", + "\n", + "# Method 2, with a custom directory for memory caching\n", + "est = TPOTClassifier(memory='/to/your/path')\n", + "\n", + "# Method 3, with a Memory object\n", + "cachedir = mkdtemp() # Create a temporary folder\n", + "memory = Memory(cachedir=cachedir, verbose=0)\n", + "est = TPOTClassifier(memory=memory)\n", + "\n", + "# Clear the cache directory when you don't need it anymore\n", + "rmtree(cachedir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note: TPOT does NOT clean up memory caches if users set a custom directory path or Memory object. We recommend that you clean up the memory caches when you don't need it anymore.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Checkpointing\n", + "\n", + "TPOT can checkpoint its progress to disk and resume from that point later if the `periodic_checkpoint_folder` parameter is used. TPOT will save its internal dataframe of pipelines and their performance to disk every generation, allowing you to interrupt TPOT’s execution and resume it later on the same or a different machine.\n", + "\n", + "This feature is useful in several scenarios:\n", + "\n", + "Interrupting TPOT’s execution and resuming it later on the same or a different machine.\n", + "Handling unexpected terminations, such as power outages, cluster job cancellations, bugs, errors, or out-of-memory issues. The checkpointed dataframe can be loaded and inspected to help diagnose problems.\n", + "Running TPOT on a cluster and periodically saving its progress to disk.\n", + "\n", + "**Note: TPOT does not clean up the checkpoint files. If the `periodic_checkpoint_folder` parameter is set, it will always continue training from the last saved point, even if the input data has changed. A common issue is forgetting to change this folder between experiments, and TPOT continuing training from pipelines optimized for another dataset. If you intend to start a run from scratch, you must either remove the parameter, supply an empty folder, or delete the original checkpoint folder.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### FAQ and Debugging\n", "\n", "If you are experiencing issues with TPOT, here are some common issues and how to address them.\n", "\n", - "* Performance is lower than expected.\n", + "* Performance is lower than expected. what can I do?\n", " * TPOT may have to be run for a longer duration, increase `max_time_mins`, `early_stop`, or `generations`.\n", " * Individual pipelines may need more time to complete fitting, increase `max_eval_time_seconds`.\n", " * The configuration may not include the optimal model types or hyperparameter ranges, explore other included templates or customize your own search space (see Tutorial 2!)\n", + " * Check that `periodic_checkpoint_folder` is set correctly. A common issue is forgetting to change this folder between experiments, and TPOT continuing training from pipelines optimized for another dataset.\n", "* TPOT is too slow! It is running forever and never terminating\n", " * Check that at least one of the three termination conditions is set to a reasonable level. These are `max_time_mins`, `early_stop`, or `generations`. Additionally check that `max_eval_time_seconds` is giving enough time for most models to train without being overly long. (Some estimators may take an unreasonably long time to fit, this parameter is intended to prevent them from slowing everything to a halt. In my experience, SVC and SVR tend to be the culprits, so removing them from the search space may also improve run time).\n", " * Set the `memory` parameter to allow TPOT to prevent repeated work when using either scikit-learn pipelines or TPOT GraphPipelines.\n", @@ -2054,10 +2084,24 @@ " * Set the `memory_limit` parameter so that n_jobs*memorylimit is less than the available RAM on your machine plus some wiggle room. This should prevent crashing due to memory concerns.\n", " * Using feature selection may also improve memory usage as described above.\n", " * Remove modules that create high RAM usage (e.g. multiple PolynomialFeatures or one with high degree).\n", + "* Why are my TPOT runs not reproducible when random_state is set?\n", + " * Check that `periodic_checkpoint_folder` is set correctly. If this is set to a non-empty folder, TPOT will continue training from the checkpoint rather than start a new run from scratch. For TPOT runs to be reproducible, they have to have the same starting points.\n", + " * If using custom search spaces, make sure to pass in a fixed `random_state` value into the configspace of the scikit-learn modules that utilize them. TPOT does not check whether estimators do or do not take in a random state value (See Tutorial 2).\n", + " * If using the pre-built search spaces provided by TPOT, make sure to pass in `random_state` to `tpot2.config.get_configspace` or `tpot2.config.template_search_spaces.get_template_search_spaces`. This ensures all estimators that support it get a fixed random_state value. (See Tutorial 2).\n", + " * If using custom Node and Pipeline types, make sure that all random decisions utilize the rng parameter passed into the mutation/crossover functions.\n", + " * If `max_eval_time_mins` is set, TPOT will terminate pipelines that go over this time limit. If the pipeline evaluation happens to be very similar to the time limit, its possible that small random fluctuations in CPU allocation may cause a give pipeline to happen to be evaluated in one run but not another. This slightly different result would throw off the random number generator thoughout the rest of the run. Setting `max_eval_time_mins` to None or a higher value may prevent this edge case.\n", + " * If using `TPOTEstimatorSteadyState` with `n_jobs`>1, it is also possible that random fluctuations in CPU allocation slightly change the order in which pipelines are evaluated, which will affect the downstream results. `TPOTEstimatorSteadyState` is more reliably reproducible when `n_jobs=1` (This is not an issue for the default `TPOTEstimator`, `TPOTClassifier`, `TPOTRegressor` as they used a batched generational approach where execution order does not impact results).\n", + "* TPOT is not using all the CPU cores I expected given my `n_jobs` setting.\n", + " * The default TPOT algorithm uses a generational approach. This means the TPOT will need to fully evaluated `population_size` (default 50) pipelines before starting the next batch. Often, TPOT will be waiting for the last few pipelines to finish evaluating, which could be less than `n_jobs`. Some estimators or pipelines can be significantly slower to evaluated than others. This can be addressed in a few ways:\n", + " * Decrease `max_eval_time_mins` to cut long running pipeline evaluations early.\n", + " * Remove estimators or hyperparameter configurations that are prone to very slow convergence (which is very often `SVC` or `SVR`).\n", + " * Alternatively, `TPOTEstimatorSteadyState` uses a slightly different backend for the evolutionary algorithm that does not utilize the generational approach. Instead, new pipelines are generated and evaluated as soon as the previous one finishes. With this estimator, all cores should be utilized at all times. \n", + "\n", + "\n", "\n", "Other things to be aware of:\n", "\n", - "* On small datasets, it is not impossible for TPOT to over fit the cross validation score itself. This can lead to lower than expected performance on held out datasets. TPOT will always return the model with the highest CV score as its final fitted_pipeline. However, if the highest performing model as evaluated by cross validation actually was just overfit to the CV score, it may actually be worse performing compared to other models on the pareto front.\n", + "* **Overfitting** On small datasets, it is not impossible for TPOT to over fit the cross validation score itself. This can lead to lower than expected performance on held out datasets. TPOT will always return the model with the highest CV score as its final fitted_pipeline. However, if the highest performing model as evaluated by cross validation actually was just overfit to the CV score, it may actually be worse performing compared to other models on the pareto front.\n", " * Using a secondary complexity objective and evaluating the entire pareto front may be beneficial. In some cases a lower performing pipeline with lower complexity can actually perform better on held out sets. These can either be evaluated and compared on a held out validation set, or sometimes, if very data limited, simply using a different seed of splitting the CV folds can work as well.\n", " * TPOT can do this automatically. The `validation_strategy` parameter than select between re-testing the final pareto front on either a held out validation set (percent of data set by `validation_fraction`) or on a different seed for splitting the CV folds. These can be selected by setting `validation_strategy` to \"split\" or \"reshuffled\", respectively.\n", " * Increasing the number of folds of cross validation can mitigate this. \n", diff --git a/tpot2/tpot_estimator/estimator.py b/tpot2/tpot_estimator/estimator.py index b8a14b75..825fb93f 100644 --- a/tpot2/tpot_estimator/estimator.py +++ b/tpot2/tpot_estimator/estimator.py @@ -154,7 +154,7 @@ def __init__(self, However, the output to the next node will come from cross_val_predict with the specified number of folds. memory: Memory object or string, default=None - If supplied, pipeline will cache each transformer after calling fit. This feature + If supplied, pipeline will cache each transformer after calling fit with joblib.Memory. This feature is used to avoid computing the fit transformers within a pipeline if the parameters and input data are identical with another fitted pipeline during optimization process. - String 'auto': diff --git a/tpot2/tpot_estimator/steady_state_estimator.py b/tpot2/tpot_estimator/steady_state_estimator.py index 0a55c2c1..bcfef964 100644 --- a/tpot2/tpot_estimator/steady_state_estimator.py +++ b/tpot2/tpot_estimator/steady_state_estimator.py @@ -214,7 +214,7 @@ def __init__(self, memory: Memory object or string, default=None - If supplied, pipeline will cache each transformer after calling fit. This feature + If supplied, pipeline will cache each transformer after calling fit with joblib.Memory. This feature is used to avoid computing the fit transformers within a pipeline if the parameters and input data are identical with another fitted pipeline during optimization process. - String 'auto': diff --git a/tpot2/tpot_estimator/templates/tpottemplates.py b/tpot2/tpot_estimator/templates/tpottemplates.py index cf529965..6b776754 100644 --- a/tpot2/tpot_estimator/templates/tpottemplates.py +++ b/tpot2/tpot_estimator/templates/tpottemplates.py @@ -87,7 +87,7 @@ def __init__( self, memory: Memory object or string, default=None - If supplied, pipeline will cache each transformer after calling fit. This feature + If supplied, pipeline will cache each transformer after calling fit with joblib.Memory. This feature is used to avoid computing the fit transformers within a pipeline if the parameters and input data are identical with another fitted pipeline during optimization process. - String 'auto': @@ -341,7 +341,7 @@ def __init__( self, memory: Memory object or string, default=None - If supplied, pipeline will cache each transformer after calling fit. This feature + If supplied, pipeline will cache each transformer after calling fit with joblib.Memory. This feature is used to avoid computing the fit transformers within a pipeline if the parameters and input data are identical with another fitted pipeline during optimization process. - String 'auto': From 1a891fbff2bcf25d0b5d3480f4a8f1c675cb7d31 Mon Sep 17 00:00:00 2001 From: perib Date: Mon, 23 Sep 2024 09:27:17 -0700 Subject: [PATCH 10/44] updated contribute, added graph light template, doc updates --- Tutorial/1_Using_TPOT.ipynb | 37 ++++++---- docs/contribute.md | 97 +++++++++++++++++++++++++- tpot2/config/template_search_spaces.py | 35 +++++++++- 3 files changed, 153 insertions(+), 16 deletions(-) diff --git a/Tutorial/1_Using_TPOT.ipynb b/Tutorial/1_Using_TPOT.ipynb index 182710da..da5ff472 100644 --- a/Tutorial/1_Using_TPOT.ipynb +++ b/Tutorial/1_Using_TPOT.ipynb @@ -178,7 +178,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Measuring Model Complexity\n", + "## Measuring Model Complexity\n", "\n", "When running TPOT, it can sometimes be beneficial to include a secondary objective that measures model complexity. More complex models can yield higher performance but this comes at the cost of interpretability. Simpler models may be more interpretable, but often have lower predictive performance. Sometimes, however, vast increases in complexity only marginally improve predictive performance. There may be other simpler and more interpretable pipelines with marginal performance decreases that could be acceptable for the increased interpretability. However, these pipelines are often missed by optimizing purely for performance. By including both performance and complexity as objective functions, TPOT will attempt to optimize the best pipeline for all complexity levels simultaneously. After optimization, the user will be able to see the complexity vs performance tradeoff and make the decision of which pipeline best suits their needs. \n", "\n", @@ -199,6 +199,7 @@ "| linear | A linear pipeline with the structure of \"Selector->(transformers+Passthrough)->(classifiers/regressors+Passthrough)->final classifier/regressor.\" For both the transformer and inner estimator layers, TPOT may choose one or more transformers/classifiers, or it may choose none. The inner classifier/regressor layer is optional. |\n", "| light | Same search space as linear, but without the inner classifier/regressor layer and with a reduced set of faster running estimators. |\n", "| graph | TPOT will optimize a pipeline in the shape of a directed acyclic graph. The nodes of the graph can include selectors, scalers, transformers, or classifiers/regressors (inner classifiers/regressors can optionally be not included). This will return a custom GraphPipeline rather than an sklearn Pipeline. More details in Tutorial 6. |\n", + "| graph-light | Same as graph search space, but with a reduced set of faster running estimators. |\n", "| mdr |TPOT will search over a series of feature selectors and Multifactor Dimensionality Reduction models to find a series of operators that maximize prediction accuracy. The TPOT MDR configuration is specialized for genome-wide association studies (GWAS), and is described in detail online here.\n", "\n", "Note that TPOT MDR may be slow to run because the feature selection routines are computationally expensive, especially on large datasets. |\n", @@ -212,7 +213,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Terminating Optimization\n", + "## Terminating Optimization (Early Stopping)\n", "\n", "Note that we use a short time duration for a quick example, but in practice you may need to run TPOT for a longer duration. by default, TPOT sets a time limit of 1 hour with a max limit of 5 minutes per pipeline. In practice you may want to increase these values.\n", "\n", @@ -228,10 +229,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Best Practices and tips:\n", + "## Best Practices and tips:\n", "\n", - "* When running tpot from an .py script, it is important to protect code with `if __name__==\"__main__\":` . This is because of how TPOT handles parallelization with Python and Dask.\n", - "* You can use the `early_stop` parameter to have TPOT terminate early. " + "* You can use the `early_stop` parameter to have TPOT terminate early. \n", + "* When running tpot from an .py script, it is important to protect code with `if __name__==\"__main__\":` . This is because of how TPOT handles parallelization with Python and Dask." ] }, { @@ -268,7 +269,8 @@ "# Example analysis and the Estimator class \n", "\n", "Here we use a toy example dataset included in scikit-learn. We will use the `light` configuration and the `complexity_scorer` to estimate complexity.\n", - "\n" + "\n", + "Note, for this toy example, we set a relatively short run time. In practice, we would recommend running TPOT for a longer duration with an `early_stop` value of around 5 to 20 (more details below)." ] }, { @@ -326,7 +328,7 @@ " search_space=\"light\",\n", " n_jobs=4, \n", " max_time_mins=60, \n", - " max_eval_time_mins=5,\n", + " max_eval_time_mins=10,\n", " early_stop=2,\n", " verbose=2,)\n", "est.fit(X_train, y_train)\n", @@ -844,7 +846,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### saving the pipeline\n", + "## Saving the Pipeline\n", "\n", "We recommend using dill or pickle to save the instance of the fitted_pipeline_. Note that we do not recommend pickling the TPOT object itself." ] @@ -1189,7 +1191,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Lets plot the performances of the different pipelines including the pareto front\n", + "## Lets plot the performances of the different pipelines including the pareto front\n", "\n", "Plotting the performance of multiple objectives in a scatterplot is a useful way to visualize the tradeoff between model complexity and predictive performance. This is best visualized when plotting the pareto front pipelines, which presents the best performing pipeline along the spectrum of complexity. Generally, higher complexity models may yield higher performance, but be more difficult to interpret. " ] @@ -1918,7 +1920,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Plot performance over time + Continuing a run from where it left off\n", + "## Plot performance over time + Continuing a run from where it left off\n", "\n", "Plotting the performance over time is a good way of trying to access whether or not the TPOT model has converged. If performance seems to asymptote over time, there may not be much more performance to be gained by running for a longer period of time. If the plot looks like it is still actively improving, it may be worth running TPOT for a longer duration. \n", "\n", @@ -2044,7 +2046,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Checkpointing\n", + "# Checkpointing\n", "\n", "TPOT can checkpoint its progress to disk and resume from that point later if the `periodic_checkpoint_folder` parameter is used. TPOT will save its internal dataframe of pipelines and their performance to disk every generation, allowing you to interrupt TPOT’s execution and resume it later on the same or a different machine.\n", "\n", @@ -2061,7 +2063,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### FAQ and Debugging\n", + "# Parallelization\n", + "\n", + "See Tutorial 7 for more details on parallelization with Dask, including information of using multiple nodes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# FAQ and Debugging\n", "\n", "If you are experiencing issues with TPOT, here are some common issues and how to address them.\n", "\n", @@ -2099,7 +2110,7 @@ "\n", "\n", "\n", - "Other things to be aware of:\n", + "## Other things to be aware of:\n", "\n", "* **Overfitting** On small datasets, it is not impossible for TPOT to over fit the cross validation score itself. This can lead to lower than expected performance on held out datasets. TPOT will always return the model with the highest CV score as its final fitted_pipeline. However, if the highest performing model as evaluated by cross validation actually was just overfit to the CV score, it may actually be worse performing compared to other models on the pareto front.\n", " * Using a secondary complexity objective and evaluating the entire pareto front may be beneficial. In some cases a lower performing pipeline with lower complexity can actually perform better on held out sets. These can either be evaluated and compared on a held out validation set, or sometimes, if very data limited, simply using a different seed of splitting the CV folds can work as well.\n", diff --git a/docs/contribute.md b/docs/contribute.md index ae86a06b..d8edf39a 100644 --- a/docs/contribute.md +++ b/docs/contribute.md @@ -1,3 +1,98 @@ # Contributing -We welcome you to check the existing issues for bugs or enhancements to work on. If you have an idea for an extension to TPOT, please file a new issue so we can discuss it. \ No newline at end of file +We welcome you to check the existing issues for bugs or enhancements to work on. If you have an idea for an extension to TPOT, please file a new issue so we can discuss it. + +# Contribution Guide + +We welcome you to [check the existing issues](https://github.com/EpistasisLab/tpot2/issues/) for bugs or enhancements to work on. If you have an idea for an extension to TPOT, please [file a new issue](https://github.com/EpistasisLab/tpot2/issues/new) so we can discuss it. + +## Project layout + +The latest stable release of TPOT is on the [main branch](https://github.com/EpistasisLab/tpot2/tree/main), whereas the latest version of TPOT in development is on the [development branch](https://github.com/EpistasisLab/tpot2/tree/dev). Make sure you are looking at and working on the correct branch if you're looking to contribute code. + +In terms of directory structure: + +* All of TPOT's code sources are in the `tpot` directory +* The documentation sources are in the `docs_sources` directory +* Images in the documentation are in the `images` directory +* Tutorials for TPOT are in the `tutorials` directory +* Unit tests for TPOT are in the `tests.py` file + +Make sure to familiarize yourself with the project layout before making any major contributions, and especially make sure to send all code changes to the `development` branch. + +## How to contribute + +The preferred way to contribute to TPOT is to fork the +[main repository](https://github.com/EpistasisLab/tpot2/) on +GitHub: + +1. Fork the [project repository](https://github.com/EpistasisLab/tpot2): + click on the 'Fork' button near the top of the page. This creates + a copy of the code under your account on the GitHub server. + +2. Clone this copy to your local disk: + + $ git clone git@github.com:YourUsername/tpot2.git + $ cd tpot + +3. Create a branch to hold your changes: + + $ git checkout -b my-contribution + +4. Make sure your local environment is setup correctly for development. Installation instructions are almost identical to [the user instructions](installing.md) except that TPOT should *not* be installed. If you have TPOT installed on your computer then make sure you are using a virtual environment that does not have TPOT installed. Furthermore, you should make sure you have installed the `pytest` package into your development environment so that you can test changes locally. + + $ conda install pytest + +5. Start making changes on your newly created branch, remembering to never work on the ``main`` branch! Work on this copy on your computer using Git to do the version control. + +6. Once some changes are saved locally, you can use your tweaked version of TPOT by navigating to the project's base directory and running TPOT directly from the command line: + + $ python -m tpot.driver + + or by running script that imports and uses the TPOT module with code similar to `from tpot import TPOTClassifier` + +7. To check your changes haven't broken any existing tests and to check new tests you've added pass run the following (note, you must have the `pytest` package installed within your dev environment for this to work): + + $ pytest -s -v + +8. When you're done editing and local testing, run: + + $ git add modified_files + $ git commit + + to record your changes in Git, then push them to GitHub with: + + $ git push -u origin my-contribution + +Finally, go to the web page of your fork of the TPOT repo, and click 'Pull Request' (PR) to send your changes to the maintainers for review. Make sure that you send your PR to the `dev` branch, as the `main` branch is reserved for the latest stable release. This will start the CI server to check all the project's unit tests run and send an email to the maintainers. + +(If any of the above seems like magic to you, then look up the +[Git documentation](http://git-scm.com/documentation) on the web.) + +## Before submitting your pull request + +Before you submit a pull request for your contribution, please work through this checklist to make sure that you have done everything necessary so we can efficiently review and accept your changes. + +If your contribution changes TPOT in any way: + +* Update the [documentation](https://github.com/EpistasisLab/tpot2/tree/main/docs) so all of your changes are reflected there. + +* Update the [README](https://github.com/EpistasisLab/tpot2/blob/main/README.md) if anything there has changed. + +If your contribution involves any code changes: + +* Update the [project unit tests](https://github.com/EpistasisLab/tpot2/tree/main/tpot2/tests) to test your code changes. + +* Make sure that your code is properly commented with [docstrings](https://www.python.org/dev/peps/pep-0257/) and comments explaining your rationale behind non-obvious coding practices. + + +If your contribution requires a new library dependency: + +* Double-check that the new dependency is easy to install via `pip` or Anaconda. If the dependency requires a complicated installation, then we most likely won't merge your changes because we want to keep TPOT easy to install. + + +## After submitting your pull request + +After submitting your pull request, GitHub will automatically run unit tests on your changes and make sure that your updated code builds and runs. We also use services that automatically check code quality and test coverage. + +Check back shortly after submitting your pull request to make sure that your code passes these checks. If any of the checks come back with a red X, then do your best to address the errors. diff --git a/tpot2/config/template_search_spaces.py b/tpot2/config/template_search_spaces.py index 23b7a324..934720fb 100644 --- a/tpot2/config/template_search_spaces.py +++ b/tpot2/config/template_search_spaces.py @@ -67,9 +67,9 @@ def get_graph_search_space(classification=True, inner_predictors=True, **get_sea if classification: if inner_predictors: - inner_search_space = tpot2.config.get_search_space(["classifiers","transformers","scalers","selectors_regression"],**get_search_space_params) + inner_search_space = tpot2.config.get_search_space(["classifiers","transformers","scalers","selectors_classification"],**get_search_space_params) else: - inner_search_space = tpot2.config.get_search_space(["transformers","scalers","selectors_regression"],**get_search_space_params) + inner_search_space = tpot2.config.get_search_space(["transformers","scalers","selectors_classification"],**get_search_space_params) else: if inner_predictors: inner_search_space = tpot2.config.get_search_space(["regressors", "transformers","scalers","selectors_regression"],**get_search_space_params) @@ -86,6 +86,35 @@ def get_graph_search_space(classification=True, inner_predictors=True, **get_sea return search_space +def get_graph_search_space_light(classification=True, inner_predictors=True, **get_search_space_params ): + + if classification: + root_search_space = get_search_space(['BernoulliNB', 'DecisionTreeClassifier', 'GaussianNB', 'KNeighborsClassifier', 'LogisticRegression', 'MultinomialNB'], **get_search_space_params) + else: + root_search_space = get_search_space(["RidgeCV", "LinearSVR", "LassoLarsCV", "KNeighborsRegressor", "DecisionTreeRegressor", "ElasticNetCV"], **get_search_space_params) + + + if classification: + if inner_predictors: + inner_search_space = tpot2.config.get_search_space(['BernoulliNB', 'DecisionTreeClassifier', 'GaussianNB', 'KNeighborsClassifier', 'LogisticRegression', 'MultinomialNB',"transformers","scalers","SelectFwe", "SelectPercentile", "VarianceThreshold"],**get_search_space_params) + else: + inner_search_space = tpot2.config.get_search_space(["transformers","scalers","SelectFwe", "SelectPercentile", "VarianceThreshold"],**get_search_space_params) + else: + if inner_predictors: + inner_search_space = tpot2.config.get_search_space(["RidgeCV", "LinearSVR", "LassoLarsCV", "KNeighborsRegressor", "DecisionTreeRegressor", "ElasticNetCV", "transformers","scalers", "SelectFwe", "SelectPercentile", "VarianceThreshold"],**get_search_space_params) + else: + inner_search_space = tpot2.config.get_search_space(["transformers", "scalers", "SelectFwe", "SelectPercentile", "VarianceThreshold"],**get_search_space_params) + + + search_space = tpot2.search_spaces.pipelines.GraphPipeline( + root_search_space= root_search_space, + leaf_search_space = None, + inner_search_space = inner_search_space, + ) + + return search_space + + def get_light_search_space(classification=True, inner_predictors=False, **get_search_space_params ): selectors = get_search_space(["SelectFwe", "SelectPercentile", "VarianceThreshold","Passthrough"], **get_search_space_params) @@ -168,6 +197,8 @@ def get_template_search_spaces(default_search_space, classification=True, inner_ return get_linear_search_space(classification, inner_predictors, **get_search_space_params) elif default_search_space == "graph": return get_graph_search_space(classification, inner_predictors, **get_search_space_params) + elif default_search_space == "graph_light": + return get_graph_search_space_light(classification, inner_predictors, **get_search_space_params) elif default_search_space == "light": return get_light_search_space(classification, inner_predictors, **get_search_space_params) elif default_search_space == "mdr": From bd927f62e7949a48cb6c0d1bbac3403ee941a216 Mon Sep 17 00:00:00 2001 From: perib Date: Mon, 23 Sep 2024 10:43:58 -0700 Subject: [PATCH 11/44] docs and bug fixes --- Tutorial/1_Using_TPOT.ipynb | 4 +-- docs/contribute.md | 11 +++----- tpot2/builtin_modules/passkbinsdiscretizer.py | 2 +- tpot2/config/regressors.py | 6 ++--- tpot2/config/template_search_spaces.py | 6 ++--- tpot2/config/templates/__init__.py | 0 tpot2/config/templates/autoqtl.py | 0 tpot2/config/templates/stc.py | 0 .../nodes/estimator_node_gradual.py | 3 +++ tpot2/tpot_estimator/estimator.py | 13 +++++++++- .../tpot_estimator/templates/tpottemplates.py | 26 ++++++++++++++++--- 11 files changed, 49 insertions(+), 22 deletions(-) delete mode 100644 tpot2/config/templates/__init__.py delete mode 100644 tpot2/config/templates/autoqtl.py delete mode 100644 tpot2/config/templates/stc.py diff --git a/Tutorial/1_Using_TPOT.ipynb b/Tutorial/1_Using_TPOT.ipynb index da5ff472..0e1461f5 100644 --- a/Tutorial/1_Using_TPOT.ipynb +++ b/Tutorial/1_Using_TPOT.ipynb @@ -197,9 +197,9 @@ "| String | Description |\n", "| :--- | :----: |\n", "| linear | A linear pipeline with the structure of \"Selector->(transformers+Passthrough)->(classifiers/regressors+Passthrough)->final classifier/regressor.\" For both the transformer and inner estimator layers, TPOT may choose one or more transformers/classifiers, or it may choose none. The inner classifier/regressor layer is optional. |\n", - "| light | Same search space as linear, but without the inner classifier/regressor layer and with a reduced set of faster running estimators. |\n", + "| linear-light | Same search space as linear, but without the inner classifier/regressor layer and with a reduced set of faster running estimators. |\n", "| graph | TPOT will optimize a pipeline in the shape of a directed acyclic graph. The nodes of the graph can include selectors, scalers, transformers, or classifiers/regressors (inner classifiers/regressors can optionally be not included). This will return a custom GraphPipeline rather than an sklearn Pipeline. More details in Tutorial 6. |\n", - "| graph-light | Same as graph search space, but with a reduced set of faster running estimators. |\n", + "| graph-light | Same as graph search space, but without the inner classifier/regressors and with a reduced set of faster running estimators. |\n", "| mdr |TPOT will search over a series of feature selectors and Multifactor Dimensionality Reduction models to find a series of operators that maximize prediction accuracy. The TPOT MDR configuration is specialized for genome-wide association studies (GWAS), and is described in detail online here.\n", "\n", "Note that TPOT MDR may be slow to run because the feature selection routines are computationally expensive, especially on large datasets. |\n", diff --git a/docs/contribute.md b/docs/contribute.md index d8edf39a..6ef12518 100644 --- a/docs/contribute.md +++ b/docs/contribute.md @@ -45,17 +45,12 @@ GitHub: 5. Start making changes on your newly created branch, remembering to never work on the ``main`` branch! Work on this copy on your computer using Git to do the version control. -6. Once some changes are saved locally, you can use your tweaked version of TPOT by navigating to the project's base directory and running TPOT directly from the command line: - $ python -m tpot.driver +6. Check your changes haven't broken any existing tests and pass all your new tests. Navigate the terminal into the `tpot2/tpot2/` folder and run the command `pytest` to start all tests. (note, you must have the `pytest` package installed within your dev environment for this to work): - or by running script that imports and uses the TPOT module with code similar to `from tpot import TPOTClassifier` + $ pytest -7. To check your changes haven't broken any existing tests and to check new tests you've added pass run the following (note, you must have the `pytest` package installed within your dev environment for this to work): - - $ pytest -s -v - -8. When you're done editing and local testing, run: +7. When you're done editing and local testing, run: $ git add modified_files $ git commit diff --git a/tpot2/builtin_modules/passkbinsdiscretizer.py b/tpot2/builtin_modules/passkbinsdiscretizer.py index 6ca5a9b5..7b8d5f3d 100644 --- a/tpot2/builtin_modules/passkbinsdiscretizer.py +++ b/tpot2/builtin_modules/passkbinsdiscretizer.py @@ -15,7 +15,7 @@ class PassKBinsDiscretizer(BaseEstimator, TransformerMixin): """ Same as sklearn.preprocessing.KBinsDiscretizer, but passes through columns that are not discretized due to having fewer than n_bins unique values instead of ignoring them. """ - def __init__(self, n_bins=5, encode='onehot-dense', strategy='quantile', subsample='warn', random_state=None): + def __init__(self, n_bins=5, encode='onehot-dense', strategy='quantile', subsample=None, random_state=None): self.n_bins = n_bins self.encode = encode self.strategy = strategy diff --git a/tpot2/config/regressors.py b/tpot2/config/regressors.py index 6348f5c2..11362cce 100644 --- a/tpot2/config/regressors.py +++ b/tpot2/config/regressors.py @@ -15,7 +15,7 @@ def get_RandomForestRegressor_ConfigurationSpace(random_state): space = { 'n_estimators': 100, - 'criterion': Categorical("criterion", ['mse', 'mae', "friedman_mse"]), + 'criterion': Categorical("criterion", ['friedman_mse', 'poisson', 'absolute_error', 'squared_error']), 'max_features': Float("max_features", bounds=(0.05, 1.0)), 'bootstrap': Categorical("bootstrap", [True, False]), 'min_samples_split': Integer("min_samples_split", bounds=(2, 21)), @@ -221,7 +221,7 @@ def get_Perceptron_ConfigurationSpace(random_state): def get_DecisionTreeRegressor_ConfigurationSpace(random_state): space = { - 'criterion': Categorical("criterion", ['squared_error', 'friedman_mse', 'mae']), + 'criterion': Categorical("criterion", ['friedman_mse', 'poisson', 'absolute_error', 'squared_error']), # 'max_depth': Integer("max_depth", bounds=(1, n_features*2)), 'min_samples_split': Integer("min_samples_split", bounds=(2, 21)), 'min_samples_leaf': Integer("min_samples_leaf", bounds=(1, 21)), @@ -334,7 +334,7 @@ def get_AdaBoostRegressor_ConfigurationSpace(random_state): def get_ExtraTreesRegressor_ConfigurationSpace(random_state): space = { 'n_estimators': 100, - 'criterion': Categorical("criterion", ["squared_error", "friedman_mse", "mae"]), + 'criterion': Categorical("criterion", ['friedman_mse', 'poisson', 'absolute_error', 'squared_error']), 'max_features': Float("max_features", bounds=(0.05, 1.0)), 'min_samples_split': Integer("min_samples_split", bounds=(2, 21)), 'min_samples_leaf': Integer("min_samples_leaf", bounds=(1, 21)), diff --git a/tpot2/config/template_search_spaces.py b/tpot2/config/template_search_spaces.py index 934720fb..8407fef8 100644 --- a/tpot2/config/template_search_spaces.py +++ b/tpot2/config/template_search_spaces.py @@ -187,7 +187,7 @@ def get_mdr_search_space(classification=True, **get_search_space_params ): def get_template_search_spaces(default_search_space, classification=True, inner_predictors=None, **get_search_space_params): if inner_predictors is None: - if default_search_space == "light": + if default_search_space == "light" or default_search_space == "graph_light": inner_predictors = False else: inner_predictors = True @@ -197,9 +197,9 @@ def get_template_search_spaces(default_search_space, classification=True, inner_ return get_linear_search_space(classification, inner_predictors, **get_search_space_params) elif default_search_space == "graph": return get_graph_search_space(classification, inner_predictors, **get_search_space_params) - elif default_search_space == "graph_light": + elif default_search_space == "graph-light": return get_graph_search_space_light(classification, inner_predictors, **get_search_space_params) - elif default_search_space == "light": + elif default_search_space == "linear-light": return get_light_search_space(classification, inner_predictors, **get_search_space_params) elif default_search_space == "mdr": return get_mdr_search_space(classification, **get_search_space_params) diff --git a/tpot2/config/templates/__init__.py b/tpot2/config/templates/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tpot2/config/templates/autoqtl.py b/tpot2/config/templates/autoqtl.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tpot2/config/templates/stc.py b/tpot2/config/templates/stc.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tpot2/search_spaces/nodes/estimator_node_gradual.py b/tpot2/search_spaces/nodes/estimator_node_gradual.py index b59e263b..b10cfba2 100644 --- a/tpot2/search_spaces/nodes/estimator_node_gradual.py +++ b/tpot2/search_spaces/nodes/estimator_node_gradual.py @@ -131,6 +131,9 @@ def gradual_hyperparameter_update(params:dict, configspace:ConfigurationSpace, r elif new_params[param] > configspace[param].upper: new_params[param] = configspace[param].upper new_params[param] = int(new_params[param]) + # TODO : add support for categorical hyperparameters + else: + new_params[param] = params[param] except: pass diff --git a/tpot2/tpot_estimator/estimator.py b/tpot2/tpot_estimator/estimator.py index 825fb93f..1077f014 100644 --- a/tpot2/tpot_estimator/estimator.py +++ b/tpot2/tpot_estimator/estimator.py @@ -113,7 +113,18 @@ def __init__(self, Parameters ---------- search_space : (String, tpot2.search_spaces.SklearnIndividualGenerator) - - String : The default search space to use for the optimization. This can be either "linear" or "graph". If "linear", will use the default linear pipeline search space. If "graph", will use the default graph pipeline search space. + - String : The default search space to use for the optimization. + | String | Description | + | :--- | :----: | + | linear | A linear pipeline with the structure of "Selector->(transformers+Passthrough)->(classifiers/regressors+Passthrough)->final classifier/regressor." For both the transformer and inner estimator layers, TPOT may choose one or more transformers/classifiers, or it may choose none. The inner classifier/regressor layer is optional. | + | linear-light | Same search space as linear, but without the inner classifier/regressor layer and with a reduced set of faster running estimators. | + | graph | TPOT will optimize a pipeline in the shape of a directed acyclic graph. The nodes of the graph can include selectors, scalers, transformers, or classifiers/regressors (inner classifiers/regressors can optionally be not included). This will return a custom GraphPipeline rather than an sklearn Pipeline. More details in Tutorial 6. | + | graph-light | Same as graph search space, but without the inner classifier/regressors and with a reduced set of faster running estimators. | + | mdr |TPOT will search over a series of feature selectors and Multifactor Dimensionality Reduction models to find a series of operators that maximize prediction accuracy. The TPOT MDR configuration is specialized for genome-wide association studies (GWAS), and is described in detail online here. + + Note that TPOT MDR may be slow to run because the feature selection routines are computationally expensive, especially on large datasets. | + + - SklearnIndividualGenerator : The search space to use for the optimization. This should be an instance of a SklearnIndividualGenerator. The search space to use for the optimization. This should be an instance of a SklearnIndividualGenerator. TPOT2 has groups of search spaces found in the following folders, tpot2.search_spaces.nodes for the nodes in the pipeline and tpot2.search_spaces.pipelines for the pipeline structure. diff --git a/tpot2/tpot_estimator/templates/tpottemplates.py b/tpot2/tpot_estimator/templates/tpottemplates.py index 6b776754..005e4747 100644 --- a/tpot2/tpot_estimator/templates/tpottemplates.py +++ b/tpot2/tpot_estimator/templates/tpottemplates.py @@ -10,7 +10,7 @@ class TPOTRegressor(TPOTEstimator): def __init__( self, - search_space = "linear", + search_space = "linear-light", scorers=['neg_mean_squared_error'], scorers_weights=[1], cv = 10, #remove this and use a value based on dataset size? @@ -44,7 +44,16 @@ def __init__( self, ---------- search_space : (String, tpot2.search_spaces.SklearnIndividualGenerator) - - String : The default search space to use for the optimization. This can be either "linear" or "graph". If "linear", will use the default linear pipeline search space. If "graph", will use the default graph pipeline search space. + - String : The default search space to use for the optimization. + | String | Description | + | :--- | :----: | + | linear | A linear pipeline with the structure of "Selector->(transformers+Passthrough)->(classifiers/regressors+Passthrough)->final classifier/regressor." For both the transformer and inner estimator layers, TPOT may choose one or more transformers/classifiers, or it may choose none. The inner classifier/regressor layer is optional. | + | linear-light | Same search space as linear, but without the inner classifier/regressor layer and with a reduced set of faster running estimators. | + | graph | TPOT will optimize a pipeline in the shape of a directed acyclic graph. The nodes of the graph can include selectors, scalers, transformers, or classifiers/regressors (inner classifiers/regressors can optionally be not included). This will return a custom GraphPipeline rather than an sklearn Pipeline. More details in Tutorial 6. | + | graph-light | Same as graph search space, but without the inner classifier/regressors and with a reduced set of faster running estimators. | + | mdr |TPOT will search over a series of feature selectors and Multifactor Dimensionality Reduction models to find a series of operators that maximize prediction accuracy. The TPOT MDR configuration is specialized for genome-wide association studies (GWAS), and is described in detail online here. + + Note that TPOT MDR may be slow to run because the feature selection routines are computationally expensive, especially on large datasets. | - SklearnIndividualGenerator : The search space to use for the optimization. This should be an instance of a SklearnIndividualGenerator. The search space to use for the optimization. This should be an instance of a SklearnIndividualGenerator. TPOT2 has groups of search spaces found in the following folders, tpot2.search_spaces.nodes for the nodes in the pipeline and tpot2.search_spaces.pipelines for the pipeline structure. @@ -263,7 +272,7 @@ def fit(self, X, y): class TPOTClassifier(TPOTEstimator): def __init__( self, - search_space = "linear", + search_space = "linear-light", scorers=['roc_auc_ovr'], scorers_weights=[1], cv = 10, @@ -298,7 +307,16 @@ def __init__( self, ---------- search_space : (String, tpot2.search_spaces.SklearnIndividualGenerator) - - String : The default search space to use for the optimization. This can be either "linear" or "graph". If "linear", will use the default linear pipeline search space. If "graph", will use the default graph pipeline search space. + - String : The default search space to use for the optimization. + | String | Description | + | :--- | :----: | + | linear | A linear pipeline with the structure of "Selector->(transformers+Passthrough)->(classifiers/regressors+Passthrough)->final classifier/regressor." For both the transformer and inner estimator layers, TPOT may choose one or more transformers/classifiers, or it may choose none. The inner classifier/regressor layer is optional. | + | linear-light | Same search space as linear, but without the inner classifier/regressor layer and with a reduced set of faster running estimators. | + | graph | TPOT will optimize a pipeline in the shape of a directed acyclic graph. The nodes of the graph can include selectors, scalers, transformers, or classifiers/regressors (inner classifiers/regressors can optionally be not included). This will return a custom GraphPipeline rather than an sklearn Pipeline. More details in Tutorial 6. | + | graph-light | Same as graph search space, but without the inner classifier/regressors and with a reduced set of faster running estimators. | + | mdr |TPOT will search over a series of feature selectors and Multifactor Dimensionality Reduction models to find a series of operators that maximize prediction accuracy. The TPOT MDR configuration is specialized for genome-wide association studies (GWAS), and is described in detail online here. + + Note that TPOT MDR may be slow to run because the feature selection routines are computationally expensive, especially on large datasets. | - SklearnIndividualGenerator : The search space to use for the optimization. This should be an instance of a SklearnIndividualGenerator. The search space to use for the optimization. This should be an instance of a SklearnIndividualGenerator. TPOT2 has groups of search spaces found in the following folders, tpot2.search_spaces.nodes for the nodes in the pipeline and tpot2.search_spaces.pipelines for the pipeline structure. From ff8137fba0acb5408d9a4adb4f07eb804ed4fecb Mon Sep 17 00:00:00 2001 From: perib Date: Mon, 23 Sep 2024 15:55:57 -0700 Subject: [PATCH 12/44] documentation, fix wrapper, fix estimatortransformer, rename GraphPipeline to GraphSearchPipeline, add cross_val_predict_cv option to templates, param update --- Tutorial/2_Search_Spaces.ipynb | 1758 ++++++++++++----- tpot2/builtin_modules/estimatortransformer.py | 30 +- tpot2/config/classifiers.py | 2 +- tpot2/config/template_search_spaces.py | 28 +- tpot2/old_config_utils/old_config_utils.py | 4 +- tpot2/search_spaces/pipelines/dynamicunion.py | 6 +- tpot2/search_spaces/pipelines/graph.py | 4 +- .../pipelines/tests/test_graphspace.py | 2 +- tpot2/search_spaces/pipelines/tree.py | 2 +- tpot2/tests/test_estimators.py | 2 +- 10 files changed, 1267 insertions(+), 571 deletions(-) diff --git a/Tutorial/2_Search_Spaces.ipynb b/Tutorial/2_Search_Spaces.ipynb index 3e975731..d97489e6 100644 --- a/Tutorial/2_Search_Spaces.ipynb +++ b/Tutorial/2_Search_Spaces.ipynb @@ -4,58 +4,89 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Everything can be done with the TPOTEstimator class. All other classes (TPOTRegressor, TPOTClassifier, TPOTSymbolicClassifier, TPOTSymbolicRegression, TPOTGeneticFeatureSetSelector, etc.) are actually just different default settings for TPOTEstimator.\n", + "# Intro\n", "\n", - "\n", - "By Default, TPOT will generate pipelines with a default set of classifiers or regressors as roots (this depends on whether classification is set to true or false). All other nodes are selected from a default list of selectors and transformers. Note: This differs from the TPOT1 behavior where by default classifiers and regressors can appear in locations other than the root. You can modify the the search space for leaves, inner nodes, and roots (final classifiers) separately through built in options or custom configuration dictionaries.\n", - "\n", - "In this tutorial we will walk through using the built in configurations, creating custom configurations, and using nested configurations." + "TPOT gives the user a lot of options for customizing the search space, from hyperparameter ranges to model selection to pipeline configuration. TPOT is able to select models, optimize their hyperparameters, as well as build a complex pipeline structure. Each level of detail has multiple options for customization. In this tutorial, first we will explore how to set up a hyperparameter search space for a single method. Next we will describe how to set up simultaneous model selection and hyperparameter tuning. And finally we will cover how to utilize these steps to configure a search space for a fixed pipeline of multiple steps as well as having TPOT optimize the pipeline structure itself.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# ConfigSpace\n", + "# Hyperparameter Search Spaces with ConfigSpace\n", + "\n", + "Hyperparameter search spaces are defined using the [ConfigSpace package found here](https://github.com/automl/ConfigSpace). More information on how to set up a hyperparameter space can be found in their [documentation here](https://automl.github.io/ConfigSpace/main/guide.html).\n", + "\n", + "TPOT uses `ConfigSpace.ConfigurationSpace` objects to define the hyperparameter search space for individual models. This object can be used to keep track of the desired hyperparameters as well as provide functions for randomly sampling from this space.\n", "\n", - "Hyperparameter search spaces are defined using the [ConfigSpace package found here](https://github.com/automl/ConfigSpace). More information on how to set up a hyperparameter space can be found in their [documentation here](https://automl.github.io/ConfigSpace/main/guide.html)." + "In short, you can use the `Integer`, `Float`, and `Categorical` functions of `ConfigSpace` to define a range of values used for each param. Alternatively, a tuple with (min,max) ints or floats can be used to specify a int/float search space, and a list is used to specify a categorical search space. A fixed value an also be provided for parameters that are not tunned. The space parameter of `ConfigurationSpace` takes in a dictionary of param name to these ranges.\n", + "\n", + "Note: If you want results to be reproducible, you need set a fixed random_state in the search space.\n", + "\n", + "Here is an example of a hyperparameter range for RandomForest" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sampled hyperparameters\n", - "{'metric': 'euclidean', 'n_jobs': 1, 'n_neighbors': 10, 'p': 2, 'weights': 'distance'}\n" - ] - } - ], + "outputs": [], "source": [ "from ConfigSpace import ConfigurationSpace\n", "from ConfigSpace import ConfigurationSpace, Integer, Float, Categorical, Normal\n", - "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.ensemble import RandomForestClassifier\n", "\n", - "knn_configspace = ConfigurationSpace(\n", + "rf_configspace = ConfigurationSpace(\n", " space = {\n", + " 'n_estimators': 128, #as recommended by Oshiro et al. (2012\n", + " 'max_features': Float(\"max_features\", bounds=(0.01,1), log=True), #log scale like autosklearn?\n", + " 'criterion': Categorical(\"criterion\", ['gini', 'entropy']),\n", + " 'min_samples_split': Integer(\"min_samples_split\", bounds=(2, 20)),\n", + " 'min_samples_leaf': Integer(\"min_samples_leaf\", bounds=(1, 20)),\n", + " 'bootstrap': Categorical(\"bootstrap\", [True, False]),\n", + " #random_state = 1, # If you want results to be reproducible, you can set a fixed random_state.\n", + " }\n", + ")\n", "\n", - " 'n_neighbors': (1, 10),\n", - " 'weights': Categorical(\"weights\", ['uniform', 'distance']),\n", - " 'p': (1, 3),\n", - " 'metric': Categorical(\"metric\", ['euclidean', 'minkowski']),\n", - " 'n_jobs': 1,\n", - " }\n", + "hyperparameters = dict(rf_configspace.sample_configuration())\n", + "print(\"sampled hyperparameters\")\n", + "print(hyperparameters)\n", + "\n", + "rf = RandomForestClassifier(**hyperparameters)\n", + "rf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "More simply:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rf_configspace = ConfigurationSpace(\n", + " space = {\n", + " 'n_estimators': 128, #as recommended by Oshiro et al. (2012\n", + " 'max_features':(0.01,1), #not log scaled\n", + " 'criterion': ['gini', 'entropy'],\n", + " 'min_samples_split': (2, 20),\n", + " 'min_samples_leaf': (1, 20),\n", + " 'bootstrap': [True, False],\n", + " #random_state = 1, # If you want results to be reproducible, you can set a fixed random_state.\n", + " }\n", ")\n", "\n", - "hyperparameters = dict(knn_configspace.sample_configuration())\n", + "hyperparameters = dict(rf_configspace.sample_configuration())\n", "print(\"sampled hyperparameters\")\n", "print(hyperparameters)\n", "\n", - "knn = KNeighborsClassifier(**hyperparameters)" + "rf = RandomForestClassifier(**hyperparameters)\n", + "rf" ] }, { @@ -68,22 +99,18 @@ "\n", "TPOT search spaces are found in the `search_spaces` module. There are two primary kinds of search spaces, node and pipeline. Node search spaces specify the search space of a single sklearn `BaseEstimator`. Pipeline search spaces define the possible structures for a group of node search spaces. These take in node search spaces and produce a pipeline using nodes from that search space. Since sklearn Pipelines are also `BaseEstimator`, pipeline search spaces are also technically node search spaces. Meaning that pipeline search spaces can take in other pipeline search spaces in order to define more complex structures. The primary differentiating factor bewteen node and pipeline search spaces is that pipeline search spaces must take in another search space as input to feed its individual nodes. Therefore, all search spaces eventually end in a node search space at the lowest level. Note that parameters for pipeline search spaces can differ, some take in only a single search space, some take in a list, or some take in multiple defined parameters.\n", "\n", - "search spaces can be found in tpot2.search_spaces.nodes and tpot2.search_spaces.pipelines\n", + "## node search spaces\n", "\n", - "### node search spaces\n", - "found in tpot2.search_spaces.nodes\n", "\n", - "\n", - "EstimatorNode, GeneticFeatureSelector\n", "| Name | Info |\n", "| :--- | :----: |\n", "| EstimatorNode | Takes in a ConfigSpace along with the class of the method. This node will optimize the hyperparameters for a single method. |\n", "| GeneticFeatureSelectorNode | Uses evolution to optimize a set of features, exports a basic sklearn Selector that simply selects the features chosen by the node. |\n", + "| FSSNode | FSS stands for FeatureSetSelector. This node takes in a list of user-defined subsets of features and selects a single predefined subset. Note that TPOT will not create new subsets nor will it select multiple subsets per node. If using a linear pipeline, this node should be set as the first step. In linear pipelines it is recommended that you only use a small number of feature sets. I recommend exploring using FSSNodes in pipelines that allow TPOT to select more than one FSSNode at a time. For example, DynamicUnionPipeline and GraphPipeline are both excellent combos for FSSNode. Use FFSNode inside a DynamicUnionPipeline at the start of linear pipeline to explore optimal combinations of subsets in linear pipelines. Set the leaf_search_space of GraphSearchPipeline TPOT can use multiple feature sets in different ways, for example, with different transformers for different sets. |\n", "\n", "\n", "\n", - "\n", - "### pipeline search spaces\n", + "## pipeline search spaces\n", "\n", "found in tpot2.search_spaces.pipelines\n", "\n", @@ -96,8 +123,10 @@ "| ChoicePipeline | Takes in a list of search spaces. Will select one node from the search space. |\n", "| SequentialPipeline | Takes in a list of search spaces. will produce a pipeline of Sequential length. Each step in the pipeline will correspond to the the search space provided in the same index. |\n", "| DynamicLinearPipeline | Takes in a single search space. Will produce a linear pipeline of variable length. Each step in the pipeline will be pulled from the search space provided. |\n", + "| UnionPipeline | Takes in a list of search spaces. The returned pipeline will include one estimator per search space joined in an sklearn FeatureUnion. Useful for having many steps in one layer. |\n", + "| DynamicUnionPipeline | Takes in a single search space. It will pull anywhere from 1 to max_estimators number of estimators from the search space and concatenate them in a FeatureUnion. |\n", "| TreePipeline |Generates a pipeline of variable length. Pipeline will have a tree structure similar to TPOT1. |\n", - "| GraphPipeline | Generates a directed acyclic graph of variable size. Search spaces for root, leaf, and inner nodes can be defined separately if desired. |\n", + "| GraphSearchPipeline | Generates a directed acyclic graph of variable size. Search spaces for root, leaf, and inner nodes can be defined separately if desired. |\n", "| WrapperPipeline | This search space is for wrapping a sklearn estimator with a method that takes another estimator and hyperparameters as arguments. For example, this can be used with sklearn.ensemble.BaggingClassifier or sklearn.ensemble.AdaBoostClassifier. |\n" ] }, @@ -105,12 +134,22 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Estimator node example" + "## Node Search Space Examples\n", + "\n", + "Node search spaces represent the smallest unit of an sklearn pipeline. All node search spaces create and optimize a single node, or estimator object. For example this could be a KNeighborsClassifier or a FeatureSetSelector.\n", + "\n", + "### EstimatorNode\n", + "\n", + "The EstimatorNode represents the hyperparameter search space for a scikit-learn estimator. \n", + "\n", + "Note that `ConfigSpace` doesn't support `None` in its search space, and does not support the booleans True or False as fixed parameters (though booleans seem to be allowed in Categorical search spaces). To get around this, use the macros defined in:\n", + "\n", + "`from tpot2.search_spaces.nodes.estimator_node import NONE_SPECIAL_STRING, TRUE_SPECIAL_STRING, FALSE_SPECIAL_STRING`" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -141,14 +180,33 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "You can sample generate an individual with the generate() function. This individual samples from the search space as well as provides mutation and crossover functions to modify the current sample.\n", - "\n", - "Note that ConfigurationSpace does not support None as a parameter. Instead, use the special string \"\\\". TPOT will automatically replace instances of this string with the Python None." + "You can sample generate an individual with the generate() function. This individual samples from the search space as well as provides mutation and crossover functions to modify the current sample." ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "knn_individual = knn_node.generate()\n", + "knn_individual" + ] + }, + { + "cell_type": "code", + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -156,17 +214,37 @@ "output_type": "stream", "text": [ "sampled hyperparameters\n", - "{'metric': 'minkowski', 'n_jobs': 1, 'n_neighbors': 6, 'p': 3, 'weights': 'distance'}\n", - "mutated hyperparameters\n", - "{'metric': 'euclidean', 'n_jobs': 1, 'n_neighbors': 6, 'p': 1, 'weights': 'uniform'}\n" + "{'metric': 'minkowski', 'n_jobs': 1, 'n_neighbors': 4, 'p': 1, 'weights': 'uniform'}\n" ] } ], "source": [ - "knn_individual = knn_node.generate()\n", - "\n", "print(\"sampled hyperparameters\")\n", - "print(knn_individual.hyperparameters)\n", + "print(knn_individual.hyperparameters)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All Individual objects have mutation and crossover operators that TPOT uses to optimize the pipelines." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mutated hyperparameters\n", + "{'metric': 'euclidean', 'n_jobs': 1, 'n_neighbors': 10, 'p': 3, 'weights': 'distance'}\n" + ] + } + ], + "source": [ "knn_individual.mutate() # mutate the individual\n", "print(\"mutated hyperparameters\")\n", "print(knn_individual.hyperparameters)" @@ -181,7 +259,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -189,14 +267,14 @@ "output_type": "stream", "text": [ "original hyperparameters for individual 1\n", - "{'metric': 'euclidean', 'n_jobs': 1, 'n_neighbors': 7, 'p': 2, 'weights': 'uniform'}\n", + "{'metric': 'euclidean', 'n_jobs': 1, 'n_neighbors': 2, 'p': 2, 'weights': 'uniform'}\n", "original hyperparameters for individual 2\n", - "{'metric': 'minkowski', 'n_jobs': 1, 'n_neighbors': 1, 'p': 2, 'weights': 'uniform'}\n", + "{'metric': 'euclidean', 'n_jobs': 1, 'n_neighbors': 10, 'p': 2, 'weights': 'uniform'}\n", "\n", "post crossover hyperparameters for individual 1\n", - "{'metric': 'euclidean', 'n_jobs': 1, 'n_neighbors': 7, 'p': 2, 'weights': 'uniform'}\n", + "{'metric': 'euclidean', 'n_jobs': 1, 'n_neighbors': 10, 'p': 2, 'weights': 'uniform'}\n", "post crossover hyperparameters for individual 2\n", - "{'metric': 'minkowski', 'n_jobs': 1, 'n_neighbors': 1, 'p': 2, 'weights': 'uniform'}\n" + "{'metric': 'euclidean', 'n_jobs': 1, 'n_neighbors': 10, 'p': 2, 'weights': 'uniform'}\n" ] } ], @@ -229,7 +307,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -639,31 +717,32 @@ " /* fitted */\n", " background-color: var(--sklearn-color-fitted-level-3);\n", "}\n", - "
KNeighborsClassifier(metric='euclidean', n_jobs=1, n_neighbors=7)
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" + "
KNeighborsClassifier(metric='euclidean', n_jobs=1, n_neighbors=10)
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" ], "text/plain": [ - "KNeighborsClassifier(metric='euclidean', n_jobs=1, n_neighbors=7)" + "KNeighborsClassifier(metric='euclidean', n_jobs=1, n_neighbors=10)" ] }, - "execution_count": 19, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "knn_individual1.export_pipeline()" + "est = knn_individual1.export_pipeline()\n", + "est" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "If a dictionary of parameters is passed instead of of a ConfigSpace, then the hyperparameters will be fixed and not learned." + "If a dictionary of parameters is passed instead of of a ConfigSpace object, then the hyperparameters will always be fixed and not learned." ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -1073,13 +1152,13 @@ " /* fitted */\n", " background-color: var(--sklearn-color-fitted-level-3);\n", "}\n", - "
KNeighborsClassifier(n_neighbors=10)
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" + "
KNeighborsClassifier(n_neighbors=10)
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" ], "text/plain": [ "KNeighborsClassifier(n_neighbors=10)" ] }, - "execution_count": 20, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1107,30 +1186,41 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Pipeline Search Spaces" + "### FSSNode and GeneticFeatureSelectorNode\n", + "\n", + "Both of these are given their own tutorials. See Tutorial 3 for FFSNode and Tutorial 5 for GeneticFeatureSelectorNode" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pipeline Search Space Examples\n", + "\n", + "Pipeline search spaces are used to define the structure and restrictions of the pipelines TPOT can search. Unlike Node search spaces, all pipeline search spaces take in other search spaces as inputs. Rather than sample hyperparameters, pipeline search spaces can select models from the input search spaces and organize them within a linear sklearn Pipeline or a TPOT GraphPipeline." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## choice search space\n", + "### ChoicePipeline\n", "\n", - "The simplest pipeline search space is the ChoicePipeline. This takes in a list of search spaces and simply selects and samples from one. In this example, we will construct a search space that takes in several options for a classifier." + "The simplest pipeline search space is the ChoicePipeline. This takes in a list of search spaces and simply selects and samples from one. In this example, we will construct a search space that takes in several options for a classifier. The resulting search space will then first select a model from KNeighborsClassifier, LogisticRegression or DecisionTreeClassifier, and then select the hyperparameters for the given model." ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 21, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1228,7 +1318,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -1645,16 +1735,16 @@ " /* fitted */\n", " background-color: var(--sklearn-color-fitted-level-3);\n", "}\n", - "
LogisticRegression(C=174.83656421187536, class_weight='balanced', dual=True,\n",
-       "                   max_iter=1000, n_jobs=1, penalty='l1', solver='saga')
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" + "
DecisionTreeClassifier(max_depth=5, max_features='sqrt', min_samples_leaf=4,\n",
+       "                       min_samples_split=4)
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" ], "text/plain": [ - "LogisticRegression(C=174.83656421187536, class_weight='balanced', dual=True,\n", - " max_iter=1000, n_jobs=1, penalty='l1', solver='saga')" + "DecisionTreeClassifier(max_depth=5, max_features='sqrt', min_samples_leaf=4,\n", + " min_samples_split=4)" ] }, - "execution_count": 22, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1668,7 +1758,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -2085,16 +2175,13 @@ " /* fitted */\n", " background-color: var(--sklearn-color-fitted-level-3);\n", "}\n", - "
KNeighborsClassifier(metric='euclidean', n_jobs=1, n_neighbors=3,\n",
-       "                     weights='distance')
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" + "
KNeighborsClassifier(n_jobs=1, n_neighbors=6, p=1)
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" ], "text/plain": [ - "KNeighborsClassifier(metric='euclidean', n_jobs=1, n_neighbors=3,\n", - " weights='distance')" + "KNeighborsClassifier(n_jobs=1, n_neighbors=6, p=1)" ] }, - "execution_count": 23, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -2111,21 +2198,29 @@ "source": [ "TPOT2 also comes with predefined search spaces. The current search spaces were adapted from a combination of the original TPOT package as well as the search spaces used in [AutoSklearn](https://github.com/automl/auto-sklearn/tree/development/autosklearn/pipeline/components). The helper function `tpot2.config.get_search_space` takes in a string or a list of strings, and returns either a EstimatorNode or a ChoicePipeline,respectively. \n", "\n", - "strings can correspond to individual methods. Tehre are also special strings that return predefined lists of methods. \n", + "strings can correspond to individual methods. There are also special strings that return predefined lists of methods. \n", "\n", - "Special strings are \"selectors\", \"classifiers\", \"transformers\"\n", - "\n", - "EstimatorNode, GeneticFeatureSelector\n", "| Special String | Included methods |\n", "| :--- | :----: |\n", - "| \"selectors\" | \"SelectFwe\", \"SelectPercentile\", \"VarianceThreshold\", \"RFE\", \"SelectFromModel\" |\n", - "| \"classifiers\" | \"LogisticRegression\", \"KNeighborsClassifier\", \"DecisionTreeClassifier\", \"SVC\", \"LinearSVC\", \"RandomForestClassifier\", \"GradientBoostingClassifier\", \"XGBClassifier\", \"LGBMClassifier\", \"ExtraTreesClassifier\", \"SGDClassifier\", \"MLPClassifier\", \"BernoulliNB\", \"MultinomialNB\" |\n", - "| \"transformers\" | \"Binarizer\", \"Normalizer\", \"PCA\", \"ZeroCount\", \"OneHotEncoder\", \"FastICA\", \"FeatureAgglomeration\", \"Nystroem\", \"RBFSampler\" |" + "| \"selectors\" | [\"SelectFwe\", \"SelectPercentile\", \"VarianceThreshold\",] |\n", + "| \"selectors_classification\" | [\"SelectFwe\", \"SelectPercentile\", \"VarianceThreshold\", \"RFE_classification\", \"SelectFromModel_classification\"] |\n", + "| \"selectors_regression\" | [\"SelectFwe\", \"SelectPercentile\", \"VarianceThreshold\", \"RFE_regression\", \"SelectFromModel_regression\"] |\n", + "| \"classifiers\" | [\"LGBMClassifier\", \"BaggingClassifier\", 'AdaBoostClassifier', 'BernoulliNB', 'DecisionTreeClassifier', 'ExtraTreesClassifier', 'GaussianNB', 'HistGradientBoostingClassifier', 'KNeighborsClassifier','LinearDiscriminantAnalysis', 'LogisticRegression', \"LinearSVC\", \"SVC\", 'MLPClassifier', 'MultinomialNB', \"QuadraticDiscriminantAnalysis\", 'RandomForestClassifier', 'SGDClassifier', 'XGBClassifier'] |\n", + "| \"regressors\" | [\"LGBMRegressor\", 'AdaBoostRegressor', \"ARDRegression\", 'DecisionTreeRegressor', 'ExtraTreesRegressor', 'HistGradientBoostingRegressor', 'KNeighborsRegressor', 'LinearSVR', \"MLPRegressor\", 'RandomForestRegressor', 'SGDRegressor', 'SVR', 'XGBRegressor'] |\n", + "| \"transformers\" | [\"PassKBinsDiscretizer\", \"Binarizer\", \"PCA\", \"ZeroCount\", \"ColumnOneHotEncoder\", \"FastICA\", \"FeatureAgglomeration\", \"Nystroem\", \"RBFSampler\", \"QuantileTransformer\", \"PowerTransformer\"] |\n", + "| \"scalers\" | [\"MinMaxScaler\", \"RobustScaler\", \"StandardScaler\", \"MaxAbsScaler\", \"Normalizer\", ] |\n", + "| \"all_transformers\" | [\"transformers\", \"scalers\"] |\n", + "| \"arithmatic\" | [\"AddTransformer\", \"mul_neg_1_Transformer\", \"MulTransformer\", \"SafeReciprocalTransformer\", \"EQTransformer\", \"NETransformer\", \"GETransformer\", \"GTTransformer\", \"LETransformer\", \"LTTransformer\", \"MinTransformer\", \"MaxTransformer\"] |\n", + "| \"imputers\" | [\"SimpleImputer\", \"IterativeImputer\", \"KNNImputer\"] |\n", + "| \"skrebate\" | [\"ReliefF\", \"SURF\", \"SURFstar\", \"MultiSURF\"] |\n", + "| \"genetic_encoders\" | [\"DominantEncoder\", \"RecessiveEncoder\", \"HeterosisEncoder\", \"UnderDominanceEncoder\", \"OverDominanceEncoder\"] |\n", + "| \"classifiers_sklearnex\" | [\"RandomForestClassifier_sklearnex\", \"LogisticRegression_sklearnex\", \"KNeighborsClassifier_sklearnex\", \"SVC_sklearnex\",\"NuSVC_sklearnex\"] |\n", + "| \"regressors_sklearnex\" | [\"LinearRegression_sklearnex\", \"Ridge_sklearnex\", \"Lasso_sklearnex\", \"ElasticNet_sklearnex\", \"SVR_sklearnex\", \"NuSVR_sklearnex\", \"RandomForestRegressor_sklearnex\", \"KNeighborsRegressor_sklearnex\"] |" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -2542,19 +2637,19 @@ " /* fitted */\n", " background-color: var(--sklearn-color-fitted-level-3);\n", "}\n", - "
LogisticRegression(C=0.09214193108798754, l1_ratio=0.6425731475282531,\n",
-       "                   max_iter=1000, n_jobs=1, penalty='elasticnet',\n",
-       "                   solver='saga')
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" + "
LogisticRegression(C=0.6262919454224, class_weight='balanced',\n",
+       "                   l1_ratio=0.1219417333128, max_iter=1000, n_jobs=1,\n",
+       "                   penalty='elasticnet', solver='saga')
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" ], "text/plain": [ - "LogisticRegression(C=0.09214193108798754, l1_ratio=0.6425731475282531,\n", - " max_iter=1000, n_jobs=1, penalty='elasticnet',\n", - " solver='saga')" + "LogisticRegression(C=0.6262919454224, class_weight='balanced',\n", + " l1_ratio=0.1219417333128, max_iter=1000, n_jobs=1,\n", + " penalty='elasticnet', solver='saga')" ] }, - "execution_count": 24, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -2569,7 +2664,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -2986,16 +3081,19 @@ " /* fitted */\n", " background-color: var(--sklearn-color-fitted-level-3);\n", "}\n", - "
DecisionTreeClassifier(class_weight='balanced', max_depth=1, min_samples_leaf=8,\n",
-       "                       min_samples_split=9)
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" + "
DecisionTreeClassifier(class_weight='balanced', max_depth=14,\n",
+       "                       max_features='sqrt', min_samples_leaf=3,\n",
+       "                       min_samples_split=16)
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" ], "text/plain": [ - "DecisionTreeClassifier(class_weight='balanced', max_depth=1, min_samples_leaf=8,\n", - " min_samples_split=9)" + "DecisionTreeClassifier(class_weight='balanced', max_depth=14,\n", + " max_features='sqrt', min_samples_leaf=3,\n", + " min_samples_split=16)" ] }, - "execution_count": 25, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -3007,7 +3105,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -3424,13 +3522,13 @@ " /* fitted */\n", " background-color: var(--sklearn-color-fitted-level-3);\n", "}\n", - "
LinearDiscriminantAnalysis(shrinkage=0.6166902161314916, solver='eigen')
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" + "
KNeighborsClassifier(n_jobs=1, n_neighbors=3, p=3, weights='distance')
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" ], "text/plain": [ - "LinearDiscriminantAnalysis(shrinkage=0.6166902161314916, solver='eigen')" + "KNeighborsClassifier(n_jobs=1, n_neighbors=3, p=3, weights='distance')" ] }, - "execution_count": 26, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -3445,7 +3543,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -3862,16 +3960,16 @@ " /* fitted */\n", " background-color: var(--sklearn-color-fitted-level-3);\n", "}\n", - "
LogisticRegression(C=0.13397662986842293, max_iter=1000, n_jobs=1, penalty='l1',\n",
-       "                   solver='saga')
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" + "
AdaBoostClassifier(algorithm='SAMME', learning_rate=0.0231006103189,\n",
+       "                   n_estimators=391)
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" ], "text/plain": [ - "LogisticRegression(C=0.13397662986842293, max_iter=1000, n_jobs=1, penalty='l1',\n", - " solver='saga')" + "AdaBoostClassifier(algorithm='SAMME', learning_rate=0.0231006103189,\n", + " n_estimators=391)" ] }, - "execution_count": 27, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -3885,14 +3983,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Sequential Example\n", + "## SequentialPipeline\n", "\n", - "SequentialPipelines are of fixed length and sample from a predefined distribution for each step. Here is an example of the form Selector-Transformer-Classifer" + "SequentialPipelines are of fixed length and sample from a predefined distribution for each step. " ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -3905,7 +4003,7 @@ { "data": { "text/html": [ - "
Pipeline(steps=[('selectpercentile',\n",
-       "                 SelectPercentile(percentile=67.96672316882378)),\n",
-       "                ('columnonehotencoder', ColumnOneHotEncoder()),\n",
+       "
Pipeline(steps=[('variancethreshold',\n",
+       "                 VarianceThreshold(threshold=0.0009684094023)),\n",
+       "                ('pca', PCA(n_components=0.8999344355157)),\n",
        "                ('logisticregression',\n",
-       "                 LogisticRegression(C=5839.203596349427,\n",
-       "                                    class_weight='balanced', max_iter=1000,\n",
-       "                                    n_jobs=1, solver='saga'))])
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" ], "text/plain": [ - "Pipeline(steps=[('robustscaler',\n", - " RobustScaler(quantile_range=(0.2187724978734,\n", - " 0.7909007640608))),\n", - " ('variancethreshold',\n", - " VarianceThreshold(threshold=0.0193318854527)),\n", - " ('featureunion',\n", - " FeatureUnion(transformer_list=[('skiptransformer',\n", - " SkipTransformer()),\n", + "Pipeline(steps=[('minmaxscaler', MinMaxScaler()),\n", + " ('selectfwe', SelectFwe(alpha=0.0014048740592)),\n", + " ('featureunion-1',\n", + " FeatureUnion(transformer_list=[('featureunion',\n", + " FeatureUnion(transformer_list=[('zerocount',\n", + " ZeroCount())])),\n", + " ('passthrough',\n", + " Passthrough())])),\n", + " ('featureunion-2',\n", + " FeatureUnion(transformer_list=[('featureunion',\n", + " FeatureUnion(transformer_list=[('estimatortransformer',\n", + " EstimatorTransformer(estimator=BernoulliNB(alpha=76.5761838773666,\n", + " fit_prior=False)))])),\n", " ('passthrough',\n", " Passthrough())])),\n", " ('kneighborsclassifier',\n", - " KNeighborsClassifier(n_jobs=1, n_neighbors=1))])" + " KNeighborsClassifier(n_jobs=1, n_neighbors=1, p=3))])" ] }, - "execution_count": 28, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1931,25 +1917,18 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Generation: : 0it [1:47:46, ?it/s]\n" - ] } ], "source": [ @@ -2011,7 +1990,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -2027,12 +2006,8 @@ "est = TPOTClassifier(memory='/to/your/path')\n", "\n", "# Method 3, with a Memory object\n", - "cachedir = mkdtemp() # Create a temporary folder\n", - "memory = Memory(cachedir=cachedir, verbose=0)\n", - "est = TPOTClassifier(memory=memory)\n", - "\n", - "# Clear the cache directory when you don't need it anymore\n", - "rmtree(cachedir)" + "memory = Memory(location='./to/your/path', verbose=0)\n", + "est = TPOTClassifier(memory=memory)\n" ] }, { @@ -2136,208 +2111,234 @@ "2. The `tpot2.TPOTEstimatorSteadyState` differs in that it will generate and evaluate the next individual as soon as an individual finishes evaluation. The number of individuals being evaluated is determined by the n_jobs parameter. There is no longer a concept of generations. The population_size parameter now refers to the size of the list of evaluated parents. When an individual is evaluated, the selection method updates the list of parents. This allows more efficient utilization when using multiple cores.\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### tpot2.TPOTEstimatorSteadyState" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Evaluations: : 21it [00:13, 1.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.9786392405063291\n" + ] + } + ], "source": [ "import tpot2\n", "import sklearn\n", "import sklearn.datasets\n", "\n", - "scorer = sklearn.metrics.get_scorer('roc_auc_ovo')\n", - "X, y = sklearn.datasets.load_iris(return_X_y=True)\n", - "X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, train_size=0.75, test_size=0.25)\n", "\n", - "\n", - "est = tpot2.TPOTClassifier(n_jobs=40, max_time_mins=30, verbose=5, generations=1, population_size=5)\n", - "est.fit(X_train, y_train)\n", - "\n", - "\n", - "print(scorer(est, X_test, y_test))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "est._evolver_instance.population.evaluated_individuals.iloc[0]['Individual'].export_pipeline()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import tpot2\n", - "import sklearn\n", - "import sklearn.metrics\n", - "import sklearn.datasets\n", - "\n", - "scorer = sklearn.metrics.get_scorer('neg_mean_squared_error')\n", - "X, y = sklearn.datasets.load_diabetes(return_X_y=True)\n", - "X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, train_size=0.75, test_size=0.25)\n", - "\n", - "est = tpot2.tpot_estimator.templates.TPOTRegressor(n_jobs=4, max_time_mins=30, verbose=2, cv=5)\n", - "est.fit(X_train, y_train)\n", - "\n", - "print(scorer(est, X_test, y_test))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### tpot2.TPOTEstimatorSteadyState" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import tpot2\n", - "import sklearn\n", - "import sklearn.datasets\n", - "\n", - "\n", - "graph_search_space = tpot2.search_spaces.pipelines.GraphPipeline(\n", - " root_search_space= tpot2.config.get_search_space([\"KNeighborsClassifier\", \"LogisticRegression\", \"DecisionTreeClassifier\"]),\n", - " leaf_search_space = tpot2.config.get_search_space(\"selectors\"), \n", - " inner_search_space = tpot2.config.get_search_space([\"transformers\"]),\n", - " max_size = 10,\n", - ")\n", - "\n", - "est = tpot2.TPOTEstimatorSteadyState( \n", - " search_space = graph_search_space,\n", - " scorers=['roc_auc_ovr'], #scorers can be a list of strings or a list of scorers. These get evaluated during cross validation. \n", - " scorers_weights=[1],\n", - "\n", - " classification=True,\n", - "\n", - " max_eval_time_mins=15,\n", - " max_time_mins=30,\n", - " verbose=2)\n", - "\n", - "\n", - "scorer = sklearn.metrics.get_scorer('roc_auc_ovo')\n", - "X, y = sklearn.datasets.load_iris(return_X_y=True)\n", - "X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, train_size=0.75, test_size=0.25)\n", - "est.fit(X_train, y_train)\n", - "print(scorer(est, X_test, y_test))\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fitted_pipeline = est.fitted_pipeline_ # access best pipeline directly\n", - "fitted_pipeline.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#view the summary of all evaluated individuals as a pandas dataframe\n", - "est.evaluated_individuals" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import tpot2\n", - "import sklearn\n", - "import sklearn.datasets\n", + "graph_search_space = tpot2.search_spaces.pipelines.GraphSearchPipeline(\n", + " root_search_space= tpot2.config.get_search_space([\"KNeighborsClassifier\", \"LogisticRegression\", \"DecisionTreeClassifier\"]),\n", + " leaf_search_space = tpot2.config.get_search_space(\"selectors\"), \n", + " inner_search_space = tpot2.config.get_search_space([\"transformers\"]),\n", + " max_size = 10,\n", + ")\n", "\n", "est = tpot2.TPOTEstimatorSteadyState( \n", " search_space = graph_search_space,\n", " scorers=['roc_auc_ovr',tpot2.objectives.complexity_scorer],\n", " scorers_weights=[1,-1],\n", "\n", + "\n", " classification=True,\n", "\n", " max_eval_time_mins=15,\n", " max_time_mins=30,\n", + " early_stop=10, #In TPOTEstimatorSteadyState, since there are no generations, early_stop is the number of pipelines to evaluate before stopping.\n", " verbose=2)\n", "\n", "\n", "scorer = sklearn.metrics.get_scorer('roc_auc_ovo')\n", - "X, y = sklearn.datasets.load_iris(return_X_y=True)\n", + "X, y = sklearn.datasets.load_breast_cancer(return_X_y=True)\n", "X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, train_size=0.75, test_size=0.25)\n", "est.fit(X_train, y_train)\n", - "print(scorer(est, X_test, y_test))\n" + "print(scorer(est, X_test, y_test))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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roc_auc_scorecomplexity_scorerParentsVariation_FunctionIndividualSubmitted TimestampCompleted TimestampEval ErrorPareto_FrontInstance
00.47480520.0NaNNaN<tpot2.search_spaces.pipelines.graph.GraphPipe...1.727144e+091.727144e+09NoneNaN[('LogisticRegression_1', 'FastICA_1'), ('Fast...
10.96298378.0NaNNaN<tpot2.search_spaces.pipelines.graph.GraphPipe...1.727144e+091.727144e+09NoneNaN[('DecisionTreeClassifier_1', 'PCA_1'), ('Quan...
20.96231057.0NaNNaN<tpot2.search_spaces.pipelines.graph.GraphPipe...1.727144e+091.727144e+09NoneNaN[('DecisionTreeClassifier_1', 'SelectFwe_1')]
30.95690866.0NaNNaN<tpot2.search_spaces.pipelines.graph.GraphPipe...1.727144e+091.727144e+09NoneNaN[('DecisionTreeClassifier_1', 'SelectFwe_2'), ...
40.87919515.0NaNNaN<tpot2.search_spaces.pipelines.graph.GraphPipe...1.727144e+091.727144e+09NoneNaN[('DecisionTreeClassifier_1', 'SelectFwe_1')]
\n", + "
" + ], + "text/plain": [ + " roc_auc_score complexity_scorer Parents Variation_Function \\\n", + "0 0.474805 20.0 NaN NaN \n", + "1 0.962983 78.0 NaN NaN \n", + "2 0.962310 57.0 NaN NaN \n", + "3 0.956908 66.0 NaN NaN \n", + "4 0.879195 15.0 NaN NaN \n", + "\n", + " Individual Submitted Timestamp \\\n", + "0 #sk-container-id-1 {\n", + " /* Definition of color scheme common for light and dark mode */\n", + " --sklearn-color-text: black;\n", + " --sklearn-color-line: gray;\n", + " /* Definition of color scheme for unfitted estimators */\n", + " --sklearn-color-unfitted-level-0: #fff5e6;\n", + " --sklearn-color-unfitted-level-1: #f6e4d2;\n", + " --sklearn-color-unfitted-level-2: #ffe0b3;\n", + " --sklearn-color-unfitted-level-3: chocolate;\n", + " /* Definition of color scheme for fitted estimators */\n", + " --sklearn-color-fitted-level-0: #f0f8ff;\n", + " --sklearn-color-fitted-level-1: #d4ebff;\n", + " --sklearn-color-fitted-level-2: #b3dbfd;\n", + " --sklearn-color-fitted-level-3: cornflowerblue;\n", + "\n", + " /* Specific color for light theme */\n", + " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n", + " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n", + " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n", + " --sklearn-color-icon: #696969;\n", + "\n", + " @media (prefers-color-scheme: dark) {\n", + " /* Redefinition of color scheme for dark theme */\n", + " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n", + " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n", + " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n", + " --sklearn-color-icon: #878787;\n", + " }\n", + "}\n", + "\n", + "#sk-container-id-1 {\n", + " color: var(--sklearn-color-text);\n", + "}\n", + "\n", + "#sk-container-id-1 pre {\n", + " padding: 0;\n", + "}\n", + "\n", + "#sk-container-id-1 input.sk-hidden--visually {\n", + " border: 0;\n", + " clip: rect(1px 1px 1px 1px);\n", + " clip: rect(1px, 1px, 1px, 1px);\n", + " height: 1px;\n", + " margin: -1px;\n", + " overflow: hidden;\n", + " padding: 0;\n", + " position: absolute;\n", + " width: 1px;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-dashed-wrapped {\n", + " border: 1px dashed var(--sklearn-color-line);\n", + " margin: 0 0.4em 0.5em 0.4em;\n", + " box-sizing: border-box;\n", + " padding-bottom: 0.4em;\n", + " background-color: var(--sklearn-color-background);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-container {\n", + " /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n", + " but bootstrap.min.css set `[hidden] { display: none !important; }`\n", + " so we also need the `!important` here to be able to override the\n", + " default hidden behavior on the sphinx rendered scikit-learn.org.\n", + " See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n", + " display: inline-block !important;\n", + " position: relative;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-text-repr-fallback {\n", + " display: none;\n", + "}\n", + "\n", + "div.sk-parallel-item,\n", + "div.sk-serial,\n", + "div.sk-item {\n", + " /* draw centered vertical line to link estimators */\n", + " background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n", + " background-size: 2px 100%;\n", + " background-repeat: no-repeat;\n", + " background-position: center center;\n", + "}\n", + "\n", + "/* Parallel-specific style estimator block */\n", + "\n", + "#sk-container-id-1 div.sk-parallel-item::after {\n", + " content: \"\";\n", + " width: 100%;\n", + " border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n", + " flex-grow: 1;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-parallel {\n", + " display: flex;\n", + " align-items: stretch;\n", + " justify-content: center;\n", + " background-color: var(--sklearn-color-background);\n", + " position: relative;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-parallel-item {\n", + " display: flex;\n", + " flex-direction: column;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n", + " align-self: flex-end;\n", + " width: 50%;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n", + " align-self: flex-start;\n", + " width: 50%;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n", + " width: 0;\n", + "}\n", + "\n", + "/* Serial-specific style estimator block */\n", + "\n", + "#sk-container-id-1 div.sk-serial {\n", + " display: flex;\n", + " flex-direction: column;\n", + " align-items: center;\n", + " background-color: var(--sklearn-color-background);\n", + " padding-right: 1em;\n", + " padding-left: 1em;\n", + "}\n", + "\n", + "\n", + "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n", + "clickable and can be expanded/collapsed.\n", + "- Pipeline and ColumnTransformer use this feature and define the default style\n", + "- Estimators will overwrite some part of the style using the `sk-estimator` class\n", + "*/\n", + "\n", + "/* Pipeline and ColumnTransformer style (default) */\n", + "\n", + "#sk-container-id-1 div.sk-toggleable {\n", + " /* Default theme specific background. It is overwritten whether we have a\n", + " specific estimator or a Pipeline/ColumnTransformer */\n", + " background-color: var(--sklearn-color-background);\n", + "}\n", + "\n", + "/* Toggleable label */\n", + "#sk-container-id-1 label.sk-toggleable__label {\n", + " cursor: pointer;\n", + " display: block;\n", + " width: 100%;\n", + " margin-bottom: 0;\n", + " padding: 0.5em;\n", + " box-sizing: border-box;\n", + " text-align: center;\n", + "}\n", + "\n", + "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n", + " /* Arrow on the left of the label */\n", + " content: \"▸\";\n", + " float: left;\n", + " margin-right: 0.25em;\n", + " color: var(--sklearn-color-icon);\n", + "}\n", + "\n", + "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n", + " color: var(--sklearn-color-text);\n", + "}\n", + "\n", + "/* Toggleable content - dropdown */\n", + "\n", + "#sk-container-id-1 div.sk-toggleable__content {\n", + " max-height: 0;\n", + " max-width: 0;\n", + " overflow: hidden;\n", + " text-align: left;\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-toggleable__content.fitted {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-toggleable__content pre {\n", + " margin: 0.2em;\n", + " border-radius: 0.25em;\n", + " color: var(--sklearn-color-text);\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-fitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n", + " /* Expand drop-down */\n", + " max-height: 200px;\n", + " max-width: 100%;\n", + " overflow: auto;\n", + "}\n", + "\n", + "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n", + " content: \"▾\";\n", + "}\n", + "\n", + "/* Pipeline/ColumnTransformer-specific style */\n", + "\n", + "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " color: var(--sklearn-color-text);\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "/* Estimator-specific style */\n", + "\n", + "/* Colorize estimator box */\n", + "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n", + "#sk-container-id-1 div.sk-label label {\n", + " /* The background is the default theme color */\n", + " color: var(--sklearn-color-text-on-default-background);\n", + "}\n", + "\n", + "/* On hover, darken the color of the background */\n", + "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n", + " color: var(--sklearn-color-text);\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "/* Label box, darken color on hover, fitted */\n", + "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n", + " color: var(--sklearn-color-text);\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "/* Estimator label */\n", + "\n", + "#sk-container-id-1 div.sk-label label {\n", + " font-family: monospace;\n", + " font-weight: bold;\n", + " display: inline-block;\n", + " line-height: 1.2em;\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-label-container {\n", + " text-align: center;\n", + "}\n", + "\n", + "/* Estimator-specific */\n", + "#sk-container-id-1 div.sk-estimator {\n", + " font-family: monospace;\n", + " border: 1px dotted var(--sklearn-color-border-box);\n", + " border-radius: 0.25em;\n", + " box-sizing: border-box;\n", + " margin-bottom: 0.5em;\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-estimator.fitted {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-0);\n", + "}\n", + "\n", + "/* on hover */\n", + "#sk-container-id-1 div.sk-estimator:hover {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-1 div.sk-estimator.fitted:hover {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n", + "\n", + "/* Common style for \"i\" and \"?\" */\n", + "\n", + ".sk-estimator-doc-link,\n", + "a:link.sk-estimator-doc-link,\n", + "a:visited.sk-estimator-doc-link {\n", + " float: right;\n", + " font-size: smaller;\n", + " line-height: 1em;\n", + " font-family: monospace;\n", + " background-color: var(--sklearn-color-background);\n", + " border-radius: 1em;\n", + " height: 1em;\n", + " width: 1em;\n", + " text-decoration: none !important;\n", + " margin-left: 1ex;\n", + " /* unfitted */\n", + " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n", + " color: var(--sklearn-color-unfitted-level-1);\n", + "}\n", + "\n", + ".sk-estimator-doc-link.fitted,\n", + "a:link.sk-estimator-doc-link.fitted,\n", + "a:visited.sk-estimator-doc-link.fitted {\n", + " /* fitted */\n", + " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n", + " color: var(--sklearn-color-fitted-level-1);\n", + "}\n", + "\n", + "/* On hover */\n", + "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n", + ".sk-estimator-doc-link:hover,\n", + "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n", + ".sk-estimator-doc-link:hover {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-3);\n", + " color: var(--sklearn-color-background);\n", + " text-decoration: none;\n", + "}\n", + "\n", + "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n", + ".sk-estimator-doc-link.fitted:hover,\n", + "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n", + ".sk-estimator-doc-link.fitted:hover {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-3);\n", + " color: var(--sklearn-color-background);\n", + " text-decoration: none;\n", + "}\n", + "\n", + "/* Span, style for the box shown on hovering the info icon */\n", + ".sk-estimator-doc-link span {\n", + " display: none;\n", + " z-index: 9999;\n", + " position: relative;\n", + " font-weight: normal;\n", + " right: .2ex;\n", + " padding: .5ex;\n", + " margin: .5ex;\n", + " width: min-content;\n", + " min-width: 20ex;\n", + " max-width: 50ex;\n", + " color: var(--sklearn-color-text);\n", + " box-shadow: 2pt 2pt 4pt #999;\n", + " /* unfitted */\n", + " background: var(--sklearn-color-unfitted-level-0);\n", + " border: .5pt solid var(--sklearn-color-unfitted-level-3);\n", + "}\n", + "\n", + ".sk-estimator-doc-link.fitted span {\n", + " /* fitted */\n", + " background: var(--sklearn-color-fitted-level-0);\n", + " border: var(--sklearn-color-fitted-level-3);\n", + "}\n", + "\n", + ".sk-estimator-doc-link:hover span {\n", + " display: block;\n", + "}\n", + "\n", + "/* \"?\"-specific style due to the `` HTML tag */\n", + "\n", + "#sk-container-id-1 a.estimator_doc_link {\n", + " float: right;\n", + " font-size: 1rem;\n", + " line-height: 1em;\n", + " font-family: monospace;\n", + " background-color: var(--sklearn-color-background);\n", + " border-radius: 1rem;\n", + " height: 1rem;\n", + " width: 1rem;\n", + " text-decoration: none;\n", + " /* unfitted */\n", + " color: var(--sklearn-color-unfitted-level-1);\n", + " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n", + "}\n", + "\n", + "#sk-container-id-1 a.estimator_doc_link.fitted {\n", + " /* fitted */\n", + " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n", + " color: var(--sklearn-color-fitted-level-1);\n", + "}\n", + "\n", + "/* On hover */\n", + "#sk-container-id-1 a.estimator_doc_link:hover {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-3);\n", + " color: var(--sklearn-color-background);\n", + " text-decoration: none;\n", + "}\n", + "\n", + "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-3);\n", + "}\n", + "" + ], + "text/plain": [ + "RandomForestClassifier(bootstrap=False, max_features=0.0109527542096,\n", + " min_samples_leaf=15, min_samples_split=4,\n", + " n_estimators=128)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from ConfigSpace import ConfigurationSpace\n", + "from ConfigSpace import ConfigurationSpace, Integer, Float, Categorical, Normal\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "import tpot2\n", + "import numpy as np\n", + "import sklearn\n", + "import sklearn.datasets\n", + "\n", + "rf_configspace = ConfigurationSpace(\n", + " space = {\n", + " 'n_estimators': 128, #as recommended by Oshiro et al. (2012\n", + " 'max_features': Float(\"max_features\", bounds=(0.01,1), log=True), #log scale like autosklearn?\n", + " 'criterion': Categorical(\"criterion\", ['gini', 'entropy']),\n", + " 'min_samples_split': Integer(\"min_samples_split\", bounds=(2, 20)),\n", + " 'min_samples_leaf': Integer(\"min_samples_leaf\", bounds=(1, 20)),\n", + " 'bootstrap': Categorical(\"bootstrap\", [True, False]),\n", + " #random_state = 1, # If you want results to be reproducible, you can set a fixed random_state.\n", + " }\n", + ")\n", + "\n", + "hyperparameters = dict(rf_configspace.sample_configuration())\n", + "print(\"sampled hyperparameters\")\n", + "print(hyperparameters)\n", + "\n", + "rf = RandomForestClassifier(**hyperparameters)\n", + "rf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "More simply:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sampled hyperparameters\n", + "{'bootstrap': True, 'criterion': 'entropy', 'max_features': 0.8366498702446, 'min_samples_leaf': 11, 'min_samples_split': 20, 'n_estimators': 128}\n" + ] + }, + { + "data": { + "text/html": [ + "
RandomForestClassifier(criterion='entropy', max_features=0.8366498702446,\n",
+       "                       min_samples_leaf=11, min_samples_split=20,\n",
+       "                       n_estimators=128)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "RandomForestClassifier(criterion='entropy', max_features=0.8366498702446,\n", + " min_samples_leaf=11, min_samples_split=20,\n", + " n_estimators=128)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rf_configspace = ConfigurationSpace(\n", + " space = {\n", + " 'n_estimators': 128, #as recommended by Oshiro et al. (2012\n", + " 'max_features':(0.01,1), #not log scaled\n", + " 'criterion': ['gini', 'entropy'],\n", + " 'min_samples_split': (2, 20),\n", + " 'min_samples_leaf': (1, 20),\n", + " 'bootstrap': [True, False],\n", + " #random_state = 1, # If you want results to be reproducible, you can set a fixed random_state.\n", + " }\n", + ")\n", + "\n", + "hyperparameters = dict(rf_configspace.sample_configuration())\n", + "print(\"sampled hyperparameters\")\n", + "print(hyperparameters)\n", + "\n", + "rf = RandomForestClassifier(**hyperparameters)\n", + "rf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TPOT Search spaces\n", + "\n", + "TPOT allows you to both hyperparameter search spaces for individual methods as well as pipeline structure search spaces. For example, TPOT can create linear pipelines, trees, or graphs. \n", + "\n", + "TPOT search spaces are found in the `search_spaces` module. There are two primary kinds of search spaces, node and pipeline. Node search spaces specify the search space of a single sklearn `BaseEstimator`. Pipeline search spaces define the possible structures for a group of node search spaces. These take in node search spaces and produce a pipeline using nodes from that search space. Since sklearn Pipelines are also `BaseEstimator`, pipeline search spaces are also technically node search spaces. Meaning that pipeline search spaces can take in other pipeline search spaces in order to define more complex structures. The primary differentiating factor bewteen node and pipeline search spaces is that pipeline search spaces must take in another search space as input to feed its individual nodes. Therefore, all search spaces eventually end in a node search space at the lowest level. Note that parameters for pipeline search spaces can differ, some take in only a single search space, some take in a list, or some take in multiple defined parameters.\n", + "\n", + "## node search spaces\n", + "\n", + "\n", + "| Name | Info |\n", + "| :--- | :----: |\n", + "| EstimatorNode | Takes in a ConfigSpace along with the class of the method. This node will optimize the hyperparameters for a single method. |\n", + "| GeneticFeatureSelectorNode | Uses evolution to optimize a set of features, exports a basic sklearn Selector that simply selects the features chosen by the node. |\n", + "| FSSNode | FSS stands for FeatureSetSelector. This node takes in a list of user-defined subsets of features and selects a single predefined subset. Note that TPOT will not create new subsets nor will it select multiple subsets per node. If using a linear pipeline, this node should be set as the first step. In linear pipelines it is recommended that you only use a small number of feature sets. I recommend exploring using FSSNodes in pipelines that allow TPOT to select more than one FSSNode at a time. For example, DynamicUnionPipeline and GraphPipeline are both excellent combos for FSSNode. Use FFSNode inside a DynamicUnionPipeline at the start of linear pipeline to explore optimal combinations of subsets in linear pipelines. Set the leaf_search_space of GraphSearchPipeline TPOT can use multiple feature sets in different ways, for example, with different transformers for different sets. |\n", + "\n", + "\n", + "\n", + "## pipeline search spaces\n", + "\n", + "found in tpot2.search_spaces.pipelines\n", + "\n", + "WrapperPipeline - This search space is for wrapping a sklearn estimator with a method that takes another estimator and hyperparameters as arguments.\n", + " For example, this can be used with sklearn.ensemble.BaggingClassifier or sklearn.ensemble.AdaBoostClassifier.\n", + "\n", + "\n", + "| Name | Info |\n", + "| :--- | :----: |\n", + "| ChoicePipeline | Takes in a list of search spaces. Will select one node from the search space. |\n", + "| SequentialPipeline | Takes in a list of search spaces. will produce a pipeline of Sequential length. Each step in the pipeline will correspond to the the search space provided in the same index. |\n", + "| DynamicLinearPipeline | Takes in a single search space. Will produce a linear pipeline of variable length. Each step in the pipeline will be pulled from the search space provided. |\n", + "| UnionPipeline | Takes in a list of search spaces. The returned pipeline will include one estimator per search space joined in an sklearn FeatureUnion. Useful for having many steps in one layer. |\n", + "| DynamicUnionPipeline | Takes in a single search space. It will pull anywhere from 1 to max_estimators number of estimators from the search space and concatenate them in a FeatureUnion. |\n", + "| TreePipeline |Generates a pipeline of variable length. Pipeline will have a tree structure similar to TPOT1. |\n", + "| GraphSearchPipeline | Generates a directed acyclic graph of variable size. Search spaces for root, leaf, and inner nodes can be defined separately if desired. |\n", + "| WrapperPipeline | This search space is for wrapping a sklearn estimator with a method that takes another estimator and hyperparameters as arguments. For example, this can be used with sklearn.ensemble.BaggingClassifier or sklearn.ensemble.AdaBoostClassifier. |\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Node Search Space Examples\n", + "\n", + "Node search spaces represent the smallest unit of an sklearn pipeline. All node search spaces create and optimize a single node, or estimator object. For example this could be a KNeighborsClassifier or a FeatureSetSelector.\n", + "\n", + "### EstimatorNode\n", + "\n", + "The EstimatorNode represents the hyperparameter search space for a scikit-learn estimator. \n", + "\n", + "Note that `ConfigSpace` doesn't support `None` in its search space, and does not support the booleans True or False as fixed parameters (though booleans seem to be allowed in Categorical search spaces). To get around this, use the macros defined in:\n", + "\n", + "`from tpot2.search_spaces.nodes.estimator_node import NONE_SPECIAL_STRING, TRUE_SPECIAL_STRING, FALSE_SPECIAL_STRING`" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import tpot2\n", + "from ConfigSpace import ConfigurationSpace\n", + "from ConfigSpace import ConfigurationSpace, Integer, Float, Categorical, Normal\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "\n", + "knn_configspace = ConfigurationSpace(\n", + " space = {\n", + "\n", + " 'n_neighbors': Integer(\"n_neighbors\", bounds=(1, 10)),\n", + " 'weights': Categorical(\"weights\", ['uniform', 'distance']),\n", + " 'p': Integer(\"p\", bounds=(1, 3)),\n", + " 'metric': Categorical(\"metric\", ['euclidean', 'minkowski']),\n", + " 'n_jobs': 1,\n", + " }\n", + ")\n", + "\n", + "\n", + "knn_node = tpot2.search_spaces.nodes.EstimatorNode(\n", + " method = KNeighborsClassifier,\n", + " space = knn_configspace,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can sample generate an individual with the generate() function. This individual samples from the search space as well as provides mutation and crossover functions to modify the current sample." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "knn_individual = knn_node.generate()\n", + "knn_individual" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sampled hyperparameters\n", + "{'metric': 'euclidean', 'n_jobs': 1, 'n_neighbors': 8, 'p': 2, 'weights': 'uniform'}\n" + ] + } + ], + "source": [ + "print(\"sampled hyperparameters\")\n", + "print(knn_individual.hyperparameters)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All Individual objects have mutation and crossover operators that TPOT uses to optimize the pipelines." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mutated hyperparameters\n", + "{'metric': 'minkowski', 'n_jobs': 1, 'n_neighbors': 1, 'p': 3, 'weights': 'distance'}\n" + ] + } + ], + "source": [ + "knn_individual.mutate() # mutate the individual\n", + "print(\"mutated hyperparameters\")\n", + "print(knn_individual.hyperparameters)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In TPOT2, crossover only modifies the individual calling the crossover function, the second individual remains the same" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "original hyperparameters for individual 1\n", + "{'metric': 'minkowski', 'n_jobs': 1, 'n_neighbors': 8, 'p': 1, 'weights': 'uniform'}\n", + "original hyperparameters for individual 2\n", + "{'metric': 'euclidean', 'n_jobs': 1, 'n_neighbors': 7, 'p': 3, 'weights': 'distance'}\n", + "\n", + "post crossover hyperparameters for individual 1\n", + "{'metric': 'minkowski', 'n_jobs': 1, 'n_neighbors': 8, 'p': 3, 'weights': 'uniform'}\n", + "post crossover hyperparameters for individual 2\n", + "{'metric': 'euclidean', 'n_jobs': 1, 'n_neighbors': 7, 'p': 3, 'weights': 'distance'}\n" + ] + } + ], + "source": [ + "knn_individual1 = knn_node.generate()\n", + "knn_individual2 = knn_node.generate()\n", + "\n", + "print(\"original hyperparameters for individual 1\")\n", + "print(knn_individual1.hyperparameters)\n", + "\n", + "print(\"original hyperparameters for individual 2\")\n", + "print(knn_individual2.hyperparameters)\n", + "\n", + "print()\n", + "\n", + "knn_individual1.crossover(knn_individual2) # crossover the individuals\n", + "print(\"post crossover hyperparameters for individual 1\")\n", + "print(knn_individual1.hyperparameters)\n", + "print(\"post crossover hyperparameters for individual 2\")\n", + "print(knn_individual2.hyperparameters)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All search spaces have an export_pipeline function that returns an sklearn `BaseEstimator`" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
KNeighborsClassifier(n_jobs=1, n_neighbors=8, p=3)
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" + ], + "text/plain": [ + "KNeighborsClassifier(n_jobs=1, n_neighbors=8, p=3)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "est = knn_individual1.export_pipeline()\n", + "est" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If a dictionary of parameters is passed instead of of a ConfigSpace object, then the hyperparameters will always be fixed and not learned." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
KNeighborsClassifier(n_neighbors=10)
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" + ], + "text/plain": [ + "KNeighborsClassifier(n_neighbors=10)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import tpot2\n", + "from ConfigSpace import ConfigurationSpace\n", + "from ConfigSpace import ConfigurationSpace, Integer, Float, Categorical, Normal\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "\n", + "space = {\n", + "\n", + " 'n_neighbors':10,\n", + "}\n", + "\n", + "knn_node = tpot2.search_spaces.nodes.EstimatorNode(\n", + " method = KNeighborsClassifier,\n", + " space = space,\n", + ")\n", + "\n", + "knn_node.generate().export_pipeline()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### FSSNode and GeneticFeatureSelectorNode\n", + "\n", + "Both of these are given their own tutorials. See Tutorial 3 for FFSNode and Tutorial 5 for GeneticFeatureSelectorNode" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pipeline Search Space Examples\n", + "\n", + "Pipeline search spaces are used to define the structure and restrictions of the pipelines TPOT can search. Unlike Node search spaces, all pipeline search spaces take in other search spaces as inputs. Rather than sample hyperparameters, pipeline search spaces can select models from the input search spaces and organize them within a linear sklearn Pipeline or a TPOT GraphPipeline." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ChoicePipeline\n", + "\n", + "The simplest pipeline search space is the ChoicePipeline. This takes in a list of search spaces and simply selects and samples from one. In this example, we will construct a search space that takes in several options for a classifier. The resulting search space will then first select a model from KNeighborsClassifier, LogisticRegression or DecisionTreeClassifier, and then select the hyperparameters for the given model." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import tpot2\n", + "from ConfigSpace import ConfigurationSpace\n", + "from ConfigSpace import ConfigurationSpace, Integer, Float, Categorical, Normal\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "\n", + "knn_configspace = ConfigurationSpace(\n", + " space = {\n", + "\n", + " 'n_neighbors': Integer(\"n_neighbors\", bounds=(1, 10)),\n", + " 'weights': Categorical(\"weights\", ['uniform', 'distance']),\n", + " 'p': Integer(\"p\", bounds=(1, 3)),\n", + " 'metric': Categorical(\"metric\", ['euclidean', 'minkowski']),\n", + " 'n_jobs': 1,\n", + " }\n", + ")\n", + "\n", + "lr_configspace = ConfigurationSpace(\n", + " space = {\n", + " 'solver': Categorical(\"solver\", ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga']),\n", + " 'penalty': Categorical(\"penalty\", ['l1', 'l2']),\n", + " 'dual': Categorical(\"dual\", [True, False]),\n", + " 'C': Float(\"C\", bounds=(1e-4, 1e4), log=True),\n", + " 'class_weight': Categorical(\"class_weight\", ['balanced']),\n", + " 'n_jobs': 1,\n", + " 'max_iter': 1000,\n", + " }\n", + " )\n", + "\n", + "dt_configspace = ConfigurationSpace(\n", + " space = {\n", + " 'criterion': Categorical(\"criterion\", ['gini', 'entropy']),\n", + " 'max_depth': Integer(\"max_depth\", bounds=(1, 11)),\n", + " 'min_samples_split': Integer(\"min_samples_split\", bounds=(2, 21)),\n", + " 'min_samples_leaf': Integer(\"min_samples_leaf\", bounds=(1, 21)),\n", + " 'max_features': Categorical(\"max_features\", ['sqrt', 'log2']),\n", + " 'min_weight_fraction_leaf': 0.0,\n", + " }\n", + " )\n", + "\n", + "knn_node = tpot2.search_spaces.nodes.EstimatorNode(\n", + " method = KNeighborsClassifier,\n", + " space = knn_configspace,\n", + ")\n", + "\n", + "lr_node = tpot2.search_spaces.nodes.EstimatorNode(\n", + " method = LogisticRegression,\n", + " space = lr_configspace,\n", + ")\n", + "\n", + "dt_node = tpot2.search_spaces.nodes.EstimatorNode(\n", + " method = DecisionTreeClassifier,\n", + " space = dt_configspace,\n", + ")\n", + "\n", + "classifier_node = tpot2.search_spaces.pipelines.ChoicePipeline(\n", + " search_spaces=[\n", + " knn_node,\n", + " lr_node,\n", + " dt_node,\n", + " ]\n", + ")\n", + "\n", + "\n", + "tpot2.search_spaces.pipelines.ChoicePipeline(\n", + " search_spaces = [\n", + " tpot2.search_spaces.nodes.EstimatorNode(\n", + " method = KNeighborsClassifier,\n", + " space = knn_configspace,\n", + " ),\n", + " tpot2.search_spaces.nodes.EstimatorNode(\n", + " method = LogisticRegression,\n", + " space = lr_configspace,\n", + " ),\n", + " tpot2.search_spaces.nodes.EstimatorNode(\n", + " method = DecisionTreeClassifier,\n", + " space = dt_configspace,\n", + " ),\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Search space objects provided by pipeline search spaces work the same as with node search spaces. Note that crossover only works when both individuals have sampled the same method. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sampled pipeline\n" + ] + }, + { + "data": { + "text/html": [ + "
LogisticRegression(C=49.6823706295106, class_weight='balanced', dual=True,\n",
+       "                   max_iter=1000, n_jobs=1, solver='liblinear')
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" + ], + "text/plain": [ + "LogisticRegression(C=49.6823706295106, class_weight='balanced', dual=True,\n", + " max_iter=1000, n_jobs=1, solver='liblinear')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "classifier_individual = classifier_node.generate()\n", + "\n", + "print(\"sampled pipeline\")\n", + "classifier_individual.export_pipeline()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mutated pipeline\n" + ] + }, + { + "data": { + "text/html": [ + "
LogisticRegression(C=997.5163561212358, class_weight='balanced', dual=True,\n",
+       "                   max_iter=1000, n_jobs=1, solver='newton-cg')
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" + ], + "text/plain": [ + "LogisticRegression(C=997.5163561212358, class_weight='balanced', dual=True,\n", + " max_iter=1000, n_jobs=1, solver='newton-cg')" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(\"mutated pipeline\")\n", + "classifier_individual.mutate()\n", + "classifier_individual.export_pipeline()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TPOT2 also comes with predefined search spaces. The current search spaces were adapted from a combination of the original TPOT package as well as the search spaces used in [AutoSklearn](https://github.com/automl/auto-sklearn/tree/development/autosklearn/pipeline/components). The helper function `tpot2.config.get_search_space` takes in a string or a list of strings, and returns either a EstimatorNode or a ChoicePipeline,respectively. \n", + "\n", + "strings can correspond to individual methods. There are also special strings that return predefined lists of methods. \n", + "\n", + "| Special String | Included methods |\n", + "| :--- | :----: |\n", + "| \"selectors\" | [\"SelectFwe\", \"SelectPercentile\", \"VarianceThreshold\",] |\n", + "| \"selectors_classification\" | [\"SelectFwe\", \"SelectPercentile\", \"VarianceThreshold\", \"RFE_classification\", \"SelectFromModel_classification\"] |\n", + "| \"selectors_regression\" | [\"SelectFwe\", \"SelectPercentile\", \"VarianceThreshold\", \"RFE_regression\", \"SelectFromModel_regression\"] |\n", + "| \"classifiers\" | [\"LGBMClassifier\", \"BaggingClassifier\", 'AdaBoostClassifier', 'BernoulliNB', 'DecisionTreeClassifier', 'ExtraTreesClassifier', 'GaussianNB', 'HistGradientBoostingClassifier', 'KNeighborsClassifier','LinearDiscriminantAnalysis', 'LogisticRegression', \"LinearSVC\", \"SVC\", 'MLPClassifier', 'MultinomialNB', \"QuadraticDiscriminantAnalysis\", 'RandomForestClassifier', 'SGDClassifier', 'XGBClassifier'] |\n", + "| \"regressors\" | [\"LGBMRegressor\", 'AdaBoostRegressor', \"ARDRegression\", 'DecisionTreeRegressor', 'ExtraTreesRegressor', 'HistGradientBoostingRegressor', 'KNeighborsRegressor', 'LinearSVR', \"MLPRegressor\", 'RandomForestRegressor', 'SGDRegressor', 'SVR', 'XGBRegressor'] |\n", + "| \"transformers\" | [\"PassKBinsDiscretizer\", \"Binarizer\", \"PCA\", \"ZeroCount\", \"ColumnOneHotEncoder\", \"FastICA\", \"FeatureAgglomeration\", \"Nystroem\", \"RBFSampler\", \"QuantileTransformer\", \"PowerTransformer\"] |\n", + "| \"scalers\" | [\"MinMaxScaler\", \"RobustScaler\", \"StandardScaler\", \"MaxAbsScaler\", \"Normalizer\", ] |\n", + "| \"all_transformers\" | [\"transformers\", \"scalers\"] |\n", + "| \"arithmatic\" | [\"AddTransformer\", \"mul_neg_1_Transformer\", \"MulTransformer\", \"SafeReciprocalTransformer\", \"EQTransformer\", \"NETransformer\", \"GETransformer\", \"GTTransformer\", \"LETransformer\", \"LTTransformer\", \"MinTransformer\", \"MaxTransformer\"] |\n", + "| \"imputers\" | [\"SimpleImputer\", \"IterativeImputer\", \"KNNImputer\"] |\n", + "| \"skrebate\" | [\"ReliefF\", \"SURF\", \"SURFstar\", \"MultiSURF\"] |\n", + "| \"genetic_encoders\" | [\"DominantEncoder\", \"RecessiveEncoder\", \"HeterosisEncoder\", \"UnderDominanceEncoder\", \"OverDominanceEncoder\"] |\n", + "| \"classifiers_sklearnex\" | [\"RandomForestClassifier_sklearnex\", \"LogisticRegression_sklearnex\", \"KNeighborsClassifier_sklearnex\", \"SVC_sklearnex\",\"NuSVC_sklearnex\"] |\n", + "| \"regressors_sklearnex\" | [\"LinearRegression_sklearnex\", \"Ridge_sklearnex\", \"Lasso_sklearnex\", \"ElasticNet_sklearnex\", \"SVR_sklearnex\", \"NuSVR_sklearnex\", \"RandomForestRegressor_sklearnex\", \"KNeighborsRegressor_sklearnex\"] |" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sampled pipeline 1\n" + ] + }, + { + "data": { + "text/html": [ + "
LogisticRegression(C=2532.59836574515, class_weight='balanced', max_iter=1000,\n",
+       "                   n_jobs=1, penalty='l1', solver='saga')
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" + ], + "text/plain": [ + "LogisticRegression(C=2532.59836574515, class_weight='balanced', max_iter=1000,\n", + " n_jobs=1, penalty='l1', solver='saga')" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#same pipeline search space as before.\n", + "classifier_choice = tpot2.config.get_search_space([\"KNeighborsClassifier\", \"LogisticRegression\", \"DecisionTreeClassifier\"])\n", + "\n", + "print(\"sampled pipeline 1\")\n", + "classifier_choice.generate().export_pipeline()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sampled pipeline 2\n" + ] + }, + { + "data": { + "text/html": [ + "
DecisionTreeClassifier(class_weight='balanced', max_depth=20,\n",
+       "                       max_features='log2', min_samples_leaf=2,\n",
+       "                       min_samples_split=4)
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" + ], + "text/plain": [ + "DecisionTreeClassifier(class_weight='balanced', max_depth=20,\n", + " max_features='log2', min_samples_leaf=2,\n", + " min_samples_split=4)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(\"sampled pipeline 2\")\n", + "classifier_choice.generate().export_pipeline()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sampled pipeline 1\n" + ] + }, + { + "data": { + "text/html": [ + "
BernoulliNB(alpha=95.0262026809266)
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" + ], + "text/plain": [ + "BernoulliNB(alpha=95.0262026809266)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#search space for all classifiers\n", + "classifier_choice = tpot2.config.get_search_space(\"classifiers\")\n", + "\n", + "print(\"sampled pipeline 1\")\n", + "classifier_choice.generate().export_pipeline()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sampled pipeline 2\n" + ] + }, + { + "data": { + "text/html": [ + "
BaggingClassifier(bootstrap=False, bootstrap_features=True,\n",
+       "                  max_features=0.8753796928965, max_samples=0.8146576017845,\n",
+       "                  n_estimators=17, n_jobs=1)
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" + ], + "text/plain": [ + "BaggingClassifier(bootstrap=False, bootstrap_features=True,\n", + " max_features=0.8753796928965, max_samples=0.8146576017845,\n", + " n_estimators=17, n_jobs=1)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(\"sampled pipeline 2\")\n", + "classifier_choice.generate().export_pipeline()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## SequentialPipeline\n", + "\n", + "SequentialPipelines are of fixed length and sample from a predefined distribution for each step. " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sampled pipeline\n" + ] + }, + { + "data": { + "text/html": [ + "
Pipeline(steps=[('variancethreshold',\n",
+       "                 VarianceThreshold(threshold=0.0038921831393)),\n",
+       "                ('pca', PCA(n_components=0.7545742110409)),\n",
+       "                ('logisticregression',\n",
+       "                 LogisticRegression(C=85638.13831296022,\n",
+       "                                    class_weight='balanced',\n",
+       "                                    l1_ratio=0.6102894736188, max_iter=1000,\n",
+       "                                    n_jobs=1, penalty='elasticnet',\n",
+       "                                    solver='saga'))])
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" + ], + "text/plain": [ + "Pipeline(steps=[('variancethreshold',\n", + " VarianceThreshold(threshold=0.0038921831393)),\n", + " ('pca', PCA(n_components=0.7545742110409)),\n", + " ('logisticregression',\n", + " LogisticRegression(C=85638.13831296022,\n", + " class_weight='balanced',\n", + " l1_ratio=0.6102894736188, max_iter=1000,\n", + " n_jobs=1, penalty='elasticnet',\n", + " solver='saga'))])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "selector_choicepipeline = tpot2.config.get_search_space(\"VarianceThreshold\")\n", + "transformer_choicepipeline = tpot2.config.get_search_space(\"PCA\")\n", + "classifier_choicepipeline = tpot2.config.get_search_space(\"LogisticRegression\")\n", + "\n", + "stc_pipeline = tpot2.search_spaces.pipelines.SequentialPipeline([\n", + " selector_choicepipeline,\n", + " transformer_choicepipeline,\n", + " classifier_choicepipeline,\n", + "])\n", + "\n", + "print(\"sampled pipeline\")\n", + "stc_pipeline.generate().export_pipeline()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is an example of the form Selector-Transformer-Classifier.\n", + "\n", + "Note that each step in the sequence is a ChoicePipeline this time. Here, the SequentialPipeline can sample from search provided search space in order." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sampled pipeline\n" + ] + }, + { + "data": { + "text/html": [ + "
Pipeline(steps=[('selectpercentile',\n",
+       "                 SelectPercentile(percentile=97.2182140589731)),\n",
+       "                ('binarizer', Binarizer(threshold=0.7460953779809)),\n",
+       "                ('gaussiannb', GaussianNB())])
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" + ], + "text/plain": [ + "Pipeline(steps=[('selectpercentile',\n", + " SelectPercentile(percentile=97.2182140589731)),\n", + " ('binarizer', Binarizer(threshold=0.7460953779809)),\n", + " ('gaussiannb', GaussianNB())])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "selector_choicepipeline = tpot2.config.get_search_space(\"selectors\")\n", + "transformer_choicepipeline = tpot2.config.get_search_space(\"transformers\")\n", + "classifier_choicepipeline = tpot2.config.get_search_space(\"classifiers\")\n", + "\n", + "stc_pipeline = tpot2.search_spaces.pipelines.SequentialPipeline([\n", + " selector_choicepipeline,\n", + " transformer_choicepipeline,\n", + " classifier_choicepipeline,\n", + "])\n", + "\n", + "print(\"sampled pipeline\")\n", + "stc_pipeline.generate().export_pipeline()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sampled pipeline\n" + ] + }, + { + "data": { + "text/html": [ + "
Pipeline(steps=[('selectfwe', SelectFwe(alpha=0.018605231348)),\n",
+       "                ('powertransformer', PowerTransformer()),\n",
+       "                ('adaboostclassifier',\n",
+       "                 AdaBoostClassifier(algorithm='SAMME',\n",
+       "                                    learning_rate=0.2576218451608,\n",
+       "                                    n_estimators=260))])
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" + ], + "text/plain": [ + "Pipeline(steps=[('selectfwe', SelectFwe(alpha=0.018605231348)),\n", + " ('powertransformer', PowerTransformer()),\n", + " ('adaboostclassifier',\n", + " AdaBoostClassifier(algorithm='SAMME',\n", + " learning_rate=0.2576218451608,\n", + " n_estimators=260))])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(\"sampled pipeline\")\n", + "stc_pipeline.generate().export_pipeline()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## DynamicLinearPipeline\n", + "\n", + "DynamicLinearPipeline takes in a single search space and randomly samples and places estimators in a list without a predefined sequence. DynamicLinearPipeline are most often used when paired with LinearPipeline. A common strategy is to use DynamicLinearPipeline to optimize a series of preprocessing or feature engineering steps, followed by a final classifier or regressor." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sampled pipeline\n" + ] + }, + { + "data": { + "text/html": [ + "
Pipeline(steps=[('minmaxscaler-1', MinMaxScaler()),\n",
+       "                ('minmaxscaler-2', MinMaxScaler()),\n",
+       "                ('minmaxscaler-3', MinMaxScaler())])
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" + ], + "text/plain": [ + "Pipeline(steps=[('minmaxscaler-1', MinMaxScaler()),\n", + " ('minmaxscaler-2', MinMaxScaler()),\n", + " ('minmaxscaler-3', MinMaxScaler())])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import tpot2.config\n", + "\n", + "\n", + "linear_feature_engineering = tpot2.search_spaces.pipelines.DynamicLinearPipeline(search_space = tpot2.config.get_search_space([\"all_transformers\",\"selectors_classification\"]), max_length=10)\n", + "print(\"sampled pipeline\")\n", + "linear_feature_engineering.generate().export_pipeline()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sampled pipeline\n" + ] + }, + { + "data": { + "text/html": [ + "
Pipeline(steps=[('powertransformer', PowerTransformer()),\n",
+       "                ('nystroem',\n",
+       "                 Nystroem(gamma=0.9541024274994, kernel='cosine',\n",
+       "                          n_components=12)),\n",
+       "                ('variancethreshold',\n",
+       "                 VarianceThreshold(threshold=0.0109488305621))])
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" + ], + "text/plain": [ + "Pipeline(steps=[('powertransformer', PowerTransformer()),\n", + " ('nystroem',\n", + " Nystroem(gamma=0.9541024274994, kernel='cosine',\n", + " n_components=12)),\n", + " ('variancethreshold',\n", + " VarianceThreshold(threshold=0.0109488305621))])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(\"sampled pipeline\")\n", + "linear_feature_engineering.generate().export_pipeline()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sampled pipeline\n" + ] + }, + { + "data": { + "text/html": [ + "
Pipeline(steps=[('pipeline',\n",
+       "                 Pipeline(steps=[('pca', PCA(n_components=0.543582719063)),\n",
+       "                                 ('quantiletransformer',\n",
+       "                                  QuantileTransformer(n_quantiles=1182)),\n",
+       "                                 ('passkbinsdiscretizer',\n",
+       "                                  PassKBinsDiscretizer(n_bins=13,\n",
+       "                                                       strategy='uniform'))])),\n",
+       "                ('randomforestclassifier',\n",
+       "                 RandomForestClassifier(bootstrap=False,\n",
+       "                                        max_features=0.078312000096,\n",
+       "                                        min_samples_leaf=7, min_samples_split=3,\n",
+       "                                        n_estimators=128))])
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" + ], + "text/plain": [ + "Pipeline(steps=[('pipeline',\n", + " Pipeline(steps=[('pca', PCA(n_components=0.543582719063)),\n", + " ('quantiletransformer',\n", + " QuantileTransformer(n_quantiles=1182)),\n", + " ('passkbinsdiscretizer',\n", + " PassKBinsDiscretizer(n_bins=13,\n", + " strategy='uniform'))])),\n", + " ('randomforestclassifier',\n", + " RandomForestClassifier(bootstrap=False,\n", + " max_features=0.078312000096,\n", + " min_samples_leaf=7, min_samples_split=3,\n", + " n_estimators=128))])" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "full_search_space = tpot2.search_spaces.pipelines.SequentialPipeline([\n", + " linear_feature_engineering,\n", + " tpot2.config.get_search_space(\"classifiers\"),\n", + "])\n", + "\n", + "print(\"sampled pipeline\")\n", + "full_search_space.generate().export_pipeline()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sampled pipeline\n" + ] + }, + { + "data": { + "text/html": [ + "
Pipeline(steps=[('pipeline',\n",
+       "                 Pipeline(steps=[('passkbinsdiscretizer',\n",
+       "                                  PassKBinsDiscretizer()),\n",
+       "                                 ('robustscaler',\n",
+       "                                  RobustScaler(quantile_range=(0.07166946516,\n",
+       "                                                               0.7478574798356))),\n",
+       "                                 ('zerocount', ZeroCount())])),\n",
+       "                ('baggingclassifier',\n",
+       "                 BaggingClassifier(bootstrap=False, bootstrap_features=True,\n",
+       "                                   max_features=0.1521715848495,\n",
+       "                                   max_samples=0.1213783267153, n_estimators=25,\n",
+       "                                   n_jobs=1))])
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" + ], + "text/plain": [ + "Pipeline(steps=[('pipeline',\n", + " Pipeline(steps=[('passkbinsdiscretizer',\n", + " PassKBinsDiscretizer()),\n", + " ('robustscaler',\n", + " RobustScaler(quantile_range=(0.07166946516,\n", + " 0.7478574798356))),\n", + " ('zerocount', ZeroCount())])),\n", + " ('baggingclassifier',\n", + " BaggingClassifier(bootstrap=False, bootstrap_features=True,\n", + " max_features=0.1521715848495,\n", + " max_samples=0.1213783267153, n_estimators=25,\n", + " n_jobs=1))])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(\"sampled pipeline\")\n", + "full_search_space.generate().export_pipeline()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### UnionPipeline\n", + "\n", + "Union pipelines can be useful when you want to either do multiple transformations in a single layer. Another common strategy is to do a union with a transformer and a passthrough for when you want to keep the original data in addition to the transformation. " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
FeatureUnion(transformer_list=[('featureagglomeration',\n",
+       "                                FeatureAgglomeration(n_clusters=257,\n",
+       "                                                     pooling_func=<function median at 0x7e7cb00dcf70>)),\n",
+       "                               ('passthrough', Passthrough())])
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" + ], "text/plain": [ - "" + "FeatureUnion(transformer_list=[('featureagglomeration',\n", + " FeatureAgglomeration(n_clusters=257,\n", + " pooling_func=)),\n", + " ('passthrough', Passthrough())])" ] }, - "execution_count": 9, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "knn_individual = knn_node.generate()\n", - "knn_individual" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sampled hyperparameters\n", - "{'metric': 'minkowski', 'n_jobs': 1, 'n_neighbors': 4, 'p': 1, 'weights': 'uniform'}\n" - ] - } - ], - "source": [ - "print(\"sampled hyperparameters\")\n", - "print(knn_individual.hyperparameters)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "All Individual objects have mutation and crossover operators that TPOT uses to optimize the pipelines." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "mutated hyperparameters\n", - "{'metric': 'euclidean', 'n_jobs': 1, 'n_neighbors': 10, 'p': 3, 'weights': 'distance'}\n" - ] - } - ], - "source": [ - "knn_individual.mutate() # mutate the individual\n", - "print(\"mutated hyperparameters\")\n", - "print(knn_individual.hyperparameters)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In TPOT2, crossover only modifies the individual calling the crossover function, the second individual remains the same" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "original hyperparameters for individual 1\n", - "{'metric': 'euclidean', 'n_jobs': 1, 'n_neighbors': 2, 'p': 2, 'weights': 'uniform'}\n", - "original hyperparameters for individual 2\n", - "{'metric': 'euclidean', 'n_jobs': 1, 'n_neighbors': 10, 'p': 2, 'weights': 'uniform'}\n", - "\n", - "post crossover hyperparameters for individual 1\n", - "{'metric': 'euclidean', 'n_jobs': 1, 'n_neighbors': 10, 'p': 2, 'weights': 'uniform'}\n", - "post crossover hyperparameters for individual 2\n", - "{'metric': 'euclidean', 'n_jobs': 1, 'n_neighbors': 10, 'p': 2, 'weights': 'uniform'}\n" - ] - } - ], - "source": [ - "knn_individual1 = knn_node.generate()\n", - "knn_individual2 = knn_node.generate()\n", - "\n", - "print(\"original hyperparameters for individual 1\")\n", - "print(knn_individual1.hyperparameters)\n", - "\n", - "print(\"original hyperparameters for individual 2\")\n", - "print(knn_individual2.hyperparameters)\n", - "\n", - "print()\n", + "transform_and_passthrough = tpot2.search_spaces.pipelines.UnionPipeline([\n", + " tpot2.config.get_search_space(\"transformers\"),\n", + " tpot2.config.get_search_space(\"Passthrough\"),\n", + "])\n", "\n", - "knn_individual1.crossover(knn_individual2) # crossover the individuals\n", - "print(\"post crossover hyperparameters for individual 1\")\n", - "print(knn_individual1.hyperparameters)\n", - "print(\"post crossover hyperparameters for individual 2\")\n", - "print(knn_individual2.hyperparameters)\n", - "\n" + "transform_and_passthrough.generate().export_pipeline()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "All search spaces have an export_pipeline function that returns an sklearn `BaseEstimator`" + "UnionPipelines are an excellent tool to expand the capabilities of the linear search spaces." ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
KNeighborsClassifier(metric='euclidean', n_jobs=1, n_neighbors=10)
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" + "
Pipeline(steps=[('selectfwe', SelectFwe(alpha=0.0043464782007)),\n",
+       "                ('featureunion',\n",
+       "                 FeatureUnion(transformer_list=[('powertransformer',\n",
+       "                                                 PowerTransformer()),\n",
+       "                                                ('passthrough',\n",
+       "                                                 Passthrough())])),\n",
+       "                ('lineardiscriminantanalysis',\n",
+       "                 LinearDiscriminantAnalysis(shrinkage=0.6381012799603,\n",
+       "                                            solver='lsqr'))])
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" ], "text/plain": [ - "KNeighborsClassifier(metric='euclidean', n_jobs=1, n_neighbors=10)" + "Pipeline(steps=[('selectfwe', SelectFwe(alpha=0.0043464782007)),\n", + " ('featureunion',\n", + " FeatureUnion(transformer_list=[('powertransformer',\n", + " PowerTransformer()),\n", + " ('passthrough',\n", + " Passthrough())])),\n", + " ('lineardiscriminantanalysis',\n", + " LinearDiscriminantAnalysis(shrinkage=0.6381012799603,\n", + " solver='lsqr'))])" ] }, - "execution_count": 14, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "est = knn_individual1.export_pipeline()\n", - "est" + "stc_pipeline2 = tpot2.search_spaces.pipelines.SequentialPipeline([\n", + " tpot2.config.get_search_space(\"selectors\"),\n", + " transform_and_passthrough,\n", + " tpot2.config.get_search_space(\"classifiers\"),\n", + "])\n", + "\n", + "stc_pipeline2.generate().export_pipeline()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "If a dictionary of parameters is passed instead of of a ConfigSpace object, then the hyperparameters will always be fixed and not learned." + "Union pipelines can also be used to create \"branches\" if you are trying to create a tree-like search space. This can be particularly useful when paired with the FeatureSetSelector node (FSSNode) as each branch can learn different feature engineering for different subsets of the features, for example." ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
KNeighborsClassifier(n_neighbors=10)
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" - ], - "text/plain": [ - "KNeighborsClassifier(n_neighbors=10)" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import tpot2\n", - "from ConfigSpace import ConfigurationSpace\n", - "from ConfigSpace import ConfigurationSpace, Integer, Float, Categorical, Normal\n", - "from sklearn.neighbors import KNeighborsClassifier\n", - "\n", - "space = {\n", - "\n", - " 'n_neighbors':10,\n", - "}\n", - "\n", - "knn_node = tpot2.search_spaces.nodes.EstimatorNode(\n", - " method = KNeighborsClassifier,\n", - " space = space,\n", - ")\n", - "\n", - "knn_node.generate().export_pipeline()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### FSSNode and GeneticFeatureSelectorNode\n", - "\n", - "Both of these are given their own tutorials. See Tutorial 3 for FFSNode and Tutorial 5 for GeneticFeatureSelectorNode" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Pipeline Search Space Examples\n", - "\n", - "Pipeline search spaces are used to define the structure and restrictions of the pipelines TPOT can search. Unlike Node search spaces, all pipeline search spaces take in other search spaces as inputs. Rather than sample hyperparameters, pipeline search spaces can select models from the input search spaces and organize them within a linear sklearn Pipeline or a TPOT GraphPipeline." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### ChoicePipeline\n", - "\n", - "The simplest pipeline search space is the ChoicePipeline. This takes in a list of search spaces and simply selects and samples from one. In this example, we will construct a search space that takes in several options for a classifier. The resulting search space will then first select a model from KNeighborsClassifier, LogisticRegression or DecisionTreeClassifier, and then select the hyperparameters for the given model." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import tpot2\n", - "from ConfigSpace import ConfigurationSpace\n", - "from ConfigSpace import ConfigurationSpace, Integer, Float, Categorical, Normal\n", - "from sklearn.neighbors import KNeighborsClassifier\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.tree import DecisionTreeClassifier\n", - "\n", - "knn_configspace = ConfigurationSpace(\n", - " space = {\n", - "\n", - " 'n_neighbors': Integer(\"n_neighbors\", bounds=(1, 10)),\n", - " 'weights': Categorical(\"weights\", ['uniform', 'distance']),\n", - " 'p': Integer(\"p\", bounds=(1, 3)),\n", - " 'metric': Categorical(\"metric\", ['euclidean', 'minkowski']),\n", - " 'n_jobs': 1,\n", - " }\n", - ")\n", - "\n", - "lr_configspace = ConfigurationSpace(\n", - " space = {\n", - " 'solver': Categorical(\"solver\", ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga']),\n", - " 'penalty': Categorical(\"penalty\", ['l1', 'l2']),\n", - " 'dual': Categorical(\"dual\", [True, False]),\n", - " 'C': Float(\"C\", bounds=(1e-4, 1e4), log=True),\n", - " 'class_weight': Categorical(\"class_weight\", ['balanced']),\n", - " 'n_jobs': 1,\n", - " 'max_iter': 1000,\n", - " }\n", - " )\n", - "\n", - "dt_configspace = ConfigurationSpace(\n", - " space = {\n", - " 'criterion': Categorical(\"criterion\", ['gini', 'entropy']),\n", - " 'max_depth': Integer(\"max_depth\", bounds=(1, 11)),\n", - " 'min_samples_split': Integer(\"min_samples_split\", bounds=(2, 21)),\n", - " 'min_samples_leaf': Integer(\"min_samples_leaf\", bounds=(1, 21)),\n", - " 'max_features': Categorical(\"max_features\", ['sqrt', 'log2']),\n", - " 'min_weight_fraction_leaf': 0.0,\n", - " }\n", - " )\n", - "\n", - "knn_node = tpot2.search_spaces.nodes.EstimatorNode(\n", - " method = KNeighborsClassifier,\n", - " space = knn_configspace,\n", - ")\n", - "\n", - "lr_node = tpot2.search_spaces.nodes.EstimatorNode(\n", - " method = LogisticRegression,\n", - " space = lr_configspace,\n", - ")\n", - "\n", - "dt_node = tpot2.search_spaces.nodes.EstimatorNode(\n", - " method = DecisionTreeClassifier,\n", - " space = dt_configspace,\n", - ")\n", - "\n", - "classifier_node = tpot2.search_spaces.pipelines.ChoicePipeline(\n", - " search_spaces=[\n", - " knn_node,\n", - " lr_node,\n", - " dt_node,\n", - " ]\n", - ")\n", + " background-color: var(--sklearn-color-background);\n", + " border-radius: 1rem;\n", + " height: 1rem;\n", + " width: 1rem;\n", + " text-decoration: none;\n", + " /* unfitted */\n", + " color: var(--sklearn-color-unfitted-level-1);\n", + " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n", + "}\n", + "\n", + "#sk-container-id-20 a.estimator_doc_link.fitted {\n", + " /* fitted */\n", + " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n", + " color: var(--sklearn-color-fitted-level-1);\n", + "}\n", + "\n", + "/* On hover */\n", + "#sk-container-id-20 a.estimator_doc_link:hover {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-3);\n", + " color: var(--sklearn-color-background);\n", + " text-decoration: none;\n", + "}\n", + "\n", + "#sk-container-id-20 a.estimator_doc_link.fitted:hover {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-3);\n", + "}\n", + "
Pipeline(steps=[('featureunion',\n",
+       "                 FeatureUnion(transformer_list=[('pipeline-1',\n",
+       "                                                 Pipeline(steps=[('selectfwe',\n",
+       "                                                                  SelectFwe(alpha=0.000231370784)),\n",
+       "                                                                 ('zerocount',\n",
+       "                                                                  ZeroCount())])),\n",
+       "                                                ('pipeline-2',\n",
+       "                                                 Pipeline(steps=[('selectpercentile',\n",
+       "                                                                  SelectPercentile(percentile=56.9207229949532)),\n",
+       "                                                                 ('rbfsampler',\n",
+       "                                                                  RBFSampler(gamma=0.9667449310006,\n",
+       "                                                                             n_components=45))]))])),\n",
+       "                ('linearsvc', LinearSVC(C=8596.144097926976, penalty='l1'))])
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" + ], + "text/plain": [ + "Pipeline(steps=[('featureunion',\n", + " FeatureUnion(transformer_list=[('pipeline-1',\n", + " Pipeline(steps=[('selectfwe',\n", + " SelectFwe(alpha=0.000231370784)),\n", + " ('zerocount',\n", + " ZeroCount())])),\n", + " ('pipeline-2',\n", + " Pipeline(steps=[('selectpercentile',\n", + " SelectPercentile(percentile=56.9207229949532)),\n", + " ('rbfsampler',\n", + " RBFSampler(gamma=0.9667449310006,\n", + " n_components=45))]))])),\n", + " ('linearsvc', LinearSVC(C=8596.144097926976, penalty='l1'))])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "st_pipeline = tpot2.search_spaces.pipelines.SequentialPipeline([\n", + " tpot2.config.get_search_space(\"selectors\"),\n", + " tpot2.config.get_search_space(\"transformers\"),\n", + "])\n", "\n", + "branched_pipeline = tpot2.search_spaces.pipelines.SequentialPipeline([\n", + " tpot2.search_spaces.pipelines.UnionPipeline([\n", + " st_pipeline,\n", + " st_pipeline,\n", + " ]),\n", + " tpot2.config.get_search_space(\"classifiers\"),\n", + "])\n", "\n", - "tpot2.search_spaces.pipelines.ChoicePipeline(\n", - " search_spaces = [\n", - " tpot2.search_spaces.nodes.EstimatorNode(\n", - " method = KNeighborsClassifier,\n", - " space = knn_configspace,\n", - " ),\n", - " tpot2.search_spaces.nodes.EstimatorNode(\n", - " method = LogisticRegression,\n", - " space = lr_configspace,\n", - " ),\n", - " tpot2.search_spaces.nodes.EstimatorNode(\n", - " method = DecisionTreeClassifier,\n", - " space = dt_configspace,\n", - " ),\n", - " ]\n", - ")" + "branched_pipeline.generate().export_pipeline()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Search space objects provided by pipeline search spaces work the same as with node search spaces. Note that crossover only works when both individuals have sampled the same method. " + "### DynamicUnionPipeline\n", + "\n", + "DynamicUnionPipeline works similarly as UnionPipeline. Whereas UnionPipeline is fixed length, with each index corresponding to the search space provided as a list, DynamicUnionPipeline takes in a single search space and will sample 1 or more estimators/pipelines and concatenate them with a FeatureUnion. \n", + "\n", + "Note that DynamicUnionPipeline will check for pipeline uniqueness, so it will never concatenate two completely identical pipelines. In other words, all steps within the feature union will be unique.\n", + "\n", + "This can be useful when you want multiple transformers (or in some cases, pipelines), but are not sure how many or which ones." ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 27, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sampled pipeline\n" - ] - }, { "data": { "text/html": [ - "
DecisionTreeClassifier(max_depth=5, max_features='sqrt', min_samples_leaf=4,\n",
-       "                       min_samples_split=4)
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" + "
FeatureUnion(transformer_list=[('powertransformer', PowerTransformer())])
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" ], "text/plain": [ - "DecisionTreeClassifier(max_depth=5, max_features='sqrt', min_samples_leaf=4,\n", - " min_samples_split=4)" + "FeatureUnion(transformer_list=[('powertransformer', PowerTransformer())])" ] }, - "execution_count": 17, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "classifier_individual = classifier_node.generate()\n", - "\n", - "print(\"sampled pipeline\")\n", - "classifier_individual.export_pipeline()" + "dynamic_transformers = tpot2.search_spaces.pipelines.DynamicUnionPipeline(tpot2.config.get_search_space(\"transformers\"), max_estimators=4)\n", + "dynamic_transformers.generate().export_pipeline()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One good strategy could be to pair this with Passthrough in a feature union so that you output all the transformations along with the original data." ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 28, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "mutated pipeline\n" - ] - }, { "data": { "text/html": [ - "
KNeighborsClassifier(n_jobs=1, n_neighbors=6, p=1)
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" + "
FeatureUnion(transformer_list=[('featureunion',\n",
+       "                                FeatureUnion(transformer_list=[('rbfsampler',\n",
+       "                                                                RBFSampler(gamma=0.3736377055485,\n",
+       "                                                                           n_components=3)),\n",
+       "                                                               ('quantiletransformer',\n",
+       "                                                                QuantileTransformer(n_quantiles=955)),\n",
+       "                                                               ('fastica',\n",
+       "                                                                FastICA(algorithm='deflation',\n",
+       "                                                                        n_components=92))])),\n",
+       "                               ('passthrough', Passthrough())])
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" ], "text/plain": [ - "KNeighborsClassifier(n_jobs=1, n_neighbors=6, p=1)" + "FeatureUnion(transformer_list=[('featureunion',\n", + " FeatureUnion(transformer_list=[('rbfsampler',\n", + " RBFSampler(gamma=0.3736377055485,\n", + " n_components=3)),\n", + " ('quantiletransformer',\n", + " QuantileTransformer(n_quantiles=955)),\n", + " ('fastica',\n", + " FastICA(algorithm='deflation',\n", + " n_components=92))])),\n", + " ('passthrough', Passthrough())])" ] }, - "execution_count": 18, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "print(\"mutated pipeline\")\n", - "classifier_individual.mutate()\n", - "classifier_individual.export_pipeline()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "TPOT2 also comes with predefined search spaces. The current search spaces were adapted from a combination of the original TPOT package as well as the search spaces used in [AutoSklearn](https://github.com/automl/auto-sklearn/tree/development/autosklearn/pipeline/components). The helper function `tpot2.config.get_search_space` takes in a string or a list of strings, and returns either a EstimatorNode or a ChoicePipeline,respectively. \n", - "\n", - "strings can correspond to individual methods. There are also special strings that return predefined lists of methods. \n", + "dynamic_transformers_with_passthrough = tpot2.search_spaces.pipelines.UnionPipeline([\n", + " dynamic_transformers,\n", + " tpot2.config.get_search_space(\"Passthrough\")],\n", + " )\n", "\n", - "| Special String | Included methods |\n", - "| :--- | :----: |\n", - "| \"selectors\" | [\"SelectFwe\", \"SelectPercentile\", \"VarianceThreshold\",] |\n", - "| \"selectors_classification\" | [\"SelectFwe\", \"SelectPercentile\", \"VarianceThreshold\", \"RFE_classification\", \"SelectFromModel_classification\"] |\n", - "| \"selectors_regression\" | [\"SelectFwe\", \"SelectPercentile\", \"VarianceThreshold\", \"RFE_regression\", \"SelectFromModel_regression\"] |\n", - "| \"classifiers\" | [\"LGBMClassifier\", \"BaggingClassifier\", 'AdaBoostClassifier', 'BernoulliNB', 'DecisionTreeClassifier', 'ExtraTreesClassifier', 'GaussianNB', 'HistGradientBoostingClassifier', 'KNeighborsClassifier','LinearDiscriminantAnalysis', 'LogisticRegression', \"LinearSVC\", \"SVC\", 'MLPClassifier', 'MultinomialNB', \"QuadraticDiscriminantAnalysis\", 'RandomForestClassifier', 'SGDClassifier', 'XGBClassifier'] |\n", - "| \"regressors\" | [\"LGBMRegressor\", 'AdaBoostRegressor', \"ARDRegression\", 'DecisionTreeRegressor', 'ExtraTreesRegressor', 'HistGradientBoostingRegressor', 'KNeighborsRegressor', 'LinearSVR', \"MLPRegressor\", 'RandomForestRegressor', 'SGDRegressor', 'SVR', 'XGBRegressor'] |\n", - "| \"transformers\" | [\"PassKBinsDiscretizer\", \"Binarizer\", \"PCA\", \"ZeroCount\", \"ColumnOneHotEncoder\", \"FastICA\", \"FeatureAgglomeration\", \"Nystroem\", \"RBFSampler\", \"QuantileTransformer\", \"PowerTransformer\"] |\n", - "| \"scalers\" | [\"MinMaxScaler\", \"RobustScaler\", \"StandardScaler\", \"MaxAbsScaler\", \"Normalizer\", ] |\n", - "| \"all_transformers\" | [\"transformers\", \"scalers\"] |\n", - "| \"arithmatic\" | [\"AddTransformer\", \"mul_neg_1_Transformer\", \"MulTransformer\", \"SafeReciprocalTransformer\", \"EQTransformer\", \"NETransformer\", \"GETransformer\", \"GTTransformer\", \"LETransformer\", \"LTTransformer\", \"MinTransformer\", \"MaxTransformer\"] |\n", - "| \"imputers\" | [\"SimpleImputer\", \"IterativeImputer\", \"KNNImputer\"] |\n", - "| \"skrebate\" | [\"ReliefF\", \"SURF\", \"SURFstar\", \"MultiSURF\"] |\n", - "| \"genetic_encoders\" | [\"DominantEncoder\", \"RecessiveEncoder\", \"HeterosisEncoder\", \"UnderDominanceEncoder\", \"OverDominanceEncoder\"] |\n", - "| \"classifiers_sklearnex\" | [\"RandomForestClassifier_sklearnex\", \"LogisticRegression_sklearnex\", \"KNeighborsClassifier_sklearnex\", \"SVC_sklearnex\",\"NuSVC_sklearnex\"] |\n", - "| \"regressors_sklearnex\" | [\"LinearRegression_sklearnex\", \"Ridge_sklearnex\", \"Lasso_sklearnex\", \"ElasticNet_sklearnex\", \"SVR_sklearnex\", \"NuSVR_sklearnex\", \"RandomForestRegressor_sklearnex\", \"KNeighborsRegressor_sklearnex\"] |" + "dynamic_transformers_with_passthrough.generate().export_pipeline()" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 29, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sampled pipeline 1\n" - ] - }, { "data": { "text/html": [ - "
LogisticRegression(C=0.6262919454224, class_weight='balanced',\n",
-       "                   l1_ratio=0.1219417333128, max_iter=1000, n_jobs=1,\n",
-       "                   penalty='elasticnet', solver='saga')
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" + "
Pipeline(steps=[('selectpercentile',\n",
+       "                 SelectPercentile(percentile=1.2220353454141)),\n",
+       "                ('featureunion',\n",
+       "                 FeatureUnion(transformer_list=[('featureunion',\n",
+       "                                                 FeatureUnion(transformer_list=[('rbfsampler',\n",
+       "                                                                                 RBFSampler(gamma=0.0989706913466,\n",
+       "                                                                                            n_components=61)),\n",
+       "                                                                                ('passkbinsdiscretizer',\n",
+       "                                                                                 PassKBinsDiscretizer(n_bins=42))])),\n",
+       "                                                ('passthrough',\n",
+       "                                                 Passthrough())])),\n",
+       "                ('bernoullinb',\n",
+       "                 BernoulliNB(alpha=7.5106513153016, fit_prior=False))])
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" ], "text/plain": [ - "LogisticRegression(C=0.6262919454224, class_weight='balanced',\n", - " l1_ratio=0.1219417333128, max_iter=1000, n_jobs=1,\n", - " penalty='elasticnet', solver='saga')" + "Pipeline(steps=[('selectpercentile',\n", + " SelectPercentile(percentile=1.2220353454141)),\n", + " ('featureunion',\n", + " FeatureUnion(transformer_list=[('featureunion',\n", + " FeatureUnion(transformer_list=[('rbfsampler',\n", + " RBFSampler(gamma=0.0989706913466,\n", + " n_components=61)),\n", + " ('passkbinsdiscretizer',\n", + " PassKBinsDiscretizer(n_bins=42))])),\n", + " ('passthrough',\n", + " Passthrough())])),\n", + " ('bernoullinb',\n", + " BernoulliNB(alpha=7.5106513153016, fit_prior=False))])" ] }, - "execution_count": 19, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "#same pipeline search space as before.\n", - "classifier_choice = tpot2.config.get_search_space([\"KNeighborsClassifier\", \"LogisticRegression\", \"DecisionTreeClassifier\"])\n", + "stc_pipeline3 = tpot2.search_spaces.pipelines.SequentialPipeline([\n", + " tpot2.config.get_search_space(\"selectors\"),\n", + " dynamic_transformers_with_passthrough,\n", + " tpot2.config.get_search_space(\"classifiers\"),\n", + "])\n", "\n", - "print(\"sampled pipeline 1\")\n", - "classifier_choice.generate().export_pipeline()" + "stc_pipeline3.generate().export_pipeline()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### WrapperPipeline\n", + "\n", + "Some sklearn estimators take in other sklearn estimators as a parameter. The wrapper pipeline is used to tune both the original estimators hyperparameters simultaneously with the inner estimators hyperparameters. In fact, the inner estimator in WrapperPipeline can be any search space defined with any of the methods described in this Tutorial.\n", + "\n", + "The `get_search_space` will automatically create an inner search space for sklearn estimators that do use require an inner estimator. For example \"SelectFromModel_classification\" will return the following search space" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 30, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sampled pipeline 2\n" - ] - }, { "data": { "text/html": [ - "
DecisionTreeClassifier(class_weight='balanced', max_depth=14,\n",
-       "                       max_features='sqrt', min_samples_leaf=3,\n",
-       "                       min_samples_split=16)
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" + "
ExtraTreesClassifier(bootstrap=True, class_weight='balanced',\n",
+       "                     criterion='entropy', max_features=0.5945413838121,\n",
+       "                     min_samples_leaf=4, min_samples_split=8, n_jobs=1)
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" ], "text/plain": [ - "DecisionTreeClassifier(class_weight='balanced', max_depth=14,\n", - " max_features='sqrt', min_samples_leaf=3,\n", - " min_samples_split=16)" + "ExtraTreesClassifier(bootstrap=True, class_weight='balanced',\n", + " criterion='entropy', max_features=0.5945413838121,\n", + " min_samples_leaf=4, min_samples_split=8, n_jobs=1)" ] }, - "execution_count": 20, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "print(\"sampled pipeline 2\")\n", - "classifier_choice.generate().export_pipeline()" + "SelectFromModel_configspace_part = ConfigurationSpace(\n", + " space = {\n", + " 'threshold': Float('threshold', bounds=(1e-4, 1.0), log=True),\n", + " }\n", + " )\n", + "\n", + "extratrees_estimator_node = tpot2.config.get_search_space(\"ExtraTreesClassifier\") #this exports an ExtraTreesClassifier node\n", + "extratrees_estimator_node.generate().export_pipeline()" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 31, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sampled pipeline 1\n" - ] - }, { "data": { "text/html": [ - "
KNeighborsClassifier(n_jobs=1, n_neighbors=3, p=3, weights='distance')
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" + "
SelectFromModel(estimator=ExtraTreesClassifier(bootstrap=True,\n",
+       "                                               class_weight='balanced',\n",
+       "                                               criterion='entropy',\n",
+       "                                               max_features=0.0157364601821,\n",
+       "                                               min_samples_leaf=14,\n",
+       "                                               min_samples_split=6, n_jobs=1),\n",
+       "                threshold=0.947435367985)
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" ], "text/plain": [ - "KNeighborsClassifier(n_jobs=1, n_neighbors=3, p=3, weights='distance')" + "SelectFromModel(estimator=ExtraTreesClassifier(bootstrap=True,\n", + " class_weight='balanced',\n", + " criterion='entropy',\n", + " max_features=0.0157364601821,\n", + " min_samples_leaf=14,\n", + " min_samples_split=6, n_jobs=1),\n", + " threshold=0.947435367985)" ] }, - "execution_count": 21, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "#search space for all classifiers\n", - "classifier_choice = tpot2.config.get_search_space(\"classifiers\")\n", + "from sklearn.ensemble import ExtraTreesClassifier\n", + "from sklearn.feature_selection import SelectFromModel\n", "\n", - "print(\"sampled pipeline 1\")\n", - "classifier_choice.generate().export_pipeline()" + "select_from_model_wrapper_searchspace = tpot2.search_spaces.pipelines.WrapperPipeline(\n", + " method=SelectFromModel,\n", + " space = SelectFromModel_configspace_part,\n", + " estimator_search_space= extratrees_estimator_node,\n", + " )\n", + "\n", + "select_from_model_wrapper_searchspace.generate().export_pipeline()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### WrapperPipeline strategy for ensembles/inner classifiers/regressors EstimatorTransformer- \n", + "\n", + "Sklearn Pipelines only allow classifiers/regressors as the final step. All other steps are expected to implement a transform function. We can get around this by wrapping it in another transformer class that returns the output of predict or predict_proba inside the transform() function.\n", + "\n", + "To wrap classifiers as transfomers, you can use the following class: `tpot2.builtin_modules.EstimatorTransformer`. You can specify whether to pass the outputs of predict, predict_proba, or decision function with the `method` parameter. \n", + "\n", + "An additional consideration is whether or not to use `cross_val_predict_cv`. Stacking classifiers can sometimes perform better when the predictions it is trained on come from out of sample predictions which is accomplished with CV. This is because the following pipelines can potentially better identify the accuracy of preceding models. Otherwise, some models may overfit the data and their predictions, making them seem like good predictors, which would then be weighted too highly by subsequent models. By default, TPOT `cross_val_predict_cv` is not enabled due to its computational cost.\n", + "\n", + "Note: This is not necessary for `GraphSearchPipeline` as the exported GraphPipeline estimator does have builtin support for inner/regressors. Instead of using a wrapper, you can set the `cross_val_predict_cv` param when initializing the `GraphSearchPipeline` object." ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 32, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sampled pipeline 2\n" - ] - }, { "data": { "text/html": [ - "
AdaBoostClassifier(algorithm='SAMME', learning_rate=0.0231006103189,\n",
-       "                   n_estimators=391)
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" + "
EstimatorTransformer(estimator=KNeighborsClassifier(n_jobs=1, n_neighbors=10,\n",
+       "                                                    p=1))
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" ], "text/plain": [ - "AdaBoostClassifier(algorithm='SAMME', learning_rate=0.0231006103189,\n", - " n_estimators=391)" + "EstimatorTransformer(estimator=KNeighborsClassifier(n_jobs=1, n_neighbors=10,\n", + " p=1))" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "classifiers = tpot2.config.get_search_space(\"classifiers\")\n", + "wrapped_estimators = tpot2.search_spaces.pipelines.WrapperPipeline(tpot2.builtin_modules.EstimatorTransformer, {}, classifiers)\n", + "\n", + "est = wrapped_estimators.generate().export_pipeline() #returns an estimator with a transform function\n", + "est" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.3, 0.7],\n", + " [0.5, 0.5],\n", + " [0.8, 0.2],\n", + " [0.7, 0.3],\n", + " [0.5, 0.5]])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "X, y = np.random.rand(100, 10), np.random.randint(0, 2, 100)\n", + "\n", + "est.fit_transform(X, y)[0:5]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "you can manually set the settings for an estimator the same way you would do it for an EstimatorNode. Here's another example with cross_val_predict and method being used." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1],\n", + " [1],\n", + " [0],\n", + " [0],\n", + " [1]])" ] }, - "execution_count": 22, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "print(\"sampled pipeline 2\")\n", - "classifier_choice.generate().export_pipeline()" + "classifiers = tpot2.config.get_search_space(\"classifiers\")\n", + "wrapped_estimators_cv = tpot2.search_spaces.pipelines.WrapperPipeline(tpot2.builtin_modules.EstimatorTransformer, {'cross_val_predict_cv':10, 'method':'predict'}, classifiers)\n", + "est = wrapped_estimators_cv.generate().export_pipeline() #returns an estimator with a transform function\n", + "est.fit_transform(X, y)[0:5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## SequentialPipeline\n", - "\n", - "SequentialPipelines are of fixed length and sample from a predefined distribution for each step. " + "These can now be used inside a linear pipeline. This is fairly similar to the default linear pipeline search space." ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 35, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sampled pipeline\n" - ] - }, { "data": { "text/html": [ - "
Pipeline(steps=[('variancethreshold',\n",
-       "                 VarianceThreshold(threshold=0.0009684094023)),\n",
-       "                ('pca', PCA(n_components=0.8999344355157)),\n",
-       "                ('logisticregression',\n",
-       "                 LogisticRegression(C=0.1195118894279, class_weight='balanced',\n",
-       "                                    max_iter=1000, n_jobs=1, solver='saga'))])
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" + "
Pipeline(steps=[('maxabsscaler', MaxAbsScaler()),\n",
+       "                ('featureunion-1',\n",
+       "                 FeatureUnion(transformer_list=[('featureunion',\n",
+       "                                                 FeatureUnion(transformer_list=[('rbfsampler',\n",
+       "                                                                                 RBFSampler(gamma=0.6486019086026,\n",
+       "                                                                                            n_components=82)),\n",
+       "                                                                                ('nystroem',\n",
+       "                                                                                 Nystroem(gamma=0.185797439118,\n",
+       "                                                                                          kernel='additive_chi2',\n",
+       "                                                                                          n_components=87))])),\n",
+       "                                                ('passthrough',\n",
+       "                                                 Passthrough())])),\n",
+       "                ('featureunion-2',\n",
+       "                 Feat...\n",
+       "                                                                                                                              missing=nan,\n",
+       "                                                                                                                              monotone_constraints=None,\n",
+       "                                                                                                                              multi_strategy=None,\n",
+       "                                                                                                                              n_estimators=100,\n",
+       "                                                                                                                              n_jobs=1,\n",
+       "                                                                                                                              nthread=1,\n",
+       "                                                                                                                              num_parallel_tree=None, ...),\n",
+       "                                                                                                      method='predict'))])),\n",
+       "                                                ('passthrough',\n",
+       "                                                 Passthrough())])),\n",
+       "                ('randomforestclassifier',\n",
+       "                 RandomForestClassifier(bootstrap=False, criterion='entropy',\n",
+       "                                        max_features=0.6976552018012,\n",
+       "                                        min_samples_leaf=8,\n",
+       "                                        min_samples_split=16,\n",
+       "                                        n_estimators=128))])
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" ], "text/plain": [ - "Pipeline(steps=[('variancethreshold',\n", - " VarianceThreshold(threshold=0.0009684094023)),\n", - " ('pca', PCA(n_components=0.8999344355157)),\n", - " ('logisticregression',\n", - " LogisticRegression(C=0.1195118894279, class_weight='balanced',\n", - " max_iter=1000, n_jobs=1, solver='saga'))])" + "Pipeline(steps=[('maxabsscaler', MaxAbsScaler()),\n", + " ('featureunion-1',\n", + " FeatureUnion(transformer_list=[('featureunion',\n", + " FeatureUnion(transformer_list=[('rbfsampler',\n", + " RBFSampler(gamma=0.6486019086026,\n", + " n_components=82)),\n", + " ('nystroem',\n", + " Nystroem(gamma=0.185797439118,\n", + " kernel='additive_chi2',\n", + " n_components=87))])),\n", + " ('passthrough',\n", + " Passthrough())])),\n", + " ('featureunion-2',\n", + " Feat...\n", + " missing=nan,\n", + " monotone_constraints=None,\n", + " multi_strategy=None,\n", + " n_estimators=100,\n", + " n_jobs=1,\n", + " nthread=1,\n", + " num_parallel_tree=None, ...),\n", + " method='predict'))])),\n", + " ('passthrough',\n", + " Passthrough())])),\n", + " ('randomforestclassifier',\n", + " RandomForestClassifier(bootstrap=False, criterion='entropy',\n", + " max_features=0.6976552018012,\n", + " min_samples_leaf=8,\n", + " min_samples_split=16,\n", + " n_estimators=128))])" ] }, - "execution_count": 27, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "selector_choicepipeline = tpot2.config.get_search_space(\"VarianceThreshold\")\n", - "transformer_choicepipeline = tpot2.config.get_search_space(\"PCA\")\n", - "classifier_choicepipeline = tpot2.config.get_search_space(\"LogisticRegression\")\n", + "dynamic_wrapped_classifiers_with_passthrough = tpot2.search_spaces.pipelines.UnionPipeline([\n", + " tpot2.search_spaces.pipelines.DynamicUnionPipeline(wrapped_estimators_cv, max_estimators=4),\n", + " tpot2.config.get_search_space(\"Passthrough\")\n", + " ])\n", "\n", - "stc_pipeline = tpot2.search_spaces.pipelines.SequentialPipeline([\n", - " selector_choicepipeline,\n", - " transformer_choicepipeline,\n", - " classifier_choicepipeline,\n", + "stc_pipeline4 = tpot2.search_spaces.pipelines.SequentialPipeline([\n", + " tpot2.config.get_search_space(\"scalers\"),\n", + " dynamic_transformers_with_passthrough,\n", + " dynamic_wrapped_classifiers_with_passthrough,\n", + " tpot2.config.get_search_space(\"classifiers\"),\n", "])\n", "\n", - "print(\"sampled pipeline\")\n", - "stc_pipeline.generate().export_pipeline()" + "stc_pipeline4.generate().export_pipeline()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Here is an example of the form Selector-Transformer-Classifier.\n", + "### GraphSearchPipeline\n", "\n", - "Note that each step in the sequence is a ChoicePipeline this time. Here, the SequentialPipeline can sample from search provided search space in order." + "The GraphSearchPipeline is a flexible search space without a prior restriction of pipeline structure. With GraphSearchPipeline, TPOT will create a pipeline in the shape of a directed acyclic graph. Throughout the optimization process, TPOT may add/remove nodes, add/remove edges, and performs model selection and hyperparameter tuning for each node.\n", + "\n", + "The primary parameters for the graph_search_space are the root_search_space, inner_search_space, and leaf_search_space.\n", + "\n", + "| Parameter | Type | Description |\n", + "|------------------------|-------------------------------------|-----------------------------------------------------------------------------------------------------------|\n", + "| root_search_space | SklearnIndividualGenerator | The search space for the root node of the graph. This node will be the final estimator in the pipeline. |\n", + "| inner_search_space | SklearnIndividualGenerator, optional| The search space for the inner nodes of the graph. If not defined, there will be no inner nodes. |\n", + "| leaf_search_space | SklearnIndividualGenerator, optional| The search space for the leaf nodes of the graph. If not defined, the leaf nodes will be drawn from the inner_search_space. |\n", + "| crossover_same_depth | bool, optional | If True, crossover will only occur between nodes at the same depth in the graph. If False, crossover will occur between nodes at any depth. |\n", + "| cross_val_predict_cv | int, cross-validation generator or an iterable, optional | Determines the cross-validation splitting strategy used in inner classifiers or regressors. |\n", + "| method | str, optional | The prediction method to use for the inner classifiers or regressors. If 'auto', it will try to use predict_proba, decision_function, or predict in that order. |\n", + "\n", + "This search space exports a `tpot2.GraphPipeline`. This is similar to a scikit-learn Pipeline, but for directed acyclic graph pipelines. You can learn more about using this module in Tutorial 6." ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "graph_search_space = tpot2.search_spaces.pipelines.GraphSearchPipeline(\n", + " root_search_space= tpot2.config.get_search_space([\"KNeighborsClassifier\", \"LogisticRegression\", \"DecisionTreeClassifier\"]),\n", + " leaf_search_space = tpot2.config.get_search_space(\"selectors\"), \n", + " inner_search_space = tpot2.config.get_search_space([\"transformers\"]),\n", + " max_size = 10,\n", + ")\n", + "\n", + "ind = graph_search_space.generate()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "sampled pipeline\n" - ] + "data": { + "image/png": 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cM3hw3jV8eB6ZPfvNVwAAbOPVDRvSunHj1p/ndHdnVldXfrF5r1NXX19+9dJLeSzJhSNH5aDm104RGDZgwA7Xt6NdsZOGDs2ctWvTSCP/46ijcu/KlTmurS3vPeSQJMms1S/nPzo7863Fi5IkL/dsysa+vhzU05MNGzbs7be8z+xy2L3nPe9J8trxa/fcc0/Gjh27Wxu66qqr8uEPfzj33ntvzjjjjDzyyCPbPN70uqpqNBrb/Dxw4MAtD6Tvt+x/P7j5N+eCNJJcPnZsLhi5bQT+60udO1z+wvr1eWztK2/6Pq5esCA3jhuXI3p7ctvq1VuPwfvfU6bkR11dWbR48ZusAQB4vUkTJ6Zp7dqtPzeS/GF7e35vc3QlSVOa8tgeHF40aejQfO3559PUlPzhkWNy+/LlmbN2bU4feujmbTbytyednCMPPnib1zU3+tLbj+4bu8tnxX7hC19Iknzwgx/M3/zN32xdPm/evK3Lt+xG7O3tzdrXfUBJ8txzz2XixIm5+uqrc+KJJ253VszkyZNz1113JUn+8R//Me973/u2H+wOThbYmSnDhuXOjhXZsPkU5ee7u/NqX99Ol08//Ij8fM2azHr5teMAGo1GfrhiRbrecIrz+r7ejBs2LD1NTXmwqytJ0tdo5KX16zPyt3zVDADs2JpXXknjdV/OnD5oUH6ydm1e3Xy83ZKenhw8dGimDBuWuzpWbD3L9eVNm5IkLU1N6X2TEy/ePWhQfr1hfV7t68uQ1tYc2zYo/+/FjkzavIdxyrDhue11txCbv/nKEn1NzWlp3e0dnPvNLo/0kksuSZJ85StfyeWXX55TTz01vb29+cAHPpC//uu/zpe//OV87nOfy6mnnpqWlpbtjhW76aab8sADD6SlpSVnnHFGzj777Dz77LNbH7/55pvzR3/0R7nuuusybty4/MM//MN2Yzho4MD07eKZru9vb8+C7q78t3m/SCPJYQMG5DsnnbzT5W0tLbnlpJNy/fPP5dpnn0tLU/I7w4bl998Qa5cdPTYXzpuXow4emJMPOSQtjaQvyXcffTSrNv8H2+LMM8/MK6+8kkajkQ996EO5+uqrt548cckll+Tyyy/Pxz72sfzu7/5ufvCDH2xz8sSVV16ZX//61+nt7c1FF12UK664Ypvn78xtt92Wb3zjG3nxxRdz+OGH58ILL8yf/dmf7dLvDAD2pnnz5uWqq67aevmtCRMm5IYbbsjDDz+cv/iLv8imTZsyYMCAnHTKKTloydI0Nzdn1MhR+f0kq5YuyRc6OpJG0n7QgPztkWPy/sGD8+S6dfn9XzyW1qamfHzkqHz6yCNzwREj89G5c3LWsGE7PXmiqakp49vaMmbga9/ITTpkaB7o7MxRm7+h+19jx+b/PPdcPjp3TnobjZw9bFi+MuS4bGxtzUFv+Bbvpz/9aS655JKsXLkyH/zgBzNt2rTcfvvt++z3uDuaGo39fN+O3fBv//ZvefqnP825D//X/h7KNl599dX885ln5L7583P//fcnSVpbW3d4uRMAYFsH6vyeJD87+3dywu/9Xj7wgQ/s76Hskv7z3WKSkSNHZs6gQdnU0pIBB9BVoJvb2tK3+Vu4Qw45JCtXrsyf/umfijoA2AUH6vy+qaUl6wYNysiROz5p80DU78KuqbU1awYPzog3HMO3P60ZPDhNra0599xz86lPfept2eb111+fH/7wh9ss+5M/+ZN85jOfeVu2DwB7y4E6v9/W2Zm//+53c+tdd6V183F2F198ca666qr9PLKd61dh197enoMHD87y9vYD6oNffthr42rffKmVt8M111yTa6655m3bHgDsKwfq/H76aRMz6rTTctkXv7jDuz0diPbpvWL3tpaWlpw6aVIWjT06vc0HxtB7m5uz8OijM+H00/vNhw4ABxLz+95zYPz2dsPEiROzqa0tSw6Q49cWjxiRnra2TJgwYX8PBQD6LfP73tHvwm748OE5dvz4PDtu7C5f+mRf6WtqynPjxubY44/P8M33mAUAdp/5fe/od2GXJFPf976sGzEiC8aM2a/jeGbMmKwbMSJTzzlnv44DACowv++5fhl2o0ePzhlTp+ZX48dn7eYb+L7d1rS15enjx+fMc87J6NGj98sYAKAS8/ue65dhlyRTp07N8KPGZM6JJ6bnbT7Qsqe5OXNOOjHtY8ZkypQpb+u2AaAy8/ue6bdh19ramvOmT0/3kUfmkZNPetv2x/c1NeWRk0/K+tFH5iPTp2+9rg0AsOfM73um34ZdkowaNSoXfOKidI4dm4dPOXmfl31Pc3MePuXkdI4dmws+cVFGjRq1T7cHAO9E5ve3rl/dK3ZnFi5cmB/f8U9pW7Ysp8+fn6Hd3Xt9G2va2jLnpBOzfvSRueATF2XcuHF7fRsAwG+Y33dfibBLkhUrVuTeGTOyesnSvGfBgoxfujTNe+Gt9TU15ZkxY/L08ePTPmZMPjJ9er8ueQDoT8zvu6dM2CVJT09PZs6cmdkzZ2bIqlV598JFOXrVqrT09e32unqbm7N4xIg8N25s1o0YkTPPOSdTpkzpt/vcAaC/Mr/vulJht8WyZcsya+bMvPDMM2nt7s64xYsz+qXOHNrVlQG9vTt93aaWlqwZPDjLD2vPwqOPTk9bW449/vhM7aenPANAJeb3N1cy7LZYvXp1Hn/88Tw+Z042dHWl0dOTIevXZ2jn6hzU05PmRl/6mpqzsbU1a9uHZ92gQWlqbc3BgwdnwumnZ8KECf3uitMAUJ35fedKh90Wvb296ezsTEdHRzo6OrJyxYps3LAhvT09aWltzUEHH5zDR43KyJEjM3LkyLS3t/erG/4CwDuR+X1774iwAwB4J+jX17EDAOA3hB0AQBHCDgCgCGEHAFCEsAMAKELYAQAUIewAAIoQdgAARQg7AIAihB0AQBHCDgCgCGEHAFCEsAMAKELYAQAUIewAAIoQdgAARQg7AIAihB0AQBHCDgCgCGEHAFCEsAMAKELYAQAUIewAAIoQdgAARQg7AIAihB0AQBHCDgCgCGEHAFCEsAMAKELYAQAUIewAAIoQdgAARQg7AIAihB0AQBHCDgCgCGEHAFCEsAMAKELYAQAUIewAAIoQdgAARQg7AIAihB0AQBHCDgCgCGEHAFCEsAMAKELYAQAUIewAAIoQdgAARQg7AIAihB0AQBHCDgCgCGEHAFCEsAMAKELYAQAUIewAAIoQdgAARQg7AIAihB0AQBHCDgCgCGEHAFCEsAMAKELYAQAUIewAAIoQdgAARQg7AIAihB0AQBHCDgCgCGEHAFCEsAMAKELYAQAUIewAAIoQdgAARQg7AIAihB0AQBHCDgCgCGEHAFCEsAMAKELYAQAUIewAAIoQdgAARQg7AIAihB0AQBHCDgCgCGEHAFCEsAMAKELYAQAUIewAAIoQdgAARQg7AIAihB0AQBHCDgCgCGEHAFCEsAMAKELYAQAUIewAAIoQdgAARQg7AIAihB0AQBHCDgCgCGEHAFCEsAMAKELYAQAUIewAAIoQdgAARQg7AIAihB0AQBHCDgCgCGEHAFCEsAMAKELYAQAUIewAAIoQdgAARQg7AIAihB0AQBHCDgCgCGEHAFCEsAMAKELYAQAUIewAAIoQdgAARQg7AIAihB0AQBHCDgCgCGEHAFCEsAMAKELYAQAUIewAAIoQdgAARQg7AIAihB0AQBHCDgCgCGEHAFCEsAMAKELYAQAUIewAAIoQdgAARQg7AIAihB0AQBHCDgCgCGEHAFCEsAMAKELYAQAUIewAAIoQdgAARQg7AIAihB0AQBHCDgCgCGEHAFCEsAMAKELYAQAUIewAAIoQdgAARQg7AIAihB0AQBHCDgCgCGEHAFCEsAMAKELYAQAUIewAAIoQdgAARQg7AIAihB0AQBHCDgCgCGEHAFCEsAMAKELYAQAUIewAAIoQdgAARQg7AIAihB0AQBHCDgCgCGEHAFCEsAMAKELYAQAUIewAAIoQdgAARQg7AIAihB0AQBHCDgCgCGEHAFCEsAMAKELYAQAUIewAAIoQdgAARQg7AIAihB0AQBHCDgCgCGEHAFCEsAMAKELYAQAUIewAAIoQdgAARQg7AIAihB0AQBHCDgCgCGEHAFCEsAMAKELYAQAUIewAAIoQdgAARQg7AIAihB0AQBHCDgCgCGEHAFCEsAMAKELYAQAUIewAAIoQdgAARQg7AIAihB0AQBHCDgCgCGEHAFCEsAMAKOL/A4WAVwMLOeW0AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "est1 = ind.export_pipeline()\n", + "est1.plot() #GraphPipelines have a helpful plotting function to visualize the pipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lets add a few more mutations and plot the final pipeline to get a sense of the diversity of pipelines that can be generated with this search space" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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QVCwhxKikp6ejd+/eqK+v19a6du2K5ORkznZ/qK6uRmBgILKzs7U1MzMzJCYmomfPnpz0RAghD0IjdoQQo6FSqRAeHs4KdTweDz/++COnW3pZW1tj586drLth6+vrER4eDpVKxVlfhBByPwp2hBCj8cUXXyAuLo5VW7ZsGYYMGcJRR/8YOnQoXn/9dVbt0qVLWLt2LUcdEUKILpqKJYQYhaSkJPTt2xeNjY3amre3N65cuQILCwsOO/tHbW0tevfujevXr2trJiYmuHz5Mnr16sVhZ4QQchuN2BFCONfQ0IA5c+awQh2fz0dERITRhDoAsLCwQEREBPj8f350NjY2Ys6cOWhoaOCwM0IIuY2CHSGEc6tXr0ZycjKrtmLFCvTv35+jjh6uf//++M9//sOqJSUl4ZNPPuGoI0II+QdNxRJCOHXp0iWEhIRArVZrawEBAYiLi4OZmRmHnT1cfX09nn76aaSmpmprAoEA58+fR9++fVnPVavVkMlkKCkpQUlJCcqKi1FfWwuNWg2+QAAzCwt0cnaGRCKBRCKBWCyGQCBo7bdECGknKNgRQjhTW1uLPn364Nq1a9qaUChEXFwcgoKCuGusCa5cuYJ+/fqx7or18fFBQkICzM3NIZfLkZSUhJSEBNQpFGBUKljV1sJWJoOJSgU+w0DD46FRKESlWIwaCwvwhEKYW1oioE8fBAYGwt7ensN3SAhpiyjYEUI489Zbb2HdunWs2urVq/H+++9z1NGTWb16NT788ENW7T//+Q/6BgcjOzMTJkolPHLz4CKTwVahgMk9o5L3axQIUGlpiSKxGLkendEoEqGrlxdCBw+Gi4uLod8KIaSdoGBHCOHEuXPnMHToUNz7Iyg4OBjnz5+HiYkJh501XWNjIwYOHIj4+HgIBAKEhIQgtG9fuKvU8M7Ph7tUCkEzth5T8/nId3RElqcHahwd0Tc0FKGhoRAKhQZ4F4SQ9oSCHSGk1dXU1CAwMBA3b97U1szMzBAfHw8/Pz8OO3tyaWlpGDVqFMaMHAk3e3t4XbuGzjduwtnRkbWgcXNoeDxkurnhmpcXxO5uGBsWBmdnZz11Tghpj+jjHyGk1a1YsYIV6gDg448/bnOhDgCsrKzw8vz5sCwqgs+pUxBVVYEBUFVVBVtb2xYdm88w8M7Ph4tMhvgqH/xSUYnJL0yHp6enfponhLQ7NGJHCGlVx48fx3PPPceqhYSE4OzZs23ubtCcnBwc2LMH4pxcdD93Duq6WtbjDg4OMDPVz529Kj4fF/18IfPwwPMzZ1K4I4Q8EAU7QkirqaysREBAAPLy8rQ1CwsLJCUlwcvLi8POnlxxcTF+2bULdtm3MDAtDRqVCmWlpWDwz49UgUCATp2cwG/hlOxdGh4P5/39UNGlK2bMmU3TsoQQHbRAMSGk1SxfvpwV6gBg7dq1bS7UqVQqHI6MhKiwCP2vXgWfYSAUCGBjY8N6nlqtRlVVpd7Oy2cY9E+7CouiQhyJjGQttUIIIQAFO0JIKzl06BB27NjBqg0bNgyvvvoqRx01X0xMDOT5BQhOT4fwnrteRZaWML1v6lWpVLIWX24poUaD4KvpkBUUIDY2Vm/HJYS0DxTsCCEGV15ejpdeeolVs7a2xo4dO1j7rrYFhYWFiIuJQc/MTNgolazHeADs7OzA47Hfk0qPwQ4AbJVKeGdk4lJ0NIqKivR6bEJI29a2fqISQtqk1157DcXFxaza//73P3Tp0oWbhlogNjoaVlIpvAoKHvi4UCC4He5w+7o6U1MzmJqa6r2PHgUFsJJKERMdrfdjE0LaLlruhBBiUPv378eePXtYtTFjxmDBggUcddR8crkc2ZmZ6J2TC/4j7juzMDeHqUQCtVoNExMT6OfWCTY+w6BbTi4SHRwgl8tp+zFCCAAasSOEGFBpaSleeeUVVs3Ozg7btm1r8eK9XEhKSoKJUgl3qfSxzxXw+TA1UKi7q7NUCqFSieTkZAOehRDSllCwI4QYBMMwePnllyG9LwR9++23cHV15air5lOr1UhJSIBHbl6ztgkzBIFGA8+8PCTHx+v1Bg1CSNtFwY4QYhC7d+/G77//zqpNnjwZs2bN4qahFpLJZKhTKOAik3HdCotL+e2+ZEbWFyGEGxTsCCF6V1BQgNdee41Vc3R0xObNm/U2BSsUChEUFAR/f39MmzYNyvvuUNW39evX4+vvvsO8M6fhE30OYVcSEHYlAZGlpXo/16XKCoxNiMfUxMTHPtdWoQCjUqGkpETvfdwrMTERAwYMgL+/P4KDg3H69GmDno8Q0jx08wQhRK8YhsHChQtRUVHBqm/evBlOTk56O4+dnR0S7wSfF198EZs3b8by5cv1dvz7DR06FG6NjXju/AX0u3Aekb37sB5XMwwEegqth8rK8Grnzhjf6fHfLxO1GiKFAiUlJfD392/xudVq9QO3drO0tMTu3bvRrVs3XLt2DWPHjtXZ75cQwj0KdoQQvdq+fTv++usvVm3WrFl4/vnnDXbOwYMHIzk5GVKpFPPmzUNOTg7EYjF+/PFHuLm5wc/PDxkZGcjIyIC3tzcKCwshkUjg5eWFrKwslJaW4uWXX0Z+fj7Mzc2xbds29OzZE3PnzoWDgwPi4+PhLJFggrU167z5dXV45epV9LK2RnJ1FQ4E9cZr6ekobWhAA6PBUg9PjHJ01D7Px8oSydXV8La0xHrvnuDxePgi+yZOymQw5fEwxrETXMzMECWVIlpegUuVlXi361P4b1YWrilqYMbn4+PuXvC1ssI3OTmQNjYgp7YOVrJy/HrpEg4ePIjU1FQUFBRg165d2LBhAxISEvD8889jzZo1AICdO3fi+++/R11dHSZNmoTVq1fj1q1bmDhxIvr164eLFy8iLi4OZmbshZbv3R3E29sbNTU1Dw2BhBDuULAjhOjNrVu38MYbb7BqLi4u2Lhxo8HOqVKpEBUVhdGjR2PVqlUYPHgw/vzzT+zduxdLly5FZGQk3NzckJ2djejoaPTp0wfR0dHw9vaGn58feDweli1bhvfffx/BwcGIi4vDsmXLtOE0Ly8Pp06dwq7t22HygDXjspQKfOntjZ6Wt4PPFz16wM7EBNUqFaYmJWKkgwMA4GatEut79sRTFhaYnZKCy1VV6C4S4YhUilNP9wWfx0O1SgVroRAXKisw2tERw8QO2J6fDyuBAIf6BCOxqgorMjLwZ5/bo4UZCiV2BQQgzcsLGzMzUF1djdOnT2P37t2YMGEC4uPj4eLiAm9vb7z55psoLS3FkSNHcP78efB4PEycOBHnz5+Hi4sL0tLS8H//93/YunXrY7/nBw8eRHBwMIU6QowQBTtCiF5oNBrMnz8fNTU1rPrWrVshFov1fr6KigoEBQUBuD1it2DBAvTr1w9HjhwBAEyfPh2vv/46ACA0NBTR0dGIjo7GihUrEB0djbKyMoSGhgIATp48ifT09AeeZ+rUqeDxeNCo1Q9cu66LhQV6Wlpqv/6xsAAnym/fyFBUX4+yxkYAQFcLC3QTiQAAvlaWKKivQ28bG1gLBFiZmYERDg4YJnbQOf7lqiq85O4OAAiysUG9RoPqO3vEDncQw5TPB5/RgNFoEBYWBgAICAiAl5cXPD09AdwebcvLy0N0dDTOnz+P4OBgAEBNTQ1u3LgBFxcX9OjRA7169Xrs9/3GjRtYsWIFoqKiHvtcQkjro2BHCNGL7777DqdOnWLVFixYgHHjxhnkfPdeY/cwd2/UCA0NxR9//IHMzExs3boVmzdvhlQqZe1TGx8f/8ARKNGdMMYXCKB5wDV0Fve85kJFBRKqqvBrYCDMBQKMir+MhjtLo5jes3Uan8eDhgGEPB5+C+qNmAo5/igtRWRpKTb6+D7yPTFgtGvjmfNvn1vD44PH52unT/n3/P/dr9VqNRiGwaJFi/DBBx+wjnnr1i3t+3wUmUyGSZMmYcuWLejevftjn08IaX10VywhpMUyMjKwYsUKVs3DwwP/+9//WrWPQYMG4eeffwZwe8eLfv36AQBCQkIQFRUFJycnCAQCWFhYICYmBk8//TSA2zdGbNmyBcDtkceUlBSdY5tZWKBR+OjPwjVqNeyEJjAXCJBUXY1btbWPfL5CrUa1SoVhYge80/UppCsUOs952sYGf5bdvvM2qboaFgIBrO7ro0EoBL8J06LPPvss9u7dC7lcDgDIz89HeXn5Y18HAA0NDZg8eTKWL1+OZ599tkmvIYS0PhqxI4S0iFqtxty5c1F7X4jZsWMHbGxsWrWXVatWYe7cudi1a5f25gkAsLW1ha2trXbqtX///qioqNCOam3cuBGLFy/G5s2boVKpMGfOHAQEBLCO3cnZGdcfM6U82N4eu4sKEXYlAT0tLdFDZPnI5yvUarxyNQ0NmttTvG936arznBddXPDfrExMSIiHKZ+Pz7166DynSmwPcwuLR54LAPz9/bFixQo888wz0Gg0sLa2xi+//PLY1wHAvn37cOHCBVRWVmLDhg0AgBMnTsDBQXf6mBDCHR7DPGLDQ0IIeYy1a9fqjNYtWbIE3377LUcdGUZqaiqO/Porxp85CxMj2uWhUSDAoaFDMHbaNL0sd0IIadtoKpYQ0mxpaWl4//33WbVu3brhiy++4Kgjw5FIJOAJhai0fPQoXGurtLQETyiERCLhuhVCiBGgqVhCSLM0NjZizpw5aGho0NZ4PB4iIiJgaWThRx/EYjHMLS1RJBbDsaqK63a0ihxu96WvO4/Ly8sxfPhwVk0kEiE2NlYvxyeEGBYFO0JIs6xZswYJCQms2ptvvqm9jq29EQgECOjTB4nl5fDNzYXgzt2u96utq0N1VRXA48HOzg6mJiYG60nN5yOnc2f00eOacg4ODo+925gQYrxoKpYQ8sQSEhLw8ccfs2o+Pj46tfYmMDAQjSIR8h0dH/h4bV0d5HI5VGoVVKpGnW3V9C3P0REqkahJ688RQjoGCnaEkCdSX1+P8PBwqO4skgvcHs2KiIiAubk5h50Znr29Pbp6eSHL00NnTbv6hoY7y4j8cz+a5iGjevqg4fFww9MDXXv0gL29vcHOQwhpWyjYEUKeyKpVq5CamsqqrVy5En379uWoo9YVOngwahwdkenmpq01NjZCJpPh3lAHAFZWVgbrI8PNDTWOjggdNMhg5yCEtD0U7AghTXbhwgWsXbuWVQsMDNS5M7Y9c3FxQd/QUFzz8kKVSASVWo1ymQwMwx6dE4ksYdXCm0gethpVpUiE6z280G/QILi4uLToHISQ9oWCHSGkSZRKJcLDw1nTiyYmJti1axdMTU057Kz1hYaGwt7dDZd79kRpRQU0Gva6dubmFrC1tW328RsaG1FUVISi4iIUl5Sg/p47j1V8PuJ9fSB2c0NISEizz0EIaZ8o2BFCmuS9995DRkYGq7Zq1aoOeeG+UCjEsBEjkGMiROrTwazr7UxNzWBvbwfdXWWbrrKiAsydaV2NRo3ycinkFRVo1Ghw0c8XtS6uGBsWBuFjtjgjhHQ8tPMEIeSxzpw5g2eeeYZV69evH2JiYjpkuGhoaMD48eNx/fp1zJgyBR7l5fC5eBFmPD4cHR3B57Uk1gGlZaWsm1MAQC0QIH3AAJR37oxpL74IHx+fFp2DENI+UbAjhDxSdXU1AgMDkZ2dra2Zm5vjypUr6NmzJ4edcUOj0eBf//oX9uzZAwDw8PDAtEmT4FZbh9DsbNjX1bX4HBWVlVAqFdqvFTY2uBb8NApFFth38CAaGxsRFRWFoKCgFp+LENK+0FQsIeSR3n77bVaoA4BPP/20Q4Y6hmHw1ltvaUMdAOTm5uLPv/6CqZ8vzg4ciOvu7jpLoTwpkzuLGmt4POR7e+PSsGFIVzXi/375BXl5eSguLsZbb73VonMQQtqnjjeHQghpsqNHj2LLli2s2uDBg/H6669z1BG3vvrqK3z99desmkgkwv/93/+hT58+iImJQZy5GfJdnNEtJxedpdKH7lDxKDwTE5R4eiKve3dIrawQExeH2NhYqNX/3KRx/1QtIYQANBVLCHmIiooK+Pv7o6CgQFsTiURITk5Gt27dOOyMG7t27UJ4eDirJhQK8eeff2L06NHaWmFhIWJjYpCdkQGhUgnPvDy4lMtgq1DARK2+/7BajQIBKi0tUeQgxi13d0gbGpCRnY2Y2FgUFxeznmttbY1jx45hwIAB+n2ThJA2j0bsCCEPtGzZMlaoA26PWHXEUBcVFYX58+fr1Hfs2MEKdQDg6uqKqdOmQS6XIzk5Gcnx8bihUIBRqWBVWwsbmRymKhX4jAYaHh8NQiGqxPaosbAATyiEuaUlgoKCMG3atDuLHusaNWoUhTpCyAPRiB0hREdkZCQmTpzIqo0YMQLHjh0Dr4XXj7U1Fy9exLPPPgulUsmqf/nll026zk2tVkMmk6GkpAQlJSUoKy5GQ10d1CoVBEIhTM3N0cnZGRKJBBKJBGKxGAKBAL169UJKSspDjxsZGYkJEya0+P0RQtoXCnaEEBapVAp/f3+UlJRoazY2NkhJSYGHhweHnbW+69evIzQ0FOXl5az68uXLsW7dOoOe++DBg5gxYwYaGhrQr18/pKWlQaH4505ZiUSCtLQ0ODg4GLQPQkjbQnfFEkJYlixZwgp1ALB+/foOF+oKCwsxatQonVD34osv4ssvvzT4+SdPnozCwkJkZGTg4sWL+Oabb1iPl5SU4N///rfB+yCEtC00YkcI0dq7dy9mzJjBqo0fPx6RkZEdagq2oqICQ4YM0ZkKHTlyJP78809OtlBjGAYTJkzA4cOHWfV9+/Zh2rRprd4PIcQ4UbAjhAAAiouL4efnx7pg397eHmlpaR1qo/m6ujqMGjUKZ8+eZdWffvppnDp1ClZWVhx1BhQVFcHPzw9yuVxbc3BwQFpaGiQSCWd9EUKMB03FEkLAMAxefvllnbswN23a1KFCnVqtxosvvqgT6ry8vHD48GFOQx0AuLi44LvvvmPVysvL8fLLL4M+oxNCAAp2hBDcXqMtMjKSVZs6dSpeeOEFjjpqfQzDYMmSJfjtt99YdWdnZxw9ehROTk4cdcY2Y8YMPP/886zaH3/8gZ9++omjjgghxoSmYgnp4PLy8hAQEIDKykptzcnJCampqejUqROHnbWujz76CKtWrWLVrK2tcfbsWaPbk7WsrAx+fn4oKyvT1mxtbZGamgp3d3cOOyOEcI1G7AjpwBiGwcKFC1mhDgC2bNnSoULdli1bdEKdqakp/vjjD6MLdQDQqVMnna3eKisrsXDhQpqSJaSDo2BHSAf2ww8/4NixY6za7NmzMWnSJG4a4sDBgwfx6quvsmo8Hg+7d+/GsGHDOOrq8SZPnox//etfrNrRo0exdetWjjoihBgDmoolpIO6efMmevXqxVr01tXVFampqbC3t+ews9Zz9uxZjBw5EvX19az6t99+iyVLlnDUVdPJ5XL4+/ujsLBQW7OyskJycjK6du3KYWeEEK7QiB0hHZBGo8G8efNYoQ4Atm/f3mFCXUpKCsLCwnRC3X//+982EeqA28vRbNu2jVWrqanBvHnzoNFoOOqKEMIlCnaEdEDffPONzpIeL730ks6G9u1VTk4ORo8erXNt4cKFC7F69WqOumqeMWPGYOHChazamTNn8O2333LUESGESzQVS0gHc/36dQQFBaGurk5b69KlC5KTk2Ftbc1hZ61DKpVi0KBBuH79OqseFhaGAwcOQCgUctRZ81VVVaFXr17IycnR1iwsLJCYmIgePXqwnqtWqyGTyVBSUoKSkhKUFRejvrYWGrUafIEAZhYW6OTsDIlEAolEArFYDIFA0NpviRDSTBTsCOlAVCoVBg0ahIsXL7LqJ0+eNOobBfRFoVBg+PDhOu8/NDQUf//9NywsLDjqrOVOnTqFZ599llUbMGAAoqOjIRAIIJfLkZSUhJSEBNQpFGBUKljV1sJWJoOJSgU+w0DD46FRKESlWIwaCwvwhEKYW1oioE8fBAYGdphpekLaMgp2hHQgn3/+OVauXMmqLV26FBs2bOCoo9bT2NiISZMm4ciRI6y6r68vzp07B7FYzFFn+rN06VJs3LiRVfvss8/g1a0bsjMzYaJUwiM3Dy4yGWwVCpio1Q89VqNAgEpLSxSJxcj16IxGkQhdvbwQOnhwh9qNhJC2hoIdIR1ESkoKgoOD0djYqK15eXkhMTERIpGIw84Mj2EYzJs3DxEREax6586dERsb224W9VUoFAgKCkJWVhYEAgFCQkIwqG8/uKtV6JGXD3epFIJm3FSh5vOR7+iILE8P1Dg6om9oKEJDQ9vktDUh7R0FO0I6gIaGBgwYMABXrlzR1vh8Ps6dO4eQkBAOO2sd77zzDr744gtWzd7eHjExMfDx8eGoK8OIjY3F888/j/FjxsDN3h5e167BMzsbTg6O4LXw2BoeD5lubrjm5QWxuxvGhoXB2dlZL30TQvSDPm4R0gF8+umnrFAHAG+99VaHCHXr16/XCXUWFhY4dOhQuwt1AODm5obFCxbAuqQEPqdOQVRVBTWAmppqWFu17OYYPsPAOz8fLjIZ4qt88EtFJSa/MB2enp76aZ4Q0mI0YkdIOxcfH4/+/ftDfc/1VH5+frh8+TLMzc057Mzw9uzZg1mzZrFqAoEABw8exIQJEzjqynBycnJwYM8eiHNy8NTZc2Dq6+55lIdOjo4wMTHRy7lUfD4u+vlC5uGB52fOpHBHiJGgdewIacfq6uowZ84cVqgTCASIiIho96Hu77//Rnh4uE5969at7TLUFRcX4+DevRDn5GJg2lU4WFsDrMlXBvKKCujrk7xQo8HA1DSIc3NxcO8+FBcX6+nIhJCWoGBHSDv24Ycf4urVq6zaf//7XwQHB3PUUeuIj4/HlClTWDeKALfvEJ03bx5HXRmOSqXC4chIiAqL0P/qVfAZBqYmJrC2srrveY2orq7S23n5DIP+aVdhUVSII5GRUKlUejs2IaR5KNgR0k7Fxsbiyy+/ZNV69+6N9957j6OOWkdWVhbGjBmDmpoaVv21117DO++8w1FXhhUTEwN5fgGC09MhvOeuVytra52p15oaBdR63G5MqNEg+Go6ZAUFiI2N1dtxCSHNQ8GOkHZIoVAgPDwc915Ca2pqil27duntGitjVFxcjFGjRqGsrIxVnz59OtavXw8er6X3hRqfwsJCxMXEoGdmJmyUStZjPAB2dva4f0pWdd9IZkvZKpXwzsjEpehoFBUV6fXYhJAnQ8GOkHZo5cqVyMrKYtVWr14Nf39/jjoyvKqqKowdOxY3b95k1Z999lns2rULfH77/HEXGx0NK6kUXgUFD3zcRCiEra0t7oY7gUAIUzMzvffRo6AAVlIpYqKj9X5sQkjT0XInhLQzJ0+e1Nl9YMCAAXjrrbc46sjw6uvrMXnyZJ0lXXr37o2DBw/CzABBxhjI5XJkZ2aid04u+I9Y4MBSJIKJiQnUajXMzMxavJ7dg/AZBt1ycpHo4AC5XE7bjxHCkfb5EZaQDqqqqgrz589n1SwsLBAREdFuN3LXaDSYM2cOTp48yap37doVR44cgY2NDUedGV5SUhJMlEq4S6WPfa6piQkszM3BN+B0dGepFEKlEsnJyQY7ByHk0SjYEdKOvPnmm8jJyWHV1qxZgx49enDUkWExDINly5Zh3759rHqnTp1w7Nixdr0rglqtRkpCAjxy85q1TZghCDQaeOblITk+nrXEDiGk9VCwI6SdiIqKwrZt21i1oUOH4rXXXuOoI8P7/PPPdaadraysEBUVhe7du3PUVeuQyWSoUyjgIpNx3QqLS/ntvmRG1hchHQXtPEFIOyCXy+Hv74/CwkJtzcrKCsnJyejatSuHnRnOzp07daadTUxMcPjwYTz33HMcdWV4QqEQ/v7+qKurQ1VFBaK8ekDUxNfm19UhuboaYzt1AgBcrKjAT0WF2OjjCwA4JpViY24u1GDAAzDLxQUvurgCABRqNQZevIAVXbtqaw+SXKvEG8XFMLOwgI2NDdatW4dnnnmmBe+YEPIk6OYJQtqBpUuXskIdAKxbt67dhrpDhw7hpZde0qlHRES061AHAHZ2dkhMTMSJEydw/ehRiM5faPJrC+rqECUt0wa7e12tqcFXt25hu78/Opubo06txpF7rt07WV4OX0srHCkre2SwswEPC8aMwaAZM+Dm5vbAO5UJIYZDU7GEtHEHDx7ETz/9xKqNGjXqgcGnPTh//jymT5+ucw3X119/jZkzZ3LUVesrKy6GrUyG3NpazExOwqQrCZiWlIisO2vZXVcoMOlKAsLu/CpvaMDXOTmIrahA2JUEHChhbwG2oyAfizt3Ruc7W82ZCwSYIpFoHz8iLcMyT09IGxtRUl//0L66WFigG3O7P29vb9TU1ND1doS0IhqxI6QNKysrw8svv8yq2draYtu2be1yMd709HSMHz8etbW1rPqKFSuwbNkybppqZRUVFQgKCoKsvBzeIhHWOTgiwj8Apnw+Eqqq8L9bt7DJ1xd7i4sw08UFLzi7oE6tBp/Hwxuenqyp14sVFdrjZimVWODm/sBz1qhUuKZQoJ+tLUY6OOJouRRzXN0e2qOpSoW6ujocPHgQwcHB7faObEKMEQU7QtoohmHwyiuv6Oyy8M0338Dd/cH/QLdl+fn5GDVqlM5F+eHh4VizZg1HXbW+u1OxO7dsgXVsLBoyMvBR1g1cVyjAB9Bw57Lp3tY2+DYvFxWNKozp5AgPc4tHHpcBHvph4O/ycjwjFoPP42GMoyNW37zxyGDHZzQoKirCts8/R1RUVHPfKiGkGSjYEdJG/fLLLzhw4ACrNnHiRMyePZujjgxHLpdj9OjRyMvLY9XHjh2LrVu3tsvRycfhCwTQ8Hj4saAQ7mbmWNfDG9LGRkxPSgQATHByQi9ra5ySyRCekoJv74zSPUx3kQhXa2rQ09JS57EoqRSpNdU4fSdUlzY0oLi+Hs4PWfi5qqER327ejIhdu9r93cmEGBu6xo6QNqiwsBBLlixh1RwcHLBly5Z2F3Jqa2sRFhaGtLQ0Vr1///7Yt29fu9779lHMLCzQKBRCoVbBydQUPB4Pf5SWah/PrauFh7k55rq5IcTOHllKJSyFAigecr3bfDd3bMnPQ35dHQCgXqPBnqIiVKlUuKqowbl+/XGqbz+c6tsP893cEfWQRZEbNBqsPX0K48aOxbPPPqv/N04IeSQasSOkjWEYBosWLYJcLmfVv//+e0juudi9PVCpVJgxYwai79t/1NvbG4cOHYLlA0aXOopOzs64LhZjhrMLXruWjj/LShFiZ6d9/EiZFJFlpRDyeHAzM8NzDg4w4fGgYhiEXUlAuKsr3M3Mtc/3s7LCcs8ueOVqGlQMAyGPh1kurvi7XIpBdvYQ3POB4TkHB3x88wbmuelOx0ZJpbhRVgbVyZMICgoCAJw4cQIODg4G+14QQv5B69gR0sbs2LEDCxYsYNVeeOEF/PLLLxx1ZBh3A+z9iy67uroiNjYWnp6eHHVmHFJTU3Hk118x/sxZmBjRXaeNAgEODR2CsdOmwd/fn+t2COlwaCqWkDYkJydH5+5PiUSC7777jpuGDOjDDz/UCXW2trb466+/OnyoA27/vvOEQlQa2ahlpaUleEJhuxs9JqStoKlYQtoIjUaDBQsWoLq6mlXfunVru5vm2rRpEz7++GNWzczMDJGRkQgICOCoK+MiFothbmmJIrEYjlVVrX5+eWMjwlNTWDULPh//nTQJ5paWEIvFrd4TIYSCHSFtxubNm3HixAlWbe7cuZgwYQJHHRnG/v378e9//5tV4/P52LNnD4YMGcJRV8ZHIBAgoE8fJJaXwzc3FwKNplXPb29igsjefVg1NZ+PqM6d0YfWriOEMzQVS0gbkJWVhbfffptVc3d3x/r167lpyEBOnz6NF198Efdf+rtp0yZMnjyZo66MV2BgIBpFIuQ7OnLdCgAgz9ERKpEIvXr14roVQjosCnaEGDm1Wo158+ZBeWerqLu2b98OW1tbjrrSv6SkJEycOBENDQ2s+qpVq3R21yC32dvbo6uXF7I8PaDheJkbDY+HG54e6NqjB+zt7TnthZCOjIIdIUZu/fr1Ost9LF68GCNHjuSoI/3Lzs7G6NGjUXXftWKLFy/GBx98wFFXbUPo4MGocXRE5gOWHmlNGW5uqHF0ROigQZz2QUhHR8udEGLE0tPT0bt3b9Tfs+l6165dkZycDCsrKw4705+ysjKEhoYiMzOTVZ8yZQr27dtH12o1wZkzZxB34iSGXbwIm/tGdltDpUiE0wP6o9/w4XQdJCEcoxE7QoyUSqVCeHg4K9TxeDz8+OOP7SbU1dTUYNy4cTqhbsiQIdi9ezeFuiYKDQ2Fvbsb4n18oOLr/8e6Sq1GcUkJCouKUFpWBtU96+ap+HzE+/pA7OaGkJAQvZ+bEPJkKNgRYqS++OILxMXFsWrLli1rNyMiDQ0NmDp1qs57DAgIwB9//AFzc/OHvJLcTygUYlxYGJSurrjo56v36+0q5HJoNGoADFSqRpSWlqKquhoqABf9fFHr4oqxYWEQCmmhBUK4RlOxhBihpKQk9O3bF42Njdqat7c3rly5AgsLCw470w+NRoPw8HD89NNPrLqnpydiY2Ph6urKUWdtW05ODg7s2QNxbi76p12FUE9LoJSWlkKlVrFqaoEA1wYMgLxLF8wMD8dTTz2ll3MRQlqGRuwIMTINDQ2YM2cOK9Tx+XxERES0i1AHACtWrNAJdQ4ODjh69CiFuhbw9PTE8zNnoqJLV5zr3RtVIpFejmtiasr6WmFjg8QhQ3HTzg5bdu7E5MmTkZOTo5dzEUJahoIdIUZm9erVSE5OZtVWrFiB/v37c9SRfq1btw5fffUVqyYSiXD48GF4e3tz1FX74enpiRlzZkPg64NT/fvjurt7i6dmTe8EOw2Ph3xvb1waNgzpqkb83y+/IC8vD8nJyViyZIk+2ieEtBBNxRJiRC5duoSQkBCo77k4PSAgAHFxcTAzM+OwM/346aefMHv2bFZNKBQiMjISY8aM4air9kmlUiEmJgZxMTGwkkrRLScXnaXSZu1QoWxsxHVLEfK6d4fUygoxcXGIjY1l/Tnt27cvLl26pM+3QAhpBgp2hBiJ2tpa9OnTB9euXdPWhEIh4uLiEBQUxF1jenL06FGMHz8eKhX7Wq2IiAjMmTOHo67av8LCQsTGxCA7IwNCpRKeeXlwKZfBVqGAyT3B7H6NAgEqLS1R5CDGLXd3SBsakJGdjZjYWBQXF7Oey+fzceDAAUyaNMnA74YQ8jh0CxMhRuL9999nhToA+OCDD9pFqIuLi8Pzzz+vE+rWrl1Loc7AXF1dMXXaNMjlciQnJyM5Ph43FAowKhWsamthI5PDVKUCn9FAw+OjQShEldgeNRYW4AmFMLe0RFBQEKZOnQq5XP7Ac8ycOZNCHSFGgkbsCDEC586dw9ChQ1l7pAYHB+P8+fMwMTHhsLOWy8jIQGhoKKRSKav+xhtvYN26deBxvBVWR6NWqyGTyVBSUoKSkhKUFRejoa4OapUKAqEQpubm6OTsDIlEAolEArFYDIFAAG9vb2RkZDzwmDweD2fPnsUg2nWCEM5RsCOEYzU1NQgMDMTNmze1NTMzM8THx8PPz4/DzlquqKgIISEhuHXrFqs+a9Ys/N///R/4BlhMlxjG1q1bsWjRIgCAu7s7iouLWSOw3bp1Q1JSEiwtLblqkRACuiuWEM6tWLGCFeoA4OOPP27zoa6yshJjxozRCXUjR47Ezp07KdS1MS+99BKSk5Nx9OhRZGVl4bPPPmM9fuPGDaxYsYKj7gghd9GIHSEcOn78OJ577jlWLSQkBGfPnm3T22nV1dVhzJgxOH36NKseHByMU6dOwdrampvGiN6o1WoMGTIEsbGxrPrx48cxfPhwjroihFCwI4QjlZWVCAgIQF5enrZmYWGBpKQkeHl5cdhZy6jVasyYMQP79+9n1bt3746YmBg4OTlx1BnRt8zMTAQGBqK2tlZb8/DwQHJyMmxtbTnsjJCOi+ZCCOHI8uXLWaEOuH2XaFsOdQzDYOnSpTqhTiKR4OjRoxTq2hkvLy988cUXrFpubi6WL1/OUUeEEBqxI4QDhw4dwoQJE1i1YcOG4fjx42362rNPPvkE77//PqtmbW2NM2fOoHfv3hx1RQxJo9FgxIgROHXqFKt+6NAhjBs3jqOuCOm4KNgR0srKy8vh7+/PWuTV2toaycnJ6NKlC3eNtdC9d03eZWpqiqioKDz77LMcdUVaw61btxAQEICamhptzdnZGWlpaRCLxRx2RkjH03aHBghpo1577TWdlfv/97//telQ98cff2Dx4sWsGo/Hw08//UShrgPo0qULvv76a1atuLgYr732GkcdEdJx0YgdIa1o//79mDZtGqs2ZswYHD58uM0u1BsdHY3nnnsOdXV1rPrGjRvx73//m6OuSGtjGAbjxo1DVFQUq75//348//zzHHVFSMdDwY6QVlJaWgo/Pz/WDgx2dnZIS0uDq6srh501X2pqKgYPHoyKigpW/b333sMnn3zCTVOEMwUFBfD392f9eXB0dERaWhrdOENIK6GpWEJaAcMwePnll3W21fr222/bbKjLzc3F6NGjdULdggUL8PHHH3PTFOGUm5sbNm7cyKpJpVIsXrwYNIZASOugETtCWsFPP/2E2bNns2pTpkzB/v372+QUbHl5OQYPHoz09HRWfcKECfjtt98gFAo56oxwjWEYPP/88zh48CCr/tNPP+HFF1/kqCtCOg4KdoQYWEFBAfz8/FBZWamtteXpKaVSieHDh+PChQusekhICP7++2+IRCKOOiPG4mGXHaSmpsLNzY31XLVaDZlMhpKSEpSUlKCsuBj1tbXQqNXgCwQws7BAJ2dnSCQSSCQSiMXiNr0rCyGGRsGOEANiGAZjx47FX3/9xaofOHAAU6ZM4air5lOpVJg8eTIOHTrEqvv6+uLcuXO0tAXRetyNQnK5HElJSUhJSECdQgFGpYJVbS1sZTKYqFTgMww0PB4ahUJUisWosbAATyiEuaUlAvr0QWBgIOzt7Tl6d4QYLwp2hBjQg9Z2mzVrFnbv3s1RR83HMAwWLFiAnTt3suru7u6IjY1F586dOeqMGKtZs2Zhz549rNp3330HJ0dHZGdmwkSphEduHlxkMtgqFDBRqx96rEaBAJWWligSi5Hr0RmNIhG6enkhdPBguLi4GPqtENJmULAjxEAetGiri4sLUlNT2+TI1rvvvos1a9awavb29jh37hz8/Pw46ooYM5lMBj8/PxQXF0MgECAkJASD+vWDu1qNHrl5cJdKIdBonvi4aj4f+Y6OyPL0QI2jI/qGhiI0NJSu7SQEFOwIMQiNRoPhw4fj9OnTrPrhw4cxduxYbppqgY0bN2Lp0qWsmrm5OU6cOIGQkBCOuiJtweHDhzF//nyEjRsHN3t7eF27hi63ctBJLEZLbxvS8HjIdHPDNS8viN3dMDYsDM7Oznrpm5C2ioIdIQbwoCC0YMECbNu2jaOOmm/v3r2YOXMma7kKPp+PgwcPIiwsjMPOSFuQk5ODHVu2wLasDD7x8RBVVQEAbG1sYWlpqZdzVIlEiPfxgdLVFZNfmA5PT0+9HJeQtoiCHSF6lpGRgaCgINTW1mprHh4eSElJgY2NDYedPbkTJ05gzJgxaGxsZNW3bduGBQsWcNQVaStycnJwYM8eiHNy0PXMGaChQfsYDzx0cnKCUE93uKr4fFz084XMwwPPz5xJ4Y50WLRAMSF6pFarMXfuXFaoA4AdO3a0uVB35coVTJ48WSfUffLJJxTqyGMVFxfj4N69EOfkYmDaVThYW7MeZ8CgQi6HvkYWhBoNBqamQZybi4N79+nsx0xIR0HBjhA9WrduHc6fP8+qLVmyBMOHD+eoo+a5ceMGxowZg+rqalZ9yZIlePfddznqirQVKpUKhyMjISosQv+rV8FnGJiZmsFSxJ56bWhsgEKh0Nt5+QyD/mlXYVFUiCORkVCpVHo7NiFtBU3FEqInaWlp6NOnDxrumW7q1q0bkpKS9HYtUWsoKSlBaGgobty4wapPmzYNe/bsocVhyWOdOXMGcSdOYtjFi7BRKrV1hmFQWlYGtfrewMWDs7MEfJ7+xhkqRSKcHtAf/YYPx5AhQ/R2XELaAhqxI0QPGhsbMWfOHFao4/F4iIiIaFOhrrq6GmPHjtUJdcOGDcP//d//Uagjj1VYWIi4mBj0zMxkhTrg9t8Jezs7gHU/LIOGBvZ0f0vZKpXwzsjEpehoFBUV6fXYhBg7CnaE6MGaNWuQkJDAqr355psIDQ3lqKMn19DQgClTpui8j8DAQBw8eBBmZmYcdUbaktjoaFhJpfAqKHjg46amprCystJ+zefxYWpqqvc+ehQUwEoqRUx0tN6PTYgxo9UcCWmhhIQEfPzxx6yaj4+PTs2YaTQazJ07F8ePH2fVu3btiqioKNja2nLUGWlL5HI5sjMz0TsnF/xHXOVjY20NExMTqFUqWIgswOe1dEU7XXyGQbecXCQ6OEAul9P2Y6TDoBE7Qlqgvr4e4eHhrIu0BQIBIiIiYG5uzmFnTccwDN58802drZ86deqEo0eP0nZNpMmSkpJgolTCXSp97HMtzM1hZWUFAd9w0/udpVIIlUokJycb7ByEGBsKdoS0wKpVq5CamsqqrVy5En379uWooyf35ZdfYv369ayapaUljhw5Ai8vL26aIm2OWq1GSkICPHLzmrVNmCEINBp45uUhOT4e6kfsQ0tIe0LBjpBmunDhAtauXcuqBQYG4v333+eooycXERGBFStWsGpCoRC//fYbnn76aY66Im2RTCZDnUIBF5mM61ZYXMpv9yUzsr4IMRQKdoQ0g1KpRHh4ODT3jEyYmJhg165dBrkQ3BCOHDnywIWGf/zxR4wcOZKDjogxEAqFCAoKgp+fHyZMmICKigoAwK1btyASiRAUFITAwEAMGTIEubm5AG7/menZsye+/u47zDtzGl/eygYA7C0uwoSE+Du/EpB4ZzsxQ7hYUYHX0q/q1G0VCjAqFUpKSpp0nEOHDsHf3x98Pl9nNJ6QtoCCHSHN8N577yEjI4NVW7VqFXr16sVRR0/mwoULmDZtms701Lp16/Diiy9y1BUxBnZ2dkhMTERaWhrs7Ozw3XffaR/z9fVFYmIikpKSMHHiRNYU/vDhw/HfOXPwZ+8+eLtLVxTX1yOisBD7AoPwZ59g7AoIgAsHd1abqNWwqq3VCXYPm5r19vbG/v37af070mbRXbGEPKEzZ87oXJPWr18//Oc//+GmoSd07do1jBs3Dsr71hh7++23sXz5co66IsYoNDQUSUlJD3ysurqadbe0UqGA7T3TneWNjbDg82HGvz1+YG9ion1sQ04OzsplqNVoMFzsgDe7dAEADIu7hDAnJ8TIKyDk8fDfbk9hbXY28uvq8U7Xrhjp6IjfSkpwQlYOhVqNovp6hLu6YpaLK6s3hVqNVVlZuFF7+8/4ZDtbiAMDsWrVKhQXFyMrKwu+vr745ptvdN4XXVdK2joKdoQ8gerqasybN49VMzc3R0REBIRC4//rVFBQgFGjRulcbzR79mx8/vnnHHVFjJFarcbff/+N+fPna2tXr15FUFAQKioqwDAMLl++rH3s7LlzSBUKYdHQgOWeXTDY3h6WAgGGX45DqJ09xnRyRKjd7SVHwl1d8bqnJzQMg5fS0pBeUwOfO2vbeZib442gILyXmYlPb95EhH8A8urqsOzaNYx0dAQApNbU4FDvPgCASYlX8KzYgdX7prxcPOfggC8dvVFcX49/xcTg40mTAAApKSk4depUm7lkgpAnZfz/EhFiRN5++21kZ2ezap9++il69uzJUUdNJ5fLMXr0aO11UXeNHj0a27dvB59PV2YQoKKiAkFBQcjPz4efnx9GjRqlfczX11cb5r766iusXLkS27ZtAwCE9O+Pha6uCLz5z9+PCP8AJFRXIUZegbeuX8dyzy6Y5uyM85UV2Jqfj0aNBmWNjbhRq9QGu7shzdtSBHsTIUz5fHQTiVDaUK897mA7e1jf+SA10NYOKTXVsBH8889ZrLwCZ2UyfJt3+896DcOgvq4OADBx4kQKdaRdo2BHSBMdPXoUW7ZsYdUGDx6M119/naOOmq62thYTJ07UuRi8X79++PXXX2FyzzQZ6djuXmOnVCrx3HPPYdOmTVi6dKnO88aPH48dO3Zov+bx+dDct9Awj8dDsI0tgm1s4WUpwsGSUoQ5OeHTmzdxIDAIEjMzfJCViQbNP4sZm975gMEDD6b37B/LsI7L7oUHdoEBgy2+fnC9s5bklW7dUHfn/0UiUdO/GYS0QfQRnZAmqKio0LmDVCQSYefOnUa/f6parcasWbNw7tw5Vr1Hjx44fPgwa3snQu4SiUTYsGED1q1bx1qA+67Y2Fg89dRT2q8FQiEa77kcoaS+HldrarRfZyiUcDU3Q71GAx5uX3NX0diIM3L5E/d2Ti5HjUqFGpUKFyor4H/fn+EQO3vsvmeP2MzKSpi2kQXDCWkpGrEjpAmWLVuGgvv2vvzqq6/QrVs3jjpqGoZh8Oqrr+L3339n1V1cXHD06FE43rlmiZAHefrppxEQEIADBw6gf//+2mvsGIaBtbW1dhoWAESWlqgUi7VfqxgGn928CWljA0x4PLiZm+MzLy/YCIUIc3LC+IQEdDY3R5C19RP31cfGBq9fu4a8ujrMd3ODs5kZcmprtY8v8fDAxzduYHxCPNQMA/eePTHA2RnXMjMfe+yjR49iwYIFKCsrw4gRIzBs2DCdXVkIMWY8hnnEhn6EEERGRmLixIms2ogRI3Ds2DHwDLDHpT6tWrUKH330EatmY2ODc+fOtZmlWUjbkJqaiiO//orxZ87CxIC7PPxWUoIMpQLvdH3q8U8G0CgQ4NDQIRg7bRr8/f0N1hchxoKmYgl5BKlUikWLFrFqNjY22L59u9GHus2bN+uEOjMzM0RGRlKoI3onkUjAEwpRaWnJdSsslZaW4AmFkEgkXLdCSKugqVhCHmHJkiU6C5uuX78eHh4eHHXUNL/99hteffVVVo3H42H37t0YOnQoR12R9kwsFsPc0hJFYjEcDbjDxJQnDGhFDrf7Et8zTQwAO3fuxIYNG1i1GTNm4J133mlxj4RwiaZiCXmIvXv3YsaMGaza+PHjERkZadSjdWfOnMGoUaNQX1/Pqn///fdYvHgxR12RjuD06dNI/PtvjI6OgeCe7fa4oubzETUoFH1GjqQPNKTDoKlYQh6guLhYZ8TL3t4eP/zwg1GHuuTkZEycOFEn1H3wwQcU6ojBBQYGolEkQr6R3JST5+gIlUhElx6QDoWCHSH3YRgGL7/8ss7uDJs2bYKLiwtHXT3erVu3MHr0aFRWVrLqixYtwqpVq7hpinQo9vb26OrlhSxPD5017VqbhsfDDU8PdO3RA/b29pz2QkhromBHyH127dqFyMhIVm3q1Kl44YUXOOro8aRSKUaNGoWie9buAoBJkybhu+++M+pRRtK+hA4ejBpHR2S6uXHaR4abG2ocHRE6aBCnfRDS2ijYEXKPvLw8nZ0knJycsGnTJqMNRwqFAuPGjUNGRgarPnjwYPz8889tYg9b0n64uLigb2gornl5oYqjXR4qRSJc7+GFfoMGGfUoOyGGQMGOkDsYhsHChQt1pjK3bNmCTp06cdTVozU2NmLatGm4dOkSq+7v748//vgDFhYWHHVGOrLQ0FDYu7sh3scHKgPsQaxhGJRJpSgqLoZMJoPmnnsAVXw+4n19IHZzQ0hIiN7PTYixo2BHyB0//PADjh07xqrNnj0bkyZN4qahx7gbRKOiolh1Dw8P/PXXX3RdEeGMUCjEuLAwKF1dcdHPV+/X28nlcjQ2NoBhNKirr0NJSQkUSiXUPB4u+vmi1sUVY8PCaLSadEi03AkhAG7evIlevXpBoVBoa66urkhNTTXagLRixQqsXbuWVROLxYiJiUHPnj056oqQf+Tk5ODAnj0Q5+aif9pVCPW0BEpJaSnUavb+tWqBANcHDkTFU09hVng4PD099XIuQtoaGrEjHZ5Go8G8efNYoQ4Atm/fbrSh7uuvv9YJdRYWFjh8+DCFOmI0PD098fzMmajo0hXnevfW2zV3piYmrK8VNjZIHDIUN2xt8f22bXjllVd07monpKOgETvS4a1fvx5vvPEGq/bSSy/hhx9+4KijR/v555/x4osvsmoCgQB//PEHxo0bx1FXhDxccXExDkdGQp5fgJ6ZmfAqKAC/Bf/0KJRKVFZWQMPjobBHD2T27IkCmQyRR46gtLQUADB9+nTs3btXX2+BkDaDgh3p0K5fv46goCDU1dVpa126dEFycjKsra057OzBjh07hvHjx6OxsZFV37lzJ+bOnctNU4Q0gUqlQkxMDOJiYmAllaJbTi46S6XN2qFC0diIDEsR8rp3h9TKCjFxcYiNjYVardY+JzAwEImJiXp8B4S0DXRlKemwVCoVwsPDWaEOAHbs2GGUoe7y5cuYMmWKTqhbs2YNhTpi9IRCIYYOHQovLy/ExsQg0cEBqUolPPPy4FIug61CAZN7gtn9GgUCVFpaoshBjGx3d5Q3NCAjOxsxkZEoLi7WeT7ttEI6KhqxIx3W559/jpUrV7JqS5cu1dkY3BhkZmYiNDQUZWVlrPrrr7+Or7/+2mjX2CPkYeRyOZKTk5EcH486hQKMSgWr2lrYyOQwVanAZzTQ8PhoEApRJbZHjYUFeEIhzC0t4RsYiKlTp6KiouKBx541axZ2797dum+IECNBwY50SCkpKQgODmaNfnl5eSExMREijhZVfZji4mKEhIQgOzubVZ8xYwZ2794NvgHWCSOktajVashkMpSUlKCkpARlxcVoqKuDWqWCQCiEqbk5Ojk7QyKRQCKRQCwWQyAQoGvXrrh169YDj2lqaor4+Hj4+/u37pshxAhQsCMdTkNDAwYMGIArV65oa3w+H9HR0Rg4cCCHnemqqqrC0KFDda4VGj58OA4fPgwzMzNuGiOEY5999hnee+89AICVlRUUCgXu/eesd+/euHjxIkzuu4OWkPaOPuqTDufTTz9lhToAePvtt40u1NXX12Py5Mk6oa5Pnz747bffKNSRDm3lypU4dOgQtmzZghs3buDtt99mPX7lyhV8+umnHHVHCHdoxI50KJcvX8aAAQNYd8/5+fkhPj7eqIKSWq3GzJkz8euvv7Lq3bp1Q0xMDCQSCUedEWKc6urqEBwcjKtXr2prQqEQFy5cQHBwMIedEdK6aMSOdBh1dXUIDw9nhTqhUIiIiAijCnUMw2DZsmU6oc7JyQlHjx6lUEfIA5ibm2PXrl0QCATa2t073+vr6znsjJDWRcGOdBgffPAB69M8ALz33ntG92l+zZo1+Pbbb1k1KysrREVFoVu3bhx1RYjxCw4O1l53d1daWho+/PBDjjoipPXRVCzpEGJjYzFo0CCjv7h6+/btWLhwIatmYmKCI0eOYMSIERx1RUjb0ZZujiLEECjYkXZPoVAgKCgIWVlZ2pqpqSkuX76MgIAADjtj+/PPPzFp0iRo7luJf8+ePZgxYwZHXRHS9rSl5YwI0TeaiiXt3sqVK1mhDgBWr15tVKEuNjYW06dP1wl1GzZsoFBHyBMKCAjA6tWrWbXMzEydBckJaY9oxI60aydPnsTw4cNZtQEDBiA6Opp1kTWX0tLSMHjwYMjlclb9nXfewZo1azjqipC2TaVSYdCgQbh48SKrfvLkSQwbNoyjrggxPAp2pN2qqqpCr169kJOTo61ZWFggMTERPXr04LCzf+Tl5SEkJAT5+fms+ty5c7Fjxw7aKoyQFrh+/TqCgoJY+0F36dIFycnJRrkfNCH6QFOxpN168803WaEOuH3HqbGEOplMhtGjR+uEunHjxuGHH36gUEdIC3l7e+uMet+6dQtvvfUWRx0RYng0YkfapaioKIwdO5ZVGzp0KE6ePGkUe6sqlUo899xziI2NZdUHDBiAEydO0AXehOiJRqPBs88+izNnzrDqUVFRGD16NEddEWI43P8LR4ieyeVynSVDrKyssHPnTs5C3f79+9G3b1+MHz8eqampmDFjhk6o69mzJw4dOkShjhA94vP52LFjBywtLVn1hQsX6lzXSkh7QCN2pN2ZPXs2fvrpJ1Zty5YtWLRoUbOOp1arIZPJUFJSgpKSEpQVF6O+thYatRp8gQBmFhbo5OwMiUQCiUQCsVjMujEjNzcXXl5eaGhoAHB7qZW7/3+Xm5sbYmNj4eHh0aweCSGPtmXLFixevJhVmz17Nnbt2sVRR4QYBgU70q4cPHgQU6ZMYdVGjRqFqKioJ75mTS6XIykpCSkJCahTKMCoVLCqrYWtTAYTlQp8hoGGx0OjUIhKsRg1FhbgCYUwt7REQJ8+CAwMhL29PTZu3IilS5c+9Dx2dnY4d+4c/P39m/WeCSGPxzAMRo8ejWPHjrHqBw8exKRJk7hpihADoGBH2o2ysjL4+fmhrKxMW7O1tUVqairc3d2bfJzCwkLERkcjOzMTJkolPHLz4CKTwVahgMk9+8zer1EgQKWlJYrEYuR6dEajSISuXl7Y++uv2L9//wNfIxQKcfLkSQwePLjpb5QQ0ix5eXkICAhAZWWltubk5IS0tDQ4Ojpy2Bkh+kPX2JF2gWEYvPLKK6xQBwDffPNNk0OdSqXCmTNn8POPP0J64QJ6J1zB6OgYBNy6BceqqkeGOgAwUavhWFWFgFu3MDo6Br0TrkB64QK8PD0xePDgB66bp1KpcO3ataa/UUJIs3Xu3BkbNmxg1UpLS/HKK6+AxjhIe0EjdqRd2LNnD2bNmsWqTZw4EQcPHmzSFGxxcTEOR0ZCnl+AnpmZ8CooAF8PfzVqGxuQ4uCAzJ49USCTIfLIEZSWlrKe4+joqBNICSGGwTAMJk2ahMjISFadtu4j7QUFO9LmFRYWwt/fn3WHm4ODA9LS0iCRSB77+pycHBzcuxeiwiIEp6fDRqnUW28VFRVQ1iqhtLFBenAwCkUi/Pr778jNzdU+x8PDQ2e9PUKI4RQXF8PPzw8ymUxbs7e3R1paGlxcXDjsjJCWo6lY0qYxDINFixbpLFvw/fffNznUHdizB/bZtzD4yhW9hjoAqL2z4r2oqgpBZ8+iq1yOGVOmaO9+lUgk2L59u17PSQh5NGdnZ3z//fesmlwux6JFi2hKlrR5NGJH2rQdO3ZgwYIFrNoLL7yAX3755bGvLS4uxi+7dsEu+xYGpqXpZer1fkVFRWDwz3E1PB6uDhyIImcXeAf4Y/r06TAzM9P7eQkhj/fCCy9g3759rNqOHTswb948jjoipOUo2JE2KycnBwEBAaiurtbWJBIJ0tLS4ODg8MjXqlQqROzYAfXVdAy+cgVCjcYgPVZWVkKhVAAAeODB0soKplZWiO37NEx8fDBn/nwIhUKDnJsQ8mhSqRT+/v4oKSnR1mxsbJCSkkJrSpI2i6ZiSZuk0WiwYMECVqgDgK1btz421AFATEwM5PkFCE5PN1ioA24vt9KpUyeIxQ5wdnGBjbU1zHk8BF9Nh6ygQGf3CUJI63F0dMQPP/zAqlVVVWHBggU0JUvaLAp2pE3avHkzTpw4warNnTsXEyZMeOxrCwsLERcTg56ZmXq/pu5BTIQmMDczw7335toqlfDOyMSl6GgUFRUZvAdCyIOFhYUhPDycVTt+/Dg2b97MUUeEtAxNxZI2JysrC4GBgVDeE8rc3d2RmpoKW1vbx75+/759kF64gGGX4w1yXV1TaXg8nHo6GI4DB2LqtGmc9UFIR1dRUQF/f38UFBRoayKRCMnJyejWrRuHnRHy5GjEjrQparUa8+bNY4U6ANi+fXuTQp1cLkd2Zia65+RyGuoAgM8w6JaTi+yMDNqMnBAO2dnZYceOHayaUqnEvHnzoH7MwuSEGBsKdqRNWb9+PaKjo1m1xYsXY+TIkU16fVJSEkyUSrhLpYZo74l1lkohVCqRnJzMdSuEdGgjR47Eyy+/zKqdO3dOZ6cKQowdTcWSNiM9PR29e/dGfX29tta1a1ckJyfDysrqsa9Xq9XYtGED3K4kIuDWLQN2+mRSunZBQVAQXn399QduO0YIaR3V1dUIDAxEdna2tmZmZoYrV67Ax8eHw84IaToasSMGde/G2rt27UJwcDAqKysxd+5cdO3aFSqVCgCQmpqKZ5555qHHUalUCAsLY4U6Ho+HH3/8kRXqVq1ahW+//Vbn9adPn8akSZNQp1DA5Z7V5luqqL4er1y9iuGX4zAm/jLevH4NlapG/FZSgs+zbzbpGC7lMtQpFKxV8O8VGRmJr7/+GsDtcBsYGIigoCBcvHgRb7/9dovfw6FDh+Dv7w8+n4/U1NQWH4+Qtsra2ho7d+5k1err6xEeHq79WUWIsaNgR1rFb7/9hi+//BJ//fWX9lo4lUqFPXv2NOn1X3zxBbKysli1ZcuWYciQIU3uob6+HoxKBbuamqY3DkD9kEFthmGwJP0qRjo44MTTfREV/DQmO0lQ+YT/ANgqFGBUKtZaWvcKCwvDG2+8AQD4448/MHPmTCQmJqJ///748ssvm/4+HnKtkLe3N/bv3/9E30tC2quhQ4di2bJlrFpcXBzWrl3LTUOEPCFaGZUY3NGjR/Huu+/ixIkT6NSpk7b+xhtv4Msvv8S//vUv1vNVKhXefPNNnD9/Hg0NDQgPD8dHH33Eek7Xrl1x+vRpDBgwAKGhoThz5gwuX74MAEhMTMSQIUOQn5+Pzz77TLuxd1lZGf5v925sKSzEKEdHvOHZBQDwQ34e/igtBQ/AIvfOCHNywsWKCmzOz4ONUIiyhgZ87d0Tr1+7BoVaDYDBl949Ud7YAEuBAJPv2bpskL09AOByZZW2dry8HJvz8tDAaOBmZoavvHvCUiDAobJSfJubCxMeH6YF+QgeMgRlZWVYunQp+Hw+TExMcPnyZfz4449ITU3F6NGjsX79egiFQsTGxmL58uX49ttvsX//ftTU1ODVV19Feno6GIbBhg0bEBoailWrVqG4uBhZWVnw9fXFN998o/P74+Xl1ezfW0Lao88++wxHjhxBRkaGtrZq1SqMGzcOgYGBHHZGyONRsCMGVV1djX/961+4cOEC3NzcWI/16NED3t7e+OOPP9C9e3dtfdu2bejSpQs2bNgAqVQKd3d3NDY2ah/n8/lwdnbGf/7zH0yaNAnvvfce67jZ2dk4efIkcnJyMGrUKG2wS09Px9pJkzA8Lx8zkpPwrNgBfABRZVL8FtQbtWo1nk9KRP87I4pJ1dWI6hMMiZkZtufno5+tLZZ36QIVw6BRo8Glygr4WFo+9nvQ19YGIxyCAADf5+Vif3Exwt3csDkvD5t9/dDFwgKnunZFWXExdv/yC/73v//hueeeQ2VlJes4I0aMwOLFi+Ho6Ih///vfOH36tPaxTz75BJMnT8auXbuQn5+PcePGISkpCQCQkpKCU6dOwdTU9LG9EkIACwsLREREIDQ0FJo7C5g3NjYiPDwcly5dor9LxKjRVCwxKJFIhF69euHnn39+4OPvvvsu1qxZw6r9/fff2LJlC4KCguDn58e6rg4AVqxYgRs3bmDixIkAoA1ud40dOxZCoRDdunVDRUWFtt69Wzc4m5vDlM/Hcw4OuFJVhfiqKox0dIAZnw87ExMMtLVDyp2p2j42NpDc2ce1l7U1DpWVYkNODm4olbAQCHB7hpaHxymsq8eclGSMT4jHr8UlyKpVao//QVYm9hUXw0TViIa6OoSGhuKdd97BN998o7Oky6P8/fff+PDDDxEUFITx48ejvLwcDQ0NAICJEyfSP0SEPKEBAwbgP//5D6uWlJSEjz/+mKOOCGkaCnbEoAQCAQ4ePIj9+/frXJQMAL1794a9vT1rFwmGYfDDDz/ghx9+QHl5Oev5jo6O+PDDD1m1+2/sNrsTxh7k3rXrHhTJmHvqFvx//nr0tbXFz70CITE1xevX0nGivBzdRSKkKx5/vd4nN2/gJffOONQnGMu7eKLhzgjAR9264w3PLsirq8OKP/9ErVKJd955Bzt27EBNTQ369u3LWjD1URiGwaFDh5CYmIjExETk5+drw5xIJGrSMQghbKtWrYK/vz+rtmbNGsTFxXHUESGPR8GOGJyNjQ2OHDmCjz/+GH/99ZfO4++++y6++uor7dcjRozAd999hzlz5rAu+Ofz+RgzZgzMzMzQp08f/PnnnwCAX3/9tUl9ZN24gTKlEg0aDY6XlyPIxgbBNjb4u7wcDRoNKlWNuFhZgQBra53XFtTVwdHUFDNcXDDRyQnXFQqE2NmhWqXCH6Wl2uedLC9Hbl0t67U1ajWcTU2hYRgcKivT1vPq6tDbxgbLPT0h5PNRW1eHGzduIDAwEO+++y58fHxYyy48yt3v2V13p2EJIc1nZmaGXbt2QSj856oltVqN8PBw1NXVcdgZIQ9HwY60Cjc3N0RGRuKll15CQkIC67EhQ4bA09NT+/XLL7+MGzdu4Pr166znTZw4EU5OTgCAr7/+Gh999BEGDBiA+vp62NjYPLYH7x49sCMuDmFXEjDI3h6B1tYIsLbGaEdHTE68gheTk7HUwxNOD5i2vFhZiYlXEjDxSgJiKyowzdkZPB4Pm3x8ESUtw4jLcRibEI8jUinshCas177a2QMvX72K2SnJcDMz19Y/z87G+IR4jL+SgL5du8LZ1RVff/01/Pz80KtXL3h4eGDgwIGP/+YC+OCDD1BaWoqAgAD4+vpi27ZtTXodcPvmFnd3d5w/fx4jRozAzJkzm/xaQtq73r174/3332fV0tPTdWqEGAtaoJgYnXPnzmHo0KGsKdbg4GCcP38eJia3Q5NSqYSFhQV4PB6+/PJLlJSUsEb9HuTEiRO4fvQonjt/waD9N8ffAwfAe9QoDB8+nOtWCCH3aWxsxMCBAxEfH6+t8Xg8nD17FoMGDeKwM0J00YgdMSo1NTWYO3cuK9SZmZkhIiJCG+oA4NKlS+jduzcCAgJw8uRJrFix4rHHlkgkqLGwQKOR7e7QKBCgxsICknuWTSGEGA8TExNERESwbkJiGAZz586FQqHgsDNCdFGwI0ZlxYoVuHmTvWPDxx9/DD8/P1btmWeeQWJiIlJSUhAVFcVaH+9hJBIJeEIhKpuwRElrqrS0BE8oNHiw27lzJ4KCgli/Pv/8c4Oek5D2ws/PT+eO2Bs3bjTpQyUhrYmmYonROH78OJ577jlWLSQkBGfPntXLHqq0VywhpCXUajUGDx6M8+fPs+rHjx+nyyiI0aARO2IUKisrMX/+fFbNwsICP/74o97CjkAgQECfPsj16Aw13zj+6Kv5fOR07oxewcEU6ggxcgKBABEREbCwsGDV58+fj6qqqoe8ipDWZRz/upEOb/ny5cjLy2PV1q5dq/ftrgIDA9EoEiHf0VGvx22uPEdHqO4s4kwIMX5eXl744osvWLXc3FwsX76co44IYaNgRzh36NAh7Nixg1UbNmwYXn31Vb2fy97eHl29vJDl6QENj71EsVqjAYPWuzJBw+PhhqcHuvboAfs7e8wSQozfkiVLMGzYMFZt+/btOHz4MEcdEfIPCnaEU+Xl5XjppZdYNWtra+zYsQN8A02Xhg4ejBpHR2S6uUHDMFAolSguKUFJSTGKioqheIKtvFoiw80NNY6OCKXlEghpU/h8Pnbs2AErKytW/aWXXoJMJuOoK0Juo2BHOPXaa6+huLiYVfv666/RpUsXg53T2dkZTu7uSPLojJtKBSorK6DR3N3hgkF1K1wrUykS4XoPL/QbNAguLi4GPx8hRL+6dOmCr7/+mlUrKirCa6+9xlFHhNxGwY5wZv/+/dizZw+rNnbsWJ2bKPTp4sWL8Pf3x/z583GrrAzpwcFQt/JNCyo+H/G+PhC7uSEkJKRVz00I0Z8FCxZgzJgxrNrPP/+MAwcOcNQRIbTcCeFISUkJ/P39IZVKtTV7e3ukpqbC1dXVIOdUq9Xo2rWr9iYNJycnzJ4xA13lcviePw/+nb8KIpEIdrZ2AICq6mrU1tbCxMQE9vb24D3s4E2k4fFw3t8PFV26Ysac2XB2dm7hEQkhXCooKIC/vz8qKiq0NUdHR6SlpWm3QCSkNdGIHWl1DMNg8eLFrFAHABs3bjRYqLuroKBA+/+lpaX49fffkevggKsDB2pH7kQWIgBARWUlamqqoVarUFdXiwq5vEXnVvH5OO/vB5mHBya/MJ1CHSHtgJubGzZu3MiqSaVSvPLKK6BxE8IFCnak1e3evRu///47qzZlyhTMmjXLoOcVCARYtmwZq5abm4tffvsN2fb2SBwyBEobW5iYmqK6pgZKJXuroPqGhmafu1Ikwtk+vVHRpSuenzkTnp6ezT4WIcS4vPjii5g8eTKr9ttvv+Hnn3/mqCPSkdFULGlVBQUF8PPzQ2VlpbbWmtMWcXFxCA0NRWNjI6vu5OSEsHHj4NmpE3pmZsIhJUU7NXsXn8d/4lE2DY+HDDc3XO/hBbGbG8aGhdFIHSHtUGlpKfz8/FgzEXZ2dkhLSzP4TAQh96JgR1oNwzAYO3Ys/vrrL1b9wIEDmDJlisHPn5WVhdDQUJSWlj7wcYFAgHnz5sHRxgaONTXonJUFx7w8CDSaO8/gwcXFpUnX2an5fOQ5OuKGpwdqHB3Rb9AghISEQCgU6u39EEKMy/79+zFt2jRWbcyYMTh8+DB4vJZeoUtI01CwI61m69atWLRoEas2a9Ys7N692+DnLi4uRmhoKG7evMmq83g87XUwAoEAarUazs7OCA0JQY+uXSFSqeCekwNxUREsKyrgKhZDKHhwOGsUCFBpaYkiBzFyOneGSiRC1x49EEpLmhDSYcyaNUvnbv+tW7di4cKFHHVEOhoKdqRV3Lp1CwEBAaipqdHWXFxckJqaCrFYbNBzV1VV4ZlnnsGVK1dY9eHDh2PZsmV48803UVFRAXNzc+Tm5moft7W1RWBgIPoEBMDS3BxCHg9itRriqmqYqlTgMxpoeHw0CIWoEtujxsICPKEQ5paW6BUcjF69etGOEoR0MDKZDH5+fqz1Oa2srJCSkmLQ9TkJuYuCHTE4jUaD4cOH4/Tp06z64cOHMXbsWIOeu76+HuPGjcOJEydY9d69e+P06dOwsbHR1qZNm4b9+/frHIPH48HBwQESiQSzZ8+Gp7s7GurqoFapIBAKYWpujk7OzpBIJJBIJBCLxRC08tp4hBDjcfjwYYwfP55VGzZsGI4fP26wHXUIuYsu+CEG99133+mEugULFhg81Gk0GsyZM0cn1D311FOIiopihTrg9nIrAoEA+/btYy1TwDAMpFIppFIp6uvrMePFFw3aNyGkbRs3bhzmz5/P2gP71KlT2LRpE/79739z2BnpCGjEjhhURkYGgoKCUFtbq615eHggJSVFJ1jpE8MwWLZsGb755htW3cnJCTExMejevfsDX1dTU4NOnTqhrq5O5zEej4dz584hNDTUID0TQtqPyspKBAQEaBdEBwALCwskJSXBy8uLw85Ie0djwsRg1Go15s6dywp1ALBjxw6DhjoA+OKLL3RCnZWVFY4cOfLQUAfcnkK5N9Tx+Xz873//w9tvv40TJ05QqCOENImtrS1rxA4AamtrMXfuXKjV6oe8ipCWo2BHDGbdunU4f/48q7ZkyRIMHz7coOfduXMnVq5cyaqZmJjg4MGDCA4OfuRr9+7dy/p6xIgReOONN7B27VoMGzZM770SQtqvESNG4NVXX2XVYmNj8b///Y+jjkhHQFOxxCDS0tLQp08fNNyzW0O3bt2QlJQES0tLg5330KFDmDRpks4n4j179mDGjBmPfG1VVRWcnJxQX1+vrW3fvh3z5883SK+EkPavpqYGQUFBuHHjhrZmamqKhIQE+Pn5cdgZaa9oxI7oXWNjI+bMmcMKdTweDxEREQYNdefPn8f06dN1Qt369esfG+oAIDIykhXqTExMdLYJIoSQJ2FlZYUff/yRtUBxQ0MDwsPDdXbAIUQfKNgRvVuzZg0SEhJYtTfffNOg16elp6dj/PjxOtfzrVixAq+//nqTjrFv3z7W1yNHjqR16AghLTZo0CAsX76cVYuPj8fnn3/OUUekPaOpWKJXCQkJ6N+/P1Qqlbbm4+ODhIQEmJubG+Sc+fn5CAkJYd19BgDh4eHYuXNnk7byqaiogJOTE+sTdEREBObMmaP3fgkhHU9dXR369OmD9PR0bU0oFOLSpUvo3bs3h52R9oZG7Ije1NfXIzw8nBXqBAIBIiIiDBbq5HI5Ro8erRPqxo4di61btzZ5f8bff/+dFepMTU0xceJEvfZKCOm4zM3NERERwVq8XKVSITw8nHUJCCEtRcGO6M2qVauQmprKqq1cuRJ9+/Y1yPlqa2sRFhaGtLQ0Vr1///7Yt28fTExMmnys+++GHTNmDGxtbfXSJyGEAEDfvn117thPSUnBRx99xFFHpD2iqViiFxcuXEBoaCg0Go22FhgYiEuXLsHU1FTv51OpVHj++ecRGRnJqnt7eyM6OhqOjo5NPlZ5eTmcnZ1ZI40///wzZs6cqbd+CSEEuH3jRL9+/ZCUlKSt8fl8xMbGon///hx2RtoLGrEjLaZUKhEeHs4KdSYmJti1a5dBQh3DMHjllVd0Qp2rqyuOHj36RKEOAA4ePMgKdebm5jr7PBJCiD6YmpoiIiKCNaOg0WgQHh6uc/MXIc1Be8WSFnvvvfeQkZHBqq1atQq9evUyyPk+/PBDbNu2jVWztbXFX3/9BU9Pzyc+3v3TsOPGjYO1tXWLeiSEkIcJDAzEhx9+iP/+97/a2vXr1/Hee+/pLF6sVqshk8lQUlKCkpISlBUXo762Fhq1GnyBAGYWFujk7AyJRAKJRAKxWMy6jo90PDQVS1rkzJkzeOaZZ1i1fv36ISYmBkKh/j83bNq0CUuWLGHVzMzMcOzYMQwZMuSJj1daWgoXFxfWaOO+ffswbdq0FvdKCCEPo1KpEBISgri4OG2Nx+Ph9OnTGDJkCORyOZKSkpCSkIA6hQKMSgWr2lrYymQwUanAZxhoeDw0CoWoFItRY2EBnlAIc0tLBPTpg8DAQFquqYOiYEearbq6GoGBgcjOztbWzM3NceXKFfTs2VPv59u/fz+mT5+Oe//I8vl87N+/v9kLCW/evBmvvPKK9muRSISysjKIRKIW90sIIY+Snp6O3r17s+6KDQ4OxlvLlyMvOxsmSiU8cvPgIpPBVqGAySP2mG0UCFBpaYkisRi5Hp3RKBKhq5cXQgcPhouLS2u8HWIkaCqWNNvbb7/NCnUA8Omnnxok1J0+fRovvvgi7v8csmnTphbtDnH/NOyECRMo1BFCWoWPjw8+++wzvPnmmxAIBAgJCUFo374oiYlB7+ISuEulENwzm/AoJmo1HKuq4FhVBd/cXOQ7OiKrvBy7s7LQNzQUoaGhBplFIcaHRuxIsxw9ehSjR49m1QYPHoxTp07p/fqOpKQkDBkyBFVVVaz6qlWr8OGHHzb7uEVFRXBzc2OFxd9++422ESOEtBq1Wo3x48fD3cUFbvb28Lp2Da4ZGehkL4aZmVmLjq3h8ZDp5oZrXl4Qu7thbFgYnJ2d9dQ5MVYU7MgTq6iogL+/PwoKCrQ1kUiE5ORkdOvWTa/nys7ORkhICIqLi1n1l19+Gd9//32TFyB+kI0bN2Lp0qXar62trVFaWmqwxZQJIeR+OTk5+HX3zxBkZ6Nn/GWI7nyAFfAF6OTkBH4LfsbdVSUSId7HB0pXV0x+YXqzbjIjbQctd0Ke2LJly1ihDgC++uorvYe6srIyjBo1SifUTZkyBd99912LQh2guzfsxIkTKdQRQlpNTk4ODuzZg055eRiSmKgNdQCg1qhRVVmpl/PYKJUYfOUK7G5l48CePcjJydHLcYlxomBHnkhkZCQiIiJYtREjRmDx4sV6PU9NTQ3GjRuHzMxMVn3IkCHYvXt3i6d78/PzER0dzapNnz69RcckhJCmKi4uxsG9eyHOycWAtDRYm5vDzJQ99aqsVaKurk4v5xNqNBiYmgZxbi4O7t2n84GZtB8U7EiTSaVSLFq0iFWzsbHB9u3bWzx6dq+GhgZMnTqVtQwAAAQEBOCPP/7Qy6jar7/+yvra1tYWI0eObPFxCSHkcVQqFQ5HRkJUWIT+V6+CzzDgAbCzswOPx/5nuaKyUuemsebiMwz6p12FRVEhjkRGshZmJ+0HBTvSZEuWLEFJSQmrtn79enh4eOjtHBqNBgsWLMDRo0dZdU9PT/z111+ws7PTy3nuvxt28uTJLb5QmRBCmiImJgby/AIEp6dDeM9drwKBALa2NqznajRq1Dc06O3cQo0GwVfTISsoQGxsrN6OS4wHBTvSJHv37tW5Jm38+PGYO3euXs+zYsUK/PTTT6yag4MDjh49CldXV72c49atW7h48SKr9sILL+jl2IQQ8iiFhYWIi4lBz8xM2CiVOo+LLEQwN2PPSuh7mRJbpRLeGZm4FB2NoqIivR6bcI+CHXms4uJivPrqq6yavb09fvjhB71Owa5btw5fffUVqyYSiXD48GF4e3vr7Tz3T8OKxWIMHz5cb8cnhJCHiY2OhpVUCq/7bkC7l729PSwtrWBmZg6xvRhCA2wR1qOgAFZSKWLuu9aYtH20WiF5JIZh8PLLL0Mmk7HqmzZt0utq5j/99BPeeustVk0gEGD//v3o37+/3s4D6E7DTpkyhbUhNyGEGIJcLkd2ZiZ65+SC/4jr5ng8HmxtbB76uD7wGQbdcnKR6OAAuVxO24+1IzRiRx5p165diIyMZNWmTp2q16nLo0ePYt68eTr1HTt2YMyYMXo7DwBkZWUhPj6eVaNpWEJIa0hKSoKJUgl3qZTrVgAAnaVSCJVKJCcnc90K0SMKduSh8vLy8Prrr7NqTk5O2LRpk96mYOPi4vD888/r3J31xRdfYM6cOXo5x73uv06wU6dOeOaZZ/R+HkIIuZdarUZKQgI8cvOavE2YoQk0Gnjm5SE5Ph7qR+xDS9oWCnbkgRiGwcKFC1F53wKZW7ZsQadOnfRyjoyMDIwdOxYKhYJVf+ONN/D222/r5Rz3uz/YTZ06lfZPJIQYjFAoRFBQEAIDA7Hmyy/h+ASjdfl1dThSVqb9+mJFBV5Lv6r9+phUigkJCRibEI9xCfHYXVSofUyhVqNXbAyr9iC2JaX45ttvYWNjo3M5DGmb6F808kA//PADjh07xqrNnj0bkyZN0svxi4qKMGrUKEjv+yE3a9YsfPXVV3q9KeOu69evIykpiVWjRYkJIYZkZ2eHxMREpKam4sivv6LT6TNNfm1BXR2ipGUY+4AP01dravDVrVvY7u+PzubmqFOrceSen6cny8vha2mFI2VleNHl4SsKONTWYviQIejUuTNqa2uf7M0Ro0TBjui4efMm3nzzTVbN1dUVGzZs0MvxKysrMWbMGNy6dYtVf+6557Bz507w+YYZSL7/pglnZ2cMHjzYIOcihJB7lZSUwKq2FoUKBVZkZqBWrYYJn481Xj3QXSTCdYUCKzKu4+4k7U4/f3ydk4NMpQJhVxIQ7uoK93uWQdlRkI/FnTuj850F280FAkyRSLSPH5GWYZmnJz68kYWS+npIHrJOpyXDoJeTE27V1xvsvZPWRcGOsGg0GsybN09nenT79u16uWuqrq4OkyZN0hk5Cw4OxoEDB2BqatriczzM/cFu2rRpLd6ajBBCHqWiogJBQUGokMvRTSTC1/ZiRPgHwJTPR0JVFf536xY2+fpib3ERZrq44AVnF9Sp1eDzeHjD0xM/FRVio48vgNtTsXdlKZVY4Ob+wHPWqFS4plCgn60tRjo44mi5FHNc3R7ao41MjmoBH5bW1np974QbFOwIyzfffIOzZ8+yai+99BJGjx7d4mOr1WrMnj0bp0+fZtW7d++OI0eOwNqAP1TS0tJw9epVVo3uhiWEGNrdqdiIbdtgER2NhuvX8VHWDVxXKMAD0KDRQFlbCz9zC/xQUICKRhXGdHKEh7nFI4/LAA+9ZOXv8nI8IxaDz+NhjKMjVt+88chgZ6pSQaXR/+UvhBsU7IjW9evXsXLlSlatS5cuWLduXYuPzTAMli5div3797PqEokER48ehZOTU4vP8Sj3j9a5ublh4MCBBj0nIYQAty8/qayshGlDA7bczIYDw2C7mzvKGurxakEBKirkGMjn4SkXVyQyDMJTUvDtnVG6h+kuEuFqTQ16WlrqPBYllSK1phqn76w/WtrQgOL6ejg/ZDqWz2igoZti2w0KdgTA7U2pw8PDUVdXx6rv2LFDLyNpn376KTZt2sSqWVtbIyoqCk899VSLj/8oDMPoBLvp06cb7Fo+QkjHJJPJcPXqVdYvuVwOOzs7zJs9G4MaGlBRXwc3ExNoNGr8XV2tfW1hYyNchELMlkiQpVQiS6lEN5EFFA9ZhmS+mzuWX7+Gfra2cDc3R71Gg99KSjCuUydcVdTgXL/+ENwZ0Vt36xaipFLMc3vwqJ2GxwefTyN27QUFOwIA+Oqrr3T2T126dCmGDRvW4mNv27YN77//PqtmamqK33//Hb17927x8R8nKSkJGRkZrBpNwxJCmoNhGJSVlekEuKtXr6KkpOShr6urr4fK1BRhNjb4oLgYx6urESwSaR8/VVODv2tqYF5SAjczMzzn4AATHg8qhnngzRN+VlZY7tkFr1xNg4phIOTxMMvFFX+XSzHIzl4b6gDgOQcHfHzzxkOD3fI/fkdFYyN4PB5++eUXXL58Gc7Oznr4bhEu8BjmEfuakA4hJSUFwcHBaGxs1Na8vLyQmJgI0T0/eJrjjz/+wJQpU6C5Z0HOuz88WmupkXfffRdr1qzRfu3p6Yns7GyDLKlCCGkfGIZBUVHRAwNceXn5Ex/v2WefxUgvLww4fvy+R3gQCAQwNTGBlbU1TDhYV/PvgQPgPWoU7ZndTtCIXQfX0NCA8PBwVqjj8/n48ccfWxzqoqOjMWPGDFaoA4ANGza0Wqh72DQshTpCCHD7Z0ReXt4DA9z9C7S3RFlZGeqCgyG0tII5jwcToRBCEyGEAiGnP48aBQLUWFhAcs9SKaRto2DXwX366ae4cuUKq/b2228jJCSkRcdNTU3FhAkTdK7Ze/fdd/Haa6+16NhPIj4+Hjdv3mTVaBqWkI5HrVbj1q1b2tCWnp6u/W9NTY3ezmNiYgJvb2/4+vrCx8cHvr6+8PX1hb29PX7esQN8d3fYVFXp7XxNJW9sRHhqCqtmwefju8FDwBMKKdi1IxTsOrD4+Hh8+umnrJqfnx8++uijFh03NzcXo0ePRsU9ay4BwPz58/HJJ5+06NhP6v7Rum7duqFPnz6t2gMhpPWoVCrcuHFDZ/Tt2rVrOh80W8Lc3Bw9e/bUBre7v5566imYmJjoPF+tVsPc0hJFYjEcOQh29iYmiOyt+7MvxUEMc0tLiMXiVu+JGAYFuw6qrq4Oc+bMYW38LBQKERERAbOH3BLfFOXl5Rg9ejQKCgpY9fHjx2PLli2tOuXAMIzO3rAvvPACTcMS0g40NDQgMzNTJ8BlZGSgoaFBb+extLRkjbzd/dWlS5cnWuBcIBAgoE8fJJaXwzc3F4L7LlHhgprPR07nzugTHEyLtbcjFOw6qA8++EBnwd733nsPwcHBzT6mUqnE+PHjkZ6ezqoPHDgQe/fuhbCVLwq+ePEicnNzWTXaG5aQtqWurg7Xr1/XCXCZmZmsD6YtZWNjoxPefH190blzZ70tjRQYGIi4mBjkOzrCs7RUL8dsiTxHR6hEIvTq1YvrVogeUbDrgGJjY/HVV1+xar1798Z7773X7GOqVCq88MILuHDhAqvu4+ODQ4cOtfhGjOa4fxrW29ubfoARYqQUCgWuXbumE+Bu3rypcwNWS9jb28PPz08nwLm6uhp8NN/e3h5dvbyQVV6OzmVl4HO4KIWGx8MNTw907dFDL9tFEuNBwa6DUSgUCA8Px72r3JiammLXrl0PvC6kKRiGwaJFi3Do0CFW3d3dHUePHuXk2g2NRoNff/2VVaNpWEK4V1lZqb1x4d6bGG7duqXX80gkEp0bGHx9feHk5MTpz4HQwYOxOysLmW5u8M7P56yPDDc31Dg6YuKgQZz1QAyDgl0Hs3LlSmRlZbFqq1evhr+/f7OP+d5772Hnzp2smp2dHf766y907ty52cdtidjYWJ3r/GgalpDW86BdGK5evarz97Kl3NzcdEbffHx84ODgoNfz6IuLiwv6hoYirq4eLjIZbJRKg5ynuqYG9fX1EFlY6MyYVIpEuN7DC/0GDYKLi4tBzk+4Q8GuAzl58iQ2btzIqg0YMABvvfVWs4+5ceNG1uK/wO27xQ4dOgQ/P79mH7el7p+G9fPz47QfQtqj5u7C0Byenp4PDHC2trZ6PU9rCA0NRdb164iv8sHgK1cg1PONFFVVVahR3F7CpaGhHgqlEnZ2djARCqHi8xHv6wOxm1uLl7UixomCXQdRVVWF+fPns2oWFhaIiIho9t1Qe/fuxeuvv86q8fl87N27F6Ghoc3utaXUajX279/PqtHadYQ0n753YXgYHo+Hp556SifA9ezZE1ZWVno7D9eEQiHGhYXhl4pKXGyox8DUNL1eb1dXz17WpbGxAWVlZTC3tMTVkIGodXHFxLCwVr+hjbQO+l3tIN58803k5OSwamvWrEGPHj2adbwTJ05g9uzZuH9Hui1btiAsLKzZferD2bNnUVxczKpRsCPk8TQaDfLy8ljXwBliFwaBQIDu3bvrBDhvb29YWFjo7TzGzNnZGZNfmI4De/bgPID+aVf1NnInEAihUqlYNbWAj8uBvZBnIYKNqhFOTk56ORcxPhTsOoCoqChs27aNVRs6dGizd4C4cuUKJk+ezNqGDAA+/vhjLFy4sNl96sv9a9cFBQU1O8AS0h7dvwvDvTcyKBQKvZ3nYbsweHl5tWi9zPbC09MTz8+ciYN79+GcqRmC09P1cs2dmZkZ6u8ZtVPY2OBa8NMoFFlg3/5fkZeXh4qKCqxdu7bF5yLGh8fcP+RC2hW5XA5/f38UFhZqa1ZWVkhOTkbXrl2f+Hg3btxAaGiozvUzS5YswcaNGzm/61SlUsHV1RVlZWXa2meffYaVK1dy2BUh3DDWXRgIW3FxMQ5HRkKeX4CemZnwKiho0dRsXX09ZLJyaHg8FPbogcyePVEgkyHyyBGU3lk/z9vbG9euXdPXWyBGhEbs2rmlS5eyQh0ArFu3rlmhrqSkBKNGjdIJdVOnTsWGDRs4D3UAcOrUKVaoA2galrR/bW0XBsLm7OyM8PnzERMTgzhzM+S7OKNbTi46S6XN26HCxAQlnp7I694dUisrxMTFITY2lrWg87Bhw/T4DogxoRG7duzgwYOYMmUKqzZq1ChERUU9cQirrq7GM888g4SEBFb9mWeeQVRUFMzNzVvcrz4sXLgQ27dv13799NNPIy4ujsOOCNGf9rQLA3mwwsJCxMbEIDsjA0KlEp55eXApl8FWoYDJI36PGwUCVFpaoshBjJzOnVFWX4+M7GzExMbqXHM8YsQI/Pnnn0bzc5voFwW7dqqsrAx+fn6s0StbW1ukpqbC3d39iY7V0NCAcePG4fjx46x6YGAgzpw5YzTLDTQ2NkIikUAul2trX375ZYuWcyGECx1hFwbyaHK5HMnJyUiOj0edQgFGpYJVbS1sZHKYqlTgMxpoeHw0CIWoEtujxsICPKEQ5paW6BUcjJdeegmZmZkPPLaTkxPS0tLg6OjYyu+KtAaaim2HGIbBK6+8ojMl+c033zxxqNNoNJg7d65OqOvSpQuioqKMJtQBwPHjx1mhDgCmTZvGUTeEPN79uzDc/XX/HewtZay7MJCHs7e3x9ChQzFo0CDIZDKUlJSgpKQEZcXFqKurg1qlgkAohKm5ObydnSGRSCCRSCAWiyEQCDBkyJCHBrvS0lK88sor2LdvH/3+t0M0YtcO7dmzB7NmzWLVJk6ciIMHDz7RX2KGYbB8+XKsX7+eVXd0dERMTIzR3Wk6d+5cREREaL8eMGAAzp8/z2FHhNxGuzCQ1lZTU4OPPvoIubm5mD17NrZu3YrIyEjWc/bs2YMZM2Zw1CExFAp27UxhYSH8/f1ZI1cODg5IS0uDRCJ5omOtXbsWK1asYNUsLS1x6tQp9O3bVy/96kt9fT0kEglrra2vv/4ay5Yt464p0qHQLgzEmBUXF8PPzw8ymUxbE4vFSE1NpW3F2hmaim1HGIbBokWLdKYjv//++ycOdRERETqhTigU4rfffjO6UAcAx44d01lAlaZhiSHQLgykLXJ2dsb333/PWiVAJpNh0aJFiIyMpCnZdoSCXTuyc+dOHD58mFV74YUXnjjgHDlyBAsWLNCp//jjjxg5cmSLejSU+/eGHTRoENzc3DjqhrQHtAsDaW+mT5+OAwcOsBZxP3ToECIiIjB37lzuGiN6RVOx7UROTg4CAgJQXV2trUkkEqSlpT3R9TUXLlzA8OHDobxv9fN169Zh+fLleutXn2pra+Hk5ISamhptbePGjfj3v//NYVekraBdGEhHIpVK4e/vz7o8wMbGBikpKfDw8OCwM6IvNGLXDmg0GixYsIAV6gBg69atTxTqrl27hnHjxumEurfeestoQx1we8u0e0Mdn8/H1KlTOeyIGCPahYGQ2ze//fDDD5g4caK2VlVVhQULFuDYsWM0JdsOULBrBzZv3owTJ06wanPnzsWECROafIyCggKMGjWKdWEtAPzrX//CF198oZc+DeX+vWGHDh0KZ2dnjrohXKNdGAh5tLCwMISHh7NWETh+/Dg2b96MV155hcPOiD7QVGwbl5WVhcDAQNYom7u7O1JTU5t8h1xFRQUGDx6M1NRUVn3UqFH4888/jXqUQaFQwMnJifX+v//+eyxevJjDrkhr4HIXBh8fH3h4eNAuDKTNqqioQEBAAPLz87U1kUiE5ORkdOvWjcPOSEvRiF0bplarMW/ePJ2p0+3btzc51NXW1iIsLEwn1PXt2xf79+836lAHAIcPH2a9f4FAgOeff57Djoi+0S4MhOifnZ0dtm/fjlGjRmlrSqUS8+bNw6lTp2jUuQ2jYNeGrV+/HtHR0aza4sWLm3znqlqtxqxZs3Du3DlW3cvLC4cPH24TSyrcfzfss88+i06dOnHUDWmJ+3dhuPv/t27d0ut5aBcGQm4bOXIkFi9ejM2bN2tr586dw4YNG4z6umryaDQV20alp6ejd+/eqK+v19a6du2K5OTkJgUyhmGwePFi/PDDD6y6i4sLYmNj0aVLF323rHfV1dVwcnJiXfi+bdu2By7VQowH7cJAiPGoqalBr169kJ2dra2ZmZnhypUr8PHx4bAz0lw0YtcGqVQqhIeHs0Idj8fDjz/+2ORRto8++kgn1NnY2OCvv/5qE6EOAP78809WqBMKhZg8eTKHHZG7aBcGQtoGKysr7Ny5E8OGDcPdcZ76+nqEh4cjNjYWQiHFhLaGfsfaoC+++AJxcXGs2rJlyzBkyJAmvX7z5s346KOPWDVTU1P88ccf6NWrl976bC61Wq2z6XV9bS00ajX4AgHMLCzQydkZR48ehaOjI8rLy8EwDJ577jmIxWKu2+9QaBcGQtq+oUOH4vXXX2ftCx4XF4e1a9fi3Xff5a4x0iw0FdvGJCUloW/fvmhsbNTWvL29ceXKlSatUv/bb79h6tSpuPe3ncfj4ddff+X8pgO5XI6kpCSkJCSgTqEAo1LBqrYWtjIZTFQq8BkGGh4PjUIhKu3tUQpAxTBQ1NUhISUFs2fPxsKFCzl9D+0VwzDIy8t7YICjXRgIaftqa2vRu3dvXL9+XVszMTFBXFwcAgMDOeyMPCkKdm1IQ0MD+vbti+TkZG2Nz+cjNjYW/fv3f+zrz5w5g1GjRrGmcAFg06ZNnK5dVFhYiNjoaGRnZsJEqYRHbh5cZDLYKhQweciyFcraWkhrqqGwtYXc1RV5Hp4w7eSIp3r0QOjgwbSpdTPdvwvD3RsY0tPTWYtAtxTtwkCI8bl48SJCQkJYd5sHBgbi0qVLMDU15bAz8iQo2LUh//3vf/Hpp5+yaitXrsRnn3322NcmJydjyJAhOqMr77//PlavXq3XPptKpVIhJiYGcTExsJJK0T0nF+5SKQRNWMKiXCZDff0/19eZWIig7NEDWZ4eqHF0RN/QUISGhtL1IQ9BuzAQQh7k3XffxZo1a1i1//73v/j444856og8KQp2bcSlS5cQEhLCWng1ICAAcXFxjx3huHXrFkJCQlBUVMSqv/TSS9iyZQsnSzwUFxfjcGQk5PkF6JmZCa+CAvCb+EdRw2hQXFwC4J/n29nZQ2RhAQ2Ph0w3N1zz8oLY3Q1jw8I69C4UtAsDIeRJ1NfXo2/fvkhJSdHWBAIBzp8/j759+3LYGWkqCnZtQG1tLfr06YNr165pa0KhEHFxcQgKCnrka6VSKUJDQ5GRkcGqT5w4Efv37+dkRCsnJwcH9+6FqLAIwenpsLlvgeXHUSqVqKis0H7NAw8SZ2fw7wmoVSIR4n18oHR1xeQXpsPT01Nf7RslLndh8PX1RefOnWkXBkLaiStXrqBfv35QqVTamo+PDxISEmBubs5hZ6QpKNi1AW+99RbWrVvHqq1evRrvv//+I1+nUCjw7LPP4tKlS6z6oEGDcOzYMU4uRs/JycGBPXvgkJOLflevQtiMnQPun4Y1N7eA2N5e53kqPh8X/Xwh8/DA8zNntotwR7swEEJaw+rVq/Hhhx+yam+99Ra+/PJLjjoiTUXBzsidO3cOQ4cOZd3FGhwcjPPnzz/yGqXGxkZMnDgRUVFRrLq/vz/Onj0L+wcEIUMrLi7GL7t2wS77FgampTV56vVeD5qGtbcXw+IhnyI1PB7O+/uhoktXzJgzu81My1ZVVbF2Ybj7S9+7MDg5OT1wBI52YSCkY2tsbMTAgQMRHx+vrfF4PJw9exaDBg3isDPyOBTsjFhNTQ0CAwNx8+ZNbc3MzAzx8fHw8/N76OsYhsHcuXOxa9cuVr1z586IjY2Fu7u7wXp+GJVKhYgdO6C+mo7BV640a6Tu7nFKy0q1X/N4PDhLnB8ZQlR8Ps726Q0THx/MmT/fqG6ooF0YCCHGKi0tDX369GFdj9utWzckJSXB0vL/27vzuCrL/P/jr/ucw3IOiGyxiIKm4MIOLiVaOVqWOkyaqaNjbmWOfmuymql+5WTLTDNjaY3a2KR91W9alpOGmjpN0gLkEiigooAhsiuy75zl94fKeMTdAweOn+fj0R9e3MuHAz2uN9d9X9flZMXKxNV0nB5OtPLCCy+YhTqAN95446qhDuDFF19sFerc3d3ZvXu3VUIdQGJiIuX5BYzIyLjpUAeg1qjRaOzQ68+t4+fs7HzNkSWN0Uj00Qy+dXEhKSnpuhdythTZhUEI0RkFBwfz5ptv8oc//KGl7cSJE7zwwgusWLHCipWJq5ERuw7qP//5D/fff79Z29ChQ/n++++vOstw2bJlrTZv1mq17Nmzh7vuuqtNar2WwsJCNq5dS7/0w/TNz7/l6xmNRuobGlCr1Tg4OHC9DwyPde/O8dAQps2a1Sbr3MkuDEIIW2MwGLjnnntISkoya//Pf/7DyJEjrVSVuBoJdh1QZWUloaGh5OXltbRptVpSU1MJDAy84nkbN25k2rRpZm1qtZovv/ySsWPHtlm917L5s88o3buXET8l39R7dZZiVBTiB0bjeffdPDJxItu3b+eHH35gzJgx3Hfffdd9HdmFQQhxO8nKyiI8PJz6+vqWNn9/f9LT03FxcbFiZeJyJNh1QHPmzOGjjz4ya1u+fDn/8z//c8Vz/v3vfzNu3DizrcYA/vd//5eZM2e2RZnXpby8nNXvv09kykECTp++9glt7KSXFwcjI0g5fJhNmzYB50bAEhMTufvuu82ONRgM5ObmtgpvbbELQ1BQUKsAJ7swCCE6iuXLl/P000+btc2ZM4fVq1dbqSJxJfKOXQezffv2VqFuxIgRzJ8//4rn/PTTT0yYMKFVqHvrrbesGurg3N62dnV1dC8ttWodF3gVFtLs38NssWaTycT7779PcXGxWXg7duyY2V+ot+rSXRguLOjbu3dv2YVBCNGhLViwgC1bthAfH9/StmbNGsaPH2/VJ0KiNRmx60DOnj1LSEgIxcXFLW1dunQhLS2Nnj17XvacrKwsYmJiOHPmjFn7008/zbvvvmvVJSsMBgPvv/cefgcPEWrhZTpulAmorq6mpqaGnNAQUv38eO/992mLX3+dTnfZJURkFwYhRGd28uRJQkNDzZ5Y+Pr6cvjwYdzd3a1YmbiYjNh1IE899ZRZqANYunTpFUNdcXExo0ePbhXqJk+ezLJly9ok1K1cuZIPP/yw5d/19fVkZ2dTWVnZ6sX9srIyGmpr8S0ru+Z1m4xGlpzMYU9ZGY4qFR52drzU6076W2AygMFo5MyZM+yoKOcunQ73oiKcevfGw8OD0lsYSZRdGIQQt5OePXuybNkynnjiiZa2oqIinnrqKTZs2GDFysTFJNh1EJs3b+aTTz4xa3vooYeYM2fOZY+vqqrioYceIicnx6x95MiRrFu3rs2CxYIFC1iwYEHLv2fNmsWECRMuOxuzpKQEk16P6/m/7gwmE+orhM23T+ZQbzCyO3ogGkXhZH09BY2W2Yy+oqIco9HArupq+jo44F9RgUZR8Pb2vq5gJ7swCCHEOXPmzOGLL74wW/x+48aNTJgwgUceecSKlYkL5FFsB3D69GmCg4PNQoarqytHjhyhW7durY5vbGzkoYceMnvXASAyMpJvv/223WYpbd26lddee419+/bR1NTE/PnzycjIwGQy8d5779HQ0MDSP/4Rl59/Jre+gT46HRO8vfljdhZNRiP9nJx5MzAQg8nEfQf2Ez9oME6XeVT5z/w8vjx9GgWY270HsV5e7Kuo4OOiQpb3HwDAUxlH+Y1vN4a4ujJ4749M8PYmobwcdzs73vT24cfKCv50+jR3aDQ4q1TMmDmLf2dlsmfPnpb7uLm5ER4e3hLcNmzYwAsvvEBsbKwEOCGEOK+goICQkBAqKipa2jw9PTly5AheXl7WK0wAIM+LrMxkMvHkk0+2GjlasWLFZUOdwWBg+vTprUJd79692blzZ7uFupKSEp566ik+/vhj7O3tefPNNxk/fjwHDhxg69atzJ8/nzPFxTjU15NZW8eHwcEs6t2bFzKP88fevdkWFY1OrWJDUSGnGhrwdXC4bKhLr65m55lSvoiI5OPQMN47lUtJY+NVa6vQ67nXzZ3tUdF42zuwX9/McOcu9HVw4DVvb/7RvTtuVZV4e3qanbd06VLi4+NZuXIlsbGxFBYWMmrUKAl1QghxET8/P5YvX27WVlpayrx589rkvWVxYyTYWdmGDRvYunWrWduECROYOnVqq2NNJhPPPPMMn3/+uVm7l5cXu3fvxtvbuy1LNfP444+zcOHCll0wvv76a1599VUiIiIYN24cZ8+epba6GrXRyEgPd+xVKqr1epqMJsK7nAufv/Ly5qfKKkwm0xUXGU6uquIBTw8cVCpc7ey4u6sr6ddYasRJreZuV1cAQpydKdEb8PHxQaPR4OjgiEpRoWlqwtHe3uy8C9trNTQ0MHnyZN5++23ZNkcIIS5j2rRpjB8/3qxty5Yt8q5dByDBzooKCgparU3n6enJP/7xj8uOEr311luttnFxdnZm586d9O7du01rvdg///lPamtrWbhwYUubyWRi+/btHDp0iEOHDpGfn49KUVBMJhxV50biLv07zoQJRYEArZbCxkbqDIZr3tsEKIBaUbh4Y7Im43+vbnfRZ6dSFAwmEypFQa1S4+LigrePDzpHR7p3707Pnj3RaDTMnj2bsWPHYjKZmDFjBmPGjGHixIk38ekIIYTtUxSFVatW4XnJk4+nnnrK4ntdixsjwc5KTCYTjz/+eKudCj744IPLvqOwZs0aXn75ZbM2Ozs7tmzZQlRUVJvWerETJ07w+uuvs27dOrPwOWrUKFauXNny79TUVFRqNaaLjnHRaLBXKaRVVwOw7cwZBrp0RadW8ysvL/6a8zOG88P42XV1/FBeTrSLC1+fPUuT0Uilvpl9lRWEdulCNwcHsuvq0JtMlDY1cbC66pq1O6nV1BoMKIBKY0efPn3IycmhqamJNWvWoFKpeOmll9DpdLzyyisW+sSEEMI2eXl5sWrVKrO2iooKnnjiCXkka0US7Kxk9erV7Nq1y6xt6tSpTJgwodWx27ZtY+7cua3a169fz6hRo9qsxsv529/+Rl1dHb/85S+JiIho+W/atGmcPn2a0NBQBgwYwOrVq3HQajFcMjv3L0FBvHYim1+mJFOrNzD1/J6tz/fshVpRuP+nnxiXkszrJ7LxsrcntEsXHvT0ZPyhg0xLS+Np/wC87O3p5ujIvW7ujEtJZvGJbPo7XXtZlAne3ryYlcnEQ4do0miwd3QEaAmo+fn5/PWvf2X//v0t39fu3bst/AkKIYTteOSRR/j1r39t1rZz507WrFljpYqEzIq1ghtZ5DEpKYmRI0fS0GC+9Md7773XanuXjuabb77h+O7d3P/jXmuX0srXd99F39GjZRNrIYS4RWVlZQQHB5utw+rs7Ex6evoV12EVbUdG7NqZ0Whk1qxZrfYaXb16datQd+TIEcaNG9cq1L344osdPtQBeHt7U6PV0tzBdltoVqup0WrbdbKJEELYKnd391Z7xtbU1DB79myMRuMVzhJtRYJdO1u5ciXffvutWducOXMYM2aMWVteXh4PPvgg5eXlZu0zZ87kz3/+c1uXaRHe3t4oGg2VHWxmaaWTE+s//5wpU6aYPU4+evSotUsTQohOaezYscyePdusLT4+nvfff99KFd2+5FFsO8rMzCQiIsJsY3l/f3/S09PN1p8rKytj+PDhrYLG2LFj2bJlS6fZML4j7RV7sfRePSmIiGD+734ne7cKIYSFVFZWEhoaSl5eXkubVqslNTWVwMBAK1Z2e5ERu3ZiMBiYOXOmWagD+Oijj8xC3YWJCZeGurvuuovPPvus04Q6ALVaTWhUFKf8e7SaRGEtBpWK3B49CIuOllAnhBAW1LVrVz766COztvr6embOnInhOpazEpbRMXrb28A777zDjz/+aNa2YMECs5f39Xo9U6ZMISkpyey4fv36sX37dnQ6XbvUaknh4eE063TkX7LWkbXkeXqi1+kICwuzdilCCGFzRo0axfz5883akpKSWLp0qZUquv3Io9h2cOTIEaKiomhqampp6927N6mpqS07G5hMJp544olWU8T9/PxISkrC39+/XWu2pM2ffUbp3r2M+CkZlRV/3YyKQvzAaDzvvpuJjz5qtTqEEMKW1dTUEBERwYkTJ1ra7O3tSUlJadmtSLQdGbFrY83NzTz22GNmoU5RFNatW2e2XdWiRYtahTpXV1d27drVqUMdQMzw4dR4epLl52fVOjL9/Kjx9CRm2DCr1iGEELbM2dmZtWvXmi1i39TUxIwZM2hubrZiZbcHCXZt7K233iIlJcWs7bnnniMmJqbl3ytWrOBPf/qT2TEODg7ExcUREhLSLnW2JV9fXwbFxHAsMJAqKz1OrtTpOB4UyOBhw/A9vyiyEEKItjFs2DCeffZZs7bk5GT+8pe/WKmi24c8im1DKSkpDBkyBL1e39LWv39/UlJScDy/68Hnn3/O5MmTzbZfUalU/Otf/+Lhhx9u75LbjF6vZ91HH2E4msHwgwfRtOPaRnqViu+jIrHr35/HZs9Go9G0272FEOJ2VV9fT1RUFMeOHWtp02g07N+/n8jISCtWZttkxK6NNDY2MmPGDLNQp1arWbduXUuoi4+P5ze/+U2rPfVWrVplU6EOzv3PPDY2lrpu3dgXPADjRUP0bcmoKOwLHkC9bzfGxMZKqBNCiHai1WpZt26d2QoEer2eGTNm0NjYaMXKbJsEuzayePFiDh8+bNb20ksvMWjQIAAOHjzIr371K7N37wBee+01nnjiiXarsz35+PgwfvIkyvz9+TEkGH0bL4GiV6n4MSSYMn9/xk+ehI+PT5veTwghhLnBgwfz4osvmrWlp6fz2muvWaki2yePYtvA3r17iYmJMdtKJTw8nP3792Nvb8/PP//M0KFDKSkpMTvvt7/9LStXrjR74dQW5ebmsmXTZ+gKC4nOyMClrs7i96jU6Uge0J96326MnzyJgIAAi99DCCHEtTU1NTFo0CDS0tJa2lQqFUlJSQwZMsSKldkmCXYWVldXR2RkJJmZmS1tdnZ2/PTTT4SFhXH69GliYmLIzs42O++RRx5h06ZNt82iucXFxeyIi6M8v4B+WVkEFhRYZCkUo6KQ6efH8aBA3P38GBMbKyN1QghhZampqQwaNMhsVmzfvn05ePAgWq3WipXZHnkUa2Evv/yyWaiDc49lw8LCqK6uZuzYsa1C3b333svHH39824Q6OPdYdsbs2Qwa+QuOhYYQPzCak15eN71DhUGl4qSXF/EDozkeGsLgkSN5bPZsCXVCCNEBhIeH8+qrr5q1HT9+nJdfftlKFdkuGbGzoO+++4777rvPrG3w4MEkJiZiNBoZN24cX3/9tdnXw8LC+O6773B1dW2/QjuYwsJCkhITycnMRFNXR0BeHr5ny+haW4vdVbahaVarqXRyosjDndwePdDrdPQKCiJGljQRQogOR6/XM3ToUA4cONDSpigK3377Lffcc48VK7MtEuwspLq6mvDwcHJyclraHB0dOXjwIEFBQUyfPp2NGzeanRMQEEBSUhLdunVr73I7pPLyctLS0khLTqahthaTXo9zfT0uZeXY6/WoTEaMioomjYYqdzdqtFoUjQZHJyfCoqMJCwvDzc3N2t+GEEKIK8jIyCAyMtJsVmyvXr1IS0vD2dnZipXZDgl2FjJv3jw++OADs7Z33nmHhQsX8txzz7Fs2TKzr3l6epKYmEhQUFB7ltkpGAwGysrKKCkpoaSkhDPFxTQ1NGDQ61FrNNg7OnKHjw/e3t54e3vj7u5+Wz3GFkKIzmzp0qU899xzZm3z5s3jH//4h5Uqsi0S7Cxg9+7dPPjgg2Ztw4cPJz4+nqVLl/KHP/zB7Gs6nY74+HgGDx7cnmUKIYQQVmcwGLjvvvtISEgwa9+9ezcPPPCAlaqyHRLsblFFRQUhISEUFBS0tOl0OtLS0khKSuKxxx4zO16j0bBt27ZWQVAIIYS4XZw4cYKwsDDqLlruqnv37qSnp9/W75xbgsyKvUXPPPOMWagDePvtt8nMzGT27Nmtjv/oo48k1AkhhLit9e7dmyVLlpi15efns3DhQitVZDsk2N2CuLg41q1bZ9Y2atQoIiMjmThxotl2YgBLlixh+vTp7VmiEEII0SHNmzePUaNGmbWtXbuWuLg4K1VkG+RR7E0qLS0lJCTEbPcIFxcXtm7dyqOPPsrZs2fNjn/22Wd555132rtMIYQQosM6deoUoaGhVFVVtbR5e3tz5MgRPDw8rFhZ5yUjdjdpwYIFrbYEW7x4MbNmzWoV6qZNm9ZqyFkIIYS43fn7+/Puu++atZWUlLBgwQLrFGQDZMTuJmzatIkpU6aYtY0ePZqCggIOHz5s1v7AAw+wbds27O3t27NEIYQQolMwmUzExsayfft2s/ZNmzYxadIkK1XVeUmwu0HFxcUEBwdTVlbW0ubm5kZQUBD79u0zO3bgwIHEx8fLootCCCHEVRQVFREcHEx5eXlLm4eHB0eOHMHb29uKlXU+8ij2BphMJp588kmzUAfQp0+fVqEuMDCQHTt2SKgTQgghrsHX15eVK1eatZ09e5a5c+ci4083RoLdNRiNRurr6wFYv359q9k6d955p9m+d3Bug/vdu3fj5eXVbnUKIYQQndmUKVOYOHGiWVtcXBz/93//Z6WKOid5FHsVX331FdOmTaO+vp5Jkybx5Zdfms3ccXJyora21uycLl268P333xMREdHO1QohhBCd25kzZwgODubMmTMtbV27duXw4cN0797dipV1HhLsrqJPnz6cOHHiuo+3t7dn165djBgxog2rEkIIIWzX1q1bGT9+vFnbAw88wK5du1AUxUpVdR63RbC73KbyjfX1GA0GVGo1Dlptq03lq6urcXNzu+57KIrCZ5991moYWQghhBA3Zvr06Xz88cdmbR988AFz5841a7uZ/l2tVrfnt9LubDrYlZeXk5qaSnpKCg21tZj0epzr6+laVoadXo/KZMKoKDRrNFS6u1Oj1aJoNDg6OXFHt27MmzePysrK67rXihUrZN0dIYQQwgLKy8sJCQmhsLCwpc3JyYn09HR69ep1S/17aFQU4eHhNzR405nYZLArLCwkKSGBnKws7Orq8D+Vh29ZGV1ra7EzGK54XrNaTaWTE0Xu7vzczZcyg4GsnBwSkpIoLi6+4nmvvPIKb7zxRlt8K0IIIcRtadeuXTz00ENmbWPHjmXG9OmczM6+6f79lH8PmnU6egUGEjN8OL6+vm39rbQrmwp2er2exMREDiQm4lxaSp/cU3QvLUVtNN7wtSrr68hxceFUYCClzs4kHjhAUlIShkt+cR5//HH++c9/ynN/IYQQwsLmzp3Lhx9+iFqtZujQocQMGkS3pib6FxbddP9uUKnI9/QkO8CfGk9PBsXEEBMTg0ajaYPvoP3ZTLArLi5mR1wc5fkF9MvKIrCgANUtfGvl5eXUN9RjVBQKg4LI6tePgrIy4r76itOnTwMwfPhw9uzZYzO/DEIIIURHUl1dzb333kt0RAR+bm4EHjuGX2YW3p6et9z3GhWFLD8/jgUG4t7djzGxsfj4+FiocuuxiWCXm5vLlk2b0BUWEZ2RgUtd3S1fs6SkBIPxv6NzdS4uZERHU6jT8fnWrXh7e/Pdd9+h1Wpv+V5CCCGEaC03N5dP16/H7tQp+icnozu/5Ji9nT0enp5Y4llZlU5Hcv/+1HXrxvjJkwgICLDAVa2n0we73Nxc/vXJJ3jknmLw0aNobmJY9nKKS0owGs0fuxrUajKGDKHQ25spjz1GaGioRe4lhBBCCHMX9+/99u6lobrK7OsuXVwstruTXqViX/AAyvz9eeTXv+7U4a5T7zxRXFzMlk2bcM89xV1Hjlgs1MG5hYYvpTYYCN23nz41NXyzc9dVJ1QIIYQQ4uZc2r+7OTmhUZs/eq2qrqZZr7fI/TRGI3cfPoL7qVNs2fRZp+7fO22w0+v17IiLQ1dYxJCjR2/pfbrLMRha/7KoFBV3uLtz19EMtEWFfBUXh95Cv1RCCCGEuHz/rigKrq6uYPbw1URFRTmW6v1VJhNDjhzt9P17pw12iYmJlOcXEJ2RYdGROoDaujpqamrM2hQU3N3d0Wg0aIxGoo9mUFZQQFJSkkXvLYQQQtzOrtS/29vb4+zkZHZsc3MzzU1NFru3LfTvnTLYFRYWciAxkX5ZWRaZKHExE+dm4ZhTcHN3w97evqWla10dfTOz2J+QQFFRkUVrEEIIIW5H1+rfu7h0QaOxM2uz9ESBzt6/d8pgl5SQgHNpKYEFBRa/tgJwyWNdV9euODo4tjo2qKAA59JSEhMSLF6HEEIIcbu5Vv9+4emZncYOUHDSOZkNulhKZ+7fO90CbOXl5eRkZRGZe8ri79Vd0LVrVyoqKwAFFxcXdFrdZY9TmUz0zj3FIQ8PysvLbXZ7EiGEEKKtXW//rlGrueOOO9q0ls7cv3e6EbvU1FTs6uroXlraZvfQarX4+vji4+ODk+7yoe6CHqWlaOrqSEtLa7N6hBBCCFvXHv37jeis/XunCnYGg4H0lBT8T+Xd1DYiN+p6Fj5UG40E5OWRlpzcarsxIYQQQlxbe/fv16Oz9u83Hew0Gg0RERGEhITw6KOPUmfhSQyXWrlyJeHh4by1ZAmTNnxM7MEUYg+mEHd+ey9L2l9ZwZiUZCYeOnRdx/ueLaOhtpZNmzbxi1/8grCwMD799FOL1lRTU8PIkSNxdnbm+eeft+i1hRBCiGt5/fXXCQkJITQ0lIEDB5KTk3PFYz09PW/o2mVl5/rRbw4cMGvvn/ADsQdTGJuSzNMZGdS3d8DKy2ffvn2UlZUBEBcXx7JlywCYOXMm27dvv+FLLliwAC8vLwYOHGjRUi+46WDn6urKoUOHOHz4MPb29qxatcqSdbUyb948Pv30U56ZO5cuajVxkVHERUYR6+UFgMGC79ttP3OG+T16sDki4prHmgBNaSl11TX8+c9/Jj4+nvT0dKZNm0ZJSckN3/tKfxXY2dnx6quvsmTJkhu+phBCCHErkpKS+Pbbbzl06BDp6els3br1/LpyllFSUoJJr2fjiWyz9i4aDXGRUeyIisZOpfBJ8fXNUrVUJqgqLyM1Pb2lP4+NjWXhwoW3dM2pU6eyc+dOS5R3WRaZPDF8+HDS0tIoLS1l1qxZ5Obm4u7uztq1a/Hz8yM4OJjMzEwyMzPp27cvhYWFeHt7ExgYSHZ2NqdPn+bJJ58kPz8fR0dHVq9eTb9+/Zg5cyYeHh4kJyczevRoBg8ejHN9fct98xsa+O3Ro4R16UJadRX/iojkqYwMTjc10WQy8rR/AKM9PVuO6+/sRFp1NX2dnHi3bz8UReGvOT+zp6wMe0XhIc878HVwYGdpKQnlFeyvrOT/9bqTV7KzOVZbg4NKxRt9Ahng7Mzfc3Mprq8np66WADs7TlaUU1Fb21Kb0Wjk4Ycfpri4mAcffJDf//73AGzevJkNGzbQ2NjI/fffz8KFC8nPz+fJJ58kLCyM1NRUtmzZgoODQ6vPuXv37uzdu5eKigp+/vlnS/zohBBCiGtKTU3F3t6eU6dOtbQ1NTXxxRdfsGLFChobGwkLC+PNN99EpVJhNBpb+qmVK1fy9ddf09TUxG9+8xumTp0KwPLly9mxYwdqtZqhQ4eSf+QI1Xo9v0xJJqqLC4vuvBMAvcGAWqVioEtXjtfWUmswsDg7mxP1554UvnznnUS7dOXvubmUNjeRW99AH52Oqb6+vJqdTaVej71KYV1IKIqiXPHc4qZGTtbXU9zYxLM9Axh3hxfLf87hRFMjDz/8MC+//DKKonD48GHefvtts89n//79PPfcc9TW1tKrVy/WrVt3xe3OYmJiOHnypEV/Phe75WCn1+vZuXMnDz74IIsXL2b48OFs27aNTZs28fTTTxMXF4efnx85OTkkJCQQFRVFQkICffv2JTg4GEVReOaZZ1i0aBHR0dEcOHCAZ555hl27dgGQl5dHfHw8iqLw6YYNdD0/HHpBdl0tS/r2pZ9TIAB/DQrC1c6Oar2eiamHeMDDA4Cf6+t4t18/7tRqmZ6ezk9VVfTR6fiqtJT4gYNQKQrVej1dNBr2VlbwoKcnI9w9WJOfj7NazfaoaA5VVfFCZiZfRkZSU1vDsbo6lnXrhp2i8FJzM3Ya849z7969AKxateqyI5rHjx9nxYoVLf8+duwYAAMGDLjm575mzZrr/REJIYQQFtG7d+8rfi0jI4NNmzZd9dhFixaxaNGiVu0D+vblkTvvZO+BA3zg6wvA6dMlGI1GTp8uwYDCntIzjPD05P28U9zv4cESz74UNzbyxJEjbIuKAiCzto71oaHYq1RMOHSQ5wJ6EuPmRo1ej6NazbLck1c8N7+hgfWhYRQ2NDD7yGHG3eHFwoAA/l5TzbOvvcaUadNYu3Ztq9qbmpp4/vnniYuLw83NjSVLlrBixQpefPHFG/psLeWmg11FRQUR5x9VDh8+nDlz5jB48GC++uorACZNmsTvfvc74Fw6TUhIICEhgRdeeIGEhATOnDlDTEwMAHv27CEjI+Oy95k4cSKKcm4aQ2N9PdpLtvjoqdXS76KVqNcWFvDN2XPhr6ixkTPNzQD00mrpfX6G6wBnJwoaG4h0caGLWs1LWZmM8vBghLtHq/v/VFXFE927AxDh4kKj0UhRRQUmk4kYnQ6787UpRiO9AgI4mZt7g5+kEEIIcXvr6uKC5jLv6tcYjczJywMgzFHLI94+TElN5fuyMlbknRs9rNA303R+wsVID3fsVSpq9Hqq9Hpizi9T4nx+4CWpvOKK597r5o5GUfDXaqm6KGuoTCaaGhquWPvx48dJS0tjxIgRwLmgd999993Kx3FLbjrYXXjH7mouBLKYmBi+/PJLsrKy+PDDD1m1ahWlpaXMnz+/5djk5GTUanWra+guWm7EaDC0WttGe9E5eysqSKmq4vPwcBzVakYn/9TyA7NX/fd1QpWiYDSBRlH4IiKSxIpyvjx9mrjTp1ne/+qjZSZMLd+Xw0XXVEymViN2QgghhLg2jUqFcpnZsM4qFWt69ABAUVTYq1SYMPHBgGC6ObbeOMBR9d9McLmVLa527sU5wfwkE4ar7BtrMpmIiopiz549VzymPVl0uZNhw4axceNG4Ny7ZIMHDwZg6NCh7Ny5Ey8vL9RqNVqtlsTExJYZIffeey8ffPABcO7dtPT09MsXq1ZjVK68CEmNwYCrxg5HtZrU6mpOXvQ+3uXUGgxU6/WMcPfgxV53knHRO3IXDHRxYduZczNvU6ur0arV+Lq6orrkF8CkKBg7yBRtIYQQojOpra/HpFKhVpTLTnxQq9S4urqiAENd3dhw0VZfGZfs7Q7nRuhcNBoSy8sBqNHr0ZtM13XuxZw0aur1etRXGbjp168fubm5LYNdtbW1ZGdnX/H4tmbRIabFixczc+ZM1q9f3zJ5As7t5NC1a9eWR69DhgyhoqKiZYLA8uXLmTdvHqtWrUKv1/PYY48RGhra6voOWi3NV/lwh7u5saGokNiDKfRzciJI53TFY+FcsPvt0SM0Gc/9Ev2+Z69Wx0zz9eWV7Cx+mZKMvUrFXwKDUAAnnRNOKgUHewcamxoxqVQ0XZLod+3axT333ENsbCyvvvoq0dHRbNiwgb///e8YjUacnZ1Zv349RqORqVOnkpiYeNV6o6OjKS4uprm5GRcXFxISEvDx8bnqOUIIIcStSklJ4dlnn23ZSz0yMpLly5fzww8/sHjxYvR6PRqNhpUrVxIZGUmPHj3IO/8IdenSpXzyySeYTCbuuOMONm/ejJOTE2+99RabN29Go9GgsbfH3sGBCT4+zC0qYkhXV17t3RtVbi6+Pr4tT8oAFvj788aJE4xLScZgMnG3qyt/dO7TquYlQX1ZlJ3FX3JycFCpWBcaet3nXtBX50SzycQfX38dg0plVscF9vb2fPrpp8yfP5+a80Hxb3/7G336XP66jz/+ODt27ODs2bN0796d5cuXM378+Ov/YVyDYjK10b5cbeCbb77h+O7d3P/jXmuXYqapuYldAwfyVUZGy1Csg4MDRUVFnWobEiGEEMIaOmr/DvD13XfRd/RoRo4cae1SrkuneinM29ubZK2WZrUauw60CrTiqMXg4cGCBQvo1q0bZ86c4fnnn5dQJ4QQQlyHjtq/N6vV1Gi1eHt7W7uU69bpgp2i0VDp5IRnVZW1y2lR6eSEotEwfPhwJkyYcEvXOnv2bKu/CnQ6HUlJSbd0XSGEEKKj6uj9+80Eu/Hjx7fanWPjxo3XtaTZrehUwc7d3R1HJyeK3N071A++yONcXe7u7rd8LQ8Pj2vONhZCCCFsiS3271u2bGmDiq7NorNi25parSY0KopT/j0wXGlacjszqFTk9uhBWHT0ZZdrEUIIIcTVSf9uOR3j07sB4eHhNOt05N/gBsNtJc/TE71OR1hYmLVLEUIIITot6d8to9MFOzc3N3oFBpId4H/VNe3ag1FROBHgT6+gIJkoIYQQQtwC6d8to9MFO4CY4cOp8fQky8/PqnVk+vlR4+lJzLBhVq1DCCGEsAXSv9+6ThnsfH19GRQTw7HAQKou2nKsPVXqdBwPCmTwsGH4nt+wWAghhBA3T/r3W9cpgx2c23/Wrbsfyf37o2/nFy31KhXJA/rj7ufH0KFD2/XeQgghhC2T/v3WdNpgp9FoGBsbS123buwLHtBuz+ONisK+4AHU+3ZjTGwsmqtscSaEEEKIGyP9+63ptMEOwMfHh/GTJ1Hm78+PIcFtnuz1KhU/hgRT5u/P+MmTZJ9WIYQQog1I/37zOtVesVeSm5vLlk2foSssJDojA5e6Oovfo1KnI3lAf+p9uzF+8iQCAgIsfg8hhBBC/Jf07zfOJoIdQHFxMTvi4ijPL6BfVhaBBQWoLPCtGRWFTD8/jgcF4u7nx5jY2E6d5IUQQojORPr3G2MzwQ5Ar9eTmJjIgcREnEtL6Z17ih6lpaiNxhu+lkGlIs/TkxMB/tR4ejJ42DCGDh3aaZ+5CyGEEJ2V9O/Xz6aC3QWFhYUkJSaSk5mJpq6OgLw8fM+W0bW2FjuD4YrnNavVVDo5UeThTm6PHuh1OnoFBRHTSac8CyGEELZE+vdrs8lgd0F5eTlpaWmkJSfTUFuLSa/Hub4el7Jy7PV6VCYjRkVFk0ZDlbsbNVotikaDo5MTYdHRhIWFdboVp4UQQghbJ/37ldl0sLvAYDBQVlZGSUkJJSUlnCkupqmhAYNej1qjwd7RkTt8fPD29sbb2xt3d/dOteGvEEIIcTuS/r212yLYCSGEEELcDjr1OnZCCCGEEOK/JNgJIYQQQtgICXZCCCGEEDZCgp0QQgghhI2QYCeEEEIIYSMk2AkhhBBC2AgJdkIIIYQQNkKCnRBCCCGEjZBgJ4QQQghhIyTYCSGEEELYCAl2QgghhBA2QoKdEEIIIYSNkGAnhBBCCGEjJNgJIYQQQtgICXZCCCGEEDZCgp0QQgghhI2QYCeEEEIIYSMk2AkhhBBC2AgJdkIIIYQQNkKCnRBCCCGEjZBgJ4QQQghhIyTYCSGEEELYCAl2QgghhBA2QoKdEEIIIYSNkGAnhBBCCGEjJNgJIYQQQtgICXZCCCGEEDbi/wNil0xBGoLeTgAAAABJRU5ErkJggg==", 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", 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", 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", 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", 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", 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", 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for i in range(0,50):\n", + " ind.mutate()\n", + " if i%5==0:\n", + " est = ind.export_pipeline()\n", + " est.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### TreePipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TreePipelines work the same way as GraphPipelines, but they are limited to a tree structure. This is similar to the search space in the original TPOT.\n", + "\n", + "(This search space is still experimental and currently built off GraphSearchPipeline. It may be rewritten with its own code in the future.)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tree_search_space = tpot2.search_spaces.pipelines.TreePipeline(\n", + " root_search_space= tpot2.config.get_search_space([\"KNeighborsClassifier\", \"LogisticRegression\", \"DecisionTreeClassifier\"]),\n", + " leaf_search_space = tpot2.config.get_search_space(\"selectors\"), \n", + " inner_search_space = tpot2.config.get_search_space([\"transformers\"]),\n", + " max_size = 10,\n", + ")\n", + "\n", + "ind = graph_search_space.generate()\n", + "exp = ind.export_pipeline()\n", + "exp.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tips and Tricks\n", + "\n", + "* Two very helpful transformers to use with search spaces are tpot2.buildin_models.Passthrough and tpot2.builtin_models.SkipTransformer. \n", + " Passthrough will simply pass through the exact inputs it receives into the next step. This is particularly useful inside UnionSearchSpace as it allows for both the transformed data as well as the original data to be passed into the next step.\n", + " SkipTransformer will always return nothing. This is helpful when inside a union with Passthrough and an optional second method. For example, if you are unsure of whether or not you will need a transformer, you can have SkipTransformer be one option that will skip the transformation step if selected." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example, the FeatureUnion layer will always have at least one transformer selected and will always have one passthrough" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ { "data": { "text/html": [ - "
Pipeline(steps=[('variancethreshold',\n",
-       "                 VarianceThreshold(threshold=0.0031706560759)),\n",
-       "                ('fastica', FastICA(n_components=8)),\n",
-       "                ('histgradientboostingclassifier',\n",
-       "                 HistGradientBoostingClassifier(early_stopping=False,\n",
-       "                                                l2_regularization=5.767e-10,\n",
-       "                                                learning_rate=0.0104696477137,\n",
-       "                                                max_features=0.1498293764962,\n",
-       "                                                max_leaf_nodes=1795,\n",
-       "                                                min_samples_leaf=40, tol=0.0001,\n",
-       "                                                validation_fraction=None))])
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" + "
FeatureUnion(transformer_list=[('featureunion',\n",
+       "                                FeatureUnion(transformer_list=[('powertransformer',\n",
+       "                                                                PowerTransformer()),\n",
+       "                                                               ('passkbinsdiscretizer',\n",
+       "                                                                PassKBinsDiscretizer(n_bins=11,\n",
+       "                                                                                     strategy='uniform'))])),\n",
+       "                               ('passthrough', Passthrough())])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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" ], "text/plain": [ - "Pipeline(steps=[('variancethreshold',\n", - " VarianceThreshold(threshold=0.0031706560759)),\n", - " ('fastica', FastICA(n_components=8)),\n", - " ('histgradientboostingclassifier',\n", - " HistGradientBoostingClassifier(early_stopping=False,\n", - " l2_regularization=5.767e-10,\n", - " learning_rate=0.0104696477137,\n", - " max_features=0.1498293764962,\n", - " max_leaf_nodes=1795,\n", - " min_samples_leaf=40, tol=0.0001,\n", - " validation_fraction=None))])" + "FeatureUnion(transformer_list=[('featureunion',\n", + " FeatureUnion(transformer_list=[('powertransformer',\n", + " PowerTransformer()),\n", + " ('passkbinsdiscretizer',\n", + " PassKBinsDiscretizer(n_bins=11,\n", + " strategy='uniform'))])),\n", + " ('passthrough', Passthrough())])" ] }, - "execution_count": 26, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "selector_choicepipeline = tpot2.config.get_search_space(\"selectors\")\n", - "transformer_choicepipeline = tpot2.config.get_search_space(\"transformers\")\n", - "classifier_choicepipeline = tpot2.config.get_search_space(\"classifiers\")\n", + "from tpot2.search_spaces.pipelines import *\n", + "from tpot2.config import get_search_space\n", "\n", - "stc_pipeline = tpot2.search_spaces.pipelines.SequentialPipeline([\n", - " selector_choicepipeline,\n", - " transformer_choicepipeline,\n", - " classifier_choicepipeline,\n", - "])\n", + "#This FeatureUnion layer will always have at least one transformer selected and will always have one passthrough\n", + "transformers_with_passthrough = UnionPipeline([\n", + " DynamicUnionPipeline(get_search_space([\"transformers\"])),\n", + " get_search_space(\"Passthrough\")\n", + " ]\n", + " )\n", "\n", - "print(\"sampled pipeline\")\n", - "stc_pipeline.generate().export_pipeline()" + "transformers_with_passthrough.generate().export_pipeline()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example, the FeatureUnion layer will always one passthrough. In addition, it may select one or more transformer, but it may skip transformers altogether and only include a Passthrough. " ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 47, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sampled pipeline\n" - ] - }, { "data": { "text/html": [ - "
Pipeline(steps=[('selectpercentile',\n",
-       "                 SelectPercentile(percentile=8.477077621045)),\n",
-       "                ('passkbinsdiscretizer',\n",
-       "                 PassKBinsDiscretizer(n_bins=22, strategy='uniform')),\n",
-       "                ('lineardiscriminantanalysis',\n",
-       "                 LinearDiscriminantAnalysis(shrinkage=0.8475252442099,\n",
-       "                                            solver='eigen'))])
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" + "
FeatureUnion(transformer_list=[('skiptransformer', SkipTransformer()),\n",
+       "                               ('passthrough', Passthrough())])
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" ], "text/plain": [ - "Pipeline(steps=[('selectpercentile',\n", - " SelectPercentile(percentile=8.477077621045)),\n", - " ('passkbinsdiscretizer',\n", - " PassKBinsDiscretizer(n_bins=22, strategy='uniform')),\n", - " ('lineardiscriminantanalysis',\n", - " LinearDiscriminantAnalysis(shrinkage=0.8475252442099,\n", - " solver='eigen'))])" + "FeatureUnion(transformer_list=[('skiptransformer', SkipTransformer()),\n", + " ('passthrough', Passthrough())])" ] }, - "execution_count": 24, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "print(\"sampled pipeline\")\n", - "stc_pipeline.generate().export_pipeline()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## DynamicLinearPipeline\n", + "final_transformers_layer =UnionPipeline([\n", + " ChoicePipeline([\n", + " DynamicUnionPipeline(get_search_space([\"transformers\"])),\n", + " get_search_space(\"SkipTransformer\"),\n", + " ]),\n", + " get_search_space(\"Passthrough\")\n", + " ]\n", + " )\n", "\n", - "DynamicLinearPipeline takes in a single search space and randomly samples and places estimators in a list without a predefined sequence. DynamicLinearPipeline are most often used when paired with LinearPipeline. A common strategy is to use DynamicLinearPipeline to optimize a series of preprocessing or feature engineering steps, followed by a final classifier or regressor." + "final_transformers_layer.generate().export_pipeline()" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 52, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sampled pipeline\n" - ] - }, { "data": { "text/html": [ - "
Pipeline(steps=[('rfe',\n",
-       "                 RFE(estimator=ExtraTreesClassifier(class_weight='balanced',\n",
-       "                                                    criterion='entropy',\n",
-       "                                                    max_features=0.5499673528175,\n",
-       "                                                    min_samples_leaf=15,\n",
-       "                                                    min_samples_split=20,\n",
-       "                                                    n_jobs=1),\n",
-       "                     step=0.719969257916)),\n",
-       "                ('zerocount', ZeroCount())])
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" + "
FeatureUnion(transformer_list=[('featureunion',\n",
+       "                                FeatureUnion(transformer_list=[('estimatortransformer-1',\n",
+       "                                                                EstimatorTransformer(cross_val_predict_cv=10,\n",
+       "                                                                                     estimator=RandomForestClassifier(criterion='entropy',\n",
+       "                                                                                                                      max_features=0.0291036830622,\n",
+       "                                                                                                                      min_samples_leaf=10,\n",
+       "                                                                                                                      min_samples_split=20,\n",
+       "                                                                                                                      n_estimators=128),\n",
+       "                                                                                     method='predict')),\n",
+       "                                                               ('estimatortransformer-2',\n",
+       "                                                                EstimatorTransformer(cross_val_predict_cv=10,\n",
+       "                                                                                     estimator=QuadraticDiscriminantAnalysis(reg_param=0.6791389504331),\n",
+       "                                                                                     method='predict')),\n",
+       "                                                               ('estimatortransformer-3',\n",
+       "                                                                EstimatorTransformer(cross_val_predict_cv=10,\n",
+       "                                                                                     estimator=QuadraticDiscriminantAnalysis(reg_param=0.8087868529112),\n",
+       "                                                                                     method='predict'))])),\n",
+       "                               ('passthrough', Passthrough())])
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" ], "text/plain": [ - "Pipeline(steps=[('rfe',\n", - " RFE(estimator=ExtraTreesClassifier(class_weight='balanced',\n", - " criterion='entropy',\n", - " max_features=0.5499673528175,\n", - " min_samples_leaf=15,\n", - " min_samples_split=20,\n", - " n_jobs=1),\n", - " step=0.719969257916)),\n", - " ('zerocount', ZeroCount())])" + "FeatureUnion(transformer_list=[('featureunion',\n", + " FeatureUnion(transformer_list=[('estimatortransformer-1',\n", + " EstimatorTransformer(cross_val_predict_cv=10,\n", + " estimator=RandomForestClassifier(criterion='entropy',\n", + " max_features=0.0291036830622,\n", + " min_samples_leaf=10,\n", + " min_samples_split=20,\n", + " n_estimators=128),\n", + " method='predict')),\n", + " ('estimatortransformer-2',\n", + " EstimatorTransformer(cross_val_predict_cv=10,\n", + " estimator=QuadraticDiscriminantAnalysis(reg_param=0.6791389504331),\n", + " method='predict')),\n", + " ('estimatortransformer-3',\n", + " EstimatorTransformer(cross_val_predict_cv=10,\n", + " estimator=QuadraticDiscriminantAnalysis(reg_param=0.8087868529112),\n", + " method='predict'))])),\n", + " ('passthrough', Passthrough())])" ] }, - "execution_count": 29, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "import tpot2.config\n", - "\n", + "inner_estimators_layer = UnionPipeline([\n", + " ChoicePipeline([\n", + " DynamicUnionPipeline(wrapped_estimators, max_estimators=4),\n", + " get_search_space(\"SkipTransformer\"),\n", + " ]),\n", + " get_search_space(\"Passthrough\")]\n", + " )\n", "\n", - "linear_feature_engineering = tpot2.search_spaces.pipelines.DynamicLinearPipeline(search_space = tpot2.config.get_search_space([\"all_transformers\",\"selectors_classification\"]), max_length=10)\n", - "print(\"sampled pipeline\")\n", - "linear_feature_engineering.generate().export_pipeline()" + "inner_estimators_layer.generate().export_pipeline()" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 53, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sampled pipeline\n" - ] - }, { "data": { "text/html": [ - "
Pipeline(steps=[('robustscaler',\n",
-       "                 RobustScaler(quantile_range=(0.181922714148,\n",
-       "                                              0.9343902611602))),\n",
-       "                ('variancethreshold',\n",
-       "                 VarianceThreshold(threshold=0.0001632138019)),\n",
-       "                ('minmaxscaler', MinMaxScaler())])
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" + "}\n", + "
Pipeline(steps=[('robustscaler',\n",
+       "                 RobustScaler(quantile_range=(0.1562687943568,\n",
+       "                                              0.8028910581685))),\n",
+       "                ('featureunion-1',\n",
+       "                 FeatureUnion(transformer_list=[('skiptransformer',\n",
+       "                                                 SkipTransformer()),\n",
+       "                                                ('passthrough',\n",
+       "                                                 Passthrough())])),\n",
+       "                ('featureunion-2',\n",
+       "                 FeatureUnion(transformer_list=[('skiptransformer',\n",
+       "                                                 SkipTransformer()),\n",
+       "                                                ('passthrough',\n",
+       "                                                 Passthrough())])),\n",
+       "                ('baggingclassifier',\n",
+       "                 BaggingClassifier(bootstrap_features=True,\n",
+       "                                   max_features=0.1392808422872,\n",
+       "                                   max_samples=0.5344888038724, n_estimators=3,\n",
+       "                                   n_jobs=1, oob_score=True))])
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" ], "text/plain": [ "Pipeline(steps=[('robustscaler',\n", - " RobustScaler(quantile_range=(0.181922714148,\n", - " 0.9343902611602))),\n", - " ('variancethreshold',\n", - " VarianceThreshold(threshold=0.0001632138019)),\n", - " ('minmaxscaler', MinMaxScaler())])" + " RobustScaler(quantile_range=(0.1562687943568,\n", + " 0.8028910581685))),\n", + " ('featureunion-1',\n", + " FeatureUnion(transformer_list=[('skiptransformer',\n", + " SkipTransformer()),\n", + " ('passthrough',\n", + " Passthrough())])),\n", + " ('featureunion-2',\n", + " FeatureUnion(transformer_list=[('skiptransformer',\n", + " SkipTransformer()),\n", + " ('passthrough',\n", + " Passthrough())])),\n", + " ('baggingclassifier',\n", + " BaggingClassifier(bootstrap_features=True,\n", + " max_features=0.1392808422872,\n", + " max_samples=0.5344888038724, n_estimators=3,\n", + " n_jobs=1, oob_score=True))])" ] }, - "execution_count": 30, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "print(\"sampled pipeline\")\n", - "linear_feature_engineering.generate().export_pipeline()" + "final_linear_pipeline = SequentialPipeline([\n", + " get_search_space(\"scalers\"),\n", + " final_transformers_layer,\n", + " inner_estimators_layer,\n", + " get_search_space(\"classifiers\"),\n", + " ])\n", + "\n", + "final_linear_pipeline.generate().export_pipeline()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Optimize Search Space with TPOTEstimator\n", + "\n", + "Once you have constructed a search space, you can use TPOTEstimator to optimize a pipeline within that space. Simply pass that search space into the `search_space` parameter. Here is a cell where you can select different search spaces that we created in this tutorial." ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "all_search_spaces ={\n", + " \"classifiers_only\" : classifier_choice,\n", + " \"stc_pipeline\" : stc_pipeline,\n", + " \"stc_pipeline2\": stc_pipeline2,\n", + " \"stc_pipeline3\": stc_pipeline3,\n", + " \"stc_pipeline4\": stc_pipeline4,\n", + " \"final_linear_pipeline\": final_linear_pipeline,\n", + " \"graph_pipeline\": graph_search_space,\n", + "}\n", + "\n", + "X, y = sklearn.datasets.load_breast_cancer(return_X_y=True)\n", + "X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, test_size=0.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "sampled pipeline\n" + "/home/perib/miniconda3/envs/myenv/lib/python3.10/site-packages/distributed/node.py:182: UserWarning: Port 8787 is already in use.\n", + "Perhaps you already have a cluster running?\n", + "Hosting the HTTP server on port 40273 instead\n", + " warnings.warn(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generation: 60%|██████ | 3/5 [00:44<00:29, 14.98s/it]\n", + "/home/perib/miniconda3/envs/myenv/lib/python3.10/site-packages/sklearn/preprocessing/_data.py:2785: UserWarning: n_quantiles (688) is greater than the total number of samples (284). n_quantiles is set to n_samples.\n", + " warnings.warn(\n" ] }, { "data": { "text/html": [ - "
Pipeline(steps=[('pipeline',\n",
-       "                 Pipeline(steps=[('quantiletransformer',\n",
-       "                                  QuantileTransformer(n_quantiles=1366)),\n",
-       "                                 ('featureagglomeration',\n",
-       "                                  FeatureAgglomeration(n_clusters=81)),\n",
-       "                                 ('selectpercentile',\n",
-       "                                  SelectPercentile(percentile=74.3065720272799))])),\n",
-       "                ('kneighborsclassifier',\n",
-       "                 KNeighborsClassifier(n_jobs=1, n_neighbors=13,\n",
-       "                                      weights='distance'))])
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" + "
TPOTEstimator(classification=True, cv=5, early_stop=2, generations=5,\n",
+       "              max_eval_time_mins=10, n_jobs=4,\n",
+       "              scorers=['roc_auc_ovr',\n",
+       "                       <function complexity_scorer at 0x7e7bacf5b9a0>],\n",
+       "              scorers_weights=[1.0, -1.0],\n",
+       "              search_space=<tpot2.search_spaces.pipelines.sequential.SequentialPipeline object at 0x7e7ba3b078b0>,\n",
+       "              verbose=2)
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" ], "text/plain": [ - "Pipeline(steps=[('pipeline',\n", - " Pipeline(steps=[('quantiletransformer',\n", - " QuantileTransformer(n_quantiles=1366)),\n", - " ('featureagglomeration',\n", - " FeatureAgglomeration(n_clusters=81)),\n", - " ('selectpercentile',\n", - " SelectPercentile(percentile=74.3065720272799))])),\n", - " ('kneighborsclassifier',\n", - " KNeighborsClassifier(n_jobs=1, n_neighbors=13,\n", - " weights='distance'))])" + "TPOTEstimator(classification=True, cv=5, early_stop=2, generations=5,\n", + " max_eval_time_mins=10, n_jobs=4,\n", + " scorers=['roc_auc_ovr',\n", + " ],\n", + " scorers_weights=[1.0, -1.0],\n", + " search_space=,\n", + " verbose=2)" ] }, - "execution_count": 31, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "full_search_space = tpot2.search_spaces.pipelines.SequentialPipeline([\n", - " linear_feature_engineering,\n", - " tpot2.config.get_search_space(\"classifiers\"),\n", - "])\n", + "selected_search_space = all_search_spaces[\"stc_pipeline\"] #change this to select a different search space\n", "\n", - "print(\"sampled pipeline\")\n", - "full_search_space.generate().export_pipeline()" + "\n", + "est = tpot2.TPOTEstimator(\n", + " scorers=[\"roc_auc_ovr\", tpot2.objectives.complexity_scorer],\n", + " scorers_weights=[1.0, -1.0],\n", + " classification = True,\n", + " cv = 5,\n", + " search_space = selected_search_space,\n", + " population_size= 50,\n", + " generations = 5,\n", + " max_eval_time_mins = 10,\n", + " early_stop = 2,\n", + " verbose = 2,\n", + " n_jobs=4,\n", + ")\n", + "\n", + "est.fit(X_train, y_train)" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 68, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "sampled pipeline\n" + "auroc score 0.9845976642022929\n" ] - }, + } + ], + "source": [ + "# score the model\n", + "auroc_scorer = sklearn.metrics.get_scorer(\"roc_auc\")\n", + "auroc_score = auroc_scorer(est, X_test, y_test)\n", + "\n", + "print(\"auroc score\", auroc_score)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ { "data": { "text/html": [ - "
Pipeline(steps=[('pipeline',\n",
-       "                 Pipeline(steps=[('zerocount', ZeroCount()),\n",
-       "                                 ('quantiletransformer',\n",
-       "                                  QuantileTransformer(n_quantiles=1844))])),\n",
-       "                ('linearsvc', LinearSVC(C=6046.824997100118, penalty='l1'))])
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" + "
Pipeline(steps=[('variancethreshold',\n",
+       "                 VarianceThreshold(threshold=0.0003237844275)),\n",
+       "                ('quantiletransformer', QuantileTransformer(n_quantiles=688)),\n",
+       "                ('baggingclassifier',\n",
+       "                 BaggingClassifier(bootstrap_features=True,\n",
+       "                                   max_features=0.2631592196919,\n",
+       "                                   max_samples=0.488886320861, n_estimators=72,\n",
+       "                                   n_jobs=1))])
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" ], "text/plain": [ - "Pipeline(steps=[('pipeline',\n", - " Pipeline(steps=[('zerocount', ZeroCount()),\n", - " ('quantiletransformer',\n", - " QuantileTransformer(n_quantiles=1844))])),\n", - " ('linearsvc', LinearSVC(C=6046.824997100118, penalty='l1'))])" + "Pipeline(steps=[('variancethreshold',\n", + " VarianceThreshold(threshold=0.0003237844275)),\n", + " ('quantiletransformer', QuantileTransformer(n_quantiles=688)),\n", + " ('baggingclassifier',\n", + " BaggingClassifier(bootstrap_features=True,\n", + " max_features=0.2631592196919,\n", + " max_samples=0.488886320861, n_estimators=72,\n", + " n_jobs=1))])" ] }, - "execution_count": 36, + "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "print(\"sampled pipeline\")\n", - "full_search_space.generate().export_pipeline()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### UnionPipeline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### DynamicUnionPipeline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### WrapperPipeline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### TreePipeline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### GraphSearchPipeline" + "#plot the best pipeline\n", + "if isinstance(est.fitted_pipeline_, tpot2.GraphPipeline):\n", + " est.fitted_pipeline_.plot()\n", + " \n", + "est.fitted_pipeline_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Optimize Search Space with TPOTEstimator\n", - "\n", - "Once you have constructed a search space, you can use TPOTEstimator to optimize a pipeline within that space." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import tpot2\n", - "import numpy as np\n", - "import sklearn\n", - "import sklearn.datasets\n", - "\n", - "# create dummy dataset\n", - "X, y = sklearn.datasets.make_classification(n_samples=200, n_features=10, n_classes=2)\n", - "\n", - "# train test split\n", - "X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, test_size=0.5)\n", - "\n", - "\n", - "\n", - "graph_search_space = tpot2.search_spaces.pipelines.GraphPipeline(\n", - " root_search_space= tpot2.config.get_search_space([\"KNeighborsClassifier\", \"LogisticRegression\", \"DecisionTreeClassifier\"]),\n", - " leaf_search_space = tpot2.config.get_search_space(\"selectors\"), \n", - " inner_search_space = tpot2.config.get_search_space([\"transformers\"]),\n", - " max_size = 10,\n", - ")\n", - "\n", - "est = tpot2.TPOTEstimator(\n", - " scorers = [\"roc_auc\"],\n", - " scorers_weights = [1],\n", - " classification = True,\n", - " cv = 5,\n", - " search_space = graph_search_space,\n", - " population_size= 10,\n", - " generations = 5,\n", - " max_eval_time_mins = 60*5,\n", - " verbose = 2,\n", - ")\n", + "## Template Search Spaces\n", "\n", - "est.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# score the model\n", + "As mentioned in Tutorial 1, TPOT has several buildin search spaces. Here is the same table:\n", "\n", - "auroc_scorer = sklearn.metrics.get_scorer(\"roc_auc\")\n", - "auroc_score = auroc_scorer(est, X_test, y_test)\n", + "| String | Description |\n", + "| :--- | :----: |\n", + "| linear | A linear pipeline with the structure of \"Selector->(transformers+Passthrough)->(classifiers/regressors+Passthrough)->final classifier/regressor.\" For both the transformer and inner estimator layers, TPOT may choose one or more transformers/classifiers, or it may choose none. The inner classifier/regressor layer is optional. |\n", + "| linear-light | Same search space as linear, but without the inner classifier/regressor layer and with a reduced set of faster running estimators. |\n", + "| graph | TPOT will optimize a pipeline in the shape of a directed acyclic graph. The nodes of the graph can include selectors, scalers, transformers, or classifiers/regressors (inner classifiers/regressors can optionally be not included). This will return a custom GraphPipeline rather than an sklearn Pipeline. More details in Tutorial 6. |\n", + "| graph-light | Same as graph search space, but without the inner classifier/regressors and with a reduced set of faster running estimators. |\n", + "| mdr |TPOT will search over a series of feature selectors and Multifactor Dimensionality Reduction models to find a series of operators that maximize prediction accuracy. The TPOT MDR configuration is specialized for genome-wide association studies (GWAS), and is described in detail online here. |\n", "\n", - "print(\"auroc score\", auroc_score)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#plot the best pipeline\n", - "est.fitted_pipeline_.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "est" + "Rather than create your own search space, you can simply pass the string into the `search_space` param. Alternatively, you can access tpot2.config.template_search_spaces.get_template_search_spaces directly which offers a few more customizable options for each template including `cross_val_predict_cv` and whether or not stacked classifiers/regressors are allowed. Or you can copy the code and customize it manually!" ] }, { @@ -7373,7 +16063,7 @@ "from tpot2.search_spaces.pipelines import *\n", "from tpot2.config import get_search_space\n", "\n", - "selectors = get_search_space([\"selectors\",\"selectors_classification\", \"Passthrough\"])\n", + "selectors = get_search_space([\"selectors_classification\", \"Passthrough\"])\n", "estimators = get_search_space([\"classifiers\"])\n", "\n", "\n", From 01f3a785e4d6652f436b86b5bfcd8b03d95a4c92 Mon Sep 17 00:00:00 2001 From: perib Date: Tue, 24 Sep 2024 07:12:30 -0700 Subject: [PATCH 15/44] removed cross_val_predict_cv from the estimators. use wrapper pipeline instead (or set in graphsearchpipeline --- tpot2/builtin_modules/estimatortransformer.py | 6 ++++- tpot2/search_spaces/pipelines/graph.py | 9 ++++--- tpot2/search_spaces/pipelines/sequential.py | 3 +-- tpot2/tpot_estimator/estimator.py | 24 ++----------------- .../tpot_estimator/steady_state_estimator.py | 24 ++----------------- 5 files changed, 16 insertions(+), 50 deletions(-) diff --git a/tpot2/builtin_modules/estimatortransformer.py b/tpot2/builtin_modules/estimatortransformer.py index 5ae15a1c..9ed10c89 100644 --- a/tpot2/builtin_modules/estimatortransformer.py +++ b/tpot2/builtin_modules/estimatortransformer.py @@ -23,7 +23,11 @@ def __init__(self, estimator, method='auto', passthrough=False, cross_val_predic passthrough : bool, default=False Whether to pass the original input through. cross_val_predict_cv : int, default=0 - If greater than 0, will use cross_val_predict with the specified cv value to generate the transformed output. + Number of folds to use for the cross_val_predict function for inner classifiers and regressors. Estimators will still be fit on the full dataset, but the following node will get the outputs from cross_val_predict. + + - 0-1 : When set to 0 or 1, the cross_val_predict function will not be used. The next layer will get the outputs from fitting and transforming the full dataset. + - >=2 : When fitting pipelines with inner classifiers or regressors, they will still be fit on the full dataset. + However, the output to the next node will come from cross_val_predict with the specified number of folds. """ self.estimator = estimator diff --git a/tpot2/search_spaces/pipelines/graph.py b/tpot2/search_spaces/pipelines/graph.py index c972b59a..f767f88e 100644 --- a/tpot2/search_spaces/pipelines/graph.py +++ b/tpot2/search_spaces/pipelines/graph.py @@ -695,7 +695,6 @@ def __init__(self, crossover_same_depth: bool = False, cross_val_predict_cv: Union[int, Callable] = 0, #signature function(estimator, X, y=none) method: str = 'auto', - memory=None, use_label_encoder: bool = False): """ @@ -723,8 +722,12 @@ def __init__(self, crossover_same_depth: bool, optional If True, crossover will only occur between nodes at the same depth in the graph. If False, crossover will occur between nodes at any depth. - cross_val_predict_cv: int, cross-validation generator or an iterable, optional - Determines the cross-validation splitting strategy used in inner classifiers or regressors + cross_val_predict_cv : int, default=0 + Number of folds to use for the cross_val_predict function for inner classifiers and regressors. Estimators will still be fit on the full dataset, but the following node will get the outputs from cross_val_predict. + + - 0-1 : When set to 0 or 1, the cross_val_predict function will not be used. The next layer will get the outputs from fitting and transforming the full dataset. + - >=2 : When fitting pipelines with inner classifiers or regressors, they will still be fit on the full dataset. + However, the output to the next node will come from cross_val_predict with the specified number of folds. method: str, optional The prediction method to use for the inner classifiers or regressors. If 'auto', it will try to use predict_proba, decision_function, or predict in that order. diff --git a/tpot2/search_spaces/pipelines/sequential.py b/tpot2/search_spaces/pipelines/sequential.py index ab9f97da..3fae8a52 100644 --- a/tpot2/search_spaces/pipelines/sequential.py +++ b/tpot2/search_spaces/pipelines/sequential.py @@ -12,10 +12,9 @@ class SequentialPipelineIndividual(SklearnIndividual): # takes in a list of search spaces. each space is a list of SklearnIndividualGenerators. # will produce a pipeline of Sequential length. Each step in the pipeline will correspond to the the search space provided in the same index. - def __init__(self, search_spaces : List[SklearnIndividualGenerator], memory=None, rng=None) -> None: + def __init__(self, search_spaces : List[SklearnIndividualGenerator], rng=None) -> None: super().__init__() self.search_spaces = search_spaces - self.memory = memory self.pipeline = [] for space in self.search_spaces: diff --git a/tpot2/tpot_estimator/estimator.py b/tpot2/tpot_estimator/estimator.py index 1077f014..1e53ee64 100644 --- a/tpot2/tpot_estimator/estimator.py +++ b/tpot2/tpot_estimator/estimator.py @@ -43,7 +43,6 @@ def __init__(self, bigger_is_better = True, export_graphpipeline = False, - cross_val_predict_cv = 0, memory = None, categorical_features = None, @@ -157,13 +156,6 @@ def __init__(self, bigger_is_better : bool, default=True If True, the objective function is maximized. If False, the objective function is minimized. Use negative weights to reverse the direction. - cross_val_predict_cv : int, default=0 - Number of folds to use for the cross_val_predict function for inner classifiers and regressors. Estimators will still be fit on the full dataset, but the following node will get the outputs from cross_val_predict. - - - 0-1 : When set to 0 or 1, the cross_val_predict function will not be used. The next layer will get the outputs from fitting and transforming the full dataset. - - >=2 : When fitting pipelines with inner classifiers or regressors, they will still be fit on the full dataset. - However, the output to the next node will come from cross_val_predict with the specified number of folds. - memory: Memory object or string, default=None If supplied, pipeline will cache each transformer after calling fit with joblib.Memory. This feature is used to avoid computing the fit transformers within a pipeline if the parameters @@ -390,13 +382,8 @@ def __init__(self, self.search_space = search_space self.export_graphpipeline = export_graphpipeline - self.cross_val_predict_cv = cross_val_predict_cv self.memory = memory - if self.cross_val_predict_cv !=0 or self.memory is not None: - if not self.export_graphpipeline: - raise ValueError("cross_val_predict_cv and memory parameters are parameters for GraphPipeline. To enable these options export_graphpipeline to be True. Otherwise these can be passed into the relevant Search spaces as parameters.") - self.categorical_features = categorical_features self.subsets = subsets @@ -629,7 +616,6 @@ def objective_function(pipeline_individual, other_objective_functions=self.other_objective_functions, export_graphpipeline=self.export_graphpipeline, memory=self.memory, - cross_val_predict_cv=self.cross_val_predict_cv, **kwargs): return objective_function_generator( pipeline_individual, @@ -641,7 +627,6 @@ def objective_function(pipeline_individual, other_objective_functions=other_objective_functions, export_graphpipeline=export_graphpipeline, memory=memory, - cross_val_predict_cv=cross_val_predict_cv, **kwargs, ) @@ -786,8 +771,6 @@ def ind_generator(rng): other_objective_functions=self.other_objective_functions, export_graphpipeline=self.export_graphpipeline, memory=self.memory, - cross_val_predict_cv=self.cross_val_predict_cv, - **kwargs: objective_function_generator( ind, X, @@ -798,7 +781,6 @@ def ind_generator(rng): other_objective_functions=other_objective_functions, export_graphpipeline=export_graphpipeline, memory=memory, - cross_val_predict_cv=cross_val_predict_cv, **kwargs, )] @@ -844,7 +826,6 @@ def ind_generator(rng): other_objective_functions=self.other_objective_functions, export_graphpipeline=self.export_graphpipeline, memory=self.memory, - cross_val_predict_cv=self.cross_val_predict_cv, **kwargs: val_objective_function_generator( ind, X, @@ -855,7 +836,6 @@ def ind_generator(rng): other_objective_functions=other_objective_functions, export_graphpipeline=export_graphpipeline, memory=memory, - cross_val_predict_cv=cross_val_predict_cv, **kwargs, )] @@ -890,7 +870,7 @@ def ind_generator(rng): #TODO #best_individual_pipeline = best_individual.export_pipeline(memory=self.memory, cross_val_predict_cv=self.cross_val_predict_cv) if self.export_graphpipeline: - best_individual_pipeline = best_individual.export_flattened_graphpipeline(memory=self.memory, cross_val_predict_cv=self.cross_val_predict_cv) + best_individual_pipeline = best_individual.export_flattened_graphpipeline(memory=self.memory) else: best_individual_pipeline = best_individual.export_pipeline(memory=self.memory) @@ -982,7 +962,7 @@ def make_evaluated_individuals(self): self.evaluated_individuals = self.evaluated_individuals.set_index(self.evaluated_individuals.index.map(object_to_int)) self.evaluated_individuals['Parents'] = self.evaluated_individuals['Parents'].apply(lambda row: convert_parents_tuples_to_integers(row, object_to_int)) - self.evaluated_individuals["Instance"] = self.evaluated_individuals["Individual"].apply(lambda ind: apply_make_pipeline(ind, preprocessing_pipeline=self._preprocessing_pipeline, export_graphpipeline=self.export_graphpipeline, memory=self.memory, cross_val_predict_cv=self.cross_val_predict_cv)) + self.evaluated_individuals["Instance"] = self.evaluated_individuals["Individual"].apply(lambda ind: apply_make_pipeline(ind, preprocessing_pipeline=self._preprocessing_pipeline, export_graphpipeline=self.export_graphpipeline, memory=self.memory)) return self.evaluated_individuals diff --git a/tpot2/tpot_estimator/steady_state_estimator.py b/tpot2/tpot_estimator/steady_state_estimator.py index bcfef964..b86b9f8f 100644 --- a/tpot2/tpot_estimator/steady_state_estimator.py +++ b/tpot2/tpot_estimator/steady_state_estimator.py @@ -40,7 +40,6 @@ def __init__(self, export_graphpipeline = False, - cross_val_predict_cv = 0, memory = None, categorical_features = None, @@ -192,13 +191,6 @@ def __init__(self, - list : a list of strings out of the above options to include the corresponding methods in the configuration dictionary. - None : If None, a leaf will not be required (i.e. the pipeline can be a single root node). Leaf nodes will be generated from the inner_config_dict. - cross_val_predict_cv : int, default=0 - Number of folds to use for the cross_val_predict function for inner classifiers and regressors. Estimators will still be fit on the full dataset, but the following node will get the outputs from cross_val_predict. - - - 0-1 : When set to 0 or 1, the cross_val_predict function will not be used. The next layer will get the outputs from fitting and transforming the full dataset. - - >=2 : When fitting pipelines with inner classifiers or regressors, they will still be fit on the full dataset. - However, the output to the next node will come from cross_val_predict with the specified number of folds. - categorical_features: list or None Categorical columns to inpute and/or one hot encode during the preprocessing step. Used only if preprocessing is not False. - None : If None, TPOT2 will automatically use object columns in pandas dataframes as objects for one hot encoding in preprocessing. @@ -431,14 +423,8 @@ def __init__(self, self.bigger_is_better = bigger_is_better self.export_graphpipeline = export_graphpipeline - self.cross_val_predict_cv = cross_val_predict_cv self.memory = memory - if self.cross_val_predict_cv !=0 or self.memory is not None: - if not self.export_graphpipeline: - raise ValueError("cross_val_predict_cv and memory parameters are parameters for GraphPipeline. To enable these options export_graphpipeline to be True. Otherwise these can be passed into the relevant Search spaces as parameters.") - - self.categorical_features = categorical_features self.subsets = subsets self.preprocessing = preprocessing @@ -673,7 +659,6 @@ def objective_function(pipeline_individual, other_objective_functions=self.other_objective_functions, export_graphpipeline=self.export_graphpipeline, memory=self.memory, - cross_val_predict_cv=self.cross_val_predict_cv, **kwargs): return objective_function_generator( pipeline_individual, @@ -685,7 +670,6 @@ def objective_function(pipeline_individual, other_objective_functions=other_objective_functions, export_graphpipeline=export_graphpipeline, memory=memory, - cross_val_predict_cv=cross_val_predict_cv, **kwargs, ) @@ -794,7 +778,6 @@ def ind_generator(rng): other_objective_functions=self.other_objective_functions, export_graphpipeline=self.export_graphpipeline, memory=self.memory, - cross_val_predict_cv=self.cross_val_predict_cv, **kwargs: objective_function_generator( ind, @@ -806,7 +789,6 @@ def ind_generator(rng): other_objective_functions=other_objective_functions, export_graphpipeline=export_graphpipeline, memory=memory, - cross_val_predict_cv=cross_val_predict_cv, **kwargs, )] @@ -848,7 +830,6 @@ def ind_generator(rng): other_objective_functions=self.other_objective_functions, export_graphpipeline=self.export_graphpipeline, memory=self.memory, - cross_val_predict_cv=self.cross_val_predict_cv, **kwargs: val_objective_function_generator( ind, X, @@ -859,7 +840,6 @@ def ind_generator(rng): other_objective_functions=other_objective_functions, export_graphpipeline=export_graphpipeline, memory=memory, - cross_val_predict_cv=cross_val_predict_cv, **kwargs, )] @@ -891,7 +871,7 @@ def ind_generator(rng): if self.export_graphpipeline: - best_individual_pipeline = best_individual.export_flattened_graphpipeline(memory=self.memory, cross_val_predict_cv=self.cross_val_predict_cv) + best_individual_pipeline = best_individual.export_flattened_graphpipeline(memory=self.memory) else: best_individual_pipeline = best_individual.export_pipeline(memory=self.memory) @@ -981,7 +961,7 @@ def make_evaluated_individuals(self): self.evaluated_individuals = self.evaluated_individuals.set_index(self.evaluated_individuals.index.map(object_to_int)) self.evaluated_individuals['Parents'] = self.evaluated_individuals['Parents'].apply(lambda row: convert_parents_tuples_to_integers(row, object_to_int)) - self.evaluated_individuals["Instance"] = self.evaluated_individuals["Individual"].apply(lambda ind: apply_make_pipeline(ind, preprocessing_pipeline=self._preprocessing_pipeline, export_graphpipeline=self.export_graphpipeline, memory=self.memory, cross_val_predict_cv=self.cross_val_predict_cv)) + self.evaluated_individuals["Instance"] = self.evaluated_individuals["Individual"].apply(lambda ind: apply_make_pipeline(ind, preprocessing_pipeline=self._preprocessing_pipeline, export_graphpipeline=self.export_graphpipeline, memory=self.memory)) return self.evaluated_individuals From 29a4745899728de34ed1bf2fc3894ecf1262cd63 Mon Sep 17 00:00:00 2001 From: perib Date: Tue, 24 Sep 2024 07:49:04 -0700 Subject: [PATCH 16/44] replaces deprecated call with replacement function --- tpot2/config/classifiers.py | 34 ++++++++++++++++----------------- tpot2/config/get_configspace.py | 2 +- tpot2/config/imputers.py | 4 ++-- tpot2/config/regressors.py | 22 ++++++++++----------- tpot2/config/transformers.py | 8 ++++---- 5 files changed, 35 insertions(+), 35 deletions(-) diff --git a/tpot2/config/classifiers.py b/tpot2/config/classifiers.py index ddcff3d0..dd30efb5 100644 --- a/tpot2/config/classifiers.py +++ b/tpot2/config/classifiers.py @@ -28,8 +28,8 @@ def get_LogisticRegression_ConfigurationSpace(random_state): cs = ConfigurationSpace(space) - cs.add_hyperparameters([penalty, C, l1_ratio, class_weight]) - cs.add_conditions([l1_ratio_condition]) + cs.add([penalty, C, l1_ratio, class_weight]) + cs.add([l1_ratio_condition]) return cs @@ -68,8 +68,8 @@ def get_BaggingClassifier_ConfigurationSpace(random_state): space = space ) - cs.add_hyperparameters([bootstrap, oob_score]) - cs.add_conditions([oob_condition]) + cs.add([bootstrap, oob_score]) + cs.add([oob_condition]) return cs @@ -107,8 +107,8 @@ def get_LinearSVC_ConfigurationSpace(random_state): cs = ConfigurationSpace(space) - cs.add_hyperparameters([penalty, C, loss]) - cs.add_conditions([loss_condition]) + cs.add([penalty, C, loss]) + cs.add([loss_condition]) return cs @@ -136,8 +136,8 @@ def get_SVC_ConfigurationSpace(random_state): cs = ConfigurationSpace(space) - cs.add_hyperparameters([kernel, C, coef0, degree, gamma, shrinking, class_weight]) - cs.add_conditions([degree_condition, gamma_condition, coef0_condition]) + cs.add([kernel, C, coef0, degree, gamma, shrinking, class_weight]) + cs.add([degree_condition, gamma_condition, coef0_condition]) return cs @@ -250,8 +250,8 @@ def get_SGDClassifier_ConfigurationSpace(random_state): space = space ) - cs.add_hyperparameters([power_t, learning_rate]) - cs.add_conditions([powertcond]) + cs.add([power_t, learning_rate]) + cs.add([powertcond]) return cs @@ -321,8 +321,8 @@ def get_LinearDiscriminantAnalysis_ConfigurationSpace(): shrinkcond = NotEqualsCondition(shrinkage, solver, 'svd') cs = ConfigurationSpace() - cs.add_hyperparameters([solver, shrinkage]) - cs.add_conditions([shrinkcond]) + cs.add([solver, shrinkage]) + cs.add([shrinkcond]) return cs @@ -360,8 +360,8 @@ def get_GradientBoostingClassifier_ConfigurationSpace(n_classes, random_state): cs = ConfigurationSpace( space = space ) - cs.add_hyperparameters([n_iter_no_change, validation_fraction, early_stop ]) - cs.add_conditions([validation_fraction_cond, n_iter_no_change_cond]) + cs.add([n_iter_no_change, validation_fraction, early_stop ]) + cs.add([validation_fraction_cond, n_iter_no_change_cond]) return cs def GradientBoostingClassifier_hyperparameter_parser(params): @@ -429,8 +429,8 @@ def get_HistGradientBoostingClassifier_ConfigurationSpace(random_state): cs = ConfigurationSpace( space = space ) - cs.add_hyperparameters([n_iter_no_change, validation_fraction, early_stop ]) - cs.add_conditions([validation_fraction_cond, n_iter_no_change_cond]) + cs.add([n_iter_no_change, validation_fraction, early_stop ]) + cs.add([validation_fraction_cond, n_iter_no_change_cond]) return cs @@ -506,7 +506,7 @@ def get_MLPClassifier_ConfigurationSpace(random_state): learning_rate_init = Float("learning_rate_init", bounds=(1e-4, 1e-1), log=True) learning_rate = Categorical("learning_rate", ['constant', 'invscaling', 'adaptive']) - cs.add_hyperparameters([n_hidden_layers, n_nodes_per_layer, activation, alpha, learning_rate, early_stopping, learning_rate_init]) + cs.add([n_hidden_layers, n_nodes_per_layer, activation, alpha, learning_rate, early_stopping, learning_rate_init]) return cs diff --git a/tpot2/config/get_configspace.py b/tpot2/config/get_configspace.py index 46b13b60..a044c215 100644 --- a/tpot2/config/get_configspace.py +++ b/tpot2/config/get_configspace.py @@ -285,7 +285,7 @@ def get_configspace(name, n_classes=3, n_samples=1000, n_features=100, random_st case "PowerTransformer": return {} case "QuantileTransformer": - return transformers.get_QuantileTransformer_configspace(random_state=random_state) + return transformers.get_QuantileTransformer_configspace(n_samples=n_samples, random_state=random_state) case "RobustScaler": return transformers.RobustScaler_configspace case "ColumnOneHotEncoder": diff --git a/tpot2/config/imputers.py b/tpot2/config/imputers.py index 2c33629f..b991bf8d 100644 --- a/tpot2/config/imputers.py +++ b/tpot2/config/imputers.py @@ -38,8 +38,8 @@ def get_IterativeImputer_config_space(n_features, random_state): space['random_state'] = random_state cs = ConfigurationSpace(space=space) - cs.add_hyperparameters([estimator, sample_posterior]) - cs.add_conditions([sampling_condition]) + cs.add([estimator, sample_posterior]) + cs.add([sampling_condition]) return cs def get_KNNImputer_config_space(n_samples): diff --git a/tpot2/config/regressors.py b/tpot2/config/regressors.py index 11362cce..9e586947 100644 --- a/tpot2/config/regressors.py +++ b/tpot2/config/regressors.py @@ -62,10 +62,10 @@ def get_SGDRegressor_ConfigurationSpace(random_state): eta0_in_inv_con = InCondition(eta0, learning_rate, ["invscaling", "constant"]) power_t_condition = EqualsCondition(power_t, learning_rate, "invscaling") - cs.add_hyperparameters( + cs.add( [l1_ratio, penalty, epsilon, loss, eta0, learning_rate, power_t] ) - cs.add_conditions( + cs.add( [elasticnet, epsilon_condition, power_t_condition, eta0_in_inv_con] ) @@ -283,8 +283,8 @@ def get_SVR_ConfigurationSpace(): gamma_condition = InCondition(gamma, kernel, ['poly', 'rbf',]) coef0_condition = InCondition(coef0, kernel, ['poly', 'sigmoid']) - cs.add_hyperparameters([kernel, degree, gamma, coef0]) - cs.add_conditions([degree_condition,gamma_condition]) + cs.add([kernel, degree, gamma, coef0]) + cs.add([degree_condition,gamma_condition]) return cs @@ -409,8 +409,8 @@ def get_GradientBoostingRegressor_ConfigurationSpace(random_state): cs = ConfigurationSpace( space = space ) - cs.add_hyperparameters([n_iter_no_change, validation_fraction, early_stop ]) - cs.add_conditions([validation_fraction_cond, n_iter_no_change_cond]) + cs.add([n_iter_no_change, validation_fraction, early_stop ]) + cs.add([validation_fraction_cond, n_iter_no_change_cond]) return cs def GradientBoostingRegressor_hyperparameter_parser(params): @@ -479,8 +479,8 @@ def get_HistGradientBoostingRegressor_ConfigurationSpace(random_state): cs = ConfigurationSpace( space = space ) - cs.add_hyperparameters([n_iter_no_change, validation_fraction, early_stop ]) - cs.add_conditions([validation_fraction_cond, n_iter_no_change_cond]) + cs.add([n_iter_no_change, validation_fraction, early_stop ]) + cs.add([validation_fraction_cond, n_iter_no_change_cond]) return cs @@ -549,7 +549,7 @@ def get_MLPRegressor_ConfigurationSpace(random_state): learning_rate_init = Float("learning_rate_init", bounds=(1e-4, 1e-1), log=True) learning_rate = Categorical("learning_rate", ['constant', 'invscaling', 'adaptive']) - cs.add_hyperparameters([n_hidden_layers, n_nodes_per_layer, activation, alpha, learning_rate, early_stopping, learning_rate_init]) + cs.add([n_hidden_layers, n_nodes_per_layer, activation, alpha, learning_rate, early_stopping, learning_rate_init]) return cs @@ -592,8 +592,8 @@ def get_BaggingRegressor_ConfigurationSpace(random_state): space = space ) - cs.add_hyperparameters([bootstrap, oob_score]) - cs.add_conditions([oob_condition]) + cs.add([bootstrap, oob_score]) + cs.add([oob_condition]) return cs diff --git a/tpot2/config/transformers.py b/tpot2/config/transformers.py index 6d393460..ced2dee2 100644 --- a/tpot2/config/transformers.py +++ b/tpot2/config/transformers.py @@ -54,8 +54,8 @@ def get_FeatureAgglomeration_configspace(n_samples): metric_condition = NotEqualsCondition(metric, linkage, 'ward') cs = ConfigurationSpace() - cs.add_hyperparameters([linkage, metric, n_clusters, pooling_func]) - cs.add_condition(metric_condition) + cs.add([linkage, metric, n_clusters, pooling_func]) + cs.add(metric_condition) return cs @@ -108,10 +108,10 @@ def get_RBFSampler_configspace(n_features=100, random_state=None): ) -def get_QuantileTransformer_configspace(random_state=None): +def get_QuantileTransformer_configspace(random_state=None, n_samples=1000): space = { - 'n_quantiles': Integer('n_quantiles', bounds=(10, 2000)), + 'n_quantiles': Integer('n_quantiles', bounds=(10, n_samples)), 'output_distribution': Categorical('output_distribution', ['uniform', 'normal']), } From b0d58a8129bfaa945ee51c5079c51d9bd9113e93 Mon Sep 17 00:00:00 2001 From: perib Date: Tue, 24 Sep 2024 10:04:46 -0700 Subject: [PATCH 17/44] add iterative imputer with learned estimators and update tutorials more --- Tutorial/1_Using_TPOT.ipynb | 916 +- Tutorial/2_Search_Spaces.ipynb | 15163 +--------------------------- tpot2/config/get_configspace.py | 13 +- tpot2/config/imputers.py | 19 + tpot2/tpot_estimator/estimator.py | 3 - 5 files changed, 776 insertions(+), 15338 deletions(-) diff --git a/Tutorial/1_Using_TPOT.ipynb b/Tutorial/1_Using_TPOT.ipynb index 34e74c2b..c1d72eeb 100644 --- a/Tutorial/1_Using_TPOT.ipynb +++ b/Tutorial/1_Using_TPOT.ipynb @@ -8,19 +8,19 @@ "Automated machine learning (AutoML) takes a higher-level approach to machine learning than most practitioners are used to, so we've gathered a handful of guidelines on what to expect when running AutoML software such as TPOT.\n", "\n", "#### AUTOML ALGORITHMS AREN'T INTENDED TO RUN FOR ONLY A FEW MINUTES\n", - "Of course, you can run TPOT for only a few minutes and it will find a reasonably good pipeline for your dataset. However, if you don't run TPOT for long enough, it may not find the best possible pipeline for your dataset. It may even not find any suitable pipeline at all, in which case a RuntimeError('A pipeline has not yet been optimized. Please call fit() first.') will be raised. Often it is worthwhile to run multiple instances of TPOT in parallel for a long time (hours to days) to allow TPOT to thoroughly search the pipeline space for your dataset.\n", + "Of course, you can run TPOT for only a few minutes, and it will find a reasonably good pipeline for your dataset. However, if you don't run TPOT for long enough, it may not find the best possible pipeline for your dataset. It may not even find any suitable pipeline at all, in which case a RuntimeError('A pipeline has not yet been optimized. Please call fit() first.') will be raised. Often it is worthwhile to run multiple instances of TPOT in parallel for a long time (hours to days) to allow TPOT to thoroughly search the pipeline space for your dataset.\n", "\n", "#### AUTOML ALGORITHMS CAN TAKE A LONG TIME TO FINISH THEIR SEARCH\n", - "AutoML algorithms aren't as simple as fitting one model on the dataset; they are considering multiple machine learning algorithms (random forests, linear models, SVMs, etc.) in a pipeline with multiple preprocessing steps (missing value imputation, scaling, PCA, feature selection, etc.), the hyperparameters for all of the models and preprocessing steps, as well as multiple ways to ensemble or stack the algorithms within the pipeline.\n", + "AutoML algorithms aren't as simple as fitting one model on the dataset; they consider multiple machine learning algorithms (random forests, linear models, SVMs, etc.) in a pipeline with multiple preprocessing steps (missing value imputation, scaling, PCA, feature selection, etc.), the hyperparameters for all of the models and preprocessing steps, and multiple ways to ensemble or stack the algorithms within the pipeline.\n", "\n", "As such, TPOT will take a while to run on larger datasets, but it's important to realize why. With the default TPOT settings (100 generations with 100 population size), TPOT will evaluate 10,000 pipeline configurations before finishing. To put this number into context, think about a grid search of 10,000 hyperparameter combinations for a machine learning algorithm and how long that grid search will take. That is 10,000 model configurations to evaluate with 10-fold cross-validation, which means that roughly 100,000 models are fit and evaluated on the training data in one grid search. That's a time-consuming procedure, even for simpler models like decision trees.\n", "\n", - "Typical TPOT runs will take hours to days to finish (unless it's a small dataset), but you can always interrupt the run partway through and see the best results so far. TPOT also provides a warm_start parameter that lets you restart a TPOT run from where it left off.\n", + "Typical TPOT runs will take hours to days to finish (unless it's a small dataset), but you can always interrupt the run partway through and see the best results so far. TPOT also provides a warm_start and a periodic_checkpoint_folder parameter that lets you restart a TPOT run from where it left off.\n", "\n", "#### AUTOML ALGORITHMS CAN RECOMMEND DIFFERENT SOLUTIONS FOR THE SAME DATASET\n", - "If you're working with a reasonably complex dataset or run TPOT for a short amount of time, different TPOT runs may result in different pipeline recommendations. TPOT's optimization algorithm is stochastic in nature, which means that it uses randomness (in part) to search the possible pipeline space. When two TPOT runs recommend different pipelines, this means that the TPOT runs didn't converge due to lack of time or that multiple pipelines perform more-or-less the same on your dataset.\n", + "If you're working with a reasonably complex dataset or run TPOT for a short amount of time, different TPOT runs may result in different pipeline recommendations. TPOT's optimization algorithm is stochastic, which means that it uses randomness (in part) to search the possible pipeline space. When two TPOT runs recommend different pipelines, this means that the TPOT runs didn't converge due to lack of time or that multiple pipelines perform more-or-less the same on your dataset.\n", "\n", - "This is actually an advantage over fixed grid search techniques: TPOT is meant to be an assistant that gives you ideas on how to solve a particular machine learning problem by exploring pipeline configurations that you might have never considered, then leaves the fine-tuning to more constrained parameter tuning techniques such as grid search." + "This is actually an advantage over fixed grid search techniques: TPOT is meant to be an assistant that gives you ideas on how to solve a particular machine learning problem by exploring pipeline configurations that you might have never considered, then leaves the fine-tuning to more constrained parameter tuning techniques such as grid search or bayesian optimization." ] }, { @@ -29,7 +29,7 @@ "source": [ "# TPOT with code\n", "\n", - "We've taken care to design the TPOT interface to be as similar as possible to scikit-learn.\n", + "We've designed the TPOT interface to be as similar as possible to scikit-learn.\n", "\n", "TPOT can be imported just like any regular Python module. To import TPOT, type:" ] @@ -85,21 +85,21 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Generation: : 3it [00:31, 10.38s/it]\n" + "Generation: : 4it [00:32, 8.03s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "auroc_score: 0.9904100529100529\n" + "auroc_score: 0.9900793650793651\n" ] } ], @@ -176,9 +176,9 @@ "source": [ "## Measuring Model Complexity\n", "\n", - "When running TPOT, it can sometimes be beneficial to include a secondary objective that measures model complexity. More complex models can yield higher performance but this comes at the cost of interpretability. Simpler models may be more interpretable, but often have lower predictive performance. Sometimes, however, vast increases in complexity only marginally improve predictive performance. There may be other simpler and more interpretable pipelines with marginal performance decreases that could be acceptable for the increased interpretability. However, these pipelines are often missed by optimizing purely for performance. By including both performance and complexity as objective functions, TPOT will attempt to optimize the best pipeline for all complexity levels simultaneously. After optimization, the user will be able to see the complexity vs performance tradeoff and make the decision of which pipeline best suits their needs. \n", + "When running TPOT, including a secondary objective that measures model complexity can sometimes be beneficial. More complex models can yield higher performance, but this comes at the cost of interpretability. Simpler models may be more interpretable but often have lower predictive performance. Sometimes, however, vast increases in complexity only marginally improve predictive performance. There may be other simpler and more interpretable pipelines with marginal performance decreases that could be acceptable for the increased interpretability. However, these pipelines are often missed when optimizing purely for performance. By including both performance and complexity as objective functions, TPOT will attempt to optimize the best pipeline for all complexity levels simultaneously. After optimization, the user will be able to see the complexity vs performance tradeoff and decide which pipeline best suits their needs. \n", "\n", - "Two methods of measuring complexity to consider would be `tpot2.objectives.number_of_nodes_objective` or `tpot2.objectives.complexity_scorer`. The number of nodes objective simply calculates the number of steps within a pipeline. This is a simple metric, however it does not differentiate between the complexity of different model types. For example, a simple LogisticRegression counts the same as the much more complex XGBoost. The complexity scorer tries to estimate the number of learned parameters included in the classifiers and regressors of the pipeline. It is challenging and potentially subjective how to exactly quantify and compare complexity between different classes of models. However, this function provides a reasonable heuristic for the evolutionary algorithm that at least separates out qualitatively more or less complex algorithms from one another. While it may be hard to exactly compare the relative complexities of LogisticRegression and XGBoost, for example, both will always be on opposite ends of the complexity values returned by this function. This allows for pareto fronts with LogisticRegression on one side, and XGBoost on the other.\n", + "Two methods of measuring complexity to consider would be `tpot2.objectives.number_of_nodes_objective` or `tpot2.objectives.complexity_scorer`. The number of nodes objective simply calculates the number of steps within a pipeline. This is a simple metric, however it does not differentiate between the complexity of different model types. For example, a simple LogisticRegression counts the same as the much more complex XGBoost. The complexity scorer tries to estimate the number of learned parameters included in the classifiers and regressors of the pipeline. It is challenging and potentially subjective how to exactly quantify and compare complexity between different classes of models. However, this function provides a reasonable heuristic for the evolutionary algorithm that at least separates out qualitatively more or less complex algorithms from one another. While it may be hard to compare the relative complexities of LogisticRegression and XGBoost exactly, for example, both will always be on opposite ends of the complexity values returned by this function. This allows for pareto fronts with LogisticRegression on one side, and XGBoost on the other.\n", "\n", "An example of this analysis is demonstrated in a following section." ] @@ -211,12 +211,12 @@ "source": [ "## Terminating Optimization (Early Stopping)\n", "\n", - "Note that we use a short time duration for a quick example, but in practice you may need to run TPOT for a longer duration. by default, TPOT sets a time limit of 1 hour with a max limit of 5 minutes per pipeline. In practice you may want to increase these values.\n", + "Note that we use a short time duration for a quick example, but in practice, you may need to run TPOT for a longer duration. By default, TPOT sets a time limit of 1 hour with a max limit of 5 minutes per pipeline. In practice, you may want to increase these values.\n", "\n", - "There are three methods of terminating a TPOT run and ending the optimization process. TPOT will always terminate as soon as one of the conditions is met.\n", - "* `max_time_mins` : (Default, 1 hour) After this many minutes, TPOT will terminate and return the best pipeline it found so far.\n", - "* `early_stop` : An int causes TPOT to terminate early if it goes that number of generations without seeing an improvement in performance. Generally a value of around 5 to 20 is sufficient to be reasonably sure that performance has converged.\n", - "* `generations` : The total number of generations of the evolutionary algorithm to run.\n", + "There are three methods of terminating a TPOT run and ending the optimization process. TPOT will terminate as soon as one of the conditions is met.\n", + "* `max_time_mins` : (Default, 60 minutes) After this many minutes, TPOT will terminate and return the best pipeline it found so far.\n", + "* `early_stop` : The number of generations without seeing an improvement in performance, after which TPOT terminates. Generally, a value of around 5 to 20 is sufficient to be reasonably sure that performance has converged.\n", + "* `generations`: The total number of generations of the evolutionary algorithm to run.\n", "\n", "By default, TPOT will run until the time limit is up, with no generation or early stop limits." ] @@ -290,14 +290,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Generation: : 4it [01:39, 24.97s/it]\n", + "Generation: : 5it [01:33, 18.64s/it]\n", "/home/perib/miniconda3/envs/myenv/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", " warnings.warn(\n" ] @@ -306,7 +306,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.9948227797690163\n" + "0.9797527706734868\n" ] } ], @@ -352,7 +352,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -762,11 +762,12 @@ " /* fitted */\n", " background-color: var(--sklearn-color-fitted-level-3);\n", "}\n", - "
Pipeline(steps=[('standardscaler', StandardScaler()),\n",
-       "                ('selectfwe', SelectFwe(alpha=0.0001226579434)),\n",
+       "
Pipeline(steps=[('maxabsscaler', MaxAbsScaler()),\n",
+       "                ('selectfwe', SelectFwe(alpha=0.0142080454732)),\n",
        "                ('featureunion-1',\n",
-       "                 FeatureUnion(transformer_list=[('skiptransformer',\n",
-       "                                                 SkipTransformer()),\n",
+       "                 FeatureUnion(transformer_list=[('featureunion',\n",
+       "                                                 FeatureUnion(transformer_list=[('passkbinsdiscretizer',\n",
+       "                                                                                 PassKBinsDiscretizer(n_bins=8))])),\n",
        "                                                ('passthrough',\n",
        "                                                 Passthrough())])),\n",
        "                ('featureunion-2',\n",
@@ -775,12 +776,14 @@
        "                                                ('passthrough',\n",
        "                                                 Passthrough())])),\n",
        "                ('logisticregression',\n",
-       "                 LogisticRegression(C=31921.0176296069, max_iter=1000, n_jobs=1,\n",
-       "                                    solver='saga'))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
MaxAbsScaler()
SelectFwe(alpha=0.0142080454732)
FeatureUnion(transformer_list=[('featureunion',\n",
+       "                                FeatureUnion(transformer_list=[('passkbinsdiscretizer',\n",
+       "                                                                PassKBinsDiscretizer(n_bins=8))])),\n",
+       "                               ('passthrough', Passthrough())])
PassKBinsDiscretizer(n_bins=8)
Passthrough()
FeatureUnion(transformer_list=[('skiptransformer', SkipTransformer()),\n",
+       "                               ('passthrough', Passthrough())])
SkipTransformer()
Passthrough()
LogisticRegression(C=462.7983711938423, class_weight='balanced', max_iter=1000,\n",
+       "                   n_jobs=1, solver='saga')
" ], "text/plain": [ - "Pipeline(steps=[('standardscaler', StandardScaler()),\n", - " ('selectfwe', SelectFwe(alpha=0.0001226579434)),\n", + "Pipeline(steps=[('maxabsscaler', MaxAbsScaler()),\n", + " ('selectfwe', SelectFwe(alpha=0.0142080454732)),\n", " ('featureunion-1',\n", - " FeatureUnion(transformer_list=[('skiptransformer',\n", - " SkipTransformer()),\n", + " FeatureUnion(transformer_list=[('featureunion',\n", + " FeatureUnion(transformer_list=[('passkbinsdiscretizer',\n", + " PassKBinsDiscretizer(n_bins=8))])),\n", " ('passthrough',\n", " Passthrough())])),\n", " ('featureunion-2',\n", @@ -808,11 +816,12 @@ " ('passthrough',\n", " Passthrough())])),\n", " ('logisticregression',\n", - " LogisticRegression(C=31921.0176296069, max_iter=1000, n_jobs=1,\n", - " solver='saga'))])" + " LogisticRegression(C=462.7983711938423,\n", + " class_weight='balanced', max_iter=1000,\n", + " n_jobs=1, solver='saga'))])" ] }, - "execution_count": 11, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -824,22 +833,22 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1,\n", - " 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1,\n", - " 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0,\n", - " 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1,\n", - " 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0,\n", - " 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0,\n", - " 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1])" + "array([0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0,\n", + " 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1,\n", + " 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0,\n", + " 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1,\n", + " 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0,\n", + " 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n", + " 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1])" ] }, - "execution_count": 12, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -859,7 +868,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -883,7 +892,7 @@ "\n", "| Column | Description |\n", "| :--- | :----: |\n", - "| | The first set of columns will correspond to each objective function. These can either be automatically named by TPOT, or passed in by the user. |\n", + "| \\ | The first set of columns will correspond to each objective function. These can either be automatically named by TPOT, or passed in by the user. |\n", "| Parents | This contains a tuple that contains the indexes of the 'parents' of the current pipeline. For example, (29, 42) means that the pipelines in indexes 29 and 42 were utilized to generate that pipeline. |\n", "| Variation_Function | The function applied to the parents to generate the new pipeline |\n", "| Individual | The individual class that represents a specific pipeline and hyperparameter configuration. This class also contains functions for mutation and crossover. To get the sklearn estimator/pipeline object from the individual you can call the `export_pipeline()` function. (as in, `pipe = ind.export_pipeline()`) |\n", @@ -896,7 +905,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -905,7 +914,7 @@ "['roc_auc_score', 'complexity_scorer']" ] }, - "execution_count": 14, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -917,7 +926,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -957,73 +966,73 @@ " \n", " \n", " 0\n", - " NaN\n", - " NaN\n", + " 0.994779\n", + " 38.8\n", " NaN\n", " NaN\n", " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", " 0.0\n", - " 1.727136e+09\n", - " 1.727136e+09\n", - " INVALID\n", + " 1.727192e+09\n", + " 1.727192e+09\n", + " None\n", " NaN\n", - " (StandardScaler(), VarianceThreshold(threshold...\n", + " (Normalizer(norm='l1'), Passthrough(), Feature...\n", " \n", " \n", " 1\n", - " 0.990146\n", - " 10.0\n", + " 0.884608\n", + " 12.0\n", " NaN\n", " NaN\n", " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", " 0.0\n", - " 1.727136e+09\n", - " 1.727136e+09\n", + " 1.727192e+09\n", + " 1.727192e+09\n", " None\n", " NaN\n", - " (MaxAbsScaler(), VarianceThreshold(threshold=0...\n", + " (MinMaxScaler(), SelectPercentile(percentile=6...\n", " \n", " \n", " 2\n", - " NaN\n", - " NaN\n", + " 0.995994\n", + " 277.0\n", " NaN\n", " NaN\n", " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", " 0.0\n", - " 1.727136e+09\n", - " 1.727136e+09\n", - " INVALID\n", + " 1.727192e+09\n", + " 1.727192e+09\n", + " None\n", " NaN\n", - " (StandardScaler(), VarianceThreshold(threshold...\n", + " (MaxAbsScaler(), Passthrough(), FeatureUnion(t...\n", " \n", " \n", " 3\n", - " 0.961892\n", - " 80.0\n", + " 0.969714\n", + " 97.0\n", " NaN\n", " NaN\n", " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", " 0.0\n", - " 1.727136e+09\n", - " 1.727136e+09\n", + " 1.727192e+09\n", + " 1.727192e+09\n", " None\n", " NaN\n", - " (RobustScaler(quantile_range=(0.2009793033711,...\n", + " (RobustScaler(quantile_range=(0.1797291876324,...\n", " \n", " \n", " 4\n", - " 0.955582\n", - " 8.0\n", + " 0.977700\n", + " 10.0\n", " NaN\n", " NaN\n", " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", " 0.0\n", - " 1.727136e+09\n", - " 1.727136e+09\n", + " 1.727192e+09\n", + " 1.727192e+09\n", " None\n", " NaN\n", - " (MaxAbsScaler(), Passthrough(), FeatureUnion(t...\n", + " (RobustScaler(quantile_range=(0.0723902721638,...\n", " \n", " \n", " ...\n", @@ -1040,93 +1049,106 @@ " ...\n", " \n", " \n", - " 245\n", - " 0.981354\n", - " 11.0\n", - " (176, 176)\n", - " ind_mutate\n", + " 295\n", + " 0.994663\n", + " 17.0\n", + " (245, 2)\n", + " ind_crossover\n", " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", - " 4.0\n", - " 1.727136e+09\n", - " 1.727136e+09\n", + " 5.0\n", + " 1.727192e+09\n", + " 1.727192e+09\n", " None\n", - " NaN\n", - " (MinMaxScaler(), SelectFwe(alpha=0.00036066272...\n", + " 1.0\n", + " (MinMaxScaler(), SelectPercentile(percentile=3...\n", " \n", " \n", - " 246\n", - " 0.972795\n", - " 10.0\n", - " (145, 58)\n", - " ind_crossover\n", + " 296\n", + " NaN\n", + " NaN\n", + " (190, 190)\n", + " ind_mutate\n", " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", - " 4.0\n", - " 1.727136e+09\n", - " 1.727136e+09\n", - " None\n", + " 5.0\n", + " 1.727192e+09\n", + " 1.727192e+09\n", + " INVALID\n", " NaN\n", - " (MaxAbsScaler(), Passthrough(), FeatureUnion(t...\n", + " (RobustScaler(quantile_range=(0.0790382918495,...\n", " \n", " \n", - " 247\n", - " 0.895754\n", - " 6.0\n", - " (195, 195)\n", - " ind_mutate\n", + " 297\n", + " 0.995224\n", + " 231.0\n", + " (168, 232)\n", + " ind_crossover\n", " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", - " 4.0\n", - " 1.727136e+09\n", - " 1.727136e+09\n", + " 5.0\n", + " 1.727192e+09\n", + " 1.727192e+09\n", " None\n", " NaN\n", - " (StandardScaler(), VarianceThreshold(threshold...\n", + " (MaxAbsScaler(), SelectFwe(alpha=0.03220532277...\n", " \n", " \n", - " 248\n", - " 0.978311\n", - " 7.0\n", - " (32, 32)\n", - " ind_mutate\n", + " 298\n", + " 0.974412\n", + " 10.0\n", + " (93, 205)\n", + " ind_mutate , ind_mutate , ind_crossover\n", " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", - " 4.0\n", - " 1.727136e+09\n", - " 1.727136e+09\n", + " 5.0\n", + " 1.727192e+09\n", + " 1.727192e+09\n", " None\n", " NaN\n", - " (MaxAbsScaler(), SelectFwe(alpha=0.00140487405...\n", + " (MaxAbsScaler(), Passthrough(), FeatureUnion(t...\n", " \n", " \n", - " 249\n", - " 0.983915\n", - " 9.0\n", - " (99, 99)\n", + " 299\n", + " 0.932393\n", + " 8.0\n", + " (234, 234)\n", " ind_mutate\n", " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", - " 4.0\n", - " 1.727136e+09\n", - " 1.727136e+09\n", + " 5.0\n", + " 1.727192e+09\n", + " 1.727192e+09\n", " None\n", " NaN\n", - " (StandardScaler(), VarianceThreshold(threshold...\n", + " (Normalizer(norm='l1'), VarianceThreshold(thre...\n", " \n", " \n", "\n", - "

250 rows × 11 columns

\n", + "

300 rows × 11 columns

\n", "" ], "text/plain": [ - " roc_auc_score complexity_scorer Parents Variation_Function \\\n", - "0 NaN NaN NaN NaN \n", - "1 0.990146 10.0 NaN NaN \n", - "2 NaN NaN NaN NaN \n", - "3 0.961892 80.0 NaN NaN \n", - "4 0.955582 8.0 NaN NaN \n", - ".. ... ... ... ... \n", - "245 0.981354 11.0 (176, 176) ind_mutate \n", - "246 0.972795 10.0 (145, 58) ind_crossover \n", - "247 0.895754 6.0 (195, 195) ind_mutate \n", - "248 0.978311 7.0 (32, 32) ind_mutate \n", - "249 0.983915 9.0 (99, 99) ind_mutate \n", + " roc_auc_score complexity_scorer Parents \\\n", + "0 0.994779 38.8 NaN \n", + "1 0.884608 12.0 NaN \n", + "2 0.995994 277.0 NaN \n", + "3 0.969714 97.0 NaN \n", + "4 0.977700 10.0 NaN \n", + ".. ... ... ... \n", + "295 0.994663 17.0 (245, 2) \n", + "296 NaN NaN (190, 190) \n", + "297 0.995224 231.0 (168, 232) \n", + "298 0.974412 10.0 (93, 205) \n", + "299 0.932393 8.0 (234, 234) \n", + "\n", + " Variation_Function \\\n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + ".. ... \n", + "295 ind_crossover \n", + "296 ind_mutate \n", + "297 ind_crossover \n", + "298 ind_mutate , ind_mutate , ind_crossover \n", + "299 ind_mutate \n", "\n", " Individual Generation \\\n", "0 " ] @@ -1206,7 +1228,7 @@ }, { "data": { - "image/png": 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", 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uq/HnmV/toaGh+uGHH2SMcdh+9OhRXbhwwWnXEYCyhdv8AJRJ3bt3lzFGf/31l5o1a5bn07BhQ8vHzl35691333VoX7Jkic6ePWvfXpJyf9m7OBQaY4q1vHW3bt20d+/ey95G1b17d504cULZ2dn5jut1111X4Hevu+46ValSRe+//77Dqnhnz57VkiVL7Cv8FVXHjh3tQeBiCxculL+/f5GWyL6SYcOG6fjx47rnnnt0+vTpQi+ZX9wZi9atW6t8+fLauXNnvuPerFmzAmeDCiu/a0qS5s6dW+hjFOX6KGhMOnbsqJ07d2rr1q0O7QsXLpTNZlOHDh2KclqF1rFjR6WlpWnZsmV5+s3dXlSFnfUEUHoxMwWgTGrdurUeeeQRDRw4UD/99JPatm2rgIAAJSUlaePGjWrYsKEef/xxS8fu3LmzunbtqjFjxig1NVWtW7fWr7/+qokTJ6pJkybq37+/k88m/xq8vb113333afTo0Tp//rzmzJmjU6dOWT7m8OHDtXjxYvXs2VNjx45V8+bNde7cOW3YsEHdu3dXhw4d1KdPH7333nu6/fbb9cQTT6h58+by8vLSn3/+qXXr1qlnz576xz/+ke/xy5UrpxkzZqhfv37q3r27Hn30UWVkZGjmzJk6ffq0nnvuOUt1T5w4UcuXL1eHDh309NNPq2LFinrvvfe0YsUKzZgxQyEhIZbH5FJ16tTRbbfdpi+//FJt2rRR48aNC/W9hg0batGiRVq8eLFq1qwpX1/fIgX6wMBAzZ49WwMGDNDJkyd1zz33qHLlyjp27Jh++eUXHTt2THPmzLF6WpKkVq1aqUKFCnrsscc0ceJEeXl56b333tMvv/xS6GMU5fooaEyefPJJLVy4UHfccYeeeeYZVa9eXStWrFB8fLwef/zxEnt587/+9S+9+uqrGjBggA4ePKiGDRtq48aNmjZtmm6//fbLPktYkNw/45deekkDBgyQl5eXrrvuukLN0AEoHQhTAMqsuXPnqkWLFpo7d67i4+OVk5OjqKgotW7dOs/D7UVhs9m0bNkyTZo0SfPnz9ezzz6rsLAw9e/fX9OmTcv3tkJnu/7667VkyRL9+9//1t13363Q0FD17dtXI0aMULdu3SwdMygoSBs3btSkSZP0+uuva/LkyapQoYJuuukmPfLII5IkDw8PffbZZ3rppZf0zjvvaPr06fL09FTVqlXVrl27KwaEvn37KiAgQNOnT1fv3r3l4eGhFi1aaN26dWrVqpWluq+77jolJCTo//7v/+zPi9WtW1fz58/Ps0iIM/Tu3VtffvllkV7kPHnyZCUlJenhhx/WmTNnVL16dR08eLBI/d5///2qVq2aZsyYoUcffVRnzpxR5cqVdcMNNzjlPENDQ7VixQqNHDlS999/vwICAtSzZ08tXrxYN954Y6GOUZTro6AxqVSpkhISEjRu3DiNGzdOqampqlmzpmbMmKERI0YU+zwL4uvrq3Xr1mn8+PGaOXOmjh07pipVqmjUqFGaOHGipWO2b99e48aN09tvv6158+YpJydH69atu+J70wCUHjZjLnkDIQAAKFDuyoMHDx6Ul5eXq8sBALgQM1MAAFxBRkaGtm7dqh9//FGffPKJXnzxRYIUAICZKQAAruTgwYOKiYlRcHCw+vbtq1deeUUeHh6uLgsA4GKEKQAAAACwgKXRAQAAAMACwhQAAAAAWECYAgAAAAALWM1PUk5Ojo4cOaKgoCD7G+ABAAAAXHuMMTpz5oyioqJUrtzl554IU5KOHDmi6OhoV5cBAAAAwE0cPnxYVatWvew+hClJQUFBkv4esODgYBdXAwAAAMBVUlNTFR0dbc8Il0OYkuy39gUHBxOmAAAAABTq8R8WoAAAAAAACwhTAAAAAGABYQoAAAAALOCZqUIyxujChQvKzs52dSlwMQ8PD3l6erKMPgAAwDWOMFUImZmZSkpKUnp6uqtLgZvw9/dXZGSkvL29XV0KAAAAXIQwdQU5OTlKTEyUh4eHoqKi5O3tzYzENcwYo8zMTB07dkyJiYmKjY294svcAAAAUDYRpq4gMzNTOTk5io6Olr+/v6vLgRvw8/OTl5eX/vjjD2VmZsrX19fVJQEAAMAF+F/qhcTsAy7G9QAAAAB+IwQAAAAACwhTAAAAAGABz0wBAAAAcJmU9EwdT8tU6vksBft5KSzAWyH+pWPFZGamyqi4uDjZbDbZbDZ5eXmpZs2aGjVqlM6ePVui/a5fv142m02nT58u9rHat29vP4eLPxcuXCh+oQU4ePCgbDabtm3bVmJ9AAAA4G9HTp/TkA9+VscXN+gf8Qnq+MIGDf3gZx05fc7VpRUKYaoMu+2225SUlKQDBw5o6tSpio+P16hRoywdK/elxVfbww8/rKSkJIePp2feCdXMzMyrXhsAAACsS0nP1Jglv+rbfccd2r/Zd1xjl/yqlHT3//2OMHWVpKRnav/RNP186JT2H0u7KheHj4+PIiIiFB0drb59+6pfv35atmyZJOndd99Vs2bNFBQUpIiICPXt21dHjx61fzd3hmnVqlVq1qyZfHx89O2338oYoxkzZqhmzZry8/NT48aN9fHHH0v6e1anQ4cOkqQKFSrIZrMpLi5OkpSRkaFhw4apcuXK8vX1VZs2bbR58+YrnoO/v78iIiIcPpJUo0YNTZ06VXFxcQoJCdHDDz8sSVqyZInq168vHx8f1ahRQy+88ILD8WrUqKFp06bpgQceUFBQkKpVq6bXX3/dvj0mJkaS1KRJE9lsNrVv377oAw8AAIArOp6WmSdI5fpm33EdTyNMQe4zfenn56esrCxJf8/kTJkyRb/88ouWLVumxMREe/C52OjRozV9+nTt2rVLjRo10r///W/Nnz9fc+bM0Y4dO/Tkk0/q/vvv14YNGxQdHa0lS5ZIkvbs2aOkpCS99NJL9uMsWbJEb7/9trZu3aratWura9euOnnypOXzmTlzpho0aKAtW7ZowoQJ2rJli+6991716dNHv/32myZNmqQJEyZowYIFDt974YUX1KxZM/38888aNGiQHn/8ce3evVuS9OOPP0qSvvrqKyUlJWnp0qWW6wMAAEDBUs9nXXb7mStsdwcsQFHCrjR9Ofu+JlflAbsff/xR77//vjp27ChJeuCBB+zbatasqZdfflnNmzdXWlqaAgMD7dueeeYZde7cWZJ09uxZvfjii1q7dq1atmxp/+7GjRs1d+5ctWvXThUrVpQkVa5cWeXLl7d/b86cOVqwYIG6desmSZo3b57WrFmjN998U0899VSBdcfHx+uNN96w//zoo4/aZ5tuvfVWh9sW+/Xrp44dO2rChAmSpDp16mjnzp2aOXOmQ1C8/fbbNWjQIEnSmDFjNGvWLK1fv17XX3+9KlWqJEkKDQ21z4IBAADA+YJ9vS67PegK290BYaqEFWb6sqTC1PLlyxUYGKgLFy4oKytLPXv21OzZsyVJP//8syZNmqRt27bp5MmTysnJkSQdOnRI9erVsx+jWbNm9n/euXOnzp8/bw9XuTIzM9WkSZMC69i/f7+ysrLUunVre5uXl5eaN2+uXbt2XfYc+vXrp/Hjx9t/zg1ol9YmSbt27VLPnj0d2lq3bq3//ve/ys7OloeHhySpUaNG9u02m00REREOtzgCAACg5IUFeqttbJi+yed35baxYQoLdP8V/QhTJcyV05cdOnTQnDlz5OXlpaioKHl5/Z3uz549qy5duqhLly569913ValSJR06dEhdu3bNs5BDQECA/Z9zA9eKFStUpUoVh/18fHwKrMMYI+nv4HJp+6VtlwoJCVHt2rXz3XZxbQUdL7fvi+WOQy6bzWY/NwAAAFwdIf7eeq5XI41d8qtDoGobG6bnezUqFcujE6ZKmCunLwMCAvINIrt379bx48f13HPPKTo6WpL0008/XfF49erVk4+Pjw4dOqR27drlu4+3998XfXZ2tr2tdu3a8vb21saNG9W3b19JUlZWln766ScNHz68qKd12fo2btzo0JaQkKA6derYZ6WuJL/6AQAAUDKiyvtp9n1NdDwtU2fOZynI10thgaXnPVOEqRLmjtOX1apVk7e3t2bPnq3HHntM27dv15QpU674vaCgII0aNUpPPvmkcnJy1KZNG6WmpiohIUGBgYEaMGCAqlevLpvNpuXLl+v222+Xn5+fAgMD9fjjj+upp55SxYoVVa1aNc2YMUPp6el68MEHnXZeI0eO1E033aQpU6aod+/e2rRpk1555RXFx8cX+hiVK1eWn5+fVq5cqapVq8rX11chISFOqxEAAACOQvxLT3i6FKv5lbDc6cu2sWEO7a6cvqxUqZIWLFigjz76SPXq1dNzzz2n//znP4X67pQpU/T0009r+vTpqlu3rrp27arPP//cvqR4lSpVNHnyZI0dO1bh4eEaMmSIJOm5555Tr1691L9/f9144436/ffftWrVKlWoUMFp53XjjTfqww8/1KJFi9SgQQM9/fTTeuaZZ/JdpbAgnp6eevnllzV37lxFRUXleQYLAAAAyGUz+T1Uco1JTU1VSEiIUlJSFBwc7LDt/PnzSkxMVExMjHx9fS33kZKeWWqnL5GXs64LAAAAuJfLZYNLcZvfVVKapy8BAAAA5MVtfgAAAABgAWEKAAAAACwgTAEAAACABYSpQmKdDlyM6wEAAACEqSvw8vr7pbrp6ekurgTuJPd6yL0+AAAAcO1hNb8r8PDwUPny5XX06FFJkr+/v2w2m4urgqsYY5Senq6jR4+qfPny8vDwcHVJAAAAcBHCVCFERERIkj1QAeXLl7dfFwAAALg2EaYKwWazKTIyUpUrV1ZWVpary4GLeXl5MSMFAAAAwlRReHh48Es0AAAAAEksQAEAAAAAlhCmAAAAAMACwhQAAAAAWECYAgAAAAALCFMAAAAAYAGr+QEAAAAospT0TB1Py1Tq+SwF+3kpLMBbIf7eri7rqiJMAQAAACiSI6fPacySX/XtvuP2traxYXquVyNFlfdzYWVXF7f5AQAAACi0lPTMPEFKkr7Zd1xjl/yqlPRMF1V29RGmAAAAABTa8bTMPEEq1zf7jut4GmEKAAAAAPJIPZ912e1nrrC9LCFMAQAAACi0YF+vy24PusL2soQwBQAAAKDQwgK91TY2LN9tbWPDFBZ47azoR5gCAAAAUGgh/t56rlejPIGqbWyYnu/V6JpaHp2l0QEAAAAUSVR5P82+r4mOp2XqzPksBfl6KSyQ90wBAAAAwBWF+F974elS3OYHAAAAABYQpgAAAADAAsIUAAAAAFhAmAIAAAAACwhTAAAAAGABYQoAAAAALCBMAQAAAIAFhCkAAAAAsIAwBQAAAAAWEKYAAAAAwALCFAAAAABYQJgCAAAAAAtcGqamT5+um266SUFBQapcubLuuusu7dmzx2EfY4wmTZqkqKgo+fn5qX379tqxY4fDPhkZGRo6dKjCwsIUEBCgO++8U3/++efVPBUAAAAA1xiXhqkNGzZo8ODB+v7777VmzRpduHBBXbp00dmzZ+37zJgxQy+++KJeeeUVbd68WREREercubPOnDlj32f48OH65JNPtGjRIm3cuFFpaWnq3r27srOzXXFaAAAAAK4BNmOMcXURuY4dO6bKlStrw4YNatu2rYwxioqK0vDhwzVmzBhJf89ChYeH6/nnn9ejjz6qlJQUVapUSe+884569+4tSTpy5Iiio6P1xRdfqGvXrnn6ycjIUEZGhv3n1NRURUdHKyUlRcHBwVfnZAEAAAC4ndTUVIWEhBQqG7jVM1MpKSmSpIoVK0qSEhMTlZycrC5dutj38fHxUbt27ZSQkCBJ2rJli7Kyshz2iYqKUoMGDez7XGr69OkKCQmxf6Kjo0vqlAAAAACUUW4TpowxGjFihNq0aaMGDRpIkpKTkyVJ4eHhDvuGh4fbtyUnJ8vb21sVKlQocJ9LjRs3TikpKfbP4cOHnX06AAAAAMo4T1cXkGvIkCH69ddftXHjxjzbbDabw8/GmDxtl7rcPj4+PvLx8bFeLAAAAIBrnlvMTA0dOlSfffaZ1q1bp6pVq9rbIyIiJCnPDNPRo0fts1URERHKzMzUqVOnCtwHAAAAAJzNpWHKGKMhQ4Zo6dKlWrt2rWJiYhy2x8TEKCIiQmvWrLG3ZWZmasOGDWrVqpUkqWnTpvLy8nLYJykpSdu3b7fvAwAAAADO5tLb/AYPHqz3339fn376qYKCguwzUCEhIfLz85PNZtPw4cM1bdo0xcbGKjY2VtOmTZO/v7/69u1r3/fBBx/UyJEjFRoaqooVK2rUqFFq2LChOnXq5MrTAwAAAFCGuTRMzZkzR5LUvn17h/b58+crLi5OkjR69GidO3dOgwYN0qlTp3TzzTdr9erVCgoKsu8/a9YseXp66t5779W5c+fUsWNHLViwQB4eHlfrVAAAAABcY9zqPVOuUpS15AEAAACUXaX2PVMAAAAAUFoQpgAAAADAAsIUAAAAAFhAmAIAAAAACwhTAAAAAGABYQoAAAAALCBMAQAAAIAFhCkAAAAAsIAwBQAAAAAWEKYAAAAAwALCFAAAAABYQJgCAAAAAAsIUwAAAABgAWEKAAAAACwgTAEAAACABYQpAAAAALDA09UFAAAAAHBvKemZOp6WqdTzWQr281JYgLdC/L1dXZbLEaYAAAAAFOjI6XMas+RXfbvvuL2tbWyYnuvVSFHl/VxYmetxmx8AAACAfKWkZ+YJUpL0zb7jGrvkV6WkZ7qoMvdAmAIAAACQr+NpmXmCVK5v9h3X8TTCFAAAAADkkXo+67Lbz1xhe1lHmAIAAACQr2Bfr8tuD7rC9rKOMAUAAAAgX2GB3mobG5bvtraxYQoLvLZX9CNMAQAAAMhXiL+3nuvVKE+gahsbpud7Nbrml0dnaXQAAAAABYoq76fZ9zXR8bRMnTmfpSBfL4UF8p4piTAFAAAA4ApC/AlP+eE2PwAAAACwgDAFAAAAABYQpgAAAADAAsIUAAAAAFhAmAIAAAAACwhTAAAAAGABYQoAAAAALCBMAQAAAIAFhCkAAAAAsIAwBQAAAAAWEKYAAAAAwALCFAAAAABYQJgCAAAAAAsIUwAAAABgAWEKAAAAACwgTAEAAACABYQpAAAAALCAMAUAAAAAFhCmAAAAAMACwhQAAAAAWECYAgAAAAALCFMAAAAAYAFhCgAAAAAsIEwBAAAAgAWEKQAAAACwgDAFAAAAABYQpgAAAADAAsIUAAAAAFhAmAIAAAAACwhTAAAAAGABYQoAAAAALCBMAQAAAIAFRQ5TFy5c0OTJk3X48OGSqAcAAAAASoUihylPT0/NnDlT2dnZJVEPAAAAAJQKlm7z69Spk9avX+/kUgAAAACg9PC08qVu3bpp3Lhx2r59u5o2baqAgACH7XfeeadTigMAAAAAd2UzxpiifqlcuYIntGw2W6m7BTA1NVUhISFKSUlRcHCwq8sBAAAA4CJFyQaWZqZycnIsFQYAAAAAZUWxl0Y/f/68M+oAAAAAgFLFUpjKzs7WlClTVKVKFQUGBurAgQOSpAkTJujNN990aoEAAAAA4I4shalnn31WCxYs0IwZM+Tt7W1vb9iwod54441CH+ebb75Rjx49FBUVJZvNpmXLljlsj4uLk81mc/i0aNHCYZ+MjAwNHTpUYWFhCggI0J133qk///zTymkBAAAAQKFZClMLFy7U66+/rn79+snDw8Pe3qhRI+3evbvQxzl79qwaN26sV155pcB9brvtNiUlJdk/X3zxhcP24cOH65NPPtGiRYu0ceNGpaWlqXv37qVuEQwAAAAApYulBSj++usv1a5dO097Tk6OsrKyCn2cbt26qVu3bpfdx8fHRxEREfluS0lJ0Ztvvql33nlHnTp1kiS9++67io6O1ldffaWuXbsWuhYAAAAAKApLM1P169fXt99+m6f9o48+UpMmTYpd1MXWr1+vypUrq06dOnr44Yd19OhR+7YtW7YoKytLXbp0sbdFRUWpQYMGSkhIKPCYGRkZSk1NdfgAAAAAQFFYmpmaOHGi+vfvr7/++ks5OTlaunSp9uzZo4ULF2r58uVOK65bt2765z//qerVqysxMVETJkzQrbfeqi1btsjHx0fJycny9vZWhQoVHL4XHh6u5OTkAo87ffp0TZ482Wl1AgAAALj2WJqZ6tGjhxYvXqwvvvhCNptNTz/9tHbt2qXPP/9cnTt3dlpxvXv31h133KEGDRqoR48e+vLLL7V3716tWLHist8zxshmsxW4fdy4cUpJSbF/Dh8+7LSaAQAAAFwbijwzdeHCBT377LN64IEHtGHDhpKoqUCRkZGqXr269u3bJ0mKiIhQZmamTp065TA7dfToUbVq1arA4/j4+MjHx6fE6wUAAABQdhV5ZsrT01MzZ850yWp5J06c0OHDhxUZGSlJatq0qby8vLRmzRr7PklJSdq+fftlwxQAAAAAFJel2/w6deqk9evXF7vztLQ0bdu2Tdu2bZMkJSYmatu2bTp06JDS0tI0atQobdq0SQcPHtT69evVo0cPhYWF6R//+IckKSQkRA8++KBGjhypr7/+Wj///LPuv/9+NWzY0L66HwAAAACUBEsLUHTr1k3jxo3T9u3b1bRpUwUEBDhsv/POOwt1nJ9++kkdOnSw/zxixAhJ0oABAzRnzhz99ttvWrhwoU6fPq3IyEh16NBBixcvVlBQkP07s2bNkqenp+69916dO3dOHTt21IIFCxzefwUAAAAAzmYzxpiifqlcuYIntGw2W6l7YW5qaqpCQkKUkpKi4OBgV5cDAAAAwEWKkg0szUzl5ORYKgwAAAAAygpLz0wBAAAAwLXOcpjasGGDevToodq1ays2NlZ33nmnvv32W2fWBgAAAABuy1KYevfdd9WpUyf5+/tr2LBhGjJkiPz8/NSxY0e9//77zq4RAAAAANyOpQUo6tatq0ceeURPPvmkQ/uLL76oefPmadeuXU4r8GpgAQoAAAAAUtGygaWZqQMHDqhHjx552u+8804lJiZaOSQAAAAAlCqWwlR0dLS+/vrrPO1ff/21oqOji10UAAAAALg7S0ujjxw5UsOGDdO2bdvUqlUr2Ww2bdy4UQsWLNBLL73k7BoBAAAAwO1YClOPP/64IiIi9MILL+jDDz+U9PdzVIsXL1bPnj2dWiAAAAAAuCNLC1CUNSxAAQAAAEC6CgtQbN68WT/88EOe9h9++EE//fSTlUMCAAAAQKliKUwNHjxYhw8fztP+119/afDgwcUuCgAAAADcnaUwtXPnTt1444152ps0aaKdO3cWuygAAAAAcHeWwpSPj4/+97//5WlPSkqSp6elNS0AAAAAoFSxFKY6d+6scePGKSUlxd52+vRp/d///Z86d+7stOIAAAAAwF1ZmkZ64YUX1LZtW1WvXl1NmjSRJG3btk3h4eF65513nFogAAAAALgjS2GqSpUq+vXXX/Xee+/pl19+kZ+fnwYOHKj77rtPXl5ezq4RAAAAANyO5QecAgIC9MgjjzizFgAAAAAoNSw9M/X2229rxYoV9p9Hjx6t8uXLq1WrVvrjjz+cVhwAAAAAuCtLYWratGny8/OTJG3atEmvvPKKZsyYobCwMD355JNOLRAAAAAA3JGl2/wOHz6s2rVrS5KWLVume+65R4888ohat26t9u3bO7M+AAAAAHBLlmamAgMDdeLECUnS6tWr1alTJ0mSr6+vzp0757zqAAAAAMBNWZqZ6ty5sx566CE1adJEe/fu1R133CFJ2rFjh2rUqOHM+gAAAADALVmamXr11VfVsmVLHTt2TEuWLFFoaKgkacuWLbrvvvucWiAAAAAAuCObMcaU1MEHDRqkZ555RmFhYSXVhVOkpqYqJCREKSkpCg4OdnU5AAAAAFykKNnA0sxUYb377rtKTU0tyS4AAAAAwCVKNEyV4KQXAAAAALhUiYYpAAAAACirCFMAAAAAYAFhCgAAAAAsIEwBAAAAgAUlGqbuv/9+lhoHAAAAUCZZClM1atTQM888o0OHDl12vzlz5rj9O6YAAAAAwApLYWrkyJH69NNPVbNmTXXu3FmLFi1SRkaGs2sDAAAAALdlKUwNHTpUW7Zs0ZYtW1SvXj0NGzZMkZGRGjJkiLZu3ersGgEAAADA7diME96sm5WVpfj4eI0ZM0ZZWVlq0KCBnnjiCQ0cOFA2m80ZdZao1NRUhYSEKCUlhWe8AAAAgGtYUbKBZ3E6ysrK0ieffKL58+drzZo1atGihR588EEdOXJE48eP11dffaX333+/OF0AAAAAgFuyFKa2bt2q+fPn64MPPpCHh4f69++vWbNm6frrr7fv06VLF7Vt29ZphQIAAACAO7EUpm666SZ17txZc+bM0V133SUvL688+9SrV099+vQpdoEAAAAA4I4shakDBw6oevXql90nICBA8+fPt1QUAAAAALg7S6v5dejQQSdOnMjTfvr0adWsWbPYRQEAAACAu7MUpg4ePKjs7Ow87RkZGfrrr7+KXRQAAAAAuLsi3eb32Wef2f951apVCgkJsf+cnZ2tr7/+WjVq1HBacQAAAADgrooUpu666y5Jks1m04ABAxy2eXl5qUaNGnrhhRecVhwAAAAAuKsihamcnBxJUkxMjDZv3qywsLASKQoAAAAA3J2l1fwSExOdXQcAAAAAlCqFDlMvv/yyHnnkEfn6+urll1++7L7Dhg0rdmEAAAAA4M5sxhhTmB1jYmL0008/KTQ0VDExMQUf0GbTgQMHnFbg1ZCamqqQkBClpKQoODjY1eUAAAAAcJGiZINCz0xdfGsft/kBAAAAuNZZes/UuXPnCtyWlJRkuRgAAAAAKC0shakmTZpo69atedo//vhjNWrUqNhFAQAAAIC7sxSmOnfurFatWum5556TMUZpaWmKi4vTgAED9PTTTzu7RgAAAABwO5aWRp89e7buuOMODRw4UCtWrNCRI0cUHByszZs3q169es6uEQAAAADcjqUwJUldunTR3XffrTlz5sjT01Off/45QQoAAADANcPSbX779+9Xy5YttXz5cq1atUqjR49Wz549NXr0aGVlZTm7RgAAAABwO5bC1A033KCYmBj98ssv6ty5s6ZOnaq1a9dq6dKlat68ubNrBAAAAAC3YylMxcfHa9GiRSpfvry9rVWrVvr555914403Oqs2AAAAAHBbNmOMsfrlzMxMJSYmqlatWvL0tPz4lcsV5S3HAAAAAMquomQDyy/tffDBB+Xv76/69evr0KFDkqRhw4bp+eeft3JIAAAAAChVLIWpsWPH6pdfftH69evl6+trb+/UqZMWLVrktOIAAAAAwF1Zujdv2bJlWrx4sVq0aCGbzWZvr1evnvbv3++04gAAAADAXVmamTp27JgqV66cp/3s2bMO4QoAAAAAyipLYeqmm27SihUr7D/nBqh58+apZcuWzqkMAAAAANyYpdv8pk+frttuu007d+7UhQsX9NJLL2nHjh3atGmTNmzY4OwaAQAAAMDtWJqZatWqlb777julp6erVq1aWr16tcLDw7Vp0yY1bdrU2TUCAAAAgNsp1numygreMwUAAABAKlo2KPRtfqmpqYUugEACAAAAoKwr9G1+5cuXV4UKFS77yd2nsL755hv16NFDUVFRstlsWrZsmcN2Y4wmTZqkqKgo+fn5qX379tqxY4fDPhkZGRo6dKjCwsIUEBCgO++8U3/++WehawAAAAAAKwo9M7Vu3Tqnd3727Fk1btxYAwcOVK9evfJsnzFjhl588UUtWLBAderU0dSpU9W5c2ft2bNHQUFBkqThw4fr888/16JFixQaGqqRI0eqe/fu2rJlizw8PJxeMwAAAABIbvTMlM1m0yeffKK77rpL0t+zUlFRURo+fLjGjBkj6e9ZqPDwcD3//PN69NFHlZKSokqVKumdd95R7969JUlHjhxRdHS0vvjiC3Xt2rVQffPMFAAAAACphJ6ZutSpU6f05ptvateuXbLZbKpbt64GDhyoihUrWj2kg8TERCUnJ6tLly72Nh8fH7Vr104JCQl69NFHtWXLFmVlZTnsExUVpQYNGighIaHAMJWRkaGMjAz7z0V5HgwAAAAAJItLo2/YsEE1atTQyy+/rFOnTunkyZN6+eWXFRMT47T3TCUnJ0uSwsPDHdrDw8Pt25KTk+Xt7Z3nOa2L98nP9OnTFRISYv9ER0c7pWYAAAAA1w5LYWrw4MHq3bu3EhMTtXTpUi1dulQHDhxQnz59NHjwYKcWaLPZHH42xuRpu9SV9hk3bpxSUlLsn8OHDzulVgAAAADXDkthav/+/Ro5cqTDAg8eHh4aMWKE9u/f75TCIiIiJCnPDNPRo0fts1URERHKzMzUqVOnCtwnPz4+PgoODnb4AAAAAEBRWApTN954o3bt2pWnfdeuXbrhhhuKW5MkKSYmRhEREVqzZo29LTMzUxs2bFCrVq0kSU2bNpWXl5fDPklJSdq+fbt9HwAAAAAoCZYWoBg2bJieeOIJ/f7772rRooUk6fvvv9err76q5557Tr/++qt930aNGhV4nLS0NP3+++/2nxMTE7Vt2zZVrFhR1apV0/DhwzVt2jTFxsYqNjZW06ZNk7+/v/r27StJCgkJ0YMPPqiRI0cqNDRUFStW1KhRo9SwYUN16tTJyqkBAAAAQKFYWhq9XLnLT2jZbDb7c0vZ2dkF7rd+/Xp16NAhT/uAAQO0YMECGWM0efJkzZ07V6dOndLNN9+sV199VQ0aNLDve/78eT311FN6//33de7cOXXs2FHx8fFFWlSCpdEBAAAASEXLBpbC1B9//FHofatXr17Uw191hCkAAAAA0lV4z1RpCEgAAAAAUJIsv7T3r7/+0nfffaejR48qJyfHYduwYcOKXRgAAAAAuDNLYWr+/Pl67LHH5O3trdDQUId3OtlsNsIUAAAAgDLP0jNT0dHReuyxxzRu3LgrLkZRGvDMFAAAAACpaNnAUhJKT09Xnz59ykSQAgAAAAArLKWhBx98UB999JGzawEAAACAUsPSbX7Z2dnq3r27zp07p4YNG8rLy8th+4svvui0Aq8GbvMDAAAAIF2FpdGnTZumVatW6brrrpOkPAtQAAAAAEBZZylMvfjii3rrrbcUFxfn5HIAAAAAoHSw9MyUj4+PWrdu7exaAAAAAKDUsBSmnnjiCc2ePdvZtQAAAABAqWHpNr8ff/xRa9eu1fLly1W/fv08C1AsXbrUKcUBAAAAgLuyFKbKly+vu+++29m1AAAAAECpYSlMzZ8/39l1AAAAAECpYilM5Tp27Jj27Nkjm82mOnXqqFKlSs6qCwAAAADcmqUFKM6ePasHHnhAkZGRatu2rW655RZFRUXpwQcfVHp6urNrBAAAAAC3YylMjRgxQhs2bNDnn3+u06dP6/Tp0/r000+1YcMGjRw50tk1AgAAAIDbsRljTFG/FBYWpo8//ljt27d3aF+3bp3uvfdeHTt2zFn1XRWpqakKCQlRSkqKgoODXV0OAAAAABcpSjawNDOVnp6u8PDwPO2VK1fmNj8AAAAA1wRLYaply5aaOHGizp8/b287d+6cJk+erJYtWzqtOAAAAABwV5ZW8/vvf/+rbt26qWrVqmrcuLFsNpu2bdsmHx8frV692tk1AgAAAIDbsfTMlPT3TNS7776r3bt3yxijevXqqV+/fvLz83N2jSWOZ6YAAAAASEXLBpZmpqZPn67w8HA9/PDDDu1vvfWWjh07pjFjxlg5LAAAAACUGpaemZo7d66uv/76PO3169fXa6+9VuyiAAAAAMDdWQpTycnJioyMzNNeqVIlJSUlFbsoAAAAAHB3lsJUdHS0vvvuuzzt3333naKioopdFAAAAAC4O0vPTD300EMaPny4srKydOutt0qSvv76a40ePVojR450aoEAAAAA4I4shanRo0fr5MmTGjRokDIzMyVJvr6+GjNmjMaNG+fUAgEAAADAHVleGl2S0tLStGvXLvn5+Sk2NlY+Pj7OrO2qYWl0AAAAANJVWBo9V2BgoG666abiHAIAAAAASiVLC1AAAAAAwLWOMAUAAAAAFhCmAAAAAMACwhQAAAAAWECYAgAAAAALCFMAAAAAYAFhCgAAAAAsIEwBAAAAgAWEKQAAAACwgDAFAAAAABYQpgAAAADAAsIUAAAAAFhAmAIAAAAACwhTAAAAAGABYQoAAAAALCBMAQAAAIAFhCkAAAAAsIAwBQAAAAAWEKYAAAAAwALCFAAAAABYQJgCAAAAAAsIUwAAAABgAWEKAAAAACwgTAEAAACABYQpAAAAALCAMAUAAAAAFhCmAAAAAMACwhQAAAAAWECYAgAAAAALCFMAAAAAYAFhCgAAAAAsIEwBAAAAgAWEKQAAAACwgDAFAAAAABYQpgAAAADAAsIUAAAAAFhAmAIAAAAAC9w+TE2aNEk2m83hExERYd9ujNGkSZMUFRUlPz8/tW/fXjt27HBhxQAAAACuBW4fpiSpfv36SkpKsn9+++03+7YZM2boxRdf1CuvvKLNmzcrIiJCnTt31pkzZ1xYMQAAAICyrlSEKU9PT0VERNg/lSpVkvT3rNR///tfjR8/XnfffbcaNGigt99+W+np6Xr//fddXDUAAACAsqxUhKl9+/YpKipKMTEx6tOnjw4cOCBJSkxMVHJysrp06WLf18fHR+3atVNCQkKBx8vIyFBqaqrDBwAAAACKwu3D1M0336yFCxdq1apVmjdvnpKTk9WqVSudOHFCycnJkqTw8HCH74SHh9u35Wf69OkKCQmxf6Kjo0v0HAAAAACUPW4fprp166ZevXqpYcOG6tSpk1asWCFJevvtt+372Gw2h+8YY/K0XWzcuHFKSUmxfw4fPlwyxQMAAAAos9w+TF0qICBADRs21L59++yr+l06C3X06NE8s1UX8/HxUXBwsMMHAAAAAIqi1IWpjIwM7dq1S5GRkYqJiVFERITWrFlj356ZmakNGzaoVatWLqwSAAAAQFnn6eoCrmTUqFHq0aOHqlWrpqNHj2rq1KlKTU3VgAEDZLPZNHz4cE2bNk2xsbGKjY3VtGnT5O/vr759+7q6dAAAAABlmNuHqT///FP33Xefjh8/rkqVKqlFixb6/vvvVb16dUnS6NGjde7cOQ0aNEinTp3SzTffrNWrVysoKMjFlQMAAAAoy2zGGOPqIlwtNTVVISEhSklJ4fkpAAAA4BpWlGxQ6p6ZAgAAAAB3QJgCAAAAAAsIUwAAAABgAWEKAAAAACwgTAEAAACABYQpAAAAALCAMAUAAAAAFhCmAAAAAMACwhQAAAAAWECYAgAAAAALCFMAAAAAYAFhCgAAAAAsIEwBAAAAgAWEKQAAAACwgDAFAAAAABZ4uroAAAAAAO4pJT1Tx9MylXo+S8F+XgoL8FaIv7ery3IbhCkAAAAAeRw5fU5jlvyqb/cdt7e1jQ3Tc70aKaq8nwsrcx/c5gcAAADAQUp6Zp4gJUnf7DuusUt+VUp6posqcy+EKQAAAAAOjqdl5glSub7Zd1zH0whTEmEKAAAAwCVSz2dddvuZK2y/VhCmAAAAADgI9vW67PagK2y/VhCmAAAAADgIC/RW29iwfLe1jQ1TWCAr+kmEKQAAAACXCPH31nO9GuUJVG1jw/R8r0Ysj/7/Y2l0AAAAAHlElffT7Pua6Hhaps6cz1KQr5fCAnnP1MUIUwAAAAAcXPqy3piwAEJUPghTAAAAAOx4WW/h8cwUAAAAAEm8rLeoCFMAAAAAJPGy3qIiTAEAAACQxMt6i4pnpgAAAIBrwKWLSoQF5F2Zj5f1Fg1hCgAAACjjCruoRO7Ler/J51Y/XtabF7f5AQAAAGVYURaV4GW9RcPMFAAAAFCGFWZRiYtDEi/rLTzCFAAAAFCGWVlUIsSf8FQY3OYHAAAAlGEsKlFyCFMAAABAGZa7qER+WFSieAhTAAAAQBnGohIlh2emAAAAgDKORSVKBmEKAAAAuAawqITzcZsfAAAAAFjAzBQAAADgxlLSM3U8LVOp57MU7OelsABmmNwFYQoAAABwU0dOn9OYJb86vHS3bWyYnuvVSFHl/VxYGSRu8wMAAADcUkp6Zp4gJUnf7DuusUt+VUp6posqQy7CFAAAAOCGjqdl5glSub7Zd1zH0whTrkaYAgAAANxQ6vmsy24/c4XtKHmEKQAAAMANBft6XXZ70BW2o+QRpgAAAAA3FBborbaxYfluaxsbprBAVvRzNcIUAAAA4IZC/L31XK9GeQJV29gwPd+rEcujuwGWRgcAAADcVFR5P82+r4mOp2XqzPksBfl6KSyQ90y5C8IUAAAAXIYX0l5ZiD9j4q4IUwAAAHAJXkiL0o5npgAAAHDV8UJalAWEKQAAAFx1vJAWZQG3+QEAAOCKnP1sEy+kRVlAmAIAOB0PlANlS0k828QLaVEWEKYAAE7FA+VA2XKlZ5tm39fE0v8syX0h7Tf53OrHC2lRWvDMFADAaXigHCh7SurZJl5Ii7KAmSk3wm0xAEq7wvzSxb/XgNKlJJ9t4oW0KO0IU26C22IAlAU8UA6UPSX9bBMvpEVpxm1+boDbYgCUFTxQDpQ9uc825Ydnm3CtI0y5Ad6zAKCs4JcuoOzh2SagYNzm5wa4LQZAWZH7S9fYJb86rNDFL11A6cazTUD+CFNugNtiAJQl/NIFlE082wTkRZhyA7xnAUBZwy9dAIBrAc9MuQHuRQYAAABKH2am3AS3xQAAAAClC2HKjXBbDAAAAFB6lJnb/OLj4xUTEyNfX181bdpU3377ratLAgAAAFCGlYkwtXjxYg0fPlzjx4/Xzz//rFtuuUXdunXToUOHXF0aAAAAgDLKZowxri6iuG6++WbdeOONmjNnjr2tbt26uuuuuzR9+vQrfj81NVUhISFKSUlRcHBwSZYKAAAAwI0VJRuU+pmpzMxMbdmyRV26dHFo79KlixISEvL9TkZGhlJTUx0+AAAAAFAUpT5MHT9+XNnZ2QoPD3doDw8PV3Jycr7fmT59ukJCQuyf6Ojoq1EqAAAAgDKk1IepXDabzeFnY0yetlzjxo1TSkqK/XP48OGrUSIAAACAMqTUL40eFhYmDw+PPLNQR48ezTNblcvHx0c+Pj5XozwAAAAAZVSpn5ny9vZW06ZNtWbNGof2NWvWqFWrVi6qCgAAAEBZV+pnpiRpxIgR6t+/v5o1a6aWLVvq9ddf16FDh/TYY4+5ujQAAAAAZVSZCFO9e/fWiRMn9MwzzygpKUkNGjTQF198oerVq7u6NAAAAABlVJl4z1Rx8Z4pAAAAAFLRskGZmJkqrtw8yfumAAAAgGtbbiYozJwTYUrSmTNnJIn3TQEAAACQ9HdGCAkJuew+3OYnKScnR0eOHFFQUFCB76YqaampqYqOjtbhw4e51fAqYtxdg3F3DcbdNRh312Dcrz7G3DUYd+czxujMmTOKiopSuXKXX/ycmSlJ5cqVU9WqVV1dhiQpODiYvwguwLi7BuPuGoy7azDursG4X32MuWsw7s51pRmpXKX+PVMAAAAA4AqEKQAAAACwgDDlJnx8fDRx4kT5+Pi4upRrCuPuGoy7azDursG4uwbjfvUx5q7BuLsWC1AAAAAAgAXMTAEAAACABYQpAAAAALCAMAUAAAAAFhCmAAAAAMACwpSTxMfHKyYmRr6+vmratKm+/fbby+7/6quvqm7duvLz89N1112nhQsX5tnn9OnTGjx4sCIjI+Xr66u6devqiy++KFa/ZY0rxn3SpEmy2WwOn4iICKefmztz9ri3b98+z5jabDbdcccdxeq3rHHFuHO9l8y/Z/773//quuuuk5+fn6Kjo/Xkk0/q/Pnzxeq3rHHFuF/r17uzxzwrK0vPPPOMatWqJV9fXzVu3FgrV64sdr9ljSvG/Vq/1p3KoNgWLVpkvLy8zLx588zOnTvNE088YQICAswff/yR7/7x8fEmKCjILFq0yOzfv9988MEHJjAw0Hz22Wf2fTIyMkyzZs3M7bffbjZu3GgOHjxovv32W7Nt2zbL/ZY1rhr3iRMnmvr165ukpCT75+jRoyV+vu6iJMb9xIkTDuO5fft24+HhYebPn2+537LGVePO9e78cX/33XeNj4+Pee+990xiYqJZtWqViYyMNMOHD7fcb1njqnG/lq/3khjz0aNHm6ioKLNixQqzf/9+Ex8fb3x9fc3WrVst91vWuGrcr+Vr3dkIU07QvHlz89hjjzm0XX/99Wbs2LH57t+yZUszatQoh7YnnnjCtG7d2v7znDlzTM2aNU1mZqbT+i1rXDXuEydONI0bN7ZeeClXEuN+qVmzZpmgoCCTlpZmud+yxlXjzvXu/HEfPHiwufXWWx32GTFihGnTpo3lfssaV437tXy9l8SYR0ZGmldeecVhn549e5p+/fpZ7rescdW4X8vXurNxm18xZWZmasuWLerSpYtDe5cuXZSQkJDvdzIyMuTr6+vQ5ufnpx9//FFZWVmSpM8++0wtW7bU4MGDFR4ergYNGmjatGnKzs623G9Z4qpxz7Vv3z5FRUUpJiZGffr00YEDB5x4du6rpMb9Um+++ab69OmjgIAAy/2WJa4a91xc784d9zZt2mjLli368ccfJUkHDhzQF198Yb+9kuvdNeOe61q83ktqzAvaZ+PGjZb7LUtcNe65rsVrvSQQporp+PHjys7OVnh4uEN7eHi4kpOT8/1O165d9cYbb2jLli0yxuinn37SW2+9paysLB0/flzS3/+S//jjj5Wdna0vvvhC//73v/XCCy/o2WeftdxvWeKqcZekm2++WQsXLtSqVas0b948JScnq1WrVjpx4kTJnbCbKKlxv9iPP/6o7du366GHHipWv2WJq8Zd4noviXHv06ePpkyZojZt2sjLy0u1atVShw4dNHbsWMv9liWuGnfp2r3eS2rMu3btqhdffFH79u1TTk6O1qxZo08//VRJSUmW+y1LXDXu0rV7rZcEwpST2Gw2h5+NMXnack2YMEHdunVTixYt5OXlpZ49eyouLk6S5OHhIUnKyclR5cqV9frrr6tp06bq06ePxo8frzlz5ljutyxyxbh369ZNvXr1UsOGDdWpUyetWLFCkvT222+XwBm6J2eP+8XefPNNNWjQQM2bNy9Wv2WRK8ad6935475+/Xo9++yzio+P19atW7V06VItX75cU6ZMsdxvWeSKcb/Wr3dnj/lLL72k2NhYXX/99fL29taQIUM0cODAPP8O4lq/+uN+rV/rzkSYKqawsDB5eHjk+T8IR48ezfN/GnL5+fnprbfeUnp6ug4ePKhDhw6pRo0aCgoKUlhYmCQpMjJSderUcbjw69atq+TkZGVmZlrqtyxx1bjnJyAgQA0bNtS+ffucdHbuq6TGPVd6eroWLVqUZ3aE6901454frvfij/uECRPUv39/PfTQQ2rYsKH+8Y9/aNq0aZo+fbpycnK43l007vm5Vq73khrzSpUqadmyZTp79qz++OMP7d69W4GBgYqJibHcb1niqnHPz7VyrZcEwlQxeXt7q2nTplqzZo1D+5o1a9SqVavLftfLy0tVq1aVh4eHFi1apO7du6tcub//SFq3bq3ff//d4V/we/fuVWRkpLy9vYvVb1ngqnHPT0ZGhnbt2qXIyMhinpX7K6lxz/Xhhx8qIyND999/v9P6LQtcNe754Xov/rinp6fn+TPw8PCQ+XtRKK53F417fq6V672k/x3j6+urKlWq6MKFC1qyZIl69uxZ7H7LAleNe36ulWu9RFy9tS7KrtxlLd98802zc+dOM3z4cBMQEGAOHjxojDFm7Nixpn///vb99+zZY9555x2zd+9e88MPP5jevXubihUrmsTERPs+hw4dMoGBgWbIkCFmz549Zvny5aZy5cpm6tSphe63rHPVuI8cOdKsX7/eHDhwwHz//feme/fuJigoiHEvxrjnatOmjendu7elfss6V40717vzx33ixIkmKCjIfPDBB+bAgQNm9erVplatWubee+8tdL9lnavG/Vq+3ktizL///nuzZMkSs3//fvPNN9+YW2+91cTExJhTp04Vut+yzlXjfi1f685GmHKSV1991VSvXt14e3ubG2+80WzYsMG+bcCAAaZdu3b2n3fu3GluuOEG4+fnZ4KDg03Pnj3N7t278xwzISHB3HzzzcbHx8fUrFnTPPvss+bChQuF7vda4Ipx7927t4mMjDReXl4mKirK3H333WbHjh0lep7upiTGfc+ePUaSWb16taV+rwWuGHeud+ePe1ZWlpk0aZKpVauW8fX1NdHR0WbQoEEOv+hcqd9rgSvG/Vq/3p095uvXrzd169Y1Pj4+JjQ01PTv39/89ddfRer3WuCKcb/Wr3VnshlTwNw2AAAAAKBAPDMFAAAAABYQpgAAAADAAsIUAAAAAFhAmAIAAAAACwhTAAAAAGABYQoAAAAALCBMAQAAAIAFhCkAAAAAsIAwBQAAAAAWEKYAAAAAwALCFACgRGVmZrq6hDKDsQQA90KYAgA4Vfv27TVkyBCNGDFCYWFh6ty5szZs2KDmzZvLx8dHkZGRGjt2rC5cuGD/Tk5Ojp5//nnVrl1bPj4+qlatmp599tlC9TdmzBjVqVNH/v7+qlmzpiZMmKCsrCz79ri4ON11110O3xk+fLjat29f7P4zMzM1ZMgQRUZGytfXVzVq1ND06dPt20+fPq1HHnlE4eHh8vX1VYMGDbR8+XL79iVLlqh+/fry8fFRjRo19MILLzgcv0aNGpo6dari4uIUEhKihx9+WJKUkJCgtm3bys/PT9HR0Ro2bJjOnj1bqPECADiPp6sLAACUPW+//bYef/xxfffddzp+/Li6dOmiuLg4LVy4ULt379bDDz8sX19fTZo0SZI0btw4zZs3T7NmzVKbNm2UlJSk3bt3F6qvoKAgLViwQFFRUfrtt9/08MMPKygoSKNHjy50vVb7f/nll/XZZ5/pww8/VLVq1XT48GEdPnxY0t8BrVu3bjpz5ozeffdd1apVSzt37pSHh4ckacuWLbr33ns1adIk9e7dWwkJCRo0aJBCQ0MVFxdn72PmzJmaMGGC/v3vf0uSfvvtN3Xt2lVTpkzRm2++qWPHjmnIkCEaMmSI5s+fX+hzBgAUn80YY1xdBACg7Gjfvr1SUlL0888/S5LGjx+vJUuWaNeuXbLZbJKk+Ph4jRkzRikpKTp79qwqVaqkV155RQ899FCx+585c6YWL16sn376SdLfM1OnT5/WsmXL7PsMHz5c27Zt0/r163XmzBnL/Q8bNkw7duzQV199ZT+3XKtXr1a3bt20a9cu1alTJ893+/Xrp2PHjmn16tX2ttGjR2vFihXasWOHpL9nppo0aaJPPvnEvs+//vUv+fn5ae7cufa2jRs3ql27djp79qx8fX2LdA4AAOu4zQ8A4HTNmjWz//OuXbvUsmVLh7DRunVrpaWl6c8//9SuXbuUkZGhjh07Wurr448/Vps2bRQREaHAwEBNmDBBhw4dKvT3i9N/XFyctm3bpuuuu07Dhg1zCEbbtm1T1apV8w1Suf22bt3aoa1169bat2+fsrOz7W0Xj6X094zWggULFBgYaP907dpVOTk5SkxMLPI5AACsI0wBAJwuICDA/s/GmDyzNrk3RdhsNvn5+Vnu5/vvv1efPn3UrVs3LV++XD///LPGjx/vsFBDuXLldOlNGBc/U1Wc/m+88UYlJiZqypQpOnfunO69917dc889hTru5cblYhePpfT37YOPPvqotm3bZv/88ssv2rdvn2rVqmX5XAAARUeYAgCUqHr16ikhIcEhKCQkJCgoKEhVqlRRbGys/Pz89PXXXxf52N99952qV6+u8ePHq1mzZoqNjdUff/zhsE+lSpWUlJTk0LZt2zb7Pxenf0kKDg5W7969NW/ePC1evFhLlizRyZMn1ahRI/3555/au3dvvt+rV6+eNm7c6NCWkJCgOnXq2J+rys+NN96oHTt2qHbt2nk+3t7els4BAGANYQoAUKIGDRqkw4cPa+jQodq9e7c+/fRTTZw4USNGjFC5cuXk6+urMWPGaPTo0Vq4cKH279+v77//Xm+++eYVj127dm0dOnRIixYt0v79+/Xyyy87PF8kSbfeeqt++uknLVy4UPv27dPEiRO1fft2+/bi9D9r1iwtWrRIu3fv1t69e/XRRx8pIiJC5cuXV7t27dS2bVv16tVLa9asUWJior788kutXLlSkjRy5Eh9/fXXmjJlivbu3au3335br7zyikaNGnXZPseMGaNNmzZp8ODB2rZtm/bt26fPPvtMQ4cOvWK9AAAnMwAAOFG7du3ME0884dC2fv16c9NNNxlvb28TERFhxowZY7Kysuzbs7OzzdSpU0316tWNl5eXqVatmpk2bVqh+nvqqadMaGioCQwMNL179zazZs0yISEhDvs8/fTTJjw83ISEhJgnn3zSDBkyxLRr167Y/b/++uvmhhtuMAEBASY4ONh07NjRbN261b79xIkTZuDAgSY0NNT4+vqaBg0amOXLl9u3f/zxx6ZevXr2PmfOnOlw/OrVq5tZs2bl6ffHH380nTt3NoGBgSYgIMA0atTIPPvss4UaLwCA87CaHwAAAABYwG1+AAAAAGABYQoA4LamTZvmsAT4xZ9u3bqV+f4BAO6N2/wAAG7r5MmTOnnyZL7b/Pz8VKVKlTLdPwDAvRGmAAAAAMACbvMDAAAAAAsIUwAAAABgAWEKAAAAACwgTAEAAACABYQpAAAAALCAMAUAAAAAFhCmAAAAAMCC/w8lcTmwylWfYAAAAABJRU5ErkJggg==", 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" ] @@ -1238,7 +1260,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -1277,88 +1299,158 @@ " \n", " \n", " \n", - " 58\n", - " 0.997081\n", - " 30.4\n", - " (17, 17)\n", + " 211\n", + " 0.997632\n", + " 231.0\n", + " (2, 2)\n", " ind_mutate\n", " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", - " 1.0\n", - " 1.727136e+09\n", - " 1.727136e+09\n", + " 4.0\n", + " 1.727192e+09\n", + " 1.727192e+09\n", " None\n", " 1.0\n", - " (StandardScaler(), SelectFwe(alpha=0.000122657...\n", + " (MaxAbsScaler(), SelectFwe(alpha=0.01420804547...\n", " \n", " \n", - " 141\n", - " 0.994594\n", - " 11.0\n", - " (46, 46)\n", + " 251\n", + " 0.996697\n", + " 213.9\n", + " (211, 211)\n", " ind_mutate\n", " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", - " 2.0\n", - " 1.727136e+09\n", - " 1.727136e+09\n", + " 5.0\n", + " 1.727192e+09\n", + " 1.727192e+09\n", " None\n", " 1.0\n", - " (MaxAbsScaler(), Passthrough(), FeatureUnion(t...\n", + " (MaxAbsScaler(), VarianceThreshold(threshold=0...\n", " \n", " \n", - 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" 1.727136e+09\n", - " 1.727136e+09\n", + " 1.727192e+09\n", + " 1.727192e+09\n", " None\n", " 1.0\n", - " (MaxAbsScaler(), SelectFwe(alpha=0.00020686984...\n", + " (MaxAbsScaler(), VarianceThreshold(threshold=0...\n", " \n", " \n", - " 201\n", - " 0.949153\n", - " 6.0\n", - " (176, 32)\n", + " 250\n", + " 0.995593\n", + " 24.3\n", + " (173, 173)\n", + " ind_mutate\n", + " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", + " 5.0\n", + " 1.727192e+09\n", + " 1.727192e+09\n", + " None\n", + " 1.0\n", + " (MinMaxScaler(), SelectFwe(alpha=0.00034913463...\n", + " \n", + " \n", + " 295\n", + " 0.994663\n", + " 17.0\n", + " (245, 2)\n", + " ind_crossover\n", + " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", + " 5.0\n", + " 1.727192e+09\n", + " 1.727192e+09\n", + " None\n", + " 1.0\n", + " (MinMaxScaler(), SelectPercentile(percentile=3...\n", + " \n", + " \n", + " 227\n", + " 0.990767\n", + " 11.0\n", + " (188, 168)\n", + " ind_crossover\n", + " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", + " 4.0\n", + " 1.727192e+09\n", + " 1.727192e+09\n", + " None\n", + " 1.0\n", + " (Passthrough(), SelectFwe(alpha=0.000109999882...\n", + " \n", + " \n", + " 226\n", + " 0.989583\n", + " 9.0\n", + " (144, 90)\n", " ind_mutate , ind_mutate , ind_crossover\n", " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", " 4.0\n", - " 1.727136e+09\n", - " 1.727136e+09\n", + " 1.727192e+09\n", + " 1.727192e+09\n", " None\n", " 1.0\n", - " (MinMaxScaler(), SelectFwe(alpha=0.00140487405...\n", + " (StandardScaler(), VarianceThreshold(threshold...\n", + " \n", + " \n", + " 213\n", + " 0.976500\n", + " 8.1\n", + " (151, 151)\n", + " ind_crossover , ind_mutate\n", + " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", + " 4.0\n", + " 1.727192e+09\n", + " 1.727192e+09\n", + " None\n", + " 1.0\n", + " (MaxAbsScaler(), SelectFwe(alpha=0.03007503043...\n", + " \n", + " \n", + " 290\n", + " 0.960114\n", + " 6.0\n", + " (240, 240)\n", + " ind_mutate\n", + " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", + " 5.0\n", + " 1.727192e+09\n", + " 1.727192e+09\n", + " None\n", + " 1.0\n", + " (MinMaxScaler(), VarianceThreshold(threshold=0...\n", " \n", " \n", "\n", @@ -1366,47 +1458,72 @@ ], "text/plain": [ " roc_auc_score complexity_scorer Parents \\\n", - "58 0.997081 30.4 (17, 17) \n", - "141 0.994594 11.0 (46, 46) \n", - "204 0.993951 9.0 (90, 90) \n", - "149 0.985930 8.0 (88, 88) \n", - "178 0.980084 7.0 (131, 131) \n", - "201 0.949153 6.0 (176, 32) \n", + "211 0.997632 231.0 (2, 2) \n", + "251 0.996697 213.9 (211, 211) \n", + "261 0.996394 182.4 (211, 182) \n", + "132 0.996313 33.0 (85, 18) \n", + "190 0.996207 27.5 (141, 122) \n", + "250 0.995593 24.3 (173, 173) \n", + "295 0.994663 17.0 (245, 2) \n", + "227 0.990767 11.0 (188, 168) \n", + "226 0.989583 9.0 (144, 90) \n", + "213 0.976500 8.1 (151, 151) \n", + "290 0.960114 6.0 (240, 240) \n", "\n", " Variation_Function \\\n", - "58 ind_mutate \n", - "141 ind_mutate \n", - "204 ind_mutate \n", - "149 ind_mutate \n", - "178 ind_mutate \n", - "201 ind_mutate , ind_mutate , ind_crossover \n", + "211 ind_mutate \n", + "251 ind_mutate \n", + "261 ind_crossover \n", + "132 ind_crossover \n", + "190 ind_mutate , ind_mutate , ind_crossover \n", + "250 ind_mutate \n", + "295 ind_crossover \n", + "227 ind_crossover \n", + "226 ind_mutate , ind_mutate , ind_crossover \n", + "213 ind_crossover , ind_mutate \n", + "290 ind_mutate \n", "\n", " Individual Generation \\\n", - "58 
Pipeline(steps=[('minmaxscaler', MinMaxScaler()),\n",
-       "                ('selectfwe', SelectFwe(alpha=0.0014048740592)),\n",
+       "                ('variancethreshold',\n",
+       "                 VarianceThreshold(threshold=0.0004675292341)),\n",
        "                ('featureunion-1',\n",
        "                 FeatureUnion(transformer_list=[('featureunion',\n",
-       "                                                 FeatureUnion(transformer_list=[('zerocount',\n",
-       "                                                                                 ZeroCount())])),\n",
+       "                                                 FeatureUnion(transformer_list=[('quantiletransformer',\n",
+       "                                                                                 QuantileTransformer(n_quantiles=104))])),\n",
        "                                                ('passthrough',\n",
        "                                                 Passthrough())])),\n",
        "                ('featureunion-2',\n",
-       "                 FeatureUnion(transformer_list=[('featureunion',\n",
-       "                                                 FeatureUnion(transformer_list=[('estimatortransformer',\n",
-       "                                                                                 EstimatorTransformer(estimator=BernoulliNB(alpha=76.5761838773666,\n",
-       "                                                                                                                            fit_prior=False)))])),\n",
+       "                 FeatureUnion(transformer_list=[('skiptransformer',\n",
+       "                                                 SkipTransformer()),\n",
        "                                                ('passthrough',\n",
        "                                                 Passthrough())])),\n",
        "                ('kneighborsclassifier',\n",
-       "                 KNeighborsClassifier(n_jobs=1, n_neighbors=1, p=3))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
MinMaxScaler()
VarianceThreshold(threshold=0.0004675292341)
FeatureUnion(transformer_list=[('featureunion',\n",
+       "                                FeatureUnion(transformer_list=[('quantiletransformer',\n",
+       "                                                                QuantileTransformer(n_quantiles=104))])),\n",
+       "                               ('passthrough', Passthrough())])
QuantileTransformer(n_quantiles=104)
Passthrough()
FeatureUnion(transformer_list=[('skiptransformer', SkipTransformer()),\n",
+       "                               ('passthrough', Passthrough())])
SkipTransformer()
Passthrough()
KNeighborsClassifier(n_jobs=1, n_neighbors=1)
" ], "text/plain": [ "Pipeline(steps=[('minmaxscaler', MinMaxScaler()),\n", - " ('selectfwe', SelectFwe(alpha=0.0014048740592)),\n", + " ('variancethreshold',\n", + " VarianceThreshold(threshold=0.0004675292341)),\n", " ('featureunion-1',\n", " FeatureUnion(transformer_list=[('featureunion',\n", - " FeatureUnion(transformer_list=[('zerocount',\n", - " ZeroCount())])),\n", + " FeatureUnion(transformer_list=[('quantiletransformer',\n", + " QuantileTransformer(n_quantiles=104))])),\n", " ('passthrough',\n", " Passthrough())])),\n", " ('featureunion-2',\n", - " FeatureUnion(transformer_list=[('featureunion',\n", - " FeatureUnion(transformer_list=[('estimatortransformer',\n", - " EstimatorTransformer(estimator=BernoulliNB(alpha=76.5761838773666,\n", - " fit_prior=False)))])),\n", + " FeatureUnion(transformer_list=[('skiptransformer',\n", + " SkipTransformer()),\n", " ('passthrough',\n", " Passthrough())])),\n", " ('kneighborsclassifier',\n", - " KNeighborsClassifier(n_jobs=1, n_neighbors=1, p=3))])" + " KNeighborsClassifier(n_jobs=1, n_neighbors=1))])" ] }, - "execution_count": 18, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1908,21 +2019,19 @@ "source": [ "## Plot performance over time + Continuing a run from where it left off\n", "\n", - "Plotting the performance over time is a good way of trying to access whether or not the TPOT model has converged. If performance seems to asymptote over time, there may not be much more performance to be gained by running for a longer period of time. If the plot looks like it is still actively improving, it may be worth running TPOT for a longer duration. \n", + "Plotting performance over time is a good way to assess whether or not the TPOT model has converged. If performance asymptotes over time, there may not be much more performance to be gained by running for a longer period. If the plot looks like it is still improving, it may be worth running TPOT for a longer duration. \n", "\n", - "There are two ways to resume TPOT. If the `warm_start` parameter is set to True, subsequent calls to `fit` will continue training where it left off (The conventional scikit-learn default is to retrain from scratch on subsequent calls to fit). Additionally, if `periodic_checkpoint_folder` is set, TPOT will periodically save its current state. If TPOT terminates normally, is interrupted (job canceled, PC shut off), or crashes (memory issues), it will be able to resume training from where it left off. ** NOTE: If the periodic_checkpoint_folder is set, TPOT will always resume from the **\n", - "\n", - "In this case we can see that performance is near optimal and has slowed, so more time is likely unnecessary." + "In this case, we can see that performance is near optimal and has slowed, so more time is likely unnecessary." ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1947,6 +2056,20 @@ "plt.show()\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Checkpointing\n", + "\n", + "There are two ways to resume TPOT. \n", + "* If the `warm_start` parameter is set to True, subsequent calls to `fit` will continue training where it left off (The conventional scikit-learn default is to retrain from scratch on subsequent calls to fit). \n", + "* If `periodic_checkpoint_folder` is set, TPOT will periodically save its current state to disk. If TPOT is interrupted (job canceled, PC shut off, crashes), you can resume training from where it left off. The checkpoint folder stores a data frame of all evaluated pipelines. This data frame can be loaded and inspected to help diagnose problems when debugging.\n", + "\n", + "\n", + "**Note: TPOT does not clean up the checkpoint files. If the `periodic_checkpoint_folder` parameter is set, training from the last saved point will always continue, even if the input data has changed. A common issue is forgetting to change this folder between experiments and TPOT continuing training from pipelines optimized for another dataset. If you intend to start a run from scratch, you must either remove the parameter, supply an empty folder, or delete the original checkpoint folder.**" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1983,14 +2106,14 @@ "source": [ "# Pipeline caching in TPOT (joblib.Memory)\n", "\n", - "With the memory parameter, pipelines can cache the results of each transformer after fitting them. This feature is used to avoid repeated computation by transformers within a pipeline if the parameters and input data are identical to another fitted pipeline during optimization process. TPOT allows users to specify a custom directory path or joblib.Memory in case they want to re-use the memory cache in future TPOT runs (or a warm_start run).\n", + "With the memory parameter, pipelines can cache the results of each transformer after fitting them. This feature is used to avoid repeated computation by transformers within a pipeline if the parameters and input data are identical to another fitted pipeline during the optimization process. TPOT allows users to specify a custom directory path or joblib.Memory in case they want to re-use the memory cache in future TPOT runs (or a warm_start run).\n", "\n", "There are three methods for enabling memory caching in TPOT:" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -2017,23 +2140,6 @@ "**Note: TPOT does NOT clean up memory caches if users set a custom directory path or Memory object. We recommend that you clean up the memory caches when you don't need it anymore.**" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Checkpointing\n", - "\n", - "TPOT can checkpoint its progress to disk and resume from that point later if the `periodic_checkpoint_folder` parameter is used. TPOT will save its internal dataframe of pipelines and their performance to disk every generation, allowing you to interrupt TPOT’s execution and resume it later on the same or a different machine.\n", - "\n", - "This feature is useful in several scenarios:\n", - "\n", - "Interrupting TPOT’s execution and resuming it later on the same or a different machine.\n", - "Handling unexpected terminations, such as power outages, cluster job cancellations, bugs, errors, or out-of-memory issues. The checkpointed dataframe can be loaded and inspected to help diagnose problems.\n", - "Running TPOT on a cluster and periodically saving its progress to disk.\n", - "\n", - "**Note: TPOT does not clean up the checkpoint files. If the `periodic_checkpoint_folder` parameter is set, it will always continue training from the last saved point, even if the input data has changed. A common issue is forgetting to change this folder between experiments, and TPOT continuing training from pipelines optimized for another dataset. If you intend to start a run from scratch, you must either remove the parameter, supply an empty folder, or delete the original checkpoint folder.**" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -2051,48 +2157,49 @@ "\n", "If you are experiencing issues with TPOT, here are some common issues and how to address them.\n", "\n", - "* Performance is lower than expected. what can I do?\n", + "* Performance is lower than expected. What can I do?\n", " * TPOT may have to be run for a longer duration, increase `max_time_mins`, `early_stop`, or `generations`.\n", - " * Individual pipelines may need more time to complete fitting, increase `max_eval_time_seconds`.\n", - " * The configuration may not include the optimal model types or hyperparameter ranges, explore other included templates or customize your own search space (see Tutorial 2!)\n", - " * Check that `periodic_checkpoint_folder` is set correctly. A common issue is forgetting to change this folder between experiments, and TPOT continuing training from pipelines optimized for another dataset.\n", + " * Individual pipelines may need more time to complete fitting; increase `max_eval_time_seconds.`\n", + " * The configuration may not include the optimal model types or hyperparameter ranges, explore other included templates, or customize your own search space (see Tutorial 2!)\n", + " * Check that `periodic_checkpoint_folder` is set correctly. A common issue is forgetting to change this folder between experiments and TPOT continuing training from pipelines optimized for another dataset.\n", "* TPOT is too slow! It is running forever and never terminating\n", - " * Check that at least one of the three termination conditions is set to a reasonable level. These are `max_time_mins`, `early_stop`, or `generations`. Additionally check that `max_eval_time_seconds` is giving enough time for most models to train without being overly long. (Some estimators may take an unreasonably long time to fit, this parameter is intended to prevent them from slowing everything to a halt. In my experience, SVC and SVR tend to be the culprits, so removing them from the search space may also improve run time).\n", + " * Check that at least one of the three termination conditions is set to a reasonable level. These are `max_time_mins`, `early_stop`, or `generations`. Additionally, check that `max_eval_time_seconds` gives enough time for most models to train without being overly long. (Some estimators may take an unreasonably long time to fit; this parameter is intended to prevent them from slowing everything to a halt. In my experience, SVC and SVR tend to be the culprits, so removing them from the search space may also improve run time).\n", " * Set the `memory` parameter to allow TPOT to prevent repeated work when using either scikit-learn pipelines or TPOT GraphPipelines.\n", " * Increase n_jobs to use more processes/CPU power. See Tutorial 7 for advanced Dask usage, including parallelizing across multiple nodes on an HPC.\n", " * Use feature selection, either the build in configuration of sklearn methods (see Tutorial 2), or genetic feature selection (see Tutorials 3 and 5 for two different strategies).\n", " * Use successive halving to reduce computational load (See tutorial 8).\n", - "* Many pipelines in the evaluated_individuals dataframe have crashed or turned up invalid!\n", - " * This may actually be normal and is expected behavior for TPOT. In some cases, TPOT may attempt an invalid hyperparameter combination which results in the pipeline not working. Other times, the pipeline configuration itself may be invalid. For example, a selector may not select any features due to its hyperparameter. Another common example is `MultinomialNB` throwing an error because it expects positive values, but a prior transformation yielded a negative value. \n", + "* Many pipelines in the evaluated_individuals data frame have crashed or turned up invalid!\n", + " * This is normal and is expected behavior for TPOT. In some cases, TPOT may attempt an invalid hyperparameter combination, resulting in the pipeline not working. Other times, the pipeline configuration itself may be invalid. For example, a selector may not select any features due to its hyperparameter. Another common example is `MultinomialNB` throwing an error because it expects positive values, but a prior transformation yielded a negative value. \n", " * If you used custom search spaces, you can use `ConfigSpace` conditionals to prevent invalid hyperparameters (this may still occur due to how TPOT uses crossover).\n", " * Setting `verbose=5` will print out the full error message for all failed pipelines. This can be useful for debugging whether or not there is something misconfigured in your pipeline, custom search space modules, or something else.\n", "* TPOT is crashing due to memory issues\n", - " * Set the `memory_limit` parameter so that n_jobs*memorylimit is less than the available RAM on your machine plus some wiggle room. This should prevent crashing due to memory concerns.\n", - " * Using feature selection may also improve memory usage as described above.\n", + " * Set the `memory_limit` parameter so that n_jobs*memorylimit is less than the available RAM on your machine, plus some wiggle room. This should prevent crashing due to memory concerns.\n", + " * Using feature selection may also improve memory usage, as described above.\n", " * Remove modules that create high RAM usage (e.g. multiple PolynomialFeatures or one with high degree).\n", "* Why are my TPOT runs not reproducible when random_state is set?\n", " * Check that `periodic_checkpoint_folder` is set correctly. If this is set to a non-empty folder, TPOT will continue training from the checkpoint rather than start a new run from scratch. For TPOT runs to be reproducible, they have to have the same starting points.\n", - " * If using custom search spaces, make sure to pass in a fixed `random_state` value into the configspace of the scikit-learn modules that utilize them. TPOT does not check whether estimators do or do not take in a random state value (See Tutorial 2).\n", + " * If using custom search spaces, pass in a fixed `random_state` value into the configspace of the scikit-learn modules that utilize them. TPOT does not check whether estimators do or do not take in a random state value (See Tutorial 2).\n", " * If using the pre-built search spaces provided by TPOT, make sure to pass in `random_state` to `tpot2.config.get_configspace` or `tpot2.config.template_search_spaces.get_template_search_spaces`. This ensures all estimators that support it get a fixed random_state value. (See Tutorial 2).\n", - " * If using custom Node and Pipeline types, make sure that all random decisions utilize the rng parameter passed into the mutation/crossover functions.\n", - " * If `max_eval_time_mins` is set, TPOT will terminate pipelines that go over this time limit. If the pipeline evaluation happens to be very similar to the time limit, its possible that small random fluctuations in CPU allocation may cause a give pipeline to happen to be evaluated in one run but not another. This slightly different result would throw off the random number generator thoughout the rest of the run. Setting `max_eval_time_mins` to None or a higher value may prevent this edge case.\n", + " * If using custom Node and Pipeline types, ensure all random decisions utilize the rng parameter passed into the mutation/crossover functions.\n", + " * If `max_eval_time_mins` is set, TPOT will terminate pipelines that exceed this time limit. If the pipeline evaluation happens to be very similar to the time limit, small random fluctuations in CPU allocation may cause a given pipeline to be evaluated in one run but not another. This slightly different result would throw off the random number generator throughout the rest of the run. Setting `max_eval_time_mins` to None or a higher value may prevent this edge case.\n", " * If using `TPOTEstimatorSteadyState` with `n_jobs`>1, it is also possible that random fluctuations in CPU allocation slightly change the order in which pipelines are evaluated, which will affect the downstream results. `TPOTEstimatorSteadyState` is more reliably reproducible when `n_jobs=1` (This is not an issue for the default `TPOTEstimator`, `TPOTClassifier`, `TPOTRegressor` as they used a batched generational approach where execution order does not impact results).\n", - "* TPOT is not using all the CPU cores I expected given my `n_jobs` setting.\n", - " * The default TPOT algorithm uses a generational approach. This means the TPOT will need to fully evaluated `population_size` (default 50) pipelines before starting the next batch. Often, TPOT will be waiting for the last few pipelines to finish evaluating, which could be less than `n_jobs`. Some estimators or pipelines can be significantly slower to evaluated than others. This can be addressed in a few ways:\n", - " * Decrease `max_eval_time_mins` to cut long running pipeline evaluations early.\n", + "* TPOT is not using all the CPU cores I expected, given my `n_jobs` setting.\n", + " * The default TPOT algorithm uses a generational approach. This means the TPOT will need to evaluate `population_size` (default 50) pipelines before starting the next batch. At the end of each generation, TPOT may leave threads unused while it waits for the last few pipelines to finish evaluating. Some estimators or pipelines can be significantly slower to evaluate than others. This can be addressed in a few ways:\n", + " * Decrease `max_eval_time_mins` to cut long-running pipeline evaluations early.\n", " * Remove estimators or hyperparameter configurations that are prone to very slow convergence (which is very often `SVC` or `SVR`).\n", " * Alternatively, `TPOTEstimatorSteadyState` uses a slightly different backend for the evolutionary algorithm that does not utilize the generational approach. Instead, new pipelines are generated and evaluated as soon as the previous one finishes. With this estimator, all cores should be utilized at all times. \n", + " * Sometimes, setting n_jobs to a multiple of the number of threads can help minimize the chances of threads being idle while waiting for others to finish\n", "\n", "\n", "\n", "## Other things to be aware of:\n", "\n", - "* **Overfitting** On small datasets, it is not impossible for TPOT to over fit the cross validation score itself. This can lead to lower than expected performance on held out datasets. TPOT will always return the model with the highest CV score as its final fitted_pipeline. However, if the highest performing model as evaluated by cross validation actually was just overfit to the CV score, it may actually be worse performing compared to other models on the pareto front.\n", - " * Using a secondary complexity objective and evaluating the entire pareto front may be beneficial. In some cases a lower performing pipeline with lower complexity can actually perform better on held out sets. These can either be evaluated and compared on a held out validation set, or sometimes, if very data limited, simply using a different seed of splitting the CV folds can work as well.\n", - " * TPOT can do this automatically. The `validation_strategy` parameter than select between re-testing the final pareto front on either a held out validation set (percent of data set by `validation_fraction`) or on a different seed for splitting the CV folds. These can be selected by setting `validation_strategy` to \"split\" or \"reshuffled\", respectively.\n", - " * Increasing the number of folds of cross validation can mitigate this. \n", - " * Nested cross validation can also be used to estimate the performance of the TPOT optimization algorithm itself.\n", - " * Removing more complex methods from the search space can reduce the changes of overfitting" + "* **Overfitting** On small datasets, it is not impossible for TPOT to overfit the cross-validation score itself. This can lead to lower-than-expected performance on held-out datasets. TPOT will always return the model with the highest CV score as its final fitted_pipeline. However, if the highest performing model, as evaluated by cross-validation, actually was just overfit to the CV score, it may actually be worse performing compared to other models on the Pareto front.\n", + "  * Using a secondary complexity objective and evaluating the entire pareto front may be beneficial. In some cases a lower performing pipeline with lower complexity can actually perform better on held out sets. These can either be evaluated and compared on a held out validation set, or sometimes, if very data limited, simply using a different seed of splitting the CV folds can work as well.\n", + "    * TPOT can do this automatically. The `validation_strategy` parameter can be set to re-test the final pareto front on either a held-out validation set (percent of data set by `validation_fraction`) or a different seed for splitting the CV folds. These can be selected by setting `validation_strategy` to \"split\" or \"reshuffled\", respectively.\n", + "  * Increasing the number of folds of cross-validation can mitigate this. \n", + "  * Nested cross-validation can also be used to estimate the performance of the TPOT optimization algorithm itself.\n", + "  * Removing more complex methods from the search space can reduce the chances of overfitting" ] }, { @@ -2102,13 +2209,13 @@ "source": [ "# More Options\n", "\n", - "`tpot2.TPOTClassifier` and `tpot2.TPOTRegressor` have a simplified set of hyperparameters with default values set for classification and regression problems. Currently, both of these use the standard evolutionary algorithm in the `tpot2.TPOTEstimator` class. If you want more control you can look into either the `tpot2.TPOTEstimator` or `tpot2.TPOTEstimatorSteadyState` class.\n", + "`tpot2.TPOTClassifier` and `tpot2.TPOTRegressor` have a simplified set of hyperparameters with default values set for classification and regression problems. Currently, both of these use the standard evolutionary algorithm in the `tpot2.TPOTEstimator` class. If you want more control, you can look into either the `tpot2.TPOTEstimator` or `tpot2.TPOTEstimatorSteadyState` class.\n", "\n", "There are two evolutionary algorithms built into TPOT2, which corresponds to two different estimator classes.\n", "\n", "1. The `tpot2.TPOTEstimator` uses a standard evolutionary algorithm that evaluates exactly population_size individuals each generation. This is similar to the algorithm in TPOT1. The next generation does not start until the previous is completely finished evaluating. This leads to underutilized CPU time as the cores are waiting for the last individuals to finish training, but may preserve diversity in the population. \n", "\n", - "2. The `tpot2.TPOTEstimatorSteadyState` differs in that it will generate and evaluate the next individual as soon as an individual finishes evaluation. The number of individuals being evaluated is determined by the n_jobs parameter. There is no longer a concept of generations. The population_size parameter now refers to the size of the list of evaluated parents. When an individual is evaluated, the selection method updates the list of parents. This allows more efficient utilization when using multiple cores.\n" + "2. The `tpot2.TPOTEstimatorSteadyState` differs in that it will generate and evaluate the next individual as soon as an individual finishes the evaluation. The number of individuals being evaluated is determined by the n_jobs parameter. There is no longer a concept of generations. The population_size parameter now refers to the size of the list of evaluated parents. When an individual is evaluated, the selection method updates the list of parents. This allows more efficient utilization when using multiple cores.\n" ] }, { @@ -2120,21 +2227,21 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Evaluations: : 21it [00:13, 1.61it/s]\n" + "Evaluations: : 25it [00:10, 2.47it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "0.9786392405063291\n" + "0.9824771007566706\n" ] } ], @@ -2174,12 +2281,12 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -2195,7 +2302,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -2234,68 +2341,68 @@ " \n", " \n", " 0\n", - " 0.474805\n", - " 20.0\n", + " 0.940426\n", + " 98.0\n", " NaN\n", " NaN\n", " <tpot2.search_spaces.pipelines.graph.GraphPipe...\n", - " 1.727144e+09\n", - " 1.727144e+09\n", + " 1.727192e+09\n", + " 1.727192e+09\n", " None\n", " NaN\n", - " [('LogisticRegression_1', 'FastICA_1'), ('Fast...\n", + " [('DecisionTreeClassifier_1', 'SelectPercentil...\n", " \n", " \n", " 1\n", - " 0.962983\n", - " 78.0\n", + " 0.954244\n", + " 70.0\n", " NaN\n", " NaN\n", " <tpot2.search_spaces.pipelines.graph.GraphPipe...\n", - " 1.727144e+09\n", - " 1.727144e+09\n", + " 1.727192e+09\n", + " 1.727192e+09\n", " None\n", " NaN\n", - " [('DecisionTreeClassifier_1', 'PCA_1'), ('Quan...\n", + " [('DecisionTreeClassifier_1', 'ColumnOneHotEnc...\n", " \n", " \n", " 2\n", - " 0.962310\n", - " 57.0\n", + " 0.967942\n", + " 13.0\n", " NaN\n", " NaN\n", " <tpot2.search_spaces.pipelines.graph.GraphPipe...\n", - " 1.727144e+09\n", - " 1.727144e+09\n", + " 1.727192e+09\n", + " 1.727192e+09\n", " None\n", " NaN\n", - " [('DecisionTreeClassifier_1', 'SelectFwe_1')]\n", + " [('KNeighborsClassifier_1', 'SelectFwe_2'), ('...\n", " \n", " \n", " 3\n", - " 0.956908\n", - " 66.0\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " <tpot2.search_spaces.pipelines.graph.GraphPipe...\n", - " 1.727144e+09\n", - " 1.727144e+09\n", - " None\n", + " 1.727192e+09\n", + " 1.727192e+09\n", + " INVALID\n", " NaN\n", - " [('DecisionTreeClassifier_1', 'SelectFwe_2'), ...\n", + " [('LogisticRegression_1', 'FeatureAgglomeratio...\n", " \n", " \n", " 4\n", - " 0.879195\n", - " 15.0\n", + " 0.953421\n", + " 80.0\n", " NaN\n", " NaN\n", " <tpot2.search_spaces.pipelines.graph.GraphPipe...\n", - " 1.727144e+09\n", - " 1.727144e+09\n", + " 1.727192e+09\n", + " 1.727192e+09\n", " None\n", " NaN\n", - " [('DecisionTreeClassifier_1', 'SelectFwe_1')]\n", + " [('DecisionTreeClassifier_1', 'SelectPercentil...\n", " \n", " \n", "\n", @@ -2303,35 +2410,35 @@ ], "text/plain": [ " roc_auc_score complexity_scorer Parents Variation_Function \\\n", - "0 0.474805 20.0 NaN NaN \n", - "1 0.962983 78.0 NaN NaN \n", - "2 0.962310 57.0 NaN NaN \n", - "3 0.956908 66.0 NaN NaN \n", - "4 0.879195 15.0 NaN NaN \n", + "0 0.940426 98.0 NaN NaN \n", + "1 0.954244 70.0 NaN NaN \n", + "2 0.967942 13.0 NaN NaN \n", + "3 NaN NaN NaN NaN \n", + "4 0.953421 80.0 NaN NaN \n", "\n", " Individual Submitted Timestamp \\\n", - "0 #sk-container-id-1 {\n", - " /* Definition of color scheme common for light and dark mode */\n", - " --sklearn-color-text: black;\n", - " --sklearn-color-line: gray;\n", - " /* Definition of color scheme for unfitted estimators */\n", - " --sklearn-color-unfitted-level-0: #fff5e6;\n", - " --sklearn-color-unfitted-level-1: #f6e4d2;\n", - " --sklearn-color-unfitted-level-2: #ffe0b3;\n", - " --sklearn-color-unfitted-level-3: chocolate;\n", - " /* Definition of color scheme for fitted estimators */\n", - " --sklearn-color-fitted-level-0: #f0f8ff;\n", - " --sklearn-color-fitted-level-1: #d4ebff;\n", - " --sklearn-color-fitted-level-2: #b3dbfd;\n", - " --sklearn-color-fitted-level-3: cornflowerblue;\n", - "\n", - " /* Specific color for light theme */\n", - " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n", - " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n", - " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n", - " --sklearn-color-icon: #696969;\n", - "\n", - " @media (prefers-color-scheme: dark) {\n", - " /* Redefinition of color scheme for dark theme */\n", - " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n", - " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n", - " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n", - " --sklearn-color-icon: #878787;\n", - " }\n", - "}\n", - "\n", - "#sk-container-id-1 {\n", - " color: var(--sklearn-color-text);\n", - "}\n", - "\n", - "#sk-container-id-1 pre {\n", - " padding: 0;\n", - "}\n", - "\n", - "#sk-container-id-1 input.sk-hidden--visually {\n", - " border: 0;\n", - " clip: rect(1px 1px 1px 1px);\n", - " clip: rect(1px, 1px, 1px, 1px);\n", - " height: 1px;\n", - " margin: -1px;\n", - " overflow: hidden;\n", - " padding: 0;\n", - " position: absolute;\n", - " width: 1px;\n", - "}\n", - "\n", - "#sk-container-id-1 div.sk-dashed-wrapped {\n", - " border: 1px dashed var(--sklearn-color-line);\n", - " margin: 0 0.4em 0.5em 0.4em;\n", - " box-sizing: border-box;\n", - " padding-bottom: 0.4em;\n", - " background-color: var(--sklearn-color-background);\n", - "}\n", - "\n", - "#sk-container-id-1 div.sk-container {\n", - " /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n", - " but bootstrap.min.css set `[hidden] { display: none !important; }`\n", - " so we also need the `!important` here to be able to override the\n", - " default hidden behavior on the sphinx rendered scikit-learn.org.\n", - " See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n", - " display: inline-block !important;\n", - " position: relative;\n", - "}\n", - "\n", - "#sk-container-id-1 div.sk-text-repr-fallback {\n", - " display: none;\n", - "}\n", - "\n", - "div.sk-parallel-item,\n", - "div.sk-serial,\n", - "div.sk-item {\n", - " /* draw centered vertical line to link estimators */\n", - " background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n", - " background-size: 2px 100%;\n", - " background-repeat: no-repeat;\n", - " background-position: center center;\n", - "}\n", - "\n", - "/* Parallel-specific style estimator block */\n", - "\n", - "#sk-container-id-1 div.sk-parallel-item::after {\n", - " content: \"\";\n", - " width: 100%;\n", - " border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n", - " flex-grow: 1;\n", - "}\n", - "\n", - "#sk-container-id-1 div.sk-parallel {\n", - " display: flex;\n", - " align-items: stretch;\n", - " justify-content: center;\n", - " background-color: var(--sklearn-color-background);\n", - " position: relative;\n", - "}\n", - "\n", - "#sk-container-id-1 div.sk-parallel-item {\n", - " display: flex;\n", - " flex-direction: column;\n", - "}\n", - "\n", - "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n", - " align-self: flex-end;\n", - " width: 50%;\n", - "}\n", - "\n", - "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n", - " align-self: flex-start;\n", - " width: 50%;\n", - "}\n", - "\n", - "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n", - " width: 0;\n", - "}\n", - "\n", - "/* Serial-specific style estimator block */\n", - "\n", - "#sk-container-id-1 div.sk-serial {\n", - " display: flex;\n", - " flex-direction: column;\n", - " align-items: center;\n", - " background-color: var(--sklearn-color-background);\n", - " padding-right: 1em;\n", - " padding-left: 1em;\n", - "}\n", - "\n", - "\n", - "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n", - "clickable and can be expanded/collapsed.\n", - "- Pipeline and ColumnTransformer use this feature and define the default style\n", - "- Estimators will overwrite some part of the style using the `sk-estimator` class\n", - "*/\n", - "\n", - "/* Pipeline and ColumnTransformer style (default) */\n", - "\n", - "#sk-container-id-1 div.sk-toggleable {\n", - " /* Default theme specific background. It is overwritten whether we have a\n", - " specific estimator or a Pipeline/ColumnTransformer */\n", - " background-color: var(--sklearn-color-background);\n", - "}\n", - "\n", - "/* Toggleable label */\n", - "#sk-container-id-1 label.sk-toggleable__label {\n", - " cursor: pointer;\n", - " display: block;\n", - " width: 100%;\n", - " margin-bottom: 0;\n", - " padding: 0.5em;\n", - " box-sizing: border-box;\n", - " text-align: center;\n", - "}\n", - "\n", - "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n", - " /* Arrow on the left of the label */\n", - " content: \"▸\";\n", - " float: left;\n", - " margin-right: 0.25em;\n", - " color: var(--sklearn-color-icon);\n", - "}\n", - "\n", - "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n", - " color: var(--sklearn-color-text);\n", - "}\n", - "\n", - "/* Toggleable content - dropdown */\n", - "\n", - "#sk-container-id-1 div.sk-toggleable__content {\n", - " max-height: 0;\n", - " max-width: 0;\n", - " overflow: hidden;\n", - " text-align: left;\n", - " /* unfitted */\n", - " background-color: var(--sklearn-color-unfitted-level-0);\n", - "}\n", - "\n", - "#sk-container-id-1 div.sk-toggleable__content.fitted {\n", - " /* fitted */\n", - " background-color: var(--sklearn-color-fitted-level-0);\n", - "}\n", - "\n", - "#sk-container-id-1 div.sk-toggleable__content pre {\n", - " margin: 0.2em;\n", - " border-radius: 0.25em;\n", - " color: var(--sklearn-color-text);\n", - " /* unfitted */\n", - " background-color: var(--sklearn-color-unfitted-level-0);\n", - "}\n", - "\n", - "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n", - " /* unfitted */\n", - " background-color: var(--sklearn-color-fitted-level-0);\n", - "}\n", - "\n", - "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n", - " /* Expand drop-down */\n", - " max-height: 200px;\n", - " max-width: 100%;\n", - " overflow: auto;\n", - "}\n", - "\n", - "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n", - " content: \"▾\";\n", - "}\n", - "\n", - "/* Pipeline/ColumnTransformer-specific style */\n", - "\n", - "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", - " color: var(--sklearn-color-text);\n", - " background-color: var(--sklearn-color-unfitted-level-2);\n", - "}\n", - "\n", - "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", - " background-color: var(--sklearn-color-fitted-level-2);\n", - "}\n", - "\n", - "/* Estimator-specific style */\n", - "\n", - "/* Colorize estimator box */\n", - "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", - " /* unfitted */\n", - " background-color: var(--sklearn-color-unfitted-level-2);\n", - "}\n", - "\n", - "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", - " /* fitted */\n", - " background-color: var(--sklearn-color-fitted-level-2);\n", - "}\n", - "\n", - "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n", - "#sk-container-id-1 div.sk-label label {\n", - " /* The background is the default theme color */\n", - " color: var(--sklearn-color-text-on-default-background);\n", - "}\n", - "\n", - "/* On hover, darken the color of the background */\n", - "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n", - " color: var(--sklearn-color-text);\n", - " background-color: var(--sklearn-color-unfitted-level-2);\n", - "}\n", - "\n", - "/* Label box, darken color on hover, fitted */\n", - "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n", - " color: var(--sklearn-color-text);\n", - " background-color: var(--sklearn-color-fitted-level-2);\n", - "}\n", - "\n", - "/* Estimator label */\n", - "\n", - "#sk-container-id-1 div.sk-label label {\n", - " font-family: monospace;\n", - " font-weight: bold;\n", - " display: inline-block;\n", - " line-height: 1.2em;\n", - "}\n", - "\n", - "#sk-container-id-1 div.sk-label-container {\n", - " text-align: center;\n", - "}\n", - "\n", - "/* Estimator-specific */\n", - "#sk-container-id-1 div.sk-estimator {\n", - " font-family: monospace;\n", - " border: 1px dotted var(--sklearn-color-border-box);\n", - " border-radius: 0.25em;\n", - " box-sizing: border-box;\n", - " margin-bottom: 0.5em;\n", - " /* unfitted */\n", - " background-color: var(--sklearn-color-unfitted-level-0);\n", - "}\n", - "\n", - "#sk-container-id-1 div.sk-estimator.fitted {\n", - " /* fitted */\n", - " background-color: var(--sklearn-color-fitted-level-0);\n", - "}\n", - "\n", - "/* on hover */\n", - "#sk-container-id-1 div.sk-estimator:hover {\n", - " /* unfitted */\n", - " background-color: var(--sklearn-color-unfitted-level-2);\n", - "}\n", - "\n", - "#sk-container-id-1 div.sk-estimator.fitted:hover {\n", - " /* fitted */\n", - " background-color: var(--sklearn-color-fitted-level-2);\n", - "}\n", - "\n", - "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n", - "\n", - "/* Common style for \"i\" and \"?\" */\n", - "\n", - ".sk-estimator-doc-link,\n", - "a:link.sk-estimator-doc-link,\n", - "a:visited.sk-estimator-doc-link {\n", - " float: right;\n", - " font-size: smaller;\n", - " line-height: 1em;\n", - " font-family: monospace;\n", - " background-color: var(--sklearn-color-background);\n", - " border-radius: 1em;\n", - " height: 1em;\n", - " width: 1em;\n", - " text-decoration: none !important;\n", - " margin-left: 1ex;\n", - " /* unfitted */\n", - " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n", - " color: var(--sklearn-color-unfitted-level-1);\n", - "}\n", - "\n", - ".sk-estimator-doc-link.fitted,\n", - "a:link.sk-estimator-doc-link.fitted,\n", - "a:visited.sk-estimator-doc-link.fitted {\n", - " /* fitted */\n", - " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n", - " color: var(--sklearn-color-fitted-level-1);\n", - "}\n", - "\n", - "/* On hover */\n", - "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n", - ".sk-estimator-doc-link:hover,\n", - "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n", - ".sk-estimator-doc-link:hover {\n", - " /* unfitted */\n", - " background-color: var(--sklearn-color-unfitted-level-3);\n", - " color: var(--sklearn-color-background);\n", - " text-decoration: none;\n", - "}\n", - "\n", - "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n", - ".sk-estimator-doc-link.fitted:hover,\n", - "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n", - ".sk-estimator-doc-link.fitted:hover {\n", - " /* fitted */\n", - " background-color: var(--sklearn-color-fitted-level-3);\n", - " color: var(--sklearn-color-background);\n", - " text-decoration: none;\n", - "}\n", - "\n", - "/* Span, style for the box shown on hovering the info icon */\n", - ".sk-estimator-doc-link span {\n", - " display: none;\n", - " z-index: 9999;\n", - " position: relative;\n", - " font-weight: normal;\n", - " right: .2ex;\n", - " padding: .5ex;\n", - " margin: .5ex;\n", - " width: min-content;\n", - " min-width: 20ex;\n", - " max-width: 50ex;\n", - " color: var(--sklearn-color-text);\n", - " box-shadow: 2pt 2pt 4pt #999;\n", - " /* unfitted */\n", - " background: var(--sklearn-color-unfitted-level-0);\n", - " border: .5pt solid var(--sklearn-color-unfitted-level-3);\n", - "}\n", - "\n", - ".sk-estimator-doc-link.fitted span {\n", - " /* fitted */\n", - " background: var(--sklearn-color-fitted-level-0);\n", - " border: var(--sklearn-color-fitted-level-3);\n", - "}\n", - "\n", - ".sk-estimator-doc-link:hover span {\n", - " display: block;\n", - "}\n", - "\n", - "/* \"?\"-specific style due to the `` HTML tag */\n", - "\n", - "#sk-container-id-1 a.estimator_doc_link {\n", - " float: right;\n", - " font-size: 1rem;\n", - " line-height: 1em;\n", - " font-family: monospace;\n", - " background-color: var(--sklearn-color-background);\n", - " border-radius: 1rem;\n", - " height: 1rem;\n", - " width: 1rem;\n", - " text-decoration: none;\n", - " /* unfitted */\n", - " color: var(--sklearn-color-unfitted-level-1);\n", - " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n", - "}\n", - "\n", - "#sk-container-id-1 a.estimator_doc_link.fitted {\n", - " /* fitted */\n", - " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n", - " color: var(--sklearn-color-fitted-level-1);\n", - "}\n", - "\n", - "/* On hover */\n", - "#sk-container-id-1 a.estimator_doc_link:hover {\n", - " /* unfitted */\n", - " background-color: var(--sklearn-color-unfitted-level-3);\n", - " color: var(--sklearn-color-background);\n", - " text-decoration: none;\n", - "}\n", - "\n", - "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n", - " /* fitted */\n", - " background-color: var(--sklearn-color-fitted-level-3);\n", - "}\n", - "" - ], - "text/plain": [ - "RandomForestClassifier(bootstrap=False, max_features=0.0109527542096,\n", - " min_samples_leaf=15, min_samples_split=4,\n", - " n_estimators=128)" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from ConfigSpace import ConfigurationSpace\n", "from ConfigSpace import ConfigurationSpace, Integer, Float, Categorical, Normal\n", @@ -501,441 +69,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sampled hyperparameters\n", - "{'bootstrap': True, 'criterion': 'entropy', 'max_features': 0.8366498702446, 'min_samples_leaf': 11, 'min_samples_split': 20, 'n_estimators': 128}\n" - ] - }, - { - "data": { - "text/html": [ - "
RandomForestClassifier(criterion='entropy', max_features=0.8366498702446,\n",
-       "                       min_samples_leaf=11, min_samples_split=20,\n",
-       "                       n_estimators=128)
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" - ], - "text/plain": [ - "RandomForestClassifier(criterion='entropy', max_features=0.8366498702446,\n", - " min_samples_leaf=11, min_samples_split=20,\n", - " n_estimators=128)" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "rf_configspace = ConfigurationSpace(\n", " space = {\n", @@ -963,9 +99,9 @@ "source": [ "# TPOT Search spaces\n", "\n", - "TPOT allows you to both hyperparameter search spaces for individual methods as well as pipeline structure search spaces. For example, TPOT can create linear pipelines, trees, or graphs. \n", + "TPOT allows you to create hyperparameter search spaces for individual methods and pipeline structure search spaces. For example, TPOT can create linear pipelines, trees, or graphs. \n", "\n", - "TPOT search spaces are found in the `search_spaces` module. There are two primary kinds of search spaces, node and pipeline. Node search spaces specify the search space of a single sklearn `BaseEstimator`. Pipeline search spaces define the possible structures for a group of node search spaces. These take in node search spaces and produce a pipeline using nodes from that search space. Since sklearn Pipelines are also `BaseEstimator`, pipeline search spaces are also technically node search spaces. Meaning that pipeline search spaces can take in other pipeline search spaces in order to define more complex structures. The primary differentiating factor bewteen node and pipeline search spaces is that pipeline search spaces must take in another search space as input to feed its individual nodes. Therefore, all search spaces eventually end in a node search space at the lowest level. Note that parameters for pipeline search spaces can differ, some take in only a single search space, some take in a list, or some take in multiple defined parameters.\n", + "TPOT search spaces are found in the `search_spaces` module. There are two primary kinds of search spaces, node and pipeline. Node search spaces specify a single sklearn `BaseEstimator` search space. Pipeline search spaces define the possible structures for a group of node search spaces. These take in node search spaces and produce a pipeline using nodes from that search space. Since sklearn Pipelines are also `BaseEstimator`, pipeline search spaces are also technically node search spaces. This means that pipeline search spaces can take in other pipeline search spaces in order to define more complex structures. The primary differentiating factor between node and pipeline search spaces is that pipeline search spaces must take in another search space as input to feed its individual nodes. Therefore, all search spaces eventually end in a node search space at the lowest level. Note that parameters for pipeline search spaces can differ, some take in only a single search space, some take in a list, or some take in multiple defined parameters.\n", "\n", "## node search spaces\n", "\n", @@ -1004,7 +140,7 @@ "source": [ "## Node Search Space Examples\n", "\n", - "Node search spaces represent the smallest unit of an sklearn pipeline. All node search spaces create and optimize a single node, or estimator object. For example this could be a KNeighborsClassifier or a FeatureSetSelector.\n", + "Node search spaces represent the smallest unit of an sklearn pipeline. All node search spaces create and optimize a single node which exports a single estimator object. For example this could be a KNeighborsClassifier or a FeatureSetSelector.\n", "\n", "### EstimatorNode\n", "\n", @@ -1017,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -1053,20 +189,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "knn_individual = knn_node.generate()\n", "knn_individual" @@ -1074,18 +199,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sampled hyperparameters\n", - "{'metric': 'euclidean', 'n_jobs': 1, 'n_neighbors': 8, 'p': 2, 'weights': 'uniform'}\n" - ] - } - ], + "outputs": [], "source": [ "print(\"sampled hyperparameters\")\n", "print(knn_individual.hyperparameters)" @@ -1100,18 +216,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "mutated hyperparameters\n", - "{'metric': 'minkowski', 'n_jobs': 1, 'n_neighbors': 1, 'p': 3, 'weights': 'distance'}\n" - ] - } - ], + "outputs": [], "source": [ "knn_individual.mutate() # mutate the individual\n", "print(\"mutated hyperparameters\")\n", @@ -1127,25 +234,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "original hyperparameters for individual 1\n", - "{'metric': 'minkowski', 'n_jobs': 1, 'n_neighbors': 8, 'p': 1, 'weights': 'uniform'}\n", - "original hyperparameters for individual 2\n", - "{'metric': 'euclidean', 'n_jobs': 1, 'n_neighbors': 7, 'p': 3, 'weights': 'distance'}\n", - "\n", - "post crossover hyperparameters for individual 1\n", - "{'metric': 'minkowski', 'n_jobs': 1, 'n_neighbors': 8, 'p': 3, 'weights': 'uniform'}\n", - "post crossover hyperparameters for individual 2\n", - "{'metric': 'euclidean', 'n_jobs': 1, 'n_neighbors': 7, 'p': 3, 'weights': 'distance'}\n" - ] - } - ], + "outputs": [], "source": [ "knn_individual1 = knn_node.generate()\n", "knn_individual2 = knn_node.generate()\n", @@ -1175,427 +266,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
KNeighborsClassifier(n_jobs=1, n_neighbors=8, p=3)
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" - ], - "text/plain": [ - "KNeighborsClassifier(n_jobs=1, n_neighbors=8, p=3)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "est = knn_individual1.export_pipeline()\n", "est" @@ -1610,427 +283,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
KNeighborsClassifier(n_neighbors=10)
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" - ], - "text/plain": [ - "KNeighborsClassifier(n_neighbors=10)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import tpot2\n", "from ConfigSpace import ConfigurationSpace\n", @@ -2079,20 +334,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import tpot2\n", "from ConfigSpace import ConfigurationSpace\n", @@ -2186,437 +430,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sampled pipeline\n" - ] - }, - { - "data": { - "text/html": [ - "
LogisticRegression(C=49.6823706295106, class_weight='balanced', dual=True,\n",
-       "                   max_iter=1000, n_jobs=1, solver='liblinear')
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" - ], - "text/plain": [ - "LogisticRegression(C=49.6823706295106, class_weight='balanced', dual=True,\n", - " max_iter=1000, n_jobs=1, solver='liblinear')" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "classifier_individual = classifier_node.generate()\n", "\n", @@ -2626,437 +442,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "mutated pipeline\n" - ] - }, - { - "data": { - "text/html": [ - "
LogisticRegression(C=997.5163561212358, class_weight='balanced', dual=True,\n",
-       "                   max_iter=1000, n_jobs=1, solver='newton-cg')
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" - ], - "text/plain": [ - "LogisticRegression(C=997.5163561212358, class_weight='balanced', dual=True,\n", - " max_iter=1000, n_jobs=1, solver='newton-cg')" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "print(\"mutated pipeline\")\n", "classifier_individual.mutate()\n", @@ -3091,437 +479,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sampled pipeline 1\n" - ] - }, - { - "data": { - "text/html": [ - "
LogisticRegression(C=2532.59836574515, class_weight='balanced', max_iter=1000,\n",
-       "                   n_jobs=1, penalty='l1', solver='saga')
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" - ], - "text/plain": [ - "LogisticRegression(C=2532.59836574515, class_weight='balanced', max_iter=1000,\n", - " n_jobs=1, penalty='l1', solver='saga')" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#same pipeline search space as before.\n", "classifier_choice = tpot2.config.get_search_space([\"KNeighborsClassifier\", \"LogisticRegression\", \"DecisionTreeClassifier\"])\n", @@ -3532,440 +492,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sampled pipeline 2\n" - ] - }, - { - "data": { - "text/html": [ - "
DecisionTreeClassifier(class_weight='balanced', max_depth=20,\n",
-       "                       max_features='log2', min_samples_leaf=2,\n",
-       "                       min_samples_split=4)
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" - ], - "text/plain": [ - "DecisionTreeClassifier(class_weight='balanced', max_depth=20,\n", - " max_features='log2', min_samples_leaf=2,\n", - " min_samples_split=4)" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "print(\"sampled pipeline 2\")\n", "classifier_choice.generate().export_pipeline()" @@ -3973,434 +502,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sampled pipeline 1\n" - ] - }, - { - "data": { - "text/html": [ - "
BernoulliNB(alpha=95.0262026809266)
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" - ], - "text/plain": [ - "BernoulliNB(alpha=95.0262026809266)" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#search space for all classifiers\n", "classifier_choice = tpot2.config.get_search_space(\"classifiers\")\n", @@ -4411,440 +515,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sampled pipeline 2\n" - ] - }, - { - "data": { - "text/html": [ - "
BaggingClassifier(bootstrap=False, bootstrap_features=True,\n",
-       "                  max_features=0.8753796928965, max_samples=0.8146576017845,\n",
-       "                  n_estimators=17, n_jobs=1)
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" - ], - "text/plain": [ - "BaggingClassifier(bootstrap=False, bootstrap_features=True,\n", - " max_features=0.8753796928965, max_samples=0.8146576017845,\n", - " n_estimators=17, n_jobs=1)" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "print(\"sampled pipeline 2\")\n", "classifier_choice.generate().export_pipeline()" @@ -4861,460 +534,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sampled pipeline\n" - ] - }, - { - "data": { - "text/html": [ - "
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-       "                ('logisticregression',\n",
-       "                 LogisticRegression(C=85638.13831296022,\n",
-       "                                    class_weight='balanced',\n",
-       "                                    l1_ratio=0.6102894736188, max_iter=1000,\n",
-       "                                    n_jobs=1, penalty='elasticnet',\n",
-       "                                    solver='saga'))])
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" - ], - "text/plain": [ - "Pipeline(steps=[('variancethreshold',\n", - " VarianceThreshold(threshold=0.0038921831393)),\n", - " ('pca', PCA(n_components=0.7545742110409)),\n", - " ('logisticregression',\n", - " LogisticRegression(C=85638.13831296022,\n", - " class_weight='balanced',\n", - " l1_ratio=0.6102894736188, max_iter=1000,\n", - " n_jobs=1, penalty='elasticnet',\n", - " solver='saga'))])" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "selector_choicepipeline = tpot2.config.get_search_space(\"VarianceThreshold\")\n", "transformer_choicepipeline = tpot2.config.get_search_space(\"PCA\")\n", @@ -5341,443 +563,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sampled pipeline\n" - ] - }, - { - "data": { - "text/html": [ - "
Pipeline(steps=[('selectpercentile',\n",
-       "                 SelectPercentile(percentile=97.2182140589731)),\n",
-       "                ('binarizer', Binarizer(threshold=0.7460953779809)),\n",
-       "                ('gaussiannb', GaussianNB())])
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" - ], - "text/plain": [ - "Pipeline(steps=[('selectpercentile',\n", - " SelectPercentile(percentile=97.2182140589731)),\n", - " ('binarizer', Binarizer(threshold=0.7460953779809)),\n", - " ('gaussiannb', GaussianNB())])" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "selector_choicepipeline = tpot2.config.get_search_space(\"selectors\")\n", "transformer_choicepipeline = tpot2.config.get_search_space(\"transformers\")\n", @@ -5795,450 +583,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sampled pipeline\n" - ] - }, - { - "data": { - "text/html": [ - "
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-       "                 AdaBoostClassifier(algorithm='SAMME',\n",
-       "                                    learning_rate=0.2576218451608,\n",
-       "                                    n_estimators=260))])
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" - ], - "text/plain": [ - "Pipeline(steps=[('selectfwe', SelectFwe(alpha=0.018605231348)),\n", - " ('powertransformer', PowerTransformer()),\n", - " ('adaboostclassifier',\n", - " AdaBoostClassifier(algorithm='SAMME',\n", - " learning_rate=0.2576218451608,\n", - " n_estimators=260))])" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "print(\"sampled pipeline\")\n", "stc_pipeline.generate().export_pipeline()" @@ -6255,440 +602,9 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sampled pipeline\n" - ] - }, - { - "data": { - "text/html": [ - "
Pipeline(steps=[('minmaxscaler-1', MinMaxScaler()),\n",
-       "                ('minmaxscaler-2', MinMaxScaler()),\n",
-       "                ('minmaxscaler-3', MinMaxScaler())])
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" - ], - "text/plain": [ - "Pipeline(steps=[('minmaxscaler-1', MinMaxScaler()),\n", - " ('minmaxscaler-2', MinMaxScaler()),\n", - " ('minmaxscaler-3', MinMaxScaler())])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import tpot2.config\n", "\n", @@ -6700,449 +616,9 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sampled pipeline\n" - ] - }, - { - "data": { - "text/html": [ - "
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-       "                          n_components=12)),\n",
-       "                ('variancethreshold',\n",
-       "                 VarianceThreshold(threshold=0.0109488305621))])
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" - ], - "text/plain": [ - "Pipeline(steps=[('powertransformer', PowerTransformer()),\n", - " ('nystroem',\n", - " Nystroem(gamma=0.9541024274994, kernel='cosine',\n", - " n_components=12)),\n", - " ('variancethreshold',\n", - " VarianceThreshold(threshold=0.0109488305621))])" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "print(\"sampled pipeline\")\n", "linear_feature_engineering.generate().export_pipeline()" @@ -7150,472 +626,9 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sampled pipeline\n" - ] - }, - { - "data": { - "text/html": [ - "
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-       "                                 ('passkbinsdiscretizer',\n",
-       "                                  PassKBinsDiscretizer(n_bins=13,\n",
-       "                                                       strategy='uniform'))])),\n",
-       "                ('randomforestclassifier',\n",
-       "                 RandomForestClassifier(bootstrap=False,\n",
-       "                                        max_features=0.078312000096,\n",
-       "                                        min_samples_leaf=7, min_samples_split=3,\n",
-       "                                        n_estimators=128))])
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" - ], - "text/plain": [ - "Pipeline(steps=[('pipeline',\n", - " Pipeline(steps=[('pca', PCA(n_components=0.543582719063)),\n", - " ('quantiletransformer',\n", - " QuantileTransformer(n_quantiles=1182)),\n", - " ('passkbinsdiscretizer',\n", - " PassKBinsDiscretizer(n_bins=13,\n", - " strategy='uniform'))])),\n", - " ('randomforestclassifier',\n", - " RandomForestClassifier(bootstrap=False,\n", - " max_features=0.078312000096,\n", - " min_samples_leaf=7, min_samples_split=3,\n", - " n_estimators=128))])" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "full_search_space = tpot2.search_spaces.pipelines.SequentialPipeline([\n", " linear_feature_engineering,\n", @@ -7628,472 +641,9 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sampled pipeline\n" - ] - }, - { - "data": { - "text/html": [ - "
Pipeline(steps=[('pipeline',\n",
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-       "                                  PassKBinsDiscretizer()),\n",
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-       "                                  RobustScaler(quantile_range=(0.07166946516,\n",
-       "                                                               0.7478574798356))),\n",
-       "                                 ('zerocount', ZeroCount())])),\n",
-       "                ('baggingclassifier',\n",
-       "                 BaggingClassifier(bootstrap=False, bootstrap_features=True,\n",
-       "                                   max_features=0.1521715848495,\n",
-       "                                   max_samples=0.1213783267153, n_estimators=25,\n",
-       "                                   n_jobs=1))])
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" - ], - "text/plain": [ - "Pipeline(steps=[('pipeline',\n", - " Pipeline(steps=[('passkbinsdiscretizer',\n", - " PassKBinsDiscretizer()),\n", - " ('robustscaler',\n", - " RobustScaler(quantile_range=(0.07166946516,\n", - " 0.7478574798356))),\n", - " ('zerocount', ZeroCount())])),\n", - " ('baggingclassifier',\n", - " BaggingClassifier(bootstrap=False, bootstrap_features=True,\n", - " max_features=0.1521715848495,\n", - " max_samples=0.1213783267153, n_estimators=25,\n", - " n_jobs=1))])" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "print(\"sampled pipeline\")\n", "full_search_space.generate().export_pipeline()" @@ -8110,437 +660,9 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
FeatureUnion(transformer_list=[('featureagglomeration',\n",
-       "                                FeatureAgglomeration(n_clusters=257,\n",
-       "                                                     pooling_func=<function median at 0x7e7cb00dcf70>)),\n",
-       "                               ('passthrough', Passthrough())])
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" - ], - "text/plain": [ - "FeatureUnion(transformer_list=[('featureagglomeration',\n", - " FeatureAgglomeration(n_clusters=257,\n", - " pooling_func=)),\n", - " ('passthrough', Passthrough())])" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "transform_and_passthrough = tpot2.search_spaces.pipelines.UnionPipeline([\n", " tpot2.config.get_search_space(\"transformers\"),\n", @@ -8559,452 +681,9 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Pipeline(steps=[('selectfwe', SelectFwe(alpha=0.0043464782007)),\n",
-       "                ('featureunion',\n",
-       "                 FeatureUnion(transformer_list=[('powertransformer',\n",
-       "                                                 PowerTransformer()),\n",
-       "                                                ('passthrough',\n",
-       "                                                 Passthrough())])),\n",
-       "                ('lineardiscriminantanalysis',\n",
-       "                 LinearDiscriminantAnalysis(shrinkage=0.6381012799603,\n",
-       "                                            solver='lsqr'))])
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" - ], - "text/plain": [ - "Pipeline(steps=[('selectfwe', SelectFwe(alpha=0.0043464782007)),\n", - " ('featureunion',\n", - " FeatureUnion(transformer_list=[('powertransformer',\n", - " PowerTransformer()),\n", - " ('passthrough',\n", - " Passthrough())])),\n", - " ('lineardiscriminantanalysis',\n", - " LinearDiscriminantAnalysis(shrinkage=0.6381012799603,\n", - " solver='lsqr'))])" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "stc_pipeline2 = tpot2.search_spaces.pipelines.SequentialPipeline([\n", " tpot2.config.get_search_space(\"selectors\"),\n", @@ -9024,472 +703,9 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Pipeline(steps=[('featureunion',\n",
-       "                 FeatureUnion(transformer_list=[('pipeline-1',\n",
-       "                                                 Pipeline(steps=[('selectfwe',\n",
-       "                                                                  SelectFwe(alpha=0.000231370784)),\n",
-       "                                                                 ('zerocount',\n",
-       "                                                                  ZeroCount())])),\n",
-       "                                                ('pipeline-2',\n",
-       "                                                 Pipeline(steps=[('selectpercentile',\n",
-       "                                                                  SelectPercentile(percentile=56.9207229949532)),\n",
-       "                                                                 ('rbfsampler',\n",
-       "                                                                  RBFSampler(gamma=0.9667449310006,\n",
-       "                                                                             n_components=45))]))])),\n",
-       "                ('linearsvc', LinearSVC(C=8596.144097926976, penalty='l1'))])
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" - ], - "text/plain": [ - "Pipeline(steps=[('featureunion',\n", - " FeatureUnion(transformer_list=[('pipeline-1',\n", - " Pipeline(steps=[('selectfwe',\n", - " SelectFwe(alpha=0.000231370784)),\n", - " ('zerocount',\n", - " ZeroCount())])),\n", - " ('pipeline-2',\n", - " Pipeline(steps=[('selectpercentile',\n", - " SelectPercentile(percentile=56.9207229949532)),\n", - " ('rbfsampler',\n", - " RBFSampler(gamma=0.9667449310006,\n", - " n_components=45))]))])),\n", - " ('linearsvc', LinearSVC(C=8596.144097926976, penalty='l1'))])" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "st_pipeline = tpot2.search_spaces.pipelines.SequentialPipeline([\n", " tpot2.config.get_search_space(\"selectors\"),\n", @@ -9522,427 +738,9 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
FeatureUnion(transformer_list=[('powertransformer', PowerTransformer())])
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" - ], - "text/plain": [ - "FeatureUnion(transformer_list=[('powertransformer', PowerTransformer())])" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "dynamic_transformers = tpot2.search_spaces.pipelines.DynamicUnionPipeline(tpot2.config.get_search_space(\"transformers\"), max_estimators=4)\n", "dynamic_transformers.generate().export_pipeline()" @@ -9957,454 +755,9 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
FeatureUnion(transformer_list=[('featureunion',\n",
-       "                                FeatureUnion(transformer_list=[('rbfsampler',\n",
-       "                                                                RBFSampler(gamma=0.3736377055485,\n",
-       "                                                                           n_components=3)),\n",
-       "                                                               ('quantiletransformer',\n",
-       "                                                                QuantileTransformer(n_quantiles=955)),\n",
-       "                                                               ('fastica',\n",
-       "                                                                FastICA(algorithm='deflation',\n",
-       "                                                                        n_components=92))])),\n",
-       "                               ('passthrough', Passthrough())])
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" - ], - "text/plain": [ - "FeatureUnion(transformer_list=[('featureunion',\n", - " FeatureUnion(transformer_list=[('rbfsampler',\n", - " RBFSampler(gamma=0.3736377055485,\n", - " n_components=3)),\n", - " ('quantiletransformer',\n", - " QuantileTransformer(n_quantiles=955)),\n", - " ('fastica',\n", - " FastICA(algorithm='deflation',\n", - " n_components=92))])),\n", - " ('passthrough', Passthrough())])" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "dynamic_transformers_with_passthrough = tpot2.search_spaces.pipelines.UnionPipeline([\n", " dynamic_transformers,\n", @@ -10416,469 +769,9 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Pipeline(steps=[('selectpercentile',\n",
-       "                 SelectPercentile(percentile=1.2220353454141)),\n",
-       "                ('featureunion',\n",
-       "                 FeatureUnion(transformer_list=[('featureunion',\n",
-       "                                                 FeatureUnion(transformer_list=[('rbfsampler',\n",
-       "                                                                                 RBFSampler(gamma=0.0989706913466,\n",
-       "                                                                                            n_components=61)),\n",
-       "                                                                                ('passkbinsdiscretizer',\n",
-       "                                                                                 PassKBinsDiscretizer(n_bins=42))])),\n",
-       "                                                ('passthrough',\n",
-       "                                                 Passthrough())])),\n",
-       "                ('bernoullinb',\n",
-       "                 BernoulliNB(alpha=7.5106513153016, fit_prior=False))])
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" - ], - "text/plain": [ - "Pipeline(steps=[('selectpercentile',\n", - " SelectPercentile(percentile=1.2220353454141)),\n", - " ('featureunion',\n", - " FeatureUnion(transformer_list=[('featureunion',\n", - " FeatureUnion(transformer_list=[('rbfsampler',\n", - " RBFSampler(gamma=0.0989706913466,\n", - " n_components=61)),\n", - " ('passkbinsdiscretizer',\n", - " PassKBinsDiscretizer(n_bins=42))])),\n", - " ('passthrough',\n", - " Passthrough())])),\n", - " ('bernoullinb',\n", - " BernoulliNB(alpha=7.5106513153016, fit_prior=False))])" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "stc_pipeline3 = tpot2.search_spaces.pipelines.SequentialPipeline([\n", " tpot2.config.get_search_space(\"selectors\"),\n", @@ -10902,433 +795,9 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
ExtraTreesClassifier(bootstrap=True, class_weight='balanced',\n",
-       "                     criterion='entropy', max_features=0.5945413838121,\n",
-       "                     min_samples_leaf=4, min_samples_split=8, n_jobs=1)
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" - ], - "text/plain": [ - "ExtraTreesClassifier(bootstrap=True, class_weight='balanced',\n", - " criterion='entropy', max_features=0.5945413838121,\n", - " min_samples_leaf=4, min_samples_split=8, n_jobs=1)" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "SelectFromModel_configspace_part = ConfigurationSpace(\n", " space = {\n", @@ -11342,449 +811,9 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
SelectFromModel(estimator=ExtraTreesClassifier(bootstrap=True,\n",
-       "                                               class_weight='balanced',\n",
-       "                                               criterion='entropy',\n",
-       "                                               max_features=0.0157364601821,\n",
-       "                                               min_samples_leaf=14,\n",
-       "                                               min_samples_split=6, n_jobs=1),\n",
-       "                threshold=0.947435367985)
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" - ], - "text/plain": [ - "SelectFromModel(estimator=ExtraTreesClassifier(bootstrap=True,\n", - " class_weight='balanced',\n", - " criterion='entropy',\n", - " max_features=0.0157364601821,\n", - " min_samples_leaf=14,\n", - " min_samples_split=6, n_jobs=1),\n", - " threshold=0.947435367985)" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from sklearn.ensemble import ExtraTreesClassifier\n", "from sklearn.feature_selection import SelectFromModel\n", @@ -11815,430 +844,9 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
EstimatorTransformer(estimator=KNeighborsClassifier(n_jobs=1, n_neighbors=10,\n",
-       "                                                    p=1))
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" - ], - "text/plain": [ - "EstimatorTransformer(estimator=KNeighborsClassifier(n_jobs=1, n_neighbors=10,\n", - " p=1))" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "classifiers = tpot2.config.get_search_space(\"classifiers\")\n", "wrapped_estimators = tpot2.search_spaces.pipelines.WrapperPipeline(tpot2.builtin_modules.EstimatorTransformer, {}, classifiers)\n", @@ -12249,24 +857,9 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.3, 0.7],\n", - " [0.5, 0.5],\n", - " [0.8, 0.2],\n", - " [0.7, 0.3],\n", - " [0.5, 0.5]])" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import numpy as np\n", "X, y = np.random.rand(100, 10), np.random.randint(0, 2, 100)\n", @@ -12283,24 +876,9 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1],\n", - " [1],\n", - " [0],\n", - " [0],\n", - " [1]])" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "classifiers = tpot2.config.get_search_space(\"classifiers\")\n", "wrapped_estimators_cv = tpot2.search_spaces.pipelines.WrapperPipeline(tpot2.builtin_modules.EstimatorTransformer, {'cross_val_predict_cv':10, 'method':'predict'}, classifiers)\n", @@ -12317,594 +895,9 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Pipeline(steps=[('maxabsscaler', MaxAbsScaler()),\n",
-       "                ('featureunion-1',\n",
-       "                 FeatureUnion(transformer_list=[('featureunion',\n",
-       "                                                 FeatureUnion(transformer_list=[('rbfsampler',\n",
-       "                                                                                 RBFSampler(gamma=0.6486019086026,\n",
-       "                                                                                            n_components=82)),\n",
-       "                                                                                ('nystroem',\n",
-       "                                                                                 Nystroem(gamma=0.185797439118,\n",
-       "                                                                                          kernel='additive_chi2',\n",
-       "                                                                                          n_components=87))])),\n",
-       "                                                ('passthrough',\n",
-       "                                                 Passthrough())])),\n",
-       "                ('featureunion-2',\n",
-       "                 Feat...\n",
-       "                                                                                                                              missing=nan,\n",
-       "                                                                                                                              monotone_constraints=None,\n",
-       "                                                                                                                              multi_strategy=None,\n",
-       "                                                                                                                              n_estimators=100,\n",
-       "                                                                                                                              n_jobs=1,\n",
-       "                                                                                                                              nthread=1,\n",
-       "                                                                                                                              num_parallel_tree=None, ...),\n",
-       "                                                                                                      method='predict'))])),\n",
-       "                                                ('passthrough',\n",
-       "                                                 Passthrough())])),\n",
-       "                ('randomforestclassifier',\n",
-       "                 RandomForestClassifier(bootstrap=False, criterion='entropy',\n",
-       "                                        max_features=0.6976552018012,\n",
-       "                                        min_samples_leaf=8,\n",
-       "                                        min_samples_split=16,\n",
-       "                                        n_estimators=128))])
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" - ], - "text/plain": [ - "Pipeline(steps=[('maxabsscaler', MaxAbsScaler()),\n", - " ('featureunion-1',\n", - " FeatureUnion(transformer_list=[('featureunion',\n", - " FeatureUnion(transformer_list=[('rbfsampler',\n", - " RBFSampler(gamma=0.6486019086026,\n", - " n_components=82)),\n", - " ('nystroem',\n", - " Nystroem(gamma=0.185797439118,\n", - " kernel='additive_chi2',\n", - " n_components=87))])),\n", - " ('passthrough',\n", - " Passthrough())])),\n", - " ('featureunion-2',\n", - " Feat...\n", - " missing=nan,\n", - " monotone_constraints=None,\n", - " multi_strategy=None,\n", - " n_estimators=100,\n", - " n_jobs=1,\n", - " nthread=1,\n", - " num_parallel_tree=None, ...),\n", - " method='predict'))])),\n", - " ('passthrough',\n", - " Passthrough())])),\n", - " ('randomforestclassifier',\n", - " RandomForestClassifier(bootstrap=False, criterion='entropy',\n", - " max_features=0.6976552018012,\n", - " min_samples_leaf=8,\n", - " min_samples_split=16,\n", - " n_estimators=128))])" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "dynamic_wrapped_classifiers_with_passthrough = tpot2.search_spaces.pipelines.UnionPipeline([\n", " tpot2.search_spaces.pipelines.DynamicUnionPipeline(wrapped_estimators_cv, max_estimators=4),\n", @@ -12945,7 +938,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -12961,20 +954,9 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "est1 = ind.export_pipeline()\n", "est1.plot() #GraphPipelines have a helpful plotting function to visualize the pipeline" @@ -12989,110 +971,9 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "for i in range(0,50):\n", " ind.mutate()\n", @@ -13119,20 +1000,9 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "tree_search_space = tpot2.search_spaces.pipelines.TreePipeline(\n", " root_search_space= tpot2.config.get_search_space([\"KNeighborsClassifier\", \"LogisticRegression\", \"DecisionTreeClassifier\"]),\n", @@ -13166,445 +1036,9 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
FeatureUnion(transformer_list=[('featureunion',\n",
-       "                                FeatureUnion(transformer_list=[('powertransformer',\n",
-       "                                                                PowerTransformer()),\n",
-       "                                                               ('passkbinsdiscretizer',\n",
-       "                                                                PassKBinsDiscretizer(n_bins=11,\n",
-       "                                                                                     strategy='uniform'))])),\n",
-       "                               ('passthrough', Passthrough())])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "FeatureUnion(transformer_list=[('featureunion',\n", - " FeatureUnion(transformer_list=[('powertransformer',\n", - " PowerTransformer()),\n", - " ('passkbinsdiscretizer',\n", - " PassKBinsDiscretizer(n_bins=11,\n", - " strategy='uniform'))])),\n", - " ('passthrough', Passthrough())])" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from tpot2.search_spaces.pipelines import *\n", "from tpot2.config import get_search_space\n", @@ -13628,430 +1062,9 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
FeatureUnion(transformer_list=[('skiptransformer', SkipTransformer()),\n",
-       "                               ('passthrough', Passthrough())])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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" - ], - "text/plain": [ - "FeatureUnion(transformer_list=[('skiptransformer', SkipTransformer()),\n", - " ('passthrough', Passthrough())])" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "final_transformers_layer =UnionPipeline([\n", " ChoicePipeline([\n", @@ -14067,482 +1080,9 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
FeatureUnion(transformer_list=[('featureunion',\n",
-       "                                FeatureUnion(transformer_list=[('estimatortransformer-1',\n",
-       "                                                                EstimatorTransformer(cross_val_predict_cv=10,\n",
-       "                                                                                     estimator=RandomForestClassifier(criterion='entropy',\n",
-       "                                                                                                                      max_features=0.0291036830622,\n",
-       "                                                                                                                      min_samples_leaf=10,\n",
-       "                                                                                                                      min_samples_split=20,\n",
-       "                                                                                                                      n_estimators=128),\n",
-       "                                                                                     method='predict')),\n",
-       "                                                               ('estimatortransformer-2',\n",
-       "                                                                EstimatorTransformer(cross_val_predict_cv=10,\n",
-       "                                                                                     estimator=QuadraticDiscriminantAnalysis(reg_param=0.6791389504331),\n",
-       "                                                                                     method='predict')),\n",
-       "                                                               ('estimatortransformer-3',\n",
-       "                                                                EstimatorTransformer(cross_val_predict_cv=10,\n",
-       "                                                                                     estimator=QuadraticDiscriminantAnalysis(reg_param=0.8087868529112),\n",
-       "                                                                                     method='predict'))])),\n",
-       "                               ('passthrough', Passthrough())])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "FeatureUnion(transformer_list=[('featureunion',\n", - " FeatureUnion(transformer_list=[('estimatortransformer-1',\n", - " EstimatorTransformer(cross_val_predict_cv=10,\n", - " estimator=RandomForestClassifier(criterion='entropy',\n", - " max_features=0.0291036830622,\n", - " min_samples_leaf=10,\n", - " min_samples_split=20,\n", - " n_estimators=128),\n", - " method='predict')),\n", - " ('estimatortransformer-2',\n", - " EstimatorTransformer(cross_val_predict_cv=10,\n", - " estimator=QuadraticDiscriminantAnalysis(reg_param=0.6791389504331),\n", - " method='predict')),\n", - " ('estimatortransformer-3',\n", - " EstimatorTransformer(cross_val_predict_cv=10,\n", - " estimator=QuadraticDiscriminantAnalysis(reg_param=0.8087868529112),\n", - " method='predict'))])),\n", - " ('passthrough', Passthrough())])" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "inner_estimators_layer = UnionPipeline([\n", " ChoicePipeline([\n", @@ -14557,482 +1097,9 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Pipeline(steps=[('robustscaler',\n",
-       "                 RobustScaler(quantile_range=(0.1562687943568,\n",
-       "                                              0.8028910581685))),\n",
-       "                ('featureunion-1',\n",
-       "                 FeatureUnion(transformer_list=[('skiptransformer',\n",
-       "                                                 SkipTransformer()),\n",
-       "                                                ('passthrough',\n",
-       "                                                 Passthrough())])),\n",
-       "                ('featureunion-2',\n",
-       "                 FeatureUnion(transformer_list=[('skiptransformer',\n",
-       "                                                 SkipTransformer()),\n",
-       "                                                ('passthrough',\n",
-       "                                                 Passthrough())])),\n",
-       "                ('baggingclassifier',\n",
-       "                 BaggingClassifier(bootstrap_features=True,\n",
-       "                                   max_features=0.1392808422872,\n",
-       "                                   max_samples=0.5344888038724, n_estimators=3,\n",
-       "                                   n_jobs=1, oob_score=True))])
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" - ], - "text/plain": [ - "Pipeline(steps=[('robustscaler',\n", - " RobustScaler(quantile_range=(0.1562687943568,\n", - " 0.8028910581685))),\n", - " ('featureunion-1',\n", - " FeatureUnion(transformer_list=[('skiptransformer',\n", - " SkipTransformer()),\n", - " ('passthrough',\n", - " Passthrough())])),\n", - " ('featureunion-2',\n", - " FeatureUnion(transformer_list=[('skiptransformer',\n", - " SkipTransformer()),\n", - " ('passthrough',\n", - " Passthrough())])),\n", - " ('baggingclassifier',\n", - " BaggingClassifier(bootstrap_features=True,\n", - " max_features=0.1392808422872,\n", - " max_samples=0.5344888038724, n_estimators=3,\n", - " n_jobs=1, oob_score=True))])" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "final_linear_pipeline = SequentialPipeline([\n", " get_search_space(\"scalers\"),\n", @@ -15055,7 +1122,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ @@ -15075,464 +1142,9 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/perib/miniconda3/envs/myenv/lib/python3.10/site-packages/distributed/node.py:182: UserWarning: Port 8787 is already in use.\n", - "Perhaps you already have a cluster running?\n", - "Hosting the HTTP server on port 40273 instead\n", - " warnings.warn(\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Generation: 60%|██████ | 3/5 [00:44<00:29, 14.98s/it]\n", - "/home/perib/miniconda3/envs/myenv/lib/python3.10/site-packages/sklearn/preprocessing/_data.py:2785: UserWarning: n_quantiles (688) is greater than the total number of samples (284). n_quantiles is set to n_samples.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/html": [ - "
TPOTEstimator(classification=True, cv=5, early_stop=2, generations=5,\n",
-       "              max_eval_time_mins=10, n_jobs=4,\n",
-       "              scorers=['roc_auc_ovr',\n",
-       "                       <function complexity_scorer at 0x7e7bacf5b9a0>],\n",
-       "              scorers_weights=[1.0, -1.0],\n",
-       "              search_space=<tpot2.search_spaces.pipelines.sequential.SequentialPipeline object at 0x7e7ba3b078b0>,\n",
-       "              verbose=2)
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" - ], - "text/plain": [ - "TPOTEstimator(classification=True, cv=5, early_stop=2, generations=5,\n", - " max_eval_time_mins=10, n_jobs=4,\n", - " scorers=['roc_auc_ovr',\n", - " ],\n", - " scorers_weights=[1.0, -1.0],\n", - " search_space=,\n", - " verbose=2)" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "selected_search_space = all_search_spaces[\"stc_pipeline\"] #change this to select a different search space\n", "\n", @@ -15543,7 +1155,6 @@ " classification = True,\n", " cv = 5,\n", " search_space = selected_search_space,\n", - " population_size= 50,\n", " generations = 5,\n", " max_eval_time_mins = 10,\n", " early_stop = 2,\n", @@ -15556,17 +1167,9 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "auroc score 0.9845976642022929\n" - ] - } - ], + "outputs": [], "source": [ "# score the model\n", "auroc_scorer = sklearn.metrics.get_scorer(\"roc_auc\")\n", @@ -15577,449 +1180,9 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Pipeline(steps=[('variancethreshold',\n",
-       "                 VarianceThreshold(threshold=0.0003237844275)),\n",
-       "                ('quantiletransformer', QuantileTransformer(n_quantiles=688)),\n",
-       "                ('baggingclassifier',\n",
-       "                 BaggingClassifier(bootstrap_features=True,\n",
-       "                                   max_features=0.2631592196919,\n",
-       "                                   max_samples=0.488886320861, n_estimators=72,\n",
-       "                                   n_jobs=1))])
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" - ], - "text/plain": [ - "Pipeline(steps=[('variancethreshold',\n", - " VarianceThreshold(threshold=0.0003237844275)),\n", - " ('quantiletransformer', QuantileTransformer(n_quantiles=688)),\n", - " ('baggingclassifier',\n", - " BaggingClassifier(bootstrap_features=True,\n", - " max_features=0.2631592196919,\n", - " max_samples=0.488886320861, n_estimators=72,\n", - " n_jobs=1))])" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#plot the best pipeline\n", "if isinstance(est.fitted_pipeline_, tpot2.GraphPipeline):\n", @@ -16047,6 +1210,134 @@ "Rather than create your own search space, you can simply pass the string into the `search_space` param. Alternatively, you can access tpot2.config.template_search_spaces.get_template_search_spaces directly which offers a few more customizable options for each template including `cross_val_predict_cv` and whether or not stacked classifiers/regressors are allowed. Or you can copy the code and customize it manually!" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Transformer-only pipelines - imputation optimization example\n", + "\n", + "Pipelines don't necessarily need to end in a classifier or regressor. Transformer only pipelines are possible as long as you have a custom objective function to match. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sklearn\n", + "import sklearn.datasets\n", + "import numpy as np\n", + "import tpot2\n", + "\n", + "#in practice, cross validation is likely better, but this simple example is fine for demonstration purposes\n", + "def rmse_obective(est, X, missing_add=.2, rng=1, fitted=False):\n", + " rng = np.random.default_rng(rng)\n", + " X_missing = X.copy()\n", + " missing_idx = rng.random(X.shape) < missing_add\n", + " X_missing[missing_idx] = np.nan\n", + " \n", + " if not fitted:\n", + " est.fit(X_missing)\n", + " \n", + " X_filled = est.transform(X_missing)\n", + " return np.sqrt(np.mean((X_filled[missing_idx] - X[missing_idx])**2))\n", + "\n", + "from sklearn.impute import SimpleImputer\n", + "\n", + "X, y = sklearn.datasets.load_diabetes(return_X_y=True)\n", + "\n", + "imp = SimpleImputer(strategy=\"mean\")\n", + "\n", + "rmse_obective(imp, X)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import tpot2.search_spaces\n", + "from ConfigSpace import ConfigurationSpace, Integer, Float, Categorical, Normal\n", + "\n", + "#set up an imputation search space that includes simple imputer, knn imputer, and iterative imputer (with an optimized ExtraTreesRegressor)\n", + "\n", + "simple_imputer = tpot2.config.get_search_space(\"SimpleImputer\")\n", + "knn_imputer = tpot2.config.get_search_space(\"KNNImputer\")\n", + "\n", + "space = ConfigurationSpace({ 'initial_strategy' : Categorical('initial_strategy', \n", + " ['mean', 'median', \n", + " 'most_frequent', 'constant']),\n", + " 'n_nearest_features' : Integer('n_nearest_features', \n", + " bounds=(1, X.shape[1])),\n", + " 'imputation_order' : Categorical('imputation_order', \n", + " ['ascending', 'descending', \n", + " 'roman', 'arabic', 'random']),\n", + "})\n", + "\n", + "# This optimizes both the iterative imputer parameters and the ExtraTreesRegressor parameters\n", + "iterative_imputer_sp = tpot2.search_spaces.pipelines.WrapperPipeline(\n", + " method = sklearn.impute.IterativeImputer,\n", + " space = space,\n", + " estimator_search_space = tpot2.config.get_search_space(\"ExtraTreesRegressor\"),\n", + ")\n", + "#this is equivalent to\n", + "# iterative_imputer_sp = tpot2.config.get_search_space(\"IterativeImputer_learned_estimators\")\n", + "\n", + "imputation_search_space = tpot2.search_spaces.pipelines.ChoicePipeline(\n", + " search_spaces = [simple_imputer, knn_imputer, iterative_imputer_sp],\n", + ")\n", + "imputation_search_space.generate().export_pipeline()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from functools import partial\n", + "\n", + "final_objective = partial(rmse_obective, X=X, missing_add=.2)\n", + "\n", + "est = tpot2.TPOTEstimator(\n", + " scorers = [],\n", + " scorers_weights = [],\n", + " other_objective_functions = [final_objective],\n", + " other_objective_functions_weights = [-1],\n", + " objective_function_names = [\"rmse\"],\n", + " classification = True,\n", + " search_space = imputation_search_space,\n", + " generations = 5,\n", + " max_eval_time_mins = 60*5,\n", + " verbose = 3,\n", + " n_jobs=20,\n", + ")\n", + "\n", + "est.fit(X, y=y)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# score the model\n", + "rmse_score = final_objective(est, fitted=True)\n", + "print(\"final rmse score\", rmse_score)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "est.fitted_pipeline_" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -16105,10 +1396,10 @@ " classification = True,\n", " cv = 5,\n", " search_space = search_space,\n", - " population_size= 10,\n", " generations = 5,\n", " max_eval_time_mins = 60*5,\n", " verbose = 2,\n", + " n_jobs=20,\n", ")\n", "\n", "est.fit(X_train, y_train)" diff --git a/tpot2/config/get_configspace.py b/tpot2/config/get_configspace.py index a044c215..da680211 100644 --- a/tpot2/config/get_configspace.py +++ b/tpot2/config/get_configspace.py @@ -355,6 +355,9 @@ def get_configspace(name, n_classes=3, n_samples=1000, n_features=100, random_st return imputers.simple_imputer_cs case "IterativeImputer": return imputers.get_IterativeImputer_config_space(n_features=n_features, random_state=random_state) + case "IterativeImputer_no_estimator": + return imputers.get_IterativeImputer_config_space_no_estimator(n_features=n_features, random_state=random_state) + case "KNNImputer": return imputers.get_KNNImputer_config_space(n_samples=n_samples) @@ -449,12 +452,10 @@ def get_node(name, n_classes=3, n_samples=100, n_features=100, random_state=None ext = get_node("ExtraTreesRegressor", n_classes=n_classes, n_samples=n_samples, random_state=random_state) return WrapperPipeline(estimator_search_space=ext, method=SelectFromModel, space=sfm_sp) # TODO Add IterativeImputer with more estimator methods - ''' - if name == "IterativeImputer_learnedestimators": - iteative_sp = get_configspace(name="IterativeImputer", n_classes=n_classes, n_samples=n_samples, random_state=random_state) - regessor_searchspace = get_search_space(["LinearRegression", ..], n_classes=n_classes, n_samples=n_samples, random_state=random_state) - return WrapperPipeline(estimator_search_space=regressor_searchspace, method=ItartiveImputer, space=iteative_sp) - ''' + if name == "IterativeImputer_learned_estimators": + iteative_sp = get_configspace(name="IterativeImputer_no_estimator", n_features=n_features, random_state=random_state) + regressor_searchspace = get_node("ExtraTreesRegressor", n_classes=n_classes, n_samples=n_samples, random_state=random_state) + return WrapperPipeline(estimator_search_space=regressor_searchspace, method=IterativeImputer, space=iteative_sp) #these are nodes that have special search spaces which require custom parsing of the hyperparameters if name == "IterativeImputer": configspace = get_configspace(name, n_classes=n_classes, n_samples=n_samples, random_state=random_state) diff --git a/tpot2/config/imputers.py b/tpot2/config/imputers.py index b991bf8d..34582b92 100644 --- a/tpot2/config/imputers.py +++ b/tpot2/config/imputers.py @@ -42,6 +42,25 @@ def get_IterativeImputer_config_space(n_features, random_state): cs.add([sampling_condition]) return cs +def get_IterativeImputer_config_space_no_estimator(n_features, random_state): + space = { 'initial_strategy' : Categorical('initial_strategy', + ['mean', 'median', + 'most_frequent', 'constant']), + 'n_nearest_features' : Integer('n_nearest_features', + bounds=(1, n_features)), + 'imputation_order' : Categorical('imputation_order', + ['ascending', 'descending', + 'roman', 'arabic', 'random']), + } + + if random_state is not None: + #This is required because configspace doesn't allow None as a value + space['random_state'] = random_state + + cs = ConfigurationSpace(space=space) + + return cs + def get_KNNImputer_config_space(n_samples): space = { 'n_neighbors': Integer('n_neighbors', bounds=(1, max(n_samples,100))), diff --git a/tpot2/tpot_estimator/estimator.py b/tpot2/tpot_estimator/estimator.py index 1e53ee64..e9e5e1ed 100644 --- a/tpot2/tpot_estimator/estimator.py +++ b/tpot2/tpot_estimator/estimator.py @@ -84,9 +84,6 @@ def __init__(self, generations_until_end_budget = 1, stepwise_steps = 5, - - - #dask parameters n_jobs=1, memory_limit = None, From 1fceb223ec870c1d477e9979c4f094269b95221d Mon Sep 17 00:00:00 2001 From: perib Date: Tue, 24 Sep 2024 17:14:06 -0700 Subject: [PATCH 18/44] removed unused param in estimator, add extra rng check so different sub-search spaces get different random seeds --- tpot2/config/get_configspace.py | 3 +++ tpot2/search_spaces/pipelines/choice.py | 1 + tpot2/search_spaces/pipelines/dynamic_linear.py | 1 + tpot2/search_spaces/pipelines/dynamicunion.py | 1 + tpot2/search_spaces/pipelines/sequential.py | 1 + tpot2/search_spaces/pipelines/tree.py | 1 + tpot2/search_spaces/pipelines/union.py | 1 + tpot2/search_spaces/pipelines/wrapper.py | 1 + tpot2/tpot_estimator/estimator.py | 10 ---------- tpot2/tpot_estimator/steady_state_estimator.py | 9 --------- 10 files changed, 10 insertions(+), 19 deletions(-) diff --git a/tpot2/config/get_configspace.py b/tpot2/config/get_configspace.py index da680211..b0fcd453 100644 --- a/tpot2/config/get_configspace.py +++ b/tpot2/config/get_configspace.py @@ -131,6 +131,8 @@ "classifiers_sklearnex" : ["RandomForestClassifier_sklearnex", "LogisticRegression_sklearnex", "KNeighborsClassifier_sklearnex", "SVC_sklearnex","NuSVC_sklearnex"], "regressors_sklearnex" : ["LinearRegression_sklearnex", "Ridge_sklearnex", "Lasso_sklearnex", "ElasticNet_sklearnex", "SVR_sklearnex", "NuSVR_sklearnex", "RandomForestRegressor_sklearnex", "KNeighborsRegressor_sklearnex"], + "genetic encoders" : ["DominantEncoder", "RecessiveEncoder", "HeterosisEncoder", "UnderDominanceEncoder", "OverDominanceEncoder"], + } @@ -456,6 +458,7 @@ def get_node(name, n_classes=3, n_samples=100, n_features=100, random_state=None iteative_sp = get_configspace(name="IterativeImputer_no_estimator", n_features=n_features, random_state=random_state) regressor_searchspace = get_node("ExtraTreesRegressor", n_classes=n_classes, n_samples=n_samples, random_state=random_state) return WrapperPipeline(estimator_search_space=regressor_searchspace, method=IterativeImputer, space=iteative_sp) + #these are nodes that have special search spaces which require custom parsing of the hyperparameters if name == "IterativeImputer": configspace = get_configspace(name, n_classes=n_classes, n_samples=n_samples, random_state=random_state) diff --git a/tpot2/search_spaces/pipelines/choice.py b/tpot2/search_spaces/pipelines/choice.py index da1fcfd0..24f597a4 100644 --- a/tpot2/search_spaces/pipelines/choice.py +++ b/tpot2/search_spaces/pipelines/choice.py @@ -50,4 +50,5 @@ def __init__(self, search_spaces : List[SklearnIndividualGenerator] ) -> None: """ def generate(self, rng=None): + rng = np.random.default_rng(rng) return ChoicePipelineIndividual(self.search_spaces, rng=rng) \ No newline at end of file diff --git a/tpot2/search_spaces/pipelines/dynamic_linear.py b/tpot2/search_spaces/pipelines/dynamic_linear.py index e58005d3..dedecc55 100644 --- a/tpot2/search_spaces/pipelines/dynamic_linear.py +++ b/tpot2/search_spaces/pipelines/dynamic_linear.py @@ -148,4 +148,5 @@ def __init__(self, search_space : SklearnIndividualGenerator, max_length: int ) """ def generate(self, rng=None): + rng = np.random.default_rng(rng) return DynamicLinearPipelineIndividual(self.search_space, self.max_length, rng=rng) \ No newline at end of file diff --git a/tpot2/search_spaces/pipelines/dynamicunion.py b/tpot2/search_spaces/pipelines/dynamicunion.py index 4f1f94ac..53b91d9d 100644 --- a/tpot2/search_spaces/pipelines/dynamicunion.py +++ b/tpot2/search_spaces/pipelines/dynamicunion.py @@ -160,4 +160,5 @@ def __init__(self, search_space : SklearnIndividualGenerator, max_estimators=Non self.allow_repeats = allow_repeats def generate(self, rng=None): + rng = np.random.default_rng(rng) return DynamicUnionPipelineIndividual(self.search_space, max_estimators=self.max_estimators, allow_repeats=self.allow_repeats, rng=rng) \ No newline at end of file diff --git a/tpot2/search_spaces/pipelines/sequential.py b/tpot2/search_spaces/pipelines/sequential.py index 3fae8a52..dde5fd6e 100644 --- a/tpot2/search_spaces/pipelines/sequential.py +++ b/tpot2/search_spaces/pipelines/sequential.py @@ -145,4 +145,5 @@ def __init__(self, search_spaces : List[SklearnIndividualGenerator] ) -> None: self.search_spaces = search_spaces def generate(self, rng=None): + rng = np.random.default_rng(rng) return SequentialPipelineIndividual(self.search_spaces, rng=rng) \ No newline at end of file diff --git a/tpot2/search_spaces/pipelines/tree.py b/tpot2/search_spaces/pipelines/tree.py index 83988656..649bbae3 100644 --- a/tpot2/search_spaces/pipelines/tree.py +++ b/tpot2/search_spaces/pipelines/tree.py @@ -47,4 +47,5 @@ def __init__(self, root_search_space : SklearnIndividualGenerator, self.crossover_same_depth = crossover_same_depth def generate(self, rng=None): + rng = np.random.default_rng(rng) return TreePipelineIndividual(self.search_space, self.leaf_search_space, self.inner_search_space, self.min_size, self.max_size, self.crossover_same_depth, rng=rng) \ No newline at end of file diff --git a/tpot2/search_spaces/pipelines/union.py b/tpot2/search_spaces/pipelines/union.py index a8fd392b..7121cb08 100644 --- a/tpot2/search_spaces/pipelines/union.py +++ b/tpot2/search_spaces/pipelines/union.py @@ -78,4 +78,5 @@ def __init__(self, search_spaces : List[SklearnIndividualGenerator] ) -> None: self.search_spaces = search_spaces def generate(self, rng=None): + rng = np.random.default_rng(rng) return UnionPipelineIndividual(self.search_spaces, rng=rng) \ No newline at end of file diff --git a/tpot2/search_spaces/pipelines/wrapper.py b/tpot2/search_spaces/pipelines/wrapper.py index 1b5807c8..f1908764 100644 --- a/tpot2/search_spaces/pipelines/wrapper.py +++ b/tpot2/search_spaces/pipelines/wrapper.py @@ -148,4 +148,5 @@ def __init__( self.wrapped_param_name = wrapped_param_name def generate(self, rng=None): + rng = np.random.default_rng(rng) return WrapperPipelineIndividual(method=self.method, space=self.space, estimator_search_space=self.estimator_search_space, hyperparameter_parser=self.hyperparameter_parser, wrapped_param_name=self.wrapped_param_name, rng=rng) \ No newline at end of file diff --git a/tpot2/tpot_estimator/estimator.py b/tpot2/tpot_estimator/estimator.py index e9e5e1ed..9a3224fd 100644 --- a/tpot2/tpot_estimator/estimator.py +++ b/tpot2/tpot_estimator/estimator.py @@ -46,7 +46,6 @@ def __init__(self, memory = None, categorical_features = None, - subsets = None, preprocessing = False, population_size = 50, initial_population_size = None, @@ -174,14 +173,6 @@ def __init__(self, - None : If None, TPOT2 will automatically use object columns in pandas dataframes as objects for one hot encoding in preprocessing. - List of categorical features. If X is a dataframe, this should be a list of column names. If X is a numpy array, this should be a list of column indices - subsets : str or list, default=None - Sets the subsets that the FeatureSetSeletor will select from if set as an option in one of the configuration dictionaries. - - str : If a string, it is assumed to be a path to a csv file with the subsets. - The first column is assumed to be the name of the subset and the remaining columns are the features in the subset. - - list or np.ndarray : If a list or np.ndarray, it is assumed to be a list of subsets. - - None : If None, each column will be treated as a subset. One column will be selected per subset. - If subsets is None, each column will be treated as a subset. One column will be selected per subset. - preprocessing : bool or BaseEstimator/Pipeline, EXPERIMENTAL A pipeline that will be used to preprocess the data before CV. Note that the parameters for these steps are not optimized. Add them to the search space to be optimized. @@ -382,7 +373,6 @@ def __init__(self, self.memory = memory self.categorical_features = categorical_features - self.subsets = subsets self.preprocessing = preprocessing self.validation_strategy = validation_strategy diff --git a/tpot2/tpot_estimator/steady_state_estimator.py b/tpot2/tpot_estimator/steady_state_estimator.py index b86b9f8f..d4819e3d 100644 --- a/tpot2/tpot_estimator/steady_state_estimator.py +++ b/tpot2/tpot_estimator/steady_state_estimator.py @@ -196,14 +196,6 @@ def __init__(self, - None : If None, TPOT2 will automatically use object columns in pandas dataframes as objects for one hot encoding in preprocessing. - List of categorical features. If X is a dataframe, this should be a list of column names. If X is a numpy array, this should be a list of column indices - subsets : str or list, default=None - Sets the subsets that the FeatureSetSeletor will select from if set as an option in one of the configuration dictionaries. - - str : If a string, it is assumed to be a path to a csv file with the subsets. - The first column is assumed to be the name of the subset and the remaining columns are the features in the subset. - - list or np.ndarray : If a list or np.ndarray, it is assumed to be a list of subsets. - - None : If None, each column will be treated as a subset. One column will be selected per subset. - If subsets is None, each column will be treated as a subset. One column will be selected per subset. - memory: Memory object or string, default=None If supplied, pipeline will cache each transformer after calling fit with joblib.Memory. This feature @@ -426,7 +418,6 @@ def __init__(self, self.memory = memory self.categorical_features = categorical_features - self.subsets = subsets self.preprocessing = preprocessing self.validation_strategy = validation_strategy self.validation_fraction = validation_fraction From 602e89e12ef99a6a1fec8270de8e975c27d7df87 Mon Sep 17 00:00:00 2001 From: perib Date: Tue, 24 Sep 2024 17:14:20 -0700 Subject: [PATCH 19/44] edits to tutorial 2, rewrote tutorial 3 --- Tutorial/2_Search_Spaces.ipynb | 18042 +++++++++++++++++++++++- Tutorial/3_Feature_Set_Selector.ipynb | 3720 ++++- 2 files changed, 21232 insertions(+), 530 deletions(-) diff --git a/Tutorial/2_Search_Spaces.ipynb b/Tutorial/2_Search_Spaces.ipynb index b07626c4..fffbffdc 100644 --- a/Tutorial/2_Search_Spaces.ipynb +++ b/Tutorial/2_Search_Spaces.ipynb @@ -28,9 +28,441 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sampled hyperparameters\n", + "{'bootstrap': False, 'criterion': 'gini', 'max_features': 0.0696410090574, 'min_samples_leaf': 7, 'min_samples_split': 8, 'n_estimators': 128}\n" + ] + }, + { + "data": { + "text/html": [ + "
RandomForestClassifier(bootstrap=False, max_features=0.0696410090574,\n",
+       "                       min_samples_leaf=7, min_samples_split=8,\n",
+       "                       n_estimators=128)
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" + ], + "text/plain": [ + "RandomForestClassifier(bootstrap=False, max_features=0.0696410090574,\n", + " min_samples_leaf=7, min_samples_split=8,\n", + " n_estimators=128)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from ConfigSpace import ConfigurationSpace\n", "from ConfigSpace import ConfigurationSpace, Integer, Float, Categorical, Normal\n", @@ -69,9 +501,441 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sampled hyperparameters\n", + "{'bootstrap': False, 'criterion': 'entropy', 'max_features': 0.2320810853841, 'min_samples_leaf': 19, 'min_samples_split': 12, 'n_estimators': 128}\n" + ] + }, + { + "data": { + "text/html": [ + "
RandomForestClassifier(bootstrap=False, criterion='entropy',\n",
+       "                       max_features=0.2320810853841, min_samples_leaf=19,\n",
+       "                       min_samples_split=12, n_estimators=128)
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" + ], + "text/plain": [ + "RandomForestClassifier(bootstrap=False, criterion='entropy',\n", + " max_features=0.2320810853841, min_samples_leaf=19,\n", + " min_samples_split=12, n_estimators=128)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "rf_configspace = ConfigurationSpace(\n", " space = {\n", @@ -153,7 +1017,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -189,9 +1053,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "knn_individual = knn_node.generate()\n", "knn_individual" @@ -199,9 +1074,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sampled hyperparameters\n", + "{'metric': 'minkowski', 'n_jobs': 1, 'n_neighbors': 7, 'p': 1, 'weights': 'uniform'}\n" + ] + } + ], "source": [ "print(\"sampled hyperparameters\")\n", "print(knn_individual.hyperparameters)" @@ -216,9 +1100,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mutated hyperparameters\n", + "{'metric': 'minkowski', 'n_jobs': 1, 'n_neighbors': 7, 'p': 2, 'weights': 'uniform'}\n" + ] + } + ], "source": [ "knn_individual.mutate() # mutate the individual\n", "print(\"mutated hyperparameters\")\n", @@ -234,9 +1127,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "original hyperparameters for individual 1\n", + "{'metric': 'minkowski', 'n_jobs': 1, 'n_neighbors': 3, 'p': 3, 'weights': 'uniform'}\n", + "original hyperparameters for individual 2\n", + "{'metric': 'euclidean', 'n_jobs': 1, 'n_neighbors': 7, 'p': 3, 'weights': 'distance'}\n", + "\n", + "post crossover hyperparameters for individual 1\n", + "{'metric': 'minkowski', 'n_jobs': 1, 'n_neighbors': 7, 'p': 3, 'weights': 'uniform'}\n", + "post crossover hyperparameters for individual 2\n", + "{'metric': 'euclidean', 'n_jobs': 1, 'n_neighbors': 7, 'p': 3, 'weights': 'distance'}\n" + ] + } + ], "source": [ "knn_individual1 = knn_node.generate()\n", "knn_individual2 = knn_node.generate()\n", @@ -266,9 +1175,427 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
KNeighborsClassifier(n_jobs=1, n_neighbors=7, p=3)
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" + ], + "text/plain": [ + "KNeighborsClassifier(n_jobs=1, n_neighbors=7, p=3)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "est = knn_individual1.export_pipeline()\n", "est" @@ -283,9 +1610,427 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
KNeighborsClassifier(n_neighbors=10)
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" + ], + "text/plain": [ + "KNeighborsClassifier(n_neighbors=10)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import tpot2\n", "from ConfigSpace import ConfigurationSpace\n", @@ -334,9 +2079,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import tpot2\n", "from ConfigSpace import ConfigurationSpace\n", @@ -430,9 +2186,437 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sampled pipeline\n" + ] + }, + { + "data": { + "text/html": [ + "
DecisionTreeClassifier(max_depth=11, max_features='sqrt', min_samples_leaf=20,\n",
+       "                       min_samples_split=10)
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" + ], + "text/plain": [ + "DecisionTreeClassifier(max_depth=11, max_features='sqrt', min_samples_leaf=20,\n", + " min_samples_split=10)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "classifier_individual = classifier_node.generate()\n", "\n", @@ -442,9 +2626,437 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mutated pipeline\n" + ] + }, + { + "data": { + "text/html": [ + "
KNeighborsClassifier(metric='euclidean', n_jobs=1, n_neighbors=6,\n",
+       "                     weights='distance')
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" + ], + "text/plain": [ + "KNeighborsClassifier(metric='euclidean', n_jobs=1, n_neighbors=6,\n", + " weights='distance')" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "print(\"mutated pipeline\")\n", "classifier_individual.mutate()\n", @@ -455,11 +3067,142 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "TPOT2 also comes with predefined search spaces. The current search spaces were adapted from a combination of the original TPOT package as well as the search spaces used in [AutoSklearn](https://github.com/automl/auto-sklearn/tree/development/autosklearn/pipeline/components). The helper function `tpot2.config.get_search_space` takes in a string or a list of strings, and returns either a EstimatorNode or a ChoicePipeline,respectively. \n", + "#### Built in search spaces for EstimatorNode and ChoicePipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TPOT2 also comes with predefined hyperparameter search spaces. The current search spaces were adapted from a combination of the original TPOT package as well as the search spaces used in [AutoSklearn](https://github.com/automl/auto-sklearn/tree/development/autosklearn/pipeline/components). The helper function `tpot2.config.get_search_space` takes in a string or a list of strings, and returns either a EstimatorNode or a ChoicePipeline (including all methods in the list), respectively. \n", + "\n", + "| String | Corresponding Method |\n", + "| --- | ----- |\n", + "| SGDClassifier | |\n", + "| RandomForestClassifier | |\n", + "| ExtraTreesClassifier | |\n", + "| GradientBoostingClassifier | |\n", + "| MLPClassifier | |\n", + "| DecisionTreeClassifier | |\n", + "| XGBClassifier | |\n", + "| KNeighborsClassifier | |\n", + "| SVC | |\n", + "| LogisticRegression | |\n", + "| LGBMClassifier | |\n", + "| LinearSVC | |\n", + "| GaussianNB | |\n", + "| BernoulliNB | |\n", + "| MultinomialNB | |\n", + "| ExtraTreesRegressor | |\n", + "| RandomForestRegressor | |\n", + "| GradientBoostingRegressor | |\n", + "| BaggingRegressor | |\n", + "| DecisionTreeRegressor | |\n", + "| KNeighborsRegressor | |\n", + "| XGBRegressor | |\n", + "| ZeroCount | |\n", + "| ColumnOneHotEncoder | |\n", + "| Binarizer | |\n", + "| FastICA | |\n", + "| FeatureAgglomeration | |\n", + "| MaxAbsScaler | |\n", + "| MinMaxScaler | |\n", + "| Normalizer | |\n", + "| Nystroem | |\n", + "| PCA | |\n", + "| PolynomialFeatures | |\n", + "| RBFSampler | |\n", + "| RobustScaler | |\n", + "| StandardScaler | |\n", + "| SelectFwe | |\n", + "| SelectPercentile | |\n", + "| VarianceThreshold | |\n", + "| SGDRegressor | |\n", + "| Ridge | |\n", + "| Lasso | |\n", + "| ElasticNet | |\n", + "| Lars | |\n", + "| LassoLars | |\n", + "| LassoLarsCV | |\n", + "| RidgeCV | |\n", + "| SVR | |\n", + "| LinearSVR | |\n", + "| AdaBoostRegressor | |\n", + "| ElasticNetCV | |\n", + "| AdaBoostClassifier | |\n", + "| MLPRegressor | |\n", + "| GaussianProcessRegressor | |\n", + "| HistGradientBoostingClassifier | |\n", + "| HistGradientBoostingRegressor | |\n", + "| AddTransformer | |\n", + "| mul_neg_1_Transformer | |\n", + "| MulTransformer | |\n", + "| SafeReciprocalTransformer | |\n", + "| EQTransformer | |\n", + "| NETransformer | |\n", + "| GETransformer | |\n", + "| GTTransformer | |\n", + "| LETransformer | |\n", + "| LTTransformer | |\n", + "| MinTransformer | |\n", + "| MaxTransformer | |\n", + "| ZeroTransformer | |\n", + "| OneTransformer | |\n", + "| NTransformer | |\n", + "| PowerTransformer | |\n", + "| QuantileTransformer | |\n", + "| ARDRegression | |\n", + "| QuadraticDiscriminantAnalysis | |\n", + "| PassiveAggressiveClassifier | |\n", + "| LinearDiscriminantAnalysis | |\n", + "| DominantEncoder | |\n", + "| RecessiveEncoder | |\n", + "| HeterosisEncoder | |\n", + "| UnderDominanceEncoder | |\n", + "| OverDominanceEncoder | |\n", + "| GaussianProcessClassifier | |\n", + "| BaggingClassifier | |\n", + "| LGBMRegressor | |\n", + "| Passthrough | |\n", + "| SkipTransformer | |\n", + "| PassKBinsDiscretizer | |\n", + "| SimpleImputer | |\n", + "| IterativeImputer | |\n", + "| KNNImputer | |\n", + "| MDR | |\n", + "| ContinuousMDR | |\n", + "| ReliefF | |\n", + "| SURF | |\n", + "| SURFstar | |\n", + "| MultiSURF | |\n", + "| LinearRegression_sklearnex | |\n", + "| Ridge_sklearnex | |\n", + "| Lasso_sklearnex | |\n", + "| ElasticNet_sklearnex | |\n", + "| SVR_sklearnex | |\n", + "| NuSVR_sklearnex | |\n", + "| RandomForestRegressor_sklearnex | |\n", + "| KNeighborsRegressor_sklearnex | |\n", + "| RandomForestClassifier_sklearnex | |\n", + "| KNeighborsClassifier_sklearnex | |\n", + "| SVC_sklearnex | |\n", + "| NuSVC_sklearnex | |\n", + "| LogisticRegression_sklearnex | |\n", + "\n", + "Some methods require a wrapped estimator. To account for both regression and classification, these have been grouped separately with their own special strings.\n", + "\n", + "| Wrapper Special String | Notes |\n", + "| :--- | :----: |\n", + "| RFE_classification | FRE with learned ExtraTreesClassifier |\n", + "| RFE_regression | RFE with learned ExtraTreesRegressor |\n", + "| SelectFromModel_classification | SelectFromModel with learned ExtraTreesClassifier |\n", + "| SelectFromModel_regression | SelectFromModel with learned ExtraTreesRegressor |\n", + "| IterativeImputer_learned_estimators | IterativeImputer with learned ExtraTreesRegressor |\n", "\n", - "strings can correspond to individual methods. There are also special strings that return predefined lists of methods. \n", "\n", - "| Special String | Included methods |\n", + "There are also special strings that include a predefined lists of methods. These will return a ChoicePipeline of the included methods.\n", + "\n", + "| List Special String | Included methods |\n", "| :--- | :----: |\n", "| \"selectors\" | [\"SelectFwe\", \"SelectPercentile\", \"VarianceThreshold\",] |\n", "| \"selectors_classification\" | [\"SelectFwe\", \"SelectPercentile\", \"VarianceThreshold\", \"RFE_classification\", \"SelectFromModel_classification\"] |\n", @@ -474,14 +3217,450 @@ "| \"skrebate\" | [\"ReliefF\", \"SURF\", \"SURFstar\", \"MultiSURF\"] |\n", "| \"genetic_encoders\" | [\"DominantEncoder\", \"RecessiveEncoder\", \"HeterosisEncoder\", \"UnderDominanceEncoder\", \"OverDominanceEncoder\"] |\n", "| \"classifiers_sklearnex\" | [\"RandomForestClassifier_sklearnex\", \"LogisticRegression_sklearnex\", \"KNeighborsClassifier_sklearnex\", \"SVC_sklearnex\",\"NuSVC_sklearnex\"] |\n", - "| \"regressors_sklearnex\" | [\"LinearRegression_sklearnex\", \"Ridge_sklearnex\", \"Lasso_sklearnex\", \"ElasticNet_sklearnex\", \"SVR_sklearnex\", \"NuSVR_sklearnex\", \"RandomForestRegressor_sklearnex\", \"KNeighborsRegressor_sklearnex\"] |" + "| \"regressors_sklearnex\" | [\"LinearRegression_sklearnex\", \"Ridge_sklearnex\", \"Lasso_sklearnex\", \"ElasticNet_sklearnex\", \"SVR_sklearnex\", \"NuSVR_sklearnex\", \"RandomForestRegressor_sklearnex\", \"KNeighborsRegressor_sklearnex\"] |\n", + "| \"genetic encoders\" | [\"DominantEncoder\", \"RecessiveEncoder\", \"HeterosisEncoder\", \"UnderDominanceEncoder\", \"OverDominanceEncoder\"] |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here are some examples of how to get search spaces using the `get_search_space` function. " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sampled pipeline 1\n" + ] + }, + { + "data": { + "text/html": [ + "
DecisionTreeClassifier(max_depth=3, max_features='sqrt', min_samples_leaf=16,\n",
+       "                       min_samples_split=8)
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" + ], + "text/plain": [ + "DecisionTreeClassifier(max_depth=3, max_features='sqrt', min_samples_leaf=16,\n", + " min_samples_split=8)" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#same pipeline search space as before.\n", "classifier_choice = tpot2.config.get_search_space([\"KNeighborsClassifier\", \"LogisticRegression\", \"DecisionTreeClassifier\"])\n", @@ -492,9 +3671,434 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sampled pipeline 2\n" + ] + }, + { + "data": { + "text/html": [ + "
LogisticRegression(C=203.4209981734027, max_iter=1000, n_jobs=1, solver='saga')
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" + ], + "text/plain": [ + "LogisticRegression(C=203.4209981734027, max_iter=1000, n_jobs=1, solver='saga')" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "print(\"sampled pipeline 2\")\n", "classifier_choice.generate().export_pipeline()" @@ -502,9 +4106,434 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sampled pipeline 1\n" + ] + }, + { + "data": { + "text/html": [ + "
GaussianNB()
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" + ], + "text/plain": [ + "GaussianNB()" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#search space for all classifiers\n", "classifier_choice = tpot2.config.get_search_space(\"classifiers\")\n", @@ -515,14 +4544,881 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sampled pipeline 2\n" + ] + }, + { + "data": { + "text/html": [ + "
MultinomialNB(alpha=0.2214451695279)
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" + ], + "text/plain": [ + "MultinomialNB(alpha=0.2214451695279)" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "print(\"sampled pipeline 2\")\n", "classifier_choice.generate().export_pipeline()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### A note on reproducibility \n", + "Many sklearn estimators, like RandomForestClassifier, are stochastic and require a random_state parameter in order to have deterministic results. If you want TPOT runs to be reproducible, it is important that the estimators used by TPOT have a random state set. TPOT will not automatically set this value. This can either be set manually in each search space, or by passing in the random state to the `get_search_space` function. For example: " + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
RandomForestClassifier(bootstrap=False, max_features=0.5976648428162,\n",
+       "                       min_samples_leaf=4, min_samples_split=7,\n",
+       "                       n_estimators=128, random_state=1)
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" + ], + "text/plain": [ + "RandomForestClassifier(bootstrap=False, max_features=0.5976648428162,\n", + " min_samples_leaf=4, min_samples_split=7,\n", + " n_estimators=128, random_state=1)" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "reproducible_random_forest = tpot2.config.get_search_space(\"RandomForestClassifier\", random_state=1)\n", + "reproducible_random_forest.generate().export_pipeline()" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -534,9 +5430,450 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sampled pipeline\n" + ] + }, + { + "data": { + "text/html": [ + "
Pipeline(steps=[('variancethreshold',\n",
+       "                 VarianceThreshold(threshold=0.0061644734724)),\n",
+       "                ('pca', PCA(n_components=0.5803735556718)),\n",
+       "                ('logisticregression',\n",
+       "                 LogisticRegression(C=0.0331002885417, class_weight='balanced',\n",
+       "                                    max_iter=1000, n_jobs=1, solver='saga'))])
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" + ], + "text/plain": [ + "Pipeline(steps=[('variancethreshold',\n", + " VarianceThreshold(threshold=0.0061644734724)),\n", + " ('pca', PCA(n_components=0.5803735556718)),\n", + " ('logisticregression',\n", + " LogisticRegression(C=0.0331002885417, class_weight='balanced',\n", + " max_iter=1000, n_jobs=1, solver='saga'))])" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "selector_choicepipeline = tpot2.config.get_search_space(\"VarianceThreshold\")\n", "transformer_choicepipeline = tpot2.config.get_search_space(\"PCA\")\n", @@ -563,9 +5900,443 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sampled pipeline\n" + ] + }, + { + "data": { + "text/html": [ + "
Pipeline(steps=[('selectpercentile',\n",
+       "                 SelectPercentile(percentile=82.0501639184698)),\n",
+       "                ('zerocount', ZeroCount()),\n",
+       "                ('multinomialnb', MultinomialNB(alpha=0.7116498874199))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "Pipeline(steps=[('selectpercentile',\n", + " SelectPercentile(percentile=82.0501639184698)),\n", + " ('zerocount', ZeroCount()),\n", + " ('multinomialnb', MultinomialNB(alpha=0.7116498874199))])" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "selector_choicepipeline = tpot2.config.get_search_space(\"selectors\")\n", "transformer_choicepipeline = tpot2.config.get_search_space(\"transformers\")\n", @@ -583,9 +6354,469 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sampled pipeline\n" + ] + }, + { + "data": { + "text/html": [ + "
Pipeline(steps=[('variancethreshold',\n",
+       "                 VarianceThreshold(threshold=0.0090024083095)),\n",
+       "                ('nystroem',\n",
+       "                 Nystroem(gamma=0.326846805684, kernel='chi2',\n",
+       "                          n_components=49)),\n",
+       "                ('mlpclassifier',\n",
+       "                 MLPClassifier(activation='identity', alpha=0.0009288789905,\n",
+       "                               early_stopping=True,\n",
+       "                               hidden_layer_sizes=[265, 265],\n",
+       "                               learning_rate='invscaling',\n",
+       "                               learning_rate_init=0.0366758440485,\n",
+       "                               n_iter_no_change=32))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "Pipeline(steps=[('variancethreshold',\n", + " VarianceThreshold(threshold=0.0090024083095)),\n", + " ('nystroem',\n", + " Nystroem(gamma=0.326846805684, kernel='chi2',\n", + " n_components=49)),\n", + " ('mlpclassifier',\n", + " MLPClassifier(activation='identity', alpha=0.0009288789905,\n", + " early_stopping=True,\n", + " hidden_layer_sizes=[265, 265],\n", + " learning_rate='invscaling',\n", + " learning_rate_init=0.0366758440485,\n", + " n_iter_no_change=32))])" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "print(\"sampled pipeline\")\n", "stc_pipeline.generate().export_pipeline()" @@ -602,9 +6833,437 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sampled pipeline\n" + ] + }, + { + "data": { + "text/html": [ + "
Pipeline(steps=[('zerocount-1', ZeroCount()), ('zerocount-2', ZeroCount()),\n",
+       "                ('minmaxscaler', MinMaxScaler())])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "Pipeline(steps=[('zerocount-1', ZeroCount()), ('zerocount-2', ZeroCount()),\n", + " ('minmaxscaler', MinMaxScaler())])" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import tpot2.config\n", "\n", @@ -616,9 +7275,479 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sampled pipeline\n" + ] + }, + { + "data": { + "text/html": [ + "
Pipeline(steps=[('selectpercentile',\n",
+       "                 SelectPercentile(percentile=61.5466222112372)),\n",
+       "                ('robustscaler',\n",
+       "                 RobustScaler(quantile_range=(0.0479806149183,\n",
+       "                                              0.9674592383627))),\n",
+       "                ('selectfrommodel',\n",
+       "                 SelectFromModel(estimator=ExtraTreesClassifier(bootstrap=True,\n",
+       "                                                                criterion='entropy',\n",
+       "                                                                max_features=0.3260066399479,\n",
+       "                                                                min_samples_leaf=6,\n",
+       "                                                                min_samples_split=8,\n",
+       "                                                                n_jobs=1),\n",
+       "                                 threshold=0.0001984121028))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "Pipeline(steps=[('selectpercentile',\n", + " SelectPercentile(percentile=61.5466222112372)),\n", + " ('robustscaler',\n", + " RobustScaler(quantile_range=(0.0479806149183,\n", + " 0.9674592383627))),\n", + " ('selectfrommodel',\n", + " SelectFromModel(estimator=ExtraTreesClassifier(bootstrap=True,\n", + " criterion='entropy',\n", + " max_features=0.3260066399479,\n", + " min_samples_leaf=6,\n", + " min_samples_split=8,\n", + " n_jobs=1),\n", + " threshold=0.0001984121028))])" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "print(\"sampled pipeline\")\n", "linear_feature_engineering.generate().export_pipeline()" @@ -626,9 +7755,471 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sampled pipeline\n" + ] + }, + { + "data": { + "text/html": [ + "
Pipeline(steps=[('pipeline',\n",
+       "                 Pipeline(steps=[('binarizer',\n",
+       "                                  Binarizer(threshold=0.4321512765788)),\n",
+       "                                 ('pca', PCA(n_components=0.6918117427918)),\n",
+       "                                 ('passkbinsdiscretizer',\n",
+       "                                  PassKBinsDiscretizer(n_bins=42))])),\n",
+       "                ('extratreesclassifier',\n",
+       "                 ExtraTreesClassifier(class_weight='balanced',\n",
+       "                                      criterion='entropy',\n",
+       "                                      max_features=0.169455524505,\n",
+       "                                      min_samples_leaf=8, min_samples_split=14,\n",
+       "                                      n_jobs=1))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "Pipeline(steps=[('pipeline',\n", + " Pipeline(steps=[('binarizer',\n", + " Binarizer(threshold=0.4321512765788)),\n", + " ('pca', PCA(n_components=0.6918117427918)),\n", + " ('passkbinsdiscretizer',\n", + " PassKBinsDiscretizer(n_bins=42))])),\n", + " ('extratreesclassifier',\n", + " ExtraTreesClassifier(class_weight='balanced',\n", + " criterion='entropy',\n", + " max_features=0.169455524505,\n", + " min_samples_leaf=8, min_samples_split=14,\n", + " n_jobs=1))])" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "full_search_space = tpot2.search_spaces.pipelines.SequentialPipeline([\n", " linear_feature_engineering,\n", @@ -641,9 +8232,506 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sampled pipeline\n" + ] + }, + { + "data": { + "text/html": [ + "
Pipeline(steps=[('pipeline',\n",
+       "                 Pipeline(steps=[('rfe',\n",
+       "                                  RFE(estimator=ExtraTreesClassifier(bootstrap=True,\n",
+       "                                                                     criterion='entropy',\n",
+       "                                                                     max_features=0.0135775754498,\n",
+       "                                                                     min_samples_leaf=8,\n",
+       "                                                                     min_samples_split=6,\n",
+       "                                                                     n_jobs=1),\n",
+       "                                      step=0.7236899597647)),\n",
+       "                                 ('columnonehotencoder', ColumnOneHotEncoder()),\n",
+       "                                 ('featureagglomeration',\n",
+       "                                  FeatureAgglomeration(linkage='average',\n",
+       "                                                       metric='l2',\n",
+       "                                                       n_clusters=150))])),\n",
+       "                ('sgdclassifier',\n",
+       "                 SGDClassifier(alpha=0.0009821180851, eta0=0.1666104101354,\n",
+       "                               l1_ratio=0.7504578619487, loss='modified_huber',\n",
+       "                               n_jobs=1, penalty='elasticnet'))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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" + ], + "text/plain": [ + "Pipeline(steps=[('pipeline',\n", + " Pipeline(steps=[('rfe',\n", + " RFE(estimator=ExtraTreesClassifier(bootstrap=True,\n", + " criterion='entropy',\n", + " max_features=0.0135775754498,\n", + " min_samples_leaf=8,\n", + " min_samples_split=6,\n", + " n_jobs=1),\n", + " step=0.7236899597647)),\n", + " ('columnonehotencoder', ColumnOneHotEncoder()),\n", + " ('featureagglomeration',\n", + " FeatureAgglomeration(linkage='average',\n", + " metric='l2',\n", + " n_clusters=150))])),\n", + " ('sgdclassifier',\n", + " SGDClassifier(alpha=0.0009821180851, eta0=0.1666104101354,\n", + " l1_ratio=0.7504578619487, loss='modified_huber',\n", + " n_jobs=1, penalty='elasticnet'))])" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "print(\"sampled pipeline\")\n", "full_search_space.generate().export_pipeline()" @@ -660,9 +8748,430 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
FeatureUnion(transformer_list=[('zerocount', ZeroCount()),\n",
+       "                               ('passthrough', Passthrough())])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "FeatureUnion(transformer_list=[('zerocount', ZeroCount()),\n", + " ('passthrough', Passthrough())])" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "transform_and_passthrough = tpot2.search_spaces.pipelines.UnionPipeline([\n", " tpot2.config.get_search_space(\"transformers\"),\n", @@ -681,9 +9190,454 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Pipeline(steps=[('selectfwe', SelectFwe(alpha=0.0104695394381)),\n",
+       "                ('featureunion',\n",
+       "                 FeatureUnion(transformer_list=[('quantiletransformer',\n",
+       "                                                 QuantileTransformer(n_quantiles=93)),\n",
+       "                                                ('passthrough',\n",
+       "                                                 Passthrough())])),\n",
+       "                ('svc',\n",
+       "                 SVC(C=0.5015595860816, coef0=0.5773095995375, kernel='sigmoid',\n",
+       "                     max_iter=3000, probability=True))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "Pipeline(steps=[('selectfwe', SelectFwe(alpha=0.0104695394381)),\n", + " ('featureunion',\n", + " FeatureUnion(transformer_list=[('quantiletransformer',\n", + " QuantileTransformer(n_quantiles=93)),\n", + " ('passthrough',\n", + " Passthrough())])),\n", + " ('svc',\n", + " SVC(C=0.5015595860816, coef0=0.5773095995375, kernel='sigmoid',\n", + " max_iter=3000, probability=True))])" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "stc_pipeline2 = tpot2.search_spaces.pipelines.SequentialPipeline([\n", " tpot2.config.get_search_space(\"selectors\"),\n", @@ -703,9 +9657,516 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Pipeline(steps=[('featureunion',\n",
+       "                 FeatureUnion(transformer_list=[('pipeline-1',\n",
+       "                                                 Pipeline(steps=[('selectfwe',\n",
+       "                                                                  SelectFwe(alpha=0.0013091622594)),\n",
+       "                                                                 ('nystroem',\n",
+       "                                                                  Nystroem(gamma=0.2527764721894,\n",
+       "                                                                           kernel='additive_chi2',\n",
+       "                                                                           n_components=40))])),\n",
+       "                                                ('pipeline-2',\n",
+       "                                                 Pipeline(steps=[('variancethreshold',\n",
+       "                                                                  VarianceThreshold(threshold=0.0130961185337)),\n",
+       "                                                                 ('featureagglomeration',\n",
+       "                                                                  Fe...ge='average',\n",
+       "                                                                                       metric='l2',\n",
+       "                                                                                       n_clusters=293,\n",
+       "                                                                                       pooling_func=<function median at 0x73f3c1bda370>))]))])),\n",
+       "                ('histgradientboostingclassifier',\n",
+       "                 HistGradientBoostingClassifier(early_stopping=False,\n",
+       "                                                l2_regularization=3.1669452e-06,\n",
+       "                                                learning_rate=0.1262523910078,\n",
+       "                                                max_features=0.8008565064114,\n",
+       "                                                max_leaf_nodes=1504,\n",
+       "                                                min_samples_leaf=32, tol=0.0001,\n",
+       "                                                validation_fraction=None))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "Pipeline(steps=[('featureunion',\n", + " FeatureUnion(transformer_list=[('pipeline-1',\n", + " Pipeline(steps=[('selectfwe',\n", + " SelectFwe(alpha=0.0013091622594)),\n", + " ('nystroem',\n", + " Nystroem(gamma=0.2527764721894,\n", + " kernel='additive_chi2',\n", + " n_components=40))])),\n", + " ('pipeline-2',\n", + " Pipeline(steps=[('variancethreshold',\n", + " VarianceThreshold(threshold=0.0130961185337)),\n", + " ('featureagglomeration',\n", + " Fe...ge='average',\n", + " metric='l2',\n", + " n_clusters=293,\n", + " pooling_func=))]))])),\n", + " ('histgradientboostingclassifier',\n", + " HistGradientBoostingClassifier(early_stopping=False,\n", + " l2_regularization=3.1669452e-06,\n", + " learning_rate=0.1262523910078,\n", + " max_features=0.8008565064114,\n", + " max_leaf_nodes=1504,\n", + " min_samples_leaf=32, tol=0.0001,\n", + " validation_fraction=None))])" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "st_pipeline = tpot2.search_spaces.pipelines.SequentialPipeline([\n", " tpot2.config.get_search_space(\"selectors\"),\n", @@ -738,9 +10199,433 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
FeatureUnion(transformer_list=[('quantiletransformer',\n",
+       "                                QuantileTransformer(n_quantiles=81)),\n",
+       "                               ('columnonehotencoder', ColumnOneHotEncoder())])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "FeatureUnion(transformer_list=[('quantiletransformer',\n", + " QuantileTransformer(n_quantiles=81)),\n", + " ('columnonehotencoder', ColumnOneHotEncoder())])" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "dynamic_transformers = tpot2.search_spaces.pipelines.DynamicUnionPipeline(tpot2.config.get_search_space(\"transformers\"), max_estimators=4)\n", "dynamic_transformers.generate().export_pipeline()" @@ -755,9 +10640,454 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
FeatureUnion(transformer_list=[('featureunion',\n",
+       "                                FeatureUnion(transformer_list=[('rbfsampler-1',\n",
+       "                                                                RBFSampler(gamma=0.6219125014396,\n",
+       "                                                                           n_components=64)),\n",
+       "                                                               ('powertransformer',\n",
+       "                                                                PowerTransformer()),\n",
+       "                                                               ('rbfsampler-2',\n",
+       "                                                                RBFSampler(gamma=0.3345729157827,\n",
+       "                                                                           n_components=23))])),\n",
+       "                               ('passthrough', Passthrough())])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "FeatureUnion(transformer_list=[('featureunion',\n", + " FeatureUnion(transformer_list=[('rbfsampler-1',\n", + " RBFSampler(gamma=0.6219125014396,\n", + " n_components=64)),\n", + " ('powertransformer',\n", + " PowerTransformer()),\n", + " ('rbfsampler-2',\n", + " RBFSampler(gamma=0.3345729157827,\n", + " n_components=23))])),\n", + " ('passthrough', Passthrough())])" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "dynamic_transformers_with_passthrough = tpot2.search_spaces.pipelines.UnionPipeline([\n", " dynamic_transformers,\n", @@ -769,9 +11099,504 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Pipeline(steps=[('variancethreshold',\n",
+       "                 VarianceThreshold(threshold=0.1286612361721)),\n",
+       "                ('featureunion',\n",
+       "                 FeatureUnion(transformer_list=[('featureunion',\n",
+       "                                                 FeatureUnion(transformer_list=[('binarizer',\n",
+       "                                                                                 Binarizer(threshold=0.736067585858)),\n",
+       "                                                                                ('rbfsampler',\n",
+       "                                                                                 RBFSampler(gamma=0.1436440722816,\n",
+       "                                                                                            n_components=44)),\n",
+       "                                                                                ('quantiletransformer',\n",
+       "                                                                                 QuantileTransformer(n_quantiles=100))])),\n",
+       "                                                ('passthrough',\n",
+       "                                                 Passthrough())])),\n",
+       "                ('histgradientboostingclassifier',\n",
+       "                 HistGradientBoostingClassifier(early_stopping=True,\n",
+       "                                                l2_regularization=2.047622e-07,\n",
+       "                                                learning_rate=0.0164428425279,\n",
+       "                                                max_features=0.3325348714186,\n",
+       "                                                max_leaf_nodes=1940,\n",
+       "                                                min_samples_leaf=78,\n",
+       "                                                n_iter_no_change=12, tol=0.0001,\n",
+       "                                                validation_fraction=None))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "Pipeline(steps=[('variancethreshold',\n", + " VarianceThreshold(threshold=0.1286612361721)),\n", + " ('featureunion',\n", + " FeatureUnion(transformer_list=[('featureunion',\n", + " FeatureUnion(transformer_list=[('binarizer',\n", + " Binarizer(threshold=0.736067585858)),\n", + " ('rbfsampler',\n", + " RBFSampler(gamma=0.1436440722816,\n", + " n_components=44)),\n", + " ('quantiletransformer',\n", + " QuantileTransformer(n_quantiles=100))])),\n", + " ('passthrough',\n", + " Passthrough())])),\n", + " ('histgradientboostingclassifier',\n", + " HistGradientBoostingClassifier(early_stopping=True,\n", + " l2_regularization=2.047622e-07,\n", + " learning_rate=0.0164428425279,\n", + " max_features=0.3325348714186,\n", + " max_leaf_nodes=1940,\n", + " min_samples_leaf=78,\n", + " n_iter_no_change=12, tol=0.0001,\n", + " validation_fraction=None))])" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "stc_pipeline3 = tpot2.search_spaces.pipelines.SequentialPipeline([\n", " tpot2.config.get_search_space(\"selectors\"),\n", @@ -795,9 +11620,430 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
ExtraTreesClassifier(max_features=0.2286391649712, min_samples_leaf=13,\n",
+       "                     min_samples_split=8, n_jobs=1)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "ExtraTreesClassifier(max_features=0.2286391649712, min_samples_leaf=13,\n", + " min_samples_split=8, n_jobs=1)" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "SelectFromModel_configspace_part = ConfigurationSpace(\n", " space = {\n", @@ -811,9 +12057,443 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
SelectFromModel(estimator=ExtraTreesClassifier(bootstrap=True,\n",
+       "                                               criterion='entropy',\n",
+       "                                               max_features=0.0311518006465,\n",
+       "                                               min_samples_split=19, n_jobs=1),\n",
+       "                threshold=0.0012368197842)
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" + ], + "text/plain": [ + "SelectFromModel(estimator=ExtraTreesClassifier(bootstrap=True,\n", + " criterion='entropy',\n", + " max_features=0.0311518006465,\n", + " min_samples_split=19, n_jobs=1),\n", + " threshold=0.0012368197842)" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from sklearn.ensemble import ExtraTreesClassifier\n", "from sklearn.feature_selection import SelectFromModel\n", @@ -844,9 +12524,463 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
EstimatorTransformer(estimator=HistGradientBoostingClassifier(early_stopping=True,\n",
+       "                                                              l2_regularization=0.000117454825,\n",
+       "                                                              learning_rate=0.122899142038,\n",
+       "                                                              max_features=0.5654219816525,\n",
+       "                                                              max_leaf_nodes=1048,\n",
+       "                                                              min_samples_leaf=1,\n",
+       "                                                              n_iter_no_change=17,\n",
+       "                                                              tol=0.0001,\n",
+       "                                                              validation_fraction=0.3473838441178))
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "EstimatorTransformer(estimator=HistGradientBoostingClassifier(early_stopping=True,\n", + " l2_regularization=0.000117454825,\n", + " learning_rate=0.122899142038,\n", + " max_features=0.5654219816525,\n", + " max_leaf_nodes=1048,\n", + " min_samples_leaf=1,\n", + " n_iter_no_change=17,\n", + " tol=0.0001,\n", + " validation_fraction=0.3473838441178))" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "classifiers = tpot2.config.get_search_space(\"classifiers\")\n", "wrapped_estimators = tpot2.search_spaces.pipelines.WrapperPipeline(tpot2.builtin_modules.EstimatorTransformer, {}, classifiers)\n", @@ -857,9 +12991,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.94963523, 0.05036477],\n", + " [0.91791762, 0.08208238],\n", + " [0.16108516, 0.83891484],\n", + " [0.05483536, 0.94516464],\n", + " [0.05482495, 0.94517505]])" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import numpy as np\n", "X, y = np.random.rand(100, 10), np.random.randint(0, 2, 100)\n", @@ -876,9 +13025,52 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 54, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/perib/miniconda3/envs/myenv/lib/python3.10/site-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=3000). Consider pre-processing your data with StandardScaler or MinMaxScaler.\n", + " warnings.warn(\n", + "/home/perib/miniconda3/envs/myenv/lib/python3.10/site-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=3000). Consider pre-processing your data with StandardScaler or MinMaxScaler.\n", + " warnings.warn(\n", + "/home/perib/miniconda3/envs/myenv/lib/python3.10/site-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=3000). Consider pre-processing your data with StandardScaler or MinMaxScaler.\n", + " warnings.warn(\n", + "/home/perib/miniconda3/envs/myenv/lib/python3.10/site-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=3000). Consider pre-processing your data with StandardScaler or MinMaxScaler.\n", + " warnings.warn(\n", + "/home/perib/miniconda3/envs/myenv/lib/python3.10/site-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=3000). Consider pre-processing your data with StandardScaler or MinMaxScaler.\n", + " warnings.warn(\n", + "/home/perib/miniconda3/envs/myenv/lib/python3.10/site-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=3000). Consider pre-processing your data with StandardScaler or MinMaxScaler.\n", + " warnings.warn(\n", + "/home/perib/miniconda3/envs/myenv/lib/python3.10/site-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=3000). Consider pre-processing your data with StandardScaler or MinMaxScaler.\n", + " warnings.warn(\n", + "/home/perib/miniconda3/envs/myenv/lib/python3.10/site-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=3000). Consider pre-processing your data with StandardScaler or MinMaxScaler.\n", + " warnings.warn(\n", + "/home/perib/miniconda3/envs/myenv/lib/python3.10/site-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=3000). Consider pre-processing your data with StandardScaler or MinMaxScaler.\n", + " warnings.warn(\n", + "/home/perib/miniconda3/envs/myenv/lib/python3.10/site-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=3000). Consider pre-processing your data with StandardScaler or MinMaxScaler.\n", + " warnings.warn(\n", + "/home/perib/miniconda3/envs/myenv/lib/python3.10/site-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=3000). Consider pre-processing your data with StandardScaler or MinMaxScaler.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[0],\n", + " [0],\n", + " [0],\n", + " [1],\n", + " [1]])" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "classifiers = tpot2.config.get_search_space(\"classifiers\")\n", "wrapped_estimators_cv = tpot2.search_spaces.pipelines.WrapperPipeline(tpot2.builtin_modules.EstimatorTransformer, {'cross_val_predict_cv':10, 'method':'predict'}, classifiers)\n", @@ -895,9 +13087,555 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Pipeline(steps=[('standardscaler', StandardScaler()),\n",
+       "                ('featureunion-1',\n",
+       "                 FeatureUnion(transformer_list=[('featureunion',\n",
+       "                                                 FeatureUnion(transformer_list=[('powertransformer',\n",
+       "                                                                                 PowerTransformer()),\n",
+       "                                                                                ('nystroem-1',\n",
+       "                                                                                 Nystroem(gamma=0.4484025909592,\n",
+       "                                                                                          kernel='polynomial',\n",
+       "                                                                                          n_components=13)),\n",
+       "                                                                                ('nystroem-2',\n",
+       "                                                                                 Nystroem(gamma=0.9023618026452,\n",
+       "                                                                                          kernel='additive_chi2',\n",
+       "                                                                                          n_component...\n",
+       "                                                                                 EstimatorTransformer(cross_val_predict_cv=10,\n",
+       "                                                                                                      estimator=GaussianNB(),\n",
+       "                                                                                                      method='predict')),\n",
+       "                                                                                ('estimatortransformer-3',\n",
+       "                                                                                 EstimatorTransformer(cross_val_predict_cv=10,\n",
+       "                                                                                                      estimator=BaggingClassifier(bootstrap=False,\n",
+       "                                                                                                                                  bootstrap_features=True,\n",
+       "                                                                                                                                  max_features=0.248985416426,\n",
+       "                                                                                                                                  max_samples=0.8328766080285,\n",
+       "                                                                                                                                  n_estimators=42,\n",
+       "                                                                                                                                  n_jobs=1),\n",
+       "                                                                                                      method='predict'))])),\n",
+       "                                                ('passthrough',\n",
+       "                                                 Passthrough())])),\n",
+       "                ('gaussiannb', GaussianNB())])
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On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "Pipeline(steps=[('standardscaler', StandardScaler()),\n", + " ('featureunion-1',\n", + " FeatureUnion(transformer_list=[('featureunion',\n", + " FeatureUnion(transformer_list=[('powertransformer',\n", + " PowerTransformer()),\n", + " ('nystroem-1',\n", + " Nystroem(gamma=0.4484025909592,\n", + " kernel='polynomial',\n", + " n_components=13)),\n", + " ('nystroem-2',\n", + " Nystroem(gamma=0.9023618026452,\n", + " kernel='additive_chi2',\n", + " n_component...\n", + " EstimatorTransformer(cross_val_predict_cv=10,\n", + " estimator=GaussianNB(),\n", + " method='predict')),\n", + " ('estimatortransformer-3',\n", + " EstimatorTransformer(cross_val_predict_cv=10,\n", + " estimator=BaggingClassifier(bootstrap=False,\n", + " bootstrap_features=True,\n", + " max_features=0.248985416426,\n", + " max_samples=0.8328766080285,\n", + " n_estimators=42,\n", + " n_jobs=1),\n", + " method='predict'))])),\n", + " ('passthrough',\n", + " Passthrough())])),\n", + " ('gaussiannb', GaussianNB())])" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "dynamic_wrapped_classifiers_with_passthrough = tpot2.search_spaces.pipelines.UnionPipeline([\n", " tpot2.search_spaces.pipelines.DynamicUnionPipeline(wrapped_estimators_cv, max_estimators=4),\n", @@ -938,7 +13676,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ @@ -954,9 +13692,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 57, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "est1 = ind.export_pipeline()\n", "est1.plot() #GraphPipelines have a helpful plotting function to visualize the pipeline" @@ -971,9 +13720,110 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 58, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "for i in range(0,50):\n", " ind.mutate()\n", @@ -1000,9 +13850,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 59, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "tree_search_space = tpot2.search_spaces.pipelines.TreePipeline(\n", " root_search_space= tpot2.config.get_search_space([\"KNeighborsClassifier\", \"LogisticRegression\", \"DecisionTreeClassifier\"]),\n", @@ -1036,9 +13897,436 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 60, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
FeatureUnion(transformer_list=[('featureunion',\n",
+       "                                FeatureUnion(transformer_list=[('binarizer',\n",
+       "                                                                Binarizer(threshold=0.1286154935127))])),\n",
+       "                               ('passthrough', Passthrough())])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "FeatureUnion(transformer_list=[('featureunion',\n", + " FeatureUnion(transformer_list=[('binarizer',\n", + " Binarizer(threshold=0.1286154935127))])),\n", + " ('passthrough', Passthrough())])" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from tpot2.search_spaces.pipelines import *\n", "from tpot2.config import get_search_space\n", @@ -1062,9 +14350,439 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 61, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
FeatureUnion(transformer_list=[('featureunion',\n",
+       "                                FeatureUnion(transformer_list=[('quantiletransformer',\n",
+       "                                                                QuantileTransformer(n_quantiles=98,\n",
+       "                                                                                    output_distribution='normal'))])),\n",
+       "                               ('passthrough', Passthrough())])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "FeatureUnion(transformer_list=[('featureunion',\n", + " FeatureUnion(transformer_list=[('quantiletransformer',\n", + " QuantileTransformer(n_quantiles=98,\n", + " output_distribution='normal'))])),\n", + " ('passthrough', Passthrough())])" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "final_transformers_layer =UnionPipeline([\n", " ChoicePipeline([\n", @@ -1080,9 +14798,442 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 62, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
FeatureUnion(transformer_list=[('featureunion',\n",
+       "                                FeatureUnion(transformer_list=[('estimatortransformer-1',\n",
+       "                                                                EstimatorTransformer(estimator=BernoulliNB(alpha=0.0316290799363))),\n",
+       "                                                               ('estimatortransformer-2',\n",
+       "                                                                EstimatorTransformer(estimator=GaussianNB()))])),\n",
+       "                               ('passthrough', Passthrough())])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "FeatureUnion(transformer_list=[('featureunion',\n", + " FeatureUnion(transformer_list=[('estimatortransformer-1',\n", + " EstimatorTransformer(estimator=BernoulliNB(alpha=0.0316290799363))),\n", + " ('estimatortransformer-2',\n", + " EstimatorTransformer(estimator=GaussianNB()))])),\n", + " ('passthrough', Passthrough())])" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "inner_estimators_layer = UnionPipeline([\n", " ChoicePipeline([\n", @@ -1097,9 +15248,479 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 63, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Pipeline(steps=[('robustscaler',\n",
+       "                 RobustScaler(quantile_range=(0.1503060406741,\n",
+       "                                              0.8118816788829))),\n",
+       "                ('featureunion-1',\n",
+       "                 FeatureUnion(transformer_list=[('skiptransformer',\n",
+       "                                                 SkipTransformer()),\n",
+       "                                                ('passthrough',\n",
+       "                                                 Passthrough())])),\n",
+       "                ('featureunion-2',\n",
+       "                 FeatureUnion(transformer_list=[('skiptransformer',\n",
+       "                                                 SkipTransformer()),\n",
+       "                                                ('passthrough',\n",
+       "                                                 Passthrough())])),\n",
+       "                ('sgdclassifier',\n",
+       "                 SGDClassifier(alpha=0.0002054334005, eta0=0.5702721028736,\n",
+       "                               l1_ratio=0.984925401959, loss='modified_huber',\n",
+       "                               n_jobs=1, penalty='elasticnet'))])
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" + ], + "text/plain": [ + "Pipeline(steps=[('robustscaler',\n", + " RobustScaler(quantile_range=(0.1503060406741,\n", + " 0.8118816788829))),\n", + " ('featureunion-1',\n", + " FeatureUnion(transformer_list=[('skiptransformer',\n", + " SkipTransformer()),\n", + " ('passthrough',\n", + " Passthrough())])),\n", + " ('featureunion-2',\n", + " FeatureUnion(transformer_list=[('skiptransformer',\n", + " SkipTransformer()),\n", + " ('passthrough',\n", + " Passthrough())])),\n", + " ('sgdclassifier',\n", + " SGDClassifier(alpha=0.0002054334005, eta0=0.5702721028736,\n", + " l1_ratio=0.984925401959, loss='modified_huber',\n", + " n_jobs=1, penalty='elasticnet'))])" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "final_linear_pipeline = SequentialPipeline([\n", " get_search_space(\"scalers\"),\n", @@ -1122,7 +15743,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ @@ -1142,9 +15763,452 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 65, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generation: 100%|██████████| 5/5 [00:55<00:00, 11.02s/it]\n" + ] + }, + { + "data": { + "text/html": [ + "
TPOTEstimator(classification=True, cv=5, early_stop=2, generations=5,\n",
+       "              max_eval_time_mins=10, n_jobs=4,\n",
+       "              scorers=['roc_auc_ovr',\n",
+       "                       <function complexity_scorer at 0x73f2b05de710>],\n",
+       "              scorers_weights=[1.0, -1.0],\n",
+       "              search_space=<tpot2.search_spaces.pipelines.sequential.SequentialPipeline object at 0x73f3bd5db070>,\n",
+       "              verbose=2)
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" + ], + "text/plain": [ + "TPOTEstimator(classification=True, cv=5, early_stop=2, generations=5,\n", + " max_eval_time_mins=10, n_jobs=4,\n", + " scorers=['roc_auc_ovr',\n", + " ],\n", + " scorers_weights=[1.0, -1.0],\n", + " search_space=,\n", + " verbose=2)" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "selected_search_space = all_search_spaces[\"stc_pipeline\"] #change this to select a different search space\n", "\n", @@ -1167,9 +16231,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 66, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "auroc score 0.9991040371034047\n" + ] + } + ], "source": [ "# score the model\n", "auroc_scorer = sklearn.metrics.get_scorer(\"roc_auc\")\n", @@ -1180,9 +16252,439 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 67, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Pipeline(steps=[('selectfwe', SelectFwe(alpha=0.0012303337733)),\n",
+       "                ('powertransformer', PowerTransformer()),\n",
+       "                ('lineardiscriminantanalysis',\n",
+       "                 LinearDiscriminantAnalysis(shrinkage=0.2481023005204,\n",
+       "                                            solver='eigen'))])
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" + ], + "text/plain": [ + "Pipeline(steps=[('selectfwe', SelectFwe(alpha=0.0012303337733)),\n", + " ('powertransformer', PowerTransformer()),\n", + " ('lineardiscriminantanalysis',\n", + " LinearDiscriminantAnalysis(shrinkage=0.2481023005204,\n", + " solver='eigen'))])" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#plot the best pipeline\n", "if isinstance(est.fitted_pipeline_, tpot2.GraphPipeline):\n", @@ -1221,9 +16723,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 68, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.04690299241236334" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import sklearn\n", "import sklearn.datasets\n", @@ -1254,9 +16767,427 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 69, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
SimpleImputer(strategy='most_frequent')
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" + ], + "text/plain": [ + "SimpleImputer(strategy='most_frequent')" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import tpot2.search_spaces\n", "from ConfigSpace import ConfigurationSpace, Integer, Float, Categorical, Normal\n", @@ -1293,9 +17224,553 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 70, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/perib/Projects/common/Projects/TPOT_Dev/tpot2/tpot2/tpot_estimator/estimator.py:512: UserWarning: Labels are not encoded as ints from 0 to N. For compatibility with some classifiers such as sklearn, TPOT has encoded y with the sklearn LabelEncoder. When using pipelines outside the main TPOT estimator class, you can encode the labels with est.label_encoder_\n", + " warnings.warn(\"Labels are not encoded as ints from 0 to N. For compatibility with some classifiers such as sklearn, TPOT has encoded y with the sklearn LabelEncoder. When using pipelines outside the main TPOT estimator class, you can encode the labels with est.label_encoder_\")\n", + "Generation: 20%|██ | 1/5 [00:13<00:53, 13.43s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generation: 1\n", + "Best rmse score: 0.03548267518369124\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generation: 40%|████ | 2/5 [00:24<00:35, 11.80s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generation: 2\n", + "Best rmse score: 0.033924799732331146\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generation: 60%|██████ | 3/5 [00:38<00:25, 12.85s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generation: 3\n", + "Best rmse score: 0.033924799732331146\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generation: 80%|████████ | 4/5 [00:56<00:14, 14.86s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generation: 4\n", + "Best rmse score: 0.033924799732331146\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generation: 100%|██████████| 5/5 [01:18<00:00, 15.68s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generation: 5\n", + "Best rmse score: 0.033924799732331146\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/html": [ + "
TPOTEstimator(classification=True, generations=5, max_eval_time_mins=300,\n",
+       "              n_jobs=20, objective_function_names=['rmse'],\n",
+       "              other_objective_functions=[functools.partial(<function rmse_obective at 0x73f2ac180d30>, X=array([[ 0.03807591,  0.05068012,  0.06169621, ..., -0.00259226,\n",
+       "         0.01990749, -0.01764613],\n",
+       "       [-0.00188202, -0.04464164, -0.05147406, ..., -0.03949338,\n",
+       "        -0.06833155, -0...\n",
+       "        -0.04688253,  0.01549073],\n",
+       "       [-0.04547248, -0.04464164,  0.03906215, ...,  0.02655962,\n",
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+       "       [-0.04547248, -0.04464164, -0.0730303 , ..., -0.03949338,\n",
+       "        -0.00422151,  0.00306441]]), missing_add=0.2)],\n",
+       "              other_objective_functions_weights=[-1], scorers=[],\n",
+       "              scorers_weights=[],\n",
+       "              search_space=<tpot2.search_spaces.pipelines.choice.ChoicePipeline object at 0x73f236dd6cb0>,\n",
+       "              verbose=3)
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" + ], + "text/plain": [ + "TPOTEstimator(classification=True, generations=5, max_eval_time_mins=300,\n", + " n_jobs=20, objective_function_names=['rmse'],\n", + " other_objective_functions=[functools.partial(, X=array([[ 0.03807591, 0.05068012, 0.06169621, ..., -0.00259226,\n", + " 0.01990749, -0.01764613],\n", + " [-0.00188202, -0.04464164, -0.05147406, ..., -0.03949338,\n", + " -0.06833155, -0...\n", + " -0.04688253, 0.01549073],\n", + " [-0.04547248, -0.04464164, 0.03906215, ..., 0.02655962,\n", + " 0.04452873, -0.02593034],\n", + " [-0.04547248, -0.04464164, -0.0730303 , ..., -0.03949338,\n", + " -0.00422151, 0.00306441]]), missing_add=0.2)],\n", + " other_objective_functions_weights=[-1], scorers=[],\n", + " scorers_weights=[],\n", + " search_space=,\n", + " verbose=3)" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from functools import partial\n", "\n", @@ -1320,9 +17795,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 71, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "final rmse score 0.0329883939925428\n" + ] + } + ], "source": [ "# score the model\n", "rmse_score = final_objective(est, fitted=True)\n", @@ -1331,9 +17814,438 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 72, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
IterativeImputer(estimator=ExtraTreesRegressor(bootstrap=True,\n",
+       "                                               max_features=0.9638745327086,\n",
+       "                                               min_samples_split=13),\n",
+       "                 n_nearest_features=6)
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" + ], + "text/plain": [ + "IterativeImputer(estimator=ExtraTreesRegressor(bootstrap=True,\n", + " max_features=0.9638745327086,\n", + " min_samples_split=13),\n", + " n_nearest_features=6)" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "est.fitted_pipeline_" ] @@ -1347,9 +18259,445 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 73, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generation: 100%|██████████| 5/5 [01:31<00:00, 18.21s/it]\n", + "/home/perib/miniconda3/envs/myenv/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/html": [ + "
TPOTEstimator(classification=True, cv=5, generations=5, max_eval_time_mins=300,\n",
+       "              n_jobs=20, scorers=['roc_auc'], scorers_weights=[1],\n",
+       "              search_space=<tpot2.search_spaces.pipelines.sequential.SequentialPipeline object at 0x73f2a93816c0>,\n",
+       "              verbose=2)
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" + ], + "text/plain": [ + "TPOTEstimator(classification=True, cv=5, generations=5, max_eval_time_mins=300,\n", + " n_jobs=20, scorers=['roc_auc'], scorers_weights=[1],\n", + " search_space=,\n", + " verbose=2)" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from tpot2.search_spaces.pipelines import *\n", "from tpot2.config import get_search_space\n", @@ -1407,9 +18755,485 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 74, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Pipeline(steps=[('minmaxscaler', MinMaxScaler()),\n",
+       "                ('variancethreshold',\n",
+       "                 VarianceThreshold(threshold=0.0008618210477)),\n",
+       "                ('featureunion-1',\n",
+       "                 FeatureUnion(transformer_list=[('skiptransformer',\n",
+       "                                                 SkipTransformer()),\n",
+       "                                                ('passthrough',\n",
+       "                                                 Passthrough())])),\n",
+       "                ('featureunion-2',\n",
+       "                 FeatureUnion(transformer_list=[('skiptransformer',\n",
+       "                                                 SkipTransformer()),\n",
+       "                                                ('passthrough',\n",
+       "                                                 Passthrough())])),\n",
+       "                ('mlpclassifier',\n",
+       "                 MLPClassifier(activation='identity', alpha=0.003748165278,\n",
+       "                               hidden_layer_sizes=[119],\n",
+       "                               learning_rate='adaptive',\n",
+       "                               learning_rate_init=0.0167284771456,\n",
+       "                               n_iter_no_change=32))])
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" + ], + "text/plain": [ + "Pipeline(steps=[('minmaxscaler', MinMaxScaler()),\n", + " ('variancethreshold',\n", + " VarianceThreshold(threshold=0.0008618210477)),\n", + " ('featureunion-1',\n", + " FeatureUnion(transformer_list=[('skiptransformer',\n", + " SkipTransformer()),\n", + " ('passthrough',\n", + " Passthrough())])),\n", + " ('featureunion-2',\n", + " FeatureUnion(transformer_list=[('skiptransformer',\n", + " SkipTransformer()),\n", + " ('passthrough',\n", + " Passthrough())])),\n", + " ('mlpclassifier',\n", + " MLPClassifier(activation='identity', alpha=0.003748165278,\n", + " hidden_layer_sizes=[119],\n", + " learning_rate='adaptive',\n", + " learning_rate_init=0.0167284771456,\n", + " n_iter_no_change=32))])" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "est.fitted_pipeline_" ] diff --git a/Tutorial/3_Feature_Set_Selector.ipynb b/Tutorial/3_Feature_Set_Selector.ipynb index 82bcf6c4..5341cf0e 100644 --- a/Tutorial/3_Feature_Set_Selector.ipynb +++ b/Tutorial/3_Feature_Set_Selector.ipynb @@ -4,20 +4,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Special Feature Selection nodes in TPOT2\n", + "# Genetic Feature Selection nodes in TPOT2\n", "\n", - "TPOT2 can use evolutionary algorithms to optimize feature selection simultaneously with pipeline optimization. There are two node search spaces included.\n", + "TPOT2 can use evolutionary algorithms to optimize feature selection simultaneously with pipeline optimization. It includes two node search spaces with different feature selection strategies: FSSNode and GeneticFeatureSelectorNode. \n", "\n", - "1. FSSNode - (Feature Set Selector) This node is useful if you have predefined groups of features that you want to select from. For example, one group could include the first x columns, the next group could include the next y columns, etc. Each FeatureSetSelector Node will select a single group to be passed to the next step in the pipeline. This node is also useful if you want to select individual columns at a time, this will be used in tutorial 4 to create a symbolic regression search space. \n", + "1. FSSNode - (Feature Set Selector) This node is useful if you have a list of predefined feature sets you want to select from. Each FeatureSetSelector Node will select a single group of features to be passed to the next step in the pipeline. Note that FSSNode does not create its own subset of features and does not mix/match multiple predefined feature sets.\n", "\n", - "2. GeneticFeatureSelectorNode - Whereas FSSNode selects from a predefine list of subsets of features, this node instead uses evolutionary algorithms to optimize a novel subset from scratch. This is useful where there is no predefined grouping of features.\n", + "2. GeneticFeatureSelectorNode—Whereas the FSSNode selects from a predefined list of subsets of features, this node uses evolutionary algorithms to optimize a novel subset of features from scratch. This is useful where there is no predefined grouping of features. \n", "\n", + "This tutorial focuses on FSSNode. See Tutorial 5 for more information on GeneticFeatureSelectorNode.\n", "\n", "It may also be beneficial to pair these search spaces with a secondary objective function to minimize complexity. That would encourage TPOT to try to produce the simplest pipeline with the fewest number of features.\n", "\n", "tpot2.objectives.number_of_nodes_objective - This can be used as an other_objective_function that counts the number of nodes.\n", "\n", - "tpot2.objectives.complexity_scorer - This is a scorer that can be used in the scorers parameter that tries to count the total number of learned parameters (number of coefficients, number of nodes in decision trees, etc.).\n" + "tpot2.objectives.complexity_scorer - This is a scorer that tries to count the total number of learned parameters (number of coefficients, number of nodes in decision trees, etc.).\n" ] }, { @@ -30,10 +31,10 @@ "The FeatureSetSelector is a subclass of sklearn.feature_selection.SelectorMixin that simply returns the manually specified columns. The parameter sel_subset specifies the name or index of the column that it selects. The transform function then simply indexes and returns the selected columns. You can also optionally name the group with the name parameter, though this is only for note keeping and does is not used by the class.\n", "\n", "\n", - "sel_subset: list or int\n", - " If X is a dataframe, items in sel_subset list must correspond to column names\n", - " If X is a numpy array, items in sel_subset list must correspond to column indexes\n", - " int: index of a single column\n", + " sel_subset: list or int\n", + " If X is a dataframe, items in sel_subset list must correspond to column names\n", + " If X is a numpy array, items in sel_subset list must correspond to column indexes\n", + " int: index of a single column\n", "\n", "\n" ] @@ -95,19 +96,22 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To use the FSS with TPOT2, you can simply pass it in to the configuration dictionary. Note that the FSS is only well defined when used in the leaf nodes of the graph. This is because downstream nodes will receive different transformations of the data such that the original indexes no longer correspond to the same columns in the raw data.\n", + "# FSSNode\n", "\n", - "TPOT2 includsing the string \"feature_set_selector\" in the leaf_config_dict parameter will include the FSS in the search space of the pipeline. By default, each FSS node will select a single column. You can also group columns into sets so that each node selects a set of features rather than a single feature.\n", + "The `FSSNode` is a node search space that simply selects one feature set from a list of feature sets. This works identically to the EstimatorNode, but provides a easier interface for defining the feature sets.\n", "\n", + "Note that the FSS is only well defined when used as the first step in a pipeline. This is because downstream nodes will receive different transformations of the data such that the original indexes no longer correspond to the same columns in the transformed data.\n", "\n", + "The `FSSNode` takes in a single parameter `subsets` which defines the groups of features. There are four ways of defining the subsets. \n", "\n", - "subsets : str or list, default=None\n", - " Sets the subsets that the FeatureSetSeletor will select from if set as an option in one of the configuration dictionaries.\n", - " - str : If a string, it is assumed to be a path to a csv file with the subsets. \n", - " The first column is assumed to be the name of the subset and the remaining columns are the features in the subset.\n", - " - list or np.ndarray : If a list or np.ndarray, it is assumed to be a list of subsets.\n", - " - None : If None, each column will be treated as a subset. One column will be selected per subset.\n", - " If subsets is None, each column will be treated as a subset. One column will be selected per subset.\n", + " subsets : str or list, default=None\n", + " Sets the subsets that the FeatureSetSeletor will select from if set as an option in one of the configuration dictionaries. \n", + " Features are defined by column names if using a Pandas data frame, or ints corresponding to indexes if using numpy arrays.\n", + " - str : If a string, it is assumed to be a path to a csv file with the subsets. \n", + " The first column is assumed to be the name of the subset and the remaining columns are the features in the subset.\n", + " - list or np.ndarray : If a list or np.ndarray, it is assumed to be a list of subsets (i.e a list of lists).\n", + " - dict : A dictionary where keys are the names of the subsets and the values are the list of features.\n", + " - None : If None, each column will be treated as a subset. One column will be selected per subset.\n", "\n", "\n", "Lets say you want to have three groups of features, each with three columns each. The following examples are equivalent:\n", @@ -117,13 +121,10 @@ "sel_subsets=simple_fss.csv\n", "\n", "\n", - "\\# simple_fss.csv\n", - "\n", - "group_one, 1,2,3\n", - "\n", - "group_two, 4,5,6\n", - "\n", - "group_three, 7,8,9\n", + " \\# simple_fss.csv\n", + " group_one, 1,2,3\n", + " group_two, 4,5,6\n", + " group_three, 7,8,9\n", "\n", "\n", "### dict\n", @@ -138,14 +139,18 @@ "### list\n", "\n", "\n", - "sel_subsets = [[1,2,3],[4,5,6],[7,8,9]]\n", - "\n", - "\n", - "\n", - "(As the FSS is just another transformer, you could also pass it in with the standard configuration dictionary format (described in tutorial 2), in which you would have to define your own function that returns a hyperparameter. Similar to the params_LogisticRegression function below. )\n", - "\n", + "sel_subsets = [[1,2,3],\n", + "[4,5,6],\n", + "[7,8,9]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Examples\n", "\n", - "(In the future, FSS will be treated as a special case node with its own mutation/crossover functions to make it more efficient when there are large numbers of features.)" + "For these examples, we create a dummy dataset where the first six columns are informative and the rest are uninformative." ] }, { @@ -183,68 +188,86 @@ " g\n", " h\n", " i\n", + " j\n", + " k\n", + " l\n", " \n", " \n", " \n", " \n", " 0\n", - " -2.170854\n", - " 1.245354\n", - " 2.139022\n", - " 0.335394\n", - " 0.459081\n", - " 0.700336\n", - " 0.578917\n", - " 0.092662\n", - " 0.161226\n", + " 2.607470\n", + " -1.799163\n", + " -1.345319\n", + " -3.155746\n", + " -1.731663\n", + " 0.699546\n", + " 0.513305\n", + " 0.507864\n", + " 0.357858\n", + " 0.439859\n", + " 0.695061\n", + " 0.449589\n", " \n", " \n", " 1\n", - " -1.249092\n", - " 0.278109\n", - " -0.498371\n", - " 0.381443\n", - " 0.551928\n", - " 0.478524\n", - " 0.656872\n", - " 0.975068\n", - " 0.497428\n", + " 0.045796\n", + " -2.830673\n", + " 1.578201\n", + " -0.098472\n", + " -0.665334\n", + " -0.130451\n", + " 0.022118\n", + " 0.808068\n", + " 0.158917\n", + " 0.328156\n", + " 0.349374\n", + " 0.927755\n", " \n", " \n", " 2\n", - " -0.997527\n", - " 1.527997\n", - " -1.360814\n", - " 0.438920\n", - " 0.257216\n", - " 0.995995\n", - " 0.411837\n", - " 0.044339\n", - " 0.073172\n", + " 0.490722\n", + " -2.026190\n", + " -1.848381\n", + " -1.112946\n", + " -1.620822\n", + " 3.430459\n", + " 0.166742\n", + " 0.504127\n", + " 0.942156\n", + " 0.556877\n", + " 0.024859\n", + " 0.430831\n", " \n", " \n", " 3\n", - " 1.511913\n", - " -1.374412\n", - " 2.422807\n", - " 0.805676\n", - " 0.051917\n", - " 0.640761\n", - " 0.094881\n", - " 0.753452\n", - " 0.214523\n", + " -1.859338\n", + " -0.196734\n", + " 1.525634\n", + " 0.244376\n", + " -0.685690\n", + " 1.995038\n", + " 0.055226\n", + " 0.751830\n", + " 0.983152\n", + " 0.702334\n", + " 0.750200\n", + " 0.294415\n", " \n", " \n", " 4\n", - " -1.120579\n", - " 1.033842\n", - " -1.099884\n", - " 0.059472\n", - " 0.682245\n", - " 0.605932\n", - " 0.745800\n", - " 0.824254\n", - " 0.903524\n", + " -0.056101\n", + " 1.386592\n", + " 1.552356\n", + " 1.446347\n", + " -0.984449\n", + " 0.742441\n", + " 0.631411\n", + " 0.217660\n", + " 0.124121\n", + " 0.814294\n", + " 0.131921\n", + " 0.917958\n", " \n", " \n", "\n", @@ -252,18 +275,18 @@ ], "text/plain": [ " a b c d e f g \\\n", - "0 -2.170854 1.245354 2.139022 0.335394 0.459081 0.700336 0.578917 \n", - "1 -1.249092 0.278109 -0.498371 0.381443 0.551928 0.478524 0.656872 \n", - "2 -0.997527 1.527997 -1.360814 0.438920 0.257216 0.995995 0.411837 \n", - "3 1.511913 -1.374412 2.422807 0.805676 0.051917 0.640761 0.094881 \n", - "4 -1.120579 1.033842 -1.099884 0.059472 0.682245 0.605932 0.745800 \n", - "\n", - " h i \n", - "0 0.092662 0.161226 \n", - "1 0.975068 0.497428 \n", - "2 0.044339 0.073172 \n", - "3 0.753452 0.214523 \n", - "4 0.824254 0.903524 " + "0 2.607470 -1.799163 -1.345319 -3.155746 -1.731663 0.699546 0.513305 \n", + "1 0.045796 -2.830673 1.578201 -0.098472 -0.665334 -0.130451 0.022118 \n", + "2 0.490722 -2.026190 -1.848381 -1.112946 -1.620822 3.430459 0.166742 \n", + "3 -1.859338 -0.196734 1.525634 0.244376 -0.685690 1.995038 0.055226 \n", + "4 -0.056101 1.386592 1.552356 1.446347 -0.984449 0.742441 0.631411 \n", + "\n", + " h i j k l \n", + "0 0.507864 0.357858 0.439859 0.695061 0.449589 \n", + "1 0.808068 0.158917 0.328156 0.349374 0.927755 \n", + "2 0.504127 0.942156 0.556877 0.024859 0.430831 \n", + "3 0.751830 0.983152 0.702334 0.750200 0.294415 \n", + "4 0.217660 0.124121 0.814294 0.131921 0.917958 " ] }, "execution_count": 2, @@ -277,11 +300,18 @@ "from sklearn.linear_model import LogisticRegression\n", "import numpy as np\n", "import pandas as pd\n", + "import tpot2\n", + "import sklearn.datasets\n", + "from sklearn.linear_model import LogisticRegression\n", + "import numpy as np\n", + "from tpot2.search_spaces.nodes import *\n", + "from tpot2.search_spaces.pipelines import *\n", + "from tpot2.config import get_search_space\n", "\n", "\n", - "X, y = sklearn.datasets.make_classification(n_samples=1000, n_features=3, n_informative=3, n_redundant=0, n_repeated=0, n_classes=2, n_clusters_per_class=2, weights=None, flip_y=0.01, class_sep=1.0, hypercube=True, shift=0.0, scale=1.0, shuffle=True, random_state=None)\n", + "X, y = sklearn.datasets.make_classification(n_samples=1000, n_features=6, n_informative=6, n_redundant=0, n_repeated=0, n_classes=2, n_clusters_per_class=2, weights=None, flip_y=0.01, class_sep=1.0, hypercube=True, shift=0.0, scale=1.0, shuffle=True, random_state=None)\n", "X = np.hstack([X, np.random.rand(X.shape[0],6)]) #add six uninformative features\n", - "X = pd.DataFrame(X, columns=['a','b','c','d','e','f','g','h','i']) # a, b ,c the rest are uninformative\n", + "X = pd.DataFrame(X, columns=['a','b','c','d','e','f','g','h','i', 'j', 'k', 'l']) # a, b ,c the rest are uninformative\n", "X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, train_size=0.75, test_size=0.25)\n", "\n", "X.head()" @@ -291,83 +321,49 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Feature Set Selector\n", - "\n", - "In this configuration, each FSS node considers a single column.\n", - "\n", - "The root node is a logistic regression and there are no other intermediate transformers. An additional objective function is included that seeks to minimize the number of leave nodes (i.e the number of selected features)" + "Lets say that either based on prior knowledge or interest, we know that the features can be grouped as follows" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/distributed/node.py:182: UserWarning: Port 8787 is already in use.\n", - "Perhaps you already have a cluster running?\n", - "Hosting the HTTP server on port 39005 instead\n", - " warnings.warn(\n", - "Generation: 100%|██████████| 5/5 [04:09<00:00, 49.87s/it]\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/preprocessing/_data.py:2762: UserWarning: n_quantiles (842) is greater than the total number of samples (750). n_quantiles is set to n_samples.\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/preprocessing/_data.py:2762: UserWarning: n_quantiles (1803) is greater than the total number of samples (750). n_quantiles is set to n_samples.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.9390338164251208\n" - ] - } - ], + "outputs": [], "source": [ - "import tpot2\n", - "import sklearn.datasets\n", - "from sklearn.linear_model import LogisticRegression\n", - "import numpy as np\n", - "\n", "subsets = { \"group_one\" : ['a','b','c',],\n", " \"group_two\" : ['d','e','f'],\n", " \"group_three\" : ['g','h','i'],\n", - " }\n", - "\n", - "fss_search_space = tpot2.search_spaces.nodes.FSSNode(subsets=subsets)\n", - "graph_search_space = tpot2.search_spaces.pipelines.GraphPipeline(\n", - " root_search_space= tpot2.config.get_search_space([\"KNeighborsClassifier\", \"LogisticRegression\", \"DecisionTreeClassifier\"]),\n", - " leaf_search_space = None, \n", - " inner_search_space = tpot2.config.get_search_space([\"transformers\"]),\n", - " max_size = 10,\n", - ")\n", - "\n", - "combined_search_space = tpot2.search_spaces.pipelines.SequentialPipeline([fss_search_space, graph_search_space])\n", - "\n", - "\n", - "est = tpot2.TPOTEstimator(population_size=10,generations=5, \n", - " scorers=['roc_auc_ovr'],\n", - " scorers_weights=[1],\n", - " n_jobs=32,\n", - " classification=True,\n", - " search_space = combined_search_space,\n", - " verbose=1,\n", - " )\n", - "\n", - "\n", - "scorer = sklearn.metrics.get_scorer('roc_auc_ovo')\n", - "\n", - "est.fit(X_train, y_train)\n", - "print(scorer(est, X_test, y_test))" + " \"group_four\" : ['j','k','l'],\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can create an FSSNode that will select from this subset. Each node in a pipeline only selects one subset." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, + "outputs": [], + "source": [ + "fss_search_space = FSSNode(subsets=subsets)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we randomly sample from this search space, we can see that we get a single selector that selects one of the predefined sets. In this case, it selects groups two, which includes ['d', 'e', 'f']. (A random seed was set in the generate function so that the same group would be selected when rerunning the notebook.)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, "outputs": [ { "data": { @@ -776,98 +772,20 @@ " /* fitted */\n", " background-color: var(--sklearn-color-fitted-level-3);\n", "}\n", - "
Pipeline(steps=[('featuresetselector',\n",
-       "                 FeatureSetSelector(name='group_one',\n",
-       "                                    sel_subset=['a', 'b', 'c'])),\n",
-       "                ('graphpipeline',\n",
-       "                 GraphPipeline(graph=<networkx.classes.digraph.DiGraph object at 0x7c2c2831c100>))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + "
FeatureSetSelector(name='group_two', sel_subset=['d', 'e', 'f'])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ - "Pipeline(steps=[('featuresetselector',\n", - " FeatureSetSelector(name='group_one',\n", - " sel_subset=['a', 'b', 'c'])),\n", - " ('graphpipeline',\n", - " GraphPipeline(graph=))])" + "FeatureSetSelector(name='group_two', sel_subset=['d', 'e', 'f'])" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "est.fitted_pipeline_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that if you want to include multiple subsets, you can instead include the node as a leaf in the graph search space. This will produce a pipeline where all leaves as FSSNodes and all FSSNodes appear in the leaves (to prevent inner nodes from also being FSSNodes). Since the graph search space allows for multiple leaves, this pipeline can select multiple feature sets. " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/distributed/node.py:182: UserWarning: Port 8787 is already in use.\n", - "Perhaps you already have a cluster running?\n", - "Hosting the HTTP server on port 42397 instead\n", - " warnings.warn(\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Generation: 100%|██████████| 5/5 [00:22<00:00, 4.46s/it]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.8384541062801932\n" - ] - } - ], - "source": [ - "import tpot2\n", - "import sklearn.datasets\n", - "from sklearn.linear_model import LogisticRegression\n", - "import numpy as np\n", - "\n", - "\n", - "graph_search_space = tpot2.search_spaces.pipelines.GraphPipeline(\n", - " root_search_space= tpot2.config.get_search_space([\"KNeighborsClassifier\", \"LogisticRegression\", \"DecisionTreeClassifier\"]),\n", - " leaf_search_space = tpot2.search_spaces.nodes.FSSNode(subsets=X_train.columns.tolist()), \n", - " inner_search_space = tpot2.config.get_search_space([\"transformers\"]),\n", - " max_size = 10,\n", - ")\n", - "\n", - "est = tpot2.TPOTEstimator(population_size=10,generations=5, \n", - " scorers=['roc_auc_ovr'],\n", - " scorers_weights=[1],\n", - " n_jobs=32,\n", - " classification=True,\n", - " search_space = graph_search_space ,\n", - " verbose=1,\n", - " )\n", - "\n", - "\n", - "scorer = sklearn.metrics.get_scorer('roc_auc_ovo')\n", - "\n", - "est.fit(X_train, y_train)\n", - "print(scorer(est, X_test, y_test))" + "fss_selector = fss_search_space.generate(rng=1).export_pipeline()\n", + "fss_selector" ] }, { @@ -877,31 +795,136 @@ "outputs": [ { "data": { - "image/png": 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" + " d e f\n", + "221 2.819930 -0.677731 -2.883151\n", + "121 -0.068495 -1.398128 1.239476\n", + "83 2.225071 -1.902156 1.425918\n", + "243 2.592917 0.764790 -2.120585\n", + "992 2.329279 2.362780 -3.780878\n", + ".. ... ... ...\n", + "254 1.850187 -2.177608 -4.088455\n", + "19 -0.949434 1.062798 -3.421324\n", + "956 2.105221 -0.169633 1.743979\n", + "150 2.171954 -1.343100 -0.346960\n", + "629 0.348571 -1.171049 0.854003\n", + "\n", + "[750 rows x 3 columns]" ] }, + "execution_count": 6, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "est.fitted_pipeline_.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Other examples" + "fss_selector.set_output(transform=\"pandas\") #by default sklearn selectors return numpy arrays. this will make it return pandas dataframes\n", + "fss_selector.fit(X_train)\n", + "fss_selector.transform(X_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## dictionary" + "We can now use this when defining our pipelines. \n", + "For this first example, we will construct a simple linear pipeline where the first step is a feature set selector, and the second is a classifier" ] }, { @@ -913,125 +936,2913 @@ "name": "stderr", "output_type": "stream", "text": [ - "Generation: 100%|██████████| 5/5 [00:44<00:00, 8.81s/it]\n" + "Generation: 100%|██████████| 5/5 [00:30<00:00, 6.12s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "0.9549114331723028\n" + "0.8989320638188367\n" ] } ], "source": [ - "import tpot2\n", - "import pandas as pd\n", - "import numpy as np\n", - "from sklearn.linear_model import LogisticRegression\n", - "import sklearn\n", - "\n", - "subsets = { \"group_one\" : ['a','b','c'],\n", - " \"group_two\" : ['d','e','f'],\n", - " \"group_three\" : ['g','h','i'],\n", - " }\n", "\n", - "fss_search_space = tpot2.search_spaces.nodes.FSSNode(subsets=subsets)\n", - "graph_search_space = tpot2.search_spaces.pipelines.GraphPipeline(\n", - " root_search_space= tpot2.config.get_search_space([\"KNeighborsClassifier\", \"LogisticRegression\", \"DecisionTreeClassifier\"]),\n", - " leaf_search_space = None, \n", - " inner_search_space = tpot2.config.get_search_space([\"transformers\"]),\n", - " max_size = 10,\n", - ")\n", - "\n", - "combined_search_space = tpot2.search_spaces.pipelines.SequentialPipeline([fss_search_space, graph_search_space])\n", + "classification_search_space = get_search_space([\"RandomForestClassifier\"])\n", + "fss_and_classifier_search_space = SequentialPipeline([fss_search_space, classification_search_space])\n", "\n", "\n", - "est = tpot2.TPOTEstimator(population_size=10,generations=5, \n", - " scorers=['roc_auc_ovr'],\n", - " scorers_weights=[1],\n", + "est = tpot2.TPOTEstimator(generations=5, \n", + " scorers=[\"roc_auc_ovr\", tpot2.objectives.complexity_scorer],\n", + " scorers_weights=[1.0, -1.0],\n", " n_jobs=32,\n", " classification=True,\n", - " search_space = combined_search_space,\n", + " search_space = fss_and_classifier_search_space,\n", " verbose=1,\n", " )\n", "\n", "\n", "scorer = sklearn.metrics.get_scorer('roc_auc_ovo')\n", - "\n", "est.fit(X_train, y_train)\n", "print(scorer(est, X_test, y_test))" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## list" - ] - }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "Generation: 100%|██████████| 5/5 [09:02<00:00, 108.52s/it]\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/decomposition/_fastica.py:595: UserWarning: n_components is too large: it will be set to 3\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/decomposition/_fastica.py:128: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/decomposition/_fastica.py:595: UserWarning: n_components is too large: it will be set to 24\n", - " warnings.warn(\n", - "/home/ribeirop/miniconda3/envs/tpot2env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.9765539452495974\n" - ] - } - ], - "source": [ - "import tpot2\n", - "import pandas as pd\n", - "import numpy as np\n", - "from sklearn.linear_model import LogisticRegression\n", - "import sklearn\n", - "\n", - "subsets = [['a','b','c'],['d','e','f'],['g','h','i']]\n", - "\n", - "fss_search_space = tpot2.search_spaces.nodes.FSSNode(subsets=subsets)\n", - "graph_search_space = tpot2.search_spaces.pipelines.GraphPipeline(\n", - " root_search_space= tpot2.config.get_search_space([\"KNeighborsClassifier\", \"LogisticRegression\", \"DecisionTreeClassifier\"]),\n", - " leaf_search_space = None, \n", - " inner_search_space = tpot2.config.get_search_space([\"transformers\"]),\n", - " max_size = 10,\n", - ")\n", - "\n", - "combined_search_space = tpot2.search_spaces.pipelines.SequentialPipeline([fss_search_space, graph_search_space])\n", - "\n", - "\n", - "est = tpot2.TPOTEstimator(population_size=10,generations=5, \n", - " scorers=['roc_auc_ovr'],\n", - " scorers_weights=[1],\n", - " n_jobs=32,\n", - " classification=True,\n", - " search_space = combined_search_space,\n", - " verbose=1,\n", - " )\n", - "\n", + "data": { + "text/html": [ + "
Pipeline(steps=[('featuresetselector',\n",
+       "                 FeatureSetSelector(name='group_two',\n",
+       "                                    sel_subset=['d', 'e', 'f'])),\n",
+       "                ('randomforestclassifier',\n",
+       "                 RandomForestClassifier(class_weight='balanced',\n",
+       "                                        max_features=0.7587731972584,\n",
+       "                                        min_samples_leaf=7,\n",
+       "                                        min_samples_split=12,\n",
+       "                                        n_estimators=128))])
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" + ], + "text/plain": [ + "Pipeline(steps=[('featuresetselector',\n", + " FeatureSetSelector(name='group_two',\n", + " sel_subset=['d', 'e', 'f'])),\n", + " ('randomforestclassifier',\n", + " RandomForestClassifier(class_weight='balanced',\n", + " max_features=0.7587731972584,\n", + " min_samples_leaf=7,\n", + " min_samples_split=12,\n", + " n_estimators=128))])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "est.fitted_pipeline_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With this setup TPOT is able to identify one of the subsets used, but the performance is not optimal. In this case we happen to know that multiple feature sets are required. If we want to include multiple features in our pipelines, we will have to modify our search space. There are three options for this.\n", + "\n", + "1. UnionPipeline - This allows you to have a fixed number of feature sets selected. If you use a UnionPipeline with two FSSNodes, you will always select two feature sets that are simply concatenated together.\n", + "2. DynamicUnionPipeline - This space allows multiple FSSNodes to be selected. Unlike UnionPipeline you don't have to specify the number of selected sets, TPOT will identify the number of sets that are optimal. Additionally, with DynamicUnionPipeline, the same feature set cannot be selected twice. Note that while DynamicUnionPipeline can select multiple feature sets, it never mixes two feature sets together.\n", + "3. GraphSearchPipeline - When set as the leave_search_space, GraphSearchPipeline can also select multiple FSSNodes which act as an input to the rest of the pipeline." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### UnionPipeline + FSSNode example" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "union_fss_space = UnionPipeline([fss_search_space, fss_search_space])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
FeatureUnion(transformer_list=[('featuresetselector-1',\n",
+       "                                FeatureSetSelector(name='group_two',\n",
+       "                                                   sel_subset=['d', 'e', 'f'])),\n",
+       "                               ('featuresetselector-2',\n",
+       "                                FeatureSetSelector(name='group_three',\n",
+       "                                                   sel_subset=['g', 'h',\n",
+       "                                                               'i']))])
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" + ], + "text/plain": [ + "FeatureUnion(transformer_list=[('featuresetselector-1',\n", + " FeatureSetSelector(name='group_two',\n", + " sel_subset=['d', 'e', 'f'])),\n", + " ('featuresetselector-2',\n", + " FeatureSetSelector(name='group_three',\n", + " sel_subset=['g', 'h',\n", + " 'i']))])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# this union search space will always select exactly two fss_search_space\n", + "selector1 = union_fss_space.generate(rng=1).export_pipeline()\n", + "selector1" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " d e f g h i\n", + "221 2.819930 -0.677731 -2.883151 0.420393 0.930431 0.223210\n", + "121 -0.068495 -1.398128 1.239476 0.583717 0.872967 0.355258\n", + "83 2.225071 -1.902156 1.425918 0.521714 0.624303 0.526185\n", + "243 2.592917 0.764790 -2.120585 0.748397 0.099224 0.640145\n", + "992 2.329279 2.362780 -3.780878 0.233820 0.054463 0.025899\n", + ".. ... ... ... ... ... ...\n", + "254 1.850187 -2.177608 -4.088455 0.955017 0.363270 0.010256\n", + "19 -0.949434 1.062798 -3.421324 0.094215 0.837656 0.856361\n", + "956 2.105221 -0.169633 1.743979 0.266365 0.446727 0.356117\n", + "150 2.171954 -1.343100 -0.346960 0.712673 0.983344 0.873176\n", + "629 0.348571 -1.171049 0.854003 0.746499 0.101473 0.609367\n", + "\n", + "[750 rows x 6 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "selector1.set_output(transform=\"pandas\") \n", + "selector1.fit(X_train)\n", + "selector1.transform(X_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### DynamicUnionPipeline + FSSNode example\n", + "The dynamic union pipeline may select a variable number of feature sets." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
FeatureUnion(transformer_list=[('featuresetselector',\n",
+       "                                FeatureSetSelector(name='group_three',\n",
+       "                                                   sel_subset=['g', 'h',\n",
+       "                                                               'i']))])
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On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "FeatureUnion(transformer_list=[('featuresetselector',\n", + " FeatureSetSelector(name='group_three',\n", + " sel_subset=['g', 'h',\n", + " 'i']))])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dynamic_fss_space = DynamicUnionPipeline(fss_search_space)\n", + "dynamic_fss_space.generate(rng=1).export_pipeline()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
FeatureUnion(transformer_list=[('featuresetselector-1',\n",
+       "                                FeatureSetSelector(name='group_one',\n",
+       "                                                   sel_subset=['a', 'b', 'c'])),\n",
+       "                               ('featuresetselector-2',\n",
+       "                                FeatureSetSelector(name='group_four',\n",
+       "                                                   sel_subset=['j', 'k',\n",
+       "                                                               'l']))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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" + ], + "text/plain": [ + "FeatureUnion(transformer_list=[('featuresetselector-1',\n", + " FeatureSetSelector(name='group_one',\n", + " sel_subset=['a', 'b', 'c'])),\n", + " ('featuresetselector-2',\n", + " FeatureSetSelector(name='group_four',\n", + " sel_subset=['j', 'k',\n", + " 'l']))])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dynamic_fss_space.generate(rng=3).export_pipeline()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### GraphSearchPipeline + FSSNode example\n", + "\n", + "FSSNodes must be set as the leaf search space as they act as the inputs to the pipeline.\n", + "\n", + "Here is an example pipeline from this search space that utilizes two feature sets." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "graph_search_space = tpot2.search_spaces.pipelines.GraphSearchPipeline(\n", + " leaf_search_space = fss_search_space,\n", + " inner_search_space = tpot2.config.get_search_space([\"transformers\"]),\n", + " root_search_space= tpot2.config.get_search_space([\"KNeighborsClassifier\", \"LogisticRegression\", \"DecisionTreeClassifier\"]),\n", + " max_size = 10,\n", + ")\n", + "\n", + "graph_search_space.generate(rng=4).export_pipeline().plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Optimize with TPOT\n", + "\n", + "For this example, we will optimize the DynamicUnion search space" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generation: 100%|██████████| 5/5 [00:34<00:00, 6.96s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.9916366443643849\n" + ] + } + ], + "source": [ + "import tpot2\n", + "import sklearn.datasets\n", + "from sklearn.linear_model import LogisticRegression\n", + "import numpy as np\n", + "\n", + "\n", + "final_classification_search_space = SequentialPipeline([dynamic_fss_space, classification_search_space])\n", + "\n", + "est = tpot2.TPOTEstimator(generations=5, \n", + " scorers=[\"roc_auc_ovr\", tpot2.objectives.complexity_scorer],\n", + " scorers_weights=[1.0, -1.0],\n", + " n_jobs=32,\n", + " classification=True,\n", + " search_space = final_classification_search_space,\n", + " verbose=1,\n", + " )\n", + "\n", + "\n", + "scorer = sklearn.metrics.get_scorer('roc_auc_ovo')\n", + "\n", + "est.fit(X_train, y_train)\n", + "print(scorer(est, X_test, y_test))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that this pipeline performed slightly better and correctly identified group one and group two as the feature sets used in the generative equation." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Pipeline(steps=[('featureunion',\n",
+       "                 FeatureUnion(transformer_list=[('featuresetselector-1',\n",
+       "                                                 FeatureSetSelector(name='group_one',\n",
+       "                                                                    sel_subset=['a',\n",
+       "                                                                                'b',\n",
+       "                                                                                'c'])),\n",
+       "                                                ('featuresetselector-2',\n",
+       "                                                 FeatureSetSelector(name='group_two',\n",
+       "                                                                    sel_subset=['d',\n",
+       "                                                                                'e',\n",
+       "                                                                                'f']))])),\n",
+       "                ('randomforestclassifier',\n",
+       "                 RandomForestClassifier(bootstrap=False,\n",
+       "                                        class_weight='balanced',\n",
+       "                                        max_features=0.3314976075207,\n",
+       "                                        min_samples_leaf=3,\n",
+       "                                        min_samples_split=15,\n",
+       "                                        n_estimators=128))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "Pipeline(steps=[('featureunion',\n", + " FeatureUnion(transformer_list=[('featuresetselector-1',\n", + " FeatureSetSelector(name='group_one',\n", + " sel_subset=['a',\n", + " 'b',\n", + " 'c'])),\n", + " ('featuresetselector-2',\n", + " FeatureSetSelector(name='group_two',\n", + " sel_subset=['d',\n", + " 'e',\n", + " 'f']))])),\n", + " ('randomforestclassifier',\n", + " RandomForestClassifier(bootstrap=False,\n", + " class_weight='balanced',\n", + " max_features=0.3314976075207,\n", + " min_samples_leaf=3,\n", + " min_samples_split=15,\n", + " n_estimators=128))])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "est.fitted_pipeline_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Other examples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## dictionary" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " d e f\n", + "221 2.819930 -0.677731 -2.883151\n", + "121 -0.068495 -1.398128 1.239476\n", + "83 2.225071 -1.902156 1.425918\n", + "243 2.592917 0.764790 -2.120585\n", + "992 2.329279 2.362780 -3.780878\n", + ".. ... ... ...\n", + "254 1.850187 -2.177608 -4.088455\n", + "19 -0.949434 1.062798 -3.421324\n", + "956 2.105221 -0.169633 1.743979\n", + "150 2.171954 -1.343100 -0.346960\n", + "629 0.348571 -1.171049 0.854003\n", + "\n", + "[750 rows x 3 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import tpot2\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.linear_model import LogisticRegression\n", + "import sklearn\n", "\n", - "scorer = sklearn.metrics.get_scorer('roc_auc_ovo')\n", + "subsets = { \"group_one\" : ['a','b','c'],\n", + " \"group_two\" : ['d','e','f'],\n", + " \"group_three\" : ['g','h','i'],\n", + " }\n", "\n", - "est.fit(X_train, y_train)\n", - "print(scorer(est, X_test, y_test))" + "fss_search_space = tpot2.search_spaces.nodes.FSSNode(subsets=subsets)\n", + "\n", + "selector = fss_search_space.generate(rng=1).export_pipeline()\n", + "selector.set_output(transform=\"pandas\")\n", + "selector.fit(X_train)\n", + "selector.transform(X_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## list" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " d e f\n", + "221 2.819930 -0.677731 -2.883151\n", + "121 -0.068495 -1.398128 1.239476\n", + "83 2.225071 -1.902156 1.425918\n", + "243 2.592917 0.764790 -2.120585\n", + "992 2.329279 2.362780 -3.780878\n", + ".. ... ... ...\n", + "254 1.850187 -2.177608 -4.088455\n", + "19 -0.949434 1.062798 -3.421324\n", + "956 2.105221 -0.169633 1.743979\n", + "150 2.171954 -1.343100 -0.346960\n", + "629 0.348571 -1.171049 0.854003\n", + "\n", + "[750 rows x 3 columns]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import tpot2\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.linear_model import LogisticRegression\n", + "import sklearn\n", + "\n", + "subsets = [['a','b','c'],['d','e','f'],['g','h','i']]\n", + "\n", + "fss_search_space = tpot2.search_spaces.nodes.FSSNode(subsets=subsets)\n", + "\n", + "selector = fss_search_space.generate(rng=1).export_pipeline()\n", + "selector.set_output(transform=\"pandas\")\n", + "selector.fit(X_train)\n", + "selector.transform(X_train)" ] }, { @@ -1045,22 +3856,127 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 19, "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "Generation: 100%|██████████| 5/5 [00:41<00:00, 8.27s/it]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.9754589371980676\n" - ] + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " d e f\n", + "221 2.819930 -0.677731 -2.883151\n", + "121 -0.068495 -1.398128 1.239476\n", + "83 2.225071 -1.902156 1.425918\n", + "243 2.592917 0.764790 -2.120585\n", + "992 2.329279 2.362780 -3.780878\n", + ".. ... ... ...\n", + "254 1.850187 -2.177608 -4.088455\n", + "19 -0.949434 1.062798 -3.421324\n", + "956 2.105221 -0.169633 1.743979\n", + "150 2.171954 -1.343100 -0.346960\n", + "629 0.348571 -1.171049 0.854003\n", + "\n", + "[750 rows x 3 columns]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -1079,61 +3995,42 @@ "'''\n", "\n", "fss_search_space = tpot2.search_spaces.nodes.FSSNode(subsets=subsets)\n", - "graph_search_space = tpot2.search_spaces.pipelines.GraphPipeline(\n", - " root_search_space= tpot2.config.get_search_space([\"KNeighborsClassifier\", \"LogisticRegression\", \"DecisionTreeClassifier\"]),\n", - " leaf_search_space = None, \n", - " inner_search_space = tpot2.config.get_search_space([\"transformers\"]),\n", - " max_size = 10,\n", - ")\n", - "\n", - "combined_search_space = tpot2.search_spaces.pipelines.SequentialPipeline([fss_search_space, graph_search_space])\n", - "\n", - "\n", - "est = tpot2.TPOTEstimator(population_size=10,generations=5, \n", - " scorers=['roc_auc_ovr'],\n", - " scorers_weights=[1],\n", - " n_jobs=32,\n", - " classification=True,\n", - " search_space = combined_search_space,\n", - " verbose=1,\n", - " )\n", "\n", - "\n", - "scorer = sklearn.metrics.get_scorer('roc_auc_ovo')\n", - "\n", - "est.fit(X_train, y_train)\n", - "print(scorer(est, X_test, y_test))" + "selector = fss_search_space.generate(rng=1).export_pipeline()\n", + "selector.set_output(transform=\"pandas\")\n", + "selector.fit(X_train)\n", + "selector.transform(X_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "note that all of the above is the same when using numpy X, but the column names are now int indeces" + "All of the above is the same when using numpy data, but the column names are replaced int indexes." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[-0.51289317 -2.65333383 -0.68124034 ... 0.66358004 0.25051107\n", - " 0.23444287]\n", - " [-2.51236119 -2.04422708 -0.40301026 ... 0.19208918 0.18041725\n", - " 0.33265205]\n", - " [ 1.49065721 -3.24328369 1.58423463 ... 0.678225 0.27643945\n", - " 0.78710293]\n", + "[[ 1.01731337 -0.93465307 -3.24039768 ... 0.39357783 0.5321955\n", + " 0.60973674]\n", + " [ 1.89576966 2.25608154 0.26643722 ... 0.7163676 0.06128163\n", + " 0.90206017]\n", + " [ 0.92384181 -0.1537421 -3.93631929 ... 0.75042522 0.99312028\n", + " 0.66668663]\n", " ...\n", - " [ 0.07953312 -1.10920624 1.0985733 ... 0.68578896 0.87562184\n", - " 0.28616797]\n", - " [-2.43045085 0.42769074 2.57608083 ... 0.0447371 0.31649605\n", - " 0.52711618]\n", - " [-2.50001651 -0.46482725 2.0546322 ... 0.34574358 0.96130892\n", - " 0.93289141]]\n" + " [-0.97817968 0.74965196 -2.18630603 ... 0.6798269 0.61574192\n", + " 0.99335315]\n", + " [ 2.30789516 0.97168873 -0.64014737 ... 0.77912995 0.80199131\n", + " 0.29313876]\n", + " [-0.18979565 0.24979476 -2.08789801 ... 0.62763484 0.98615568\n", + " 0.46710305]]\n" ] } ], @@ -1155,22 +4052,24 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 21, "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "Generation: 100%|██████████| 5/5 [00:34<00:00, 6.92s/it]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.8762676829662166\n" - ] + "data": { + "text/plain": [ + "array([[-2.28091426, -1.08355329, -2.53836197],\n", + " [ 3.39870077, -2.05411876, -1.40671745],\n", + " [-0.34207793, 1.60073506, -1.42660153],\n", + " ...,\n", + " [-0.68208863, 2.89047595, -0.97955455],\n", + " [-0.28393195, -0.62493036, 0.21022641],\n", + " [-1.21225128, 1.97437192, 1.27051581]])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -1186,30 +4085,9 @@ " }\n", "\n", "fss_search_space = tpot2.search_spaces.nodes.FSSNode(subsets=subsets)\n", - "graph_search_space = tpot2.search_spaces.pipelines.GraphPipeline(\n", - " root_search_space= tpot2.config.get_search_space([\"KNeighborsClassifier\", \"LogisticRegression\", \"DecisionTreeClassifier\"]),\n", - " leaf_search_space = None, \n", - " inner_search_space = tpot2.config.get_search_space([\"transformers\"]),\n", - " max_size = 10,\n", - ")\n", - "\n", - "combined_search_space = tpot2.search_spaces.pipelines.SequentialPipeline([fss_search_space, graph_search_space])\n", - "\n", - "\n", - "est = tpot2.TPOTEstimator(population_size=10,generations=5, \n", - " scorers=['roc_auc_ovr'],\n", - " scorers_weights=[1],\n", - " n_jobs=32,\n", - " classification=True,\n", - " search_space = combined_search_space,\n", - " verbose=1,\n", - " )\n", - "\n", - "\n", - "scorer = sklearn.metrics.get_scorer('roc_auc_ovo')\n", - "\n", - "est.fit(X_train, y_train)\n", - "print(scorer(est, X_test, y_test))" + "selector = fss_search_space.generate(rng=1).export_pipeline()\n", + "selector.fit(X_train)\n", + "selector.transform(X_train)" ] } ], From a21aa7b8f44ae599b7c325573a68e708cdaf2527 Mon Sep 17 00:00:00 2001 From: perib Date: Tue, 24 Sep 2024 17:22:12 -0700 Subject: [PATCH 20/44] add fancy search space --- Tutorial/3_Feature_Set_Selector.ipynb | 585 ++++++++++++++++++++++++++ 1 file changed, 585 insertions(+) diff --git a/Tutorial/3_Feature_Set_Selector.ipynb b/Tutorial/3_Feature_Set_Selector.ipynb index 5341cf0e..a175a587 100644 --- a/Tutorial/3_Feature_Set_Selector.ipynb +++ b/Tutorial/3_Feature_Set_Selector.ipynb @@ -3537,6 +3537,591 @@ "est.fitted_pipeline_" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Getting Fancy\n", + "\n", + "If you want to get fancy, you can combine more search spaces in order to set up unique preprocessing pipelines per feature set. Here's an example:" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "dynamic_transformers = DynamicUnionPipeline(get_search_space(\"all_transformers\"), max_estimators=4)\n", + "dynamic_transformers_with_passthrough = tpot2.search_spaces.pipelines.UnionPipeline([\n", + " dynamic_transformers,\n", + " tpot2.config.get_search_space(\"Passthrough\")],\n", + " )\n", + "multi_step_engineering = DynamicLinearPipeline(dynamic_transformers_with_passthrough, max_length=4)\n", + "fss_engineering_search_space = SequentialPipeline([fss_search_space, multi_step_engineering])\n", + "union_fss_engineering_search_space = DynamicUnionPipeline(fss_engineering_search_space)\n", + "\n", + "final_fancy_search_space = SequentialPipeline([union_fss_engineering_search_space, classification_search_space])" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Pipeline(steps=[('featureunion',\n",
+       "                 FeatureUnion(transformer_list=[('pipeline-1',\n",
+       "                                                 Pipeline(steps=[('featuresetselector',\n",
+       "                                                                  FeatureSetSelector(name='group_one',\n",
+       "                                                                                     sel_subset=[0,\n",
+       "                                                                                                 1,\n",
+       "                                                                                                 2])),\n",
+       "                                                                 ('pipeline',\n",
+       "                                                                  Pipeline(steps=[('featureunion',\n",
+       "                                                                                   FeatureUnion(transformer_list=[('featureunion',\n",
+       "                                                                                                                   FeatureUnion(transformer_list=[('pca',\n",
+       "                                                                                                                                                   PCA(n_components=0.93113403057))])),\n",
+       "                                                                                                                  ('passthrough...\n",
+       "                                                                                   FeatureUnion(transformer_list=[('featureunion',\n",
+       "                                                                                                                   FeatureUnion(transformer_list=[('quantiletransformer',\n",
+       "                                                                                                                                                   QuantileTransformer(n_quantiles=87)),\n",
+       "                                                                                                                                                  ('columnonehotencoder',\n",
+       "                                                                                                                                                   ColumnOneHotEncoder())])),\n",
+       "                                                                                                                  ('passthrough',\n",
+       "                                                                                                                   Passthrough())]))]))]))])),\n",
+       "                ('randomforestclassifier',\n",
+       "                 RandomForestClassifier(class_weight='balanced',\n",
+       "                                        criterion='entropy',\n",
+       "                                        max_features=0.021545996678,\n",
+       "                                        min_samples_leaf=11,\n",
+       "                                        n_estimators=128))])
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" + ], + "text/plain": [ + "Pipeline(steps=[('featureunion',\n", + " FeatureUnion(transformer_list=[('pipeline-1',\n", + " Pipeline(steps=[('featuresetselector',\n", + " FeatureSetSelector(name='group_one',\n", + " sel_subset=[0,\n", + " 1,\n", + " 2])),\n", + " ('pipeline',\n", + " Pipeline(steps=[('featureunion',\n", + " FeatureUnion(transformer_list=[('featureunion',\n", + " FeatureUnion(transformer_list=[('pca',\n", + " PCA(n_components=0.93113403057))])),\n", + " ('passthrough...\n", + " FeatureUnion(transformer_list=[('featureunion',\n", + " FeatureUnion(transformer_list=[('quantiletransformer',\n", + " QuantileTransformer(n_quantiles=87)),\n", + " ('columnonehotencoder',\n", + " ColumnOneHotEncoder())])),\n", + " ('passthrough',\n", + " Passthrough())]))]))]))])),\n", + " ('randomforestclassifier',\n", + " RandomForestClassifier(class_weight='balanced',\n", + " criterion='entropy',\n", + " max_features=0.021545996678,\n", + " min_samples_leaf=11,\n", + " n_estimators=128))])" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final_fancy_search_space.generate(rng=3).export_pipeline()" + ] + }, { "cell_type": "markdown", "metadata": {}, From 9ff1187b876d34274af14e579225357a425ec0fb Mon Sep 17 00:00:00 2001 From: perib Date: Tue, 24 Sep 2024 18:33:19 -0700 Subject: [PATCH 21/44] fssnode now always selects a new feature set when mutation is called - previously it could sometimes select the same set and not actually change --- tpot2/search_spaces/nodes/fss_node.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tpot2/search_spaces/nodes/fss_node.py b/tpot2/search_spaces/nodes/fss_node.py index a5dfe2b8..386e9450 100644 --- a/tpot2/search_spaces/nodes/fss_node.py +++ b/tpot2/search_spaces/nodes/fss_node.py @@ -47,7 +47,9 @@ def __init__( self, def mutate(self, rng=None): rng = np.random.default_rng(rng) - self.selected_subset_name = rng.choice(self.names_list) + #get list of names not including the current one + names = [name for name in self.names_list if name != self.selected_subset_name] + self.selected_subset_name = rng.choice(names) self.sel_subset = self.subset_dict[self.selected_subset_name] From 8a80f5741fa557264c54649054a314aa2c875067 Mon Sep 17 00:00:00 2001 From: perib Date: Tue, 24 Sep 2024 18:34:04 -0700 Subject: [PATCH 22/44] reorder tutorials, fill in feature set selector tutorial and genetic feature set selector --- Tutorial/2_Search_Spaces.ipynb | 510 +++ Tutorial/3_Feature_Set_Selector.ipynb | 3216 ++++++++++++----- Tutorial/4_Genetic_Feature_Selection.ipynb | 2330 ++++++++++++ Tutorial/5_Genetic_Feature_Selection.ipynb | 626 ---- ...phPipeline.ipynb => 5_GraphPipeline.ipynb} | 0 ...bolic_Regression_and_Classification.ipynb} | 0 .../nodes/genetic_feature_selection.py | 29 +- 7 files changed, 5183 insertions(+), 1528 deletions(-) create mode 100644 Tutorial/4_Genetic_Feature_Selection.ipynb delete mode 100644 Tutorial/5_Genetic_Feature_Selection.ipynb rename Tutorial/{6_GraphPipeline.ipynb => 5_GraphPipeline.ipynb} (100%) rename Tutorial/{4_Symbolic_Regression_and_Classification.ipynb => 6_Symbolic_Regression_and_Classification.ipynb} (100%) diff --git a/Tutorial/2_Search_Spaces.ipynb b/Tutorial/2_Search_Spaces.ipynb index fffbffdc..7eb6755a 100644 --- a/Tutorial/2_Search_Spaces.ipynb +++ b/Tutorial/2_Search_Spaces.ipynb @@ -16712,6 +16712,516 @@ "Rather than create your own search space, you can simply pass the string into the `search_space` param. Alternatively, you can access tpot2.config.template_search_spaces.get_template_search_spaces directly which offers a few more customizable options for each template including `cross_val_predict_cv` and whether or not stacked classifiers/regressors are allowed. Or you can copy the code and customize it manually!" ] }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Pipeline(steps=[('passthrough', Passthrough()),\n",
+       "                ('variancethreshold',\n",
+       "                 VarianceThreshold(threshold=0.0033508168395)),\n",
+       "                ('featureunion-1',\n",
+       "                 FeatureUnion(transformer_list=[('skiptransformer',\n",
+       "                                                 SkipTransformer()),\n",
+       "                                                ('passthrough',\n",
+       "                                                 Passthrough())])),\n",
+       "                ('featureunion-2',\n",
+       "                 FeatureUnion(transformer_list=[('featureunion',\n",
+       "                                                 FeatureUnion(transformer_list=[('estimatortransformer',\n",
+       "                                                                                 EstimatorTransformer(estimator=AdaBoostClassifier(algorithm='SAMME',\n",
+       "                                                                                                                                   learning_rate=0.0473135874378,\n",
+       "                                                                                                                                   n_estimators=436)))])),\n",
+       "                                                ('passthrough',\n",
+       "                                                 Passthrough())])),\n",
+       "                ('linearsvc', LinearSVC(C=0.012266617842, penalty='l1'))])
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" + ], + "text/plain": [ + "Pipeline(steps=[('passthrough', Passthrough()),\n", + " ('variancethreshold',\n", + " VarianceThreshold(threshold=0.0033508168395)),\n", + " ('featureunion-1',\n", + " FeatureUnion(transformer_list=[('skiptransformer',\n", + " SkipTransformer()),\n", + " ('passthrough',\n", + " Passthrough())])),\n", + " ('featureunion-2',\n", + " FeatureUnion(transformer_list=[('featureunion',\n", + " FeatureUnion(transformer_list=[('estimatortransformer',\n", + " EstimatorTransformer(estimator=AdaBoostClassifier(algorithm='SAMME',\n", + " learning_rate=0.0473135874378,\n", + " n_estimators=436)))])),\n", + " ('passthrough',\n", + " Passthrough())])),\n", + " ('linearsvc', LinearSVC(C=0.012266617842, penalty='l1'))])" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "linear_search_space = tpot2.config.template_search_spaces.get_template_search_spaces(\"linear\")\n", + "linear_search_space.generate().export_pipeline()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "linear_est = tpot2.TPOTEstimator(population_size=10,generations=5, \n", + " scorers=['roc_auc_ovr',tpot2.objectives.complexity_scorer],\n", + " scorers_weights=[1,-1],\n", + " n_jobs=32,\n", + " classification=True,\n", + " search_space = linear_search_space,\n", + " verbose=1,\n", + " )\n", + "\n", + "#alternatively, you can use the template search space to generate a pipeline\n", + "linear_est = tpot2.TPOTEstimator(population_size=10,generations=5, \n", + " scorers=['roc_auc_ovr',tpot2.objectives.complexity_scorer],\n", + " scorers_weights=[1,-1],\n", + " n_jobs=32,\n", + " classification=True,\n", + " search_space = \"linear\",\n", + " verbose=1,\n", + " )" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/Tutorial/3_Feature_Set_Selector.ipynb b/Tutorial/3_Feature_Set_Selector.ipynb index a175a587..47aeb176 100644 --- a/Tutorial/3_Feature_Set_Selector.ipynb +++ b/Tutorial/3_Feature_Set_Selector.ipynb @@ -196,78 +196,78 @@ " \n", " \n", " 0\n", - " 2.607470\n", - " -1.799163\n", - " -1.345319\n", - " -3.155746\n", - " -1.731663\n", - " 0.699546\n", - " 0.513305\n", - " 0.507864\n", - " 0.357858\n", - " 0.439859\n", - " 0.695061\n", - " 0.449589\n", + " -0.988411\n", + " -3.270714\n", + " -1.816697\n", + " 0.384124\n", + " 1.258591\n", + " -1.577232\n", + " 0.101273\n", + " 0.657975\n", + " 0.770880\n", + " 0.882366\n", + " 0.637714\n", + " 0.002812\n", " \n", " \n", " 1\n", - " 0.045796\n", - " -2.830673\n", - " 1.578201\n", - " -0.098472\n", - " -0.665334\n", - " -0.130451\n", - " 0.022118\n", - " 0.808068\n", - " 0.158917\n", - " 0.328156\n", - " 0.349374\n", - " 0.927755\n", + " -0.531157\n", + " -1.298541\n", + " -2.630749\n", + " 0.036662\n", + " -2.097307\n", + " -1.711751\n", + " 0.894172\n", + " 0.727579\n", + " 0.211429\n", + " 0.223319\n", + " 0.496683\n", + " 0.840040\n", " \n", " \n", " 2\n", - " 0.490722\n", - " -2.026190\n", - " -1.848381\n", - " -1.112946\n", - " -1.620822\n", - " 3.430459\n", - " 0.166742\n", - " 0.504127\n", - " 0.942156\n", - " 0.556877\n", - " 0.024859\n", - " 0.430831\n", + " -0.896734\n", + " -1.805453\n", + " -2.736948\n", + " -0.310169\n", + " 1.802988\n", + " -0.269441\n", + " 0.765178\n", + " 0.341713\n", + " 0.847770\n", + " 0.696190\n", + " 0.824104\n", + " 0.297523\n", " \n", " \n", " 3\n", - " -1.859338\n", - " -0.196734\n", - " 1.525634\n", - " 0.244376\n", - " -0.685690\n", - " 1.995038\n", - " 0.055226\n", - " 0.751830\n", - " 0.983152\n", - " 0.702334\n", - " 0.750200\n", - " 0.294415\n", + " 1.637719\n", + " -0.930537\n", + " -0.229303\n", + " 0.198907\n", + " 1.184137\n", + " -0.411545\n", + " 0.870378\n", + " 0.811312\n", + " 0.142528\n", + " 0.707361\n", + " 0.201967\n", + " 0.867956\n", " \n", " \n", " 4\n", - " -0.056101\n", - " 1.386592\n", - " 1.552356\n", - " 1.446347\n", - " -0.984449\n", - " 0.742441\n", - " 0.631411\n", - " 0.217660\n", - " 0.124121\n", - " 0.814294\n", - " 0.131921\n", - " 0.917958\n", + " -1.709777\n", + " -2.701615\n", + " 0.297434\n", + " -0.909832\n", + " 1.436884\n", + " 0.120985\n", + " 0.866854\n", + " 0.352461\n", + " 0.690270\n", + " 0.172950\n", + " 0.056518\n", + " 0.806867\n", " \n", " \n", "\n", @@ -275,18 +275,18 @@ ], "text/plain": [ " a b c d e f g \\\n", - "0 2.607470 -1.799163 -1.345319 -3.155746 -1.731663 0.699546 0.513305 \n", - "1 0.045796 -2.830673 1.578201 -0.098472 -0.665334 -0.130451 0.022118 \n", - "2 0.490722 -2.026190 -1.848381 -1.112946 -1.620822 3.430459 0.166742 \n", - "3 -1.859338 -0.196734 1.525634 0.244376 -0.685690 1.995038 0.055226 \n", - "4 -0.056101 1.386592 1.552356 1.446347 -0.984449 0.742441 0.631411 \n", + "0 -0.988411 -3.270714 -1.816697 0.384124 1.258591 -1.577232 0.101273 \n", + "1 -0.531157 -1.298541 -2.630749 0.036662 -2.097307 -1.711751 0.894172 \n", + "2 -0.896734 -1.805453 -2.736948 -0.310169 1.802988 -0.269441 0.765178 \n", + "3 1.637719 -0.930537 -0.229303 0.198907 1.184137 -0.411545 0.870378 \n", + "4 -1.709777 -2.701615 0.297434 -0.909832 1.436884 0.120985 0.866854 \n", "\n", " h i j k l \n", - "0 0.507864 0.357858 0.439859 0.695061 0.449589 \n", - "1 0.808068 0.158917 0.328156 0.349374 0.927755 \n", - "2 0.504127 0.942156 0.556877 0.024859 0.430831 \n", - "3 0.751830 0.983152 0.702334 0.750200 0.294415 \n", - "4 0.217660 0.124121 0.814294 0.131921 0.917958 " + "0 0.657975 0.770880 0.882366 0.637714 0.002812 \n", + "1 0.727579 0.211429 0.223319 0.496683 0.840040 \n", + "2 0.341713 0.847770 0.696190 0.824104 0.297523 \n", + "3 0.811312 0.142528 0.707361 0.201967 0.867956 \n", + "4 0.352461 0.690270 0.172950 0.056518 0.806867 " ] }, "execution_count": 2, @@ -821,34 +821,34 @@ " \n", " \n", " \n", - " 221\n", - " 2.819930\n", - " -0.677731\n", - " -2.883151\n", + " 28\n", + " -2.393671\n", + " 2.653494\n", + " 1.336840\n", " \n", " \n", - " 121\n", - " -0.068495\n", - " -1.398128\n", - " 1.239476\n", + " 540\n", + " -1.598037\n", + " -2.639941\n", + " -1.787062\n", " \n", " \n", - " 83\n", - " 2.225071\n", - " -1.902156\n", - " 1.425918\n", + " 980\n", + " -1.562249\n", + " 1.573867\n", + " -0.135207\n", " \n", " \n", - " 243\n", - " 2.592917\n", - " 0.764790\n", - " -2.120585\n", + " 812\n", + " 0.084835\n", + " 1.809188\n", + " -1.525609\n", " \n", " \n", - " 992\n", - " 2.329279\n", - " 2.362780\n", - " -3.780878\n", + " 117\n", + " 0.647414\n", + " 1.437139\n", + " 1.873279\n", " \n", " \n", " ...\n", @@ -857,34 +857,34 @@ " ...\n", " \n", " \n", - " 254\n", - " 1.850187\n", - " -2.177608\n", - " -4.088455\n", + " 630\n", + " 0.102721\n", + " 0.463829\n", + " -0.220689\n", " \n", " \n", - " 19\n", - " -0.949434\n", - " 1.062798\n", - " -3.421324\n", + " 963\n", + " -0.530709\n", + " 0.353686\n", + " 0.621369\n", " \n", " \n", - " 956\n", - " 2.105221\n", - " -0.169633\n", - " 1.743979\n", + " 943\n", + " 3.850193\n", + " 0.948248\n", + " -2.042764\n", " \n", " \n", - " 150\n", - " 2.171954\n", - " -1.343100\n", - " -0.346960\n", + " 930\n", + " 1.051634\n", + " 1.240570\n", + " -1.477092\n", " \n", " \n", - " 629\n", - " 0.348571\n", - " -1.171049\n", - " 0.854003\n", + " 116\n", + " -0.126476\n", + " -1.599799\n", + " -0.610169\n", " \n", " \n", "\n", @@ -893,17 +893,17 @@ ], "text/plain": [ " d e f\n", - "221 2.819930 -0.677731 -2.883151\n", - "121 -0.068495 -1.398128 1.239476\n", - "83 2.225071 -1.902156 1.425918\n", - "243 2.592917 0.764790 -2.120585\n", - "992 2.329279 2.362780 -3.780878\n", + "28 -2.393671 2.653494 1.336840\n", + "540 -1.598037 -2.639941 -1.787062\n", + "980 -1.562249 1.573867 -0.135207\n", + "812 0.084835 1.809188 -1.525609\n", + "117 0.647414 1.437139 1.873279\n", ".. ... ... ...\n", - "254 1.850187 -2.177608 -4.088455\n", - "19 -0.949434 1.062798 -3.421324\n", - "956 2.105221 -0.169633 1.743979\n", - "150 2.171954 -1.343100 -0.346960\n", - "629 0.348571 -1.171049 0.854003\n", + "630 0.102721 0.463829 -0.220689\n", + "963 -0.530709 0.353686 0.621369\n", + "943 3.850193 0.948248 -2.042764\n", + "930 1.051634 1.240570 -1.477092\n", + "116 -0.126476 -1.599799 -0.610169\n", "\n", "[750 rows x 3 columns]" ] @@ -923,55 +923,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can now use this when defining our pipelines. \n", - "For this first example, we will construct a simple linear pipeline where the first step is a feature set selector, and the second is a classifier" + "Under the hood, mutation will randomly select another feature set and crossover will swap the feature sets selected by two individuals" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Generation: 100%|██████████| 5/5 [00:30<00:00, 6.12s/it]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.8989320638188367\n" - ] - } - ], - "source": [ - "\n", - "classification_search_space = get_search_space([\"RandomForestClassifier\"])\n", - "fss_and_classifier_search_space = SequentialPipeline([fss_search_space, classification_search_space])\n", - "\n", - "\n", - "est = tpot2.TPOTEstimator(generations=5, \n", - " scorers=[\"roc_auc_ovr\", tpot2.objectives.complexity_scorer],\n", - " scorers_weights=[1.0, -1.0],\n", - " n_jobs=32,\n", - " classification=True,\n", - " search_space = fss_and_classifier_search_space,\n", - " verbose=1,\n", - " )\n", - "\n", - "\n", - "scorer = sklearn.metrics.get_scorer('roc_auc_ovo')\n", - "est.fit(X_train, y_train)\n", - "print(scorer(est, X_test, y_test))" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, "outputs": [ { "data": { @@ -1380,77 +1338,25 @@ " /* fitted */\n", " background-color: var(--sklearn-color-fitted-level-3);\n", "}\n", - "
Pipeline(steps=[('featuresetselector',\n",
-       "                 FeatureSetSelector(name='group_two',\n",
-       "                                    sel_subset=['d', 'e', 'f'])),\n",
-       "                ('randomforestclassifier',\n",
-       "                 RandomForestClassifier(class_weight='balanced',\n",
-       "                                        max_features=0.7587731972584,\n",
-       "                                        min_samples_leaf=7,\n",
-       "                                        min_samples_split=12,\n",
-       "                                        n_estimators=128))])
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" + "
FeatureSetSelector(name='group_two', sel_subset=['d', 'e', 'f'])
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" ], "text/plain": [ - "Pipeline(steps=[('featuresetselector',\n", - " FeatureSetSelector(name='group_two',\n", - " sel_subset=['d', 'e', 'f'])),\n", - " ('randomforestclassifier',\n", - " RandomForestClassifier(class_weight='balanced',\n", - " max_features=0.7587731972584,\n", - " min_samples_leaf=7,\n", - " min_samples_split=12,\n", - " n_estimators=128))])" + "FeatureSetSelector(name='group_two', sel_subset=['d', 'e', 'f'])" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "est.fitted_pipeline_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With this setup TPOT is able to identify one of the subsets used, but the performance is not optimal. In this case we happen to know that multiple feature sets are required. If we want to include multiple features in our pipelines, we will have to modify our search space. There are three options for this.\n", - "\n", - "1. UnionPipeline - This allows you to have a fixed number of feature sets selected. If you use a UnionPipeline with two FSSNodes, you will always select two feature sets that are simply concatenated together.\n", - "2. DynamicUnionPipeline - This space allows multiple FSSNodes to be selected. Unlike UnionPipeline you don't have to specify the number of selected sets, TPOT will identify the number of sets that are optimal. Additionally, with DynamicUnionPipeline, the same feature set cannot be selected twice. Note that while DynamicUnionPipeline can select multiple feature sets, it never mixes two feature sets together.\n", - "3. GraphSearchPipeline - When set as the leave_search_space, GraphSearchPipeline can also select multiple FSSNodes which act as an input to the rest of the pipeline." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### UnionPipeline + FSSNode example" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "union_fss_space = UnionPipeline([fss_search_space, fss_search_space])" + "ind1 = fss_search_space.generate(rng=1)\n", + "ind1.export_pipeline()" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -1860,219 +1766,74 @@ " /* fitted */\n", " background-color: var(--sklearn-color-fitted-level-3);\n", "}\n", - "
FeatureUnion(transformer_list=[('featuresetselector-1',\n",
-       "                                FeatureSetSelector(name='group_two',\n",
-       "                                                   sel_subset=['d', 'e', 'f'])),\n",
-       "                               ('featuresetselector-2',\n",
-       "                                FeatureSetSelector(name='group_three',\n",
-       "                                                   sel_subset=['g', 'h',\n",
-       "                                                               'i']))])
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" + "
FeatureSetSelector(name='group_three', sel_subset=['g', 'h', 'i'])
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" ], "text/plain": [ - "FeatureUnion(transformer_list=[('featuresetselector-1',\n", - " FeatureSetSelector(name='group_two',\n", - " sel_subset=['d', 'e', 'f'])),\n", - " ('featuresetselector-2',\n", - " FeatureSetSelector(name='group_three',\n", - " sel_subset=['g', 'h',\n", - " 'i']))])" + "FeatureSetSelector(name='group_three', sel_subset=['g', 'h', 'i'])" ] }, - "execution_count": 10, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# this union search space will always select exactly two fss_search_space\n", - "selector1 = union_fss_space.generate(rng=1).export_pipeline()\n", - "selector1" + "ind1.mutate()\n", + "ind1.export_pipeline()" ] }, { - "cell_type": "code", - "execution_count": 11, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " d e f g h i\n", - "221 2.819930 -0.677731 -2.883151 0.420393 0.930431 0.223210\n", - "121 -0.068495 -1.398128 1.239476 0.583717 0.872967 0.355258\n", - "83 2.225071 -1.902156 1.425918 0.521714 0.624303 0.526185\n", - "243 2.592917 0.764790 -2.120585 0.748397 0.099224 0.640145\n", - "992 2.329279 2.362780 -3.780878 0.233820 0.054463 0.025899\n", - ".. ... ... ... ... ... ...\n", - "254 1.850187 -2.177608 -4.088455 0.955017 0.363270 0.010256\n", - "19 -0.949434 1.062798 -3.421324 0.094215 0.837656 0.856361\n", - "956 2.105221 -0.169633 1.743979 0.266365 0.446727 0.356117\n", - "150 2.171954 -1.343100 -0.346960 0.712673 0.983344 0.873176\n", - "629 0.348571 -1.171049 0.854003 0.746499 0.101473 0.609367\n", - "\n", - "[750 rows x 6 columns]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "selector1.set_output(transform=\"pandas\") \n", - "selector1.fit(X_train)\n", - "selector1.transform(X_train)" + "We can now use this when defining our pipelines. \n", + "For this first example, we will construct a simple linear pipeline where the first step is a feature set selector, and the second is a classifier" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 9, "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generation: 100%|██████████| 5/5 [00:30<00:00, 6.11s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.90263107355483\n" + ] + } + ], "source": [ - "#### DynamicUnionPipeline + FSSNode example\n", - "The dynamic union pipeline may select a variable number of feature sets." + "\n", + "classification_search_space = get_search_space([\"RandomForestClassifier\"])\n", + "fss_and_classifier_search_space = SequentialPipeline([fss_search_space, classification_search_space])\n", + "\n", + "\n", + "est = tpot2.TPOTEstimator(generations=5, \n", + " scorers=[\"roc_auc_ovr\", tpot2.objectives.complexity_scorer],\n", + " scorers_weights=[1.0, -1.0],\n", + " n_jobs=32,\n", + " classification=True,\n", + " search_space = fss_and_classifier_search_space,\n", + " verbose=1,\n", + " )\n", + "\n", + "\n", + "scorer = sklearn.metrics.get_scorer('roc_auc_ovr')\n", + "est.fit(X_train, y_train)\n", + "print(scorer(est, X_test, y_test))" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -2482,44 +2243,84 @@ " /* fitted */\n", " background-color: var(--sklearn-color-fitted-level-3);\n", "}\n", - "
FeatureUnion(transformer_list=[('featuresetselector',\n",
-       "                                FeatureSetSelector(name='group_three',\n",
-       "                                                   sel_subset=['g', 'h',\n",
-       "                                                               'i']))])
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" + "
Pipeline(steps=[('featuresetselector',\n",
+       "                 FeatureSetSelector(name='group_one',\n",
+       "                                    sel_subset=['a', 'b', 'c'])),\n",
+       "                ('randomforestclassifier',\n",
+       "                 RandomForestClassifier(criterion='entropy',\n",
+       "                                        max_features=0.4070021568844,\n",
+       "                                        min_samples_leaf=4, min_samples_split=3,\n",
+       "                                        n_estimators=128))])
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" ], "text/plain": [ - "FeatureUnion(transformer_list=[('featuresetselector',\n", - " FeatureSetSelector(name='group_three',\n", - " sel_subset=['g', 'h',\n", - " 'i']))])" + "Pipeline(steps=[('featuresetselector',\n", + " FeatureSetSelector(name='group_one',\n", + " sel_subset=['a', 'b', 'c'])),\n", + " ('randomforestclassifier',\n", + " RandomForestClassifier(criterion='entropy',\n", + " max_features=0.4070021568844,\n", + " min_samples_leaf=4, min_samples_split=3,\n", + " n_estimators=128))])" ] }, - "execution_count": 12, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dynamic_fss_space = DynamicUnionPipeline(fss_search_space)\n", - "dynamic_fss_space.generate(rng=1).export_pipeline()" + "est.fitted_pipeline_" ] }, { - "cell_type": "code", - "execution_count": 13, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
FeatureUnion(transformer_list=[('featuresetselector-1',\n",
-       "                                FeatureSetSelector(name='group_one',\n",
-       "                                                   sel_subset=['a', 'b', 'c'])),\n",
+       "                                FeatureSetSelector(name='group_two',\n",
+       "                                                   sel_subset=['d', 'e', 'f'])),\n",
        "                               ('featuresetselector-2',\n",
-       "                                FeatureSetSelector(name='group_four',\n",
-       "                                                   sel_subset=['j', 'k',\n",
-       "                                                               'l']))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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" ], "text/plain": [ - "FeatureUnion(transformer_list=[('featuresetselector-1',\n", - " FeatureSetSelector(name='group_one',\n", - " sel_subset=['a', 'b', 'c'])),\n", - " ('featuresetselector-2',\n", - " FeatureSetSelector(name='group_four',\n", - " sel_subset=['j', 'k',\n", - " 'l']))])" + "Pipeline(steps=[('featureunion',\n", + " FeatureUnion(transformer_list=[('featuresetselector-1',\n", + " FeatureSetSelector(name='group_one',\n", + " sel_subset=['a',\n", + " 'b',\n", + " 'c'])),\n", + " ('featuresetselector-2',\n", + " FeatureSetSelector(name='group_two',\n", + " sel_subset=['d',\n", + " 'e',\n", + " 'f']))])),\n", + " ('randomforestclassifier',\n", + " RandomForestClassifier(bootstrap=False,\n", + " class_weight='balanced',\n", + " max_features=0.4909664847192,\n", + " min_samples_leaf=2, min_samples_split=4,\n", + " n_estimators=128))])" ] }, - "execution_count": 13, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dynamic_fss_space.generate(rng=3).export_pipeline()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### GraphSearchPipeline + FSSNode example\n", - "\n", - "FSSNodes must be set as the leaf search space as they act as the inputs to the pipeline.\n", - "\n", - "Here is an example pipeline from this search space that utilizes two feature sets." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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zczRxcYGzszOcnZ3h4OAAiUSi8RzDhw/H3r17NWZffvklxo0bp8sfhYjoqbHYERE9JD8/H35+fsjLy1PPbGxskJSUBA8PDwGTERE9Hq9jR0T0EEdHR3z++ecas+LiYkyYMAH8uzAR6TMWOyKiGgwcOBBjx47VmB09ehRbtmwRKBER0ZPxKJaISIvCwkL4+fkhKytLPbO0tERiYiK8vLwETEZEVDPu2BERaWFnZ4cvv/xSY1ZWVoZx48ZB8ZhLqRARCYXFjojoMV588UW8+eabGrM//vgD69evFygREZF2PIolInqCe/fuISAgAOnp6eqZmZkZzp8/Dx8fHwGTERFp4o4dEdETNGrUCDt27NCYVVZWYuzYsZDL5QKlIiJ6FIsdEVEt9OzZE9OnT9eYxcbGYtWqVcIEIiKqAY9iiYhqqby8HIGBgRr3kjUxMUFsbCwCAgIETEZE9Bfu2BER1ZKFhQV27doFsfh//+qsrq7G2LFjUVVVJWAyIqK/sNgRET2FLl26YNasWRqzhIQELF68WKBERET/w6NYIqKnVFlZiU6dOiE5OVk9k0gkOH36NIKDgwVMRkQNHYsdEdEzOH/+PDp37qzxrVgfHx/Ex8fD3NxcwGRE1JDxKJaI6Bl06NABCxYs0JhdunTpkRkRkS5xx46I6BlVV1eja9euiIuLU89EIhFOnjyJbt26CZiMiBoqFjsioueQkpKCoKAgjW/Fenl5ISEhAVZWVgImI6KGiEexRETPwdfXF0uWLNGYXb9+HbNnzxYoERE1ZNyxIyJ6TgqFAj169EBMTIzG/OjRo+jdu7dAqYioIWKxIyKqA6mpqQgICEB5ebl65uHhgcTERNja2gqYjIgaEh7FEhHVAW9vb6xcuVJjlpmZiRkzZgiUiIgaIu7YERHVEaVSiYiICBw/flxj/ssvv6Bfv34CpSKihoTFjoioDt24cQP+/v4oKSlRz1xcXJCSkgIHBwcBkxFRQ8CjWCKiOtSiRQusXbtWY5abm4upU6cKlIiIGhLu2BER1TGVSoV+/frh0KFDGvN9+/bh1VdfFSgVETUELHZERPUgKysLfn5+KCwsVM8cHR2RkpICJycn4YIRkVHjUSwRUT1wc3PDhg0bNGb5+fl46623wL9PE1F9YbEjIqono0aNwuDBgzVmP//8M3bv3i1QIiIydjyKJSKqR7dv34avry/y8/PVMzs7OyQnJ8PNzU3AZERkjLhjR0RUj5ycnLB582aNWWFhISZOnMgjWSKqcyx2RET1bMiQIRg5cqTG7NChQ9i+fbtAiYjIWPEolohIB2QyGXx9fZGbm6ueWVtbIykpCS1atBAuGBEZFe7YERHpgIODA7Zt26YxKykpwfjx46FUKgVKRUTGhsWOiEhH+vXrh/Hjx2vMjh8/jk2bNgmUiIiMDY9iiYh0qKioCP7+/rh586Z6ZmFhgYSEBHh7ewuYjIiMAXfsiIh0yNbWFl9++aXGrLy8HK+//joUCoVAqYjIWLDYERHpWEREBKZMmaIxi4mJwSeffCJQIiIyFjyKJSISQElJCQIDA3H9+nX1zNTUFPHx8fD19RUwGREZMu7YEREJwNraGjt37oRIJFLPqqqqMHbsWFRXVwuYjIgMGYsdEZFAunXrhhkzZmjM4uLisGLFCoESEZGh41EsEZGAysvLERQUhMuXL6tnUqkUZ8+eRYcOHQRMRkSGiDt2REQCsrCwwK5duyCRSNQzuVyOsWPHorKyUsBkRGSIWOyIiATWuXNnzJkzR2OWlJSEjz76SKBERGSoeBRLRKQHqqqqEBwcjMTERPVMLBYjJiYGISEhAiYjIkPCYkdEpCcSEhIQHBys8a3Y1q1b4/z587C0tBQwGREZCh7FEhHpiYCAACxatEhjdvXqVcybN0+gRERkaLhjR0SkR+RyOUJDQxEbG6ueiUQiHD9+HD179hQwGREZAhY7IiI9c+nSJXTo0EHjW7Genp5ITEyEtbW1gMmISN/xKJaISM/4+Phg2bJlGrP09HS8//77AiUiIkPBHTsiIj2kUCgQHh6OU6dOacwPHz6MF198UaBURKTvWOyIiPTU9evX0b59e5SVlaln7u7uSEpKgp2dnXDBiEhv8SiWiEhPeXl5YfXq1RqzW7duYfr06cIEIiK9xx07IiI9plQq8dJLL+Ho0aMa8/3792PgwIECpSIifcViR0Sk5zIzM+Hv74/i4mL1zNnZGSkpKWjcuLGAyYhI3/AolohIz3l4eGDdunUas7y8PLz99tvCBCIivcUdOyIiA6BSqTBw4ED88ssvGvM9e/Zg2LBhAqUiIn3DYkdEZCBycnLg6+uLgoIC9axx48ZITk6Gi4uLgMmISF/wKJaIyEC4urpi48aNGrO7d+/izTffBP+OTkQAix0RkUEZMWIEhgwZojGLiorC119/LVAiItInPIolIjIwd+7cga+vL+7cuaOe2draIjk5Ge7u7gImIyKhcceOiMjANGnSBJ9//rnGrKioCBMmTOCRLFEDx2JHRGSABg0ahH/+858as19//fWRwkdEDQuPYomIDFRBQQH8/PyQnZ2tnllZWSExMREtW7YUMBkRCYU7dkREBsre3h7bt2/XmJWWlmL8+PFQKpUCpSIiIbHYEREZsD59+mDixIkasxMnTmDDhg0CJSIiIfEolojIwN27dw/+/v7IyMhQz8zNzXHhwgW0adNGwGREpGvcsSMiMnCNGjXCjh07NGYVFRV4/fXXIZfLBUpFREJgsSMiMgK9evXC1KlTNWZ//vknPv74Y4ESEZEQeBRLRGQkysrKEBgYiNTUVPXM1NQU586dg7+/v4DJiEhXuGNHRGQkLC0tsXPnTojF//tXe1VVFcaOHYuqqioBkxGRrrDYEREZkdDQUMycOVNjdv78eSxbtkygRESkSzyKJSIyMhUVFejUqRNSUlLUM4lEgjNnzqBjx44CJiOi+sYdOyIiI2Nubo5du3ZBIpGoZwqFAmPHjkVFRYWAyYiovrHYEREZoY4dO2L+/Pkas5SUFCxatEigRESkCzyKJSIyUtXV1QgJCcH58+fVM5FIhFOnTiE0NFTAZERUX1jsiIiMWFJSEjp16qTxrdhWrVrhwoULsLKyEjAZEdUHHsUSERkxf39/fPTRRxqza9eu4YMPPhAoERHVJ+7YEREZOblcju7du+PPP//UmB87dgy9evUSKBUR1QcWOyKiBuDKlSsIDAzU+FZs8+bNkZiYCBsbGwGTEVFd4lEsEVED0KZNG6xYsUJjlpGR8cjFjInIsHHHjoiogVAqlfi///s/nDhxQmN+8OBB9O3bV6BURFSXWOyIiBqQ9PR0+Pv7o7S0VD1r2rQpkpOTYW9vL2AyIqoLPIolImpAPD09sWbNGo1ZdnY23n33XYESEVFd4o4dEVEDo1Kp0KdPH/z6668a859++gmDBw8WKBUR1QUWOyKiBujWrVvw8/NDUVGRetakSROkpKSgSZMmAiYjoufBo1giogbI3d0dn376qcbszp07mDx5Mvj3fSLDxR07IqIGSqVSYdCgQYiKitKY7969GyNHjhQoFRE9DxY7IqIGLDc3F35+frh79656Zm9vj5SUFLi6ugqYjIieBY9iiYgaMBcXF2zatEljVlBQgIkTJ/JIlsgAsdgRETVww4YNw/DhwzVmBw4cwM6dO4UJRETPjEexRESEu3fvwtfXF3l5eepZo0aNkJycDA8PDwGTEdHT4I4dERGhcePG+PzzzzVm9+7dw4QJE6BUKgVKRURPi8WOiIgAAAMHDsTYsWM1ZkePHsWWLVsESkRET4tHsUREpFZYWAh/f3/cunVLPbO0tERiYiK8vLwETEZEtcEdOyIiUrOzs8P27ds1ZmVlZXj99dehUCgESkVEtcViR0REGl588UW89dZbGrNTp05h/fr1AiUiotriUSwRET2ipKQE7du3R3p6unpmZmaG8+fPw8fHR8BkRPQ43LEjIqJHWFtbY8eOHRCJROpZZWUlxo4dC7lcLmAyInocFjsiIqpRz549MW3aNI1ZbGwsVq5cKVAiInoSHsUSEZFW5eXl6NChA65cuaKemZiYIDY2FgEBAQImI6KacMeOiIi0srCwwK5duyAW/+/torq6GmPGjEFVVZWAyYioJix2RET0WCEhIZg9e7bGLDExEYsXLxYoERFpw6NYIiJ6osrKSgQHByMpKUk9k0gkOH36NIKDgwVMRkQPYrEjIqJaOX/+PDp37qzxrVgfHx/ExcXBwsJCwGREdB+PYomIqFY6dOiABQsWaMwuXbr0yIyIhMMdOyIiqrXq6mp07doVcXFx6plIJMLJkyfRrVs3AZMREcBiR0RETyklJQVBQUEa34r18vJCQkICrKysBExGRDyKJSKip+Lr64slS5ZozK5fv/7IN2eJSPe4Y0dERE9NoVCgR48eiImJ0ZgfPXoUvXv3FigVEbHYERHRM0lNTUVAQADKy8vVMw8PDyQmJsLW1lbAZEQNF49iiYjomXh7ez9y39jMzEzMmDFDoERExB07IiJ6ZkqlEhERETh+/LjG/JdffkG/fv0ESkXUcLHYERHRc7lx4wb8/f1RUlKinrm4uCAlJQUODg4CJiNqeHgUS0REz6VFixZYu3atxiw3NxdTp04VKBFRw8UdOyIiem4qlQr9+vXDoUOHNOb79u3Dq6++KlAqooaHxY6IiOpEVlYW/Pz8UFhYqJ45OjoiJSUFTk5OwgUjakB4FEtERHXCzc0NGzZs0Jjl5+fjrbfeAvcQiHSDxY6IiOrMqFGjMHjwYI3Zzz//jN27dwuUiKhh4VEsERHVqdu3b8PX1xf5+fnqmZ2dHZKTk+Hm5iZgMiLjxx07IiKqU05OTti8ebPGrLCwEBMnTuSRLFE9Y7EjIqI6N2TIEIwcOVJjdujQIWzfvl2gREQNA49iiYioXshkMvj6+iI3N1c9s7a2RlJSElq0aCFcMCIjxh07IiKqFw4ODti2bZvGrKSkBOPHj4dSqRQoFZFxY7EjIqJ6069fP4wfP15jdvz4cWzatEmgRETGjUexRERUr4qKiuDv74+bN2+qZxYWFkhISIC3t7eAyYiMD3fsiIioXtna2uLLL7/UmJWXl+P111+HQqEQKBWRcWKxIyKiehcREYEpU6ZozGJiYvDJJ58IlIjIOPEoloiIdKKkpASBgYG4fv26emZqaor4+Hj4+voKmIzIeHDHjoiIdMLa2ho7d+6ESCRSz6qqqjB27FhUV1cLmIzIeLDYERGRznTr1g0zZszQmMXFxWHFihUCJSIyLjyKJSIinSovL0dQUBAuX76snkmlUpw9exYdOnQQMBmR4eOOHRER6ZSFhQV27doFiUSinsnlcowdOxaVlZUCJiMyfCx2RESkc507d8acOXM0ZklJSfjoo48ESkRkHHgUS0REgqiqqkJwcDASExPVM7FYjJiYGISEhAiYjMhwsdgREZFgEhISEBwcrPGt2DZt2uD8+fOwsLAQMBmRYeJRLBERCSYgIACLFi3SmF25cgXz5s0TKBGRYeOOHRERCUoulyM0NBSxsbHqmUgkwvHjx9GzZ08BkxEZHhY7IiIS3KVLl9ChQweNb8V6enoiMTER1tbWAiYjMiw8iiUiIsH5+Phg2bJlGrP09HS8//77AiUiMkzcsSMiIr2gUCgQHh6OU6dOacwPHz6MF198UaBURIaFxY6IiPTG9evX0b59e5SVlaln7u7uSEpKgp2dnXDBiAwEj2KJiEhveHl5YfXq1RqzW7duYfr06cIEIjIw3LEjIiK9olQq8dJLL+Ho0aMa8/3792PgwIFQKBQatyMjov/hjh0REekVsViM7du3w8bGRmM+ceJETJkyBXZ2dvDw8Hjks3hExB07IiLSUzt27MD48eO1/npgYCDOnz+vw0RE+o/FjoiI9JJKpUK/fv1w6NChGn9dJBKhvLwcZmZmUCgUkMlkyMvLQ15eHu7k5qKyvBxKhQJiiQRmFhZo4uICZ2dnODs7w8HBgce5ZJSkQgcgIiKqSU5ODtLS0rT+ukqlQkpKCoqLi5EUH4+K0lKo5HJYl5fDViaDhVwOsUoFpUiEaqkUVxwcEGdhAZFUCnMrK/gHBSEgIAD29vY6/KmI6hd37IiISC+NGDECe/bsqfHXXFxc0C00FB38/WFZXQ2PzJtwlclgW1oKE4VC63NWSyQosrJCjoMDMj2aodrSEp7e3gjr3h2urq719aMQ6Qx37IiISC/l5+c/MpNIJAgNDUVYcDAcS0rQ9lwcvO7dg0SprNVzmigUcCwuhmNxMdplZuKWoyOu3b2Lb69dQ3BYGMLCwiCV8q2RDBd37IiISC8dPXoUAwYMQEVFBQDAyckJA/v1g5u9PbwvX0bTq1dhbWEJO1vb53odpUiEVDc3XPb2hoO7G14eOBAuLi518SMQ6RyLHRER6a1r165h9uzZOHfuHIYNGgTXsjL4xMXBsrgYACCRSOHs5FQnr1VsaYk4Hx+UNW2KwcOHoXnz5nXyvES6xGJHRER6LSMjA9999RVsr19Hm9OnIXngM3R1WewAQC4W44xvO8g8PPDqyJEsd2RweIFiIiLSW7m5ufh5zx64ZOegV1o6HKwbARCpf93a2rpOX0+qVKJrcgocMjPx8569yM3NrdPnJ6pvLHZERKSX5HI5DkRFwTI7ByEXL0KiUsHK0hKuLi6wt3eAk5MzrCwt6/x1xSoVQlIuwiInGwejoiCXy+v8NYjqC4sdERHppejoaBTcykLHS5cgfeBbryKRCBbm5pDW4wWGpUolOl68BFlWFmJiYurtdYjqGosdERHpnezsbMRGR6NtaipsysoEyWBbVoY2V1Nx9tQp5OTkCJKB6Gmx2BERkd6JOXUK1vn58M7KEjRH66wsWOfnI/rUKUFzENUWix0REemVgoICpKemolVGJsQCX7hBrFLBKyMT6VevoqCgQNAsRLXBYkdERHolISEBJmVlcK/hzhNCaJafD2lZGRITE4WOQvRELHZERKQ3FAoFkuLj4ZF5s9a3CatvEqUSzW/eRGJcHBSPuQ8tkT5gsSMiIp2TSqUIDAxEYGAggoODceHCBQDA7t278euRI3CVyZ77NX67exc76ugzeq53ZagoLYXs71zbtm2Dt7c3RCIRSkpK6uQ1iOoCix0REemcnZ0dLly4gAsXLmDOnDmIjIwEAHTo0AHdO3eG3XOWJYVKhd6NG2Ocm9tzPw8A2JaWQiWXIy8vDwAQEhKCX3/9lXemIL0jFToAERE1bMXFxbC1tQUAfPXVV4g/fhyDzS0w++oVNJJIkVByD4XV1Vjq7Y3OtnbILC/H7NSrKFcoYCIWY7l3a7SytMRPeXn4o6AAJQo5LCUS9LR3wNWyUszxbImB5+PVr5daWoqjnYJhJhZjwbVryKuqhKlYjKWtvOFlaYnZV6/ATmqClJISdLO3x1vNmsFEoYB1eTny8vLg5+cHf39/oX67iB6LxY6IiHSusLAQgYGBKCsrQ35+vvoiwPeKimBWUQGYWwAAihVy/BAQiNOFhfgsMxNf+duhiakpdvn5w1QsRnxxMT65cQOb2rUDACSW3MP+wA6wlkrx09+7awAQ1SEIAPBDbi5OFMjgZm6O9y5fxtsezeBn3QiJ9+5hWVoatvv5AQByqyrxtb8/RKL/3b7MRlaAO7zFGOk5FjsiItK5+0exALBv3z688847OHr0KKqrqiB+4EsTvR0aAwD8rK2RVVkJAKhSKfHRteu4UloKMYCqBy6J0t3OHtbSmt/aLpWU4KvsLHzXPgAA8GdRIa6X13zx45caO2qUOgAwlctRUVHxTD8vka6w2BERkaD69++PMWPGAABUSiUerFOm4r/+l1gkgvLvArczKxvuZuZY07oN8qurMSzhgnq9uaTmj47fk8sx6+oVrGrdRqP4/RzYAZKHChwAWNTwPGKVEgreN5b0HL88QUREgoqJiUHLli0BACKxGE+6JHGpQg4nU1OIRCLsv327Vq/xQepVjG3qBh9ra/Wss60tvs/961ZhSpUKV0pLH/scSpEYEi27gUT6gv+EEhGRzt3/jJ1KpYJUKsXnn38OADAxNYVS/Pg9hxEurph6+RL+c+c2Qu3snvhaWRUV+O3uXWRWVOCrnGwAwBftfLGgpRcWXruG73NyIFepMMjJGW2srLQ+T5VUClNzcwDA9u3bsWjRIuTm5qJNmzYYNWoUVq1aVcufnqj+iFQqge/XQkRE9LfffvsNVw4fxgun/3zk16rlcpSVlkKhVMLKygpmpqY6zXakaxe0eekl9O7dW6evS/Q0uGNHRER6w9nZGXEWFqiWSGDy910equVy3Lt3DxUV5ep1lRUVcHJ2huQJu3t1pVoiQYmFBZydnXXyekTPisWOiIj0hrOzM0RSKYqsrGBz9y5KSu7V+E1UFVRQKOSQiOt3127zzUwcys+HQixByZXL+OrHHzFr1iz1lz2I9A2LHRER6Q0HBwdAIsF1Cws0y7+jdZ2JiSlMpCb1nmdyMw9MbuaBJM8WyAoMxJRp0yCRSOr9dYmeFb8VS0REeiEmJgb9+vXDL4cP44ZbUyhqOGYVicRoZN0IjRs3fuQ6c/VFIRYjo1kztO/YkaWO9B6LHRERCerEiROIiIhAWFgYDh8+jISEBJSZmOCuu7t6jVgkRqNGNnB2dkajRo0g1lGpA4Cbjo6QW1qiffv2OntNomfFo1giItI5lUqFY8eOITIyEidPntT4taKiIqSmp6OxtzecsrJgY2kFSysrnZa5+5QiEa4394Bn69awt7fX+esTPS3u2BERkc6oVCocPnwY3bp1Q0RExCOl7r6Lly+j3MkJxUFBsLa2FqTUAcBVNzeUODoirFs3QV6f6Gmx2BERUb1TqVQ4cOAAunTpgj59+iAmJqbGdU2bNsW6desQGxuL7hERuOLdGsWWlvWWS6FUoLyiXH27sgcVWVriSmtvdO7WDa6urvWWgagu8SiWiIjqjUqlQlRUFCIjIxEfH691nbu7Oz744AOMHz8e5n/f3SEsLAzXrlxBXLEPup8/D6lSWafZyisqUFBQAEAFQAQbGxtYWVlBBEAuFiOunQ8c3NwQGhpap69LVJ+4Y0dERHVOqVRi37596NChAwYNGqS11DVv3hxbt27FtWvXMGXKFHWpAwCpVIp+AweirGlTnPFtB2UdH8feu3cPUN+ZVoXi4iLcuXMH5VVVOOPbDuWuTfHywIGQ8v6wZEBY7IiIqM4oFAp8//33aN++PYYOHYqEhIQa17Vs2RLbt29HamoqJk2aBDMzsxrXubi4YPDwYZB5eOC0ny/kdXiniZoul1KpUuKPNq1x3cYGHUI6w8XFpc5ej0gXeK9YIiJ6bnK5HN9//z2WLFmCK1euaF3n7e2N+fPn47XXXnuqnbCMjAz8vGcvLLOz0fHSJdiUlT135uJ791BSck/9v0ttbHC5YydkW1pg788/IycnB3v37sXgwYOf+7WIdIXFjoiInll1dTW+/fZbLF26FNeuXdO6zsfHB/Pnz8fw4cOf+SK/ubm5OBAVhYJbWWibmgrvrCyIn+MtrKy8HIWFBVCKRMhu3RqpbdsiSyZD1MGDuH37NgCgY8eOOHfu3DO/BpGu8YMDRET01KqqqvDVV19h2bJlSE9P17rOz88PCxYswKuvvvrcd21wcXHB2PHjER0djVhzM9xydYFXRiaa5edD8gxfrBCZmiCveXPcbNUK+dbWiI6NRUxMDBQKhXqNo6Pjc2Um0jXu2BERUa1VVlZix44dWL58OTIzM7WuCwwMxMKFC/GPf/wD4jr8XNx92dnZiImORvrVq5CWlaH5zZtwvSuDbWkpTB4oZg+rlkhQZGWFnMYOuOHujvyqKlxNT0d0TAxyc3M11np4eODYsWPw8vKq8/xE9YXFjoiInqi8vBzbtm3DypUrkZWVpXVdp06dsHDhQvTv318n93ItKChAYmIiEuPiUFFaCpVcDuvyctjICmAql0OsUkIpEqNKKkWxgz1KLCwgkkphbmUF/6AgjBw58pFCd19oaCj++OOPeimmRPWFxY6IiLQqKyvD1q1bsWrVKq0FCABCQkKwaNEi9OnTRyeF7mEKhQIymQx5eXnIy8vDndxcVFVUQCGXQyKVwtTcHE1cXODs7AxnZ2c4ODhAIpGgV69e+P3337U+7yeffIL33ntPdz8I0XNisSMiokeUlJRg8+bN+Pjjj9VfJKhJt27dsHDhQkRERAhS6J5XfHw8hg8fjuzsbIwcORJHjhzROGI2MzPDhQsX0LZtWwFTEtUeix0REakVFxdj48aNWLNmDe7evat1XXh4OBYuXIjw8HCDLHQPUygUkEgkOHHiBMLDwzV+rXPnzoiOjuaFiskg8IMDRESEwsJCREZGokWLFpg7d67WUvfCCy/g5MmTOH78OHr16mUUpQ6A+hu7PXv2xLRp0zR+7ezZs1i1apUQsYieGnfsiIgaMJlMhnXr1mH9+vUoLi7Wuq5v375YsGABunbtqsN0wigrK0OHDh1w9epV9czExATnzp1D+/btBUxG9GQsdkREDVB+fj4++eQTbNiwASUlJVrXDRgwAAsWLEBwcLAO0wnvzz//RFhYGJQPXB8vICAAZ8+ehampqYDJiB6PR7FERA1IXl4eZs2ahRYtWmD58uVaS93gwYMRHx+PqKioBlfqAKBLly6YNWuWxiwhIQFLliwRKBFR7XDHjoioAcjJycHq1auxZcsWlJeX17hGJBJhyJAhmD9/Po8c8dfFmDt16oTk5GT1TCKR4PTp0w2y7JJhYLEjIjJit27dwsqVK/HFF1+gsrKyxjVisRgjRozAvHnz0K5dOx0n1G/x8fEICQmBXC5Xz3x8fBAfHw9zc3MBkxHVjEexRERGKCMjA5MnT4aXlxc+++yzGkudRCLBmDFjcPHiRXz77bcsdTUICgrCggULNGaXLl16ZEakL7hjR0RkRNLS0rB8+XLs3LlTY5fpQVKpFGPGjMEHH3yAVq1a6Tih4amurkbXrl0RFxennolEIvzxxx8ICwsTMBnRo1jsiIiMQGpqKpYtW4avv/4aCoWixjUmJiYYN24c5syZA09PTx0nNGwpKSkICgpCVVWVeubl5YWEhARYWVkJmIxIE49iiYgM2OXLlzF69Gi0bdsWO3furLHUmZqaYsqUKbh27Rq2bt3KUvcMfH19sXjxYo3Z9evXMXv2bIESEdWMO3ZERAYoJSUFS5YswZ49e6DtX+Pm5uaYNGkSZs2aBTc3Nx0nND4KhQLdu3fH6dOnNeZHjx5F7969BUpFpInFjojIgCQkJGDx4sX48ccfta6xsLDAlClTMHPmTLi4uOgwnfFLTU1FQECAxiVjPDw8kJSUBBsbGwGTEf2FR7FERAYgLi4OgwYNQmBgoNZSZ2VlhdmzZ+PGjRv4+OOPWerqgbe3N1auXKkxy8zMxIwZMwRKRKSJO3ZERHrszJkzWLx4MQ4cOKB1TaNGjfDuu+9i+vTpcHR01GG6hkmpVCIiIgLHjx/XmP/yyy/o16+fQKmI/sJiR0Skh6Kjo7F48WIcPnxY6xpbW1tMnz4d06ZNg729vQ7T0Y0bN+Dv769xSzZXV1ckJyfDwcFBwGTU0PEolohIj5w4cQK9e/dGt27dtJY6e3t7LF68GBkZGfjwww9Z6gTQokULfPLJJxqznJwcTJ06VaBERH/hjh0RkcBUKhWOHTuGyMhInDx5Uus6R0dHzJw5E1OmTEGjRo10mJBqolKp8PLLL+O///2vxnzfvn149dVXBUpFDR2LHRGRQFQqFX799VdERkYiJiZG6zonJyfMmjULb731Fi+Gq2eysrLg5+eHwsJC9czR0REpKSlwcnISLhg1WDyKJSLSMZVKhQMHDqBLly7o06eP1lLn6uqKdevWIT09Hf/6179Y6vSQm5sbNmzYoDHLz8/H5MmTtV5fkKg+cceOiEhHVCoVoqKiEBkZifj4eK3r3N3dMWfOHEyYMAHm5uY6TEjPQqVS4ZVXXsG///1vjfk333yDUaNGCROKGiwWOyKieqZUKvHTTz9hyZIlSEhI0LquefPm+OCDD/D666/DzMxMhwnpeeXl5cHPzw/5+fnqmZ2dHVJSUtC0aVMBk1FDw6NYIqJ6olAo8P3336N9+/YYOnSo1lLXsmVLbN++HampqXjzzTdZ6gyQs7MzNm/erDErLCzEG2+8wSNZ0ikWOyKiOiaXy/HNN9/A19cXI0eOREpKSo3rvL29sWvXLly5cgXjx4+HiYmJjpNSXRoyZAhGjhypMTt06BC+/PJLgRJRQ8SjWCKiOlJdXY1vv/0WS5cuxbVr17Su8/Hxwfz58zF8+HBIJBIdJqT6JpPJ4Ovri9zcXPWsUaNGSEpKQvPmzQVMRg0Fd+yIiJ5TVVUVtm3bhjZt2mDcuHFaS52fnx/27NmDpKQkvPbaayx1RsjBwQFffPGFxuzevXsYP348lEqlQKmoIWGxIyJ6RpWVldiyZQu8vb0xceJEpKen17guMDAQP/74IxISEjBs2DAWOiPXv39/jBs3TmN27NgxbNq0SaBE1JDwKJaI6CmVl5dj27ZtWLlyJbKysrSu69SpExYuXIj+/ftDJBLpMCEJraioCP7+/rh586Z6ZmlpiQsXLsDb21vAZGTsWOyIiGqprKwMW7duxapVqzQ+Q/WwkJAQLFq0CH369GGha8COHj2KF154QWMWGhqKkydPcteW6g2PYomInqCkpASrV6+Gp6cnZsyYobXUhYWF4ddff8Xp06fRt29flroGLiIiAlOmTNGYxcTEYO3atQIlooaAO3ZERFoUFxdj48aNWLNmDe7evat1XXh4OBYuXIjw8HCWOdJQUlKCgIAApKWlqWdmZmaIj49Hu3btBExGxorFjojoIYWFhfj000+xbt06FBQUaF0XERGBBQsWoEePHjpMR4bmjz/+QM+ePTUuVNypUyfExMTw2oVU53gUS0T0N5lMhoULF6J58+ZYtGiR1lLXt29fxMTE4MiRIyx19ETdu3fHe++9pzE7d+4cVqxYIVAiMmbcsSOiBi8/Px+ffPIJNmzYgJKSEq3rBgwYgAULFiA4OFiH6cgYlJeXIygoCJcvX1bPpFIpYmNjERgYKFwwMjosdkTUYOXl5WHNmjXYtGkTSktLta4bPHgw5s+fj6CgIB2mI2Nz9uxZdO3aVeNCxf7+/oiNjeX9ganO8CiWiBqcnJwczJgxA56enli9enWNpU4kEmHo0KFISEjATz/9xFJHz61z58744IMPNGZJSUmIjIwUKBEZI+7YEVGDcevWLaxcuRJffPEFKisra1wjFosxfPhwzJs3D76+vjpOSMauqqoKwcHBSExMVM/EYjFiYmIQEhIiYDIyFix2RGT0MjIysGLFCnz55ZeoqqqqcY1YLMY///lPzJ07F23atNFxQmpIEhISEBwcjOrqavWsTZs2OH/+PCwsLARMRsaAR7FEZLTS0tIwceJEtGrVClu2bKmx1EmlUowfPx5XrlzBrl27WOqo3gUEBGDhwoUasytXrmDevHkCJSJjwh07IjI6qampWLZsGb7++msoFIoa15iYmGDcuHGYM2cOPD09dZyQGjq5XI7Q0FDExsaqZyKRCL///jsvoUPPhcWOiIzG5cuXsXTpUuzevVvjm4cPMjU1xRtvvIHZs2fDw8NDxwmJ/ufSpUvo0KGDxuc9PT09kZiYCGtrawGTkSHjUSwRGbzk5GSMGDEC7dq1wzfffFNjqTM3N8e7776LtLQ0bNy4kaWOBOfj44OlS5dqzNLT0zFr1iyBEpEx4I4dERmshIQELF68GD/++KPWNRYWFpg8eTJmzpwJV1dXHaYjejKFQoGePXsiOjpaY/7rr7/ihRdeECgVGTIWOyIyOHFxcVi8eDH279+vdY2VlRXeeecdzJgxA05OTjpMR/R0rl27hoCAAJSVlaln7u7uSE5Ohq2trYDJyBDxKJaIDMaZM2fQv39/dOrUSWupa9SoEebNm4cbN25gxYoVLHWk91q1aoVVq1ZpzG7duoXp06cLE4gMGnfsiEjvRUdHY/HixTh8+LDWNba2tpg+fTqmTZsGe3t7HaYjen5KpRIvvvgifvvtN415VFQUBgwYIFAqMkQsdkSkt06cOIHIyEgcO3ZM6xp7e3vMmDEDU6dO5bEVGbTMzEz4+fnh3r176pmzszNSUlLQuHFjAZORIeFRLBHpFZVKhd9++w09e/ZEeHi41lLn6OiI5cuXIyMjA/Pnz2epI4Pn4eGBdevWaczy8vLwzjvvCBOIDBJ37IhIL6hUKvz666+IjIxETEyM1nVOTk54//338dZbb/FaX2R0VCoV+vfvj4MHD2rM9+7di6FDhwqUigwJix0RCUqlUuHgwYOIjIzE2bNnta5zdXXF7NmzMXHiRFhaWuowIZFuZWdnw8/PDwUFBepZ48aNkZKSAmdnZwGTkSHgUSwRCUKlUmH//v3o1KkT+vfvr7XUubu747PPPkNaWhqmTZvGUkdGr2nTpvjss880Znfv3sWbb74J7sXQk3DHjoh0SqlU4qeffsKSJUuQkJCgdZ2Hhwfmzp2L119/HWZmZjpMSCQ8lUqFoUOHPnLx7a+++gqjR48WKBUZAhY7ItIJhUKBH374AUuWLEFKSorWdS1btsTcuXMxevRomJqa6jAhkX65c+cOfH19cefOHfXM1tYWycnJcHd3FzAZ6TMexRJRvZLL5fjmm2/g6+uLkSNHai113t7e2LlzJy5fvowJEyaw1FGD16RJE2zZskVjVlRUhDfeeINHsqQVix0R1Yvq6mrs3LkTPj4+GD16NK5cuVLjurZt2+Kbb77BxYsXMXbsWJiYmOg4KZH+euWVVzBq1CiN2eHDh/HFF18IlIj0HY9iiahOVVVV4auvvsKyZcuQnp6udZ2fnx8WLFiAV199FRKJRIcJiQxLQUEB/Pz8kJ2drZ5ZW1sjMTERnp6eAiYjfcQdOyKqE5WVldi8eTO8vb0xceJEraUuICAAP/74IxISEjBs2DCWOqInsLe3x7Zt2zRmJSUlGDduHJRKpUCpSF+x2BHRcykvL8eGDRvg5eWFKVOmIDMzs8Z1HTt2xP79+3H+/Hm88sorEIv5rx+i2urbty/eeOMNjdmJEyceuSwKEY9iieiZlJWVYevWrVi1ahVyc3O1rgsJCcHChQvRt29fiEQiHSYkMi7FxcXw9/fX+MuThYUFLly4gNatWwuYjPQJix0RPZWSkhJs3rwZH3/8MW7fvq11XVhYGBYtWoSIiAgWOqI6cuzYMfTu3Vtj1qVLF5w6dYofayAAPIololoqLi7G8uXL0aJFC8yaNUtrqQsPD8exY8fwxx9/4IUXXmCpI6pD//d//4d33nlHY/bnn3/i448/FigR6Rvu2BHRYxUWFuLTTz/FunXrNO5d+bCIiAgsWLAAPXr00GE6ooantLQUgYGBuHbtmnpmamqKuLg4+Pn5CZiM9AGLHRHVSCaTYd26dVi/fj2Ki4u1ruvTpw8WLFiA0NBQHaYjatiio6PRvXt3jQsVd+jQAWfOnOG1IBs4HsUSkYb8/HzMnTsXzZs3x+LFi7WWugEDBuDMmTM4dOgQSx2RjoWFhWHmzJkas/Pnz2PZsmUCJSJ9wR07IgIA5OXlYc2aNdi0aRNKS0u1rhs8eDDmz5+PoKAgHaYjoodVVFSgY8eOuHjxonomlUpx5swZ/v+zAWOxI2rgcnJysHr1amzZsgXl5eU1rhGJRBgyZAjmz5+P9u3b6zghEWlz7tw5dOnSBQqFQj3z9fVFXFwczMzMBExGQuFRLFEDdevWLUydOhWenp5Yu3ZtjaVOJBJh5MiRSEpKwt69e1nqiPRMp06dMHfuXI1ZSkoKFi1aJFAiEhp37IgamIyMDKxYsQJffvklqqqqalwjFosxatQozJ07F23bttVxQiJ6GlVVVQgJCcGFCxfUM7FYjFOnTqFr167CBSNBsNgRNRBpaWlYvnw5du7cCblcXuMaiUSCMWPGYO7cuWjVqpWOExLRs0pKSkLHjh1RXV2tnnl7e+PChQuwtLQUMBnpGo9iiYxcamoqxo0bh9atW2Pbtm01ljoTExNMmjQJqamp+PLLL1nqiAyMv78/PvroI41ZamrqI8e0ZPy4Y0dkpC5fvoylS5di9+7dUCqVNa4xNTXFG2+8gdmzZ8PDw0PHCYmoLsnlcnTr1g1nzpzRmB8/fhzh4eHChCKdY7EjMjLJyclYsmQJ9u7dC23/9zY3N8ekSZMwa9YsuLm56TghEdWXK1euIDAwEBUVFepZixYtkJiYiEaNGgmYjHSFR7FERiIhIQFDhgyBv78/9uzZU2Ops7CwwHvvvYe0tDSsX7+epY7IyLRp0wbLly/XmN24ceORixmT8eKOHZGBi4uLw+LFi7F//36ta6ysrPD222/jX//6F5ycnHSYjoh0TalUolevXjh58qTG/NChQ+jTp49AqUhXWOyIDNSZM2ewePFiHDhwQOuaRo0a4d1338X06dPh6Oiow3REJKS0tDS0b99e4y4ybm5uSEpKgr29vYDJqL7xKJbIwERHR6NPnz7o0qWL1lJna2uLRYsW4caNG1iyZAlLHVED07JlS3z88ccas6ysLEybNk2gRKQr3LEjMhAnTpxAZGQkjh07pnWNvb09ZsyYgalTp8LW1laH6YhI36hUKrz00ks4cuSIxvznn3/GoEGDhAlF9Y7FjkiPqVQqHDt2DJGRkY98XuZBjo6O+Ne//oUpU6bAxsZGhwmJSJ/dvHkTfn5+KC4uVs+cnJyQkpLCnXwjxaNYIj2kUqlw+PBhdOvWDREREVpLnZOTE1avXo309HTMmTOHpY6INDRr1gyffvqpxuz27duYPHmy1sshkWHjjh2RHlGpVDh48CAiIyNx9uxZretcXV0xa9YsTJo0ibcLIqLHUqlU+Mc//oH//Oc/GvPvvvsOI0aMECgV1RcWOyI9oFKpEBUVhcjISMTHx2td5+7ujjlz5mDChAkwNzfXYUIiMmS5ubnw9fWFTCZTzxwcHJCcnAxXV1cBk1Fd41EskYCUSiX27duHDh06YNCgQVpLnYeHB7Zs2YJr167h7bffZqkjoqfi4uKCTZs2acxkMhkmTZrEI1kjwx07IgEoFAr88MMPWLJkCVJSUrSua9myJebOnYvRo0fD1NRUhwmJyBgNHz4ce/fu1Zjt2LEDr7/+ujCBqM6x2BHpkFwux/fff48lS5bgypUrWtd5e3tj3rx5eO2112BiYqLDhERkzPLz8+Hr64vbt2+rZzY2NkhOTkazZs0ETEZ1hUexRDpQXV2NnTt3wsfHB6NHj9Za6tq2bYtvvvkGFy9exNixY1nqiKhOOTo64osvvtCYFRcXY/z48TySNRIsdkT1qKqqCtu2bUObNm0wbtw4XLt2rcZ1fn5+2LNnD5KTkzFq1ChIpVIdJyWihmLgwIEYM2aMxuzo0aPYsmWLQImoLvEolqgeVFZWYseOHVi+fDkyMzO1rgsICMDChQsxaNAgiMX8exYR6UZhYSH8/PyQlZWlnllZWSEhIQFeXl4CJqPnxXcSojpUXl6ODRs2wMvLC5MnT9Za6jp27Ij9+/fj/PnzeOWVV1jqiEin7OzssH37do1ZaWkpxo0bB6VSKVAqqgt8NyGqA2VlZVi7di1atmyJd999V+NvwQ8KCQnBgQMHEBsbi4EDB0IkEuk4KRHRX1566SW8+eabGrM//vgD69evFygR1QUexRI9h5KSEmzevBkff/yxxrfMHhYWFoZFixYhIiKCZY6I9Ma9e/fQvn173LhxQz0zMzPDhQsX0LZtW+GC0TNjsSN6BsXFxdi4cSPWrFmDu3fval0XHh6OhQsXIjw8nIWOiPTSiRMnEB4erjHr3LkzoqOj+UUuA8SjWKKnUFhYiMWLF6NFixaYO3eu1lIXERGBEydO4Pjx4+jVqxdLHRHprZ49e2LatGkas7Nnz2LVqlUCJaLnwR07olqQyWRYv3491q9fj6KiIq3r+vTpgwULFiA0NFSH6YiInk9ZWRk6dOiAq1evqmcmJiY4d+4c2rdvL2AyelosdkSPkZ+fj08++QSfffYZ7t27p3XdgAEDMH/+fHTu3FmH6YiI6s6ff/6JsLAwjW/FBgQE4OzZs7yloQHhUSxRDfLy8jBr1iy0aNECy5cv11rqBg8ejLi4OERFRbHUEZFB69KlC2bNmqUxS0hIwJIlSwRKRM+CO3ZED8jJycHq1auxZcsWlJeX17hGJBJhyJAhmD9/Po8oiMioVFZWolOnTkhOTlbPJBIJTp8+jeDgYAGTUW2x2BEBuHXrFlatWoXPP/8clZWVNa4RiUQYMWIE5s2bB19fXx0nJCLSjfj4eISEhEAul6tnPj4+iI+Ph7m5uYDJqDZ4FEsNWkZGBqZMmQIvLy9s2LChxlInFosxevRoXLx4Ebt372apIyKjFhQUhPnz52vMLl26hAULFgiUiJ4Gd+yoQUpLS8Py5cuxc+dOjb+VPkgikWDMmDGYO3cuWrVqpeOERETCqa6uRpcuXRAfH6+eiUQi/PHHHwgLCxMwGT0Jix01KKmpqVi2bBm+/vprKBSKGteYmJhg3LhxmDNnDjw9PXWckIhIP6SkpCAoKAhVVVXqmZeXFxISEmBlZSVgMnocHsVSg3D58mWMHj0abdu2xc6dO2ssdaamppgyZQquXbuGrVu3stQRUYPm6+uLxYsXa8yuX7+O2bNnC5SIaoM7dmTUUlJSsGTJEuzZswfa/lE3NzfHpEmTMGvWLLi5uek4IRGR/lIoFOjevTtOnz6tMT969Ch69+4tUCp6HBY7Mkr3r720b98+rWssLCwwefJkzJw5E66urjpMR0RkOFJTUxEQEKBxCSgPDw8kJSXBxsZGwGRUEx7FklGJi4vDoEGDEBgYqLXUWVlZYfbs2bhx4wbWrFnDUkdE9Bje3t5YuXKlxiwzMxMzZswQKBE9DnfsyCicPXsWkZGROHDggNY1jRo1wrvvvovp06fD0dFRh+mIiAybUqlEREQEjh8/rjH/5Zdf0K9fP4FSUU1Y7MigxcTEIDIyEocPH9a6xtbWFtOnT8e0adNgb2+vw3RERMbjxo0b8Pf3R0lJiXrm6uqK5ORkODg4CJiMHsSjWDJIJ0+eREREBMLCwrSWOnt7eyxevBgZGRn48MMPWeqIiJ5DixYt8Mknn2jMcnJyMHXqVIESUU24Y0cGQ6VS4fjx44iMjMSJEye0rnN0dMS//vUvvP3222jUqJEOExIRGTeVSoWXX34Z//3vfzXm+/btw6uvvipQKnoQix3pPZVKhSNHjiAyMhLR0dFa1zk5OeH999/HW2+9BWtrax0mJCJqOLKysuDn54fCwkL1zNHRESkpKXBychIuGAHgUSzpMZVKhQMHDqBLly546aWXtJY6V1dXrFu3Dunp6Zg5cyZLHRFRPXJzc8OGDRs0Zvn5+Zg8ebLW64WS7nDHjvSOSqVCVFQUIiMjNe5T+DB3d3fMmTMHEyZMgLm5uQ4TEhE1bCqVCq+88gr+/e9/a8y/+eYbjBo1SphQBIDFjvSIUqnEzz//jMWLFyMhIUHrOg8PD8ydOxevv/46zMzMdJiQiIjuy8vLg5+fH/Lz89UzOzs7pKSkoGnTpgIma9h4FEuCUygU2LNnD9q3b48hQ4ZoLXUtW7bEtm3bkJqaijfffJOljohIQM7Ozti8ebPGrLCwEG+88QaPZAXEHTsSjFwux/fff4+lS5fi8uXLWtd5e3tj/vz5eO211yCVSnWYkIiInuS1117Dd999pzHbtm0bJkyYIFCiho3FjnSuuroa3377LZYuXYpr165pXde2bVssWLAAw4cPh0Qi0WFCIiKqLZlMBl9fX+Tm5qpnjRo1QlJSEpo3by5gsoaJR7GkM1VVVdi2bRvatGmDcePGaS11fn5+2LNnD5KTk/Haa6+x1BER6TEHBwd88cUXGrN79+5h/PjxUCqVAqVquFjsqN5VVlZiy5Yt8Pb2xsSJE5Genl7juoCAAPz4449ISEjAsGHDWOiIiAxE//79MW7cOI3ZsWPHsGnTJoESNVw8iqV6U1FRgW3btmHFihXIysrSuq5jx45YuHAhBgwYAJFIpMOERERUV4qKiuDv74+bN2+qZ5aWlrhw4QK8vb0FTNawsNhRnSsrK8PWrVuxevVq5OTkaF0XEhKCRYsWoU+fPix0RERG4OjRo3jhhRc0ZqGhoTh58iRPYXSER7FUZ0pKSrB69Wp4enpixowZWktdWFgYfv31V5w+fRp9+/ZlqSMiMhIRERGYMmWKxiwmJgZr164VKFHDwx07em7FxcXYuHEj1qxZg7t372pdFx4ejoULFyI8PJxljojISJWUlCAgIABpaWnqmZmZGeLj49GuXTsBkzUMLHb0zAoLC7FhwwasXbsWBQUFWtdFRERgwYIF6NGjhw7TERGRUP744w/07NlT40LFnTp1QkxMDExMTARMZvx4FEtPTSaTYdGiRWjRogUWLlyotdT16dMH0dHROHLkCEsdEVED0r17d7z33nsas3PnzmHFihUCJWo4uGNHtZafn4+1a9diw4YNuHfvntZ1AwYMwPz589G5c2cdpiMiIn1SXl6OoKAgjTsLSaVSxMbGIjAwULhgRo7Fjp4oLy8Pa9aswaZNm1BaWqp13eDBgzF//nwEBQXpMB0REemrs2fPomvXrhoXKvb390dsbCzv911PeBRLWuXk5GDGjBnw9PTE6tWrayx1IpEIQ4cORUJCAn766SeWOiIiUuvcuTM++OADjVlSUhIiIyMFSmT8uGNHj7h16xZWrVqFzz//HJWVlTWuEYvFGD58OObNmwdfX18dJyQiIkNRVVWF4OBgJCYmqmdisRgxMTEICQkRMJlxYrEjtczMTKxYsQLbt29HVVVVjWvEYjH++c9/Yu7cuWjTpo2OExIRkSFKSEhAcHAwqqur1bM2bdrg/PnzsLCwEDCZ8eFRLCE9PR2TJk1Cq1atsHnz5hpLnVQqxfjx43HlyhXs2rWLpY6IiGotICAAixYt0phduXIF8+bNEyiR8eKOXQOWmpqKZcuW4euvv4ZCoahxjYmJCcaNG4c5c+bA09NTxwmJiMhYyOVyhIaGIjY2Vj0TiUT4/fffeUmsOsRi1wBdvnwZS5cuxe7duzW+qfQgU1NTvPHGG5g9ezY8PDx0nJCIiIzRpUuX0KFDB43Pb3t6eiIxMRHW1tYCJjMePIptQFJSUjBy5Ei0a9cO33zzTY2lztzcHO+++y7S0tKwceNGljoiIqozPj4+WLp0qcYsPT0d77//vkCJjA937BqAhIQELFmyBPv27dO6xsLCApMnT8bMmTPh6uqqw3RERNSQKBQKhIeH49SpUxrzw4cP48UXXxQolfFgsTNi8fHxWLx4Mf79739rXWNlZYV33nkHM2bMgJOTk+7CERFRg3X9+nW0b98eZWVl6pm7uzuSkpJgZ2cnXDAjwKNYI3T27FkMGDAAHTt21FrqGjVqhHnz5uHGjRtYsWIFSx0REemMl5cXVq9erTG7devWI/eXpafHHTsjEhMTg8jISBw+fFjrGltbW0yfPh3Tpk2Dvb29DtMRERH9j1KpxIsvvojffvtNY75//34MHDhQoFSGj8XOCJw8eRKRkZGP/J/jQfb29pgxYwamTp0KW1tbHaYjIiKqWWZmJvz8/HDv3j31zNnZGSkpKWjcuLGAyQwXj2INlEqlwrFjxxAeHo6ePXtqLXWOjo5YsWIFMjIyMH/+fJY6IiLSGx4eHli3bp3GLC8vD2+//bYwgYwAd+wMjEqlwpEjRxAZGYno6Git65ycnDBr1iy89dZbsLKy0mFCIiKi2lOpVBgwYAAOHDigMd+zZw+GDRsmUCrDxWJnIFQqFQ4dOoTIyEicOXNG6zpXV1fMnj0bEydOhKWlpQ4TEhERPZucnBz4+vqioKBAPWvcuDFSUlLg7OwsYDLDw6NYPadSqbB//34EBwejX79+Wkudu7s7PvvsM6SlpWHatGksdUREZDBcXV2xceNGjdndu3cxadIkcP/p6XDHTk8plUr8/PPPWLx4MRISErSua968OT744AO8/vrrMDMz02FCIiKiuqNSqTB06FD8+OOPGvNdu3ZhzJgxAqUyPCx2ekahUGDfvn1YvHgxUlJStK5r2bIl5s2bh9GjR8PExESHCYmIiOrHnTt34Ovrizt37qhntra2SE5Ohru7u4DJDAePYvWEXC7Ht99+Cz8/P4wYMUJrqfP29sauXbtw5coVjB8/nqWOiIiMRpMmTbB161aNWVFRESZMmMAj2VpqEDt2CoUCMpkMeXl5yMvLw53cXFSWl0OpUEAskcDMwgJNXFzg7OwMZ2dnODg4QCKR6CRbdXU1du/ejaVLlyI1NVXrurZt22LBggUYPny4zrIREREJYfTo0fjmm280Zlu3bsWkSZM0Zvr8/i4Uoy52BQUFSEhIQFJ8PCpKS6GSy2FdXg5bmQwmcjnEKhWUIhGqpVIUOTigxMICIqkU5lZW8A8KQkBAQL3dnaGqqgpff/01li1bhrS0NK3r/Pz8sGDBArz66qtG/w8jERER8Nf7t5+fH7Kzs9UzKysrJCUlwdPTU6/f34VmlMUuOzsbMadOIT01FSZlZfDIvAlXmQy2paUwUSi0Pq5aIkGRlRVyHByQ6dEM1ZaW8PT2Rlj37nB1da2TbJWVldixYweWL1+OzMxMresCAwOxYMECDBo0CGIxT8yJiKhhOXToEF5++WWNWb9+/TB29GjcuHZN797f9YVRFTu5XI7o6GjERkfDOj8frTIy4Z6fD4lS+dTPpRCLccvREdeae6DE0RHBYWEICwuDVCp9pmwVFRXYtm0bVqxYgaysLK3rOnXqhIULF6J///4QiUTP9FpERETGYOLEidi2bRskEglCQ0MRFhyMplVV8MnO0Zv3d31jNMUuNzcXB6KiUHArC21TU+GdlQVxHfxoSpEIqW5uuOztDQd3N7w8cCBcXFxq/fiysjJ8/vnnWLVqFXJycrSuCwkJwaJFi9CnTx8WOiIiIgDFxcXo2bMnOnXoADd7e3hfvgy3q6lwdnR87iL2vO/v+sooil1GRgZ+3rMHltk56HjpEmzKyur8NYotLRHn44Oypk0xePgwNG/eHMBfhfLw4cPw9/dHUFCQen1JSQm2bNmC1atX4/bt21qfNywsDIsWLUJERAQLHRER0QMyMjLw/VdfwSQzEz5xcbAsLgYAmJqYorGjI+riXVPb+7uhMvhil5GRgR+/+w6NMzLR+eJFSJ9hW7a25GIxzvi2g8zDA6+OHImqqip0794deXl5AP66r13fvn2xceNGrFmzBvn5+VqfKzw8HAsXLkR4eDgLHRER0UMefH9v++dpVNy7p/HrNo1sYG1tXSev9fD7uyGXO4Mudrm5ufj+q69gl34DXVNS6uTo9UmUIhFO+/mioHkL/PTLf3Dq1Cn1r9nY2EAsFqOwsFDr4yMiIrBgwQL06NGj3rMSEREZooff30VKJW7fuQOFQv7AKhGaNGkCkzr6bNz99/fCFp4YMWa0wR7LGuzXLeVyOQ5ERcEyOwchFy/qpNQBgFilQkjKRUgybqBtq1YalyApLi7WWur69u2LmJgYHDlyhKWOiIhIi5re30UiEezt7ACNw1cVCgsLUFfv/vff3y1ysnEwKgpyufzJD9JDBlvsoqOjUXArCx0vXarX49eaVJWWotWff8LNwQGhoaGPXTtgwACcPXsWBw8eRNeuXXWUkIiIyDBpe383NTWFtZWVxtrq6mpUV1XV2WtLlUp0vHgJsqwsxMTE1Nnz6pJBFrvs7GzERkejbWpqvXxR4nGqqqtRWFgIq+JieF++jLDg4Bq3awcPHoy4uDhERUUhODhYpxmJiIgM0ZPe3xvZNIJUqnkrzbo+r7MtK0Obq6k4e+rUY69moa8MstjFnDoF6/x8eD/menD1QQXg7t27uP+PUdOrV+FYUoKwh3bt3NzcsG/fPo1vyRIREdHjPen9XQQRHBwcYCI1ASCCpaUVTE1N6zxH66wsWOfnI/qBz9EbCoMrdgUFBUhPTUWrjEydfa7uPnl1NVSq/20Li1UqNLt2Da09PWFra6ueZ2Vl4caNGzrNRkREZMhq+/4ulUjQpEkTNHV1hZ2tbZ1c8uRhYpUKXhmZSL96FQUFBfXwCvXH4IpdQkICTMrK4P6YS4nUF6nJX39DeJDjzZuwlMsREBCgnjk5OcHd3V3H6YiIiAyXkO/vNWmWnw9pWRkSExOFjvJUDOr+GQqFAknx8fDIvPlMtxF5XiIAjo0bo6i4GCqVChKJGCKRCC1uZaFbSAisra3h6uqK9957r162homIiIyR0O/vNZEolWh+8yYS4+LQrVs3jatg6LN63bE7d+4c3n//fa2/HhUVhbVr19b6+WQyGSpKS3H14kUMPB+Pgefj4Rt9CgPi4zDwfDy23br1XHmvlpZiTFIiXjgXiz5x5/DhtWuoVirxaUYGvs7OBvDXt3KaODrCqUkTNHZoDAd7B3iVl8OuUSOMGjUKaWlpGDlyJL7//vsaX2PLli3Ys2cPAODEiRPw9fVFSEjIU/9eaLNt2zZ4e3tDJBKhpKTkuZ+PiIgaBqlUisDAQPV/qp7h26arVq16pte+//7uKpNpzD/LzEC/+Dj0j4/DKxfO42ZFxWOf54tbN5/r8Z3/PK3xv13v/pVL9lCuhz3rz/2wGzduICwsDObm5vjss8+e6TlqfYHizz//HJMmTXqmF6krycnJOPjDDxjw+wn1V6B7xZ7FL0EdYfVAk1apVFABED/FHR3KFQr0Px+PSK9WCLO3h0qlwn/u3EHvxo2x/dYt2JuYYHTTpo88TgWgpLoa/+nRAz8cPICUlBQAgFgsRnZ2NpydnbW+5ltvvYXw8HCMGDGi1jnvUygUNf7tISkpCdbW1ujVqxeSk5Pr7KrcRERk3BwdHR97x6T6eg6FQoFLly498v4eX1yMdRk38KWfP6QiEXIrK2EhEcP2oW/FPqjzn6dxtkvXOnk8AFRLJPilZw+8PHQo/Pz86uzn1vYeLpPJkJqaiqioKLi6uuKdd96p9XPeV+uj2E2bNmHSpEkoKSnBlClTcOnSJahUKqxfvx5hYWEoLi7G5MmTkZSUBLFYjE2bNqGqqgqfffYZ9u3bh+PHj+Pdd9+FWCyGiYkJzp07h507dyI5ORkff/wx0tLSMH78eMhkMrRo0QI7d+6Eg4MDwsPDERISgmPHjiE/Px+jevXSet26zn+exlAXF5wuLMSaNm3w3/x8HLl7F9VKJV5zbYqRrq4AgM03Mx+Z/+fOHQTb2CLM3h4AIBKJMNDJ6ZHX+C4nB/vyclGpVKKthQVmNnaEUiHHuVOnkJ6erl6nVCqxePFi/Pe//4WJiQnc3d2xdetWrF+/Hvb29rC2tsb333+PgwcPIioqCoGBgbh69Srmzp2L/Px8zJs3D7m5uTAzM8Py5cvh5eWF999/H/b29khOTkb37t0xefLkR/JZWVlBpVJBLpcjPT0dVg9d84eIiKgmSqUSaWlpGrPjx4/js88+Q2VlJdq3b48lS5ZALBZj7ty5SE5ORlVVFUaNGoXRo0fj448/RmFhIdq1a4eOHTti4sSJePvtt7F//34AwLJly9C6dWsMGTIEPXr0wNChQ3HixAm89957+PPPP3Hgp5+wvbgYve0dMNXDA3kVFWgkkQBKJVRiMVzMzNS5fpfJsPFmJiqVSrS3boTIVq2wLiMD9+RyDDwfjyAbG3S1tYONVArp35s8T3r8w5tB97uCLCUZ2UVFWLduHQAgMjISe/fuhUQiwRtvvIHc3FwUFhYiMDAQYWFh2LhxI1auXImvv/4aIpEIc+bMwahRo/D7779j2bJlsLOzQ25uLk6ePPnIn4GDgwNCQkJw6NChZ/5zrPWOnYWFBcrLyzFnzhyEhIRg8ODBuHXrFvr164eEhATMnDkTZmZmWLp0KRQKBUpLSxEfH68udgMGDMC7776LF154AUVFRbC1tdUodv3798eYMWMwbNgwrFy5EllZWfj0008RHh6OHj16IDIyEm9OmoQrJ07gC6f/7YI9uGPX+tQf2NrOF70cHHCyQIaTsgLM9/JClVKJkYkJ2OjTDlfLSmucf5mVBTczM4x1c3vkZ/80I0O9Y1dYXQ0bqRS3b9/GqtwcdLGyQjcrK7x88yb69e+PH3766Zn/MIiIiBqiZu7umBsWhjaxsZiXm4t/2tvD09QUb2dlQaFSIdjSCoNdXNC5SRPIqqvx3uXL+NzXF2ZiMT66fg0dbWzQv4mTxo5biVyOEYkJkKtU6GZnj4FOTmjfqFGtHv9gh4hu1QpLTp7A7ydO4MKFC1i7di0OHjwIMzMzyGQyODg4aOzYnTt3Dm+++Saio6NRVlaG4OBg/PHHH7h69Sr+8Y9/4NKlS2hawwnggz788EM4OjrW747dfUeOHMHBgwfx0UcfAfjrum5VVVU4duwYoqKiAAASiQQ2NjYajwsLC8OcOXNw6dIlDB06VOPyIAAQGxuL//znPwCA0aNHo1+/fupf+8c//gEAcHd1RUxxMeBU8/GmuViMXg4OAIDogkIck8lwtrgIwF9/wJkV5VrngAq1Obm9XFqKj9Ou455cjnsKBVxNTNDNygot7e1xJjb2yU9AREREGu7KZFj23//CtLwc5Uolsqqr4Wtuji/c3XGhvBxx5eV4M/UqPpVKUa1U4kpZKYYmXAAAVCqVcDY1e+Q5raVS/LtDEM4UFiKmsBDjkpOwvq0Pqmrx+Ae7QnlKMkolEly/fh3Hjh3DuHHjYPb37p/D353jQadOncKrr74Kc3NzmJubo3fv3oiNjYWtrS3CwsKeWOqeV62LXdu2bQH89fm1X375BR4eHk/1QnPmzEHfvn1x4MABBAcH48yZMxq/LnqgVan+vi/cffd/A6FSQfmYDUZz8f++C6ICMNXDA4Mf+ozb0buyGufp5eU4X3zviT/H3NRUfNKiBZzk1fi2oABVf+eZHhqKn0pLkXnz5hOegYiIiB7k07o13vb0RMuHLi0iFYnQydISnSwtYSuR4pjsLrrZ2aOXvQOWt279xOeVikQIs7dHmL097E1M8FstH/9gh0ho6Yl7oaHo2bOn+lj5aTzYaSwtLZ/68U+r1t+Kvb8dGBERgY0bN6rnCQkJ6vnmzZsB/PWhwOLiYo3HX79+HQEBAZg7dy58fHw0Po8GAJ06dcKPP/4IANi9eze6d+/+aNin+KpxqJ0d9uXlokKhAACklZWhUqnUOh/YxAlni4oQU/jXhQhVKhV+yM1F6d/r7itXKtDczg4KsRgnSksBAEqVCnfLy+Fcw2fyiIiI6PGup6ejRC4HANyWy1GkUCCzqgpZ1dUAALFIjGyo4GZmjsBGjXCmqBA5lZUAgILqauT+/d8lIhEUf2+4pJWVIbO8HMBf7+mpZaVPfPx9D3YFpUiM2/n5qKioQEREBHbs2IHKv9ff/7asRCKB4u++0K1bN/z000+orKxEQUEBjh8/rtNbi9Z6x27ChAkAgIULF2Lq1Knw9/eHQqFA7969sWHDBixYsABvvfUW/P39IZFI1CXvvrVr1+L48eOQSCQIDg5G165dce3aNfWvf/rppxg3bhwiIyPRvHlz7Nq165EMpmZmUNbym67hDg5ILSvFkIQLUAFobGKCLe18tc4tJRJsatcOS9Ou48Nr1yERAV3s7DDoobI2pZkHXr1wAe7mZvCzsYFUpYISwOfnziH/738A7wsNDUVRURFUKhX69euHjz76CEuWLEHjxo0xefJkTJo0CYMGDcLLL7+Mr7/+GhcvXsTy5cuRl5eHd999F+np6ZDL5Xjttdcwc+ZMjfXa7Ny5E0uWLEFeXh6cnJwwYsQILF26tFa/Z0RE1HA1a9YMNx86dfr111/x4YcfQi6XQyqVYuPGjejQoQMmTJiACxcuoFWrVigtLcWsWbPQo0cPzJs3D4cOHUKPHj2wbt06fPrpp/jiiy/g5eUFKysr9OnTB6NHj0bbtm1x7tw59ZUb3ps2Dat//BEWFRWwkkjwSZs2kFfLsTjtOkrkCkAE+FpZ45+urjCXSPBhq1aYcvEi5ColpCIxlnh7w8XMDIOdnNE/Pg4hdnYY4uyMyOvXUfJ34arN4+97sCuUXroE26aueGf6dLz88suIi4tDUFAQTExM8MYbb+Cdd97B2LFj4e/vj169emHjxo0YOnQoOnbsCJFIhI8++giurq64cuXKE/8MiouL0a5dOxQXF0MikeDjjz9+6jtZ1frLE/rgt99+w5XDh/HC6T+FjqKhqroK/+3UCQcvXcKxY8cA/HV8nJOTA/u/v2VLRERENdPX93cAONK1C9q89BJ69+4tdJRaMag7Tzg7OyPOwgLVEglMHjoiFZLI3AKKxo3x9ttvo2nTprhz5w5mzpzJUkdERFQL+vr+Xi2RoMTC4rHXpNU3BlfsRFIpiqys4PjQZ/iEVGRlBZFUiu7du+OVV17RyWsuXboUP/zwg8ZsxowZGDNmjE5en4iIqK7o+/t7XRe7pKQkjB49WmPWqlUr7Nu377mf26CKnYODA8ytrJDj4KBXf/A5jf/KVdPXnuvLvHnzMG/ePJ29HhERUX1paO/v/v7+uHDhQp0+5331eq/YuiaRSOAfFIRMj2ZQiPUjukIsRkazZmjfsaPB3CCYiIhIn/D9ve7ox+/eUwgICEC1pSVuOToKHQUAcNPREXJLS7Rv317oKERERAaL7+91w+CKnb29PTy9vXGtuUetL31SX5QiEa4394Bn69b8ogQREdFz4Pt73TC4YgcAYd27o8TREak13NdVl666uaHE0RFh3boJmoOIiMgY8P39+RlksXN1dUVwWBgue3ujWAe356hJkaUlrrT2Rudu3eDq6ipIBiIiImPC9/fnZ5DFDgDCwsJg7+6GOB8fyHX8QUu5WIy4dj5wcHNDaGioTl+biIjImPH9/fkYbLGTSqXoN3Agypo2xRnfdjo7j1eKRDjj2w7lrk3x8sCBkEoN6ooxREREeo3v78/HYIsdALi4uGDw8GGQeXjgtJ9vvTd7uViM036+kHl4YPDwYXBxcanX1yMiImqI+P7+7AzqXrHaZGRk4Oc9e2GZnY2Oly7Bpqyszl+jyNISce18UO7aFIOHD0Pz5s3r/DWIiIjof/j+/vSMotgBQG5uLg5ERaHgVhbapqbCOysL4jr40ZQiEa66ueFKa284uLnh5YEDDbrJExERGRK+vz8doyl2ACCXyxEdHY3Y6GhY5+fDKyMTzfLzIVEqn/q5FGIxbjo64npzD5Q4OqJzt24IDQ012DN3IiIiQ8X399ozqmJ3X3Z2NmKio5F+9SqkZWVofvMmXO/KYFtaChOFQuvjqiUSFFlZIaexAzKaNYPc0hKerVsjzEC/8kxERGRM+P7+ZEZZ7O4rKChAYmIiEuPiUFFaCpVcDuvyctjICmAql0OsUkIpEqNKKkWxgz1KLCwgkkphbmWF9h07on379gZ3xWkiIiJjx/d37Yy62N2nUCggk8mQl5eHvLw83MnNRVVFBRRyOSRSKUzNzdHExQXOzs5wdnaGg4ODQd3wl4iIqCHi+/ujGkSxIyIiImoIDPo6dkRERET0Pyx2REREREaCxY6IiIjISLDYERERERkJFjsiIiIiI8FiR0RERGQkWOyIiIiIjASLHREREZGRYLEjIiIiMhIsdkRERERGgsWOiIiIyEiw2BEREREZCRY7IiIiIiPBYkdERERkJFjsiIiIiIwEix0RERGRkWCxIyIiIjISLHZERERERoLFjoiIiMhIsNgRERERGQkWOyIiIiIjwWJHREREZCRY7IiIiIiMBIsdERERkZFgsSMiIiIyEix2REREREaCxY6IiIjISPw/UaPnfRLk2TkAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "graph_search_space = tpot2.search_spaces.pipelines.GraphSearchPipeline(\n", - " leaf_search_space = fss_search_space,\n", - " inner_search_space = tpot2.config.get_search_space([\"transformers\"]),\n", - " root_search_space= tpot2.config.get_search_space([\"KNeighborsClassifier\", \"LogisticRegression\", \"DecisionTreeClassifier\"]),\n", - " max_size = 10,\n", - ")\n", - "\n", - "graph_search_space.generate(rng=4).export_pipeline().plot()" + "est.fitted_pipeline_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### Optimize with TPOT\n", - "\n", - "For this example, we will optimize the DynamicUnion search space" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Generation: 100%|██████████| 5/5 [00:34<00:00, 6.96s/it]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.9916366443643849\n" - ] - } - ], - "source": [ - "import tpot2\n", - "import sklearn.datasets\n", - "from sklearn.linear_model import LogisticRegression\n", - "import numpy as np\n", - "\n", - "\n", - "final_classification_search_space = SequentialPipeline([dynamic_fss_space, classification_search_space])\n", - "\n", - "est = tpot2.TPOTEstimator(generations=5, \n", - " scorers=[\"roc_auc_ovr\", tpot2.objectives.complexity_scorer],\n", - " scorers_weights=[1.0, -1.0],\n", - " n_jobs=32,\n", - " classification=True,\n", - " search_space = final_classification_search_space,\n", - " verbose=1,\n", - " )\n", + "### Combining with existing search spaces\n", "\n", + "As with all search spaces, FSSNode can be combined with any other search space. \n", "\n", - "scorer = sklearn.metrics.get_scorer('roc_auc_ovo')\n", - "\n", - "est.fit(X_train, y_train)\n", - "print(scorer(est, X_test, y_test))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that this pipeline performed slightly better and correctly identified group one and group two as the feature sets used in the generative equation." + "You can also pair this with the existing prebuilt templates, for example:" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
Pipeline(steps=[('featureunion',\n",
-       "                 FeatureUnion(transformer_list=[('featuresetselector-1',\n",
-       "                                                 FeatureSetSelector(name='group_one',\n",
-       "                                                                    sel_subset=['a',\n",
-       "                                                                                'b',\n",
-       "                                                                                'c'])),\n",
-       "                                                ('featuresetselector-2',\n",
-       "                                                 FeatureSetSelector(name='group_two',\n",
-       "                                                                    sel_subset=['d',\n",
-       "                                                                                'e',\n",
-       "                                                                                'f']))])),\n",
-       "                ('randomforestclassifier',\n",
-       "                 RandomForestClassifier(bootstrap=False,\n",
-       "                                        class_weight='balanced',\n",
-       "                                        max_features=0.3314976075207,\n",
-       "                                        min_samples_leaf=3,\n",
-       "                                        min_samples_split=15,\n",
-       "                                        n_estimators=128))])
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" + "
Pipeline(steps=[('featuresetselector',\n",
+       "                 FeatureSetSelector(name='group_two', sel_subset=[3, 4, 5])),\n",
+       "                ('pipeline',\n",
+       "                 Pipeline(steps=[('maxabsscaler', MaxAbsScaler()),\n",
+       "                                 ('rfe',\n",
+       "                                  RFE(estimator=ExtraTreesClassifier(max_features=0.0390676831531,\n",
+       "                                                                     min_samples_leaf=8,\n",
+       "                                                                     min_samples_split=14,\n",
+       "                                                                     n_jobs=1),\n",
+       "                                      step=0.753983388654)),\n",
+       "                                 ('featureunion-1',\n",
+       "                                  FeatureUnion(transformer_list=[('f...\n",
+       "                                                                  FeatureUnion(transformer_list=[('powertransformer',\n",
+       "                                                                                                  PowerTransformer()),\n",
+       "                                                                                                 ('pca',\n",
+       "                                                                                                  PCA(n_components=0.9286371732844))])),\n",
+       "                                                                 ('passthrough',\n",
+       "                                                                  Passthrough())])),\n",
+       "                                 ('featureunion-2',\n",
+       "                                  FeatureUnion(transformer_list=[('skiptransformer',\n",
+       "                                                                  SkipTransformer()),\n",
+       "                                                                 ('passthrough',\n",
+       "                                                                  Passthrough())])),\n",
+       "                                 ('kneighborsclassifier',\n",
+       "                                  KNeighborsClassifier(n_jobs=1, n_neighbors=21,\n",
+       "                                                       weights='distance'))]))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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" ], "text/plain": [ - "Pipeline(steps=[('featureunion',\n", - " FeatureUnion(transformer_list=[('featuresetselector-1',\n", - " FeatureSetSelector(name='group_one',\n", - " sel_subset=['a',\n", - " 'b',\n", - " 'c'])),\n", - " ('featuresetselector-2',\n", - " FeatureSetSelector(name='group_two',\n", - " sel_subset=['d',\n", - " 'e',\n", - " 'f']))])),\n", - " ('randomforestclassifier',\n", - " RandomForestClassifier(bootstrap=False,\n", - " class_weight='balanced',\n", - " max_features=0.3314976075207,\n", - " min_samples_leaf=3,\n", - " min_samples_split=15,\n", - " n_estimators=128))])" + "Pipeline(steps=[('featuresetselector',\n", + " FeatureSetSelector(name='group_two', sel_subset=[3, 4, 5])),\n", + " ('pipeline',\n", + " Pipeline(steps=[('maxabsscaler', MaxAbsScaler()),\n", + " ('rfe',\n", + " RFE(estimator=ExtraTreesClassifier(max_features=0.0390676831531,\n", + " min_samples_leaf=8,\n", + " min_samples_split=14,\n", + " n_jobs=1),\n", + " step=0.753983388654)),\n", + " ('featureunion-1',\n", + " FeatureUnion(transformer_list=[('f...\n", + " FeatureUnion(transformer_list=[('powertransformer',\n", + " PowerTransformer()),\n", + " ('pca',\n", + " PCA(n_components=0.9286371732844))])),\n", + " ('passthrough',\n", + " Passthrough())])),\n", + " ('featureunion-2',\n", + " FeatureUnion(transformer_list=[('skiptransformer',\n", + " SkipTransformer()),\n", + " ('passthrough',\n", + " Passthrough())])),\n", + " ('kneighborsclassifier',\n", + " KNeighborsClassifier(n_jobs=1, n_neighbors=21,\n", + " weights='distance'))]))])" ] }, - "execution_count": 16, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "est.fitted_pipeline_" + "linear_search_space = tpot2.config.template_search_spaces.get_template_search_spaces(\"linear\", classification=True)\n", + "fss_and_linear_search_space = SequentialPipeline([fss_search_space, linear_search_space])\n", + "\n", + "# est = tpot2.TPOTEstimator( \n", + "# population_size=32,\n", + "# generations=10, \n", + "# scorers=[\"roc_auc_ovr\", tpot2.objectives.complexity_scorer],\n", + "# scorers_weights=[1.0, -1.0],\n", + "# other_objective_functions=[number_of_selected_features],\n", + "# other_objective_functions_weights = [-1],\n", + "# objective_function_names = [\"Number of selected features\"],\n", + "\n", + "# n_jobs=32,\n", + "# classification=True,\n", + "# search_space = fss_and_linear_search_space,\n", + "# verbose=2,\n", + "# )\n", + "\n", + "fss_and_linear_search_space.generate(rng=1).export_pipeline()" ] }, { @@ -3548,7 +4969,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -3566,13 +4987,13 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
Pipeline(steps=[('featureunion',\n",
+       "
Pipeline(steps=[('featureunion',\n",
        "                 FeatureUnion(transformer_list=[('pipeline-1',\n",
        "                                                 Pipeline(steps=[('featuresetselector',\n",
        "                                                                  FeatureSetSelector(name='group_one',\n",
-       "                                                                                     sel_subset=[0,\n",
-       "                                                                                                 1,\n",
-       "                                                                                                 2])),\n",
+       "                                                                                     sel_subset=['a',\n",
+       "                                                                                                 'b',\n",
+       "                                                                                                 'c'])),\n",
        "                                                                 ('pipeline',\n",
        "                                                                  Pipeline(steps=[('featureunion',\n",
        "                                                                                   FeatureUnion(transformer_list=[('featureunion',\n",
        "                                                                                                                   FeatureUnion(transformer_list=[('pca',\n",
        "                                                                                                                                                   PCA(n_components=0.93113403057))])),\n",
-       "                                                                                                                  ('passthrough...\n",
+       "                                                                                                                  ('passt...\n",
        "                                                                                   FeatureUnion(transformer_list=[('featureunion',\n",
        "                                                                                                                   FeatureUnion(transformer_list=[('quantiletransformer',\n",
        "                                                                                                                                                   QuantileTransformer(n_quantiles=87)),\n",
@@ -4001,19 +5422,19 @@
        "                                        criterion='entropy',\n",
        "                                        max_features=0.021545996678,\n",
        "                                        min_samples_leaf=11,\n",
-       "                                        n_estimators=128))])
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"
       ],
       "text/plain": [
-       "Pipeline(steps=[('maxabsscaler', MaxAbsScaler()),\n",
-       "                ('selectfwe', SelectFwe(alpha=0.0142080454732)),\n",
+       "Pipeline(steps=[('passthrough', Passthrough()),\n",
+       "                ('selectfwe', SelectFwe(alpha=0.0012275167982)),\n",
        "                ('featureunion-1',\n",
-       "                 FeatureUnion(transformer_list=[('featureunion',\n",
-       "                                                 FeatureUnion(transformer_list=[('passkbinsdiscretizer',\n",
-       "                                                                                 PassKBinsDiscretizer(n_bins=8))])),\n",
+       "                 FeatureUnion(transformer_list=[('skiptransformer',\n",
+       "                                                 SkipTransformer()),\n",
        "                                                ('passthrough',\n",
        "                                                 Passthrough())])),\n",
        "                ('featureunion-2',\n",
@@ -863,13 +911,12 @@
        "                                                 SkipTransformer()),\n",
        "                                                ('passthrough',\n",
        "                                                 Passthrough())])),\n",
-       "                ('logisticregression',\n",
-       "                 LogisticRegression(C=462.7983711938423,\n",
-       "                                    class_weight='balanced', max_iter=1000,\n",
-       "                                    n_jobs=1, solver='saga'))])"
+       "                ('adaboostclassifier',\n",
+       "                 AdaBoostClassifier(learning_rate=0.9052253032837,\n",
+       "                                    n_estimators=273))])"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -881,22 +928,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
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-       "array([0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0,\n",
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-       "       0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
-       "       0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1])"
+       "array([1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0,\n",
+       "       1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1,\n",
+       "       0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1,\n",
+       "       1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0,\n",
+       "       0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
+       "       1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1])"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -916,7 +963,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -953,7 +1000,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -962,7 +1009,7 @@
        "['roc_auc_score', 'complexity_scorer']"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -974,7 +1021,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -1014,73 +1061,73 @@
        "  \n",
        "    \n",
        "      0\n",
-       "      0.994779\n",
-       "      38.8\n",
+       "      0.964012\n",
+       "      1745.5\n",
        "      NaN\n",
        "      NaN\n",
        "      <tpot2.search_spaces.pipelines.sequential.Sequ...\n",
        "      0.0\n",
-       "      1.727192e+09\n",
-       "      1.727192e+09\n",
+       "      1.727568e+09\n",
+       "      1.727568e+09\n",
        "      None\n",
        "      NaN\n",
-       "      (Normalizer(norm='l1'), Passthrough(), Feature...\n",
+       "      (Normalizer(norm='l1'), SelectPercentile(perce...\n",
        "    \n",
        "    \n",
        "      1\n",
-       "      0.884608\n",
-       "      12.0\n",
+       "      NaN\n",
+       "      NaN\n",
        "      NaN\n",
        "      NaN\n",
        "      <tpot2.search_spaces.pipelines.sequential.Sequ...\n",
        "      0.0\n",
-       "      1.727192e+09\n",
-       "      1.727192e+09\n",
-       "      None\n",
+       "      1.727568e+09\n",
+       "      1.727568e+09\n",
+       "      INVALID\n",
        "      NaN\n",
-       "      (MinMaxScaler(), SelectPercentile(percentile=6...\n",
+       "      (MaxAbsScaler(), SelectFromModel(estimator=Ext...\n",
        "    \n",
        "    \n",
        "      2\n",
-       "      0.995994\n",
-       "      277.0\n",
+       "      NaN\n",
+       "      NaN\n",
        "      NaN\n",
        "      NaN\n",
        "      <tpot2.search_spaces.pipelines.sequential.Sequ...\n",
        "      0.0\n",
-       "      1.727192e+09\n",
-       "      1.727192e+09\n",
-       "      None\n",
+       "      1.727568e+09\n",
+       "      1.727568e+09\n",
+       "      INVALID\n",
        "      NaN\n",
-       "      (MaxAbsScaler(), Passthrough(), FeatureUnion(t...\n",
+       "      (MaxAbsScaler(), VarianceThreshold(threshold=0...\n",
        "    \n",
        "    \n",
        "      3\n",
-       "      0.969714\n",
-       "      97.0\n",
+       "      NaN\n",
+       "      NaN\n",
        "      NaN\n",
        "      NaN\n",
        "      <tpot2.search_spaces.pipelines.sequential.Sequ...\n",
        "      0.0\n",
-       "      1.727192e+09\n",
-       "      1.727192e+09\n",
-       "      None\n",
+       "      1.727568e+09\n",
+       "      1.727568e+09\n",
+       "      INVALID\n",
        "      NaN\n",
-       "      (RobustScaler(quantile_range=(0.1797291876324,...\n",
+       "      (Normalizer(norm='l1'), RFE(estimator=ExtraTre...\n",
        "    \n",
        "    \n",
        "      4\n",
-       "      0.977700\n",
-       "      10.0\n",
+       "      0.991667\n",
+       "      24030.0\n",
        "      NaN\n",
        "      NaN\n",
        "      <tpot2.search_spaces.pipelines.sequential.Sequ...\n",
        "      0.0\n",
-       "      1.727192e+09\n",
-       "      1.727192e+09\n",
+       "      1.727568e+09\n",
+       "      1.727568e+09\n",
        "      None\n",
        "      NaN\n",
-       "      (RobustScaler(quantile_range=(0.0723902721638,...\n",
+       "      (RobustScaler(quantile_range=(0.1798922078332,...\n",
        "    \n",
        "    \n",
        "      ...\n",
@@ -1097,106 +1144,93 @@
        "      ...\n",
        "    \n",
        "    \n",
-       "      295\n",
-       "      0.994663\n",
-       "      17.0\n",
-       "      (245, 2)\n",
-       "      ind_crossover\n",
+       "      345\n",
+       "      0.992793\n",
+       "      4374.0\n",
+       "      (237, 237)\n",
+       "      ind_mutate\n",
        "      <tpot2.search_spaces.pipelines.sequential.Sequ...\n",
-       "      5.0\n",
-       "      1.727192e+09\n",
-       "      1.727192e+09\n",
+       "      6.0\n",
+       "      1.727568e+09\n",
+       "      1.727568e+09\n",
        "      None\n",
-       "      1.0\n",
-       "      (MinMaxScaler(), SelectPercentile(percentile=3...\n",
+       "      NaN\n",
+       "      (Passthrough(), SelectFwe(alpha=0.022268001122...\n",
        "    \n",
        "    \n",
-       "      296\n",
-       "      NaN\n",
-       "      NaN\n",
-       "      (190, 190)\n",
+       "      346\n",
+       "      0.520972\n",
+       "      9.0\n",
+       "      (128, 128)\n",
        "      ind_mutate\n",
        "      <tpot2.search_spaces.pipelines.sequential.Sequ...\n",
-       "      5.0\n",
-       "      1.727192e+09\n",
-       "      1.727192e+09\n",
-       "      INVALID\n",
+       "      6.0\n",
+       "      1.727568e+09\n",
+       "      1.727568e+09\n",
+       "      None\n",
        "      NaN\n",
-       "      (RobustScaler(quantile_range=(0.0790382918495,...\n",
+       "      (MaxAbsScaler(), RFE(estimator=ExtraTreesClass...\n",
        "    \n",
        "    \n",
-       "      297\n",
-       "      0.995224\n",
-       "      231.0\n",
-       "      (168, 232)\n",
+       "      347\n",
+       "      NaN\n",
+       "      NaN\n",
+       "      (109, 85)\n",
        "      ind_crossover\n",
        "      <tpot2.search_spaces.pipelines.sequential.Sequ...\n",
-       "      5.0\n",
-       "      1.727192e+09\n",
-       "      1.727192e+09\n",
-       "      None\n",
+       "      6.0\n",
+       "      1.727568e+09\n",
+       "      1.727568e+09\n",
+       "      INVALID\n",
        "      NaN\n",
-       "      (MaxAbsScaler(), SelectFwe(alpha=0.03220532277...\n",
+       "      (StandardScaler(), SelectPercentile(percentile...\n",
        "    \n",
        "    \n",
-       "      298\n",
-       "      0.974412\n",
-       "      10.0\n",
-       "      (93, 205)\n",
-       "      ind_mutate , ind_mutate , ind_crossover\n",
+       "      348\n",
+       "      0.976466\n",
+       "      21.0\n",
+       "      (296, 128)\n",
+       "      ind_crossover , ind_mutate\n",
        "      <tpot2.search_spaces.pipelines.sequential.Sequ...\n",
-       "      5.0\n",
-       "      1.727192e+09\n",
-       "      1.727192e+09\n",
+       "      6.0\n",
+       "      1.727568e+09\n",
+       "      1.727568e+09\n",
        "      None\n",
        "      NaN\n",
-       "      (MaxAbsScaler(), Passthrough(), FeatureUnion(t...\n",
+       "      (Passthrough(), RFE(estimator=ExtraTreesClassi...\n",
        "    \n",
        "    \n",
-       "      299\n",
-       "      0.932393\n",
-       "      8.0\n",
-       "      (234, 234)\n",
-       "      ind_mutate\n",
+       "      349\n",
+       "      0.990725\n",
+       "      14.0\n",
+       "      (297, 213)\n",
+       "      ind_crossover\n",
        "      <tpot2.search_spaces.pipelines.sequential.Sequ...\n",
-       "      5.0\n",
-       "      1.727192e+09\n",
-       "      1.727192e+09\n",
+       "      6.0\n",
+       "      1.727568e+09\n",
+       "      1.727568e+09\n",
        "      None\n",
        "      NaN\n",
-       "      (Normalizer(norm='l1'), VarianceThreshold(thre...\n",
+       "      (MinMaxScaler(), SelectFwe(alpha=0.00016890355...\n",
        "    \n",
        "  \n",
        "\n",
-       "
300 rows × 11 columns
\n",
+       "
350 rows × 11 columns
\n",
        "
" ], "text/plain": [ - " roc_auc_score complexity_scorer Parents \\\n", - "0 0.994779 38.8 NaN \n", - "1 0.884608 12.0 NaN \n", - "2 0.995994 277.0 NaN \n", - "3 0.969714 97.0 NaN \n", - "4 0.977700 10.0 NaN \n", - ".. ... ... ... \n", - "295 0.994663 17.0 (245, 2) \n", - "296 NaN NaN (190, 190) \n", - "297 0.995224 231.0 (168, 232) \n", - "298 0.974412 10.0 (93, 205) \n", - "299 0.932393 8.0 (234, 234) \n", - "\n", - " Variation_Function \\\n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \n", - ".. ... \n", - "295 ind_crossover \n", - "296 ind_mutate \n", - "297 ind_crossover \n", - "298 ind_mutate , ind_mutate , ind_crossover \n", - "299 ind_mutate \n", + " roc_auc_score complexity_scorer Parents Variation_Function \\\n", + "0 0.964012 1745.5 NaN NaN \n", + "1 NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN \n", + "4 0.991667 24030.0 NaN NaN \n", + ".. ... ... ... ... \n", + "345 0.992793 4374.0 (237, 237) ind_mutate \n", + "346 0.520972 9.0 (128, 128) ind_mutate \n", + "347 NaN NaN (109, 85) ind_crossover \n", + "348 0.976466 21.0 (296, 128) ind_crossover , ind_mutate \n", + "349 0.990725 14.0 (297, 213) ind_crossover \n", "\n", " Individual Generation \\\n", "0 " ] @@ -1276,7 +1310,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -1308,7 +1342,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1347,231 +1381,164 @@ " \n", " \n", " \n", - " 211\n", - " 0.997632\n", - " 231.0\n", - " (2, 2)\n", - " ind_mutate\n", + " 330\n", + " 0.995556\n", + " 4373.0\n", + " (237, 52)\n", + " ind_crossover\n", " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", - " 4.0\n", - " 1.727192e+09\n", - " 1.727192e+09\n", + " 6.0\n", + " 1.727568e+09\n", + " 1.727568e+09\n", " None\n", " 1.0\n", - " (MaxAbsScaler(), SelectFwe(alpha=0.01420804547...\n", + " (Passthrough(), SelectFwe(alpha=0.001227516798...\n", " \n", " \n", - " 251\n", - " 0.996697\n", - " 213.9\n", - " (211, 211)\n", + " 144\n", + " 0.995000\n", + " 68.6\n", + " (61, 61)\n", " ind_mutate\n", " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", - " 5.0\n", - " 1.727192e+09\n", - " 1.727192e+09\n", - " None\n", - " 1.0\n", - " (MaxAbsScaler(), VarianceThreshold(threshold=0...\n", - " \n", - " \n", - " 261\n", - " 0.996394\n", - " 182.4\n", - " (211, 182)\n", - " ind_crossover\n", - " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", - " 5.0\n", - " 1.727192e+09\n", - " 1.727192e+09\n", + " 2.0\n", + " 1.727568e+09\n", + " 1.727568e+09\n", " None\n", " 1.0\n", - " (MaxAbsScaler(), SelectFwe(alpha=0.01420804547...\n", + " (RobustScaler(quantile_range=(0.2808423658106,...\n", " \n", " \n", - " 132\n", - " 0.996313\n", - " 33.0\n", - " (85, 18)\n", - " ind_crossover\n", + " 320\n", + " 0.994059\n", + " 31.0\n", + " (184, 184)\n", + " ind_mutate\n", " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", - " 2.0\n", - " 1.727192e+09\n", - " 1.727192e+09\n", + " 6.0\n", + " 1.727568e+09\n", + " 1.727568e+09\n", " None\n", " 1.0\n", - " (StandardScaler(), SelectFwe(alpha=0.000377258...\n", + " (MaxAbsScaler(), SelectFwe(alpha=0.01352548659...\n", " \n", " \n", - " 190\n", - " 0.996207\n", - " 27.5\n", - " (141, 122)\n", - " ind_mutate , ind_mutate , ind_crossover\n", + " 161\n", + " 0.994028\n", + " 23.2\n", + " (123, 123)\n", + " ind_mutate\n", " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", " 3.0\n", - " 1.727192e+09\n", - " 1.727192e+09\n", + " 1.727568e+09\n", + " 1.727568e+09\n", " None\n", " 1.0\n", - " (MaxAbsScaler(), VarianceThreshold(threshold=0...\n", + " (MaxAbsScaler(), SelectFromModel(estimator=Ext...\n", " \n", " \n", - " 250\n", - " 0.995593\n", - " 24.3\n", - " (173, 173)\n", + " 297\n", + " 0.992577\n", + " 13.0\n", + " (193, 193)\n", " ind_mutate\n", " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", " 5.0\n", - " 1.727192e+09\n", - " 1.727192e+09\n", + " 1.727568e+09\n", + " 1.727568e+09\n", " None\n", " 1.0\n", - " (MinMaxScaler(), SelectFwe(alpha=0.00034913463...\n", + " (MaxAbsScaler(), SelectFwe(alpha=0.00098089598...\n", " \n", " \n", - " 295\n", - " 0.994663\n", - " 17.0\n", - " (245, 2)\n", - " ind_crossover\n", + " 306\n", + " 0.991165\n", + " 8.0\n", + " (167, 167)\n", + " ind_mutate\n", " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", - " 5.0\n", - " 1.727192e+09\n", - " 1.727192e+09\n", + " 6.0\n", + " 1.727568e+09\n", + " 1.727568e+09\n", " None\n", " 1.0\n", - " (MinMaxScaler(), SelectPercentile(percentile=3...\n", + " (MaxAbsScaler(), SelectFwe(alpha=0.00057722163...\n", " \n", " \n", - " 227\n", - " 0.990767\n", - " 11.0\n", - " (188, 168)\n", + " 106\n", + " 0.965015\n", + " 7.0\n", + " (11, 85)\n", " ind_crossover\n", " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", - " 4.0\n", - " 1.727192e+09\n", - " 1.727192e+09\n", - " None\n", - " 1.0\n", - " (Passthrough(), SelectFwe(alpha=0.000109999882...\n", - " \n", - " \n", - " 226\n", - " 0.989583\n", - " 9.0\n", - " (144, 90)\n", - " ind_mutate , ind_mutate , ind_crossover\n", - " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", - " 4.0\n", - " 1.727192e+09\n", - " 1.727192e+09\n", - " None\n", - " 1.0\n", - " (StandardScaler(), VarianceThreshold(threshold...\n", - " \n", - " \n", - " 213\n", - " 0.976500\n", - " 8.1\n", - " (151, 151)\n", - " ind_crossover , ind_mutate\n", - " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", - " 4.0\n", - " 1.727192e+09\n", - " 1.727192e+09\n", + " 2.0\n", + " 1.727568e+09\n", + " 1.727568e+09\n", " None\n", " 1.0\n", - " (MaxAbsScaler(), SelectFwe(alpha=0.03007503043...\n", + " (StandardScaler(), SelectPercentile(percentile...\n", " \n", " \n", - " 290\n", - " 0.960114\n", + " 195\n", + " 0.945486\n", " 6.0\n", - " (240, 240)\n", + " (25, 25)\n", " ind_mutate\n", " <tpot2.search_spaces.pipelines.sequential.Sequ...\n", - " 5.0\n", - " 1.727192e+09\n", - " 1.727192e+09\n", + " 3.0\n", + " 1.727568e+09\n", + " 1.727568e+09\n", " None\n", " 1.0\n", - " (MinMaxScaler(), VarianceThreshold(threshold=0...\n", + " (MaxAbsScaler(), SelectFwe(alpha=0.00098089598...\n", " \n", " \n", "\n", "
" ], "text/plain": [ - " roc_auc_score complexity_scorer Parents \\\n", - "211 0.997632 231.0 (2, 2) \n", - "251 0.996697 213.9 (211, 211) \n", - "261 0.996394 182.4 (211, 182) \n", - "132 0.996313 33.0 (85, 18) \n", - "190 0.996207 27.5 (141, 122) \n", - "250 0.995593 24.3 (173, 173) \n", - "295 0.994663 17.0 (245, 2) \n", - "227 0.990767 11.0 (188, 168) \n", - "226 0.989583 9.0 (144, 90) \n", - "213 0.976500 8.1 (151, 151) \n", - "290 0.960114 6.0 (240, 240) \n", - "\n", - " Variation_Function \\\n", - "211 ind_mutate \n", - "251 ind_mutate \n", - "261 ind_crossover \n", - "132 ind_crossover \n", - "190 ind_mutate , ind_mutate , ind_crossover \n", - "250 ind_mutate \n", - "295 ind_crossover \n", - "227 ind_crossover \n", - "226 ind_mutate , ind_mutate , ind_crossover \n", - "213 ind_crossover , ind_mutate \n", - "290 ind_mutate \n", + " roc_auc_score complexity_scorer Parents Variation_Function \\\n", + "330 0.995556 4373.0 (237, 52) ind_crossover \n", + "144 0.995000 68.6 (61, 61) ind_mutate \n", + "320 0.994059 31.0 (184, 184) ind_mutate \n", + "161 0.994028 23.2 (123, 123) ind_mutate \n", + "297 0.992577 13.0 (193, 193) ind_mutate \n", + "306 0.991165 8.0 (167, 167) ind_mutate \n", + "106 0.965015 7.0 (11, 85) ind_crossover \n", + "195 0.945486 6.0 (25, 25) ind_mutate \n", "\n", " Individual Generation \\\n", - "211 
Pipeline(steps=[('minmaxscaler', MinMaxScaler()),\n",
-       "                ('variancethreshold',\n",
-       "                 VarianceThreshold(threshold=0.0004675292341)),\n",
+       "
Pipeline(steps=[('maxabsscaler', MaxAbsScaler()),\n",
+       "                ('selectfwe', SelectFwe(alpha=0.0009808959816)),\n",
        "                ('featureunion-1',\n",
-       "                 FeatureUnion(transformer_list=[('featureunion',\n",
-       "                                                 FeatureUnion(transformer_list=[('quantiletransformer',\n",
-       "                                                                                 QuantileTransformer(n_quantiles=104))])),\n",
+       "                 FeatureUnion(transformer_list=[('skiptransformer',\n",
+       "                                                 SkipTransformer()),\n",
        "                                                ('passthrough',\n",
        "                                                 Passthrough())])),\n",
        "                ('featureunion-2',\n",
@@ -2016,13 +1981,12 @@
        "                                                ('passthrough',\n",
        "                                                 Passthrough())])),\n",
        "                ('kneighborsclassifier',\n",
-       "                 KNeighborsClassifier(n_jobs=1, n_neighbors=1))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
MaxAbsScaler()
SelectFwe(alpha=0.0009808959816)
FeatureUnion(transformer_list=[('skiptransformer', SkipTransformer()),\n",
+       "                               ('passthrough', Passthrough())])
SkipTransformer()
Passthrough()
FeatureUnion(transformer_list=[('skiptransformer', SkipTransformer()),\n",
+       "                               ('passthrough', Passthrough())])
SkipTransformer()
Passthrough()
KNeighborsClassifier(n_jobs=1, n_neighbors=1, p=1, weights='distance')
" ], "text/plain": [ - "Pipeline(steps=[('minmaxscaler', MinMaxScaler()),\n", - " ('variancethreshold',\n", - " VarianceThreshold(threshold=0.0004675292341)),\n", + "Pipeline(steps=[('maxabsscaler', MaxAbsScaler()),\n", + " ('selectfwe', SelectFwe(alpha=0.0009808959816)),\n", " ('featureunion-1',\n", - " FeatureUnion(transformer_list=[('featureunion',\n", - " FeatureUnion(transformer_list=[('quantiletransformer',\n", - " QuantileTransformer(n_quantiles=104))])),\n", + " FeatureUnion(transformer_list=[('skiptransformer',\n", + " SkipTransformer()),\n", " ('passthrough',\n", " Passthrough())])),\n", " ('featureunion-2',\n", @@ -2053,10 +2014,11 @@ " ('passthrough',\n", " Passthrough())])),\n", " ('kneighborsclassifier',\n", - " KNeighborsClassifier(n_jobs=1, n_neighbors=1))])" + " KNeighborsClassifier(n_jobs=1, n_neighbors=1, p=1,\n", + " weights='distance'))])" ] }, - "execution_count": 13, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -2081,12 +2043,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -2195,7 +2157,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -2306,21 +2268,23 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Evaluations: : 25it [00:10, 2.47it/s]\n" + "Evaluations: : 113it [00:21, 5.15it/s]\n", + "/home/perib/miniconda3/envs/myenv/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "0.9824771007566706\n" + "0.9957890070921986\n" ] } ], @@ -2348,6 +2312,7 @@ " max_eval_time_mins=15,\n", " max_time_mins=30,\n", " early_stop=10, #In TPOTEstimatorSteadyState, since there are no generations, early_stop is the number of pipelines to evaluate before stopping.\n", + " n_jobs=30,\n", " verbose=2)\n", "\n", "\n", @@ -2360,20 +2325,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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roc_auc_scorecomplexity_scorerParentsVariation_FunctionIndividualSubmitted TimestampCompleted TimestampEval ErrorPareto_FrontInstance
00.94042698.0NaNNaN<tpot2.search_spaces.pipelines.graph.GraphPipe...1.727192e+091.727192e+09NoneNaN[('DecisionTreeClassifier_1', 'SelectPercentil...
10.95424470.0NaNNaN<tpot2.search_spaces.pipelines.graph.GraphPipe...1.727192e+091.727192e+09NoneNaN[('DecisionTreeClassifier_1', 'ColumnOneHotEnc...
20.96794213.0NaNNaN<tpot2.search_spaces.pipelines.graph.GraphPipe...1.727192e+091.727192e+09NoneNaN[('KNeighborsClassifier_1', 'SelectFwe_2'), ('...
3NaNNaNNaNNaN<tpot2.search_spaces.pipelines.graph.GraphPipe...1.727192e+091.727192e+09INVALIDNaN[('LogisticRegression_1', 'FeatureAgglomeratio...
40.95342180.0NaNNaN<tpot2.search_spaces.pipelines.graph.GraphPipe...1.727192e+091.727192e+09NoneNaN[('DecisionTreeClassifier_1', 'SelectPercentil...
\n", - "
" - ], - "text/plain": [ - " roc_auc_score complexity_scorer Parents Variation_Function \\\n", - "0 0.940426 98.0 NaN NaN \n", - "1 0.954244 70.0 NaN NaN \n", - "2 0.967942 13.0 NaN NaN \n", - "3 NaN NaN NaN NaN \n", - "4 0.953421 80.0 NaN NaN \n", - "\n", - " Individual Submitted Timestamp \\\n", - "0 " ] @@ -78,7 +78,7 @@ "import matplotlib.pyplot as plt\n", "import tpot2\n", "\n", - "population_size=60\n", + "population_size=30\n", "initial_population_size=100\n", "population_scaling = .5\n", "generations_until_end_population = 50\n", @@ -107,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -122,7 +122,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Generation: 2%|▏ | 1/50 [00:08<07:17, 8.93s/it]" + "Generation: 2%|▏ | 1/50 [00:25<20:56, 25.64s/it]" ] }, { @@ -130,14 +130,14 @@ "output_type": "stream", "text": [ "Generation: 1\n", - "Best roc_auc_score score: 1.0\n" + "Best roc_auc_score score: 0.9909340659340659\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Generation: 4%|▍ | 2/50 [00:15<06:00, 7.50s/it]" + "Generation: 4%|▍ | 2/50 [00:47<18:41, 23.37s/it]" ] }, { @@ -145,14 +145,14 @@ "output_type": "stream", "text": [ "Generation: 2\n", - "Best roc_auc_score score: 1.0\n" + "Best roc_auc_score score: 0.9918914418914418\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Generation: 6%|▌ | 3/50 [00:25<06:51, 8.76s/it]" + "Generation: 6%|▌ | 3/50 [01:25<23:39, 30.21s/it]" ] }, { @@ -160,14 +160,14 @@ "output_type": "stream", "text": [ "Generation: 3\n", - "Best roc_auc_score score: 1.0\n" + "Best roc_auc_score score: 0.9925990675990676\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Generation: 8%|▊ | 4/50 [00:33<06:22, 8.30s/it]" + "Generation: 8%|▊ | 4/50 [02:19<30:20, 39.58s/it]" ] }, { @@ -175,14 +175,14 @@ "output_type": "stream", "text": [ "Generation: 4\n", - "Best roc_auc_score score: 1.0\n" + "Best roc_auc_score score: 0.9925990675990676\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Generation: 10%|█ | 5/50 [00:48<08:13, 10.97s/it]" + "Generation: 10%|█ | 5/50 [02:56<28:55, 38.56s/it]" ] }, { @@ -190,14 +190,14 @@ "output_type": "stream", "text": [ "Generation: 5\n", - "Best roc_auc_score score: 1.0\n" + "Best roc_auc_score score: 0.9933816183816184\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Generation: 12%|█▏ | 6/50 [01:09<10:27, 14.26s/it]" + "Generation: 12%|█▏ | 6/50 [03:46<31:15, 42.63s/it]" ] }, { @@ -205,14 +205,14 @@ "output_type": "stream", "text": [ "Generation: 6\n", - "Best roc_auc_score score: 1.0\n" + "Best roc_auc_score score: 0.9933816183816184\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Generation: 14%|█▍ | 7/50 [01:20<09:23, 13.10s/it]" + "Generation: 14%|█▍ | 7/50 [04:41<33:15, 46.42s/it]" ] }, { @@ -220,14 +220,14 @@ "output_type": "stream", "text": [ "Generation: 7\n", - "Best roc_auc_score score: 1.0\n" + "Best roc_auc_score score: 0.9934065934065934\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - 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"total time: 555.5690805912018\n" + "total time: 983.1856114864349\n" ] } ], @@ -898,26 +464,18 @@ "from sklearn.linear_model import LogisticRegression\n", "import sklearn\n", "\n", - "X, y = sklearn.datasets.load_iris(return_X_y=True)\n", - "\n", - "graph_search_space = tpot2.search_spaces.pipelines.GraphSearchPipeline(\n", - " root_search_space= tpot2.config.get_search_space([\"KNeighborsClassifier\", \"LogisticRegression\", \"DecisionTreeClassifier\"]),\n", - " leaf_search_space = tpot2.config.get_search_space(\"selectors\"), \n", - " inner_search_space = tpot2.config.get_search_space([\"transformers\"]),\n", - " max_size = 10,\n", - " )\n", + "X, y = sklearn.datasets.load_breast_cancer(return_X_y=True)\n", "\n", "est = tpot2.TPOTEstimator(\n", " scorers = [\"roc_auc_ovr\"],\n", " scorers_weights = [1],\n", " classification = True,\n", " cv = 5,\n", - " search_space = graph_search_space,\n", + " search_space = 'linear-light',\n", " generations = 50,\n", - " max_eval_time_mins = 60*5,\n", - " verbose = 3,\n", - "\n", + " max_time_mins=None,\n", "\n", + " verbose = 3,\n", " population_size=population_size,\n", " initial_population_size=initial_population_size,\n", " population_scaling = population_scaling,\n", @@ -940,32 +498,32 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### CV early pruning\n", + "## CV early pruning\n", "\n", - "2. Most often, we will be evaluating pipelines using cross validation. However, we can often tell within the first few folds whether or not the pipeline is going have a reasonable change of outperforming the previous best pipelines. For example, if the best score so far is .92 AUROC and the average score of the first five folds of our current pipeline is only around .61, we can be reasonably confident that the next five folds are unlikely to this pipeline ahead of the others. We can save a significant amount of compute by not computing the rest of the folds. There are two strategies that TPOT can use to accomplish this (More information on these strategies in Tutorial 8).\n", + "Most often, we will be evaluating pipelines using cross validation. However, we can often tell within the first few folds whether or not the pipeline is going have a reasonable change of outperforming the previous best pipelines. For example, if the best score so far is .92 AUROC and the average score of the first five folds of our current pipeline is only around .61, we can be reasonably confident that the next five folds are unlikely to this pipeline ahead of the others. We can save a significant amount of compute by not computing the rest of the folds. There are two strategies that TPOT can use to accomplish this (More information on these strategies in Tutorial 8).\n", " 1. Threshold Pruning: Pipelines must achieve a score above a predefined percentile threshold (based on previous pipeline scores) to proceed in each cross-validation (CV) fold.\n", " 2. Selection Pruning: Within each population, only the top N% of pipelines (ranked by performance in the previous CV fold) are selected to evaluate in the next fold.\"\n", "\n", "\n", "We can further reduce computational load by terminating the evaluation of individual pipelines early if the first few CV scores are not promising. Note that this is different than early stopping of the full algorithm. In this section we will cover:\n", "\n", - "`threshold_evaluation_early_stop`\n", + "`threshold_evaluation_pruning`\n", "\n", "`threshold_evaluation_scaling`\n", "\n", "`min_history_threshold`\n", "\n", - "`selection_evaluation_early_stop`\n", + "`selection_evaluation_pruning`\n", "\n", "`selection_evaluation_scaling`\n", "\n", "Threshold early stopping uses previous scores to identify and terminate the cross validation evaluation of poorly performing pipelines. We calculate the percentile scores from the previously evaluated pipelines. A pipeline must reach the given percentile each fold for the next to be evaluated, otherwise the pipeline is discarded.\n", "\n", - "The `threshold_evaluation_early_stop` parameter is a list that specifies the starting and ending percentiles to use as a threshold for the evaluation early stopping. W The `threshold_evaluation_scaling` parameter is a float that controls the rate at which the threshold moves from the start to end percentile. The `min_history_threshold` parameter specifies the minimum number of previous scores needed before using threshold early stopping. This ensures that the algorithm has enough historical data to make an informed decision about when to stop evaluating pipelines.\n", + "The `threshold_evaluation_pruning` parameter is a list that specifies the starting and ending percentiles to use as a threshold for the evaluation early stopping. W The `threshold_evaluation_scaling` parameter is a float that controls the rate at which the threshold moves from the start to end percentile. The `min_history_threshold` parameter specifies the minimum number of previous scores needed before using threshold early stopping. This ensures that the algorithm has enough historical data to make an informed decision about when to stop evaluating pipelines.\n", "\n", "Selection early stopping uses a selection algorithm after each fold to select which algorithms will be evaluated for the next fold. For example, after evaluating 100 individuals on fold 1, we may want to only evaluate the best 50 for the remaining folds.\n", "\n", - "The `selection_evaluation_early_stop` parameter is a list that specifies the lower and upper percentage of the population size to select each round of CV. This is used to determine which individuals to evaluate in the next generation. The `selection_evaluation_scaling` parameter is a float that controls the rate at which the selection threshold moves from the start to end percentile.\n", + "The `selection_evaluation_pruning` parameter is a list that specifies the lower and upper percentage of the population size to select each round of CV. This is used to determine which individuals to evaluate in the next generation. The `selection_evaluation_scaling` parameter is a float that controls the rate at which the selection threshold moves from the start to end percentile.\n", "\n", "By manipulating these parameters, we can control how the algorithm selects individuals to evaluate in the next generation and when to stop evaluating pipelines that are not performing well.\n", "\n", @@ -976,12 +534,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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aIgAAiAStLjM///nPQz+vWrWqI7IAAAC0WVjnzAwaNEinTp1qMX769GnuzQQAAK6osMrMX/7yFzU2trwBWzAY1GeffXbZoQAAAFqrTZflfO2110I/b926tdm1ZxobG7Vt2zYlJye3XzoAAIBv0aYyc/fdd0uSHA6H5syZ02xddHS0Bg4cqF/84hftFg4AAODbtKnMNDU1SZKSk5NVWlqqa6+9tkNCAQAAtFZYd387cuRIe+cAAAAIS9i3st22bZu2bdumqqqq0IzNV37zm99cdjAAAIDWCKvMPPnkk1q9erVSU1OVkJDABfQAAIBtwioz69ev16ZNm/TAAw+0dx4AAIA2Ces6Mw0NDUpLS2vvLAAAAG0WVpl56KGHVFhY2C4Bjh8/rtmzZ6t3795yu90aMWKE9uzZE1pvWZZWrVqlxMREuVwuTZgwQeXl5e3yuwEAgPnCOsz017/+VQUFBXrjjTc0fPhwRUdHN1ufm5vbqv1UV1dr3Lhxmjhxol5//XX17dtXhw8f1tVXXx3aJicnR7m5udq0aZMGDx6srKwsTZ48WYcOHVJsbGw48QEAQCcSVpn54IMPNGLECEnShx9+2GxdW04GXrt2rZKSkrRx48bQ2MCBA0M/W5alvLw8rVixQtOnT5ckbd68WR6PR4WFhZo3b1448QEAQCcSVpl566232uWXv/baa5oyZYruu+8+lZSU6LrrrlNGRoZ+8pOfSDp/PZvKykqlp6eHnuN0OjV+/Hjt2LHjgmUmGAwqGAyGHgcCgXbJCgAAIlNY58x85ZNPPtHWrVtVX18v6fxMSlt8+umnWrdunXw+n7Zu3aqHH35YCxYs0G9/+1tJUmVlpSTJ4/E0e57H4wmt+6bs7GzFx8eHlqSkpLa+LAAAYJCwysypU6d0++23a/Dgwbrjjjt08uRJSedPDP7nf/7nVu+nqalJo0aNkt/v18iRIzVv3jz95Cc/0bp165pt981DV5ZlXfRw1vLly1VTUxNajh071sZXBwAATBJWmXnssccUHR2to0ePyu12h8Z/9KMfacuWLa3eT0JCglJSUpqN3XzzzTp69Kgkyev1SlKLWZiqqqoWszVfcTqdiouLa7YAAIDOK6wyU1RUpLVr16pfv37Nxn0+nyoqKlq9n3HjxunQoUPNxj766CMNGDBA0vkbWnq9XhUXF4fWNzQ0qKSkhOvcAAAASWGeAHzmzJlmMzJf+fzzz+V0Olu9n8cee0xpaWny+/364Q9/qPfee08FBQUqKCiQdP7wUmZmpvx+v3w+n3w+n/x+v9xut2bOnBlOdAAA0MmENTNz2223hU7Slc6XjqamJj3zzDOaOHFiq/fzne98R3/4wx/0u9/9TkOHDtVTTz2lvLw8zZo1K7TNkiVLlJmZqYyMDKWmpur48eMqKiriGjMAAECS5LDa+hUkSQcOHNCECRM0evRovfnmm/rBD36g8vJyffHFF3r33Xd1/fXXd0TWsAQCAcXHx6umpobzZ4ArpK7hnFKe2CpJOrB6itwxYU0CA+jC2vL5HdbMTEpKij744AN997vf1eTJk3XmzBlNnz5d+/bti6giAwAAOr+w/7vk9Xr15JNPtmcWAACANgtrZmbjxo36/e9/32L897//vTZv3nzZoQAAAForrDLz9NNP69prr20x3rdvX/n9/ssOBQAA0FphlZmKigolJye3GB8wYEDogncAAABXQlhlpm/fvvrggw9ajL///vvq3bv3ZYcCAABorbDKzIwZM7RgwQK99dZbamxsVGNjo958800tXLhQM2bMaO+MAAAAFxXWt5mysrJUUVGh22+/Xd27n99FU1OTHnzwQc6ZAQAAV1Sby4xlWTp58qQ2btyorKwslZWVyeVyadiwYaF7KgEAAFwpYZUZn8+n8vLy0P2SAAAA7NLmc2a6desmn8+nU6dOdUQeAACANgnrBOCcnBwtXrxYH374YXvnAQAAaJOwTgCePXu26urq9Dd/8zeKiYmRy+Vqtv6LL75ol3AAAADfJqwyk5eX184xAAAAwhNWmZkzZ0575wAAAAhL2HfNPnz4sDZu3KjDhw/r2WefVd++fbVlyxYlJSVpyJAh7ZkR6FIsy1L92Ua7Y1yWugaz8wMwS1hlpqSkRFOnTtW4ceP0zjvvaM2aNaFbHPz617/Wv/3bv7V3TqBLsCxL967fqT0V1XZHAQBjhPVtpmXLlikrK0vFxcWKiYkJjU+cOFE7d+5st3BAV1N/trFTFZnUAb3kio6yOwaATi6smZn9+/ersLCwxXifPn24/gzQTnavnCR3jNlFwBUdJYfDYXcMAJ1cWGXm6quv1smTJ5WcnNxsfN++fbruuuvaJRjQ1bljouSOCfu0NgDoMsI6zDRz5kwtXbpUlZWVcjgcampq0rvvvqvHH39cDz74YHtnBAAAuKiwysyaNWvUv39/XXfddfryyy+VkpKi733ve0pLS9PKlSvbOyMAAMBFhTWHHR0drX/913/VU089pd27d8vhcGjkyJG64YYb2jsfAADAJYV9QP7555/XL3/5S3388ceSJJ/Pp8zMTD300EPtFg4AAODbhFVmfvazn+mXv/ylHn30UY0dO1aStHPnTj322GP6y1/+oqysrHYNCQAAcDFhlZl169Zpw4YNuv/++0NjP/jBDzR8+HA9+uijlBkAAHDFhHUCcGNjo1JTU1uMjx49WufOnbvsUAAAAK0VVpmZPXu21q1b12K8oKBAs2bNuuxQAAAArXVZJwAXFRXplltukSTt2rVLx44d04MPPqhFixaFtsvNzb38lAAAABcRVpn58MMPNWrUKEnn754tnb+VQZ8+ffThhx+GtuMy5gAAoKOFVWbeeuut9s4BAAAQlrDOmQEAAIgUlBkAAGA0ygwAADAaZQYAABiNMgMAAIxGmQEAAEajzAAAAKNRZgAAgNEoMwAAwGiUGQAAYDTKDAAAMBplBgAAGI0yAwAAjEaZAQAARrO1zKxatUoOh6PZ4vV6Q+sty9KqVauUmJgol8ulCRMmqLy83MbEAAAg0tg+MzNkyBCdPHkytOzfvz+0LicnR7m5ucrPz1dpaam8Xq8mT56s2tpaGxMDAIBIYnuZ6d69u7xeb2jp06ePpPOzMnl5eVqxYoWmT5+uoUOHavPmzaqrq1NhYaHNqQEAQKSwvcx8/PHHSkxMVHJysmbMmKFPP/1UknTkyBFVVlYqPT09tK3T6dT48eO1Y8eOi+4vGAwqEAg0WwAAQOdla5kZM2aMfvvb32rr1q3asGGDKisrlZaWplOnTqmyslKS5PF4mj3H4/GE1l1Idna24uPjQ0tSUlKHvgYAAGAvW8vM1KlTdc8992jYsGGaNGmS/uu//kuStHnz5tA2Doej2XMsy2ox9nXLly9XTU1NaDl27FjHhAcAABHB9sNMX9ezZ08NGzZMH3/8cehbTd+chamqqmoxW/N1TqdTcXFxzRYAANB5RVSZCQaDOnjwoBISEpScnCyv16vi4uLQ+oaGBpWUlCgtLc3GlAAAIJJ0t/OXP/7447rzzjvVv39/VVVVKSsrS4FAQHPmzJHD4VBmZqb8fr98Pp98Pp/8fr/cbrdmzpxpZ2wAABBBbC0zn332me6//359/vnn6tOnj2655Rbt2rVLAwYMkCQtWbJE9fX1ysjIUHV1tcaMGaOioiLFxsbaGRsAAEQQh2VZlt0hOlIgEFB8fLxqamo4fwYRr67hnFKe2CpJOrB6itwxtv5/AwBs05bP74g6ZwYAAKCtKDMAAMBolBkAAGA0ygwAADAaZQYAABiNr0qg07AsS/VnG+2OcVnqGszODwB2oMygU7AsS/eu36k9FdV2RwEAXGEcZkKnUH+2sVMVmdQBveSKjrI7BgAYgZkZdDq7V06SO8bsIuCKjrrk3eEBAP+PMoNOxx0TxZVzAaAL4TATAAAwGmUGAAAYjTIDAACMRpkBAABGo8wAAACjUWYAAIDRKDMAAMBolBkAAGA0ygwAADAaZQYAABiNMgMAAIxGmQEAAEajzAAAAKNRZgAAgNEoMwAAwGiUGQAAYDTKDAAAMBplBgAAGI0yAwAAjEaZAQAARqPMAAAAo1FmAACA0SgzAADAaJQZAABgNMoMAAAwGmUGAAAYjTIDAACMRpkBAABGo8wAAACjUWYAAIDRKDMAAMBolBkAAGA0ygwAADAaZQYAABiNMgMAAIwWMWUmOztbDodDmZmZoTHLsrRq1SolJibK5XJpwoQJKi8vty8kAACIOBFRZkpLS1VQUKDhw4c3G8/JyVFubq7y8/NVWloqr9eryZMnq7a21qakAAAg0nS3O8CXX36pWbNmacOGDcrKygqNW5alvLw8rVixQtOnT5ckbd68WR6PR4WFhZo3b55dkTsdy7JUf7bR7hiXpa7B7PwAgPDZXmbmz5+vadOmadKkSc3KzJEjR1RZWan09PTQmNPp1Pjx47Vjx46LlplgMKhgMBh6HAgEOi58J2BZlu5dv1N7KqrtjgIAQFhsLTMvvfSS9u7dq9LS0hbrKisrJUkej6fZuMfjUUVFxUX3mZ2drSeffLJ9g3Zi9WcbO1WRSR3QS67oKLtjAACuINvKzLFjx7Rw4UIVFRWpR48eF93O4XA0e2xZVouxr1u+fLkWLVoUehwIBJSUlHT5gbuA3SsnyR1jdhFwRUdd8u8HAKDzsa3M7NmzR1VVVRo9enRorLGxUe+8847y8/N16NAhSednaBISEkLbVFVVtZit+Tqn0ymn09lxwTsxd0yU3DG2H3kEAKBNbPs20+233679+/errKwstKSmpmrWrFkqKyvToEGD5PV6VVxcHHpOQ0ODSkpKlJaWZldsAAAQYWz7b3hsbKyGDh3abKxnz57q3bt3aDwzM1N+v18+n08+n09+v19ut1szZ860IzIAAIhAEX1MYcmSJaqvr1dGRoaqq6s1ZswYFRUVKTY21u5oAAAgQjgsy7LsDtGRAoGA4uPjVVNTo7i4OLvjRJy6hnNKeWKrJOnA6imcMwMAiAht+fyOiCsAAwAAhIsyAwAAjEaZAQAARqPMAAAAo1FmAACA0SgzAADAaJQZAABgNMoMAAAwGmUGAAAYjTIDAACMRpkBAABGo8wAAACjUWYAAIDRKDMAAMBolBkAAGA0ygwAADAaZQYAABiNMgMAAIxGmQEAAEajzAAAAKNRZgAAgNEoMwAAwGiUGQAAYDTKDAAAMBplBgAAGI0yAwAAjEaZAQAARutudwCTWZal+rONdse4LHUNZucHAIAyEybLsnTv+p3aU1FtdxQAALo0DjOFqf5sY6cqMqkDeskVHWV3DAAA2oyZmXawe+UkuWPMLgKu6Cg5HA67YwAA0GaUmXbgjomSO4Y/SgAA7MBhJgAAYDTKDAAAMBplBgAAGI0yAwAAjEaZAQAARqPMAAAAo1FmAACA0SgzAADAaJQZAABgNMoMAAAwGmUGAAAYjTIDAACMRpkBAABGs7XMrFu3TsOHD1dcXJzi4uI0duxYvf7666H1lmVp1apVSkxMlMvl0oQJE1ReXm5jYgAAEGlsLTP9+vXT008/rd27d2v37t3627/9W911112hwpKTk6Pc3Fzl5+ertLRUXq9XkydPVm1trZ2xAQBABLG1zNx555264447NHjwYA0ePFhr1qzRVVddpV27dsmyLOXl5WnFihWaPn26hg4dqs2bN6uurk6FhYV2xgYAABEkYs6ZaWxs1EsvvaQzZ85o7NixOnLkiCorK5Wenh7axul0avz48dqxY8dF9xMMBhUIBJotAACg87K9zOzfv19XXXWVnE6nHn74Yf3hD39QSkqKKisrJUkej6fZ9h6PJ7TuQrKzsxUfHx9akpKSOjQ/AACwV3e7A9x4440qKyvT6dOn9corr2jOnDkqKSkJrXc4HM22tyyrxdjXLV++XIsWLQo9DgQCHVJoXNFROrB6SuhnAABgD9vLTExMjG644QZJUmpqqkpLS/Xss89q6dKlkqTKykolJCSEtq+qqmoxW/N1TqdTTqezY0PrfMlyx9j+xwcAQJdn+2Gmb7IsS8FgUMnJyfJ6vSouLg6ta2hoUElJidLS0mxMCAAAIomtUws//elPNXXqVCUlJam2tlYvvfSS3n77bW3ZskUOh0OZmZny+/3y+Xzy+Xzy+/1yu92aOXOmnbEBAEAEsbXM/Pd//7ceeOABnTx5UvHx8Ro+fLi2bNmiyZMnS5KWLFmi+vp6ZWRkqLq6WmPGjFFRUZFiY2PtjA0AACKIw7Isy+4QHSkQCCg+Pl41NTWKi4uzOw4AAGiFtnx+R9w5MwAAAG1BmQEAAEajzAAAAKNRZgAAgNEoMwAAwGiUGQAAYDTKDAAAMBplBgAAGI0yAwAAjNbpb/v81QWOA4GAzUkAAEBrffW53ZobFXT6MlNbWytJSkpKsjkJAABoq9raWsXHx19ym05/b6ampiadOHFCsbGxcjgc7brvQCCgpKQkHTt2jPs+RQDej8jC+xFZeD8iC+/Ht7MsS7W1tUpMTFS3bpc+K6bTz8x069ZN/fr169DfERcXx1/GCML7EVl4PyIL70dk4f24tG+bkfkKJwADAACjUWYAAIDRKDOXwel06uc//7mcTqfdUSDej0jD+xFZeD8iC+9H++r0JwADAIDOjZkZAABgNMoMAAAwGmUGAAAYjTIDAACMRpkJ069+9SslJyerR48eGj16tP70pz/ZHalLys7O1ne+8x3Fxsaqb9++uvvuu3Xo0CG7Y+H/ZGdny+FwKDMz0+4oXdrx48c1e/Zs9e7dW263WyNGjNCePXvsjtUlnTt3TitXrlRycrJcLpcGDRqk1atXq6mpye5oRqPMhOHll19WZmamVqxYoX379ul73/uepk6dqqNHj9odrcspKSnR/PnztWvXLhUXF+vcuXNKT0/XmTNn7I7W5ZWWlqqgoEDDhw+3O0qXVl1drXHjxik6Olqvv/66Dhw4oF/84he6+uqr7Y7WJa1du1br169Xfn6+Dh48qJycHD3zzDN67rnn7I5mNL6aHYYxY8Zo1KhRWrduXWjs5ptv1t13363s7Gwbk+F//ud/1LdvX5WUlOi2226zO06X9eWXX2rUqFH61a9+paysLI0YMUJ5eXl2x+qSli1bpnfffZfZ4wjx/e9/Xx6PR88//3xo7J577pHb7dYLL7xgYzKzMTPTRg0NDdqzZ4/S09Objaenp2vHjh02pcJXampqJEnXXHONzUm6tvnz52vatGmaNGmS3VG6vNdee02pqam677771LdvX40cOVIbNmywO1aXdeutt2rbtm366KOPJEnvv/++tm/frjvuuMPmZGbr9DeabG+ff/65Ghsb5fF4mo17PB5VVlbalArS+TusLlq0SLfeequGDh1qd5wu66WXXtLevXtVWlpqdxRI+vTTT7Vu3TotWrRIP/3pT/Xee+9pwYIFcjqdevDBB+2O1+UsXbpUNTU1uummmxQVFaXGxkatWbNG999/v93RjEaZCZPD4Wj22LKsFmO4sh555BF98MEH2r59u91Ruqxjx45p4cKFKioqUo8ePeyOA0lNTU1KTU2V3++XJI0cOVLl5eVat24dZcYGL7/8sl588UUVFhZqyJAhKisrU2ZmphITEzVnzhy74xmLMtNG1157raKiolrMwlRVVbWYrcGV8+ijj+q1117TO++8o379+tkdp8vas2ePqqqqNHr06NBYY2Oj3nnnHeXn5ysYDCoqKsrGhF1PQkKCUlJSmo3dfPPNeuWVV2xK1LUtXrxYy5Yt04wZMyRJw4YNU0VFhbKzsykzl4FzZtooJiZGo0ePVnFxcbPx4uJipaWl2ZSq67IsS4888oheffVVvfnmm0pOTrY7Upd2++23a//+/SorKwstqampmjVrlsrKyigyNhg3blyLyxV89NFHGjBggE2Jura6ujp169b8ozcqKoqvZl8mZmbCsGjRIj3wwANKTU3V2LFjVVBQoKNHj+rhhx+2O1qXM3/+fBUWFuqPf/yjYmNjQzNm8fHxcrlcNqfremJjY1ucr9SzZ0/17t2b85hs8thjjyktLU1+v18//OEP9d5776mgoEAFBQV2R+uS7rzzTq1Zs0b9+/fXkCFDtG/fPuXm5mru3Ll2RzObhbD8y7/8izVgwAArJibGGjVqlFVSUmJ3pC5J0gWXjRs32h0N/2f8+PHWwoUL7Y7Rpf3Hf/yHNXToUMvpdFo33XSTVVBQYHekLisQCFgLFy60+vfvb/Xo0cMaNGiQtWLFCisYDNodzWhcZwYAABiNc2YAAIDRKDMAAMBolBkAAGA0ygwAADAaZQYAABiNMgMAAIxGmQEAAEajzAAAAKNRZgBEFMuy9I//+I+65ppr5HA4VFZWdsnt3377bTkcDp0+ffqi22zatElXX311u+YEEDm4NxOAiLJlyxZt2rRJb7/9tgYNGqRrr73W7kgAIhxlBkBEOXz4sBISErgLPYBW4zATgIjx4x//WI8++qiOHj0qh8OhgQMHKhgMasGCBerbt6969OihW2+9VaWlpZfcz6ZNm9S/f3+53W793d/9nU6dOnWFXgEAO1BmAESMZ599VqtXr1a/fv108uRJlZaWasmSJXrllVe0efNm7d27VzfccIOmTJmiL7744oL7+POf/6y5c+cqIyNDZWVlmjhxorKysq7wKwFwJVFmAESM+Ph4xcbGKioqSl6vV263W+vWrdMzzzyjqVOnKiUlRRs2bJDL5dLzzz9/wX08++yzmjJlipYtW6bBgwdrwYIFmjJlyhV+JQCuJMoMgIh1+PBhnT17VuPGjQuNRUdH67vf/a4OHjx4weccPHhQY8eObTb2zccAOhfKDICIZVmWJMnhcLQY/+bYN58DoOugzACIWDfccINiYmK0ffv20NjZs2e1e/du3XzzzRd8TkpKinbt2tVs7JuPAXQufDUbQMTq2bOn/umf/kmLFy/WNddco/79+ysnJ0d1dXX6h3/4hws+Z8GCBUpLS1NOTo7uvvtuFRUVacuWLVc4OYAriZkZABHt6aef1j333KMHHnhAo0aN0ieffKKtW7eqV69eF9z+lltu0a9//Ws999xzGjFihIqKirRy5cornBrAleSwOMAMAAAMxswMAAAwGmUGAAAYjTIDAACMRpkBAABGo8wAAACjUWYAAIDRKDMAAMBolBkAAGA0ygwAADAaZQYAABiNMgMAAIz2v7zJkDR+tK61AAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -993,15 +551,18 @@ "source": [ "import matplotlib.pyplot as plt\n", "import tpot2\n", + "import time\n", + "import sklearn\n", + "import sklearn.datasets\n", "\n", - "threshold_evaluation_early_stop = [30, 90]\n", + "threshold_evaluation_pruning = [30, 90]\n", "threshold_evaluation_scaling = .5\n", - "cv = 5\n", + "cv = 10\n", "\n", "#Population and budget use stepwise\n", "fig, ax1 = plt.subplots()\n", "\n", - "interpolated_values = tpot2.utils.beta_interpolation(start=threshold_evaluation_early_stop[0], end=threshold_evaluation_early_stop[-1], n=cv, n_steps=cv, scale=threshold_evaluation_scaling)\n", + "interpolated_values = tpot2.utils.beta_interpolation(start=threshold_evaluation_pruning[0], end=threshold_evaluation_pruning[-1], n=cv, n_steps=cv, scale=threshold_evaluation_scaling)\n", "ax1.step(list(range(len(interpolated_values))), interpolated_values, label=f\"threshold\")\n", "ax1.set_xlabel(\"fold\")\n", "ax1.set_ylabel(\"percentile\")\n", @@ -1011,7 +572,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1019,43 +580,37 @@ "output_type": "stream", "text": [ "/home/perib/Projects/common/Projects/TPOT_Dev/tpot2/tpot2/tpot_estimator/estimator.py:423: UserWarning: Both generations and max_time_mins are set. TPOT will terminate when the first condition is met.\n", - " warnings.warn(\"Both generations and max_time_mins are set. TPOT will terminate when the first condition is met.\")\n", - "/home/perib/miniconda3/envs/myenv/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n" + " warnings.warn(\"Both generations and max_time_mins are set. TPOT will terminate when the first condition is met.\")\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "total time: 98.39543533325195\n" + "Generation: 20%|██ | 1/5 [00:32<02:11, 32.94s/it]" ] } ], "source": [ - "graph_search_space = tpot2.search_spaces.pipelines.GraphSearchPipeline(\n", - " root_search_space= tpot2.config.get_search_space([\"KNeighborsClassifier\", \"LogisticRegression\", \"DecisionTreeClassifier\"]),\n", - " leaf_search_space = tpot2.config.get_search_space(\"selectors\"), \n", - " inner_search_space = tpot2.config.get_search_space([\"transformers\"]),\n", - " max_size = 10,\n", - " )\n", + "X, y = sklearn.datasets.load_breast_cancer(return_X_y=True)\n", "\n", "\n", "est = tpot2.TPOTEstimator( \n", - " generations=5,\n", + " generations=50,\n", + " max_time_mins=None,\n", " scorers=['roc_auc_ovr'],\n", " scorers_weights=[1],\n", " classification=True,\n", - " search_space = graph_search_space,\n", + " search_space = 'linear-light',\n", " n_jobs=32,\n", " cv=cv,\n", " \n", " # budget_range = [.3,1],\n", " # generations_until_end_budget=4,\n", "\n", - " threshold_evaluation_early_stop = threshold_evaluation_early_stop,\n", + " threshold_evaluation_pruning = threshold_evaluation_pruning,\n", " threshold_evaluation_scaling = threshold_evaluation_scaling,\n", - " verbose=0)\n", + " verbose=2)\n", "\n", "\n", "start = time.time()\n", @@ -1065,7 +620,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -1083,14 +638,14 @@ "import matplotlib.pyplot as plt\n", "import tpot2\n", "\n", - "selection_evaluation_early_stop = [.1, 1]\n", + "selection_evaluation_pruning = [.1, 1]\n", "selection_evaluation_scaling = .5\n", "cv = 5\n", "\n", "#Population and budget use stepwise\n", "fig, ax1 = plt.subplots()\n", "\n", - "interpolated_values = tpot2.utils.beta_interpolation(start=selection_evaluation_early_stop[0], end=selection_evaluation_early_stop[-1], n=cv, n_steps=cv, scale=selection_evaluation_scaling)\n", + "interpolated_values = tpot2.utils.beta_interpolation(start=selection_evaluation_pruning[0], end=selection_evaluation_pruning[-1], n=cv, n_steps=cv, scale=selection_evaluation_scaling)\n", "ax1.step(list(range(len(interpolated_values))), interpolated_values, label=f\"threshold\")\n", "ax1.set_xlabel(\"fold\")\n", "ax1.set_ylabel(\"percent to select\")\n", @@ -1100,24 +655,18 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/perib/Projects/common/Projects/TPOT_Dev/tpot2/tpot2/tpot_estimator/estimator.py:423: UserWarning: Both generations and max_time_mins are set. TPOT will terminate when the first condition is met.\n", - " warnings.warn(\"Both generations and max_time_mins are set. TPOT will terminate when the first condition is met.\")\n", - "/home/perib/miniconda3/envs/myenv/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:349: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total time: 73.06747031211853\n" + "ename": "NameError", + "evalue": "name 'time' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[4], line 16\u001b[0m\n\u001b[1;32m 1\u001b[0m est \u001b[38;5;241m=\u001b[39m tpot2\u001b[38;5;241m.\u001b[39mTPOTEstimator( \n\u001b[1;32m 2\u001b[0m generations\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m,\n\u001b[1;32m 3\u001b[0m scorers\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mroc_auc_ovr\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 12\u001b[0m \n\u001b[1;32m 13\u001b[0m verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m---> 16\u001b[0m start \u001b[38;5;241m=\u001b[39m \u001b[43mtime\u001b[49m\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m 17\u001b[0m est\u001b[38;5;241m.\u001b[39mfit(X, y)\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtotal time: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtime\u001b[38;5;241m.\u001b[39mtime()\u001b[38;5;241m-\u001b[39mstart\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mNameError\u001b[0m: name 'time' is not defined" ] } ], @@ -1126,15 +675,15 @@ "\n", "\n", "est = tpot2.TPOTEstimator( \n", - " generations=5,\n", - " scorers=['roc_auc_ovr'],\n", + " generations=50,\n", + " max_time_mins=None,\n", " scorers_weights=[1],\n", " classification=True,\n", - " search_space = graph_search_space,\n", + " search_space = \"linear-light\",\n", " n_jobs=32,\n", " cv=cv,\n", "\n", - " selection_evaluation_early_stop = selection_evaluation_early_stop,\n", + " selection_evaluation_pruning = selection_evaluation_pruning,\n", " selection_evaluation_scaling = selection_evaluation_scaling,\n", "\n", " verbose=0)\n", @@ -1201,11 +750,12 @@ ], "source": [ "est = tpot2.TPOTEstimator( \n", - " generations=5,\n", + " generations=50,\n", + " max_time_mins=None,\n", " scorers=['roc_auc_ovr'],\n", " scorers_weights=[1],\n", " classification=True,\n", - " search_space = graph_search_space,\n", + " search_space = 'linear-light',\n", " n_jobs=32,\n", " cv=cv,\n", "\n", @@ -1217,10 +767,10 @@ " budget_range = budget_range,\n", " generations_until_end_budget=generations_until_end_budget,\n", " \n", - " threshold_evaluation_early_stop = threshold_evaluation_early_stop,\n", + " threshold_evaluation_pruning = threshold_evaluation_pruning,\n", " threshold_evaluation_scaling = threshold_evaluation_scaling,\n", "\n", - " selection_evaluation_early_stop = selection_evaluation_early_stop,\n", + " selection_evaluation_pruning = selection_evaluation_pruning,\n", " selection_evaluation_scaling = selection_evaluation_scaling,\n", "\n", " verbose=0)\n", diff --git a/tpot2/evolvers/base_evolver.py b/tpot2/evolvers/base_evolver.py index 3cb0a3ce..54995803 100644 --- a/tpot2/evolvers/base_evolver.py +++ b/tpot2/evolvers/base_evolver.py @@ -97,10 +97,10 @@ def __init__( self, generations_until_end_budget = 1, stepwise_steps = 5, - threshold_evaluation_early_stop = None, + threshold_evaluation_pruning = None, threshold_evaluation_scaling = .5, min_history_threshold = 20, - selection_evaluation_early_stop = None, + selection_evaluation_pruning = None, selection_evaluation_scaling = .5, evaluation_early_stop_steps = None, final_score_strategy = "mean", @@ -154,7 +154,7 @@ def __init__( self, Maximum time to evaluate a single individual. If none or inf, there will be no time limit per evaluation. n_jobs : int, default=1 Number of processes to run in parallel. - memory_limit : str, default="4GB" + memory_limit : str, default=None Memory limit for each job. See Dask [LocalCluster documentation](https://distributed.dask.org/en/stable/api.html#distributed.Client) for more information. client : dask.distributed.Client, default=None A dask client to use for parallelization. If not None, this will override the n_jobs and memory_limit parameters. If None, will create a new client with num_workers=n_jobs and memory_limit=memory_limit. @@ -186,7 +186,7 @@ def __init__( self, The number of generations to run before reaching the max budget. stepwise_steps : int, default=1 The number of staircase steps to take when interpolating the budget and population size. - threshold_evaluation_early_stop : list [start, end], default=None + threshold_evaluation_pruning : list [start, end], default=None Starting and ending percentile to use as a threshold for the evaluation early stopping. The evolver will interpolate between these values over the evaluation_early_stop_steps. Values between 0 and 100. At each step of the evaluation, a threshold is calculated based on the previous evaluations. All individuals that are below the performance threshold are not evaluated for further steps. @@ -196,7 +196,7 @@ def __init__( self, Must be greater than zero. Higher numbers will move the threshold to the end faster. min_history_threshold : int, default=0 The minimum number of previous scores needed before using threshold early stopping. - selection_evaluation_early_stop : list, default=None + selection_evaluation_pruning : list, default=None A lower and upper percent of the population size to select each round of CV. Values between 0 and 1. Selects a percentage of the population to evaluate at each step of the evaluation. @@ -243,9 +243,9 @@ def __init__( self, self.rng = np.random.default_rng(rng) - if threshold_evaluation_early_stop is not None or selection_evaluation_early_stop is not None: + if threshold_evaluation_pruning is not None or selection_evaluation_pruning is not None: if evaluation_early_stop_steps is None: - raise ValueError("evaluation_early_stop_steps must be set when using threshold_evaluation_early_stop or selection_evaluation_early_stop") + raise ValueError("evaluation_early_stop_steps must be set when using threshold_evaluation_pruning or selection_evaluation_pruning") self.individual_generator = individual_generator self.population_size = population_size @@ -292,11 +292,11 @@ def __init__( self, self.generation = 0 - self.threshold_evaluation_early_stop =threshold_evaluation_early_stop + self.threshold_evaluation_pruning =threshold_evaluation_pruning self.threshold_evaluation_scaling = max(0.00001,threshold_evaluation_scaling ) self.min_history_threshold = min_history_threshold - self.selection_evaluation_early_stop = selection_evaluation_early_stop + self.selection_evaluation_pruning = selection_evaluation_pruning self.selection_evaluation_scaling = max(0.00001,selection_evaluation_scaling ) self.evaluation_early_stop_steps = evaluation_early_stop_steps self.final_score_strategy = final_score_strategy @@ -616,21 +616,21 @@ def evaluate_population(self,): #Get the current thresholds per step self.thresholds = None - if self.threshold_evaluation_early_stop is not None: + if self.threshold_evaluation_pruning is not None: old_data = self.population.evaluated_individuals[self.objective_names] old_data = old_data[old_data[self.objective_names].notnull().all(axis=1)] if len(old_data) >= self.min_history_threshold: self.thresholds = np.array([get_thresholds(old_data[obj_name], - start=self.threshold_evaluation_early_stop[0], - end=self.threshold_evaluation_early_stop[1], + start=self.threshold_evaluation_pruning[0], + end=self.threshold_evaluation_pruning[1], scale=self.threshold_evaluation_scaling, n=self.evaluation_early_stop_steps) for obj_name in self.objective_names]).T #Get the selectors survival rates per step - if self.selection_evaluation_early_stop is not None: - lower = self.cur_population_size*self.selection_evaluation_early_stop[0] - upper = self.cur_population_size*self.selection_evaluation_early_stop[1] + if self.selection_evaluation_pruning is not None: + lower = self.cur_population_size*self.selection_evaluation_pruning[0] + upper = self.cur_population_size*self.selection_evaluation_pruning[1] #survival_counts = self.cur_population_size*(scipy.special.betainc(1,self.selection_evaluation_scaling,np.linspace(0,1,self.evaluation_early_stop_steps))*(upper-lower)+lower) survival_counts = np.array(beta_interpolation(start=lower, end=upper, scale=self.selection_evaluation_scaling, n=self.evaluation_early_stop_steps, n_steps=self.evaluation_early_stop_steps)) diff --git a/tpot2/evolvers/steady_state_evolver.py b/tpot2/evolvers/steady_state_evolver.py index bea6dd2d..6a3730f8 100644 --- a/tpot2/evolvers/steady_state_evolver.py +++ b/tpot2/evolvers/steady_state_evolver.py @@ -144,7 +144,7 @@ def __init__( self, Maximum time to evaluate a single individual. If none or inf, there will be no time limit per evaluation. n_jobs : int, default=1 Number of processes to run in parallel. - memory_limit : str, default="4GB" + memory_limit : str, default=None Memory limit for each job. See Dask [LocalCluster documentation](https://distributed.dask.org/en/stable/api.html#distributed.Client) for more information. client : dask.distributed.Client, default=None A dask client to use for parallelization. If not None, this will override the n_jobs and memory_limit parameters. If None, will create a new client with num_workers=n_jobs and memory_limit=memory_limit. diff --git a/tpot2/tpot_estimator/estimator.py b/tpot2/tpot_estimator/estimator.py index 7202175d..ab51ee94 100644 --- a/tpot2/tpot_estimator/estimator.py +++ b/tpot2/tpot_estimator/estimator.py @@ -63,9 +63,9 @@ def __init__(self, early_stop = None, scorers_early_stop_tol = 0.001, other_objectives_early_stop_tol =None, - threshold_evaluation_early_stop = None, + threshold_evaluation_pruning = None, threshold_evaluation_scaling = .5, - selection_evaluation_early_stop = None, + selection_evaluation_pruning = None, selection_evaluation_scaling = .5, min_history_threshold = 20, @@ -175,7 +175,7 @@ def __init__(self, - List of categorical features. If X is a dataframe, this should be a list of column names. If X is a numpy array, this should be a list of column indices preprocessing : bool or BaseEstimator/Pipeline, - EXPERIMENTAL + EXPERIMENTAL - will be changed in future versions A pipeline that will be used to preprocess the data before CV. Note that the parameters for these steps are not optimized. Add them to the search space to be optimized. - bool : If True, will use a default preprocessing pipeline which includes imputation followed by one hot encoding. - Pipeline : If an instance of a pipeline is given, will use that pipeline as the preprocessing pipeline. @@ -232,7 +232,7 @@ def __init__(self, -int If an int is given, it will be used as the tolerance for all objectives - threshold_evaluation_early_stop : list [start, end], default=None + threshold_evaluation_pruning : list [start, end], default=None starting and ending percentile to use as a threshold for the evaluation early stopping. Values between 0 and 100. @@ -240,7 +240,7 @@ def __init__(self, A scaling factor to use when determining how fast we move the threshold moves from the start to end percentile. Must be greater than zero. Higher numbers will move the threshold to the end faster. - selection_evaluation_early_stop : list, default=None + selection_evaluation_pruning : list, default=None A lower and upper percent of the population size to select each round of CV. Values between 0 and 1. @@ -290,7 +290,7 @@ def __init__(self, n_jobs : int, default=1 Number of processes to run in parallel. - memory_limit : str, default="4GB" + memory_limit : str, default=None Memory limit for each job. See Dask [LocalCluster documentation](https://distributed.dask.org/en/stable/api.html#distributed.Client) for more information. client : dask.distributed.Client, default=None @@ -403,10 +403,10 @@ def __init__(self, self.budget_scaling = budget_scaling self.generations_until_end_budget = generations_until_end_budget self.stepwise_steps = stepwise_steps - self.threshold_evaluation_early_stop =threshold_evaluation_early_stop + self.threshold_evaluation_pruning =threshold_evaluation_pruning self.threshold_evaluation_scaling = threshold_evaluation_scaling self.min_history_threshold = min_history_threshold - self.selection_evaluation_early_stop = selection_evaluation_early_stop + self.selection_evaluation_pruning = selection_evaluation_pruning self.selection_evaluation_scaling = selection_evaluation_scaling self.warm_start = warm_start self.verbose = verbose @@ -623,7 +623,7 @@ def objective_function(pipeline_individual, - if self.threshold_evaluation_early_stop is not None or self.selection_evaluation_early_stop is not None: + if self.threshold_evaluation_pruning is not None or self.selection_evaluation_pruning is not None: evaluation_early_stop_steps = self.cv else: evaluation_early_stop_steps = None @@ -698,11 +698,11 @@ def ind_generator(rng): max_eval_time_mins = self.max_eval_time_mins, periodic_checkpoint_folder = self.periodic_checkpoint_folder, - threshold_evaluation_early_stop = self.threshold_evaluation_early_stop, + threshold_evaluation_pruning = self.threshold_evaluation_pruning, threshold_evaluation_scaling = self.threshold_evaluation_scaling, min_history_threshold = self.min_history_threshold, - selection_evaluation_early_stop = self.selection_evaluation_early_stop, + selection_evaluation_pruning = self.selection_evaluation_pruning, selection_evaluation_scaling = self.selection_evaluation_scaling, evaluation_early_stop_steps = evaluation_early_stop_steps, diff --git a/tpot2/tpot_estimator/steady_state_estimator.py b/tpot2/tpot_estimator/steady_state_estimator.py index 57b3df28..8dcbfe5d 100644 --- a/tpot2/tpot_estimator/steady_state_estimator.py +++ b/tpot2/tpot_estimator/steady_state_estimator.py @@ -56,7 +56,7 @@ def __init__(self, early_stop = None, - early_stop_seconds = None, + early_stop_mins = None, scorers_early_stop_tol = 0.001, other_objectives_early_stop_tol = None, max_time_mins=None, @@ -251,7 +251,7 @@ def __init__(self, early_stop : int, default=None Number of evaluated individuals without improvement before early stopping. Counted across all objectives independently. Triggered when all objectives have not improved by the given number of individuals. - early_stop_seconds : float, default=None + early_stop_mins : float, default=None Number of seconds without improvement before early stopping. All objectives must not have improved for the given number of seconds for this to be triggered. scorers_early_stop_tol : @@ -277,7 +277,7 @@ def __init__(self, n_jobs : int, default=1 Number of processes to run in parallel. - memory_limit : str, default="4GB" + memory_limit : str, default=None Memory limit for each job. See Dask [LocalCluster documentation](https://distributed.dask.org/en/stable/api.html#distributed.Client) for more information. client : dask.distributed.Client, default=None @@ -314,7 +314,7 @@ def __init__(self, stepwise_steps : int, default=1 The number of staircase steps to take when scaling the budget and population size. - threshold_evaluation_early_stop : list [start, end], default=None + threshold_evaluation_pruning : list [start, end], default=None starting and ending percentile to use as a threshold for the evaluation early stopping. Values between 0 and 100. @@ -325,7 +325,7 @@ def __init__(self, min_history_threshold : int, default=0 The minimum number of previous scores needed before using threshold early stopping. - selection_evaluation_early_stop : list, default=None + selection_evaluation_pruning : list, default=None A lower and upper percent of the population size to select each round of CV. Values between 0 and 1. @@ -426,7 +426,7 @@ def __init__(self, self.initial_population_size = initial_population_size self.early_stop = early_stop - self.early_stop_seconds = early_stop_seconds + self.early_stop_mins = early_stop_mins self.scorers_early_stop_tol = scorers_early_stop_tol self.other_objectives_early_stop_tol = other_objectives_early_stop_tol self.max_time_mins = max_time_mins @@ -699,7 +699,7 @@ def ind_generator(rng): early_stop_tol = self.early_stop_tol, early_stop= self.early_stop, - early_stop_seconds = self.early_stop_seconds, + early_stop_mins = self.early_stop_mins, budget_range = self.budget_range, budget_scaling = self.budget_scaling, diff --git a/tpot2/tpot_estimator/templates/tpottemplates.py b/tpot2/tpot_estimator/templates/tpottemplates.py index 5b007950..15870747 100644 --- a/tpot2/tpot_estimator/templates/tpottemplates.py +++ b/tpot2/tpot_estimator/templates/tpottemplates.py @@ -159,7 +159,7 @@ def __init__( self, 6. evaluations progress bar. (Temporary: This used to be 2. Currently, using evaluation progress bar may prevent some instances were we terminate a generation early due to it reaching max_time_mins in the middle of a generation OR a pipeline failed to be terminated normally and we need to manually terminate it.) - memory_limit : str, default="4GB" + memory_limit : str, default=None Memory limit for each job. See Dask [LocalCluster documentation](https://distributed.dask.org/en/stable/api.html#distributed.Client) for more information. client : dask.distributed.Client, default=None @@ -422,7 +422,7 @@ def __init__( self, 6. evaluations progress bar. (Temporary: This used to be 2. Currently, using evaluation progress bar may prevent some instances were we terminate a generation early due to it reaching max_time_mins in the middle of a generation OR a pipeline failed to be terminated normally and we need to manually terminate it.) - memory_limit : str, default="4GB" + memory_limit : str, default=None Memory limit for each job. See Dask [LocalCluster documentation](https://distributed.dask.org/en/stable/api.html#distributed.Client) for more information. client : dask.distributed.Client, default=None From d4128846a8f4466e9989bf60be2effa30414c766 Mon Sep 17 00:00:00 2001 From: perib Date: Sun, 29 Sep 2024 07:47:57 -0700 Subject: [PATCH 33/44] pass through memory/kwards to all inner search spaces --- Tutorial/2_Search_Spaces.ipynb | 4425 ++++++++--------- tpot2/builtin_modules/estimatortransformer.py | 3 +- .../search_spaces/pipelines/dynamic_linear.py | 2 +- tpot2/search_spaces/pipelines/dynamicunion.py | 2 +- tpot2/search_spaces/pipelines/graph.py | 2 +- tpot2/search_spaces/pipelines/sequential.py | 2 +- tpot2/search_spaces/pipelines/union.py | 2 +- 7 files changed, 2144 insertions(+), 2294 deletions(-) diff --git a/Tutorial/2_Search_Spaces.ipynb b/Tutorial/2_Search_Spaces.ipynb index fcc6a8e9..782fbb19 100644 --- a/Tutorial/2_Search_Spaces.ipynb +++ b/Tutorial/2_Search_Spaces.ipynb @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -36,7 +36,7 @@ "output_type": "stream", "text": [ "sampled hyperparameters\n", - "{'bootstrap': False, 'criterion': 'gini', 'max_features': 0.0696410090574, 'min_samples_leaf': 7, 'min_samples_split': 8, 'n_estimators': 128}\n" + "{'bootstrap': False, 'criterion': 'entropy', 'max_features': 0.1574830347299, 'min_samples_leaf': 10, 'min_samples_split': 6, 'n_estimators': 128}\n" ] }, { @@ -446,19 +446,19 @@ " /* fitted */\n", " background-color: var(--sklearn-color-fitted-level-3);\n", "}\n", - "
RandomForestClassifier(bootstrap=False, max_features=0.0696410090574,\n",
-       "                       min_samples_leaf=7, min_samples_split=8,\n",
-       "                       n_estimators=128)
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" + "
RandomForestClassifier(bootstrap=False, criterion='entropy',\n",
+       "                       max_features=0.1574830347299, min_samples_leaf=10,\n",
+       "                       min_samples_split=6, n_estimators=128)
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" ], "text/plain": [ - "RandomForestClassifier(bootstrap=False, max_features=0.0696410090574,\n", - " min_samples_leaf=7, min_samples_split=8,\n", - " n_estimators=128)" + "RandomForestClassifier(bootstrap=False, criterion='entropy',\n", + " max_features=0.1574830347299, min_samples_leaf=10,\n", + " min_samples_split=6, n_estimators=128)" ] }, - "execution_count": 21, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -501,7 +501,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -509,7 +509,7 @@ "output_type": "stream", "text": [ "sampled hyperparameters\n", - "{'bootstrap': False, 'criterion': 'entropy', 'max_features': 0.2320810853841, 'min_samples_leaf': 19, 'min_samples_split': 12, 'n_estimators': 128}\n" + "{'bootstrap': True, 'criterion': 'entropy', 'max_features': 0.2601475241557, 'min_samples_leaf': 17, 'min_samples_split': 3, 'n_estimators': 128}\n" ] }, { @@ -919,19 +919,19 @@ " /* fitted */\n", " background-color: var(--sklearn-color-fitted-level-3);\n", "}\n", - "
RandomForestClassifier(bootstrap=False, criterion='entropy',\n",
-       "                       max_features=0.2320810853841, min_samples_leaf=19,\n",
-       "                       min_samples_split=12, n_estimators=128)
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" + "
RandomForestClassifier(criterion='entropy', max_features=0.2601475241557,\n",
+       "                       min_samples_leaf=17, min_samples_split=3,\n",
+       "                       n_estimators=128)
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" ], "text/plain": [ - "RandomForestClassifier(bootstrap=False, criterion='entropy',\n", - " max_features=0.2320810853841, min_samples_leaf=19,\n", - " min_samples_split=12, n_estimators=128)" + "RandomForestClassifier(criterion='entropy', max_features=0.2601475241557,\n", + " min_samples_leaf=17, min_samples_split=3,\n", + " n_estimators=128)" ] }, - "execution_count": 22, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -1017,7 +1017,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -1053,16 +1053,16 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 24, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -1074,7 +1074,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -1082,7 +1082,7 @@ "output_type": "stream", "text": [ "sampled hyperparameters\n", - "{'metric': 'minkowski', 'n_jobs': 1, 'n_neighbors': 7, 'p': 1, 'weights': 'uniform'}\n" + "{'metric': 'euclidean', 'n_jobs': 1, 'n_neighbors': 9, 'p': 1, 'weights': 'uniform'}\n" ] } ], @@ -1100,7 +1100,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -1108,7 +1108,7 @@ "output_type": "stream", "text": [ "mutated hyperparameters\n", - "{'metric': 'minkowski', 'n_jobs': 1, 'n_neighbors': 7, 'p': 2, 'weights': 'uniform'}\n" + "{'metric': 'minkowski', 'n_jobs': 1, 'n_neighbors': 3, 'p': 3, 'weights': 'distance'}\n" ] } ], @@ -1127,7 +1127,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -1135,14 +1135,14 @@ "output_type": "stream", "text": [ "original hyperparameters for individual 1\n", - "{'metric': 'minkowski', 'n_jobs': 1, 'n_neighbors': 3, 'p': 3, 'weights': 'uniform'}\n", + "{'metric': 'minkowski', 'n_jobs': 1, 'n_neighbors': 6, 'p': 2, 'weights': 'distance'}\n", "original hyperparameters for individual 2\n", - "{'metric': 'euclidean', 'n_jobs': 1, 'n_neighbors': 7, 'p': 3, 'weights': 'distance'}\n", + "{'metric': 'euclidean', 'n_jobs': 1, 'n_neighbors': 4, 'p': 2, 'weights': 'uniform'}\n", "\n", "post crossover hyperparameters for individual 1\n", - "{'metric': 'minkowski', 'n_jobs': 1, 'n_neighbors': 7, 'p': 3, 'weights': 'uniform'}\n", + "{'metric': 'euclidean', 'n_jobs': 1, 'n_neighbors': 6, 'p': 2, 'weights': 'uniform'}\n", "post crossover hyperparameters for individual 2\n", - "{'metric': 'euclidean', 'n_jobs': 1, 'n_neighbors': 7, 'p': 3, 'weights': 'distance'}\n" + "{'metric': 'euclidean', 'n_jobs': 1, 'n_neighbors': 4, 'p': 2, 'weights': 'uniform'}\n" ] } ], @@ -1175,7 +1175,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -1585,13 +1585,13 @@ " /* fitted */\n", " background-color: var(--sklearn-color-fitted-level-3);\n", "}\n", - "
KNeighborsClassifier(n_jobs=1, n_neighbors=7, p=3)
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" + "
KNeighborsClassifier(metric='euclidean', n_jobs=1, n_neighbors=6)
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" ], "text/plain": [ - "KNeighborsClassifier(n_jobs=1, n_neighbors=7, p=3)" + "KNeighborsClassifier(metric='euclidean', n_jobs=1, n_neighbors=6)" ] }, - "execution_count": 28, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -1610,7 +1610,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -2026,7 +2026,7 @@ "KNeighborsClassifier(n_neighbors=10)" ] }, - "execution_count": 29, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -2079,16 +2079,16 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 30, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -2186,7 +2186,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -2603,16 +2603,16 @@ " /* fitted */\n", " background-color: var(--sklearn-color-fitted-level-3);\n", "}\n", - "
DecisionTreeClassifier(max_depth=11, max_features='sqrt', min_samples_leaf=20,\n",
-       "                       min_samples_split=10)
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" + "
LogisticRegression(C=0.0008500633703, class_weight='balanced', max_iter=1000,\n",
+       "                   n_jobs=1, penalty='l1', solver='saga')
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" ], "text/plain": [ - "DecisionTreeClassifier(max_depth=11, max_features='sqrt', min_samples_leaf=20,\n", - " min_samples_split=10)" + "LogisticRegression(C=0.0008500633703, class_weight='balanced', max_iter=1000,\n", + " n_jobs=1, penalty='l1', solver='saga')" ] }, - "execution_count": 31, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -2626,7 +2626,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -3043,16 +3043,16 @@ " /* fitted */\n", " background-color: var(--sklearn-color-fitted-level-3);\n", "}\n", - "
KNeighborsClassifier(metric='euclidean', n_jobs=1, n_neighbors=6,\n",
-       "                     weights='distance')
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" + "
LogisticRegression(C=0.1054489422979, class_weight='balanced', max_iter=1000,\n",
+       "                   n_jobs=1, penalty='l1', solver='liblinear')
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" ], "text/plain": [ - "KNeighborsClassifier(metric='euclidean', n_jobs=1, n_neighbors=6,\n", - " weights='distance')" + "LogisticRegression(C=0.1054489422979, class_weight='balanced', max_iter=1000,\n", + " n_jobs=1, penalty='l1', solver='liblinear')" ] }, - "execution_count": 32, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -3230,7 +3230,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -3647,16 +3647,13 @@ " /* fitted */\n", " background-color: var(--sklearn-color-fitted-level-3);\n", "}\n", - "
DecisionTreeClassifier(max_depth=3, max_features='sqrt', min_samples_leaf=16,\n",
-       "                       min_samples_split=8)
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" + "
KNeighborsClassifier(n_jobs=1, n_neighbors=55, weights='distance')
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" ], "text/plain": [ - "DecisionTreeClassifier(max_depth=3, max_features='sqrt', min_samples_leaf=16,\n", - " min_samples_split=8)" + "KNeighborsClassifier(n_jobs=1, n_neighbors=55, weights='distance')" ] }, - "execution_count": 33, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -3671,7 +3668,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -4088,13 +4085,16 @@ " /* fitted */\n", " background-color: var(--sklearn-color-fitted-level-3);\n", "}\n", - "
LogisticRegression(C=203.4209981734027, max_iter=1000, n_jobs=1, solver='saga')
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" + "
LogisticRegression(C=0.012915602763, l1_ratio=0.2577823332886, max_iter=1000,\n",
+       "                   n_jobs=1, penalty='elasticnet', solver='saga')
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" ], "text/plain": [ - "LogisticRegression(C=203.4209981734027, max_iter=1000, n_jobs=1, solver='saga')" + "LogisticRegression(C=0.012915602763, l1_ratio=0.2577823332886, max_iter=1000,\n", + " n_jobs=1, penalty='elasticnet', solver='saga')" ] }, - "execution_count": 34, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -4106,7 +4106,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -4523,13 +4523,19 @@ " /* fitted */\n", " background-color: var(--sklearn-color-fitted-level-3);\n", "}\n", - "
GaussianNB()
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" + "
SGDClassifier(alpha=0.0038384092036, class_weight='balanced',\n",
+       "              eta0=0.7197535254246, l1_ratio=0.8816063677431,\n",
+       "              loss='modified_huber', n_jobs=1, penalty='elasticnet')
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" ], "text/plain": [ - "GaussianNB()" + "SGDClassifier(alpha=0.0038384092036, class_weight='balanced',\n", + " eta0=0.7197535254246, l1_ratio=0.8816063677431,\n", + " loss='modified_huber', n_jobs=1, penalty='elasticnet')" ] }, - "execution_count": 35, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -4544,7 +4550,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -4961,13 +4967,13 @@ " /* fitted */\n", " background-color: var(--sklearn-color-fitted-level-3);\n", "}\n", - "
MultinomialNB(alpha=0.2214451695279)
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" + "
KNeighborsClassifier(n_jobs=1, n_neighbors=1, p=1, weights='distance')
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" ], "text/plain": [ - "MultinomialNB(alpha=0.2214451695279)" + "KNeighborsClassifier(n_jobs=1, n_neighbors=1, p=1, weights='distance')" ] }, - "execution_count": 36, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -4987,13 +4993,13 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
RandomForestClassifier(bootstrap=False, max_features=0.5976648428162,\n",
-       "                       min_samples_leaf=4, min_samples_split=7,\n",
-       "                       n_estimators=128, random_state=1)
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" + "
RandomForestClassifier(bootstrap=False, criterion='entropy',\n",
+       "                       max_features=0.0121463021153, min_samples_leaf=10,\n",
+       "                       min_samples_split=14, n_estimators=128, random_state=1)
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" ], "text/plain": [ - "RandomForestClassifier(bootstrap=False, max_features=0.5976648428162,\n", - " min_samples_leaf=4, min_samples_split=7,\n", - " n_estimators=128, random_state=1)" + "RandomForestClassifier(bootstrap=False, criterion='entropy',\n", + " max_features=0.0121463021153, min_samples_leaf=10,\n", + " min_samples_split=14, n_estimators=128, random_state=1)" ] }, - "execution_count": 75, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -5430,7 +5436,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -5443,7 +5449,7 @@ { "data": { "text/html": [ - "
Pipeline(steps=[('variancethreshold',\n",
-       "                 VarianceThreshold(threshold=0.0061644734724)),\n",
-       "                ('pca', PCA(n_components=0.5803735556718)),\n",
+       "
Pipeline(steps=[('variancethreshold',\n",
+       "                 VarianceThreshold(threshold=0.0008293708451)),\n",
+       "                ('pca', PCA(n_components=0.5048643890372)),\n",
        "                ('logisticregression',\n",
-       "                 LogisticRegression(C=0.0331002885417, class_weight='balanced',\n",
-       "                                    max_iter=1000, n_jobs=1, solver='saga'))])
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VarianceThreshold(threshold=0.0008293708451)
PCA(n_components=0.5048643890372)
LogisticRegression(C=7.7606337566295, class_weight='balanced',\n",
+       "                   l1_ratio=0.123465163557, max_iter=1000, n_jobs=1,\n",
+       "                   penalty='elasticnet', solver='saga')
" ], "text/plain": [ "Pipeline(steps=[('variancethreshold',\n", - " VarianceThreshold(threshold=0.0061644734724)),\n", - " ('pca', PCA(n_components=0.5803735556718)),\n", + " VarianceThreshold(threshold=0.0008293708451)),\n", + " ('pca', PCA(n_components=0.5048643890372)),\n", " ('logisticregression',\n", - " LogisticRegression(C=0.0331002885417, class_weight='balanced',\n", - " max_iter=1000, n_jobs=1, solver='saga'))])" + " LogisticRegression(C=7.7606337566295, class_weight='balanced',\n", + " l1_ratio=0.123465163557, max_iter=1000,\n", + " n_jobs=1, penalty='elasticnet',\n", + " solver='saga'))])" ] }, - "execution_count": 37, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -5900,7 +5913,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -5913,7 +5926,7 @@ { "data": { "text/html": [ - "
Pipeline(steps=[('selectpercentile',\n",
-       "                 SelectPercentile(percentile=82.0501639184698)),\n",
-       "                ('zerocount', ZeroCount()),\n",
-       "                ('multinomialnb', MultinomialNB(alpha=0.7116498874199))])
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" + "
Pipeline(steps=[('variancethreshold',\n",
+       "                 VarianceThreshold(threshold=0.1215210592814)),\n",
+       "                ('fastica', FastICA(n_components=83)),\n",
+       "                ('baggingclassifier',\n",
+       "                 BaggingClassifier(bootstrap_features=True,\n",
+       "                                   max_features=0.9057563115025,\n",
+       "                                   max_samples=0.2313759070451, n_estimators=89,\n",
+       "                                   n_jobs=1))])
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" ], "text/plain": [ - "Pipeline(steps=[('selectpercentile',\n", - " SelectPercentile(percentile=82.0501639184698)),\n", - " ('zerocount', ZeroCount()),\n", - " ('multinomialnb', MultinomialNB(alpha=0.7116498874199))])" + "Pipeline(steps=[('variancethreshold',\n", + " VarianceThreshold(threshold=0.1215210592814)),\n", + " ('fastica', FastICA(n_components=83)),\n", + " ('baggingclassifier',\n", + " BaggingClassifier(bootstrap_features=True,\n", + " max_features=0.9057563115025,\n", + " max_samples=0.2313759070451, n_estimators=89,\n", + " n_jobs=1))])" ] }, - "execution_count": 38, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -6354,7 +6380,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -6367,7 +6393,7 @@ { "data": { "text/html": [ - "
Pipeline(steps=[('variancethreshold',\n",
-       "                 VarianceThreshold(threshold=0.0090024083095)),\n",
-       "                ('nystroem',\n",
-       "                 Nystroem(gamma=0.326846805684, kernel='chi2',\n",
-       "                          n_components=49)),\n",
-       "                ('mlpclassifier',\n",
-       "                 MLPClassifier(activation='identity', alpha=0.0009288789905,\n",
-       "                               early_stopping=True,\n",
-       "                               hidden_layer_sizes=[265, 265],\n",
-       "                               learning_rate='invscaling',\n",
-       "                               learning_rate_init=0.0366758440485,\n",
-       "                               n_iter_no_change=32))])
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" + "
Pipeline(steps=[('selectpercentile',\n",
+       "                 SelectPercentile(percentile=25.1697450346144)),\n",
+       "                ('kbinsdiscretizer',\n",
+       "                 KBinsDiscretizer(encode='onehot-dense', n_bins=40,\n",
+       "                                  strategy='uniform')),\n",
+       "                ('lineardiscriminantanalysis',\n",
+       "                 LinearDiscriminantAnalysis(shrinkage=0.755769834898,\n",
+       "                                            solver='eigen'))])
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" ], "text/plain": [ - "Pipeline(steps=[('variancethreshold',\n", - " VarianceThreshold(threshold=0.0090024083095)),\n", - " ('nystroem',\n", - " Nystroem(gamma=0.326846805684, kernel='chi2',\n", - " n_components=49)),\n", - " ('mlpclassifier',\n", - " MLPClassifier(activation='identity', alpha=0.0009288789905,\n", - " early_stopping=True,\n", - " hidden_layer_sizes=[265, 265],\n", - " learning_rate='invscaling',\n", - " learning_rate_init=0.0366758440485,\n", - " n_iter_no_change=32))])" + "Pipeline(steps=[('selectpercentile',\n", + " SelectPercentile(percentile=25.1697450346144)),\n", + " ('kbinsdiscretizer',\n", + " KBinsDiscretizer(encode='onehot-dense', n_bins=40,\n", + " strategy='uniform')),\n", + " ('lineardiscriminantanalysis',\n", + " LinearDiscriminantAnalysis(shrinkage=0.755769834898,\n", + " solver='eigen'))])" ] }, - "execution_count": 39, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -6833,7 +6845,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -6846,7 +6858,7 @@ { "data": { "text/html": [ - "
Pipeline(steps=[('zerocount-1', ZeroCount()), ('zerocount-2', ZeroCount()),\n",
-       "                ('minmaxscaler', MinMaxScaler())])
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" + "
Pipeline(steps=[('rbfsampler',\n",
+       "                 RBFSampler(gamma=0.1991726671256, n_components=7)),\n",
+       "                ('zerocount', ZeroCount()),\n",
+       "                ('binarizer', Binarizer(threshold=0.5354245073766))])
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" ], "text/plain": [ - "Pipeline(steps=[('zerocount-1', ZeroCount()), ('zerocount-2', ZeroCount()),\n", - " ('minmaxscaler', MinMaxScaler())])" + "Pipeline(steps=[('rbfsampler',\n", + " RBFSampler(gamma=0.1991726671256, n_components=7)),\n", + " ('zerocount', ZeroCount()),\n", + " ('binarizer', Binarizer(threshold=0.5354245073766))])" ] }, - "execution_count": 40, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -7275,7 +7293,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -7288,7 +7306,7 @@ { "data": { "text/html": [ - "
Pipeline(steps=[('selectpercentile',\n",
-       "                 SelectPercentile(percentile=61.5466222112372)),\n",
-       "                ('robustscaler',\n",
-       "                 RobustScaler(quantile_range=(0.0479806149183,\n",
-       "                                              0.9674592383627))),\n",
-       "                ('selectfrommodel',\n",
-       "                 SelectFromModel(estimator=ExtraTreesClassifier(bootstrap=True,\n",
-       "                                                                criterion='entropy',\n",
-       "                                                                max_features=0.3260066399479,\n",
-       "                                                                min_samples_leaf=6,\n",
-       "                                                                min_samples_split=8,\n",
-       "                                                                n_jobs=1),\n",
-       "                                 threshold=0.0001984121028))])
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" + "
Pipeline(steps=[('selectfwe', SelectFwe(alpha=0.0014251225737)),\n",
+       "                ('powertransformer', PowerTransformer())])
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" ], "text/plain": [ - "Pipeline(steps=[('selectpercentile',\n", - " SelectPercentile(percentile=61.5466222112372)),\n", - " ('robustscaler',\n", - " RobustScaler(quantile_range=(0.0479806149183,\n", - " 0.9674592383627))),\n", - " ('selectfrommodel',\n", - " SelectFromModel(estimator=ExtraTreesClassifier(bootstrap=True,\n", - " criterion='entropy',\n", - " max_features=0.3260066399479,\n", - " min_samples_leaf=6,\n", - " min_samples_split=8,\n", - " n_jobs=1),\n", - " threshold=0.0001984121028))])" + "Pipeline(steps=[('selectfwe', SelectFwe(alpha=0.0014251225737)),\n", + " ('powertransformer', PowerTransformer())])" ] }, - "execution_count": 41, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -7755,7 +7731,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -7768,7 +7744,7 @@ { "data": { "text/html": [ - "
Pipeline(steps=[('pipeline',\n",
-       "                 Pipeline(steps=[('binarizer',\n",
-       "                                  Binarizer(threshold=0.4321512765788)),\n",
-       "                                 ('pca', PCA(n_components=0.6918117427918)),\n",
-       "                                 ('passkbinsdiscretizer',\n",
-       "                                  PassKBinsDiscretizer(n_bins=42))])),\n",
-       "                ('extratreesclassifier',\n",
-       "                 ExtraTreesClassifier(class_weight='balanced',\n",
-       "                                      criterion='entropy',\n",
-       "                                      max_features=0.169455524505,\n",
-       "                                      min_samples_leaf=8, min_samples_split=14,\n",
-       "                                      n_jobs=1))])
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" + "
Pipeline(steps=[('pipeline',\n",
+       "                 Pipeline(steps=[('nystroem',\n",
+       "                                  Nystroem(gamma=0.3480554902065,\n",
+       "                                           kernel='sigmoid', n_components=20)),\n",
+       "                                 ('binarizer',\n",
+       "                                  Binarizer(threshold=0.6696149189758)),\n",
+       "                                 ('minmaxscaler', MinMaxScaler())])),\n",
+       "                ('multinomialnb', MultinomialNB(alpha=0.0016967794962))])
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" ], "text/plain": [ "Pipeline(steps=[('pipeline',\n", - " Pipeline(steps=[('binarizer',\n", - " Binarizer(threshold=0.4321512765788)),\n", - " ('pca', PCA(n_components=0.6918117427918)),\n", - " ('passkbinsdiscretizer',\n", - " PassKBinsDiscretizer(n_bins=42))])),\n", - " ('extratreesclassifier',\n", - " ExtraTreesClassifier(class_weight='balanced',\n", - " criterion='entropy',\n", - " max_features=0.169455524505,\n", - " min_samples_leaf=8, min_samples_split=14,\n", - " n_jobs=1))])" + " Pipeline(steps=[('nystroem',\n", + " Nystroem(gamma=0.3480554902065,\n", + " kernel='sigmoid', n_components=20)),\n", + " ('binarizer',\n", + " Binarizer(threshold=0.6696149189758)),\n", + " ('minmaxscaler', MinMaxScaler())])),\n", + " ('multinomialnb', MultinomialNB(alpha=0.0016967794962))])" ] }, - "execution_count": 42, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -8232,7 +8196,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -8245,7 +8209,7 @@ { "data": { "text/html": [ - "
Pipeline(steps=[('pipeline',\n",
-       "                 Pipeline(steps=[('rfe',\n",
-       "                                  RFE(estimator=ExtraTreesClassifier(bootstrap=True,\n",
-       "                                                                     criterion='entropy',\n",
-       "                                                                     max_features=0.0135775754498,\n",
-       "                                                                     min_samples_leaf=8,\n",
-       "                                                                     min_samples_split=6,\n",
-       "                                                                     n_jobs=1),\n",
-       "                                      step=0.7236899597647)),\n",
-       "                                 ('columnonehotencoder', ColumnOneHotEncoder()),\n",
-       "                                 ('featureagglomeration',\n",
-       "                                  FeatureAgglomeration(linkage='average',\n",
-       "                                                       metric='l2',\n",
-       "                                                       n_clusters=150))])),\n",
-       "                ('sgdclassifier',\n",
-       "                 SGDClassifier(alpha=0.0009821180851, eta0=0.1666104101354,\n",
-       "                               l1_ratio=0.7504578619487, loss='modified_huber',\n",
-       "                               n_jobs=1, penalty='elasticnet'))])
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" + "
Pipeline(steps=[('pipeline',\n",
+       "                 Pipeline(steps=[('zerocount', ZeroCount()),\n",
+       "                                 ('variancethreshold',\n",
+       "                                  VarianceThreshold(threshold=0.0020422211173)),\n",
+       "                                 ('binarizer',\n",
+       "                                  Binarizer(threshold=0.9681763702))])),\n",
+       "                ('bernoullinb',\n",
+       "                 BernoulliNB(alpha=0.0816524714629, fit_prior=False))])
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" ], "text/plain": [ "Pipeline(steps=[('pipeline',\n", - " Pipeline(steps=[('rfe',\n", - " RFE(estimator=ExtraTreesClassifier(bootstrap=True,\n", - " criterion='entropy',\n", - " max_features=0.0135775754498,\n", - " min_samples_leaf=8,\n", - " min_samples_split=6,\n", - " n_jobs=1),\n", - " step=0.7236899597647)),\n", - " ('columnonehotencoder', ColumnOneHotEncoder()),\n", - " ('featureagglomeration',\n", - " FeatureAgglomeration(linkage='average',\n", - " metric='l2',\n", - " n_clusters=150))])),\n", - " ('sgdclassifier',\n", - " SGDClassifier(alpha=0.0009821180851, eta0=0.1666104101354,\n", - " l1_ratio=0.7504578619487, loss='modified_huber',\n", - " n_jobs=1, penalty='elasticnet'))])" + " Pipeline(steps=[('zerocount', ZeroCount()),\n", + " ('variancethreshold',\n", + " VarianceThreshold(threshold=0.0020422211173)),\n", + " ('binarizer',\n", + " Binarizer(threshold=0.9681763702))])),\n", + " ('bernoullinb',\n", + " BernoulliNB(alpha=0.0816524714629, fit_prior=False))])" ] }, - "execution_count": 43, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -8748,13 +8664,13 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
FeatureUnion(transformer_list=[('zerocount', ZeroCount()),\n",
-       "                               ('passthrough', Passthrough())])
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" + "
FeatureUnion(transformer_list=[('fastica',\n",
+       "                                FastICA(algorithm='deflation',\n",
+       "                                        n_components=66)),\n",
+       "                               ('passthrough', Passthrough())])
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" ], "text/plain": [ - "FeatureUnion(transformer_list=[('zerocount', ZeroCount()),\n", + "FeatureUnion(transformer_list=[('fastica',\n", + " FastICA(algorithm='deflation',\n", + " n_components=66)),\n", " ('passthrough', Passthrough())])" ] }, - "execution_count": 44, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -9190,13 +9112,13 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
Pipeline(steps=[('selectfwe', SelectFwe(alpha=0.0104695394381)),\n",
+       "
Pipeline(steps=[('variancethreshold',\n",
+       "                 VarianceThreshold(threshold=0.0009494718313)),\n",
        "                ('featureunion',\n",
-       "                 FeatureUnion(transformer_list=[('quantiletransformer',\n",
-       "                                                 QuantileTransformer(n_quantiles=93)),\n",
+       "                 FeatureUnion(transformer_list=[('binarizer',\n",
+       "                                                 Binarizer(threshold=0.8136655878085)),\n",
        "                                                ('passthrough',\n",
        "                                                 Passthrough())])),\n",
-       "                ('svc',\n",
-       "                 SVC(C=0.5015595860816, coef0=0.5773095995375, kernel='sigmoid',\n",
-       "                     max_iter=3000, probability=True))])
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VarianceThreshold(threshold=0.0009494718313)
FeatureUnion(transformer_list=[('binarizer',\n",
+       "                                Binarizer(threshold=0.8136655878085)),\n",
+       "                               ('passthrough', Passthrough())])
Binarizer(threshold=0.8136655878085)
Passthrough()
AdaBoostClassifier(learning_rate=0.1727096029044, n_estimators=446)
" ], "text/plain": [ - "Pipeline(steps=[('selectfwe', SelectFwe(alpha=0.0104695394381)),\n", + "Pipeline(steps=[('variancethreshold',\n", + " VarianceThreshold(threshold=0.0009494718313)),\n", " ('featureunion',\n", - " FeatureUnion(transformer_list=[('quantiletransformer',\n", - " QuantileTransformer(n_quantiles=93)),\n", + " FeatureUnion(transformer_list=[('binarizer',\n", + " Binarizer(threshold=0.8136655878085)),\n", " ('passthrough',\n", " Passthrough())])),\n", - " ('svc',\n", - " SVC(C=0.5015595860816, coef0=0.5773095995375, kernel='sigmoid',\n", - " max_iter=3000, probability=True))])" + " ('adaboostclassifier',\n", + " AdaBoostClassifier(learning_rate=0.1727096029044,\n", + " n_estimators=446))])" ] }, - "execution_count": 45, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -9657,13 +9581,13 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
Pipeline(steps=[('featureunion',\n",
+       "
Pipeline(steps=[('featureunion',\n",
        "                 FeatureUnion(transformer_list=[('pipeline-1',\n",
-       "                                                 Pipeline(steps=[('selectfwe',\n",
-       "                                                                  SelectFwe(alpha=0.0013091622594)),\n",
-       "                                                                 ('nystroem',\n",
-       "                                                                  Nystroem(gamma=0.2527764721894,\n",
-       "                                                                           kernel='additive_chi2',\n",
-       "                                                                           n_components=40))])),\n",
-       "                                                ('pipeline-2',\n",
        "                                                 Pipeline(steps=[('variancethreshold',\n",
-       "                                                                  VarianceThreshold(threshold=0.0130961185337)),\n",
-       "                                                                 ('featureagglomeration',\n",
-       "                                                                  Fe...ge='average',\n",
-       "                                                                                       metric='l2',\n",
-       "                                                                                       n_clusters=293,\n",
-       "                                                                                       pooling_func=<function median at 0x73f3c1bda370>))]))])),\n",
-       "                ('histgradientboostingclassifier',\n",
-       "                 HistGradientBoostingClassifier(early_stopping=False,\n",
-       "                                                l2_regularization=3.1669452e-06,\n",
-       "                                                learning_rate=0.1262523910078,\n",
-       "                                                max_features=0.8008565064114,\n",
-       "                                                max_leaf_nodes=1504,\n",
-       "                                                min_samples_leaf=32, tol=0.0001,\n",
-       "                                                validation_fraction=None))])
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VarianceThreshold(threshold=0.1996640297479)
PowerTransformer()
SelectFwe(alpha=0.0045323854667)
FastICA(n_components=34)
QuadraticDiscriminantAnalysis(reg_param=0.8833282196313)
" + ], "text/plain": [ "Pipeline(steps=[('featureunion',\n", " FeatureUnion(transformer_list=[('pipeline-1',\n", - " Pipeline(steps=[('selectfwe',\n", - " SelectFwe(alpha=0.0013091622594)),\n", - " ('nystroem',\n", - " Nystroem(gamma=0.2527764721894,\n", - " kernel='additive_chi2',\n", - " n_components=40))])),\n", - " ('pipeline-2',\n", " Pipeline(steps=[('variancethreshold',\n", - " VarianceThreshold(threshold=0.0130961185337)),\n", - " ('featureagglomeration',\n", - " Fe...ge='average',\n", - " metric='l2',\n", - " n_clusters=293,\n", - " pooling_func=))]))])),\n", - " ('histgradientboostingclassifier',\n", - " HistGradientBoostingClassifier(early_stopping=False,\n", - " l2_regularization=3.1669452e-06,\n", - " learning_rate=0.1262523910078,\n", - " max_features=0.8008565064114,\n", - " max_leaf_nodes=1504,\n", - " min_samples_leaf=32, tol=0.0001,\n", - " validation_fraction=None))])" + " VarianceThreshold(threshold=0.1996640297479)),\n", + " ('powertransformer',\n", + " PowerTransformer())])),\n", + " ('pipeline-2',\n", + " Pipeline(steps=[('selectfwe',\n", + " SelectFwe(alpha=0.0045323854667)),\n", + " ('fastica',\n", + " FastICA(n_components=34))]))])),\n", + " ('quadraticdiscriminantanalysis',\n", + " QuadraticDiscriminantAnalysis(reg_param=0.8833282196313))])" ] }, - "execution_count": 46, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -10199,13 +10079,13 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
FeatureUnion(transformer_list=[('quantiletransformer',\n",
-       "                                QuantileTransformer(n_quantiles=81)),\n",
-       "                               ('columnonehotencoder', ColumnOneHotEncoder())])
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" + "
FeatureUnion(transformer_list=[('zerocount', ZeroCount()),\n",
+       "                               ('powertransformer', PowerTransformer())])
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" ], "text/plain": [ - "FeatureUnion(transformer_list=[('quantiletransformer',\n", - " QuantileTransformer(n_quantiles=81)),\n", - " ('columnonehotencoder', ColumnOneHotEncoder())])" + "FeatureUnion(transformer_list=[('zerocount', ZeroCount()),\n", + " ('powertransformer', PowerTransformer())])" ] }, - "execution_count": 47, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -10640,13 +10517,13 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
FeatureUnion(transformer_list=[('featureunion',\n",
-       "                                FeatureUnion(transformer_list=[('rbfsampler-1',\n",
-       "                                                                RBFSampler(gamma=0.6219125014396,\n",
-       "                                                                           n_components=64)),\n",
-       "                                                               ('powertransformer',\n",
-       "                                                                PowerTransformer()),\n",
-       "                                                               ('rbfsampler-2',\n",
-       "                                                                RBFSampler(gamma=0.3345729157827,\n",
-       "                                                                           n_components=23))])),\n",
-       "                               ('passthrough', Passthrough())])
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" + "
FeatureUnion(transformer_list=[('featureunion',\n",
+       "                                FeatureUnion(transformer_list=[('powertransformer',\n",
+       "                                                                PowerTransformer())])),\n",
+       "                               ('passthrough', Passthrough())])
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" ], "text/plain": [ "FeatureUnion(transformer_list=[('featureunion',\n", - " FeatureUnion(transformer_list=[('rbfsampler-1',\n", - " RBFSampler(gamma=0.6219125014396,\n", - " n_components=64)),\n", - " ('powertransformer',\n", - " PowerTransformer()),\n", - " ('rbfsampler-2',\n", - " RBFSampler(gamma=0.3345729157827,\n", - " n_components=23))])),\n", + " FeatureUnion(transformer_list=[('powertransformer',\n", + " PowerTransformer())])),\n", " ('passthrough', Passthrough())])" ] }, - "execution_count": 48, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -11099,13 +10958,13 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
Pipeline(steps=[('variancethreshold',\n",
-       "                 VarianceThreshold(threshold=0.1286612361721)),\n",
+       "
Pipeline(steps=[('selectpercentile',\n",
+       "                 SelectPercentile(percentile=3.5688237635159)),\n",
        "                ('featureunion',\n",
        "                 FeatureUnion(transformer_list=[('featureunion',\n",
-       "                                                 FeatureUnion(transformer_list=[('binarizer',\n",
-       "                                                                                 Binarizer(threshold=0.736067585858)),\n",
-       "                                                                                ('rbfsampler',\n",
-       "                                                                                 RBFSampler(gamma=0.1436440722816,\n",
-       "                                                                                            n_components=44)),\n",
-       "                                                                                ('quantiletransformer',\n",
-       "                                                                                 QuantileTransformer(n_quantiles=100))])),\n",
+       "                                                 FeatureUnion(transformer_list=[('featureagglomeration',\n",
+       "                                                                                 FeatureAgglomeration(n_clusters=28,\n",
+       "                                                                                                      pooling_func=<function max at 0x78ec455b4e30>))])),\n",
        "                                                ('passthrough',\n",
        "                                                 Passthrough())])),\n",
-       "                ('histgradientboostingclassifier',\n",
-       "                 HistGradientBoostingClassifier(early_stopping=True,\n",
-       "                                                l2_regularization=2.047622e-07,\n",
-       "                                                learning_rate=0.0164428425279,\n",
-       "                                                max_features=0.3325348714186,\n",
-       "                                                max_leaf_nodes=1940,\n",
-       "                                                min_samples_leaf=78,\n",
-       "                                                n_iter_no_change=12, tol=0.0001,\n",
-       "                                                validation_fraction=None))])
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SelectPercentile(percentile=3.5688237635159)
FeatureUnion(transformer_list=[('featureunion',\n",
+       "                                FeatureUnion(transformer_list=[('featureagglomeration',\n",
+       "                                                                FeatureAgglomeration(n_clusters=28,\n",
+       "                                                                                     pooling_func=<function max at 0x78ec455b4e30>))])),\n",
+       "                               ('passthrough', Passthrough())])
FeatureAgglomeration(n_clusters=28,\n",
+       "                     pooling_func=<function max at 0x78ec455b4e30>)
Passthrough()
LogisticRegression(C=9762.07332929782, max_iter=1000, n_jobs=1, solver='saga')
" ], "text/plain": [ - "Pipeline(steps=[('variancethreshold',\n", - " VarianceThreshold(threshold=0.1286612361721)),\n", + "Pipeline(steps=[('selectpercentile',\n", + " SelectPercentile(percentile=3.5688237635159)),\n", " ('featureunion',\n", " FeatureUnion(transformer_list=[('featureunion',\n", - " FeatureUnion(transformer_list=[('binarizer',\n", - " Binarizer(threshold=0.736067585858)),\n", - " ('rbfsampler',\n", - " RBFSampler(gamma=0.1436440722816,\n", - " n_components=44)),\n", - " ('quantiletransformer',\n", - " QuantileTransformer(n_quantiles=100))])),\n", + " FeatureUnion(transformer_list=[('featureagglomeration',\n", + " FeatureAgglomeration(n_clusters=28,\n", + " pooling_func=))])),\n", " ('passthrough',\n", " Passthrough())])),\n", - " ('histgradientboostingclassifier',\n", - " HistGradientBoostingClassifier(early_stopping=True,\n", - " l2_regularization=2.047622e-07,\n", - " learning_rate=0.0164428425279,\n", - " max_features=0.3325348714186,\n", - " max_leaf_nodes=1940,\n", - " min_samples_leaf=78,\n", - " n_iter_no_change=12, tol=0.0001,\n", - " validation_fraction=None))])" + " ('logisticregression',\n", + " LogisticRegression(C=9762.07332929782, max_iter=1000, n_jobs=1,\n", + " solver='saga'))])" ] }, - "execution_count": 49, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -11620,13 +11440,13 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
ExtraTreesClassifier(max_features=0.2286391649712, min_samples_leaf=13,\n",
-       "                     min_samples_split=8, n_jobs=1)
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" + "
ExtraTreesClassifier(class_weight='balanced', max_features=0.6642237575313,\n",
+       "                     min_samples_leaf=17, min_samples_split=3, n_jobs=1)
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" ], "text/plain": [ - "ExtraTreesClassifier(max_features=0.2286391649712, min_samples_leaf=13,\n", - " min_samples_split=8, n_jobs=1)" + "ExtraTreesClassifier(class_weight='balanced', max_features=0.6642237575313,\n", + " min_samples_leaf=17, min_samples_split=3, n_jobs=1)" ] }, - "execution_count": 50, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -12057,13 +11877,13 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
SelectFromModel(estimator=ExtraTreesClassifier(bootstrap=True,\n",
-       "                                               criterion='entropy',\n",
-       "                                               max_features=0.0311518006465,\n",
-       "                                               min_samples_split=19, n_jobs=1),\n",
-       "                threshold=0.0012368197842)
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" + "
SelectFromModel(estimator=ExtraTreesClassifier(bootstrap=True,\n",
+       "                                               class_weight='balanced',\n",
+       "                                               max_features=0.3007313724684,\n",
+       "                                               min_samples_leaf=12,\n",
+       "                                               min_samples_split=17, n_jobs=1),\n",
+       "                threshold=0.0048046738992)
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" ], "text/plain": [ "SelectFromModel(estimator=ExtraTreesClassifier(bootstrap=True,\n", - " criterion='entropy',\n", - " max_features=0.0311518006465,\n", - " min_samples_split=19, n_jobs=1),\n", - " threshold=0.0012368197842)" + " class_weight='balanced',\n", + " max_features=0.3007313724684,\n", + " min_samples_leaf=12,\n", + " min_samples_split=17, n_jobs=1),\n", + " threshold=0.0048046738992)" ] }, - "execution_count": 51, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -12528,13 +12351,13 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
EstimatorTransformer(estimator=HistGradientBoostingClassifier(early_stopping=True,\n",
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-       "                                                              max_features=0.5654219816525,\n",
-       "                                                              max_leaf_nodes=1048,\n",
-       "                                                              min_samples_leaf=1,\n",
-       "                                                              n_iter_no_change=17,\n",
-       "                                                              tol=0.0001,\n",
-       "                                                              validation_fraction=0.3473838441178))
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" + "
EstimatorTransformer(estimator=SVC(C=140.9223338924506, gamma=0.0007253447995,\n",
+       "                                   max_iter=3000, probability=True,\n",
+       "                                   shrinking=False))
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" ], "text/plain": [ - "EstimatorTransformer(estimator=HistGradientBoostingClassifier(early_stopping=True,\n", - " l2_regularization=0.000117454825,\n", - " learning_rate=0.122899142038,\n", - " max_features=0.5654219816525,\n", - " max_leaf_nodes=1048,\n", - " min_samples_leaf=1,\n", - " n_iter_no_change=17,\n", - " tol=0.0001,\n", - " validation_fraction=0.3473838441178))" + "EstimatorTransformer(estimator=SVC(C=140.9223338924506, gamma=0.0007253447995,\n", + " max_iter=3000, probability=True,\n", + " shrinking=False))" ] }, - "execution_count": 52, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -12995,20 +12790,20 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[0.94963523, 0.05036477],\n", - " [0.91791762, 0.08208238],\n", - " [0.16108516, 0.83891484],\n", - " [0.05483536, 0.94516464],\n", - " [0.05482495, 0.94517505]])" + "array([[0.5 , 0.5 ],\n", + " [0.50964815, 0.49035185],\n", + " [0.50681558, 0.49318442],\n", + " [0.51565809, 0.48434191],\n", + " [0.52006004, 0.47993996]])" ] }, - "execution_count": 53, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -13029,48 +12824,20 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 35, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/perib/miniconda3/envs/myenv/lib/python3.10/site-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=3000). Consider pre-processing your data with StandardScaler or MinMaxScaler.\n", - " warnings.warn(\n", - "/home/perib/miniconda3/envs/myenv/lib/python3.10/site-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=3000). Consider pre-processing your data with StandardScaler or MinMaxScaler.\n", - " warnings.warn(\n", - "/home/perib/miniconda3/envs/myenv/lib/python3.10/site-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=3000). Consider pre-processing your data with StandardScaler or MinMaxScaler.\n", - " warnings.warn(\n", - "/home/perib/miniconda3/envs/myenv/lib/python3.10/site-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=3000). Consider pre-processing your data with StandardScaler or MinMaxScaler.\n", - " warnings.warn(\n", - "/home/perib/miniconda3/envs/myenv/lib/python3.10/site-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=3000). Consider pre-processing your data with StandardScaler or MinMaxScaler.\n", - " warnings.warn(\n", - "/home/perib/miniconda3/envs/myenv/lib/python3.10/site-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=3000). Consider pre-processing your data with StandardScaler or MinMaxScaler.\n", - " warnings.warn(\n", - "/home/perib/miniconda3/envs/myenv/lib/python3.10/site-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=3000). Consider pre-processing your data with StandardScaler or MinMaxScaler.\n", - " warnings.warn(\n", - "/home/perib/miniconda3/envs/myenv/lib/python3.10/site-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=3000). Consider pre-processing your data with StandardScaler or MinMaxScaler.\n", - " warnings.warn(\n", - "/home/perib/miniconda3/envs/myenv/lib/python3.10/site-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=3000). Consider pre-processing your data with StandardScaler or MinMaxScaler.\n", - " warnings.warn(\n", - "/home/perib/miniconda3/envs/myenv/lib/python3.10/site-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=3000). Consider pre-processing your data with StandardScaler or MinMaxScaler.\n", - " warnings.warn(\n", - "/home/perib/miniconda3/envs/myenv/lib/python3.10/site-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=3000). Consider pre-processing your data with StandardScaler or MinMaxScaler.\n", - " warnings.warn(\n" - ] - }, { "data": { "text/plain": [ "array([[0],\n", " [0],\n", - " [0],\n", + " [1],\n", " [1],\n", " [1]])" ] }, - "execution_count": 54, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -13091,13 +12858,13 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
Pipeline(steps=[('standardscaler', StandardScaler()),\n",
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-       "                                                                                ('nystroem-1',\n",
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-       "                                                                                          kernel='polynomial',\n",
-       "                                                                                          n_components=13)),\n",
-       "                                                                                ('nystroem-2',\n",
-       "                                                                                 Nystroem(gamma=0.9023618026452,\n",
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+       "                                                                                            n_components=50)),\n",
+       "                                                                                ('columnonehotencoder',\n",
+       "                                                                                 ColumnOneHotEncoder()),\n",
+       "                                                                                ('nystroem',\n",
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        "                                                                                          kernel='additive_chi2',\n",
-       "                                                                                          n_component...\n",
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-       "                                                                                 EstimatorTransformer(cross_val_predict_cv=10,\n",
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-       "                                                                                                                                  n_estimators=42,\n",
-       "                                                                                                                                  n_jobs=1),\n",
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+       "                                                ('...\n",
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+       "                                                                                                                              learning_rate='constant',\n",
+       "                                                                                                                              loss='modified_huber',\n",
+       "                                                                                                                              n_jobs=1,\n",
+       "                                                                                                                              penalty='elasticnet'),\n",
        "                                                                                                      method='predict'))])),\n",
        "                                                ('passthrough',\n",
        "                                                 Passthrough())])),\n",
-       "                ('gaussiannb', GaussianNB())])
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BaggingClassifier(bootstrap=False, bootstrap_features=True,\n",
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BaggingClassifier(bootstrap=False, bootstrap_features=True,\n",
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ExtraTreesClassifier(bootstrap=True, criterion='entropy',\n",
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ExtraTreesClassifier(bootstrap=True, criterion='entropy',\n",
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+       "                     min_samples_split=10, n_jobs=1)
SGDClassifier(alpha=0.0009170388361, class_weight='balanced',\n",
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+       "              penalty='elasticnet')
SGDClassifier(alpha=0.0009170388361, class_weight='balanced',\n",
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+       "              penalty='elasticnet')
Passthrough()
MLPClassifier(alpha=0.0867902302825, hidden_layer_sizes=[35],\n",
+       "              learning_rate='invscaling', learning_rate_init=0.0152961651727,\n",
+       "              n_iter_no_change=32)
" ], "text/plain": [ - "Pipeline(steps=[('standardscaler', StandardScaler()),\n", + "Pipeline(steps=[('normalizer', Normalizer(norm='max')),\n", " ('featureunion-1',\n", " FeatureUnion(transformer_list=[('featureunion',\n", - " FeatureUnion(transformer_list=[('powertransformer',\n", - " PowerTransformer()),\n", - " ('nystroem-1',\n", - " Nystroem(gamma=0.4484025909592,\n", - " kernel='polynomial',\n", - " n_components=13)),\n", - " ('nystroem-2',\n", - " Nystroem(gamma=0.9023618026452,\n", + " FeatureUnion(transformer_list=[('rbfsampler',\n", + " RBFSampler(gamma=0.7809991844556,\n", + " n_components=50)),\n", + " ('columnonehotencoder',\n", + " ColumnOneHotEncoder()),\n", + " ('nystroem',\n", + " Nystroem(gamma=0.3179172515929,\n", " kernel='additive_chi2',\n", - " n_component...\n", - " EstimatorTransformer(cross_val_predict_cv=10,\n", - " estimator=GaussianNB(),\n", - " method='predict')),\n", - " ('estimatortransformer-3',\n", - " EstimatorTransformer(cross_val_predict_cv=10,\n", - 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"outputs": [], "source": [ @@ -13696,12 +13468,12 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 38, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -13724,12 +13496,12 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 39, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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", 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", + "image/png": 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/Nw+aUin4jBxyHh9lQiEKjQxRpKMDnlAIbV1duLm7w8nJiU6UIITUe9RjRwhRe4GBgZg0aRIrNmrUKOzbt4/zWJlMhtzcXGRnZyM7OxvPsrJQVlICmVQKgVAITW1tNDE3h5mZGczMzGBkZASBArdIIYQQVaLCjhCi1lJTU+Ho6IgXL15UxMzMzHD79m0YGxurMDNCCFE/tN0JIURtyeVyTJo0iVXUAcD27dupqCOEkEpQYUcIUVtbt27F2bNnWTF/f38MHjxYRRkRQoh6o6FYQohaun//PpydnSGRSCpizZo1Q0JCAho3VtxmxIQQ0pBQjx0hRO3IZDJMmDCBVdQBwM6dO6moI4SQd6DCjhCidtavX4/w8HBW7Msvv8Qnn3yioowIIaR+oKFYQohauXPnDlxdXVFaWloRa9myJeLj46Gnp6fCzKqnsu1WSouLIZfJwBcIoKWjQ9utEEKUhjYoJoSoDalUivHjx7OKOh6Ph6CgILUv6vLy8hAXF4dbMTEoeWOD5Ma5udCRSsFnGMh5PJQLhbhnZIToNzZIdnRzg7OzM22QTAipNSrsCCFqY/Xq1YiKimLFvvnmG/To0UNFGb1fZmYmIsPDkZKcDA2JBFZp6bDI/YAjzYyMcPP5c0RFRKCljQ08u3enI80IITVGQ7GEELUQFxeHjh07ory8vCLWtm1bxMbGQkdHR4WZVU4qlSIiIgJRERHQy8lBm9Q0NMvJgUAu/+B7yfh8PDYxwX1rKxSZmKCjpyc8PT0hFNJ3b0LIh6HCjhCicmVlZejYsSPi4+MrYnw+H5GRkejcubMKM6tcVlYWToSEIO9xBtolJ8MmIwN8BfwqlfN4SLa0xF0bGxg1s4SXry/Mzc0VkDEh5GNBXwcJISq3bNkyVlEHAPPnz1fLoi41NRVH9u+HKPMJet+5A/23tmSpDT7DoO3jx7DIzUV0oR325RfAb9RIWFtbK6wNQkjDRj12hBCVun79Ojw8PCB7Yz6ao6MjoqKioKWlpcLMuFJTU3Fo714Yp6ahU2IihDUYdq0uKZ+Pa/btkWtlheGffUbFHSGkWmgfO0KIyhQXF2P8+PGsok4oFGLXrl1qV9RlZWXhyP79MEpNQ5fbt5Va1AGAUC5H14TbMEpLw5H9B5CVlaXU9gghDQMVdoQQlVm8eDHu3r3Lii1ZsgQuLi6qSagKUqkUJ0JCIMp8gs6JiQqZT1cdfIZB59uJ0HmSibCQEEil0jpplxBSf1FhRwhRicuXL2PdunWsmLu7OxYsWKCijKoWERGBvMcZcL9zR+k9dW8TyuVwT7yD3IwMREZG1mnbhJD6hwo7QkidKyoqgr+/P96c4qulpYXg4GBoaGioMDOuzMxMREVEoF1yskIXSnyIxhIJ2iYl43p4OJ48eaKSHAgh9QMVdoSQOjd//nw8fPiQFVu+fDns7e1VlFHVIsPDoZeTA5uMDJXmYZuRAb2cHES8dYYuIYS8iQo7QkidOnPmDDZv3syKeXh4YPbs2SrKqGp5eXlISU5Gm9S0OptXVxU+w6B1ahpSkpKQl5en0lwIIeqLCjtCSJ0pKCjAxIkTWTEdHR0EBQVBIBCoKKuqxcXFQUMiQbOcHFWnAgBonpMDoUTC2fOPEEJeo8KOEFJnZs+ejfT0dFbsp59+go2NjYoyqppMJsOtmBhYpaXX6JgwZRDI5bBOT0d8dDRrixhCCHmNCjtCSJ0IDQ1FYGAgK9a7d29MmzatxvdctmwZHBwc4OjoiA4dOiAlJaXKx5qYmHzQvXNzc1EiFuNsVBQrbhd+Gb6xMfCOicbMO3dQXNcFVvpjXLt2Dbm5uQCAkJAQ/PLLLwAAf39/hIaGfvAtp0+fDlNTU3To0EGhqRJC6h4VdoQQpXv+/DkmT57MijVq1AiBgYHg82v2aygyMhIXLlzAzZs3cevWLRw9ehQGBgYKyPal7OxsMFIp9jy4z4o3EgoR4uqGE27u0ODzsDereqtUZQqao1eYl4u4W7eQnZ0NAPD19cWsWbNqdc8xY8bg5MmTikiPEKJidFYsIUTpvvrqK87JCevWrUOLFi1qfM+srCwYGhpCKHz5a6xZs2YAgLCwMCxbtgwlJSXo1KkTtm7dyikeV65ciaNHj6K0tBTTpk3Dl19+CeBlD+CBAwcgEAjQvXt33I+OxgupFL6xMXDT18fS1m1Y9+mg3xj3xGKIZTIsvX8fD4pfboeyqFUruOs3xobUVOSUlyG1uARtRCKMsbDAf+/fR4FUCk0+D8EOjuDxeFU+N6usFI+Ki5FVWobZLazh08QUGx+m4EFZKYYOHYpFixaBx+MhISEBa9asYeV2/fp1zJkzB2KxGC1btkRwcDD09PQqfS89PT3x6NGjGv8sCCHqg3rsCCFKdfDgQezdu5cVGzRoECZNmlSr+/bv3x9JSUmws7PD119/jaioKOTk5GDdunUVPXmampo4cOAA63mnTp3C06dPERUVhRs3biAwMBCPHz9GaGgoLl68iOjoaMTFxcHF0RFj7e0reujeLuqkDINLebmw1RVhc3oa+hsb47CLKzbbtcfS+w8qHpcklmC7vT0Wt26NeUn3MLV5cxx3c0OwgyO0BYJ3PvdxSQl2OTohyMEB61NTAQCzrK3hYGGBFd9/jwkTJlT63pSVlWHu3LkICQlBTEwMunTpgk2bNtXq/SaE1A/UY0cIUZqnT59i6tSprJiBgQF27NgBHo9Xq3s3atQIsbGxOH/+PM6cOYP+/fsjODgY8fHx6NKlC4CXZ9FaWlqynvfvv//i+PHjuHjxIoCXK3UfPHiAc+fOYcKECRVn1GoIBNCo5Aiv1z14ANBBXx8jzMwxKi4Ol3JzsSk9DQCQLy1H2asFF32NjaDJ56NIKkWhVApPQ0MAgN6rnsbIvPwqn9vT0AhCHg9WOjoofCMXPsOgrKSkyvfm3r17iI+PR+/evQG8LPR69epV3beWEFKPUWFHCFEKhmEwZcoU5Ly1VcimTZvQtGlThbQhFArRv39/9O/fHyYmJpg1axZ8fHw4izTezmvp0qUYN24cK37s2DHWn+UyWaV7173uwWPdEwx+b2+PptranMdr8/+3jUtlpey7nqtZ1fxDhoHsHefGMgwDNzc3nDt3rsrHEEIaJhqKJYQoxV9//YWjR4+yYn5+fhgzZoxC7n/v3j08ePBy2JJhGNy+fRtTpkzB+fPnK7ZUef78OR4/fsx6Xr9+/bBz504UFxdX3KekpAT9+vXDH3/8gdLSUgCApKQEch4PAh7vvQsfPAwM8dcbR33dKSriPEZPKIS+UIiIV5sLF0mlkDJMtZ77Jl2hAMVSKQTCqr+Xt2vXDqmpqbh58yYAQCwW4/79+1U+nhDScFCPHSFE4TIyMvDVV1+xYiYmJti6dWuth2BfKyoqwowZM1BYWAgAcHd3x8yZM+Hs7IyhQ4eivLwcGhoa2L59e8XCCgDw8vJCQkICOnXqBIZhYGpqiuPHj8PLywvR0dFwc3ODhoYGXJ2d0U9DA36mZvCJiUZnAwPOPLvXpltZYfmDB/CJiYaMYdDVwABL9LiP/dm2LRbfT8aPKSnQ4vMR7OhY7ee+1laki3KGwZJlyyDj8yt9PzU1NbFv3z5MmzYNRa8KxZ9++glt2lR+34CAAJw4cQLPnz9Hs2bNsHHjRvj5+VX95hNC1BaPYVR8Tg4hpEFhGAZeXl44deoUK37w4EEMHz5cRVl9uLNnz+Le6dPof+WqqlPh+LdrF7QdMAB9+/ZVdSqEEDVDQ7GEEIXauXMnp6gbM2ZMvSrqAMDMzAxFOjooV7OjzsoFAhTp6MDMzEzVqRBC1BANxRJCFObRo0eczXItLCywceNGFWVUc2ZmZuAJhSjQ1YXJq+FedVCgqwueUFijws7Pz49zOseePXvQvn17RaVHCFExKuwIIQohl8sxceLEijldr23fvh1GRkYqyqrmjIyMoK2riydGRu8s7GRyOUpLSqChoQENDQ2l5/XE+GVeNXlPjxw5ooSMCCHqhIZiCSEK8dtvv+H8+fOs2KRJk+Dt7a2ijGpHIBDA0c0NaVbNIati25GysjI8ffoU+QX5eJaTU7GiVllkfD5SmzeHk7s7BGo2REwIUQ9U2BFCai0pKQnz589nxaysrLBu3ToVZaQYzs7OKBeJ8NjEhHNNKpMiNzcXDCN/FWFQ/I5NgxUh3cQEUpEITk5OSm2HEFJ/UWFHCKkVmUwGf3//in3hXgsMDIS+vr6KslIMQ0NDtLSxwX1rK8jf2FZELpfj+fNcyCuKupc0NZU3FCvn8fDA2gotbW1h+Or0CkIIeRsVdoSQWlm7di2uXLnCik2fPr3BbMXh2b07ikxMkPzqaDKGYZCbmwuZjH3yg5aWNnR0RErLI8nSEkUmJvDs1k1pbRBC6j/ax44QUmO3b9+Gm5sbysrKKmKtW7dGXFwcdHV1VZiZYl28eBFRZ8+h17VrkGVkoKSE3TupoaEJY2Nj8BW0+fLbCkQiXOjSGZ369kWPHj2U0gYhpGGgHjtCSI2Ul5dj3LhxrKKOx+MhODi4QRV1AODp6QnDZpa40roVxOVlrGsCgQBGRkZKK+qkfD6i29vByNISHh4eSmmDENJwUGFHCKmRVatWISYmhhWbM2cOPD09VZSR8giFQhSKxUjV0MCdzp0r5tvxeHwYGRlDUMWq2dqS83i4Zt8exRZN4eXrC+E7zoclhBCAhmIJITUQExODzp07Qyr93zwzOzs7xMTEQFtbW4WZKcexY8fg5+eH5s2bY/SwYbB6/hx2167D1MAAWpqaSmlTyufjmn175FpZYfhnn8Ha2lop7RBCGhYq7AghH6S0tBQdOnRAQkJCRUwgEODKlSvo2LGjCjNTjuvXr6NXr14Vq36trKzw6dChaCmToUvyfehLJApvs0AkQnR7OxRbNIXfqJFU1BFCqo369QkhH2Tp0qWsog4AFi5c2CCLupSUFAwePJi1lUtaWhoaGRlB28IC542M0S45GTYZGeAr4DuynMdDkqUl7tnawMjSEkN8fWFubl7r+xJCPh7UY0cIqbarV6/C09MTcvn/9m9zdnbG9evXoamkIUlVyc3NhYeHB+7du8eK+/v7IzAwEDKZDBEREYiKiIBeTg5ap6aheU4OBHJ5FXesmozPR7qJCR5YW6HIxASdunWDh4cHzakjhHwwKuwIIdUikUjg6uqKpKSkipiGhgZu3LjR4E5CKC0txSeffIJLly6x4v3798eJEydYZ8JmZmYiMiICKUlJEEoksE5Ph8XzXDQWi6Ehk1XZRrlAgAJdXTwxNkJq8+aQikRoaWsLz27dYGFhobTXRghp2OjrICGkWhYtWsQq6oCXw7INraiTy+Xw9/fnFHWOjo44ePAgq6gDgKZNm2LEp58iLy8P8fHxiI+OxgOxGIxUCr3iYujn5kFTKgWfkUPO46NMKEShkSGKdHTAEwqhrasLN3d3ODk50YkShJBaox47Qsh7Xbx4Eb169WLFOnXqhIiIiAY3XLhw4UL8+OOPrFjTpk1x7do1NGvW7L3Pl8lkyM3NRXZ2NrKzs/EsKwtlJSWQSaUQCIXQ1NZGE3NzmJmZwczMDEZGRhAIBMp6OYSQjwwVdoSQd3rx4gWcnZ2RkpJSEdPW1kZsbCzatWunwswUb9u2bZgyZQorpqenh8uXL8PFxUU1SRFCyAegDYoJIe80b948VlEHACtXrmxwRd3Jkycxbdo0VkwgEODgwYNU1BFC6g3qsSOEVOn06dMYOHAgK9a9e3ecP3++QQ0fxsbGonv37hCLxaz49u3bERAQoKKsCCHkw1FhRwipVH5+PhwcHJCRkVERE4lEiI+PR+vWrVWYmWKlpaWhS5cuePLkCSv+f//3f1i5cqWKsiKEkJqhoVhCSKW++eYbVlEHAGvWrGlQRV1BQQG8vb05Rd2YMWOwYsUKFWVFCCE1Rz12hBCOkJAQDBkyhBXr168f/vnnH/B4PBVlpVhlZWXw8vLC2bNnWfGePXvi9OnT0NLSUlFmNVfZitzS4mLIZTLwBQJo6ejQilxCGjgq7AghLDk5OXBwcEB2dnZFTF9fH7du3YKVlZUKM1MchmEwYcIEBAcHs+Lt2rVDZGRkvdtPLi8vD3FxcbgVE4OSN/bQa5ybCw2pFHyGgZzHQ7lQiAIjI9Yeeo5ubnB2dq53r5kQUrmGtQEVIaTWpk+fzirqAGD9+vUNpqgDgGXLlnGKOlNTU4SFhdWrAiczMxOR4eFISU6GhkQCq7R0WOR+wKkXRka4+fw5oiIi0NLGBp7du9OpF4TUc9RjRwipsH//fowePZoV8/HxQUhISIMZgg0ODoa/vz8rJhKJcPHiRXTo0EE1SX0gqVTKOqe2TWoamtXinNrHJia4/+qc2o6envD09GxwG08T8rGgwo4QAgDIysqCvb09cnNzK2KGhoa4fft2g+nFOXv2LAYOHAipVFoR4/P5OHr0KAYPHqzCzKovKysLJ0JCkPc4A+2Sk2GTkQG+An6Ny3k8JFta4q6NDYyaWcLL1xfm5uYKyJgQUpfoKxkhBAzDYMqUKayiDgA2b97cYIq6W7duYdiwYayiDgA2bNhQb4q61NRUHNm/H6LMJ+h95w70JRKF3ZvPMGj7+DEscnMRXWiHffkF8Bs1EtbW1gprgxCifNRjRwipdHhyxIgROHDgQIMYgs3MzESXLl2Qnp7Ois+ZMwdr1qxRUVYfJjU1FYf27oVxaho6JSZCWINh1+qS8vm4Zt8euVZWGP7ZZ1TcEVKPUGFHyEcuPT0djo6OKCgoqIiZmpoiISEBTZo0UWFmivHixQv06NEDN2/eZMVHjBiB/fv3g89X/+08s7KysG/XLhikPELX27cVMvT6PnIeD1cc7JHfoiVGjxtLw7KE1BPq/xuNEKI0DMMgICCAVdQBwO+//94gijqpVIqRI0dyirquXbti165d9aKok0qlOBESAlHmE3ROTKyTog54OTTb+XYidJ5kIiwkhDOETQhRT+r/W40QojTbtm3DP//8w4qNHTsWQ4cOVU1CCsQwDKZPn45Tp06x4m3atEFISAh0dHRUlNmHiYiIQN7jDLjfuaPU4dfKCOVyuCfeQW5GBiIjI+u0bUJIzVBhR8hH6uHDh5gzZw4r1rRpU/z6668qykixVq9ejW3btrFixsbGOHnyJExMTFSU1YfJzMxEVEQE2iUnK3ShxIdoLJGgbVIyroeHc45eI4SoHyrsCPkIyeVyTJgwAWKxmBXfuXNnvdqgtyp79+7FwoULWTEtLS2EhISgTZs2Ksrqw0WGh0MvJwc2b53ZW9dsMzKgl5ODiPBwleZBCHk/KuwI+Qht2LABly5dYsUmT56MgQMHqigjxbl06RJnhS+Px8Pu3bvh4eGhmqRqIC8vDynJyWiTmlZn8+qqwmcYtE5NQ0pSEvLy8lSaCyHk3aiwI+Qjc+/ePU5vVosWLbB27VoVZaQ4d+/exdChQ1FWVsaK//zzzxgxYoSKsqqZuLg4aEgkaJaTo+pUAADNc3IglEgQHx+v6lQIIe9AhR0hHxGpVIrx48ejpKSEFQ8MDESjRo1UlJViZGdnw8vLi9OjNGPGDMyePVtFWdWMTCbDrZgYWKWl1+iYMGUQyOWwTk9HfHQ0ZO84h5YQolpU2BHyEVmzZg2uXbvGis2cORO9e/dWUUaKIRaLMXjwYKSkpLDivr6+WL9+vVI2WV62bBkcHBzg6OiIDh06cNp+04cu1sjNzUWJWIyzUVGsuF34ZfjGxsA7Jhoz79xBcV0XWOmPce3atYoTSkJCQvDLL78AAPz9/REaGvpBt5NIJPDy8kK7du3g4OCAjRs3KjxlQj42dKQYIR+JW7duYcmSJayYjY0NVq1apaKMFEMmk2HMmDGIeqsI6tChA/bs2QOBQKDwNiMjI3HhwgXcvHkTQqEQjx8/hq6ursLun52dDUYqxZ4H9/HNG/sJNhIKEeLqBgCYc+8u9mY9wUTLZu+9n4xhIFBAcVuYl4u47CxkZ2ejSZMm8PX1rfU958+fj549e0IsFqNDhw4YNGhQvVrgQoi6ocKOkI9AWVkZxo8fj/Ly8ooYn89HUFAQRCKRCjOrHYZhMGvWLISEhLDiLVq0QGhoqEKLrTdlZWXB0NAQQuHLX6HNmr0srsLCwrBs2TKUlJSgU6dO2Lp1K2cT5JUrV+Lo0aMoLS3FtGnT8OWXXwJ42QN44MABCAQCdO/eHfejo/FCKoVvbAzc9PWxtDW72Omg3xj3xGKIZTIsvX8fD4pfboeyqFUruOs3xobUVOSUlyG1uARtRCKMsbDAf+/fR4FUCk0+D8EOjuDxeFU+N6usFI+Ki5FVWobZLazh08QUGx+m4EFZKYYOHYpFixaBx+MhISGBcyzb9evXMWfOHIjFYrRs2RLBwcHQ09PjvI8ikQg9e/YEAOjq6sLGxgZPnjyhwo6QWqChWEI+AitXrkRsbCwrNnfu3Hq1SrQy69ev5wzfGRgYICwsDGZmZkprt3///khKSoKdnR2+/vprREVFIScnB+vWravoydPU1MSBAwdYzzt16hSePn2KqKgo3LhxA4GBgXj8+DFCQ0Nx8eJFREdHIy4uDi6Ojhhrb1/RQ/d2USdlGFzKy4Wtrgib09PQ39gYh11csdmuPZbef1DxuCSxBNvt7bG4dWvMS7qHqc2b47ibG4IdHKEtELzzuY9LSrDL0QlBDg5Yn5oKAJhlbQ0HCwus+P57TJgwodL3pqysDHPnzkVISAhiYmLQpUsXbNq06b3vaXp6OuLj4+Hm5lbtnwMhhIt67Ahp4KKjo7Fy5UpWzN7eHt9//72KMlKMQ4cOcTZY1tTUxNGjR2FnZ6fUths1aoTY2FicP38eZ86cQf/+/REcHIz4+Hh06dIFAFBcXAxLS0vW8/79918cP34cFy9eBAAUFBTgwYMHOHfuHCZMmAAtLS0AgIZAAI1KjvB63YMHAB309THCzByj4uJwKTcXm9LTAAD50nKUvVpw0dfYCJp8PoqkUhRKpfB8tUeh3quexsi8/Cqf29PQCEIeD1Y6Oih8Ixc+w6DsrcU3b7p37x7i4+Mr5m2WlZWhV69e73w/S0pKMGrUKKxZs0ZpvayEfCyosCOkASspKcG4ceNYqxgFAgGCg4Ohra2twsxq58qVK/j888/BvLW/2x9//FExtKdsQqEQ/fv3R//+/WFiYoJZs2bBx8cHgYGBVT6HYRgsXboU48aNY8WPHTvG+rNcJqt077o359hV3BMMfm9vj6aV/Dy1+f+bX1jZDLt3PVezqnN0GQayd5wbyzAM3NzccO7cuSof8/bjx48fDy8vr3q3JQ0h6oiGYglpwP773/8iMTGRFfvuu+/g7u6uooxq7/79+/D19eVs2bJy5UqMGTOmTnK4d+8eHjx4OWzJMAxu376NKVOm4Pz580hPTwcAPH/+HI8fP2Y9r1+/fti5cyeKi4sr7lNSUoJ+/frhjz/+QGlpKQBAUlICOY8HAY8H2Xs2J/YwMMRfbxz1daeoiPMYPaEQ+kIhIl5tBVMklULKMNV67pt0hQIUS6UQCKvuE2jXrh1SU1Nx8+ZNAC9XLN+/f7/Kxy9cuBAikQjffffdO9smhFQP9dgR0kBFRkbi559/ZsVcXV2xaNEiFWVUezk5OfDy8kLOW5v2BgQEcDZdVqaioiLMmDEDhYWFAAB3d3fMnDkTzs7OGDp0KMrLy6GhoYHt27dXLKwAAC8vLyQkJKBTp05gGAampqY4fvw4vLy8EB0dDTc3N2hoaMDV2Rn9NDTgZ2oGn5hodDYw4Myze226lRWWP3gAn5hoyBgGXQ0MsESP+9ifbdti8f1k/JiSAi0+H8GOjtV+7mttRbooZxgsWbYMMj6/0m1kNDU1sW/fPkybNg1FrwrFn376qdIFEY8fP8bq1avRvn17uLi4AHh5xu+AAQOqfvMJIe/EY94eyyCE1HtisRguLi6snhJNTU1ER0fDwcFBhZnV3OuerYiICFZ8wIABOH78ODQ0NFSUmeKdPXsW906fRv8rV1WdCse/Xbug7YAB6Nu3r6pTIYRUgoZiCWmAFi5cyBn+er2hbn0kl8sxbtw4TlHn7OyMv//+u0EVdQBgZmaGIh0dlCthD77aKBcIUKSjo9QVx4SQ2qGhWEIamHPnznG2AOnSpQvmzp2rooxqb8GCBfj7779ZsWbNmuHEiRP1/ii0ypiZmYEnFKJAVxcmr4Z7K8Og8kURylKgqwueUFijws7Pz49zOseePXvQvn17RaVHCAEVdoQ0KIWFhZg4cSIrpqOjg+DgYKWcwFAXNm/ezJkrqK+vj7CwMM52Ig2FkZERtHV18cTIqMrCTiwW40VREcAwaGxgAJ06WOX8xPhlXkZGRh/83CNHjighI0LI22golpAGZM6cOUh9tZnsa6tWrYKtra2KMqqd48eP46uvvmLFhEIhDh06BEdHRxVlpXwCgQCObm5Is2oO2VvbjjAACgoLUVBYALlcBjkjR35+vtJzkvH5SG3eHE7u7vX2SwIhHwMq7AhpIE6ePIkdO3awYj179uQURvXFjRs3MHr0aMhfbZj72vbt29GvXz8VZVV3nJ2dUS4S4bGJSUWMYRjk5eVBLOZuS6LsVXDpJiaQikRwcnJSckuEkNqgwo6QBiAvLw8BAQGsmJ6eHv744w/OWaX1waNHj+Dj4wOJRMKKL1myBP7+/qpJqo4ZGhqipY0N7ltbQc7jQS6X43nuc5SUFHMe27ixvlLn2sl5PDywtkJLW1sYvjq9ghCinurfb3xCCMfMmTORmZnJiq1duxYtW7ZUUUY1l5eXBy8vL2RnZ7Pi48aNw9KlS1WTlIp4du+OIhMT3LWwQM7zHJSVlb31CB4MDQwh0hEpNY8kS0sUmZjAs1s3pbZDCKk9KuwIqeeOHDmC3bt3s2IDBgzA5MmTVZRRzZWWlmLYsGG4c+cOK96nTx9s37690g1xGzILCws0a9UKN5s3Q6GIXbzxeHwYGxtDR0dHqTkUiES4Z2uDTt26wcLCQqltEUJqjwo7QuqxZ8+eYcqUKaxY48aNsWPHjnpXBDEMg4CAAFy4cIEVt7e3x6FDh6CpqamaxFTo9OnT+PLLL/E4Nxd33N0he7VoQcAXwMTEBFpKfk+kfD6i29vByNISHh4eSm2LEKIYVNgRUk8xDIOpU6fi2bNnrPiGDRtYx1jVF0uWLOH0PJqbm+PEiRMwMDBQTVIqFBQUBB8fHxQUFCDkxAlkikS407kz+BqaMDExgcY7zmtVBDmPh2v27VFs0RRevr4QKrk9QohiUGFHSD21b98+HDp0iBUbMmQIxo4dq6KMai4wMBArVqxgxXR1dXHixAlYW1urKCvVYBgGK1aswIQJEyCVSgEAT58+xd9HjyLLwgIPevUEo+STNqR8Pq442CPXygp+o0bC3Nxcqe0RQhSHzoolpB7KzMyEg4MD8vLyKmLGxsa4fft2vTvu6Z9//oGXlxdkMllFjM/n4/jx4/Dy8lJhZnVPKpVi2rRp2L59O+fa6NGjsXz5coQePgJRZibc79yB/lurhhWhQCRCdHs7FFs0hd+okR9dYU1IfUd964TUMwzD4IsvvmAVdQCwZcuWelfUxcXFYcSIEayiDnh52sTHVtSJxWKMGjUKJ06c4FybO3cuVq9eDT6fj9HjxuJESAjO6+ujXXIybDIywFfA93M5j4ckS0vcs7WBkaUlhvj6Uk8dIfUQ9dgRUs8EBgZi0qRJrNioUaOwb98+FWVUM48fP0aXLl2QkZHBis+fPx8//vijirJSjadPn8LHxwdRUVGsOI/Hw/r16zFz5kxWXCqVIiIiAlEREdDLyUHr1DQ0z8mB4K3NnKtDxucj3cQED6ytUGRigk7dusHDw4Pm1BFST1FhR0g9kpqaCkdHR7x48aIiZmZmhtu3b8PY2FiFmX2YwsJCdO/eHfHx8az46NGj8ddff9XLTZVr6v79+xg4cCAePHjAimtpaeGvv/7C8OHDq3xuZmYmIiMikJKUBKFEAuv0dFg8z0VjsRgab/WCvqlcIECBri6eGBshtXlzSEUitLS1hSdtaUJIvUeFHSH1hFwuxyeffIKzZ8+y4iEhIRg8eLCKsvpw5eXl8Pb2xr///suKd+vWDf/++y+06+Awe3Vx7do1+Pj4ICcnhxU3NDTE8ePH4enpWa375OXlIT4+HvHR0SgRi8FIpdArLoZ+bh40pVLwGTnkPD7KhEIUGhmiSEcHPKEQ2rq6cHJ3h5OTE50oQUgDQYUdIfXE5s2bMX36dFbM398ff/zxh4oy+nCv96oLDAxkxW1tbREZGVmveh1rKyQkBKNHj0ZxMfuIMGtra5w6dQrt2rX74HvKZDLk5uYiOzsb2dnZeJaVhbKSEsikUgiEQmhqa6OJuTnMzMxgZmYGIyMjCF7tjUcIaRiosCOkHrh//z6cnZ1ZZ6c2a9YMCQkJaNy4sQoz+zArVqzA4sWLWbEmTZrg6tWraNWqlYqyqntbt27F9OnTIX9rTpyrqytOnDhBw6GEkBr7eCayEFJPyWQyTJgwgVXUAcDOnTvrVVG3e/duTlGno6OD48ePfzRFHcMwWLRoEaZOncop6gYMGICLFy9SUUcIqRVa9kSImlu/fj3Cw8NZsS+//BKffPKJijL6cOfPn8fEiRNZMR6Phz179qBz584qyqpulZWVYfLkydi1axfnmr+/P7Zt2wYNJW88TAhp+GgolhA1dufOHbi6uqK0tLQi1rJlS8THx0NPT0+FmVVfYmIiPDw8UFBQwIqvX78eX3/9tYqyqluFhYUYMWIEZ8EIACxevBjff/99vTvblxCinqjHjhA1JZVKMX78eFZRx+PxEBQUVG+KuqysLHh5eXGKuq+//vqjKeoyMzPh5eWFuLg4VpzP52PLli344osvVJQZIaQhosKOEDW1evVqzoa133zzDXr06KGijD5MUVERfHx8kJqayor7+flh7dq1KsqqbiUmJmLQoEFIS0tjxUUiEfbv3w8fHx8VZUYIaahoKJYQNRQXF4eOHTuivLy8Ita2bVvExsZCR0dHhZlVj1QqhZ+fH0JDQ1nxzp0749y5cxCJRCrKrO5cvnwZvr6+yM/PZ8WbNGmC0NBQdOrUSTWJKVFl262UFhdDLpOBLxBAS0eHtlshRMmox44QNVNWVoZx48axijo+n4/g4OB6UdQxDIOZM2dyirpWrVohJCTkoyjqDh48iM8//5w1jA4Abdq0wcmTJ9GmTRsVZaYceXl5iIuLw62YGNYGyY1zc6EjlYLPMJDzeCgXCnHPyAjRb2yQ7OjmBmdnZ9ogmRAFocKOEDWzbNkyzlFb8+fPrzerR9esWYMtW7awYkZGRjh58iRMTU1VlFXdWb9+PWbPno23B0M6d+6M48ePo0mTJirKTPEyMzMRGR6OlORkaEgksEpLh0XuBxxpZmSEm8+fIyoiAi1tbODZvTtt90JILdFQLCFq5Pr16/Dw8IDsjQ9FR0dHREVFQUtLS4WZVc+BAwcwatQoVkxLSwtnzpxBt27dVJRV3ZDL5Zg3bx7WrVvHuTZ48GDs27evwfRWSqVSREREICoiAno5OWiTmoZmOTkQvLU3X3XI+Hw8NjHBfWsrFJmYoKOnJzw9PSEUUr8DITVBhR0haqK4uBhubm64e/duRUwoFCIqKgouLi6qS6yaIiIi0LdvX87w4759+zjFXkNTUlKC8ePH48CBA5xrU6ZMwaZNmxpMoZKVlYUTISHIe5yBdsnJsMnIAF8BHyNyHg/Jlpa4a2MDo2aW8PL1hbm5uQIyJuTj0jB+0xDSACxevJhV1AHAkiVL6kVRl5SUBF9fX05Rt3r16gZf1OXl5WHo0KG4dOkS59rKlSuxcOHCBrNHXWpqKo7s3w9R5hP0vnMH+m+dhlIbfIZB28ePYZGbi+hCO+zLL4DfqJGwtrZWWBuEfAyox44QNXD58mX07NmTNS/L3d0dV65cUfvTCJ49e4YuXbrg4cOHrPiXX36JzZs3N5iipjJpaWkYNGgQEhMTWXGhUIidO3di3LhxKspM8VJTU3Fo714Yp6ahU2IihDUYdq0uKZ+Pa/btkWtlheGffUbFHSEfgAo7QlSsqKgIzs7OrMJIS0sL0dHRsLe3V2Fm71dcXIw+ffrg6tWrrLi3tzeOHj3aYIYfKxMXFwcvLy9kZmay4o0aNcKhQ4fQv39/FWWmeFlZWdi3axcMUh6h6+3bChl6fR85j4crDvbIb9ESo8eNpWFZQqqJr+oECPnYzZ8/n9PbtXz5crUv6mQyGT7//HNOUefm5oZ9+/Y16KLu7Nmz6N69O6eos7CwwKVLlxpUUSeVSnEiJASizCfonJhYJ0Ud8HJotvPtROg8yURYSAikUmmdtEtIfUeFHSEqdObMGWzevJkV8/DwwOzZs1WUUfXNmzcPhw8fZsWsrKwQGhpab448q4ndu3dj4MCBePHiBStuZ2eHK1eu1Is5kR8iIiICeY8z4H7njlKHXysjlMvhnngHuRkZiIyMrNO2CamvqLAjREUKCgowceJEVkxHRwdBQUFqvxv/xo0b8csvv7BijRs3RlhYWIPdh4xhGPz4448YO3Ysp/eoe/fuCA8Pb3BzwTIzMxEVEYF2yckKXSjxIRpLJGiblIzr4eF48uSJSnIgpD6hwo4QFZk9ezbS09NZsZ9++gk2NjYqyqh6jh07hq+//poV09DQwJEjR9R++LimZDIZpk+fjoULF3KujRgxAv/88w+MjIxUkJlyRYaHQy8nBzYZGSrNwzYjA3o5OYgID1dpHoTUB1TYEaICoaGhCAwMZMV69+6NadOmqSij6rl+/To+++wzzqkKO3fuRO/evVWUlXJJJBIMHz6cc5oGAHzzzTfYv38/tLW1VZCZcuXl5SElORltUtPqbF5dVfgMg9apaUhJSkJeXp5KcyFE3VFhR0gde/78OSZPnsyKNWrUCIGBgeDz1fef5MOHD+Hj44Pi4mJWfNmyZRg7dqyKslKunJwc9O3bF8eOHeNcW7duHX755Re1/pnVRlxcHDQkEjTLyVF1KgCA5jk5EEoknOP2CCFsDfM3EiFq7KuvvkJWVhYrtm7dOrRo0UI1CVVDbm4uvLy88OzZM1Z84sSJ+O6771SUlXI9fPgQHh4enFW/mpqa2L9/P2bNmqWizJRPJpPhVkwMrNLSa3RMmDII5HJYp6cjPjqadeQeIYSNCjtC6tDBgwexd+9eVmzQoEGYNGmSijJ6v5KSEgwdOhT37t1jxfv374+tW7c2yA2Io6Ki0LVrVyQnJ7PiBgYG+OeffzBy5Eil59CzZ0/OaRZTp07F1q1b3/vcGzduYN68eTVuOzc3FyViMSxyc9/5uIkJt+AbG4OeUdfR5dpV+MbGwDc2BvfEYgy7GVvj9qtsb98+lIjFyH1PXq+1aNECRUVFnLi/vz9CQ0OrfJ6fnx8MDQ0xYsSIGudKiKpQYUdIHXn69CmmTp3KihkYGGDHjh1qWxzJ5XJMmDABly9fZsUdHR1x8OBBtT8VoybCwsLQq1cvPH36lBVv3rw5wsPD0bNnzzrJY+TIkayzZ2UyGUJCQjB8+PB3Pk8mk6FDhw74+eefa9x2dnY2GKkUBpUURW8KdHBEiKsbvrayxlBTU4S4uiHE1Q261VzVLfvAuXs8hgEjlSI7O/uDnvehZs6ciV27dim1DUKUhQo7QuoAwzCYMmUKct6ar7Rp0yY0bdpURVm936JFi7Bv3z5WrGnTpggLC4O+vr6KslKenTt3wtfXF5K3tvZwcnLClStX6nTV74gRI3D06FHIXw2FXrx4Eba2thg1ahTc3Nzg6uqK8FerRC9cuIBPPvkEI0eORO/evXHhwoWK3qarV6/Cw8MDrq6u6NOnT8WWIUuXLkVAQAB69OiBVq1asX7Oa9aswfrNm+F34wZ2Zb5cEXshNxefxt2Eb2wMvktOhvw9RVm5nMG3SfcwMPoGvr57p2LBTe+o69iUlopRcTdxrSAfh7KzMPxmLAbHROPX1EcAALFMhkkJCfCJiYZPTDQuv7Fg4p+wMAwePBh9+/aFWCwGAMTExKBTp05wcnLCuHHjUFJSwsln8eLFsLOzg7e3N6dof1vv3r3RqFGjdz6GEHVFhR0hdeCvv/7C0aNHWbFhw4ZhzJgxqkmoGrZt24Yff/yRFdPT08OJEyfQrFkzFWWlHAzDVBQ6b8/f6tu3Ly5fvgxLS8s6zcnMzAy2trYVvaUHDhzAmDFjcOzYMcTExODYsWOseX7Xrl3D+vXrOcO37du3x+XLlxEbG4uAgAD89NNPFddSUlJw7tw5/PvvvxVzJUNDQ3EjKgo/+PjguJsbfJuYIre8HH9kZGC3oxNCXN2gwechLIc93/JtD4slmNKsOU66ueN5WTluFBZWXDMQamC/swtMNTVxMTcPB5xdcMzVDYlFYsQWFiI8Lw8GGkKEurnjuKsbXF8VWflSKTo2McWqFStgaWlZsUH2+PHjsXHjRsTHx0NXV5ez6ff169dx6tQpxMXFYceOHbTZMWnQqLAjRMkyMjIwY8YMVszExARbtmxR2yHYkydPcrZeEQgEOHjwYIM7WaG8vByTJk3C999/z7n2+eefq7R3ctSoUfj7778hk8lw/Phx+Pr64ttvv4WjoyN8fX2RmJhY8VhPT89Ke3/z8vLg5+cHBwcHLFu2jPUcLy8vCIVCtG7dGvn5+QCAc+fOwbNrV4he9bAZaGjgZmEh7knEFT12kfn5eFxS+s7cW+rooLVIBB6Ph/Z6usgo/V8v2iATEwBAZH4+Yl8Uwu9mLIbejMWDYgnSSkpgqyvCjcJC/JSSgpsvXkDv1fF0ugIBXE1NUVZSAnd3dzx69AgFBQUoLS1F586dAQBjx47lTB2IjIyEn58fNDU1YWFhgT59+lT3R0BIvdNwD3MkRA0wDIOAgAAUFBSw4r///jtMTU1VlNW7xcbG4tNPP+X0XG3duhUDBgxQUVbKUVRUhE8//RSnTp3iXFuwYAF++OEHlRbfw4cPx4oVKzBkyBA4OTkhLCwMYrEYsbGxEAgEEIlEFY998//ftGTJEnh7e2PKlCm4evUqFixYUHFNS0ur0ucwcjlr7zoGQG9DI6yyta127ppvbAPD5/Egf2PkVvuNOXijzM0xw4p7YsdRF1dcyM3FiocPMNTUDGObNoUGjwc+I4dMKoVAIIBMJuPsqcgwDOdnVlmMkIaKeuwIUaIdO3ZwioYxY8Zg2LBhKsro3dLS0uDt7V0xd+m1RYsWISAgQEVZKUdWVhZ69uzJ+fnw+Xz89ttvWLVqlcqLARMTE9jZ2WHOnDkYOXIkCgsLYWZmBqFQiIMHD1Y6l+xthYWFFUPnu3fvfu/j+/Xrh/ArV1D6am5ffnk5XBo1wrWCfDwpfdlLl1dejqzSd/fYVUeXxgYIy8lBgbQcAJBVWoq88nJkl5ZCJBDAz8wM45ta4o74f4s45Dw+BML/9UkYGBhAS0sLUVFRAIA9e/age/furHY8PT1x5MgRlJWVISsrC+fPn6917oSoK+qxI0RJHj16hNmzZ7NiFhYW2Lhxo4oyerf8/Hx4eXlxzuP8z3/+g+XLl6soK+W4d+8eBg4ciEePHrHiOjo62Lt3L4YMGaKaxCoxatQoTJ06FUOHDoVUKoW3tzc6deqEbt26wdjY+L3Pnzt3Lvz9/bFq1Sp4eHi89/FeXl4ICgzE/NBQNCovx6dm5hjbtCmWtmmDaYmJkDJyCHl8rLCxgXkVPX7VZauri8mWzfB5/C0wYKArEOCXtu3woLgYq1Megs/jQZvPxw9vHLNXJhRCU1sbeGOj7KCgIEydOhUlJSVwcXHhrD7v1KkTBgwYACcnJ7Rt2xY9evR4Z14DBgxATEwMxGIxmjVrhiNHjqBjx461eq2E1BUe83Y/NiGk1uRyOfr27YsLFy6w4idOnICXl5dqknqHsrIyDBo0COfOnWPFe/XqhVOnTlU5ZFcfRUZGYvDgwZy90IyNjREaGoouXbqoKDP1cfbsWdw7fRr9r1x9/4Pr2L9du6DtgAHo27evqlMhRC3RUCwhSvDbb79xirpJkyapZVHHMAwmT57MKers7Oxw+PDhBlXUHTlyBH379uUUdS1btkRkZCQVda+YmZmhSEcH5ZXsRydnGBS+KMSznBwUFhaiLnsGygUCFOnowMzMrA5bJaR+oaFYQhQsKSkJ8+fPZ8WsrKywbt06FWX0bt9//z1nM1YzMzOEhYXB0NBQRVkp3qZNmzBz5kzOZPsOHTogNDSUioU3mJmZgScUokBXFyZvbFNSWlqK/Px8yOQvF9aUl5dBIBRCt4qFG4pWoKsLnlCosJ9V586dUfrWXMELFy7AwMBAIfcnRBWosCNEgWQyGfz9/VH8xvwfAAgMDFTLDX2DgoI423yIRCKEhoaq9dm1H0Iul2PhwoWs/dte8/Lywv79+6Gnp6eCzNSXkZERtHV18cTICCaFhS976QoLIZGIOY+ty3Nbnxi/zMvIyEgh97t27ZpC7kOIOqGhWEIUaO3atbhy5QorNn36dLWcD3TmzBlMnjyZFePz+di3bx86dOigoqwUq7S0FGPHjq20qAsICMCxY8eoqKuEQCCAo5sb0qyaQyKV4tmzZ5UWdXwev8a9dZLiYhS+eAGpTFqtx8v4fKQ2bw4nd3cIqnlkGSEfIyrsCFGQ27dvY/HixaxY69atsXr1ahVlVLVbt25h+PDhkErZH6obNmzA4MGDVZSVYhUUFGDQoEHYs2cP59r333+Pbdu2QSikQYuq2NjYoABAko42ZJUUX1qaWjBp0qSiyCorK0PhixeQFBe/d95dUVER8vPzUFT0As+e5aBc+v7iLt3EBFKRCE5OTjV4NYR8PKiwI0QBysvLMW7cOJSVlVXEeDwegoODoaurq8LMuDIzM+Ht7Y3CN+ZOAcCcOXMwffp0FWWlWI8fP0b37t05+5UJBAIEBgZiyZIlKt+jTp1dvHgR3bt3R8Ldu0izsYH8jfeKx+OhcWMDGBkbQ/i6qCsvR87z5ygqeoH8/DxkZ2VB+o4h2jfntTGMHPn5+e8sBuU8Hh5YW6GlrW2DmvdJiDJQYUeIAqxatQoxMTGs2Jw5c+Dp6amijCr34sULeHt7Iz09nRUfMWJEpcOV9VFCQgK6du2KW7duseK6uroIDQ3FhAkTVJSZ+hOLxZg5cyZ69eqFlJQUhEdGIkdPD5mvTpzQ1NRCkyam0BWJ8GZZ/OJFIfBGaSZn5Ch68aLKdt5eaV1eXoaioqIqHg0kWVqiyMQEnt261eh1EfIxoXEIQmopJiaGs4GvnZ2d2m3qK5VKMXLkSNy8eZMV79q1K3bt2gU+v/5/zzt//jz8/Pw4R7iZmZnhxIkTcHd3V1Fm6u/y5cuYMGECHjx4UBHLyspCRFQUtDt2glXhCxgDeLufs1wq5awsBSp54Bt0dXUhKS6G9NWJE8DLLx3a2lrQEGqwHlsgEuGerQ06desGCwuLGrwyQj4u9f83OSEqVFpaivHjx7PmqgkEAgQHB0NbW1uFmbExDIPp06dzjs9q06YNQkJCoKOjo6LMFGffvn0YOHAgp6iztbXFlStXqKirgkQiwaxZs9CzZ09WUfcmE2sr3HF3h6yS4r+qnjYtrar//vN4vFdbirxZ/THIy2MPyUr5fES3t4ORpWW1Ts0ghFBhR0itLF26FAkJCazYwoUL1e74odWrV2Pbtm2smImJCU6ePAkTExMVZaUYDMNgzZo1+Oyzz1hzHAHAw8MDkZGRaNmypYqyU2+RkZFwcXHB+vXrOfv7aWtr45dffsGFCxcwfORISJo2xTX79qz5djK5jLO1DwDweHxov2dja00NDc6KZKm0HC9eDeHKeTxcs2+PYoum8PL1pYUuhFQTHSlGSA1dvXoVnp6ekL86LB0AnJ2dcf36dWhqaqowM7Y9e/bgP//5Dyumra2Nc+fOoWvXrirKSjFkMhlmz56NDRs2cK75+fnhr7/+ahC9kYpWXFyMJUuWYO3atZyCDnhZEP/xxx+wfTW3DgBSU1NxaO9eGKWlofPtRAjlchQWFqJIzO2x09ERwbAam/wyAJ49e8YakgV4MDQ1RYyLM3KtrDD8s89gbW1dg1dJyMeJCjtCakAikcDV1RVJSUkVMQ0NDdy4cUOttmO4dOkS+vfvz1mt+/fff2P48OEqzKz2iouLMXbsWBw6dIhzbcaMGVi/fj3td1aJq1evwt/fH/fu3eNc09LSwsqVK/HNN99U+t6lpqbiyP4DEGVmwjUxEcUpKWAYOedxRoZG1Z6KUP5qn7zXiy/E+vpI6tgRTKvWGDZ6FBV1hHwgGoolpAYWLVrEKuqAl8Oy6lTU3b17F0OHDuUMT65Zs6beF3XPnz9H//79Ky3qVq9ejQ0bNlBR95aSkhLMnz8fnp6elRZ1nTt3xs2bNzFnzpwq3ztra2uMHjcWgvZ2OOPujnRb9lYowMthWK0PmF+qIRSiUaNGkPN4eNy2La737o3bZWXIzHlGRR0hNUA9doR8oIsXL6JXr16sWKdOnRAREaE284Cys7PRpUsXPHr0iBWfMWMGNmzYUK/3cHv06BEGDhzIKU40NDQQFBSEMWPGqCgz9RUVFYXx48fjzp07nGuamppYvnw5Zs+eXe2/vyUlJRg5ciTsbWxgUlSE5vfvwyQ9HQK5vNrDsK/J+Hykm5ggwbQJnopEiIiKQmRkJORyecV+eoSQ6qPCjpAP8OLFCzg7OyMlJaUipq2tjdjYWLRr106Fmf2PWCxG7969ERUVxYr7+vri8OHD9bonKzY2Fl5eXsjKymLF9fX1ceTIEfTp00dFmamn0tJSLFu2DKtXr670TNeOHTsiKCgI7du3/6D77tu3D5999hnMzc3h6eEB25YtIZJK0Sw1FS3EEpiWlUHjHRsUlwsEKNDVxRNjI6Q2bw6pSARjc3N8v2wZ0tLSKh7XqlUrxMXF0bFvhHwA9eheIKSemDdvHquoA4CVK1eqTVEnk8kwZswYTlHXoUMH7Nmzp14XdadPn8aIESM422tYWlri5MmTcHR0VFFm6ik6Ohr+/v6cVdvAy97N77//HvPmzfvgXubXq5CBl/vcHTp8GI0bN4azszM6ODvjaZMmgFQKveJi6OfmQVMqBZ+RQ87jo0woRKGRIYp0dMATCqGtqws3d3c4OTnB0NAQefn5mDt3bkVbDx8+xLfffovNmzfX7s0g5CNCPXaEVNPp06cxcOBAVuz1sVXqUDAxDIOvv/4aGzduZMVbtGiBq1evwszMTEWZ1V5QUBAmT57MOdvW3t4eJ0+eRPPmzVWUmfopKyvD8uXLsWrVqkp76dzc3BAcHAwHB4ca3b+yqQivTZw4ET/++COys7ORnZ2NZ1lZKCspgUwqhUAohKa2NpqYm8PMzAxmZmYwMjJi/duRyWTo1asXwsPDWff9559/0L9//xrlS8hHhyGEvFdeXh5jaWnJ4OXSPQYAIxKJmPv376s6tQrr1q1j5QeAMTAwYBITE1WdWo3J5XJm+fLlnNcFgOnVqxeTl5en6hTVSkxMDOPk5FTp+6WhocEsX76cKSsrq1UbPj4+ld4fAHP69Olav4b79+8zIpGIdd9mzZox+fn5tb43IR8DKuwIqYbx48dzPsQ2b96s6rQqHDx4kOHxeKz8NDU1mQsXLqg6tRorLy9nJk+eXGkBMXr0aKakpETVKaqNsrIyZunSpYxQKKz0/XJxcWHi4uJq3c6dO3eqLOpMTEyY8vJyBbwahvntt9849/f391fIvQlp6KiwI+Q9jh07xvmQ6devHyOXy1WdGsMwDBMZGcloa2tzcvzrr79UnVqNFRUVMd7e3pUWEHPnzmVkMpmqU1QbN2/eZFxcXCp9r4RCIbN06dJa99K9VlWhDYCZMmWKQtpgGIaRyWRM3759OW2EhIQorA1CGioq7Ah5h2fPnjFmZmasDxd9fX0mNTVV1akxDMMwycnJjImJCecD8IcfflB1ajWWnZ3NdOzYkfOaeDwe8+uvv6o6PbVRVlbGLFu2jNHQ0Ki00HJ0dGRiYmIU1l5WVhajpaVVZWF39uxZhbXFMAyTmprKNGrUiNWGmZkZk5OTo9B2CGloqLAj5B1GjhzJ+QALDAxUdVoMw7wsOtu0acPJb/LkyWrTm/ihkpOTmdatW3Nek5aWFnPw4EFVp6c2bt26xbi5uVVaYAkEAmbx4sVMaWmpQttcsmRJlUWdmZkZI5VKFdoewzDMzp07Kx2GJ4RUjVbFElKF/fv3Y/To0ayYj48PQkJCVL7Bb3FxMfr164fIyEhWfODAgTh+/LjabJT8Ia5duwYfHx/k5OSw4oaGhjh+/Dg8PT1VlJn6kEql+Pnnn7F06VLOiSLAy1XCwcHBcHd3V2i7EokEVlZWeP78eaXXp0+fjk2bNkEmkyE3N5e1Kra0uBhymQx8gQBaOjrvXBX7NoZhMHjwYJw4cYIVP3DgAD799FOFvkZCGgoq7AipRFZWFuzt7ZGbm1sRMzQ0xO3bt2FhYaHCzAC5XI7Ro0fj77//ZsVdXFxw6dIlNGrUSEWZ1VxISAhGjx6N4uJiVtza2hqnTp1Sm30CVSkxMRH+/v6cPQoBgM/nY8GCBViyZAm0tLQU3vaWLVswbdq0Kq+fPn0ampqauBUTgxKxGMyrfewa5+ZCQyoFn2Eg5/FQLhSiwMiItY+do5sbnJ2dYWhoWOm9nzx5Ant7e+Tl5VXEjI2Ncfv27Xq9hQ8hykKFHSFvYRgGQ4cORUhICCu+d+9eTg+eKsybN69ig9jXmjVrhmvXrqFp06Yqyqrmtm7diunTp0MuZx8m7+rqihMnTqi8kFY1mUyGtWvXYvHixZX20tnZ2SE4OBgdO3ZUWvt2dnZITk7mXDM3N0f/vn3haGcHDYkEVmnpsMjNRWOxuHonTxgZIc2qOcpFIrS0sYFn9+6V/rz37t3LOSpuyJAhOHLkiMp7zwlRN1TYEfKW4OBg+Pv7s2IjRozAgQMHVP4hsnnzZkyfPp0V09fXR3h4eL07eYFhGHz33Xf44YcfONcGDBiAv//+u172PirS3bt3MWHCBFy9epVzjc/nY968eVi6dCm0tbWVlsPRo0fh5+fHigkEAnh4eMCzY0dYlJai/ZMsNMvJgeCt4rw6ZHw+HpuY4L61FYpMTNDR0xOenp6s6QQMw+DTTz/FoUOHWM/dtWsXxo4dW7MXRkgDRYUdIW9IT0+Ho6MjCgoKKmKmpqZISEhAkyZNVJgZcPz4cQwdOpTVsyUUCnHy5En069dPhZl9uLKyMkyePBm7du3iXPP398e2bdugoaGhgszUg0wmw/r167Fo0SKUlpZyrrdt2xZBQUHo0qWL0nPp1q0bIiIiKv5samoKX29vWBoawubuXTjl5kJbWPuflZzHQ7KlJe7a2MComSW8fH1hbm5ecf3Zs2ewt7fHs2fPKmKNGzdGQkICmjVrVuv2CWko+KpOgBB1wTAMAgICWEUdAPz+++8qL+pu3LiB0aNHc4Yrt2/fXu+KusLCQvj4+FRa1C1evBiBgYEfdVGXlJSEHj16YO7cuZyijsfjYe7cuYiNja2Tou7q1ausos7KygrjRo+GnUCAzufPw/r+fYUUdQDAZxi0ffwYva9dgzTxDvbt+hOpqakV15s0aYLff/+d9ZyCggIEBASA+icI+R8q7Ah5Zdu2bfjnn39YsbFjx2Lo0KGqSeiVR48ewcfHBxKJhBVfsmQJZ8hY3WVmZqJHjx74999/WXE+n4/ff/8dy5YtU/lwt6rI5XKsX78ezs7OnNXOAGBjY4Pw8HD8/PPP0NHRqZOc1q5dW/H/VlZWGD18OFrk5sHl0iWICguho634PPQlEnSPjYXBoxQc2ruXVdz5+fnh888/Zz3+9OnT2L59u8LzIKS+oqFYQgA8fPgQTk5OEIvFFbGmTZsiISGhytV6dSEvLw+enp64c+cOKz5u3DgEBQXVqyIoMTERgwYNQlpaGisuEomwf/9++Pj4qCgz1bt//z4mTpyIy5cvc67xeDx88803WLFiBUQiUZ3l9ODBA9ja2kIul8PU1BTjRo9Gi7x8tL8SCf6rjw0TkybQVFLvqpzHwxUHe+S3aInR48ZWDMvm5eXBwcEBmZmZFY/V09NDfHw8WrZsqZRcCKlPqMeOfPTkcjkmTJjAKuoAYOfOnSot6kpLSzFs2DBOUdenTx9s3769XhV1ly9fhqenJ6eoa9KkCc6fP//RFnVyuRwbN26Ek5NTpUVd69atcfHiRaxbt65OizoAWL9+PeRyOQQCAXy9vWEhkcDu2tWKok4gECqtqANeDs12vp0InSeZCAsJgVQqBfBy26EdO3awHltUVIQJEyZwpioQ8jGiwo589DZs2IBLly6xYpMnT8bAgQNVlNHL+X6TJk3ChQsXWHF7e3scOnQImpqaqkmsBg4ePIj+/fsjPz+fFW/Tpg2uXLmCTp06qSYxFXv48CH69OmDmTNncvbvA4CZM2ciLi4O3bt3r/PccnNzERgYCADw8PCApaEh7KJjIHhjC5O6GA4WyuVwT7yD3IwM1vD0oEGDEBAQwHrsxYsXsWnTJqXnRIi6o8KOfNTu3buHhQsXsmItWrRgzS1ShcWLF+Ovv/5ixSwsLBAWFgYDAwPVJFUD69evx8iRIzmLADp37ozIyEi0bt1aRZmpjlwux+bNm+Hk5ISLFy9yrrds2RLnz5/Hr7/+Cl1dXRVk+HJvQYlEAnNzc3h27Aibu3chKmQvKtLRUd4WK29qLJGgbVIyroeH48mTJxXxtWvXwtramvXYBQsWICkpqU7yIkRdUWFHPlpSqRTjx49HSUkJKx4YGKjS/dN27NiBlStXsmK6uro4ceIErKysVJTVh5HL5ZgzZw5mzZrFWbE4ePBgnDt3TuUrjVXh0aNH6N+/P6ZPn84Z+gdeHs0VHx+PXr161X1yr5SWlmLjxo0AgG4eHjApKoLVwxTWY4QCITQUtBq2OmwzMqCXk4OI8PCKmL6+Pv744w/W44qLizF+/HjI3rE5MiENHRV25KO1Zs0aXLt2jRWbOXMmevfuraKMXq7w+/LLL1kxgUCAAwcOwNXVVUVZfZiSkhJ89tlnWLduHefalClTcPjw4TqfL6ZqDMPg999/h6OjI86dO8e5bm1tjbNnz2LTpk3Q09NTQYb/s2fPHmRlZaFx48awadkSVsnJwFuFknYdrcp9jc8waJ2ahpSkJNbRYr1798ZXX33FeuzVq1c5J7MQ8jGhVbHko3Tr1i24u7ujvLy8ImZjY4ObN2+qrOiIi4tDt27dUFRUxIpv3boVU6ZMUUlOHyovLw9Dhw7lzFkEgJUrV2LhwoX1atGHIqSmpiIgIABnzpyp9PqXX36Jn376SS1O2WAYBg4ODkhMTESPHj3Q38UFPc+cgfytofQmTUyh8cbJEHVBxufjZDdPuH3yCXr27FkRl0gkcHFxYR15pqmpiejoaDg4ONRpjoSoA+qxIx+dsrIyjB8/nlXU8fl8BAcHq6yoe/z4Mby9vTlF3fz58+tNUZeWloZu3bpxijqhUIjg4GD83//930dV1DEMgx07dsDR0bHSoq558+b4559/sGXLFrUo6gDg1KlTSExMBI/Hg5ujI5qnpUET7J+ZUKhR50UdAAjkclinpyM+Opo11CoSiRAUFAQ+/38fZ2VlZRg3bhzr3zghHwsq7MhHZ+XKlYiNjWXF5s2bh65du6okn8LCQnh7eyMjI4MVHz16dKXnqKqjuLg4dO3aFYmJiax4o0aNEBYWhnHjxqkoM9V4/PgxBg0ahMmTJ+PFixec6wEBAUhISED//v2rvEfPnj05RfLUqVOxdevW97Z/48YNzJs374Pzfr1oyNjYGLra2miS/RRlbxVHby6amJhwC76xMegZdR1drl2Fb2wMfGNjcE8sxrCb7H9jijBx3z6UiMXIzc1lxT08PDB37lxWLDY2Fk2aNOF8WQJeHlsXGhpaaRsvXrxAx44d4eLiAkdHR9r8mNQ7df+1ixAVunHjBmdhgr29Pb7//nuV5FNeXo4RI0YgPj6eFe/WrRv++OMPVi+Eujp79iz8/Pw4BczrVbwuLi6qSUwFGIZBUFAQvvnmGxQWFnKuN2vWDDt27MCAAQPee6+RI0fiwIED6NGjB4CX58eGhIRg2bJl73yeTCZDhw4d0KFDhw/KPTY2FmfPngUAmJmZQcjjwbikBCVy9vy6N0+bCHRwBAAczs5GkkSMBS1bAQAev7UgqcpcGQaCD+jF5TEMGKkU2dnZnMU333//PUJDQ1lfLgoKCnDz5k1069at2m2IRCJcvHgRIpEIEokEDg4OGDZsGIyNjat9D0JUSf0/NQhRkJKSEs6KudfDhFpaWnWeD8Mw+PLLLznHa9na2uLo0aPQ1q6b7SRqY/fu3Rg4cCCnqLOzs8OVK1c+qqIuIyMDPj4+mDhxYqVF3cSJE5GQkFCtog4ARowYgaNHj1Zsunvx4kXY2tpi1KhRcHNzg6urK8JfrRK9cOECPvnkE4wcORK9e/fGhQsXMGLECAAvFxN4eHjA1dUVffr0qdgyZOnSpQgICECPHj3QqlUr1iIEiUSCrTt34vM7iTj86uzkq2IxpmVkYNiteHyXnAz5e6Znl8sZfJt0DwOjb+Dru3cqVkf3jrqOTWmpGBV3E9cK8nEoOwvDb8ZicEw0fk19BAAQy2SYlJAAn5ho+MRE4/IbCyb+CQvD4MGD0bdv34qVxTExMejRowfKyso4w/2TJ09GaWkpFi9eDDs7O3h7e+Pp06dV5i0QCCqmZJSUlEAmk9FZtKReocKOfDSWLFnCGSpctGgR3N3dVZLPypUrKzaBfa1JkyY4efKk2vcOMAyDH3/8EWPHjq04EeC17t27Izw8nLPHWEPFMAx27doFe3t7hIWFca43bdoUJ06cwM6dO9G4ceNq39fMzAy2trYVJ1IcOHAAY8aMwbFjxxATE4Njx45h1qxZFY+/du0a1q9fzxm+bd++PS5fvozY2FgEBATgp59+qriWkpKCc+fOITg4GBERERVxiViMZV7eCGxuhX56esiXyXCgoAA7bNsixNUNGnwewnKevTP/h8USTGnWHCfd3PG8rBw33ih2DYQa2O/sAlNNTVzMzcMBZxccc3VDYpEYsYWFCM/Lg4GGEKFu7jju6gbXV3MQ86VSdGxiilUrVsDS0hKHDx8GAIwfPx4bN25EcnIy3NzcWHncvXsXkydPxqlTpxAXF4cdO3ZUehbvm/Lz8+Hs7IxmzZrh22+/hYmJyTsfT4g6ocKOfBQiIyM5WyC4urpi0aJFKsln9+7dWLx4MSumo6OD48ePo1WrVirJqbpkMhmmT5/O2dgZeNnL9M8//8DIyEgFmdW9J0+eYMiQIRg/fjwKCgo418eNG4eEhAR4eXnV6P6jRo3C33//DZlMhuPHj8PX1xfffvstHB0d4evry/qi4unpiaZNm3LukZeXBz8/Pzg4OGDZsmWs53h5eUEoFCIkJIT1nHa2ttADIJfLoC8QILGkBA9LSzHhfjJ8Y2MQmZ+PxyWleJeWOjpoLRKBx+OhvZ4uMkr/Nzw76FWhFJmfj9gXhfC7GYuhN2PxoFiCtJIS2OqKcKOwED+lpODmixfQe7VYQ1cggKupKcpKSuDu7o5Hjx6hoKAApaWl6Ny5M4CX8wTfLqB3794Nd3d3aGpqwsLCAn369Hln7gYGBoiLi0NKSgr27NmD7Ozsdz6eEHVChR1p8MRiMcaPH88aTtHU1ERwcDA0lHjWZVXOnz+PiRMnsmI8Hg979+6t+HBSVxKJBMOHD8eWLVs417755hvs37+/Xgwh1xbDMPjrr79gb2+P48ePc66bm5sjJCQEwcHBtTpvePjw4Th27BjOnTsHJycnhIWFQSwWIzY2FrGxsayzUata0b1kyRJ4e3sjISEBQUFBrFNAtLS0UFhYiG3btrGeY2BgAPkbiyYYAJ6NGuG4mztCXN1w2r0Dvmze/J25a74xP5TP40H+xmimtkBQ8f+jzM0R4uqGEFc3nOnQEUNMTdFSR4SjLq6wEYmw4uED/JmZCQDQ4PHAZ+SQSaUQCASVDpMKhUJ06NCB9W+bYRgcPHgQEonknTm/zczMDE5OTpVu30OIuqLCjjR4CxcuxP3791mxZcuWwdHRsc5zuX37Nvz8/DjbMKxfvx5Dhgyp83w+RE5ODvr27Ytjx45xrq1btw6//PJLvVjsUVvZ2dkYNmwYPv/8c9Zmua99/vnnuH37NgYPHlzrtkxMTGBnZ4c5c+Zg5MiRKCwsfLmwQSjEwYMHOaemVKawsBDNmjUD8LLn6m07duzgzAm8m5QESXnZy+fLZGivpYXY4mI8eVUU5pWXI6v03T121dGlsQHCcnJQIH357yGrtBR55eXILi2FSCCAn5kZxje1xB3x/1a2ynl8CN7YbsXAwABaWlqIiooC8HKDZW9vb84ik+fPn+Pbb79FVlYWzp8/X2VO2dnZFe9HYWEhLl26hLZt29b6tRJSV2hVLGnQzp07V3E80mtdunThbI1QF548eQIvLy/OkN0333yDmTNn1nk+H+LBgwcYNGgQaxNY4GXP559//omRI0eqKLO6wzAM9u/fjxkzZuD58+ec66ampvj9998xdOhQhbY7atQoTJ06FUOHDoVUKoW3tzc6deqEbt26VWsu5ty5c+Hv749Vq1bBw8ODdU0mk2H9+vWs2LBhw1AikWDp2bMQlZTAW18fwxo3xn9btca0xERIGTmEPD5W2NjAvJaLjmx1dTHZshk+j78FBgx0BQL80rYdHhQXY3XKQ/B5PGjz+fjBxqbiOWVCITS1tYHi4opYUFAQpk6dipKSEri4uGDq1KkQCoX473//i7KysorH/fbbb4iNja1YaVyZx48fY9KkSWAYBgzDYMaMGXBycqrV6ySkLtHJE6TBKiwshJOTE1JTUytiOjo6uHnzJmxtbes0l6KiIvTs2RMxMTGsuJ+fH/7++28I3hiaUjdRUVHw8fHhrCQ0MDDA0aNHWacANFRPnz7FtGnTcOjQoUqvjx49Ghs3bqx3k+z37NmD//znP6xYZGQkDhw4APPiYnR5tbGyhoYmmqjJa/u3axe0HTAAffv2fe9j7927BxcXF1bPZosWLRAfH682m0ITomgNf9yEfLTmzJnDKuoAYNWqVXVe1EmlUowePZpT1HXu3Bm7d+9W66IuLCwMvXr14hR1zZs3R3h4+EdR1P3999+wt7evtKhr0qQJDh48iL1799a7oo5hmIoNiV/z8PCAu7s7IiIiUNyoEaSvhjx16vhs2KqUCwQo0tGBmZlZtR7ftm1brFq1ihV79OiRSnrsCakrVNiRBunkyZPYsWMHK9azZ0/OgeHKxjAMZs6ciRMnTrDirVq1QkhIiMqOMKuOnTt3wtfXlzPh3MnJCVeuXIG9vb2KMqsbOTk5GDVqFEaOHImcnBzO9U8//RS3b9/G8OHDVZBd7V24cIHzZWPOnDn4559/kJKSAinDQPxqdamOmiyIKdDVBU8orHZhBwAzZ87kfAHZtm0bTp06hc6dO8PFxYX1X35+voKzJqRu0Rw70uDk5eUhICCAFdPT01PJSQ5r1qzhrCA1MjLCyZMnYWpqWqe5VBfDMPj+++8rPY2jb9++OHz4MPT19VWQWd05fPgwpk6dWulGtsbGxti8eXO9n1f49vY/rVu3xpAhQ+Dv74/nz59DXFKCvKZN0eTFC6X2KssZBjweD9U5f+KJsRG0dXU/aDsdPp+PwMBAODk5VWxoDLw81u3WrVu1WrVMiDqiHjvS4MycOROZr7ZHeG3t2rVo2bJlneZx4MABfPvtt6yYlpYWjh07VufDwdVVXl6OSZMmVVrUff755wgLC2vQRd3z588xZswYDB8+vNKibtiwYbh9+3a9L+oSExM5mynPnj0b5eXlOHbsGBiGQcytW0i3soKmSFcpOcgZBjnPnyMr6wmePXuG8rc2un6bjM9HavPmcHJ3/+BCs1WrVpxh54yMDHz99dcfnDch6o4KO9KgHDlyhLOlw4ABAzB58uQ6zSMiIqLSg++Dg4M/6NzKulRUVARfX1/88ccfnGsLFizArl27oKmpqYLM6saxY8dgb2+PvXv3cq4ZGRlh7969OHjw4AcNA6qrdevWsf5sZGQEf39/nDp1quJ4uLi4OEg0NPDc6t371dVUaUkJyspebpkilZbj+fPnrOP+3pZuYgKpSFTjFapffPEFPvnkE1bszz//xNGjR2t0P0LUFRV2pMF49uwZpkyZwoo1btwYO3bs4JwfqUxJSUnw9fVlbQQLAKtXr8aoUaPqLI8PkZWVhZ49e+LUqVOsOJ/Px2+//YZVq1bV6XtYl3JzczF27FgMHTq00hMGhgwZgtu3b2P06NEN4j3IysrCn3/+yYpNmzYNIpEI+/fvr4gVFBQgXyzGwxYtIFfG637rnnK5DM9zn7M2Xa64xuPhgbUVWtra1njolMfjYceOHZxTKaZMmVLpHEpC6isq7EiDwDAMpk6dimfP2OdXbtiwoWJz1rrw7NkzDBo0CLm5uaz41KlTMW/evDrL40Pcu3cPXbt25Uyk19HRweHDhzFt2jQVZaZ8oaGhcHBwqHTjXkNDQ/z55584cuQIzM3NVZCdcmzatIm1t5umpiZmzJgBiUTCOUWjnZ0dikxMkGxpqfA8tLW1oanJ3gdPKpXieW4u5zSJJEtLFJmYwLOWvd3NmzfHr7/+yoo9ffoUU6dO5bRJSH1FhR1pEPbt28fZjmLIkCEYO3ZsneUgkUgwePBgPHz4kBX39vbGhg0b1LK3JzIyEh4eHnj06BErbmxsjHPnzqn9aRg1lZ+fD39/fwwePBhPnjzhXPfx8UFCQgI+//xztfy51ZRYLOYs5hk3bhzMzMwqjit7jc/nY9SoUejo6Ym7NjYoVPAKbh5eDgELhexj/crLy5Cbl4fXZVaBSIR7tjbo1K0bLCwsat3uuHHj4Ovry4odPHiQ1VtJSH1GhR2p9zIzMzF9+nRWzNjYGL///nudfSjLZDJ8/vnnuHbtGivu5uaGffv2QShUvwXoR44cQd++fTm9iy1btkRkZCS6dOmiosyUKywsDPb29ggODuZca9y4MYKDgxESEoKmTZuqIDvlCgoK4vy8Z8+eDQCcwqZ3794wMzODp6cnDJtZItrODlIFryrn83gwNjaCgM9eDFFaWoL8/HxI+XxEt7eDkaUl59SMmuLxePj99985K2unT59eaZFPSH1DhR2p1xiGwRdffME5s3PLli11Osl97ty5OHLkCCtmZWWF0NBQ6Onp1Vke1bVp0yYMHz6cc9Zohw4dcOXKFbVdtVsbBQUFmDRpEry9vTmrpgFg0KBBuH37NsaNG9egeulek8lknEUT3t7esLOzQ1FREWevxdfzQYVCIbx9fSFp2hTX7NsrfL6dgC+AsbEx+Dz2x5G4pBiXbdqg2KIpvHx9FfrlyNzcnNNzmZubiy+++IKGZEm9R4Udqdf++OOPSj+QPv300zrLYcOGDZzzNhs3boywsDCFDB0pklwux/z58/HVV19xPsC8vLxw/vz5BrHq823//PMPHBwcEBgYyLmmr6+PnTt34sSJE7BUwlwydXHs2DHONIHXJzAcP34cxW+cvSoQCODn51fxZ3Nzc/iNGolcKytccbBXeM+dUCiEkbERXu9mJxMIkNi1Kx40agSZgK+UOY4jR47kbFsTGhpaaU8uIfUJnRVL6q3U1FQ4OjpWbM8AAGZmZrh9+3a1DkdXhKNHj2LYsGGsIklDQwOnT59G79696ySH6iotLcXEiROxZ88ezrWAgABs2bJFLYeMa6OwsBBz587F9u3bK73+ySefYMeOHWjeXDlbeqgTDw8PXLlypeLPbm5uuHHjBng8Hvz8/FjbfgwYMICzQhp4+W/uyP4DEGVmwv3OHei/dSpJbZWUliJdKsVdd3dkinRw4MgRpKenY+/evRg9erRC2wJeni7i4ODAWg2tr6+PhISEj+LvBGmYqMeO1EtyuRyTJk1iFXUAsH379jor6q5fv44xY8Zwer527typdkVdQUEBBg0aVGlR9/3332Pbtm0Nrqg7c+YMHB0dKy3qGjVqhO3bt+PUqVMfxQd4ZGQkq6gDXvbW8Xg8FBYW4uTJk6xrVW3LY21tjdHjxkLQ3g7nO3fGvWbNFDY0K+fx8Kh1a8QNHIA70nL8uW8f0tPTAbxc8PDvv/8qpJ03mZiYYNu2baxYYWEhJk6cSEOypN6iwo7US1u3bsXZs2dZsderHOvCw4cP4ePjwxq+AoBly5bV6Urc6nj8+DG6d++O8+fPs+ICgQCBgYFYsmRJg5pT9uLFC0ydOhX9+/dHWloa53rfvn1x69YtBAQENKjX/S5vn7pgZWWFESNGAHg5RPvmnosaGhoYOnRolfcyNzfH+IkT0bFvH9x1dMD5Du54ZGoKWQ2HZ2V8Ph6ZmuJ8B3fcc3SA58CBaGljwzr5o7y8HMOGDUN0dHSN2ngXX19fjB8/nhU7c+YMtm7dqvC2CKkLNBRL6p379+/D2dmZdTh9s2bNkJCQwNl8VBlyc3Ph4eGBe/fuseITJ06s882Q3ychIQGDBg3C48ePWXFdXV0cPHgQAwcOVFFmynH+/HlMnDiRs30L8PI1r1mzBlOmTFGrn5Gy3b9/H7a2tqweqLVr11ashvXx8WHNU/Xx8eHsZ1eVzMxMREZEICUpCUKJBNbp6bB4novGYjE03nGKRLlAgAJdXTwxNkJq8+aQikRoaWsLz1dbmjAMg2+++QYbNmxgPc/U1BQRERFo06bNh7wF75Wfnw9HR0fWvxNdXV3ExcWhdevWCm2LEGWjwo7UKzKZDL169UJ4eDgrfvr0ac5xQcpQUlKCTz75BJcvX2bF+/fvjxMnTkBDQ6OKZ9a98+fPw8/PDwUFBay4mZkZTpw4AXd3dxVlpnhFRUVYsGABfvvtt0qv9+7dGzt37qzz84LVwfTp07F58+aKP+vr6yM9PR36+vrIy8uDmZkZysvLK67v2rXrg3ud8/LyEB8fj/joaJSIxWCkUugVF0M/Nw+aUin4jBxyHh9lQiEKjQxRpKMDnlAIbV1dOLm7w8nJiXOihFwux5gxYzjbsLRq1QoREREKX1Dxzz//YMCAAaxY9+7dceHCBfAVvFiEEKViCKlH1qxZwwBg/ffll1/WSdsymYwZPXo0p31HR0emoKCgTnKorr179zKampqcXG1tbZmHDx+qOj2FunjxItOqVSvOawXAiEQi5rfffmNkMpmq01SJnJwcRkdHh/WezJs3r+J6YGAg65qWllat/i5LpVLm6dOnzK1bt5gzZ84we3fvZoJ37GACt25lgnfsYPbu3s2cOXOGuXXrFvP06VNGKpW+834lJSVM3759OT9XV1dXpfyb+/LLLzltrVu3TuHtEKJMVNiReiMxMZHR0tJi/dJt2bIl8+LFizppf8GCBZxf+paWlkx6enqdtF8dcrmc+fnnnystcjw8PJicnBxVp6gwRUVFzMyZMyt9rQCYHj16MPfv31d1miq1fPly1nsiFApZf18HDBjAuj506FAVZlu5goICxtXVlfPz7dOnD1NSUqLQtl68eMG0bNmSU+zeuXNHoe0QokxU2JF6oby8nOnYsSPrFy6Px2MuXrxYJ+1v3bqV88HSqFEj5ubNm3XSfnVIpdIqCx0/Pz9GIpGoOkWFuXz5MtOmTZtKX6uOjg7z66+/frS9dK8VFxczpqamrPfm888/Z65evcosW7aM2b59O8Pn81nX9+7dq+q0K5WVlVVpr+zIkSMV/nO+cOECw+PxWO106tSJKS8vV2g7hCgLFXakXlixYgXnl/qsWbPqpO0TJ05wPgAFAgFz6tSpOmm/OiQSCTN8+PBKC50ZM2a8d8irvhCLxcysWbM4H7yv//P09GSSk5NVnaZa2LFjB+f9CQoK4vxdfv2ftrZ2nfV+10RycjKnUAXAfPXVV4xcLldoW9988w2nnZUrVyq0DUKUhQo7ovZu3rzJaGhosH7Jtm3btk56oKKjoxldXV3OL/kdO3Yove3qysnJYTw9PSv9sF69erXCP/RUJTIykrG1ta2yKFm3bl2DKWBrSyaTMXZ2dqz3qG/fvsx///vfKoeuhUIh88UXXzC5ubmqTr9KN27cYPT09Di5//DDDwptRyKRMG3btmW1oaGhwcTFxSm0HUKUgQo7otZKS0sZJycn1i9YPp/PXL16Veltp6amMubm5pwPkUWLFim97epKSUnhfAC9/hD666+/VJ2eQkgkEmbu3LlV9jR17dqVuXv3rqrTVCuhoaGc9+nkyZOVLj56+79x48apOv13+vfffzlf9AAwgYGBCm3n6tWrnL9zzs7OTGlpqULbIUTRaA03UWvLli1DfHw8KzZ//nx07txZqe3m5+fDy8sLWVlZrPh//vMfLF++XKltV1dsbCy6du3K2U9PX18fp06dwpgxY1SUmeJcu3YNbm5uWLNmDeRyOeualpYWfv75Z1y+fBlt27ZVUYbqRSaT4dmzZ9i1axf69OmDz0aOxISxY/H11KnIfvwYfAB9+vSBvb09TExMKt3PLzExse4T/wD9+vXDrl27OPHJkycjNDRUYe107twZ8+fPZ8Xi4uKwYsUKhbVBiDLQPnZEbV2/fh0eHh6QvbHRqaOjI6KioqClpaW0dsvKyjBo0CCcO3eOFe/VqxdOnTql1Lar6/Tp0xgxYgSKiopYcUtLS5w8eRKOjo4qykwxSkpKsHTpUvz888+cgg4AOnXqhODgYLRr104F2amfvLw8xMXF4VZMDMSFhSgRi6Hz4gUMCwshLCuDjqYmBJqaKOULkKmpgeJGjSBlGIhLShBz6xbi4uIq9jvctGkTpk+fruJX9H6//vorvvnmG1ZMR0cHZ8+eRdeuXRXSRmlpKTp27Ihbt25VxAQCAa5cuYKOHTsqpA1CFI0KO6KWiouL4ebmhrt371bEhEIhoqKi4OLiorR2GYaBv78/p0fAzs4OERERnE1UVSEoKAiTJ0+GVCplxe3t7XHy5Ml6f/ZpVFQU/P39K+050tTUxLJlyzBnzpwGd7ZtTWRmZiIyPBwpycnQkEhglZYOvdRHEDx9CuGrvx98vgBmZmbg4eXf7ydZTyAVCiFu3Bh5TZsi3coKEg0NJKekoLWNDVatWlVvTuZYsGABVq9ezYoZGRkhPDwcdnZ2CmkjNjYWnTp1Yv17s7OzQ0xMDLS1tRXSBiGKREOxRC0tXryYVdQBwJIlS5Ra1AHA999/zynqzMzMEBYWpvKijmEYLF++HBMmTOAUda9P46jPRV1paSkWLVqErl27VlrUdejQATExMZg/f/5HX9RJpVJcvHgRe4KCkHP1KlxjYjEwPALtHzyAVuaTiqIOeHk01usyjcfjgcfjQyiVovHz52hx6xY8Tp6Ea0wMultYwERfH5cuXeL8/VJXq1at4pzzmpubiwEDBnCO0aspV1dXLF68mBW7c+cOJ0aIuqAeO6J2Ll++jJ49e7LOtnR3d8eVK1eUemRXUFAQJkyYwIqJRCJcvHgRHTp0UFq71SGVSjFt2jRs376dc2306NEICgpSiyHimoqJicH48eORkJDAuaahoYGlS5fi22+//egLOgDIysrCiZAQ5D3OQLvkZNhkZID/6t9KQWEhxOL/Dc/zeDyYmZmBz/vfd/is7GzI5exzXPX0GkFPXx/Jlpa4a2MDo2aW8PL1VfixXcpQXl6OoUOHIiwsjBW3t7fH5cuXFfKFrLy8HF27dkV0dHRFjMfj4dKlS+jWrVut70+IIlFhR9RKUVERnJ2d8fDhw4qYlpYWoqOjYW9vr7R2z5w5g0GDBrF6Kvh8Po4ePYrBgwcrrd3qEIvFGDVqFOug9tfmzZuHH3/8sd6eZVlWVoYVK1bghx9+YM2lfM3NzQ1BQUH1fs6goqSmpuLI/v0QZT6B+5070JdIKq7JGQbZ2dlgmP/NSdTV1UVj/casezzPzUVpaUnFn0U6IhgYGFT8uVAkQrSdHSRNm8Jv1EhYW1sr7wUpiFgsRt++fXHt2jVWvFu3bvjnn3+go6NT6zZu374NNzc3lJWVVcRat26NuLg46Orq1vr+hChK/fw0IA3W/PnzWUUdACxfvlypRd2tW7cwfPhwzvDThg0bVF7UPX36FL179+YUdTweDxs2bMBPP/1Ub4u6mzdvomPHjli+fDmnqBMKhVi2bBmuXr1KRd0rqampOLR3LwxTHqF7bCyrqAMAiUTCKuoAHnR19Tj3MTI0hJamFvh8AXR19VhFHQDoSyToHhsLg0cpOLR3L1JTU5XwahRLV1cXJ06c4CymCQ8Px+jRoxUytGxvb89ZEfvgwQPOyllCVI167IjaOHPmDPr378+KeXh44NKlSxAIBEppMzMzE126dEF6ejorPnfuXPz8889KabO67t+/j4EDB+LBgwesuJaWFv766y8MHz5cRZnVTnl5OX744QesWLGi0g9cFxcXBAUFwdnZWQXZqaesrCzs27ULBimP0PX27Yqh19cYAE+zsyF7Y4hVW1sHRrUYhpTzeLjiYI/8Fi0xetzYejEsm5aWhq5duyIzM5MVDwgIwLZt22q9KEQmk6FHjx6IjIxkxc+cOYO+ffvW6t6EKEr9/KpPGpyCggJMnDiRFdPR0UFQUJDSiroXL17A29ubU9R9+umnnJV2de3atWvo2rUrp6gzNDTE2bNn621RFx8fj86dO2Pp0qWcok4oFOK///0vrl27RkXdG6RSKU6EhECU+QSdExM5RR3wchW5jDNvjttb9yH4DIPOtxOh8yQTYSEh9WJBhZWVFU6dOoXGjdnDzzt27MB///vfWt9fIBAgKCiIM7Q7ceJEFBYW1vr+hCgCFXZELcyePZtTYP3000+wsbFRSnvl5eUYOXIkbt68yYp7eHhg165dKh3eDAkJQe/evZGTk8OKW1tbIzIyEp6enirKrOakUilWrlyJDh06IDY2lnPd0dER169fx9KlS6GpqamCDNVXREQE8h5nwP3OHQgr2dMPAMRv7WeoqakFTQUsNBLK5XBPvIPcjAxOL5W6cnR0xPHjxzmLiZYvX47NmzfX+v42NjacL35paWmYPXt2re9NiCJQYUdULjQ0FIGBgaxY7969MW3aNKW0xzAMpk+fjlOnTrHiNjY2OHbsmEr3ptq6dSv8/PxQXFzMiru6uuLKlSv1ckPehIQEdOnSBd999x3Ky8tZ1wQCAb777jvcuHEDrq6uKspQfWVmZiIqIgLtkpM5c+peKy0rRbmU/b7qKXAyf2OJBG2TknE9PBxPnjxR2H2VqXv37ti3bx/nC9qMGTNw8ODBWt9/+vTp6N27Nyu2c+fOShc4EVLXqLAjKvX8+XNMnjyZFWvUqBECAwOV1mv2448/crYNMTExQVhYGExMTJTS5vswDINFixZh6tSpnJMWBgwYgIsXL8LCwkIludWUVCrFqlWr4O7uztom4jV7e3tcvXoVy5cvp166KkSGh0MvJwc2GRlVPqaoSMz6s1AghJaCv5zYZmRALycHEeHhCr2vMg0dOhRbtmxhxRiGwX/+8x9cuHChVvfm8/kIDAzkDHdPnjwZubm5tbo3IbVFhR1Rqa+++opzHusvv/yCFi1aKKW9PXv24P/+7/9YMW1tbYSEhKBNmzZKafN9ysrK4O/vjx9++IFzzd/fH8ePH0ejRo1UkFnNJSYmwsPDA//3f//H2h4CePmhuHDhQkRHR6t8f0B1lpeXh5TkZLRJTat0Xh0AlEulrK1LAEBXTw+KPjeCzzBonZqGlKQk5OXlKfjuyvPFF1/g+++/Z8XKysowZMgQxMXF1ereLVq0wC+//MKKPXnyBF999VWt7ktIbVFhR1Tm4MGD2Lt3Lyvm5eXFWUShKJcuXeJsQMzj8bB7926FnS35oQoLC+Hj41PpoeaLFy9GYGCgUjdlVjSZTIaff/4Zbm5uiIqK4ly3s7PDlStX8MMPP9TrDZXrQlxcHDQkEjR7a67lm96eW8fn8SFSwJ5tlWmekwOhRIL4+Hil3F9ZFi9ejC+//JIVKywsxMCBA5GSklKre0+aNAmDBg1ixfbs2YNDhw7V6r6E1AYVdkQlsrOzMXXqVFbM0NAQ27dvV8o5lXfv3sXQoUM5vUdr1qxR2QrTzMxM9OjRA//++y8rzufz8fvvv2PZsmX15sxOALh37x66deuGb7/9FqWlpaxrfD4f3377LWJiYtCpUycVZVh/yGQy3IqJgVVaOgRVLJiQyeWQvDUXU1dXV2l/ZwRyOazT0xEfHV3pZtLqisfjYdOmTRg2bBgrnpWVhQEDBuDZs2e1uvf27ds5ewF++eWXePr0aY3vS0htUGFH6hzDMPjyyy85qz43btyIpk2bKry97OxsDBo0iDOENGPGDMyaNUvh7VVHYmIiunbtyhkOEolEOHbsGL744guV5FUTMpkMa9euhYuLC65evcq53rZtW0RERGD16tV0aHo15ebmokQshsU75muJxWK83MHuJR54ECn5BASL5y/zqm/zyAQCAf766y/07NmTFU9OToa3tzeK3ur5/BCWlpbYuHEjK5aTk4OpU6eCtoklqkCFHalzf/31F44ePcqKDRs2DGPGjFF4W2KxGIMHD8ajR49YcV9fX6xfv14lPWKXL1+Gp6cn0tLSWPEmTZrg/Pnz8PHxqfOcaiopKQk9evTA3LlzUVLCnuvF4/EwZ84cxMbGokuXLirKULWEQiFcXFxgb2+PwYMHIz8/HwDw6NEjiEQiuLi4wNnZGT169Kj4+xAUFIR27drhl99+w4SLF/Dzo5fDhfuznmBwTHTFf1HP2V+MdEQiCBS04Ohafj6+upPIiTcWi8FIpcjOzq7WfUJDQ+Hg4AA+n1/pOcB1SVtbG0ePHuWcZBIVFYURI0ZwevM/xH/+8x/4+fmxYocPH8aePXtqfE9CaooKO1KnMjIyMGPGDFbMxMQEW7ZsUXiRJZPJMGbMGM5crw4dOmDPnj1K2/j4XQ4ePIj+/ftXfMC/1qZNG1y5cqXeDFPK5XL8+uuvcHFxqXR/szZt2uDy5ctYs2aNQs7prK8MDAxw8+ZN3L59GwYGBvjtt98qrrVv3x43b95EXFwchgwZgvXr11dc69u3L74bNw7HXd0wr0VLZJWWIjgzEwecXXDczR1bW7dBk7f+/ipyi5OqaMhk0Csu5hR2VQ3Ntm3bFgcPHkSPHj2Unlt1GBgY4NSpU5zzb0+fPo1JkyZxVqRXF4/Hw9atWzmr6mfMmME5BYMQZROqOgHy8WAYBgEBASgoKGDFf//9d5iamiq8rVmzZiEkJIQVb9GiBUJDQ1VyaPf69esxe/ZszvBM586dcfz4cTRp0qTOc6qJ+/fvY+LEibh8+TLnGo/Hw9dff42VK1dCJBKpIDv15enpWeVKzBcvXrBOS5CIxWj8xnDn8/Jy6PD50OLzwQAQlpbCRPjy1/cfubmIKi5B2ZMn6GtkjDmvVpT3jroOX1NTROTlQ8jj4bvWrfBTSgoel5RiQcuW+MTEBIezs3E29znEMhmelJZifNOmGGPBng4hlsmw9P59PCh+uY+en0FjGDk7Y+nSpcjKysL9+/fRvn17bNiwgfO6lLXBeG00bdoUp0+fhqenJ54/f14R3717N8zMzLBmzZoa3dfU1BRbtmzBp59+WhHLz89HQEAATpw4Ua/my5L6jQo7Umd27NjB2RR4zJgxnEnNirB+/XrOvBdDQ0OcPHkSZmZmCm/vXeRyOebOncvZGgEABg8ejH379tWLIkgul+O3337DggULIKlks9zWrVvjjz/+QPfu3VWQnXqTyWT4999/WSu+ExMT4eLigvz8fDAMgxs3blRcu3T5MhKEQuiUlWG2dQt0NzSErkCAvjei0KWRPrpqCNHh1d+Z4Y0bY66NLTQ0NDD59m3cKSqC3av91ay0tTHLxQWLkpOx8uFDBDs4Ir2kBN/cvYtPXvUuJRQVIdTVDQAw9GYs+hgZs3LfnJ6G/sbG+NmkLbJKS/F5RASWDx0KALh16xbOnz9f7/YhbNu2LU6cOIE+ffqw/i6vXbsW5ubmmDt3bo3uO2LECIwZM4Y1BHvy5EkEBgZi0qRJtc6bkOqgoVhSJx49esQ5csfCwoJTfCnCoUOHMGfOHFZMU1MTR48erfOTG0pKSvDZZ59VWtRNmTIFhw8frhdF3cOHD9GnTx/MnDmz0qLuq6++QlxcHBV1b8nPz4eLiwvMzMyQk5ODAQMGVFx7PRT76NEjfPXVV1i4cGHFNY/OnbFm8GCEuLqhl5ERBDwegh0csaZtWzRmGKx8+hQnXp1NGldWjs8Sb2NIbAxui4sqetYAVBRpbXVFcNfXhyafj9YiEZ6W/W/VcncDQzQSCtFIKETXxga4VfSC9Roi8/KxMS0VvrEx+CLxNopKS1H6aj7lkCFD6l1R91rnzp1x6NAhCIXs/o158+bhzz//rPF9N27cyNlMfNasWUhNTa3xPQn5EFTYEaWTy+WYMGECZ+XZjh07YGRkpNC2rly5gs8//5wz3BkUFFTn83zy8vIwYMAAHDhwgHNt5cqV2LJlC+dDRd3I5XJs2bIFTk5OuHjxIud6ixYtcP78eWzYsEElw9vq7vUcu7S0NEil0irPKvXx8WHNVeTx+ZC/NXTH4/HgqK2DcQaN8bWJCS6LxSiVy7Hh2VNssWuP427u+MTYGGXy//3d13y1mIIHHjR5//t1z7Duy86F99b2xgwY/N7eHiGubghxdcOWT0dWnGxRH76UvMvAgQM5xxkCwMSJEzmjC9VlZGTEOdnmxYsXmDhxYo3n8BHyIaiwI0r322+/cY7wmTRpEry8vBTazv379+Hr68tZnfnDDz/gs88+U2hb75OWloZu3brh0qVLrLhQKERwcDD+7//+T+3n3Dx69Aj9+/fHtGnTXm2twTZt2jTcunULvXr1qvvk6hmRSIRff/0Va9euhVQq5VyPjIxEq1atKv4sEApR/kbRn11aisSiIhS9+jmklJXBXCiEjC8An8eDoYYG8svLcbEGp0JczstDkVSKIqkUVwvy4fDWMVkeBob4640zYpMLCqDZgLatGTt2LH766SdWTCqVYsSIEbh+/XqN7unt7c3ZaP3cuXNVFvaEKBIVdkSpkpKSMH/+fFbMysoK69atU2g7OTk5GDRoEGdvvMmTJ2PBggUKbet94uLi0LVrVyQmsreLaNSoEcLCwjBu3Lg6zedDMQyD33//HY6Ojjh37hznurW1Nc6ePYvffvuNc1YmqVqHDh3g6OhYcSrB6zl2zs7OCAwMZE3aF+nqouCN3mwpw2DlwwcYmXQPE9PTkVxaCn8jI5jr68PX1BQ+MTGYc+8eXGpw9Jybvj6+vnsXw27exERLS5i/dSLIdCsrPC8vh09MNAZF38C/qY/QxNy8Wvc+ffo0mjVrhitXrqBfv351/gWruubOncuZKiIWi+Ht7Y2kpKQa3fOXX36BlZUVKzZ//nwkJyfXOE9CqoPH0A6KRElkMhm6d++OK1eusOJnzpxB3759FdZOcXEx+vXrx9l2Y+DAgTh+/HidDneePXsWfn5+ePGCPU/JwsICYWFhcHFxqbNcaiItLQ0BAQGc0zBemzJlCn7++ed6d3ZtfZOQkICwv/+Gz8VL0Hi1lUh+QQEkkv/1nPJ4fJiZmYFfi57fw9nZSJKIsaBlK5RLpSgrK4OWlhaEVWwFVC4QILRnD3h9+ikcHBxq3K46ksvlGDduHP766y9WvEWLFoiMjOTMm6uOs2fPol+/fqyYh4cHLl26pJLtlsjHgXrsiNKsXbuWU9RNnz5doUWdXC7H+PHjOUWdi4sLDhw4UKdF3e7duzFw4EBOUff6fFR1LuoYhsHOnTvh4OBQaVHXvHlznD59Glu3bqWirg6YmZmBJxSi4NW8RblcjuK3Fq3o6opqVdS9qaS0FM+ePUVBQT6ePn2KgsICyCv5zl+gqwueUFjnK8vrAp/PR2BgID755BNW/NGjRxg4cCBnm6bq6Nu3L6ZPn86KRUZGVrqYihBFocKOKMXt27exePFiVqx169ZYvXq1QtuZP38+/v77b1asWbNmOHHiRJ0VIAzD4Mcff8TYsWM586e6d++O8PBwzoao6uTx48fw8vJCQEAApygFgICAANy6dYvzgUeUx8jICNq6unjyajhWLJGAYS95gK5u7YfBh5mZYUHLVihhnTnLQCwW4+nTbEgkElarT4xf5vX2oqc//vgDLi4urP9+/PHHWudX1zQ1NXHw4EF06NCBFY+Pj8eQIUM483erY/Xq1WjdujUr9t1333GmahCiKDQUSxSuvLwcXbp0QUxMTEWMx+NVHKWlKJs3b+Z8G9bX10d4eDjn2CBlkclk+Oqrr7BlyxbOtREjRuDPP//8//buOiyqrI8D+HeCGrpLMUGQEMQkRFddXVQUW1cFa3XXWHVF17Vdu2NtxVwVW+xYExQDkRIBgxAEpTtm5rx/qPN6vQMSQ3o+z8PzyLn3nnPu4Mz9zclauz8qIQT79+/H9OnTpbZGGBsbY8+ePejZs2cN1I66ffs2nl2/jh73/JCSlASx+P+7OygpCaD51cbzlZGbl4fMzAypx+Tk5KGurg6eggIuOzmi9Y8/svZcrW/ev38PR0dHvHz5kpE+YMAA+Pj4lLsb1c/PD506dWLM1m/Tpg3u378POTk5mdSZoj6jLXaUzK1YsYIR1AHAH3/8IdOg7vz585gyZQojjc/n49SpU9UW1OXl5WHAgAFSg7pp06bBx8en1gZ1iYmJ6N27N8aMGSM1qBs9ejTCwsJoUFeDWrVqhWKBAK/V1BhBHQCZT1pRFgigqqrGWuoEAIqLi5CS8gGRAiUUKSrCxsZGpmXXRnp6erh27RoMvpokcurUKUydOpW1nNK3ODk5sSZnPHnypE62alK1H22xo2Tq6dOnaN++PaNL0sLCAk+fPpVZkPPkyRO4uLiwFsrdt28fPD09ZVLGt6SkpKBPnz4ICAhgHVu/fj2mT59eLfUoL0IIDh8+jKlTp7L2qwU+TvLYvXs3evXqVf2Vo1hO+Pgg/vZt2N24Ae6nj2oFBQVof7U7hKwIRSJkZWWhoCCfkS7mcPD0hx/wJCMD7Tt2xKRJk76LlqZnz56hU6dOrCEKS5YsYQ01+ZaCggK0bt0aERERkjQ+n4/Hjx/X6vG3VN1DW+womSksLISHhwcjqOPxeDhw4IDMgrqYmBj07t2bFdQtXLiw2oK6V69ewcHBgRXUycvLw8fHp9YGde/evUPfvn0xatQoqUHdqFGjEB4eToO6WkQM4INAgEQzM0maigzG1pWEz+NBS1MT2lrajIlHiWZmSFFRwe27dzF9+nTY2dlJXQqnvrG1tcW5c+dYu2ssWLCAtQjxtygqKuLAgQOMblyhUIhRo0ahsLCwlCspqnxoYEfJzKJFixAWFsZImzNnDtq2bSuT/NPT0+Hq6ork5GRGuoeHBxYuXCiTMr7l8ePHcHBwYK1FpaGhgWvXrmHw4MHVUo/yIITg33//haWlJc6fP886bmBgAF9fXxw4cACampo1UEOqJNu3b4f/48eINjdHnpoa+Hw5KHy1zlxVUFBQgK6uHtTU1JGnroFoc3P4P36MpKQkAB8nR3Xt2hWDBg1CXFxcldenJnXp0gWHDx9mLSg+ceJEnDt3rlx5tW3blrF1HPBxv90lS5ZUup4U9RntiqVkIiAgAI6Ojowtc1q1aoVHjx7JZC/JwsJC9OzZk7WDxQ8//IDLly9Xy36Vly5dwqBBg1ithQ0bNsTly5dhaWlZ5XUor+TkZEycOBFnz56Vevznn3/G5s2bZb61G1V5jx8/Rrt27cDj8TB61ChY8HjoHBwC1WoI7D4Tcrm4Y2eLWHl5rFm/HiKRiHWOkpIS/vrrL8ycObPWjimVha1bt2Ly5MmMNEVFRVy/fh1OTk5lzqeoqAjt2rVDcHCwJI3L5eL+/fto3769zOpLfb9oix1VaXl5efDw8GAEdXJycjh48KBMAi5CCMaOHcsK6iwtLXHq1KlqCer27t0LNzc3VlBnY2ODgICAWhfUEULg4+MDS0tLqUGdnp4eTp8+jcOHD9OgrpZat24dgI8zr30vXkSyqipC7O1Ze8hWFTGHg4eWLVFgZIzfZ8zAnTt3pI4Fy8/Px/z582FpaQlfX99yTyyoKyZNmoS5c+cy0goKCtCnTx9WT0Vp5OXlceDAAcYYxc/rcebn55dyJUWVDQ3sqEqbO3cua9udRYsWyWz23Pz581mrwX/eyUFDhks+SEMIwcKFCzFu3DhWa0XXrl1x7949GBkZVWkdyuv9+/cYNGgQhg4ditTUVNbxoUOHIjw8HO7u7jVQO6osYmJicPLkScnv79+/h7qODtIameCBlSWE3Kr96BZyuXhgZYk0ExO4DxkMAwMDODo64smTJ9i+fbvULwOvX79G37594erqWuFtuGq7v//+G2PHjmWkZWRkoGfPnuXqkm7VqhVr+EhkZCQrcKSoiqBdsVSl3Llzh7UJfLt27eDv7y+TXR/27NmD8ePHM9KUlZVx79492NnZVTr/0hQXF2PChAnYt28f69iIESOwd+/eamktLI8TJ07gt99+Y+2ZCwA6OjrYvn07Bg4cWAM1o8pj+vTp2Lhxo+R3ZWVlxMfHIysrC2d8jkOQmAj7iAiofdWCLAuZAgECW1og39AI7kMGS11cOzU1FfPnz8fOnTsZLfWfycnJYfr06Zg3b16926lEKBRiwIAB8PX1ZaSbm5vDz88P2tplm7EsFArh4OCAx48fS9I4HA5u376NTp06ybTO1PeFBnZUhWVnZ6NVq1Z48+aNJE1RURFBQUEwNzevdP5Xr15Fr169GC1lPB4Pvr6+cHV1rXT+pcnJycGgQYNw5coV1rE///wTy5cvZw2mrkkpKSmYPHkyfHx8pB4fOHAgtm7dCj09vWquGVVeGRkZaNiwIXJyciRpv//+uyTQS0pKwkVfX6S/TYB5dDRMExIkS6FUhpjDQZSxMSLNTKFlbAxXNzfWOm5fe/bsGSZPngx/f3+pxw0NDbFmzRoMHz68Vr1fKisvLw/du3dnbWXYoUMH3LhxA8qftoL7loiICNjZ2TFmxTZp0gQhISEyX6uQ+n7Qrliqwry8vBhBHQAsW7ZMJkFdcHAwBg4cyOr+3Lp1a5UHdUlJSXBxcWEFdVwuF1u3bsWKFStq1UPqzJkzsLS0lBrUaWtrw8fHBydOnKBBXR2xa9cuRlDH5XIxbdo0ye8GBgbwGDMGbbv+gBfWVrjVxh4xenoQVbB7VsTlIkZPD7fa2CPS2grtunbFqDFjvhnUAR+XA7l37x4OHz4MQ0ND1vF3795hxIgR6NSpE549e1ah+tVGAoEA58+fR8uWLRnpAQEBGDJkCIqLi8uUj4WFBZYvX85Ie/PmDWbNmiWzulLfH9piR1XI1atXWbsSODs749atW+Xebudrb9++RYcOHZCQkMBInz17dpWv1B4ZGYmePXsiJiaGka6kpISjR4+ib9++VVp+eaSmpmLKlCk4evSo1OPu7u7Yvn17vdywvb4qKipCkyZNkJiYKEkbPHhwiS2xiYmJuO/vjzdRUeDn5aFRfDwMU9OgnpsLOSkzWD8r5vGQqayMd9paiG3YEEKBAE3MzODo5CQ1QCuL7OxsLF26FBs2bJAa2HC5XEyYMAFLly6tNxN24uPj4eDggLdv3zLSR48ejb1795bpC6BIJELnzp3h5+fHSL927Rq6d+8u0/pS3wca2FHllpGRASsrK0bgJRAIEBISwtrsuryysrLg7OyMkJAQRvrQoUPx77//gluFg8bv37+PPn36IC0tjZGura2NCxcuoEOHDlVWdnmdO3cOEyZMYK3pBwCamprYunUrhg4dWqtaFqlvO3jwIDw8PBhpjx49+uZakOnp6QgJCUFIYCAKcnNBhEKo5OdDLS0d8kIhuEQMMYeLIj4fWVqayFFSAofPh6KyMmzs7WFjYyOzNQwjIyMxbdo0qcMYAEBLSwvLli3D+PHjK/0lsDZ4/vw5nJyckJ6ezkifM2cOqzWuJK9evYKNjQ1j1n2DBg0QFhYGdXV1mdaX+g4QiionDw8PAoDxs23btkrnW1RURLp3787K28nJieTn58ug5iU7ffo0UVRUZJXdpEkTEhkZWaVll0daWhoZOXIkq56ff9zc3EhiYmJNV5OqALFYTGxsbBh/z06dOpUrD6FQSN6/f09CQ0PJjRs3yNHDh8mBPXuI944d5MCePeTo4cPkxo0bJDQ0lLx//54IhcIqu5dz586RJk2alPh/1c7Ojvj7+1dJ+dXN39+fKCkpse5x06ZNZc5j69atrOs9PT2rsNZUfUUDO6pczp07x/rw6datGxGLxZXKVywWkzFjxrDybtGiBUlNTZVR7aXbsmUL4XA4rLLbtGlDkpKSqrTs8rhw4QIxNDSU+pDU0NAghw4dqvTfgao5165dY/1dz507V9PVqpS8vDyyZMkSqUHP55+RI0fWiy8jvr6+hMfjMe6Nw+GQY8eOlel6kUhEunXrVu/+D1DVjwZ2VJl9+PCB6OvrMz501NTUSGxsbKXz/vvvv1kfaLq6uuTVq1cyqLl0IpGIzJo1S+rDxtXVlWRnZ1dZ2eWRnp4utZX080/v3r1JQkJCTVeTqqQff/yR8Xc1MzMjIpGopqslEzExMWTgwIEl/h9WVVUla9asIYWFhTVd1UrZu3cv697k5OTI9evXy3R9bGwsUVNTY1yvr69PUlJSqrjmVH1CAzuqzAYPHsz60PL29q50vocOHWLlq6SkRB4+fCiDWktXUFBAhg8fLvUhM27cOFJcXFxlZZfHpUuXiLGxsdR6qqurk/3799NWujqqqKiI/Pvvv2TLli3k0qVLrL/vzp07a7qKMnfjxg3SsmXLEgO8Fi1akKtXr9Z0NStl2bJlrPtSUVEhgYGBZbre29ubdf2QIUOquNZUfUIDO6pMjh07JrWlqLJBxc2bN4mcnByr++Ls2bMyqjlbRkYG6dKli9QHy+LFi2tFoJSRkUHGjh1b4gOwZ8+eJD4+vqarSVXClClTGP/nv26tzsvLq+kqVomioiKyYcMGVsvUlz/9+vUjr1+/rumqVohYLCaTJ09m3ZOenh55+fJlma7v3bs363ofH59qqD1VH9DAjvqmd+/eES0tLcaHjKamZqXHxYSFhRF1dXXWB1h5BhyXV3x8PLG2tmaVyePxZNL6KAtXr14lDRo0kPrAU1NTI3v37q0VwSdVOQYGBiUGNsOGDas33bAlSUpKIp6eniW+BoqKimThwoV1MsAVCoVSeziaNWtWpnG7iYmJRFNTk3GttrZ2rRrzS9VeNLCjSiUWi4mbmxvrA+ro0aOVyjcxMZGYmJiw8p02bZqMas4WGhoqNWBSVlYmly9frrJyyyozM5OMHz++xAfdjz/+SOLi4mq6mpSM6Orqlvi3BkAGDBjwXQTwDx48IG3atCnxdWjUqBE5depUnXstCgoKyA8//MC6n9atW5OsrKxvXn/kyBHWtW5ubnXudaCqHw3sqFLt37+f9eEycODASn24ZGdnk9atW7PydXd3r7LlF27evCm1dVBfX588efKkSsosjxs3bkgNdIGP43N27dpFP9DrCGlLjuzfvZt479hB9u/eLVlyxMXFhejo6Eidkf35JygoqKZvp1qIRCKye/duoqOjU+Jr0a1bN/L8+fOarmq5ZGZmEltbW9a9dO3alRQUFJR6rVgsljrh5MCBA9VUe6quogsUUyWKj4+HtbU1MjMzJWl6enoICwuDrq5uhfIUCoXo168fLl68yEhv3749bt68CYFAUKk6S3Ps2DF4eHigqKiIkd6iRQtcvnwZTZo0kXmZZZWTk4NZs2Zh+/btUo937doVe/fulboRO1W7pKenIzg4GKFPnzIWCVZPS4OcUAguIRBzOCjm85GppYUPAIrFYuQWFOBpaCiCg4MZ7zUul4vXr19/V3/79PR0LFy4ENu2bWNtJwgAfD4fU6dOxcKFC6GmplYDNSy/pKQkODg4sLZfHDJkCI4cOVLqousfPnyApaUlPnz4IElTV1dHWFgYGjRoUGV1puo2GthRUhFC0LNnT1y7do2RfubMGfTr16/CeU6aNIkVxDRt2hQPHjyQ+V6mhBCsW7cOXl5erGMODg7w9fWFtra2TMssj1u3bmHMmDGs7csAQFlZGWvXrsWECRPo7hG1XGJiIu77+eFNdDTk8vJgEhcPw7Rvb+v1LiMDGQIlpBsZId7EBHlycoh+8wZ+9+8jOTkZmzZtwpQpU6rxTmqP0NBQTJkyBXfu3JF6XF9fH6tWrcLIkSOrdDcaWXn58iUcHBwYARoA/P7779iwYUOp7/GzZ8/C3d2dkfbjjz/iypUr9LOBkooGdpRUO3fuxMSJExlpI0eOxMGDByuc55o1a1ibW2tpaeHBgwcwMzOrcL7SiEQizJgxA5s3b2Ydc3d3x7///gslJSWZlllWubm5+PPPP/HPP/9IPd65c2d4e3vXaEsi9W1CoRD+/v547O8PlZQUNI+NQ4OUFPDE4jJd//7DBwiFH/dUFXG5SG3QAHGmpkhVVUVjMzOMGTMGfD6/Km+hViOE4Pjx45g5cyZrL9bPOnbsiC1btsDe3r6aa1d+T548QefOnZGbm8tIX7lyJWbPnl3qtSNHjsThw4cZaTt37sQvv/wi83pSdR8N7CiW169fw8bGhvEBZGRkhLCwsArvJ3n8+HEMGTKEkaagoIAbN27AycmpUvX9Wn5+PkaMGIHTp0+zjk2ePBkbN26ssT0q7969i9GjR+P169esYwKBAKtWrcJvv/1WJ1ohvmdJSUm46OuL9LcJMI+OhmlCArjl/ChNTk6GSMxs0ePy5ZDWygbRLcyh1cAYrm5uMDAwkGXV65zc3FwsX74ca9euZQ2nAAAOh4Nx48Zh+fLl0NHRqYEalt3169fRq1cvFBcXM9L37dsHT0/PEq9LT0+HlZUVEhMTJWnKysoIDQ2lXwApFhrYUQxisRhdunTB3bt3GemXL19Gz549K5Snv78/unbtisLCQka6j48PBg8eXOG6SpOamoq+ffvC39+fdWzVqlXw8vKqke6LvLw8/PXXX9i8eTOkveWcnZ2xb98+NGvWrNrrRpVPbGwszvj4QJD4DvYREVD7YuP28khNTUVh0f/fE3JyctDW1gGXw0GWQIBACwvkGRnBfcjg72qcXUlevnyJ6dOn48KFC1KPa2hoYOnSpZgwYUKtbuk8evQohg8fzkjj8Xg4d+4cevXqVeJ1V65cwU8//cRIc3Fxwc2bN+kXQYqB/m+gGDZv3swK6saPH1/hoC4qKgpubm6soG716tUyD+piYmLg6OjICurk5OTw77//YtasWTUS1Pn5+aFVq1bYtGkTK6hTUlLCpk2bcPv2bRrU1QGxsbE4dfQoNN/EwDkoqMJBHQBoamlBTk4OHA4XiopK0NHRBffT/0+1vDw4BwVBI+YNTh09itjYWFndQp3VvHlznD9/HhcuXEDz5s1ZxzMyMjB58mTY29uzPsNqk2HDhmH9+vWMNJFIhEGDBiEgIKDE63r27Inx48cz0u7cuYMtW7ZUST2puou22FESkZGRsLW1RUFBgSStcePGCAkJgaqqarnz+/DhAzp06MDqdvz111+xdetWmQZZQUFBcHV1RVJSEiNdTU0NZ8+eRZcuXWRWVlnl5+dj3rx52LBhg9RWOkdHR+zbtw+mpqbVXjeq/JKSknDs4EFovIlBx/Dwcne9VoSYw8EDK0tkNG6CoaNGfvfdsp8VFhZi/fr1WLp0KfJKCK6HDRuGNWvWwNjYuJprVzazZ8/G6tWrGWlaWlrw8/ODhYWF1Guys7NhbW3NCPQVFRXx7NkztGjRokrrS9UdtMWOAvBxILiHhwcjqAMAb2/vCgV1+fn5cHNzYwV1vXr1wubNm2Ua1F29ehWdOnViBXXGxsbw8/OrkaDuwYMHsLW1xfr161lBnaKiItatW4c7d+7QoK6OEAqFuOjrC0HiO7R//rxagjoA4BKC9uHPofQuEZd8fSEUCqul3NpOQUEBc+bMQWRkJIYOHSr1nKNHj6JFixZYuXIlq8egNli5ciVGjRrFSEtLS0OPHj2QkJAg9RpVVVXs27ePkVZQUABPT0+py8NQ3yca2FEAgLVr1+Lhw4eMtKlTp1YoKBKJRBgxYgSrW6F169Y4duyYTMe/7N+/H71790ZOTg4j3dLSEg8ePIC1tbXMyiqLgoICzJo1C05OToiKimId79ChA549e4YZM2bU2AQOqvz8/f2R/jYB9hER4Jdx1qus8MVi2D+PQFpCAu7fv1+tZdd2DRo0wNGjR3H79m2p7/Xc3FzMmTMH1tbWuHTpUg3UsGQcDgd79uxhjZuLj49Hz549kZ6eLvW6Ll26sJbBCQgIwNq1a6usrlTdQrtiKYSGhsLe3p4xU8vU1BTPnj2r0ILBM2bMwIYNGxhpJiYmCAgIgKGhYaXrC3xcCmHp0qVYsGAB61jnzp1x5swZaGhoyKSssnr48CE8PT3x4sUL1jEFBQUsXboU06dPpwFdHZOYmIgj+/fDPDQMLUpYdqM6vGjQAJHWVvh59GiZvY/qE6FQiB07dmD+/PnIyMiQek7v3r2xcePGWjWeNTc3F127dmV9sXZycsK1a9ekLsuUl5cHW1tbREdHS9Lk5eURGBgIKyurKq8zVbvRFrvvXFFRETw8PBhBHZfLxf79+ysU1G3evJkV1Kmrq+PSpUsyexgJhUJMmDBBalA3dOhQXLlypVqDusLCQsyZMwcODg5Sg7p27dohKCgIM2fOpEFdHXTfzw8qKSkwLaF7rLqYJSRAJSUF/n5+NVqP2orP52Py5MmIiorC+PHjpQ73uHDhAlq2bIm5c+ey1pOrKcrKyrhw4QJrjJyfnx+GDx8utftdIBBg//79jNmwRUVFGDVqFGspFer7QwO779yyZcsQFBTESPPy8oKDg0O58zp79iymTZvGSJOTk8OZM2dgaWlZmWpK5Obmol+/fti9ezfrmJeXF/79918oKCjIpKyyePLkCezt7bFy5UqIv+qik5eXx4oVK+Dv71/iYGiqdktPT8eb6Gg0j42rtnF1JeESgmaxcXgTFVViNx0F6OrqYteuXXj06BHat2/POl5UVITly5fD3NwcPj4+Uic2VTcdHR1cvXoVRkZGjPSzZ89i0qRJUuvo4OCAmTNnMtKCgoKwbNmyKq0rVfvRrtjvWGBgINq3b88YdGtpaYnAwMByB0ePHj1C586dkZ+fz0g/ePAgRo4cKZP6vn//Hr1798bjx48Z6RwOp9q3XyosLMTff/+NlStXSh20bG9vjwMHDsgsoKVqxu3bt/Hs+nX09PMv844SVUnE5eKykyNa//gjXFxcaro6tZ5YLMbBgwcxe/ZsvH//Xuo5nTt3xpYtW2pFF2ZoaCicnZ0ZewYDwIIFC7B48WLW+QUFBbC3t8fz588laXw+HwEBAXViNw6qatAWu+9UQUEBRo0axQhK+Hw+Dhw4UO6g7vXr1+jduzcrqFuyZInMgrro6Gg4ODiwgjoFBQWcOHGiWoO6p0+fok2bNli2bBkrqJOTk8OyZcsQEBBAg7o6TiQSIfTpU5jExdeKoA4AeGIxGsXHIyQwkM6CLAMulwtPT09ERUVh+vTpUidu3b59G7a2tvj9999LHJtXXaytreHr68v6DF6yZAl27NjBOl9RUREHDx5kDPH4vMJBbZwJTFUPGth9pxYsWMD4lgcAc+fOLfe3vLS0NLi6urI2tx4zZgzmzZtX6XoCHyclODg44NWrV4x0TU1N/PfffxgwYIBMyvmWoqIiLFy4EO3atUNYWBjruJ2dHZ48eYK//vqrVq98X9vFxMSgZ8+eMDMzg6mpqcxn+2VkZGDXrl2S3588eQIvLy8AwKJFiyR7+KalpaEgNxeGaWmSc9fHxMAt6Cl+CnwCm/v+cAt6CregpwiogoDg4ocP6Bn4BL999T41TP1Yr7Qv6lVdPi8CrqioWOJex7WRuro61q9fj+DgYHTt2pV1XCQSYfPmzTAzM8PevXtZwyqqU6dOnXDkyBHWbhK//fYbTp06xTrf3t4ec+fOZaSFh4dj4cKFVVpPqvaigd136P79+6yHpZ2dHevD4VsKCgrQr18/REZGMtK7d++OHTt2yGStOl9fX3Tp0gUpKSmM9EaNGuH+/ftwdHSsdBllERwcjHbt2mHJkiWslhI+n4/Fixfj4cOHsLGxqZb61FeEELi7u2PMmDGIiopCYGAgTp06BR8fH5mV8XVg16ZNG6xZs4Z1XnJyMohQCI0vltKZ0bgxfO1aY7elFZoLBPC1aw1fu9bo8GmyjkiGI1tOJydjpakZtrVsyUhXz80FEQqRnJzMSJdlC15JeampqWH9+vX4448/ZFZWdWrZsiWuX7+OkydPwsTEhHX8w4cPGDduHDp06IBHjx7VQA0/6t+/P7Zt28ZII4Rg+PDhuHPnDuv8uXPnws7OjpG2Zs0aPHjwoErrSdVShPqu5OTkkObNmxMAkh95eXkSGhparnxEIhEZOnQoIx8AxMbGhmRmZsqkrtu3bydcLpdVhp2dHUlMTJRJGd9SVFREFi9eTPh8PqseAEirVq1IUFBQtdTle3Dt2jXSuXNnRtqtW7dI+/btiYeHBzl//jwhhJDs7GzSqFEjQgghL1++JE5OTsTOzo60b9+ePH/+nBBCyL59+8igQYNIt27dSLNmzcjatWsJIYQMHz6cKCkpkVatWpGlS5eSW7dukQEDBhBCCFm4cCHZsmULIYSQ/fv3E/NGjYilsgpx1NAgfu3akygnZxLl5ExutmlLrFRUSJSTMzlkZU0cNTTITzo6pI2aGnnaoSPpoK5OWiorEwtlZXLE2kZynoOGBumqpU0aKyoRTyNjEuXkTCIcnYibri5ppqREzAQCssLUlPzRqDERcLmkiZISmWpiQgLadyBdNLVIC4GAtFNTJ0t++YXcuHGDeHh4kBkzZhAXFxeyfPly4uLiQv744w/i6OhIrK2tSWBgIHF1dSXNmjWT3BchhCxdupS0adOGWFtbk+3bt0te5+7du5NBgwYRZ2fnUv9OX75OdVVubi5ZsGABUVBQkPreBkBGjx5NkpOTa6yOCxcuZNVJTU2NBAcHs84NCQkhcnJyjHNNTU1Jbm5uDdScqkk0sPvOTJkyhfVBsXLlynLn8+eff7LyMTY2JvHx8ZWuo1gsJn/99ZfUD9oePXqQrKysSpdRFiEhIcTOzk5qPXg8HlmwYAEpLCyslrp8LzZu3EimT5/OSMvIyCDq6uolBna5ubmkoKCAEEKIv78/6devHyHkY2BnYWFBcnJySHp6OtHT0yOFhYXkzZs3xN7eXpJ/SYGdlZUV2Th4MIlyciabzM3JUAODEgM7FR6P3GvbjkQ5OZNwB0fytENHEuXkTG5/dZ4Gn08C2rcnYQ6OpJGiIrndpi05bWtLWquqSfIO/HRtOzV1csGuNYlyciY/GxoSr8aNSZSTM9nQwpzYmZiQo4cPEw8PDzJo0CAiFosJIYS4uLiQBQsWEEI+Bm/Nmzcn6enpJDU1lRgYGBBCCLl8+TKZOnUqIYSQwsJC0rZtWxIfH09u3bpF1NTUSEJCwjf/TvUhsPvs9evXpF+/fiUGd+rq6mTjxo2kqKio2usmFovJhAkTWHUyNDQkb968YZ2/YsUK1rmf/9bU94N2xX5Hbt68ydowukOHDqwp89+ya9curFy5kpGmqqqKixcvokGDBpWqY1FRETw9PbF8+XLWMU9PT5w/f75CW5yVh1AoxLJly2Bvb89aCgb4OMD50aNHWLx4MeTl5au0LhS+2aVfWFiI0aNHw8rKChMnTmSMHe3atSuUlZWhoaEBIyMjVvdlSbKzsxEVFYUN16/DLegptsbFIbmwqMTzW6upQf/TgHcCYE3MG/R+GoiJEc/x6ou9TO1U1aAlJw95LhemAmUkFBaioaIi3hcVYtGrl/BLT4eqlPGZgVlZcNPVAwC46ujg9fv3KPq0/d/AgQMZr5GbmxuAj/9P27RpAw0NDWhpaUFVVRXp6em4fv06zp8/D1tbW7Rr1w4fPnyQjF91dHRkLblR3zVp0gRnzpzBlStXYGZmxjqemZmJadOmwc7ODrdu3arWunE4HGzduhXu7u6M9Hfv3qFHjx6ssc0zZ85kLfGyefPmaq83VbNoYPedyMrKwpgxYxhpSkpKOHDgQLkWzb18+TJ+++03RhqPx8OJEyfQqlWrStexV69eOHjwIOvY/Pnz4e3tDTk5uUqV8S3h4eHo2LEj5s2bx1rok8fjYe7cuXj8+DFat25dpfX4XllYWODp06eMtM+zkPl8vmRQ+5cz/jZu3IgmTZogNDQU165dYxz7cnYhj8cr8zg0QgjU1NSwtk8f+Nq1xoXW9thZyixnpS8Gup//8B55IjHO2rXGOVs7fDkMX577/wCMxwHEhECdL4fzre3RXl0d3glvseL1KwhFpe8Jy+FwIPq0cO3XC4l/vmcul8u4fy6XC5FIBEIIFi1ahGfPnuHZs2d48+aNZOmUiixKXl/06NEDoaGhWL16NVRUVFjHw8PD8cMPP2Dw4MGIi4urtnrxeDwcOXIEzs7OjPSoqCjWdoqfVzZQVFRknDtmzBhkZ2dXS32pmkcDu+/EH3/8gdjYWEbaihUrpH5DLUlQUBAGDRrEejju3LkTPXr0qFT9EhMT0alTJ9y4cYORzuVysXPnTixZskQmkzFKIhQKsXLlSrRu3RpPnjxhHW/ZsiUCAgKwdOnSal0A+XvTrVs3pKen4/jx4wA+Bvvz5s3DvHnz0KhRIzx79gwAcPr0ack1WVlZMDIyAofDwaFDh75Zhqqq6jcfcmpqalBTVUXgp90misVivPyi5a00OUIRdOTlwOdwcCU1BYXfmGGZVlwMQgh+0tHFeAMDhKSn4/379ygqKkRGRgayc3Jgq6yC8x8+rsN2JTUFzXV0wKvgzOtu3bph7969kuWJIiMjUfCp9e97Jy8vDy8vL0RFRZW4VNOJEydgbm6OpUuXVtvrpqioCF9fX9Z+uI8ePcLAgQMZX0JbtGiBFStWMM6LiYkpd88MVXfRwO47cPnyZezZs4eR5uLiUq613+Li4tCrVy/WNjxz587F2LFjK1W/58+fo2PHjggODmakCwQCnDt3Dr/88kul8v+WiIgIODo6Ys6cOSgqYna3cblc/Pnnn5JWI6pqcblcnDlzBnv27IGpqSkMDAwwbtw4dO7cGePGjcOFCxfQoUMHxMfHS66ZOHEitm/fDgcHhzK1Smhra6N169awtrYudZX+KZMm4XxEBPo8fYq+z4IQUsYWjz56uniUmYkBz4IQlJUNjW8EYMmFhfg5NAR9nj7F8jcx8NDUBPCxS7eouAjZ2VkYKlDCvffv8dPjRzgYH4+h7duDV8HWa1dXV/Tq1Qvt2rWDlZUVfv311zK3ZGZlZaFBgwZYv3495s+fj8aNG1eoDrWdoaEhDh48CD8/P9ja2rKO5+fnY/78+bC0tISvr2+17F6hoaGBK1eusGbzXr16FWPHjmUs0TJ16lTWAta7du3ClStXqryeVM2jO0/Uc+np6bCyskJiYqIkTUVFBSEhIWjSpEmZ8sjIyICTkxPCw8MZ6T///DMOHTpUqZa0e/fuwc3NjbUwqK6uLi5evIi2bdtWOO9vEYlEkgeUtMU8zc3NsX//fqnbElHV4+jRo1i+fDnu3r0LzU8BT3X577//EHn1Kro/CKi2MtPS08rUChTQrTtuvHqJ/Px8uLi4wMXFBQ4ODlK7EKnKEYlE2L17N+bOnVvi2oE9e/bEpk2bytUDUlEvXryAk5MTUlNTGeleXl5YvXq15PfXr1/DxsaG8WXc2NgYoaGh1f5eoqoXbbGr56ZOncoI6gBg3bp1ZQ7qioqKMGDAAFZQ17lzZ+zdu7dSQd3JkyfRvXt3VlDXvHlzPHjwoEqDusjISDg7O2PWrFmsoI7L5cLLywtBQUE0qKthw4YNq7EHkb6+PnKUlFBcjjGolaWmpg4F+dK7+oV8PvJVVfDu3Tv4+/tj+fLl6NGjBzQ0NNChQwfMnj0bFy9eZG1LRVUMj8fDxIkTERUVhV9//ZW1cDAAXLlyBVZWVpg9e3aVj2UzNzfHxYsXWeMh16xZg/Xr10t+b9q0KdatW8c4JyEhAb///nuV1o+qebTFrh47c+YM+vfvz0jr0aMHLl++XKaAjBACT09P1mQGCwsL+Pv7V+phu3HjRsyYMYPVhdG+fXucP38eurq6Fc67NCKRCJs2bcLcuXOltoyYmZlh//796NixY5WUT9UdHz58wP4dO+AU8BA6WVnVWrZYLEZhURGKiopQVFiIYqEQHztngUwdHTxycsLOAwdYC3d/icvlwtbWVtKi5+zsDC0trTKVHxoayhpj1rx5c5w8ebLC91RfBAUFYcqUKfD395d63MjICKtXr8bw4cOrdFzw5cuX0adPH1Y3+uHDh/Hzzz8D+PgZ3rNnT1y7do1xzpkzZ9CvX78qqxtVs2hgV099+PABlpaWjOnw6urqCAsLK/OSJIsWLWJtPK2vr4+AgIAKj60Ri8WYOXMmNmzYwDrWp08fHDt2rMpm5kVHR2P06NFSP5A5HA6mT5+OpUuXQklJqUrKp+oWkUiEbZs2wTjoGaxjYmq0LmJCUFRUiMLCIkQ0b46nhgbYtG1bucZ2cTgcWFtbSwK9Tp06VdkXqPqOEIIjR47Ay8sL7969k3qOs7MztmzZUunVAkpz4MABeHp6MtL4fD4uXryIH3/8EQAQHx8Pa2trRguunp4ewsPDoaOjU2V1o2oODezqIUIIBg0axNpX8MCBAxg1alSZ8ti/fz9Gjx7NSBMIBLhz506FJxEUFBTAw8NDMuPxSxMmTMA///xTJXusisVibNmyBXPmzJHMBPxS8+bNsW/fPjg5Ocm8bKpuu337Np5dv46efv7g1eD+oZ+JuFxcdnKEpYsLRCIR7t69izt37uDx48cQCktfIkWali1bSgI9FxcXGBgYVEGt66/s7GwsXboUGzZsYC2PBHxsNZ04cSL+/vvvMreWltfq1asxe/ZsRpqysjJu3bolGc4iLQAcOHAgjh8/XqWtilTNoIFdPXT06FEMHz6ckda3b1+cOXOmTG/iGzdu4KeffmI8KLhcLs6ePYs+ffpUqE7p6eno168f7t69yzq2fPly/Pnnn1XyAfPq1SuMGTNGarkcDgdTp07F8uXLv+v1u6iSpaenY8+2bbB7GoRG79/XdHUQo6eHZ63tMO633xhDIXJzc/HgwQPcuXMHd+7cwcOHD1kzvMvCzMyMEehVdsHx70VkZCSmTZtW4qxTbW1tLFu2DOPGjSvXuqFlQQjBjBkzsHHjRka6jo4O7t+/D1NTUxBC0K9fP/j6+jLOOXr0KIYOHSrT+lA1jwZ29UxiYiKsrKyQnp4uSdPW1kZ4eDj09fW/eX1oaCicnJyQ9dWYoq1bt7IWJi6ruLg4/PTTT4wdAYCPXQbe3t4lrhdVGWKxGNu3b8esWbOQJ2X9saZNm2Lfvn3o1KmTzMum6peTx48jJSAAXZ4EgluDH5diDge32thDp2NHDBw0qNRz8/Pz8fDhQ0mg9+DBgwqtuda0aVNGoFdflzeRBUIIzp8/j2nTpuHNmzdSz2ndujW2bNkCBwcHmZYtFosxYsQIHD16lJHepEkT+Pv7w9DQEElJSbC0tGTM7NXS0kJYWBgMDQ1lWh+qZtHArh4hhKBPnz64ePEiI/348eMY9I0HAfAxKPx6jTDg4zY1a9asqVCdgoOD4erqypqZq6qqilOnTqF79+4Vyrc0b968wZgxY3D79m2pxydPnoyVK1dCWVlZ5mVT9c+7d+/w7759MA8NQ4u3b2usHi8aNECktRV+Hj263A/iwsJCPH78WBLo+fv7S/3C8y0mJibo1KmTJNBr3rw57cr7SkFBAdasWYMVK1ZIHfoBACNHjsSqVatkGlAVFRWhV69erEXeW7VqhTt37kBdXR3Hjx/HkCFDGMd79+4NX19f+nesR2hgV494e3uzFgseMmQIjh079s1rs7Oz0alTJ8nK/p8NGjQIx44dkzrF/1tu3LiB/v37s6b/Gxoa4tKlS1IX/qwMsViMnTt3wsvLi7WQMgA0btwY3t7e6NKli0zLpeq/O3fu4PF/N9Hl4UOoVSAgqqxMgQC3O7RHu65dZdLKXFxcjMDAQEmg5+fnV6FlOoyMjBiBnrm5OQ0QPomNjcXMmTNLnEmsqqqKBQsWYOrUqTLbczo7OxtdunRBYGAgI71z5864cuUKFBQUMHToUPj4+DCO79u3jzUGj6q7aGBXT8TGxsLa2prx4ayvr4/w8HBoa2uXeq1QKESfPn1Y40McHBzw33//sfYdLIvDhw9j9OjRrAHdFhYWuHz5Mho1alTuPEsTGxuLsWPH4r///pN6/Ndffy1xD0iK+hahUIgD3t4QPY+Ac1AQ+NU4kULI5eJuazvIWVhg1JgxVTLBSCgU4tmzZ5JA7969e6z1JctCT0+PEehZWlpW6EthffLff/9h6tSprKEon5mbm2Pz5s0y6714//49HB0d8fLlS0b6wIEDcezYMWRkZMDS0hLJycmSY2pqaggLC0PDhg1lUgeqZtHArh4Qi8X48ccfWUGNr6/vNyc7EEIwceJE7Nq1i5FuamqK+/fvl3s6PCEEq1atwpw5c1jHnJ2dcfbsWZnODiOEYM+ePZgxYwZjM+zPTExM4O3tja5du8qsTOr7lJSUhGMHD0Ej5g06hoVXy3g7MYeDB1aWyGjcBENHjay2WatisRihoaGSQO/u3bulrplXEm1tbTg7O0sCPRsbG5lPHqgLiouLsXXrVixcuJA1fvkzd3d3rF+/XibjGF+/fg0HBwdG8AYAkyZNwpYtW3D+/Hn07duXcaxbt264du0abXGtB2hgVw9s27YNkyZNYqR5enpi375937x25cqVrCBMR0cHDx48QPPmzctVD5FIhClTpmD79u2sY4MGDcLBgwcr1PpXkvj4eIwbN461+OZnv/zyC9asWQM1NTWZlUl932JjY3Hq6FFoxcWhffjzKm25E3K5eGjZEmkmJhgwbJjMW7nLQywWIyIiQhLo3blzhxU0lIW6ujoj0LOzs6uSFsjaKjk5GXPmzCnxs1lRURGzZ8/G7NmzK72eZlBQEFxcXFhd7EuXLsXcuXPh6emJAwcOMI5t27YNv/76a6XKpWoeDezquJcvX6JVq1aMgdANGjRAWFgY1NXVS71W2rIoioqKuHnzZrl3XsjLy8Pw4cNx7tw51rFp06Zh3bp1MuuSIYTA29sbM2bMkPrtt2HDhtizZ49kgU6KkqXY2Fic8TkOQWIi7CMiqmTMXaZAgMCWFsg3NIL7kME1GtRJQwhBVFQUI9BLSEgodz6qqqpwdHSUdN+2adNGZuPNarOHDx9i8uTJePLkidTjjRo1wvr16+Hu7l6pFrSbN2/ip59+Yi19s3v3bgwcOBDW1tZ4+8WEIGVlZQQHB6NZs2YVLpOqeTSwq8NEIhE6d+4MPz8/RvrVq1e/GdTcvXsX3bt3Z7zhORwOTpw4gQEDBpSrHikpKejTpw8CAtibpa9fvx7Tp08vV36lefv2LcaPH1/ielFjx47FunXrvhnUUlRlJCUl4aKvL9LfJsA8OhqmCQky6ZoVcziIMjZGpJkptIyN4ermVicWDSaE4PXr14xALzY2ttz5CAQCdOzYUdKi165dO5m28tcmYrEY3t7emDNnTond3N26dcPmzZthYWFR4XKOHz+OoUOHMnYp4XK5OHPmDBQVFdGjRw/G+c7Ozrh9+/Z3PzayLqOBXR22bt06zJw5k5E2ceJEqV2hX3rx4gUcHBwYa919zm/GjBnlqsOrV6/w008/ITo6mpEuLy+PQ4cOYfDgweXKrySEEBw8eBC///671M3NjYyMsGfPHvz0008yKY+ivkUoFMLf3x+P/f2hkpKCZrFxaJiSUqEdKkRcLuJ1dPCqkQlydHTQzskJDg4OdbqbMjY2VrIzxp07d1iD+ctCQUEBHTp0kAR6HTp0qHeLiaenp2PhwoXYtm0ba99X4ON6n1OnTsXChQsrPKxky5YtmDp1KiNNUVERN27cwOHDh7Fjxw7GMVl/IaeqFw3s6qiIiAjY2dmhsLBQktakSROEhISUOvMzOTkZHTp0QMxXe19OnjwZmzdvLlez/+PHj9G7d2+8/2pFfg0NDZw7d05mi/8mJibil19+Ya3P95mnpyc2bNgADQ0NmZRHUeWRmJiI+/7+eBMVBX5eHhrFx8MwNQ3qubmQk/Kg/qyYx0OmsjLeaWshtmFDCAUCNDEzg6OTU71cMDYhIYER6L148aLcecjJyaFdu3aSQM/BwaHezHQPDQ3FlClTcOfOHanH9fX1sWrVKowcObJCrWlz587F8uXLGWmampq4evUqhgwZwlhUWUFBAc+ePYO5uXm5y6FqHg3s6iChUAgHBwc8fvxYksbhcHD79u1Sg6nc3Fx06dKFcR0AuLm54fTp0+WarXbp0iUMGjSItchpw4YNcfnyZVhaWpY5r5IQQnD48GFMnTpV6tILhoaG2L17N3r16lXpsiiqstLT0xESEoKQwEAU5OaCCIVQyc+HWlo65IVCcIkYYg4XRXw+srQ0kaOkBA6fD0VlZdjY28PGxoaxTVh9l5yczAj0wsLCyp0Hj8dDmzZt4OLigk6dOsHJyalOD8MghOD48eOYOXMmY+zblzp27IgtW7bA3t6+3HmPGzcO3t7ejPQGDRpg/fr1GDJkCKO7tl27dvD396/TrcbfKxrY1UHLli3DvHnzGGnTp0/H+vXrS7xGJBKhf//+rL0C27Zti1u3bpVrF4a9e/diwoQJrG4DGxsbXL58GUZGRmXOqyRJSUmYMGECq76fjRw5Eps2bfquHoRU3SASiZCWlobk5GQkJyfjQ1ISigoKIBIKwePzIa+oCF0DA+jr60NfXx9aWlrf5RIgX0tJScG9e/ckgV5wcDDK+3jicrmwtbWVtOg5OzvLdHml6pKbm4vly5dj7dq1Uvf85XA4GD9+PJYtW1auJamEQiHc3d1x4cIFRrqFhQVcXFxYXbLLli3DX3/9VbGboGoOoeqUZ8+eETk5OQJA8tOiRQuSl5dX4jVisZhMmTKFcQ0A0qRJE5KUlFTmssViMVmwYAErHwCka9euJDMzs9L3JxaLyZEjR4iWlpbUcvT19cnZs2crXQ5FUbVbWloa8fX1JX/88Qdp06YN4XK5Uj8TSvvhcDjExsaGTJkyhZw8eZK8f/++pm+rXKKjo0nv3r1LvD9NTU3yzz//kOLi4jLnmZubSzp27MjKq3379sTU1JSRJicnR4KDg6vwDqmqQAO7OqSwsJDY2Ngw3nhcLpcEBASUet369eulfiBERESUueyioiIyevRoqR8uI0aMIIWFhZW9PZKUlETc3d1L/BAbPnw4SUlJqXQ5FEXVPZmZmeTSpUvkzz//JB07diR8Pr/cgR4A0rJlS/Lrr7+SY8eOkXfv3tX0bZXJxYsXSfPmzUu8JxsbG3Lnzp0y55eamkosLCxY+Tg6OhIOh8NIa9WqlUw+36nqQwO7OmTu3LmsN+KcOXNKvebkyZOsN6q8vHy5PgSys7NJz549pX6g/Pnnn0QsFlf21oiPjw/R1taWWoaenh45ffp0pcugKKr+yMnJIdevXyfz5s0jzs7ORF5evkKBnpmZGRk/fjw5fPgwiY+Pr+nbKlFBQQFZsWIFUVZWLvFehg0bRt6+fVum/OLi4oixsbHUIPHrtPnz51fx3VGyRAO7OuLhw4eEx+Mx3mzW1takoKCgxGvu379PFBUVWW/SI0eOlLncd+/ekdatW7Py4HK5ZOvWrZW+r/fv35NBgwaV+EE1ZMgQ8uHDh0qXQ1FU/ZaXl0du3bpFFi1aRLp06SL1s68sP02bNiWjR48m+/fvJ2/evKnp22KJj48nw4YNK7H+ysrKZMWKFaU+Gz4LCwsjGhoarDx0dXUZv/N4PBIQEEBOnDhB5syZQx4/flwNd0pVFA3s6oC8vDxibm7OeKPx+XwSFBRU4jXR0dFER0eH9YZdvnx5mct98eIFady4MSsPJSUlmYxzO3nyJOsD5POPjo4OOX78eKXLoCjq+1RQUEDu3btHli5dSrp3704EAkGFAj0TExMyYsQIsnv3bhIVFSWTHgpZuH37NrG2ti6x3qampuTixYvfzMfPz09qEPz1mMYvXz85OblyDeWhqhedFVsHzJw5E+vWrWOkLVmyBPPnz5d6fkpKChwcHFiLBo8fPx47d+4s01p19+/fR58+fZCWlsZI19bWxoULF9ChQ4dy3gWzfpMnT4aPj4/U4wMGDMC2bdugp6dX4TIoiqK+VFxcjMDAQMmsWz8/P9Y+qmVhZGQk2QLNxcUF5ubmldr2qzKEQiF27NiB+fPnS10SCgB69+6NjRs3lrpNmK+vL9zd3SH+YnFtDodT6qzkL2fMSpsJXpifD7FIBC6PBwUlJToTvBrRwK6Wu3fvHlxcXBhvMHt7ezx48ABycnKs8wsKCtCtWzf4+/sz0nv27Inz58+XaU2iM2fOYPjw4SgoKGCkN2nSBFeuXIGZmVkF7+Zj3hMnTmQtagwAWlpa2LZtGwYPHlxjH5QURX0fhEIhgoODJYHevXv3WLvxlIWenh4j0LO0tKz27bg+fPiAuXPnYs+ePVKDMXl5ecycORN//fVXiUtb7dmzB+PHjy9zmWPGjMHatWsRHByM0KdPGWs3qqelQU4oBJcQiDkcFPP5yNTSYqzdaN26NVq1akWXrKoCNLCrxXJyctCqVSu8fv1akqagoIDAwECpCwCLxWIMHToUJ06cYKTb2tri7t27UFVV/WaZ//zzD6ZOncr6cGjTpg0uXLgAfX39Ct1LWloapk6din///Vfq8X79+mH79u11Yl9MiqLqH7FYjNDQUEmgd/fu3RL3cC2NtrY2nJ2dJYGejY1NtbVOPXnyBFOmTJG6bzfwcTHitWvXlvjleenSpSX2BH3JwMAA/fv1Q+MGDSCXlweTuHgYppVjtxUtLcSZNESxQIAmpqZwdHaul7ut1BQa2NVikyZNwrZt2xhpq1evhpeXl9Tzvby8sHbtWkZagwYN8PDhw28uGiwWizFnzhysXr2adczV1RU+Pj4V3rrH19cXEyZMQFJSEuuYpqYm/vnnHwwbNoy20lEUVWuIxWJERERIAr07d+4gOTm53Pmoq6szAj07O7sq3c1BLBbj0KFDmD17don17dy5M7Zs2QIrKytGOiEEnp6eOHjwoNTreDweHBwc4Ni2LXRzc2HzIQUNKrE/8lsdHbz8tD9yW0dHODo60p0uZIAGdrXUjRs30L17d0aag4MD7t69K/Xb37Zt2zBp0iRGmpqaGvz8/GBtbV1qWYWFhRgzZgyOHDnCOjZu3Dhs3769Qm+29PR0/P777zh06JDU43369MHOnTvpNzWKomo9QgiioqIYgV5CQkK581FVVYWjo6Mk0GvTpo3UYTWVlZmZiSVLlmDz5s0QCoWs4zweD5MmTcLixYsl+2y/f/8e1tbWUofK6Onpwa1XLxhrasL0xQsYRUXD2MAAlf06LuZwEG1sjBemptBqYAxXNzfac1NJNLCrhTIzM2FtbY34+HhJmpKSEoKDg2Fqaso6//z58+jXrx9j4Cufz8fly5fRrVu3UsvKyMhA//79cevWLdaxxYsXY/78+RVqSbt48SJ++eUXJCYmso5paGhg8+bNGDFiBG2loyiqTiKE4PXr14z9bmNiYsqdj0AggIODgyTQa9euHRQUFGRWz4iICEydOhU3btyQelxXVxcrVqzAzz//DFtbW0RGRrLOMTExweB+/WCYlweLwEAIsrIAAHq6ejJrYcsSCBBoYYE8IyO4DxmMRo0aySTf7xEN7GqhsWPHsjZq3rJlCyZPnsw698mTJ3BxcUFeXh4jff/+/fDw8Ci1nLdv3+Knn35ibb7N4/Gwe/dujB49utx1z8jIwIwZM7Bv3z6px11dXbFr1y4YGxuXO2+KoqjaLC4ujtGi9/Lly3LnoaCggA4dOkgCvY4dO0JJSalS9SKE4PTp05gxYwbi4uKkntOgQQO8ffuWlW5iYoKh/fvDJCUVFo8egvfFGDoNDU0IKlm3Lwm5XDy0bIk0ExMMGDaMBncVRAO7WubChQvo06cPI61Lly64ceMGa6ZVTEwMOnTowBpHsXDhQixatKjUcsLCwvDTTz+x3sjKyso4efIkevbsWe66X7lyBePGjZPaPaGmpoZNmzbBw8ODttJRFPVdSEhIYLTovXjxotx5yMnJoV27dpJAz8HBocLjnfPy8rBq1SqsWrUKhYWF3zxfT08Po4YOReP0DLR8cB/cr8IFfT19mU8MEXM4eGBliYzGTTB01EjaLVsBNLCrRVJTU2FlZcWYZKCqqoqQkBA0btyYcW56ejocHR0RERHBSPfw8MC+fftKDZ5u3boFd3d3ZGZmMtL19fVx8eJF2Nvbl6vemZmZ+OOPP7B3716px3v27Indu3ejQYMG5cqXoiiqPklOTmYEel/3lpQFn8+Hvb29JNBzcnKCmppaufJ48+YNZsyYgbNnz5Z4Do/Hw+hRo2DB48H27l1GSx2Hw4W6urpMW+u+JORycbe1HeQsLDBqzBg6oaKcaGBXiwwfPhxHjx5lpO3evRvjxo1jpBUWFqJnz564ffs2I/2HH37A5cuXIS8vX2IZx44dg4eHB4qKihjpLVq0wOXLl9GkSZNy1fn69esYO3YsYzzgZ6qqqtiwYQPGjBlDW+koiqK+kpKSgnv37kkCveDg4FIXBZaGy+XCzs5OEug5OzuXeW24a9euYfz48VK7Z52dnfFD27Zof+sWNAuLPrXwEXA5XOjp64NbxZ/pmQIBbndoj3Zdu6JTp05VWlZ9QwO7WuLkyZMYNGgQI+2nn37CxYsXGUERIQSjRo3C4cOHGedaWlrCz89PMrvpa4QQrFu3TupSKQ4ODvD19YW2tnaZ65udnQ0vLy/s3LlT6vFu3bph7969MDExKXOeFEVR37OMjAz4+flJAr2nT59CVMq6cNJwOBzY2NhIAr1OnTpBR0enxPOtrKwQHh7OSDMwMIDn8OGwevECDSIjwePxoaerC5FYDH417hjxokEDRFpb4efRo+nqCeVAA7ta4P3797C0tGQshqmhoYHw8HDW+nPz58/H0qVLGWmGhoYICAgoMYgSiUSYPn06tmzZwjrm7u6Of//9t1yDc//77z+MHTsWsbGxrGMqKipYt24dxo8fT1vpKIqiKiE7Oxv+/v6SQO/x48dSly75FktLS8buGJ/HrQUGBqJNmzas8wf2748OOjpoffOmZFydtrYOFErpDaoKYg4Ht9rYQ6djRwz8quGDKhntuK5hhBBMmDCBtcL5P//8wwrqvL29WUGdsrIyLl68WGJQl5+fjxEjRuD06dOsY5MnT8bGjRvLPPg1JycHs2fPZi2a/NkPP/yAvXv3ssYDUhRFUeWnqqqKnj17Siaz5ebm4sGDB5JA7+HDh6xhNdKEh4cjPDwc27dvBwCYmZnBxcUF7969Y52rrq4O0yZNYPL0KWOyRE18TecSgmaxcXimrY309HS6/VgZ0Ra7Gnb48GGMHDmSkda/f3+cPHmS0eJ17do1uLq6MprleTwefH194erqKjXv1NRU9O3bl7VvLACsWrUKXl5eZW5Vu337NsaMGYM3b96wjikrK2P16tWYOHFite+RSFEU9b3Kz8/Hw4cPJYHegwcPWHt8l1enTp3Q3dYWDpevgCf++LxRUFCAtlbZh+rIkojLxWUnR7T+8Ue4uLjUSB3qGhrY1aCEhARYWloyZqfq6OggPDwcenp6krTg4GA4OzsjOzubcf2OHTswYcIEqXnHxMSgZ8+erMUm5eTksH//fgwfPrxMdczNzcWcOXOkduMCgIuLC7y9vdG0adMy5UdRFEVVjcLCQjx58kQS6Pn7+yM3N7fM13M4HPz+22+wTUhA49AwqKmpQVlZuUZa674U2qQxEmxt8dvvv1fbvrt1Ge2KrSGEEIwbN4615MjOnTsZQd3bt2/Rq1cvVlA3e/bsEoO6oKAguLq6svZmVVNTw9mzZ9GlS5cy1fHevXsYPXo0Xr16xTomEAiwatUq/Pbbb7SVjqIoqhZQUFCA46c9V//66y8UFxfj6dOnkkDPz88PWZ92jZBGW1sbyoqK0ExMBECQlZUJOTm5ah9b9zXD1DS8ys1FWloadHV1a7QudQF9IteQPXv24MqVK4y04cOHo3///pLfs7Ky0KtXL9aCv0OHDsXy5cul5nv16lV06tSJFdQZGxvDz8+vTEFdXl4epk+fDhcXF6lBnZOTE4KDgzF58mQa1FEURZVBfHw8+vXrh2bNmqFly5b4+eefkZ6eLvXcRYsW4Z9//ql0mXJycmjfvj1mzZqFixcvIi0tDU+ePMG6devQvHlz1vna2trgczhQyciQpOXn55eprC6PHyH3i6FChxITsVnKBLsvnU5ORuoXYwRHhISgR+ATuAU9hVvQUyx//fH5o56bCyIUshbjr4zGjRsjJyenUnnExMTA0dERioqKMvl7yQptsasBMTExmDFjBiPN0NCQ0d1ZXFyMgQMHIiQkhHGes7Mz9u3bJzWg2r9/P8aNG8eaHm9paYnLly+jYcOG36zb/fv34enpiejoaNYxJSUlLF++HFOnTqUBHUVRVBkRQuDu7o6pU6dKFgW+du1atU8I4PF4sLe3h729PXbv3s06npqaCqXsbMZixFW1CDHwMbCzUlGB9hctglvMLWCmrMw4T04kgkp+PpKTk2FlZVVl9SmJSCSS2gWspqaG9evXw9fXt9rrVBr6dK5mYrEYo0ePZn1T2LNnD7S0tAB8/BCYOHEirl+/zjinRYsWOHv2LBQVFRnphBD8/fffGD16NCuo69y5M/z8/L4Z1OXn52PmzJlwcnKSGtQ5ODjg2bNnmDZtGg3qKIqiyuG///6DqqoqRo0aJUn78ccfYWhoiJEjR8La2hrt2rXDs2fPWNd27txZskNFWFgYOnfuDOBjq96YMWPQvXt3NGnSBFeuXMGvv/4KCwsLjBgxQnK9jo4OZs6cCWtra3Tt2lUy5k7aosQF+fkoSkhASEEhvN4lYXFqKnqHhGD569eSc26npWFQ8DO4BT3FvOhoiMswTD+uIB8jQkLQ52kgJj4PR0ZxMa6lpCAsJxtTXkRgoJT7/tKIkBBcunIVv4wfz1h3LysrCz///DNsbGxga2uL+/fvgxCCadOmwcrKCra2trhx4waAj+PF3d3dYWtri19++YWxEPSyZcvQtm1b2NjYYMeOHR/v8/Zt/Pjjjxg8eHCJPV1aWlpo37495OTkvvkaVCfaYlfNtm7dytoxYuzYsYyZrcuWLYO3tzfjHF1dXVy6dEkS/H0mFArx22+/Sf32NXToUOzfvx8KCgql1ikgIACenp6siRYAoKioiGXLluF3OmiVoiiqQp4/fw5bW1tW+tatW6GqqorQ0FAEBATAw8MDwcHBZc43NjYW165dQ0BAAHr06IGbN29i27ZtcHBwQFBQEOzs7JCamgpXV1esXbsWo0aNwunTp9G3b1/Iy8vjhx9+QHh4OMzNzdGxY0ec8PFBVm4ummhqIjo5CVesraDC46PX00B4GhlBkcfDvoQEHLa2gQKXi8WvXuJSygf01v04Lnxo8DPJSgvpxcUYpP9xvbylr15juKEhXHV1settPLbExWF+s2Y4mJiIBc2aMVropryIgMKnxgN3PX2MNjYGAChyOFg4dy6EHA7Wr1+PvXv3YsmSJWjcuDH+/fdfiEQi5Obm4tSpU3j58iVCQkIQFxeHzp0748WLF9i6dSsaNmyIM2fO4NKlS5Jn5pUrV/D+/Xs8fvwYRUVFcHJyQu/evQEADx8+REREBGvpsdqOBnbVKCoqCrNnz2akmZiYYP369ZLfDx8+jPnz5zPOUVJSwoULF1gzT3NzczFkyBBcvHiRVZaXlxdWrlxZautaQUEBFi5ciLVr10IsFrOOt2/fHvv374e5uXmZ7o+iKIpiI4RIXVrKz88Ps2bNAgB06NAB+fn5rAl1pXF1dQWPx4O1tTVUVVXRrl07AIC1tTViYmJgZ2cHFRUV/PDDDwAAe3t7xMTEgBACLpeL8+fPM/I7c+qUpBvPTlUNWnIfu0hNBcpIKCxEtlCIyLxcDAp+BgAoFIuhL///hoNjrWyh/KkB4FBiItKLiwEAoTnZ2NmyJQCgr64efnnO3OniS9K6YgGgbcMGEAmFsO/QAf/++y8A4ObNm5JuUB6PBzU1Nfj5+WH48OHgcrlo3LgxzMzMEBkZifv370tea1dXV0kX+PXr13H+/HncuXMHwMe9zz+PLXd0dKxzQR1AA7tqIxKJ4OnpyRqI6u3tLdnA+datWxgzZgzjOIfDwdGjRyVv2M/ev3+P3r174/Hjx6zzN23ahClTppRan8ePH8PDwwMRERGsYwoKCliyZAn++OMP2kpHURRVSS1btpSMrSuNtACQz+dLvnh/3K/1/z73xnC5XEbPDJfLlQzL+TKdx+NBJBJBXV0dioqKiImJkSwoLxaL8TYhAUZNmwIfUiDP/X89eBxATAgIgC6aWlhhZlbmeweYixsTVGyxYx6PDx6fL7mHsvr8mpYUXBNCsGjRIkY3OfCxK1YgEFSgpjWPDpaqJuvWrcODBw8YaZMmTULXrl0BfGyqd3d3R/Gnbzifbdy4EX379mWkRUdHo2PHjqygTkFBASdOnCg1qCssLMRff/2FDh06SA3q2rZti6dPn2LWrFk0qKMoipKBbt26ITMzk7HH9/nz5+Hg4IAjR44AAB49egRlZWXJF/3PGjVqJBl7J20HoYqaPn06pk+fLtm5Yt26dWjYsCF0vhru8yVbVVU8zMzAu08BZnpxMZK+CjalsVJRxdXUj7srXfjwAW3U1AEAyjweYyZtaYp5PMh/Nb68W7dukt00RCIRsrKy4OTkhGPHjkEsFiM2NhYvX76EmZkZHB0d4ePjA+Bj9+vnGcmf9zX/3OgSGRlZ6UWeaxptsasG4eHhrO7VZs2aYdWqVQCApKQkuLq6sprgp02bhqlTpzLSHj58iN69e7O2INPU1MT58+fh6OhYYj2ePHkCT09P1obPACAvL4/Fixdj5syZ4PPpfwuKoihZ4XA4OHv2LCZPnoxFixZBQUEBrVu3xurVq+Hl5QUbGxsoKipi3759rGtnzJiBIUOGYNeuXejYsaPM6jR9+nSkpKTAwsICHA4Htra2mDtvHpICAqBWwjXa8vJY1Lw5fnv+HEIiBp/DxVJTUxh8Yxz3vGZNMScqClvj4mCkoIhVn1r8+uvr48/oKKjy+Dj5aQzil2PsWgiUsaZFCwBArroadD/tcfvZ/PnzMXHiRFhbW4PH42H79u3o378//Pz8YGNjAz6fj927d0NRURGTJk3CiBEjYGtrCxcXF8k2nK6urggLC0O7du1ACIGenh6ri7okWVlZaNmyJbKyssDj8bB27VrExMSU6dqqRHeeqGLFxcXo0KEDnj59KknjcDi4d+8eHB0dkZOTg86dOyMwMJBxnbu7O06cOMFoNfP19cXQoUNZ3bmNGjXClStXShwLV1RUhL///hsrVqyQ2oRtb2+P/fv318g0coqiKKp2CAsLw6UTJ9D7zl3IlaO7s6oV83i44NIJroMG0edUGdCu2Cq2YsUKRlAHAH/88QccHR0hFAoxbNgwVlDXvn17HD58mBHU7dixA+7u7qygzs7ODgEBASUGdUFBQWjbti2WLl3KCurk5OTw999/48GDB/TNQlEU9Z3T19cHh89HppTJCzUpU1kZHD4f+vr6NV2VOoH2uVWhp0+f4u+//2akWVhY4O+//wYhBFOnTsWFCxcYx5s2bQpfX1/JoE1CCObNmyd1p4kePXrgxIkTUFVVZR0rKirC8uXLsWzZMgiFQtZxW1tbHDhwADY2NpW5RYqiKKqe0NLSgqKyMt5paUGnlK3Hqts77Y/1+nq5r+oSGhqKkSNHMtKaN2+OkydP1kh9voUGdlWksLAQHh4ejKCKx+PhwIEDUFRUxJo1aySDPj/T0tLC5cuXJXvFFhUVYdy4cTh06BArf09PT+zatUvqwojBwcHw9PSUutgln8/H/PnzMWfOnFq3qCJFURRVc3g8Hqxbt8az1FS0jIsDT8oyWNVNxOUitmFDtLa3r7EJfdbW1lKfp7UV7YqtIosWLZKsFv7ZnDlz0LZtWxw/flyyns5nCgoKOHfuHMw+DSr9vE+stKBu/vz58Pb2ZgVmxcXF+Pvvv9G2bVup/wltbGzw+PFjLFiwgAZ1FEVRFEurVq1QLBDgrY6OzPMmAHLz8pCZlcVaAaIk8To6EAoEtHepHGiLXRUICAjA6tWrGWmtWrXC/Pnz4e/vz1ovBwAOHToEJycnAEBiYiJcXV1ZK5B/nvUzfvx41vVhYWHw8PBgjef7fN1ff/2FefPmQf6LPfkoiqIo6kuamppoYmqKl6mpaPjhA7gynF+Zm5ODrOyPXbx5ubnQ1tGBfCmNDGIOB68amaCJmVm17qlb19EWOxnLy8uDh4cHYycHOTk5HDx4EDExMXBzc2MtMrl69WoMGjQIwMf17Dp27MgK6gQCAc6dO8cK6oRCIZYvX47WrVtLDeqsrKzw8OFDLFmyhAZ1FEVR1Dc5OjsjR0cH0Z+285KVz2vmAQABQUZGOkpbmCPK2Bg5Ojpw/NToQZUNbbGTsblz5yIqKoqRtmjRIhgaGqJDhw5IS0tjHPv1118xc+ZMAMC9e/fg5uaGjIwMxjm6urq4ePEi2rZty0h//vw5PDw88OTJE1Y9uFwu/vzzTyxYsOCbe8VSFEVR1GeGhoZo6+iIxwWFMExLg1penkzyVVBUREHh/xf/FQqFyMrOhroae+W8TIEAkWamaOfkBENDQ5mU/72gLXYydOfOHWzcuJGR1q5dO0yZMgVubm54/fo141ivXr2wefNmcDgcnDhxAt27d2cFdc2bN8eDBw8YQZ1QKMSqVatgZ2cnNahr2bIlAgICsGzZMhrUURRFUeXm6OgIzQbGCLSwgLCUPcfLQyAQQE6O2XOUm5uLwi9a8gBAyOUisKUFtIyN4eDgIJOyvyc0sJOR7OxsjB49mpGmqKgIb29veHp6IiAggHGsdevWOHbsGPh8PjZu3IghQ4awumjbt2+P+/fvo1mzZpK0Fy9ewMnJCX/++SejWRv42Eo3e/ZsBAYGslr3KIqiKKqs+Hw+erm5Ic/ICA8tW0IsZZ/V8uIA0NTQAOer3WMzMjIg/tQlK+Zw8NCyJfINjeDq5kZ3QqoAGtjJiJeXF968ecNIW7ZsGfbu3cva38/ExAQXLlyAQCDAjBkzMH36dNY4gz59+uDmzZvQ1dUF8HEfvLVr18LW1hYPHz5kld+iRQvcv38fK1euhOJX++lRFEVRVHkZGBjAfchgpJmY4IGVpUxa7vh8PlS/6noViYTIysqCkMvFAytLpJmYwH3IYBh8tYUYVTY0sJOBq1evYufOnYw0Z2dn8Hg8bNiwgZGurq6OS5cuQVNTE8OGDWMdB4CJEyfi9OnTkkWKo6Ki4OzsDC8vL1arHofDwcyZMxEUFIT27dvL+M4oiqKo71mjRo0wYNgwZDRugnt2dsj69FyqDGVlZcjLM4cJfeDzcKuVDTIaN8GAYcPQqFGjSpfzvaJ7xVZSRkYGrKyskJCQIEkTCARYt24dfvvtN0ZLnJycHK5evQpbW1v069cPd+/eZeW3fPly/Pnnn+BwOBCJRNi8eTP++usvFBQUsM41NTXF/v376RgEiqIoqkolJSXhoq8v0t8mwDw6GqYJCZVaCkUoEuHDh/cQAUg0M0O0uTk+5ORgupcXmjdvLruKf4do53UlTZs2jRHUAcDkyZMxY8YMVvfq3r170axZMzg5OeH58+eMY3w+H97e3pJtS16+fInRo0fDz8+PVSaHw8G0adOwdOlSSaseRVEURVUVAwMDeIwZA39/fzxWVMBbQwM0i41Dw5SUCu1QwZGTQ5a5OSKNjJCiogL/x49x//595BUVYd++fVVwB98P2mJXCb6+vujbty8jzdHREdHR0Xj//j0jfcmSJXBzc8NPP/2Ed+/eMY6pqqri1KlT6N69O8RiMbZu3YrZs2cjPz+fVWazZs2wf/9+yWLGFEVRFFWdEhMTcd/fH2+iosDPy0Oj+HgYpqZBPTcXciJRidcV83jIVFbGO20txDZsCKFAgNiEBJw6cwZJSUmS83x9fdGnT5/quJV6iQZ2FZSSkgIrKyskJydL0lRVVaGnp4dXr14xzh0zZgyGDh2KAQMGIDs7m3HM0NAQly5dgq2tLV6/fo0xY8bgzp07UsucOnUqli9fDmVlZdnfEEVRFEWVQ3p6OkJCQhASGIiC3FwQoRAq+flQS0uHvFAILhFDzOGiiM9HlpYmcpSUwOHzoaisDBt7e9jY2CA7OxtWVlaMZ6O+vj7Cw8Ohra1dg3dXd9HAroKGDBmC48ePM9LMzMxYixN3794dw4cPx/jx4yEUChnHLCwscPnyZTRs2BDbt2/H7NmzkZubyyqradOm8Pb2houLi+xvhKIoiqIqQSQSIS0tDcnJyUhOTsaHpCQUFRRAJBSCx+dDXlERugYG0NfXh76+PrS0tMDj8STXe3t7Y+zYsYw8hw4diqNHj1b3rdQLNLCrAB8fHwwdOpSRZmRkhMTEREaatbU1+vfvj8WLF7PycHZ2xrlz55CZmYkxY8bg1q1bUsuaNGkSVq5cCRUVFdndAEVRFEXVEoQQ9OnTBxcvXmSk+/j4YPDgwTVUq7qLBnbllJSUBEtLS8bWYIqKiqxZq0ZGRujatSsOHTrEymPQoEE4cOAADh48iJkzZyInJ4d1TuPGjeHt7Y0uXbrI/iYoiqIoqhZ59+4dLC0tkZ6eLknT1tZGeHg49PX1a7BmdQ9dx64cCCGYMGECa7/Xr4M6FRUVmJmZSQ3qpk2bhlWrVsHNzQ0TJ06UGtRNnDgRISEhNKijKIqivguGhobYunUrIy01NRW//PILa4UJqnQ0sPsGsVgsmZ168OBB+Pr6Mo5zvtpmhcvlokGDBrh9+zYrr3Xr1qFly5Zo1aoVbty4wTpuYmKC69evY/v27VBVVZXdTVAURVFULTd06FAMHDiQkebr6ytpJMnPz4e4AkurfG9oYFeKS5cuQVtbG5qamhg1ahSmTp3KOM7hcFjfJHR1dfHixQtGmry8PLZu3Ypr167hl19+Yc2MBYDx48cjNDQU3bp1k/2NUBRFUVQtx+FwsG3bNslWmp9NmTIFo0aNgqamJrS1tXHp0qUaqmHdQMfYlaJ58+aspUtKo6yszJrVqqGhgQkTJmD79u3IyspiXdOgQQPs2bMHPXr0qHR9KYqiKKquO3v2LNzd3Us83qxZM7x8+bIaa1S3fBeBnbSp2IX5+RCLRODyeFBQUmJNxc7OzoampmaZy+DxeBB9tTCjkZERmjVrhnv37km9ZsyYMVi/fj3U1dUrdX8URVEUVZ8MGzYMx44dK/F4RkYG1NXVK/R8/3KplfqoXm8plp6ejuDgYIQ+fcpYPFE9LQ1KQiG4hEDM4aCYz0eklhYCv1g8UdfICOrq6sjMzCxTWV8HdQ0bNkRGRobUoM7IyAi7d++Gq6urTO6ToiiKouqLhIQEBAYGlnpOWFgYiouLK/R8t27dGq1atSpX401dUi9b7BITE3Hfzw9voqMhl5cHk7h4GKaVY7sTLS28NjJEmkiE6Ddv4Hf/PmO7k2/R0dFBSkqK1GMeHh7YsGFDvf0PRVEURVGVMXLkSBw+fFjqMQMDAzg5OMDW2hrKxcUVer7HmTREsUCAJqamcHR2hqGhYVXdSo2oV4GdUCj8uEGxvz9UUlLQPDYODSq4QXFmfh7eqKkhztSUsUHx1y1zX5OXl0dRUREr3cDAALt27aL731EURVFUKfr27ctagYLH48HBwQGObdtCJycHLRIS0Dw7p0LPdxGXi7c6OnjZyAQ5Ojpo6+gIR0dH8Pn1oxOz3gR2SUlJuOjri/S3CTCPjoZpQgK4lbi19PR05BfkQ8zhINHMDNHm5khIS4PvpUt4//59ufIaMWIENm3aBC0trQrXh6IoiqK+B48ePcKPP/4oGQqlp6cHt169YKypCdMXL2AUFQVlRUVoalSu50vM4SDa2BgvTE2h1cAYrm5uMDAwkMUt1Kh6EdjFxsbijI8PBInvYB8RAbW8vErnmZycDJH4/61zeWpqiLC3R6JAgBNnzyIuLu6beejr62Pnzp3o27dvpetDURRFUd+LpKQkzJ8/H9evX8egvn1hmJcHi8BACD6tLsHj8mS2I0WWQIBACwvkGRnBfchgNGrUSCb51pQ6H9jFxsbi1NGj0I6NQ7vnz8GX0eKFScnJEIuZ3a4iHg8R7dsjTlsbx06fLjW4GzZsGLZs2QJtbW2Z1IeiKIqiviexsbE4dugQ1F+9gtn9++B9MRRKloEdAAi5XDy0bIk0ExMMGDasTgd3dXqB4qSkJJzx8YFWbBw6hIfLLKgDADUpOz/wRCK0fPAAJqmpGNSvH/T09Fjn6Ojo4OTJkzhy5AgN6iiKoiiqAj4/3/UTEtHl1WvoqKmDw/kcsnBkvjsTXyxGx7BwaMXF4YzP8XJNmKxt6mxgJxQKcdHXF4LEd2j//HmlxtNJIxAIoK9v8MV/pI+4hMDi4UMY5eXDzdWVtR7O8OHDMWDAAJnWhaIoiqK+F9Ke70qKijAwMIC2tg709fUhEAhkXi6XELQPfw6ld4m45OsLoVAo8zKqQ50N7Pz9/ZH+NgH2EREyban7Eo/Lhb6+PnhcZvDGE4lgHvgExlpacHBwYBw7ePAg3bCYoiiKoiqopOc7B4CCvDx43KoLXfhiMeyfRyAtIQH379+vsnKqUp0M7BITE/HY3x/m0dEymShRGi6HAx1dXXC/arlTzsqC6YsXcGzbljGLRlNTExwOp0rrRFEURVH1UXU+30uinpeHFlHReOTnh3fv3tVIHSqjTgZ29/38oJKSAtOEhGopj8flQkdXh5VuFBUFnZwcOH5qtbOxscGhQ4eqpU4URVEUVd9U9/O9JGYJCVBJSYG/n1+N1qMi6txqfOnp6XgTHQ272DiZj6srDZ/Hh7q6BnJycsDhcKAgLw++nBwskpJQbG+Pf7ZurRfr31AURVFUTaip57s0XELQLDYOz7S1kZ6eXqd2i6pzLXbBwcGQy8tDgxK27KpKygIB9PX0oKerC3V1dSgLBGiakQmFwkJERkZWe30oiqIoqr6oyee7NA1TUsDPy0NISEhNV6Vc6lRgJxKJEPr0KUzi4iu0jUhV4InFaBQfj5DAwG9uN0ZRFEVRFBt9vstOhQI7HR32eLPycnV1RX5+fonHV69eLfl3YmIifv75Z6SlpaEgNxeGaWms8y387sEt6ClcnwZiQng4sqpxmrJh6sd6+fj44IcffoCNjQ2OHTsGANixYwd8fHxkVtaePXtgamoKDoeDnJwcmeVLURRFUSX58rl/8OBB2NvbIzMzE56enmjatClsbW1hYWGB9evXS87r0qVLmfP/8vk+IiQEPQKfoPfTQPQMfILVb96g4FNgFZqdjVVvXsvuxr7wPCcHfunpkt+PvnuH8NAwFOTmIk1K3FEZS5cuhYmJiUziqa9VaOcJHR0dpFRxU6m0MsLCwnDpxAn0uX2HtcRJu4AHeNShIwBgZmQkmgmU8GtDk0rVQUQIeN+Y4UoA5BQX43ynTjhx6SLCw8MBAFwuF4mJiTJdGRsAQkNDoaKigi5duiAsLAwqKioyzZ+iKIqivvb5mXz69GksXLgQN2/ehK6uLjw9PTFw4ED07t0bubm5MDU1RUREBNTV1cuV/5fPd89nz7CgWTOYKSsjXyTCgpcvIQLB+hbmlb6P0p7rp5OTEZWXiz+bNJWkFfN4uODSCa6DBsHKyqr85YlErPVuAeDx48do0KABrK2tZR5PyWzyxLVr1zBr1iwIhUL8+OOPWLduHTgcDrZv344NGzagYcOG0NXVhZOTEyZPnozGjRsjLCwMADBw4EAkfJoBs3btWty9excZGRmwtbWFo6MjvLy8MHDgQKxatQpKOTlYHh2FR5mZ4ICDySYm6PFVxGuvpoYXuR9bs1KKijD/5UskFxVCnsvFsuamaCYQ4E1+Hv6IjASPw0FrVTU8zsrEaVs7bI6NRUpxEWLzC9BcIMBIIyMsevUSmcVCaMjxscqsBfTk5bEvIQFHEhPAJ4ClogIcbG0ZdRCLxbhx4wYePXoETU1NjBo1CmFhYZg/fz4KCwthYWGB5cuXQ0FBAZ06dcKAAQNw48YN8Hg87Nq1S+quFgCgrKwMQgiEQiHevHkDZWVlWf0JKYqiKEoqsViM/fv34++//8ahQ4eQnZ0t+UlKSsLr16+RmpoKDoeDt2/fIjU1FW3atMGTJ08QEBCAbdu2QUlJCS9fvsQPP/yAuXPnQiQSwcvLC+Hh4SgqKoKzhQX6FBeDgEAkFkEoEkEOwLwmTdAl8AnSi4sRlZuLw+8SscWiJQIyMrD09StwwIEcl4PTtnYoFoux4s1rRoxgqaKCX58/h42qKkKys3DK1g6r37xBUHYWignBFBMT/KCljU1xsSgUi3E/IwNejZsgKCsLmnJyUPzwAf369YOKigrEYjFCQ0NBCEF0dDR+++03pKWlQVtbGwcOHIChoSE6d+4MBwcH+Pn5Ydy4cRg1ahTr9Wzbtm2V/a1kEtjl5+dj/PjxuHPnDkxMTODm5oYzZ86gffv2WLduHQIDA8Hn89G6dWs4OTkxrr169Sq0tbVx5coVEEKQnZ2NHj16YOfOnXj27BkAICYmBgDwISkJTx48QLZQBF+71uByOMgUFjPyExEC/4x0DND/OEN12evXmGTSEFYqqgjJzsby16+x18oKy16/xq8NG6K7tg7Wf8r/s6jcPBy0toY8lwvPsFAsa24KY0VFXE75gH/iYrGoWXP8ExuD440aQYnLRY5IhISsTLx9+5aRz4gRIyT/Xrx4MeNYZGQkzp49K/l98+bNkn937NixTK+7jY1Nmc6jKIqiqMoaPXo0AMDR0ZGRfvr0acbvX7ZsNWvWjJWPt7c3vL29GWnDBg9GW5EI71++RFFREVJTU6H+xXAjAx4fkRkZ4MjJSdL2JSRgTpOmcNTURPan4VfHkpJYMUK2UISXeblY06IFzJVNcfTdOxgrKmJes2bIEQoxMPgZXDS18LtJI0aLXVBWFgDApKgYSxcvxtCff8b8+fPRrVs3AMBvv/2GPXv2oFGjRjhx4gQWL16MHTt2AACKi4tx9+7dcry6siOTwC4yMhItWrRA48aNAXzcVuvevXvgcrno2rWrpEm2d+/erGutra0xffp0zJo1C+7u7qUGNYX5+YhISMB0AwNwPzWlqvM//pGzhUK4BT1FUmEhTAUCOH+amhyQmYFX+exFDsNzctBN6+Nerr10deGX8f9+9a7aWpDncpEjFOJpVhZ+jXgOABATAmMFRWRnZ8NcQQHL3r9HZ2VlOCkrg19UBAM9PWRmZpb35aMoiqKo71pqaipUNTRKPE5AkJOTA5Uvlh1praaGtTExeJWfh546ulDFx2f+aCNjRoyQLRShsZISzD/1cPlnpCM6Lw9n3icDAPLFYiQVFZZYtrxQiIKCAly5cgW3b9/GzZs3kZ2dDX9/f/Tt2xfAxy7XzzEQAAwaNKiCr0TlVck6doQQcDgc1tZa0obzmZmZISgoCBcvXsTvv/+OUaNGYfLkyVLzFYtEKGnEmyqfD1+71sgXiTA6LAxH3iVilJExAOCMrV2pY+W+rpXiF1uI6cjJw9euNeN4VnY2Vhoa4ll+Pu7l5uJ4Zib+tLODc8eOiIyOLrEciqIoiqLYwiMicFwgwBwpx/LEYiQJhWgoL4/0L9InNGyITpqauJ2ehgHPgnCilW2J+St9Mc6NAFja3BRtvxoH+CQzS+q1XCLG+/fvsWLtWty4cQNycnLIz8+HgYGBpGfxa1Wxl21ZyWS5kxYtWiAqKgqxsbEQi8U4duwYnJ2d0bZtW9y8eRNZWVnIy8vDpUuXWNcmJiZCWVkZo0aNwu+//y55kXg8Hmt6MZfHg5WhIXySkiD+FCR+3RWrxONhbtOm8E5IgJAQtFNXx7Gkj1uCiAlBZG4uAKCligpufprlciXlg9T7UuHzoSUnh9ufzisWi/EyLw/KKipIA2AvEOA3HR0kFRdDBCCdttZRFEVRVLm1srZGnJRnaKFYjE0pKXBUVoaJtjajcScuPx8WKir4taEJmgkEeFtQAAcNjVJjBABw0NDA0aR3EH065/mnLl9lHg+5UpY1KRIT7Ny7F5s3b4ax8ccGIzU1Nejq6krimuLiYkRERFTqNZCVCrXYpaeno0GDBpLfN2zYgF27dqFv376SyRP9+vUDh8PBtGnT0KZNG5iYmMDOzg5qamqMvEJDQzFz5kzweDwoKSlh7969AAAPDw9YW1ujS5cu8PLyAgAoKCnBpWVL3IiLR++gp+CVMHnCWlUVZgJlXE1JwfymzbDg5Usce/cOQkLQT08fLZSV8VeTppgZGYkdb+PRVk0dKlJmrQDAuhYtsODlS6yLiYEIBOOMG6CRoiJWfEhBtrAYIrEYo7W0IFZQwMOv+tOvXLmCu3fvQltbG7/++iuePn2K33//HYWFhbCxscE///wDRUVFmJub48mTJ1BRUcGlS5dw9uxZ7Nq1S2p99u/fj6VLlyI5ORl6enoYOnQoli1bVr4/IEVRFEWVQ8OGDREfHw8ACA8PR79+/XD8+HFs374dfn5+UFVVRVFREQYNGoS//vqLcc3du3exY8cOHDlyBMDH4VoTJ06EhoYGfvnlFxBC8PzFC4xo1w6GGZlQ+PABf6emQo7DgZAQ/KClhakmjaDI4wEFBZI67UtMwMPMTPDw8blvp6YGWzU1vM7LZ8QIll+tHjHUwBDxBQXoG/QUBEBjJSVstWiJ9urq2Pk2Hn2DnmJm4yaS88PT0hAXHy+JRQDg2bNn+PfffzFx4kTMmTMHQqEQXl5esLCwKNPruWjRIuzZs0cST82aNQtTp06tyJ+GpULLnZRHbm4ulJWVkZ+fj06dOsHb2xvW1tYVyuu///5D5NWr6P4goNL1yheJoMjlgsPhYM/bt0gpLmJMcS6PouIiXGnTBpciInDz5k0AgIKCAt69e1entiGhKIqiqJogy+e7rF3v2AEtevRA165da7oqZVLle8XOmzcPt27dQkFBAUaNGlXhoA4A9PX1EaikhGIeD3KVXAU6JDsby968hpgQ6CsoYI2ZWYXz4igqQaStjUmTJsHIyAgfPnzAzJkzaVBHURRFUWUgy+e7LBXzeMhRUpL5mrRVqcoDuw0bNsgsL319fXD4fGQqK0MnS/ogx7Jqr6HBmhRRUZnKyuDw+XB2dkb//v1lkueyZctw4sQJRtqMGTOkrodDURRFUXWZLJ/vsvT5+V7RwG7SpEnw9/dnpK1du1ayZEpVqPLATpa0tLSgqKyMd1pateoP/077Y720tLRklufcuXMxd+5cmeVHURRFUbVVfX2+b926VcY1+jaZzIqtLjweD9atWyPOpCFE3NpRdRGXi9iGDWFjby912xCKoiiKokpHn++yUztevXJo1aoVigUCvK2CjXMrIl5HB0KBgO4CQVEURVGVQJ/vslHnAjtNTU00MTXFy0YmEJey6HB1EHM4eNXIBE3MzOhECYqiKIqqBPp8l406F9gBgKOzM3J0dBD9aaHAmhJlbIwcHR04frX/LUVRFEVR5Uef75VXJwM7Q0NDtHV0xAtTU2TV0LYdmQIBIs1M0c7JCYaGhjVSB4qiKIqqT+jzvfLqZGAHAI6OjtBsYIxACwsIq3mgpZDLRWBLC2gZG8PBwaFay6YoiqKo+ow+3yunzgZ2fD4fvdzckGdkhIeWLautP17M4eChZUvkGxrB1c0NfH6dWjGGoiiKomo1+nyvnDob2AGAgYEB3IcMRpqJCR5YWVZ5ZC/kcvHAyhJpJiZwHzIYBgYGVVoeRVEURX2P6PO94qp8r9jqEBsbizM+xyFITIR9RATU8vJkXkamQIDAlhbINzSC+5DBaNSokczLoCiKoijq/+jzvfzqRWAHAElJSbjo64v0twkwj46GaUICuDK4NTGHgyhjY0SamULL2Biubm51OpKnKIqiqLqEPt/Lp94EdgAgFArh7++Px/7+UElJQbPYODRMSQFPLC53XiIuF/E6OnjVyAQ5Ojpo5+QEBweHOtvnTlEURVF1FX2+l129Cuw+S0xMxH1/f7yJigI/Lw+N4uNhmJoG9dxcyIlEJV5XzOMhU1kZ77S1ENuwIYQCAZqYmcGxjk55piiKoqj6hD7fv61eBnafpaenIyQkBCGBgSjIzQURCqGSnw+1tHTIC4XgEjHEHC6K+HxkaWkiR0kJHD4fisrKsLG3h42NTZ1bcZqiKIqi6jv6fC9ZvQ7sPhOJREhLS0NycjKSk5PxISkJRQUFEAmF4PH5kFdUhK6BAfT19aGvrw8tLa06teEvRVEURX2P6POd7bsI7CiKoiiKor4HdXodO4qiKIqiKOr/aGBHURRFURRVT9DAjqIoiqIoqp6ggR1FURRFUVQ9QQM7iqIoiqKoeoIGdhRFURRFUfUEDewoiqIoiqLqCRrYURRFURRF1RM0sKMoiqIoiqonaGBHURRFURRVT9DAjqIoiqIoqp6ggR1FURRFUVQ9QQM7iqIoiqKoeoIGdhRFURRFUfUEDewoiqIoiqLqCRrYURRFURRF1RM0sKMoiqIoiqonaGBHURRFURRVT9DAjqIoiqIoqp6ggR1FURRFUVQ9QQM7iqIoiqKoeoIGdhRFURRFUfUEDewoiqIoiqLqCRrYURRFURRF1RM0sKMoiqIoiqonaGBHURRFURRVT9DAjqIoiqIoqp74H2KvTb1LY/ZeAAAAAElFTkSuQmCC", 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", 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", 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", 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", 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", + "image/png": 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BSyMj2N2+jTZJSTDWN4DohcUVL6O0rAySgnxkdOqE5C5dkC6RoEwqxZ49e8BT8VYrhBDSUGjCCCFE7bVq1Yr19ePHj7ErMBC9e/dGRffuyLKyQtesbNjm57/SCRVyPh+PbWyQIO6ObB0dhISHIzQ0FHK5HCNHjsSUKVNU9VYIIaRBUY8dIUTtJScnY8CAAUhPT6/ymrm5Obx690Z3V1dolpfDJi0NFk8kMCgpgYZcXuM9pQIBCnR1kWlijJS2bSETiWBhbY3Va9bgzp07ynZGRkZISEigbU4IIU0CFXaEELXHMAyGDRuGs2fPVvu6UChEVlYW4uPjERsRgfKSEjAyGfTKyqAvyYOmTAY+o4CCx0elUIhCYyMU6+iAJxRCW1cXzh4ecHZ2hpGREf744w9MmjSJdf+RI0ciKCiIhmQJIWqPCjtCiNrbtWsXZs6cWePr3bt3x40bNwAAcrkcEokE2dnZyM7ORk5WFirLyyGXySAQCqGprY3W5uYQi8UQi8UwNjaG4IUVtlOmTMHBgwdZsZ07d2LGjBmqf3OEEKJCVNgRQtRaamoqHB0dUVRUpIy1bt0agwYNwp9//glDQ0McPnwYffv2Vdkznzx5AgcHB2RnZytj+vr6iIuLg7W1tcqeQwghqkaFHSFEbSkUCgwdOhTnz59nxYOCgjBq1CiUlpZCR0enQYZIjx8/Dh8fH1Zs8ODBOHv2LA3JEkLUFm1QTAhRW1u3bq1S1Pn7+2PUqFEAAJFI1GBF1qhRo+Dv78+KnT9/Hlu3bm2Q5xFCiCpQjx0hRC3du3cPzs7OKC0tVcasrKwQHx//yhsRv6yCggI4Ojri0aNHyphIJEJsbCw6dOjQKDkQQsjLoB47Qojakcvl8Pf3ZxV1ALBjx45GK+oAwMDAADt27GDFSktLMWPGDMhr2UqFEEK4QoUdIUTtbNq0CcHBwazYvHnzMGTIkEbPZciQIZg3bx4rdvXqVWzatKnRcyGEkLrQUCwhRK3cunULbm5uqKioUMZsbW0RGxsLPT09TnIqLi6Gs7MzHjx4oIxpaWkhKioK9vb2nORECCHVoR47QojakMlk8PPzYxV1PB4Pu3fv5qyoAwA9PT3s3r2btVCjoqICfn5+kMlknOVFCCEvosKOEKI21q1bh/DwcFbsww8/VOkeda+qb9+++PDDD1mx8PBwrF+/npuECCGkGjQUSwhRCzExMejevTukUqky1rlzZ0RFRUFHR4fDzP5TVlYGNzc31lmyGhoaCA8Ph4uLC4eZEULIU9RjRwjhXGVlJXx9fVlFHZ/PR0BAgNoUdQCgo6ODgIAA8Pn//eqUSqXw8/NDZWUlh5kRQshTVNgRQjj39ddfIzY2lhX77LPP4OnpyVFGNfP09MRnn33GisXExODrr7/mKCNCCPkPDcUSQl6LXC6HRCJBdnY2srOzkZOVhYqyMijkcvAFAmjp6KC1uTnEYjHEYjGMjY0hEAiU19+4cQO9e/dm7Qvn5OSE8PBwaGlpcfGW6lRRUYHu3bsjLi5OGRMIBLh27Rq6d+/OYWaEkJaOCjtCyCvJy8tDTEwM4iIjUV5SAkYmg15ZGQwkEmjIZOAzDBQ8HqRCIQqMjVGsowOeUAhtXV04ubvDxcUF2tracHd3x+3bt5X3FQqFCA8Ph6urK3dvrh6io6PRvXt31qpYe3t7REZGQltbm8PMCCEtmZDrBAghTUtGRgZCg4PxIDkZGqWlsE5Ng4VEAoOSEmjUchqDVCBAga4uMo2NEf3kCcJDQlBQUoL8/HxWuxUrVqh9UQcArq6uWLFiBVasWKGM3bp1C8uXL8eGDRs4zIwQ0pJRjx0hpF5kMhlCQkIQHhICvdxcdExJhVVuLgQKxUvfS87n44GhIRLNxcjV00NIeDhCQ0Ph6uqKa9euQUNDowHegepJpVL06tULERERyhiPx8OVK1fg7e3NYWaEkJaKCjtCSJ2ysrJwMigIeY/S0SU5GXbp6eC/xq8OBcMgJycHUoUcGZ06IblLF2Tk58Nv5kx4eXmpMPOGl5CQAA8PD9amyh06dEBMTAx0dXU5zIwQ0hLRqlhCSK1SUlJwIDAQ8sRbGBAWhs6PHr1WUQcARYWFkMufzsOzunMHnhcvwllbG+EhoUhJSVFR5o3DwcGhyorYe/fuVVk5SwghjYF67AghNUpJScGf+/fDJCUVPRITIXyFYdcXVVRU4InkCSumqaEJAzMz3HDoCom1NcZPnQobG5vXflZjkcvl6Nu3L0JDQ1nx8+fPY9CgQRxlRQhpiaiwI4RUKysrCwcCA2H44CF6JSS8di8d8GwI9vFjyBX/LbLggYfWZmYQCgRQ8Hi45uiA/Ha2mOI7Hebm5q/9zMaSnJwMFxcXlJWVKWPW1taIi4uDvr4+h5kRQloSGoolhFQhk8lwMigIooxMeCYmqqSoA4DCggJWUQcA+vr6ED7b147PMPBMSIROZgb+DgpibSWi7uzs7KqcG5uamoqPP/6Yo4wIIS0RFXaEkCpCQkKQ9ygdHrduqWT4FXg6XFlaVsqKaWpqQfTCAgOhQgGPxFuQpKdXGdpUd++++y4GDBjAiu3YsQMnT57kKCNCSEtDhR0hhCUjIwPhISHokpwM/dLSui+oJ9kLe9zxeHwYGhqCV01bg9JSdE5Kxo3gYGRmZqosh4bG5/Oxc+dOtGrVihV/5513IJFIOMqKENKSUGFHCGEJDQ6GXm4u7NLTVXpfTU1NaGo+PSKMBx4MDQ2VQ7DV6ZSeDr3cXIQEB6s0j4bWrl07fP/996xYZmYm3n//fY4yIoS0JFTYEUKU8vLy8CA5GR1TUlU2r+5fPAAmJiYwNW0NM7EYOnUcu8VnGHRIScWDpCTk5eWpNJeGNmvWLLz55pus2O+//44///yTo4wIIS0FFXaEEKWYmBholJbCKje3Qe7PA6CpoQEBv36/etrm5kJYWorY2NgGyaeh8Hg8bN++HYaGhqz4vHnz8PjxY26SIoS0CFTYEUIAPF3cEBcZCevUtFc6JqwhCBQK2KSlITYiAvJazqFVR23atMHPP//MiuXm5mL+/PmgXaYIIQ2FCjtCCABAIpGgvKQEFmo2yd/iydO8muLig2nTpmHs2LGs2JEjR/D7779zlBEhpLmjwo4QNdOvXz9cuXKFFZs/fz62bt1a57U3b97E4sWLX+m52dnZYGQyGBYX19l2ZnwcfKIi0S/8BnqGXYdPVCR8oiJxp6QE46KjXun5NTEoKcHKtWuRnZ1d72vatWuH4mreh7+/P06cOFHjdWPHjoWRkREmTJjwSrm+iMfjYevWrTA1NWXF33vvPWRkZKjkGYQQ8jwq7AhRM5MmTcKhQ4eUX8vlcgQFBWH8+PG1XieXy9GtWzds2LDhlZ6bnZ0NvbKyeu1bt9PRCUFu7vjA2gZjzMwQ5OaOIDd36NayypWV60sMRWrI5eA9y6+hLVy4EIGBgSq9p5mZWZWiPD8/H7Nnz6YhWUKIylFhR4iamTBhAo4dOwbFswLr8uXL6NSpEyZPngx3d3e4ubkh+NkWIJcuXcKQIUMwadIkDBgwAJcuXVL2Nl2/fh29e/eGm5sbBg4cqNwPbuXKlZg9ezb69u2L9u3b48CBAwCAnKwsXPjnH4yIjMCoyEgEZjzd7uSSRIKJMdHwiYrEsuRkKOooRqQKBp8m3cGwiJv44PYtZfEyIPwGfk5NweSYaIQV5OPP7CyMj47CqMgIbEp5CAAokcsxKz4eIyMjMDIyAlefrYblKRT44fvv4eTkhEGDBqGkpAQAEBkZiR49esDZ2Rm+vr4oLy+vks/y5cthb2+PESNG1LlwYcCAAVX2oFOF8ePHY9q0aazYqVOnsGPHDpU/ixDSslFhR4iaEYvF6NSpE65evQoAOHToEKZNm4a//voLkZGR+Ouvv/DRRx8p24eFheHHH3+sMnzbtWtXXL16FVFRUZg9ezbruKsHDx7gwoULOHfuHJYtW/b0Ptev405GBo66uuG4uzt8WptBIpViV3o69jo5I8jNHRp8Hv7Ozak1//tlpZhr1Ran3D3wpFKKm4WFytcMhRo46OIKM01NXJbk4ZCLK/5yc0dicQmiCgsRnJcHQw0hTrh74LibO9yeFVnFFRVwtLdHXFwcLC0tceTIEQCAn58fNm/ejNjYWOjq6mLLli2sXG7cuIHTp08jJiYG27dv5/Qki82bN8PCwoIV++ijj/Dw4UNuEiKENEtU2BGihiZPnow//vgDcrkcx48fh4+PDz799FM4OTnBx8cHiYmJyrZeXl5o06ZNlXvk5eVh7NixcHR0xKpVq1jXDB8+HEKhEB06dEB+fj4AIPHWLQzo2BGaz7YiMdTQQHRhIe6Ulih77ELz8/GovKLW3G11dNBBJAKPx0NXPV2kV/zXi/bms7lmofn5iCoqxNjoKIyJjsK9slKklpejk64INwsLsf7BA0QXFUFPKAQAaAuF6GxnBwDw8PDAw4cPUVBQgIqKCnh6egIApk+friyG/xUaGoqxY8dCU1MTFhYWGDhwYL2+/w3B2NgYv/32GytWXFyMmTNnKntnCSHkdVFhR4gaGj9+PP766y9cuHABzs7O+Pvvv1FSUoKoqChERUWxCgGRSFTtPVasWIERI0YgPj4eu3fvRkXFfwWZlpZWlfY8Hg8vDrIyAAYYGSvn0J3x6IZ5bdvWmrvmc3vU8Xk8KJ67qfZzc/Amm5sr73u+W3eMNjODrY4Ix1zdYCcSYfX9e9jzbIGBhkAAwbMiTyAQQC6XV5mfxjAMeDxenTEujRgxAjNnzmTFLl68WKWnkRBCXhUVdoSoIVNTU9jb2+OTTz7BpEmTUFhYCLFYDKFQiMOHD1c7l+xFhYWFsLKyAgDs3bu3zvYurq64cO8eKp8VjflSKVxbtUJYQT4ynxWFeVIpsipq77Grj54Ghvg7NxcFMikAIKuiAnlSKbIrKiASCDBWLIZfG0vcKnm6spUBoPnCSRWGhobQ0tJCeHg4gKcnO/Tp04fVxsvLC0ePHkVlZSWysrJw8eLF1879df3www+wtrZmxT799FMkJydzlBEhpDkRcp0AIaR6kydPxvz58zFmzBjIZDKMGDECPXr0gLe3N0xMTOq8ftGiRfD398fatWvRu3fvOtsPGTYMideuYUxUFIQ8HiaKzTG9TRus7NgR7yYmQsYoIOTxsdrODubV9Pi9jE66unjH0gpvx8aBAQNdgQA/dO6Ce2VlWPfgPvg8HrT5fHzzbPiV4fPR2ty8yn12796N+fPno7y8HK6urpg/fz7r9R49emDo0KFwdnZG586d0bdv31rzGjp0KCIjI1FSUgIrKyscPXoU3bt3f633+iJ9fX3s3LkTgwcPVsbKysrg7++PK1euQFDPlcWEEFIdHkPr7QkhAOLj4/H3H39g5OUr0FCjUx6kAgFO9OuL4RMnwtHRket0VOa9997DL7/8woqtX7/+lfchJIQQgIZiCSHPiMVi8IRCFOjqcp0KS4GuLnhCIcRiMdepqNS6devQoUMHVmzZsmVISEjgKCNCSHNAhR0hBMDTVZvaurrINDbmOhWWTJOneRmrMC9PT0+4urqy/vy7Orix6OrqYvfu3azFHZWVlfDz84NUKm3UXAghzQcVdoQQAE9Xmzq5uyPVui3kfPX41SDn85HSti2cPTxUOvcsLCwM0dHRrD+GhoYqu399eXt74+OPP2bFIiIi8O233zZ6LoSQ5kE9fnsTQtSCi4sLpCIRHr1wtumL5Ao5nkgkePz4MUpKSxssnzRTU8hEIjg7OzfYM7i2evVq2Nvbs2KrVq1CVJRqz9wlhLQMVNgRQpSMjIxga2eHuzbWUNSw/xsD4MkTCSoqyiGTy1BQkI+S0lKUlpbWedzYy1DweLhnYw3bTp1gZGSksvuqG21tbQQEBLB6JGUyGfz8/Fh7DxJCSH1QYUcIYfHq0wfFpqZItrSs9vWioiLIZOw5YAUF+cgvyMeTJ0+qbHL8qpIsLVFsagovb28V3VF9de/eHUuWLGHF4uLi8NVXX3GUESGkqaLCjhDCYmFhge5eXrhtZ4fCF061qJRKUVxcXOO1UmklFIrX3yqlQCTCnU526OHtXeV81eZq+fLlcHFxYcXWrVuHsLAwjjIihDRFVNgRQqrw8vKCkZUlIuztIXu2kIJhGOTn5QGsPjn2cC0PPPB5r/drRcbnI6KrPYwtLeu1sXJzoampiYCAAGhoaChjCoUCfn5+KCsr4zAzQkhTQoUdIaQKoVCIET4+KG3TBmEOXaHg8VBYVASZXMZq9+JKVS1t7dc6m1XB4yHMoSvKLNpguI8PhMKWdTiOi4sLvvzyS1bszp07WLp0KUcZEUKaGjp5ghBSo5SUFPy5fz8MHzxAu0uXIXiusNPQ0IBUKsPzPXhGRsbQeeFM1/qS8fkIc+gKibU1xk+dChsbm9dNv0mSyWTo3bu38gxcAODxeLh06VKdR6IRQgj12BFCamRjY4PhY8YgSVcX0X37oFRfH8DTIVdtLW08X9TxeDxov+IZsgUiEa64uyG/nW2LLuqAp72lAQEB0Hrue8kwDPz9/Wud30gIIQAVdoSQOmzatAm79u3DLbkcYQMG4FHnztA1MEBFZSWrnbbWyw/DKng83LaywqWentCwt8cU3+ktuqj7l729Pb755htW7MGDB3SOLCGkTjQUSwip0dmzZzF06FAAT+fT9e7dG/179YZFZQVMYuNgmpYKgUIB4OWGYeV8PtJMTXHPxhrFpqbo4e2N3r17t7g5dbWRy+Xo378/goODWfEzZ85gyJAhHGVFCFF3VNgRQqqVn58PJycnPHr0SBkTiUS4ePEi/jl/HkUSCUQyGaxSUmCSmYX2WlrQfFbkVUcqEKBAVxeZJsZIadsWMpEItp06wasFbWnysu7duwdnZ2eUPne6h5WVFeLi4jg5Ao0Qov6osCOEVMvf3x8BAQGs2JYtWzB//nz06dMHcXFxcHFxgbuTEwz19aGjoQG9sjLoS/KgKZOBzyig4PFRKRSi0NgIxTo64AmF0NbVhbOHB5ydnZv1iRKqsmXLFixYsIAV8/f3x65duzjKiBCizqiwI4RUERQUhNGjR7NigwcPxtmzZ5GRkQErKytlnMfj4cCBA+jatSuys7ORk5WFyvJyyGUyCIRCaGpro7W5OcRiMcRiMYyNjatsk0JqplAoMHToUJw/f54V/+uvv+Dj48NRVoQQdUWFHSGE5cmTJ3BwcEB2drYypq+vj7i4OFhbW+PHH3/ERx99pHzNwMAA2dnZrFWcRLVSU1Ph5OSEwsJCZUwsFiMhIQEmJiYcZkYIUTe0KpYQwrJgwQJWUQcAP/74I6ytrQEABw8eZL02ZswYKuoa2L8F9fOys7OrDNESQggVdoQQpUOHDlUp3EaOHAl/f38ATzcsvn79Ouv1yZMnN1Z6LZq/vz9GjhzJih08eBCHDh3iKCNCiDqioVhCCAAgKysLjo6OePLkiTJmZGSEhIQE5arVDRs24NNPP1W+bmxsjKysLNb5pqThZGZmwsHBAXl5ecqYiYkJEhISIBaLOcyMEKIuqMeOEAKGYTB37lxWUQc8XZH5/FYkL/YOjRs3joq6RmRhYYFffvmFFXvy5AnmzJkD+oxOCAGosCOEANizZw+CgoJYsQkTJrCGWe/du4ebN2+y2kyaNKlR8iP/mTJlCiZMmMCKBQUFYc+ePRxlRAhRJzQUS0gL9+jRIzg6OqKgoEAZMzMzQ3x8PFq3bq2MrV27Fl988YXy69atWyMjI4NOi+BATk4OHBwckJOTo4wZGBggPj6etRUNIaTloR47QlowhmEwa9YsVlEHAL/++iurqAOqroYdP348FXUcad26NbZt28aKFRQUYNasWTQkS0gLR4UdIS3Ytm3bcPbsWVZs+vTpGDNmDCt2584dxMTEsGK0GpZbY8aMwdtvv82KnT17Fr/99htHGRFC1AENxRLSQt2/fx/Ozs4oKSlRxiwtLREXF1flqK+vv/4aK1asUH5tbm6OR48e0QkSHMvLy4OjoyMyMjKUMV1dXcTFxcHW1pbDzAghXKEeO0JaIIVCgZkzZ7KKOgDYvn17tee3vjgMO2HCBCrq1ICRkRF27NjBipWUlGDGjBlQKBQcZUUI4RIVdoS0QJs3b8bly5dZsTlz5mDYsGFV2iYkJCAhIYEVo2FY9TFs2DC88847rNjly5exefNmjjIihHCJhmIJaWHu3LkDV1dXlJeXK2Pt2rVDbGwsWrVqVaX9l19+iVWrVim/trS0RGpqKvj8p58L5XI5JBIJsrOzkZ2djZysLFSUlUEhl4MvEEBLRwetzc0hFoshFothbGxMvX0qVlRUBCcnJ6SkpChj2traiI6ORufOnTnMjBDS2GhJGyEtiEwmg7+/P6uoA4Bdu3ZVW9QxDFNlGHbixIng8/nIy8tDTEwM4iIjUV5SAkYmg15ZGQwkEujIZOAzDBQ8HqRCIe4YGyNCRwc8oRDaurpwcneHi4tLtcO+5OW1atUKu3btwsCBA5Wx8vJy+Pv7Izg4mAppQloQ6rEjpAX59ttvsWTJElZs4cKF2LRpU7XtY2Ji4OrqyoqdPn0aRQUFeJCcDI3SUlinpsFCIoFBSQk05PIany0VCFCgq4tMY2OkWreFVCSCrZ0dvPr0YZ1uQV7dwoULqwzBfvvtt/jss884yogQ0tiosCOkhYiLi4OHhwekUqkyZmdnh+joaIhEomqv+eKLL7B27VoAgEAgwPDhw9G3Z0/o5eaiY0oqrHJzIXiFSfpyPh+PTE1x18Yaxaam6O7lBS8vL9oX7zWVlpbC1dUVycnJypimpiYiIiLg6OjIYWaEkMZChR0hLUBlZSV69uyJqKgoZYzP5yM4OBi9evWq9hqGYWBnZ4d79+7BzMwMPiNGwNbMDM4pqbBLTwdfBb86FDweki0tcdvODsZWlhju4wNzc/PXvm9LFhoaij59+rBWxbq5uSEsLIzO9SWkBaBVsYS0AGvWrGEVdQCwePHiGos6AIiMjMS9e/dgbW0N3ylTYC8QYGB4ODo/eqSSog4A+AyDzo8eYUBYGGSJt3AgcA9rAQB5eb1798aiRYtYsaioKKxZs4ajjAghjYl67Ahp5iIiIuDp6Qn5c/PfHBwcEBERAS0trRqv+/TTT3Hw4EFMGTcO1rlP4BgRgTYmJg2Wp4zPR5hDV0isrTF+6lTY2Ng02LOau/Lycnh4eCAxMVEZEwqFuH79Ojw8PDjMjBDS0KiwI6QZe9X/wTMMA3d3dwzu2xft8vLR9Voo9HX1oF/NyllVUvB4uObogPx2tpjiO52GZV/Dqxb0hJCmjYZiCWnGvvzyS1ZRBwBLly6ts9fm2rVr6ObmBovSUtiHXQefYaCjo9OQqQJ4OjTrmZAIncwM/B0UBJlM1uDPbK48PDywdOlSViwhIQFffvklRxkRQhoD9dgR0kyFhobC29sbz/8Tr+8k+s8//xzaUik8L16EqLAQQoEQZmZmDZ2yUoFIhEs9PdFj0CD07du30Z7b3LzKohlCSNNGPXaENEMlJSXw8/NjFXWampoIDAyss6hLT0+HQC6H3e3bEBUWAgC0G6G37nkGpaXonJSMG8HByMzMbNRnNyeampoICAhg/Z0rFAr4+fmhtLSUw8wIIQ2FCjtCmqElS5bg7t27rNiqVavqtZdZ0F9/wbiwEG2SkpSxxhiGfVGn9HTo5eYiJDi40Z/dnDg5ObGOhAOA5OTkKhtVE0KaByrsCGlmLl68WOX0gZ49e1bZAqM6eXl5eJyeDuvkZOWWJkKhBjQ42DiYzzDokJKKB0lJyMvLa/TnNyeLFi2Cp6cnK/bTTz/h4sWLHGVECGkoVNgR0owUFhZixowZrJiOjg4CAgLqdV5odHQ0hCUlMHn0iHU9V9rm5kJYWorY2FjOcmgOhEIhAgICoK2tzYrPnDkTRUVFHGVFCGkIVNgR0owsWrSoyga/a9euRadOneq8Vi6XIzw0FFYpKaxjwnReKAYak0ChgE1aGmIjIljbdpCX17lzZ+XxcP96+PBhvXpyCSFNBxV2hDQTp06dwm+//caK9evXD++//369rpdIJCgvKYFRRoYypiHU4Pz8VosnT/OSSCSc5tEcLFy4EP369WPFtm3bhtOnT3OUESFE1aiwI0TN9OvXD1euXGHF5s+fj61bt9Z4TV5eHmbPns2K6enpYdeuXeDz6/fPPCMjA4xUBr38fGWsptWwM+Pj4BMViX7hN9Az7Dp8oiLhExWJOyUlGBcdVe01r8qgpAQr165FdnZ2va9p164diouLq8T9/f1x4sSJaq9JS0tD//790bVrVzg7O+OPP/545ZzVFZ/Px86dO6Grq8uKz549m+YxEtJMUGFHiJqZNGkSDh06pPxaLpcjKCgI48ePr/GahQsXIuO5njYA+O6772Bra1vv54aHh0O7qBCC54Y8a5pft9PRCUFu7vjA2gZjzMwQ5OaOIDd36NZjHh8AyF9i+0wNuRw84KUKu1chFArx448/IjExEefPn8dHH32EkpKSBn0mF9q3b4/vvvuOFUtPT8cHH3zAUUaEEFWiwo4QNTNhwgQcO3YMimfz3C5fvoxOnTph8uTJcHd3h5ubG4KfbQFy6dIluLq6Yu/evax7DB06FE5OTujduzfc3NwwcOBA5X5wK1euxOzZs9G3b1+0b98eBw4cAADcTUpCSGgo/FNTMSstDceKiiEUCHBJIsHEmGj4REViWXIyFHUUZVIFg0+T7mBYxE18cPuWci+9AeE38HNqCibHRCOsIB9/ZmdhfHQURkVGYFPKQwBAiVyOWfHxGBkZgZGREbj6rBeJp1Dgh++/h5OTEwYNGqQsuCIjI9GjRw84OzvD19cX5eXlVfJZvnw57O3tMWLECDx+/LjGvC0sLODq6goAMDMzg7GxcbMd/p0zZw6GDBnCiu3ZswfHjh3jJiFCiMpQYUeImhGLxejUqROuXr0KADh06BCmTZuGv/76C5GRkfjrr7/w0UcfAQDy8/OrrBgVCoXYvn07HBwccPXqVURFRWH27NlYv369ss2DBw9w4cIFnDt3DsuWLYNUKkV0VBSSs7KwrW1b7GjbFqPFZpBIpdiVno69Ts4IcnOHBp+Hv3Nzas3/flkp5lq1xSl3DzyplOLms02OAcBQqIGDLq4w09TEZUkeDrm44i83dyQWlyCqsBDBeXkw1BDihLsHjru5w+3Z2bTFFRVwtLdHXFwcLC0tceTIEQCAn58fNm/ejNjYWOjq6mLLli2sXG7cuIHTp08jJiYG27dvR2hoaL3+Dm7evAmFQoG2bdvWq31Tw+PxsH37dhgYGLDic+fORW5uLkdZEUJUgQo7QtTQ5MmT8ccff0Aul+P48ePw8fHBp59+CicnJ/j4+CAxMREMw+CHH37Ai6cCurq6wsrKCnl5eRg7diwcHR2xatUq1pmxw4cPh1AoRIcOHZCfn49//vkH6RkZ6GNtDU0eDwBgrtcK0YWFuFNaouyxC83Px6Pyilpzt9XRQQeRCDweD131dJFe8V8v2pumpgCA0Px8RBUVYmx0FMZER+FeWSlSy8vRSVeEm4WFWP/gAaKLiqD3bOGGtlCIznZ2AJ6egfrw4UMUFBSgoqJCuT/b9OnTlcXwv0JDQzF27FhoamrCwsICAwcOrPN7/+TJE/j6+mLbtm11tm3K2rZti02bNrFijx8/xvz586v8TBFCmg4q7AhRQ+PHj8dff/2FCxcuwNnZGX///TdKSkoQFRWFqKgoKBQKHDhwoMoiCy8vL9jY2AAAVqxYgREjRiA+Ph67d+9GRcV/BZmWlhbruoMHD4JhGDDPFlpoaGhCIBCAATDAyFg5h+6MRzfMq6MXS/O5xRp8Hg+K52oE7efm4E02N1fe93y37hhtZgZbHRGOubrBTiTC6vv3sOfZvEENgQCCZ0WeQCCAXC6vUnwwDAPes6K0tlhtKioqMHbsWCxZsgS9e/eu93VNla+vL3x8fFixw4cP4+DBgxxlRAh5XVTYEaKGTE1NYW9vj08++QSTJk1CYWEhxGIxhEIhDh8+jPLycixYsIB1jYmJCT7++GPl14WFhbCysgKAKnPwXnT06FGYmpriamoqKhkGOjo6yJdK4dqqFcIK8pH5rCjMk0qRVVF7j1199DQwxN+5uSiQSQEAWRUVyJNKkV1RAZFAgLFiMfzaWOJWydOVrQwAzRf20zM0NISWlhbCw8MBAL///jv69OnDauPl5YWjR4+isrISWVlZtZ60wDAM/P39MXDgQEyfPv2132NTwOPx8Ouvv8LExIQVX7BgAZ3RS0gTRYUdIWpq8uTJuH37NsaMGYNp06bh8uXL6NGjB0JDQ6GhoVFle4r//e9/MDY2Vn69aNEifPjhh/D29oZIJKrxOZWVlSgoKIBAQwPWbdtiTloapt25jeM5OTDR1MTKjh3xbmIiRkVGYGZ8PJ5Ipa/93jrp6uIdSyu8HRuHkZER+OD2LZTJ5UgqLcX46Cj4REVib2YGZlpaAgAYPh+tzc2r3Gf37t1YsGABnJ2dUVRUhPnz57Ne79GjB4YOHQpnZ2fMnTsXffv2rTGnkJAQHDx4EMeOHYOrqytcXV0RFxf32u9V3Zmbm1eZmyiRSDBnzhwakiWkCeIx9C+XkCZl165dmDlzJis2efJk5erWlzV9+nTs3bsXDg4OmPDmmxh05gzMDQxVkKlqSAUCnOjXF8MnToSjoyPX6TRbU6ZMqTIEu2vXLvj7+3OTECHklVCPHSFNSGpqapX9xsRiMX755ZdXul95eTn++usvAE/3iZMxDKSmrV87T1Uq0NUFTyiEWCzmOpVm7ZdffqnyPf7ggw+QlpbGUUaEkFdBhR0hTYRCocCsWbOqHNr+22+/VZkjVV+nTp1S3u/JkycoLa+ApE2b185VlTJNjKGtq8saZn5dnp6eyuHWf//kP3fiRktkYmJSZSVwYWEhZs6cSUOyhDQhVNgR0kRs3boV58+fZ8X8/f0xatSoV77n80NvDMNAUlyERzbWkNfzGLKGJufzkdK2LZw9PCCo56kW9REWFobo6GjWH0NDQ5Xdv6ny8fGBn58fK3b+/Plaj7MjhKgXmmNHSBNw7949ODs7o7S0VBmzsrJCfHx8lU1m66u0tBStW7dm3XPz5s0oKyiAW2QUbGo5peFVlVdUoKSkBEKBAK309cGvYyuSh2ZmiHZ3w+x334WRkZHK8yFV5efnw8nJCY8ePVLGdHV1ERMTgw4dOnCYGSGkPtTjYzkhpEZyuRz+/v6sAgwAduzY8cpFHQCcPHmSdU8+n49JkybB1s4Od22soXiJ/d/qQ65QQCKRoKKiHCWlJciTSFDbp0oFj4d7Ntaw7dSJirpGZGhoiB07drBiJSUlmDFjhvKYO0KI+qLCjhA1t2nTJuXZsP+aN29elbM+X9aLKyAHDhwIMzMzePXpg2JTUyQ/22pEVWQyGfBcKVdRWVGlWH1ekqUlik1N4eXtrdI8SN2GDBmCefPmsWJXr16tclIFIUT90FAsIWrs1q1bcHNzY50aYWtri9jYWOjp6b3yfYuKimBmZoby8v+O+/rtt98we/ZsAMDly5cR/s8FDAgLg34txdfLYAA8zs6GXCFXxng8Hlq3NoPwhflzBSIRLvX0RI9Bg2rde440nOLiYjg7O+PBgwfKmJaWFqKjo9GlSxcOMyOE1IZ67AhRUzKZDH5+fqyijsfjYffu3a9V1AHA8ePHWUWdUCjEuHHjlF97eXnByMoSEfb2kKloIQUPqLJAgWEY5Ofns4ZkZXw+Irraw9jSskUc66Wu9PT0sGvXLtaRbBUVFfDz83vW+0oIUUdU2BGiptatW6c8LutfH374oUp6sA4dOsT6+o033mBtJyIUCjHCxwelbdogzKGryubbaWlpQSTSZcUqK58uqACezqsLc+iKMos2GO7jA+Gz82EJN/r161dl38QbN25g/fr1HGVECKkLDcUSooZiYmLQvXt3SJ87vqtz586IioqCjo7Oa927oKAAZmZmqKysVMZqOmEgJSUFf+7fD+PUVHgmJEKogsnzCoZBTk4O5PL/en144MFILEaEizMk1tYYP3UqbGxsXvtZ5PWVlZXBzc0Nd+7cUcY0NDRw8+ZNODs7c5gZIaQ61GNHiJqprKyEr68vq6jj8/kICAh47aIOAP766y9WUaepqYkxY8ZU29bGxgbjp05FfjtbXHVzQ2EtZ87WF5/HezYk+18vYLF+K/zj5Ij8du2oqFMzOjo6CAgIAP+5IXmpVApfX1/WzxEhRD1QYUeImlm1ahViY2NZsc8++wyenp4quf+Lq2GHDh1a6+a8NjY2mOI7HYKu9rjo6Yk7VlavPTSrpakJXV1dKHg8POrcGTcGDEBCRQWKKyupqFNDnp6e+Oyzz1ixmJgYrF69mqOMCCE1oaFYQtTIjRs30Lt3b8jl/60cdXJyQnh4OLS0tF77/nl5eTAzM2NNft+7dy/eeuutOq+VyWQICQlBeEgI9HJz0SElFW1zcyF4heFZOZ+PNFMTxJmZIUckQkh4OEJDQ8Hn8xEeHg4XF5eXvidpWBUVFejevTvi4uKUMYFAgGvXrqF79+4cZkYIeR4VdoSoibKyMri7u+P27dvKmFAoRHh4OFxdXVXyjJ07d2LWrFnKr7W0tPD48WPo6+vX+x4ZGRkIDQnBg6QkCEtLYZOWBosnEhiUlEDjuYL0RVKBAAW6usg0MUZK27aQiUTQNzHBqq+/RkZGhrKds7MzwsPDoamp+WpvkjSYqKgo9OjRg/XBwN7eHpGRkdDW1uYwM0LIv2jJGSFqYvny5ayiDgBWrFihsqIOqDoMO3z48Jcq6gCgTZs2mDBxIvLy8hAbG4vYiAjcKykBI5NBr6wM+pI8aMpk4DMKKHh8VAqFKDQ2QrGODnhCIbR1deHu4QFnZ2cYGRkhNS0N33zzjfL+sbGxWLVqFQ3zqSE3NzcsX74cX375pTJ269YtLF++HBs2bOAwM0LIv6jHjhA1cPXqVfTr1w/P/3Ps1q0bQkNDoaGhoZJn5OTkwMLCgjXMe+DAAUyePPm17iuXyyGRSJCdnY3s7GzkZGWhsrwccpkMAqEQmtraaG1uDrFYDLFYDGNjYwie25C4piG+0NBQ9OjR47VyI6onlUrRq1cvREREKGM8Hg9Xr16Fl5cXh5kRQgAq7AjhXHFxMVxcXHD//n1lTEtLC5GRkejatavKnrNt2zbMnTtX+bWOjg5ycnKgq6tby1WNIzo6Gt27d2cN8XXp0gWRkZEqWQlMVCshIQHu7u6sVbEdOnRATEyMWvw8EdKS0apYQjj22WefsYo6AFi9erVKizqg6jDsyJEj1eZ/wq6urlixYgUrdvv2bSxfvpyjjEhtHBwcqgyV37t3D59//jlHGRFC/kU9doRw6Pz583jjjTdYMS8vL1y+fJk1XPm6srOz0aZNGyieW8F6+PBhjB8/XmXPeF1SqRS9e/fGzZs3lTEej4fLly+jT58+HGZGqiOXy9G3b1+Ehoay4ufPn8egQYM4yooQQoUdIRwpKCiAk5MT0tLSlDGRSISYmBh07NhRpc/65Zdf8N577ym/1tPTw+PHj9VumDMxMRHu7u6s83Hbt2+PmJiY1z4fl6hecnIyXFxcUFZWpoxZW1sjLi7upRflEEJUg4ZiCeHIxx9/zCrqgKfnw6q6qAOqng3r4+OjdkUdAHTt2rXKEN/9+/erbI5L1IOdnR3WrVvHiqWmpuLjjz/mKCNCCPXYEcKBEydOYNSoUazYwIEDce7cOdbRTaqQkZEBKysr1orbY8eOYfTo0Sp9jqrI5XL069cPISEhrPi5c+cwePBgjrIiNVEoFBg8eDAuXrzIip88eRLDhw/nKCtCWi4q7AhpZE+ePIGjoyOysrKUsVatWiEuLq5BjtPatGkTPvzwQ+XX+vr6ePz4sUpOsmgod+/ehYuLC0pLS5Wxtm3bIi4uDgYGBhxmRqrz8OFDODk5obi4WBmzsLBAfHw8jI2NOcyMkJaHhmIJaWTvv/8+q6gDgB9++KHBzkh9cTXsmDFj1LqoA4COHTtWGeJLS0vDRx99xFFGpDbt2rXDDz/8wIplZmZi4cKFHGVESMtFPXaENKLDhw9j4sSJrNjw4cNx4sQJ8Hg8lT3nzJkzOH/+PMRiMRYvXsx6rakMkSkUCrzxxhu4cOECK378+HGMHDmSo6xITRiGwYgRI3Dq1ClW/M8//8S4ceM4yoqQlocKO0IayePHj+Hg4IDc3FxlzMjICPHx8WjTpo3KnnP27FkMHTq02tcMDQ2RnZ3dZM5hTUlJgZOTE4qKipQxc3NzxMfHw8TEhMPMSHXS09Ph6OiI/Px8Zax169aIj4+HmZkZd4kR0oLQUCwhjYBhGMydO5dV1AHA5s2bVVrUAU/3EatJUVERZs+eXSUPdWVjY1NliC8rKwvvv/8+RxmR2lhaWmLz5s2sWE5ODubPnw/qQyCkcVBhR0gj2LdvH44dO8aKjRs3DtOmTVP5swwNDWt8TS6XY8+ePfj0009V/tyGMnPmzCpDx/v378fhw4c5yojU5q233sLYsWNZsSNHjmD//v0cZURIy0JDsYQ0sOqGp0xNTZGQkPBKw1NyuRwSiQTZ2dnIzs5GTlYWKsrKoJDLwRcIkCuR4O8zZ5SvP3nypEpvSZ8+fXDlypXXfWuNJiMjA46OjsjLy1PGTE1NER8fD7FYzGFmpDrVTTswNDREQkKCynuoCSFsVNgR0oAYhsHw4cNx+vRpVvxVJpTn5eUhJiYGcZGRKC8pASOTQa+sDAYSCTRkMvAZBgoeD+V8PjI1NVHWqhVkDIOS8nJExsUhJiYGBQUFAIDff/8dU6dOVdn7bAz79u3D22+/zYqNGTMGR44cUenCE6Ia1S0UevPNN3Hy5En6+yKkAVFhR0gD2r59O9555x1WbNq0adi3b1+975GRkYHQ4GA8SE6GRmkprFPTYCGRwKCkBBpyeZX2CoZBVlYmZEIhSgwMkNemDdKsrVGqoYHkBw/Q3dOzSQ3F/othGEyYMAFHjhxhxffs2VOl4CPqYdq0aVWGYLdv345Zs2ZxlBEhzR8VdoQ0kNfdtFUmkyEkJAThISHQy81Fx5RUWOXmQqBQ1HltZmYmGPz3T1vO5+OJlRWyHBxQYWGB7l5e8PLyglAofLU3x5HHjx/D0dEROTk5ypiBgQESEhJgaWnJYWakOhKJBA4ODo22GTchhBZPENIgFAoFZs6cySrqgKe9FfUp6rKyshCwcyfC/7mALnHxGHAzAjaPH9erqANQZahLoFCgY34BhsTEoktcPML/uYDAnTurbJSs7szMzLB161ZWrKCgALNnz6ZVl2rI2NgY27dvZ8WKioowc+ZMKOr5s0wIeTlU2BHSAH755ZcqZ2fOmjWrXhsDp6Sk4EBgIOSJtzAgLAydHz0C/yWLFo0X9qnT02sFPV1d8BkGnR89woCwMMgSb+FA4B6kpKS81L25Nm7cOLz11lus2OnTp6sUEEQ9jBgxAjNnzmTFLly4gC1btnCUESHNGw3FEqJiycnJcHFxQVlZmTJmbW2NuLg46Ovr13ptSkoK/ty/HyYpqeiRmAjhK/ZqMADyJBJIZTLoikTQ09Or0kbG5yPMoSsk1tYYP3Vqkxoay8vLg4ODAzIzM5UxPT09xMXFoV27dtwlRqpVUFAAJycnpKWlKWMikQjR0dGws7PjMDNCmh/qsSNEheRyOfz8/FhFHQDs3LmzzqIuKysLRw8ehHFKKnomJLxyUQcAPDwdBhObmVVb1AGAUKFAr/gEGKem4ujBQ01qWNbIyKhKD11xcTFmzJhBQ3xqyMDAADt37mTFSktL4e/vD3k1C4AIIa+OCjtCVOj777/HtWvXWLEFCxZg0KBBtV4nk8lwMigIooxMeCYmvvTQ66viMww8ExKhk5mBv4OCIJPJGuW5qjB8+PAqqysvXbqEX375haOMSG0GDx6Md999lxULDQ2tcrIIIeT10FAsISqSkJAAd3d3VFZWKmMdOnRATEwMdHV1a7328uXLCP/nAgaEhUG/tLShU62iQCTCpZ6e6DFoEPr27dvoz39VhYWFcHJyQmpqqjKmo6OD6OhodOrUicPMSHWKi4vh6uqKe/fuKWNaWlqIjIxE165dOcyMkOaDeuwIUQGpVAo/Pz9WUcfj8RAQEFBnUZeRkYHwkBB0SU7mpKgDAIPSUnROSsaN4GDWvDV1p6+vj127drFiZWVlNMSnpvT09LB7927Wqu2Kigr4+flBKpVymBkhzQcVdoSowNq1axEREcGKffLJJ/Dy8qrz2tDgYOjl5sIuPb2h0quXTunp0MvNRUhwMKd5vKyBAwfivffeY8WuXbuG7777jqOMSG28vb3x8ccfs2I3b97Et99+y1FGhDQvNBRLyGuKjIyEp6cna36avb09IiMjoa2tXeu1eXl52L5lC9wio2Dz+HFDp1qnh2ZmiHZ3w+x334WRkRHX6dRbSUkJXF1dcffuXWVMU1MTkZGRcHBw4DAzUp2ysjK4u7vj9u3byphQKER4eDhcXV25S4yQZoB67Ah5Df8OIz1f1AkEAgQEBNRZ1AFATEwMNEpLYfXcYelcapubC2FpKWJjY7lO5aXo6upWGeKrrKyEr68vDfGpIR0dHQQEBEAgEChjMpkMvr6+qKio4DAzQpo+KuwIeQ0rV65EfHw8K7ZkyRJ07969zmvlcjniIiNhnZpW7xMlGppAoYBNWhpiIyKa3Bw1Ly8vfPLJJ6xYZGQkvvnmG44yIrXp0aMHPv/8c1YsLi4Oq1at4igjQpoHGool5BVdv34dXl5erH3TXFxccOPGDWi+cPJDdXJycrB761Z4Xw+DaWFhQ6b6UnL19RHc0xP+8+ahdevWXKfzUsrLy+Hu7o5bt24pY0KhEGFhYXB3d+cwM1KdyspKdO/endVDzOfzERoaCk9PTw4zI6Tpoh47Ql5BaWkp/Pz8WEWdhoYGAgMD61XUAUB2djYYmQyGL5wnax98FT5RkRgRGYGFt26h7FnPWWZFBeYnJmLQzXC8GXETn9y5jQLZf8OMi+7cxvjoqDqfuyU1Ff3Cb6DH9WvVvm5QUgJGJkN2dna93oc60dbWRmBgIA3xNRGampoIDAyEhoaGMqZQKKrd5JsQUj9U2BHyCpYuXYqkpCRWbOXKlXB2dq73PbKzs6FXVlblhIlWQiGC3Nxx0t0DGnwe9mdlgmEYLLiViCEmJvinW3ec8uiGsWZiFDyb21ehUCC8sBDlCgXSystrfa63kRH+cHGt8XUNuRx6ZWVNsrADgG7duuGLL75gxRISErBy5UpuEiK1cnFxwZdffsmK3blzB0uXLuUoI0KaNirsCHlJly9fxo8//siK9ejRA59++ulL3ScnKwsGEkmtbbrpGyC1rByhBfnQFQgwVixWvuZtZARrbZ2nOUkk8DQwwMjWrXEqN6fWezq3agWzOnoV9SV5yGlCR4y9aNmyZVVWV65fvx7Xr1/nJiFSq88++6zKvNQff/wRV65c4SgjQpouKuwIeQlFRUWYMWMGK6atrY2AgAAIhcKXuldFWRk0ajnCS8YwuJInQSddEe6VlsK+lo2OT+XmYrhpawwzNcWpnNdfYaspk6Gyjp4/daapqYmAgIBqh/hKOdoEmtRMKBQiICAAWlpayhjDMPD390fxC1MVCCG1o8KOkJewePFiPHjwgBVbs2YNunTp8tL3Usjl1Z4JWySTwScqEuOio2ChpYUJYnM8bcar0hYAyuVyRBYVwsvQELY6IsjB4OFrzk/iMwrIm9C5sdVxdnauMvyalJRUZZiWqAd7e3usWbOGFXvw4MFL94QT0tJRYUdIPZ05cwa//vorK9anTx988MEHr3Q/vkAABa9qsfbvHLsgN3es6NARmnw+OopEuFVSfc/FpTwJCqRSDIm4iQHhN5BRXlHncGxdFDw+BC/ZA6mOPv30U/To0YMV27RpEy5dusRNQqRWH374Iby9vVmx//3vfzh37hxHGRHS9FBhR0g95OfnY9asWayYSCTCrl27WCswX4aWjg6k9Syeehsaokgmw1/PnU5x4ckTpJaX4e+cXGzs3AUXu/fAxe498IerC/5+zeHYSqEQmvXYYFnd/TvE9+Jm0TNmzEBRURFHWZGaCAQC7N69GyKRiBWfOXMmCgoKOMqKkKaFCjtC6uHDDz9E+gtnuW7cuBEdOnR45Xu2NjdHgbFxvdryeDxsse+KU7k5GHwzHMMjI/B3bi40eXyEFeTD29BQ2dZWRwQFGNyrZi4ZA+CH+/fR50YYCmUy9LkRhsCMqmfUFhobobW5+au+NbXSpUuXKpsUP3z4EIsXL+YoI1KbDh06YMOGDazYo0eP8OGHH3KTECFNDG1QTEgdgoKCMHr0aFZs8ODBOHv2LOsIq5cVHx+Pv//4AyMvX4FGI5zywAB48iQXlZWVAAAtTS3otWoFrRdWyEoFApzo1xfDJ06Eo6Njg+fVGBQKBfr374+rV6+y4qdPn8bQoUM5yorURKFQYMiQIfjnn39Y8aCgIIwaNYqjrAhpGqjHjpBa5ObmYs6cOayYvr4+duzY8VpFHQCIxWLwhEIU1LLaVZWkUqmyqAOAisoKPHmSi9wnT1DxXLxAVxc8oRDi57ZWaer4fD527doF3Re+17NmzUJ+fj43SZEa8fl87Ny5E61atWLF33nnHTx58oSjrAhpGqiwI6QWCxYsqLJR748//ghra+vXvrexsTG0dXWRWc/h2Je18t5d+ERFKv9MiIvDzdKqq2UrlQVeLioqKpBp8jQv4wbKiyvVDfGlp6e/8uIX0rCsra2r7BeZnZ2N9957j5uECGkiaCiWkBocPHgQU6ZMYcVGjhyJoKCg1+6t+9elS5cQfe4chgWHQPDCCRQNobyiAvn5+VAoqh/6lfP5uD5yJFrb22P27Nkqe5/qgmEYDB06tMoqy2PHjlUZbifcYxgGo0aNwsmTJ1nxQ4cOYeLEiRxlRYh6ox47QqqRlZWFd999lxUzMjLCtm3bVFrsuLi4QCoS4ZGpqcruWRttLS2IzcxgoG8AAb/qat7ctm1RzOdj8eLF6N69O4KCgtCcPvvxeDzs2LED+vr6rPicOXOQm/v6GzsT1eLxePjtt99gZGTEis+fP7/JHnlHSEOjwo6QFzAMg7lz50LywnFfW7ZsgYWFhUqfZWRkBFs7O9y1sa52T7uGwOPxoKurCzOxGAYGhsrtWhQ8HtI6dkTSgwcoKChAREQERo8eDTc3Nxw5cgSKRuhRbAxt27bFpk2bWLHHjx9jwYIFHGVEamNhYYFffvmFFXvy5Anmzp3brD50EKIqVNgR8oLAwEAEBQWxYhMnTsTkyZMb5Hleffqg2NQUyZaWDXL/mvAA6IpEMDMTw9DAEJlduiBXTw8hoaGsdjExMRg/fjxcXFxw8OBByBthBW9D8/Pzw8iRI1mxQ4cO4eDBgxxlRGozZcoUjB8/nhX766+/sHfvXo4yIkR90Rw7Qp6TlpYGJycn1maoZmZmSEhIgGkDDpdevnwZ4f9cwICwMOhzcJZpgUiES549IDQ2xi+//IK7d+/W2LZLly5YtmwZJk+e/NLn46qTzMxMODo6snpmjY2NkZCQAPNmsodfc5KTkwMHBwfk5Px3qoqBgQHi4+NhZWXFYWaEqBfqsSPkGYZhMHv27Co73P/6668NWtQBgJeXF4ysLBFhbw8Zv3H/Wcr4fER0tYexlRXee+893Lp1C3v37q3x/Nvbt2/j7bffRteuXREQEABZEz1TtrohPolEgjlz5tAQnxpq3bp1lSP9CgoKMHv2bPr7IuQ5VNgR8sy2bdtw9uxZVmz69OkYM2ZMgz9bKBRihI8PStu0QZhD10abb6fg8RDm0BVlFm0w3McHQqEQQqEQb731FuLj43HgwAE4ODhUe21ycjL8/f3RuXNn7Nixg7VHXlMxefLkKqsrjx8/jsDAQI4yIrUZO3Ys3n77bVbszJkz+O233zjKiBD1Q0OxhAC4f/8+nJ2dUVJSooxZWloiLi6uyoq8hpSSkoI/9++HcWoqPBMSIWzABQsyPh9hDl0hsbbG+KlTYWNjU207hUKBo0ePYtWqVYiNja3xfjY2NliyZAn8/f2hpaXVUGmrXG5uLhwcHPD4uXN49fX1ER8fj7Zt23KYGalOXl4eHB0dkZGRoYzp6ekhNjYWtra2HGZGiHqgHjvS4ikUCsyYMYNV1AHA9u3bG7WoA54WR+OnTkV+O1tcdXND4QuHoatKgUiEK+5uyG9nW2tRBzw9BWD8+PGIiorCsWPH4O7uXm27lJQUzJs3Dx07dsQvv/yC8vLyBsld1UxNTasM8RUWFmLWrFk0xKeGjIyMsH37dlasuLgYM2bMaDYrtwl5HVTYkRbvp59+wpUrV1ixOXPmYNiwYZzkY2Njgym+0yHoao+Lnp64Y2WlsqFZBY+H21ZWuNTTExr29pjiO73Wou55fD4fo0ePxs2bN3HixAn06NGj2naPHj3Ce++9h/bt22PTpk0o5WAxyMsaM2YMfH19WbFz585VKfiIenjzzTcxe/ZsVuzy5cv4+eefOcqIEPVBQ7GkRbtz5w5cXV1ZvUvt2rVDbGxslXMqG5tMJkNISAjCQ0Kgl5uLDimpaJub+0onVMj5fKSZmuKejTWKTU3Rw9sbvXv3fq1VrQzD4Ny5c/jqq68Q+sIWKc8Ti8VYtGgR5s+fX+WsVnWSn58PR0dHpKenK2O6urqIjY1F+/btOcyMVKewsBDOzs5ISUlRxnR0dBAdHY1OnTpxmBkh3KLCjrRYMpkM3t7eCAsLY8UvXryI/v37c5NUNTIyMhAaEoIHSUkQlpbCJi0NFk8kMCgpgUYte8pJBQIU6Ooi08QYKW3bQiYSwbZTJ3h5e6t0o2WGYXDx4kWsWrUKly9frrGdqakpPvnkEyxYsIDzorkmZ86cqdJT27dvX1y8eBH8Rl6tTOp28eJFDBw4kBXr2bMngoODlRtvE9LSUGFHWqxvv/0WS5YsYcUWLlxY5VQCdZGXl4fY2FjERkSgvKQEjEwGvbIy6EvyoCmTgc8ooODxUSkUotDYCMU6OuAJhdDW1YWzhwecnZ0bfM7g5cuX8fXXX+Off/6psY2xsTE++ugjvP/++zAwMGjQfF7F3LlzsW3bNlbshx9+wIcffshNQqRWCxcuxObNm1mxb7/9Fp999hlHGRHCLSrsSIsUFxcHDw8PSKVSZczOzg7R0dEQNdCCBVWRy+WQSCTIzs5GdnY2crKyUFleDrlMBoFQCE1tbbQ2N4dYLIZYLIaxsXGj916EhITg66+/xpkzZ2psY2BggA8//BAffPBBoy9SqU1RURGcnZ3x8OFDZUxbWxvR0dHo3Lkzd4mRapWUlMDV1ZW1qbampiYiIiLg6OjIYWaEcIMKO9LiVFZWomfPnoiKilLG+Hw+goOD0atXLw4za35u3LiBr7/+GidOnKixTatWrbBw4UJ89NFHMDExacTsanbp0iUMGDCAFfP09ERwcHCTPm2juQoNDUWfPn1Yq2Ld3NwQFhYGDQ0NDjMjpPHRpBHS4qxZs4ZV1AHA4sWLqahrAD169MDx48dx8+bNGjd6Lioqwpo1a9CuXTt8/vnnrP3kuNK/f3988MEHrFhYWBg2bNjAUUakNr1798Ynn3zCikVFRWHNmjUcZUQId6jHjrQoERER8PT0ZB1k7+DggIiIiCa1qW5TFRMTg9WrV+Pw4cM1thGJRJg/fz4WLVrE6ZmtpaWlcHV1RXJysjKmoaGBiIgIODk5cZYXqV55eTk8PDyQmJiojAmFQly/fh0eHh4cZkZI46LCjrQY9ItffcTHx2PNmjU4ePBgjZsAa2trY86cOfj0009haWnZyBk+de3aNXh7e7OG+FxdXREWFgZNTU1OciI1ow9uhNBQLGlBVqxYwSrqAGDp0qVU1HHA0dER+/fvR2JiIqZPn17tViLl5eX46aef0L59eyxYsACpqamNnmevXr2wePFiViw6OpqG+NSUh4cHli5dyoolJCTgyy+/5CgjQhof9diRFiE0NBTe3t6s3iGaXK0+kpOTsXbtWgQGBrJ6W56noaGBGTNmYMmSJWjXrl2j5VZRUQEPDw8kJCQoYwKBANevX0e3bt0aLQ9SP5WVlfD09ER0dLQyRoujSEtChR1p9mg7hKbj/v37+Pbbb7Fr1y7IZLJq2wiFQvj6+uKLL75Ahw4dGiWvyMhIeHp6snLq2rUrIiIioK2t3Sg5kPprytsZEfK6aCiWNHtLlixhFXUAsGrVKirq1FD79u2xbds23L17F/Pmzat2HptMJsPOnTvRuXNn+Pn54c6dOw2el7u7O5YtW8aKJSYmYsWKFQ3+bPLynJyc8NVXX7FiycnJVTYkJ6Q5oh470qxduHABgwYNYsXoyKGm49GjR1i/fj1+++031nm+z+Pz+Zg8eTKWLVuGrl27NlguUqkUPXv2RGRkpDLG4/EQHByM3r17N9hzyaup6cjACxcuVNmjkJDmhAo70mzRIeHNR2ZmJjZu3Ij//e9/KCsrq7YNj8fDhAkTsGzZMjg7OzdIHvHx8fDw8EBlZaUy1rFjR0RHR0NXV7dBnkle3Z07d+Dq6sr6UNCuXTvExsaq7XnFhLwuGoolzdYnn3zCKuoAYO3atVTUNUEWFhb47rvv8PDhQ3z66afVFlEMw+CPP/6Ai4sLxo0bV2UTalVwdHTEqlWrWLG7d+9iyZIlyM3NxW+//YagoKAat3Ahjatz585Yu3YtK/bw4UMsWrSIo4wIaXjUY0eapVOnTmH48OGsWL9+/XDhwoVqt9YgTUtubi5++OEHbN68GUVFRTW2GzVqFJYvX47u3bur7NlyuRze3t64fv06K96qVStlLsuWLcPXX3+tsmeSV6dQKDBgwABcuXKFFT916hSGDRvGUVaENBwq7Eizk5eXB0dHR2RkZChjenp6iI2Nha2tLYeZEVWTSCTYtGkTNm3ahIKCghrbvfnmm1i+fLnKtrtISkqCq6trjcPCYrEYWVlZKnkWeX3379+Hs7MzSkpKlDFLS0vExcXByMiIw8wIUT0q7EizM336dOzdu5cV+/XXXzFnzhyOMiINLT8/H5s3b8YPP/yAvLy8GtsNHjwYK1asQJ8+fV77mbNnz8aOHTtqfL2goAD6+vqQy+WQSCTIzs5GdnY2crKyUFFWBoVcDr5AAC0dHbQ2N4dYLIZYLIaxsTEt7GkAv/76K+bNm8eKTZ8+HYGBgRxlREjDoMKONCtHjx7FuHHjWLGhQ4fi1KlT4PF4HGVFGkthYSG2bNmCjRs34smTJzW269+/P1asWIH+/fu/0s/F7t27MWPGjFrbhISEoLKyEnGRkSgvKQEjk0GvrAwGEgk0ZDLwGQYKHg9SoRAFxsYo1tEBTyiEtq4unNzd4eLiQr1JKsQwDIYNG4azZ8+y4kePHsWYMWO4SYqQBkCFHWk2cnJy4ODggJycHGXMwMAA8fHxsLKy4jAz0tiKi4uxdetWbNiwAY8fP66xnbe3N1asWIHBgwe/VIHXq1evKnPs/mVubg7v3r3h5uQEkVQK69Q0WEgkMCgpgUYNp2oAgFQgQIGuLjKNjZFq3RZSkQi2dnbw6tMHFhYW9c6N1CwtLQ1OTk6sYXszMzMkJCTA1NSUw8wIUR0q7EizwDAMJk6ciD///JMVDwgIgK+vL0dZEa6VlpZi27ZtWLduXa1z3nr27IkVK1Zg2LBh9Srw/P39ERAQwIoJBAL07t0bXt27w7S4GJ3T09GxqBgCheKl85bz+Xhkaoq7NtYoNjVFdy8veHl5QSgUvvS9CFtAQAD8/f1ZsQkTJuDQoUPUq0+aBSrsSLOwf/9+TJs2jRUbPXo0jh49Sr+sCcrKyrBjxw58++23SE9Pr7Fdt27dsHz5cowaNarWn5uCggL4+/vj2LFjAJ72+viMGAFLIyPY3b6NNklJ0NHUgomx8WvlreDxkGxpidt2djC2ssRwHx+Ym5u/1j1bOoZhMGbMGAQFBbHi+/fvx5QpUzjKihDVocKONHkZGRlwdHRkTZo3MTFBQkICxGIxh5kRdVNRUYFdu3Zh7dq1SE1NrbGdq6srli9fjjFjxoDP56OwsBCffPIJkpOTMXfuXEydOhUAcPHiRaxevRrujo6wKC2FfUQERIWFAJ724InNVPPzVygSIcLeHqVt2mDs5EmwsbFRyX1bqqysLDg4OEAikShjxsbGiI+Pp2Fv0uRRYUeaNIZhMGrUKJw8eZIVP3ToECZOnMhRVkTdVVZWYs+ePVizZg0ePHhQYztHR0csX74cf/75Jw4dOqSM//nnnxg3bhxSUlJweP9+GNy7hw5Xg8GX/XfovKamFkxNTFSWs4zPR5hDV0isrTF+6lQq7l7ToUOHMHnyZFZs5MiRCAoKol5+0qRRYUeatJ07d2LWrFms2OTJk3HgwAGOMiJNiVQqxe+//47Vq1fj7t279b5OX18f58+fR8jFizB88BC9EhLAUyiQl5+PiooKCAUCmJiYqHwzbAWPh2uODshvZ4spvtNpWPY1TZ48mVWwA8CuXbuqzMEjpCmhwo40WSkpKXBycmKdPCAWi5GQkAATFfaUkOZPJpPh4MGDWL16NW7fvl1ne4FAgPfmzYO9UIi+UdEQvsICiVcl4/Nxxd0NGvb28J05kxZUvIbc3Fw4OjoiOztbGdPX10d8fDzatm3LYWaEvDo6W4k0SQqFArNmzapynNRvv/1GRR15aUKhEG+99Rbi4+Nx4MABODg41Nq+d+/eMNTSQqcbNxq1qAMAoUIBj8RbkKSnIzQ0tFGf3dyYmppi27ZtrFhhYSFmzpxJ5/2SJosKO9Ikbd26Ff/88w8r5u/vj1GjRnGUEWkOBAIBJk+ejNjYWBw+fBiGhoZV2pibm8Ore3fY3b4N/uPHKCoubvQ8DUpL0TkpGTeCg5GZmdnoz29OfHx84Ofnx4qdP38eW7du5SgjQl4PDcWSJufu3btwcXFBaWmpMmZlZYX4+HgYGBhwmBlpTnJycmBubg7FCz1yE8aNQ09TU7hfuAA+wwDgwcLCAo093V7B4+FiNw+Y9uqFCbRQ6LXk5+fD0dGRtRWOrq4uYmJi0KFDBw4zI+TlUY8daVLkcjlmzJjBKuoAYMeOHVTUEZXKysqqUtQZGBjAztYW1snJz4o6AGDANPJwLADwGQYdUlLxICmp1vNxSd0MDQ2xc+dOVqykpAQzZsyo8jNAiLqjwo40KT/++COCg4NZsXnz5mHIkCEcZUSaKwcHBwwaNIgVc3FxgUgqhcmjR8qYhoamyle/1lfb3FwIS0sRGxvLyfObkyFDhmDu3Lms2NWrV7Fp0yaOMiLk1dBQLGkybt26BTc3N1RUVChjtra2iI2NhZ6eHoeZkeZKKpUiLCwMjx8/Bo/Hw534eLS/dQv2d+9BoZCDzxdAW0uL0xzjbNsh3dUV737wAQQCAae5NHVFRUVwcXFh7W2opaWF6OhodOnShcPMCKk/6rEjTYJMJoOfnx+rqOPxeNi9ezcVdQQAWIe4BwYGwsPDQ3n0V/v27eHq6gp7e3t8//33ynYDBgyo9Z4aGhrw9vbGuHHj4O3tDQGAtgWFmH3nDsYmJmJCQjyGRdzE+gcPUC6XAwDiioqw7sH9BnmPicXFCH5u2HV/ZiYS4uJRXlLCOkVBFVavXg1ra2vW97W5a9WqFXbt2sWKVVRUwM/PDzKZjKOsCHk5VNiRJmHdunUIDw9nxT788EP07duXo4yIujpy5Ag2bNiA06dPK+dd/vTTT4iOjsbNmzexceNGFBQUAHh6JFh9ZWdng5HJYPhsFezmLvY44e6Bo65uyKmsxBd3kwEATq1a4TPb9q+cv7yWQZTbJSUIzv+vsJtqYYHxBgZgZDLWXmwv9bxnBemLhg4dirCwsFe6Z1PWr18/fPjhh6zYjRs3sH79em4SIuQl0c6WRO3FxMTgq6++YsU6d+6MNWvWcJQRUVdnzpzBF198gX/++QetW7eu8nppaSk0NDSgoaEB4GkvX25uLi5duoQ1a9ZAV1cXiYmJGDlyJL7//nvI5XL4+fkhMjISZWVl8O7SBWNfmEyvIxDgyw4d0Df8BvKkUiSVlGBvZgY223fF9fx8rL5/DzzwoMHn4YirG6QKBdY+uI8bBQXggYf3rK3hoKeH+YmJcG7VCrFFhfjT1Q3rHzxAVFEhpAyD962tMdDYBJtSU1ChUCA0Px+L29kiqrAQRhoa0M7JwZgxY6CnpweFQoG4uDgwDIPk5GS8++67kEgkMDExQUBAACwsLNC/f3/07t0bwcHBmD17Nnx9fat8r7p3794wf0lNwDfffIO///4bSUlJytjKlSsxcuRIODs7c5gZIXWjwo6otcrKSvj6+kIq/e8MTj6fj4CAAOjo6HCYGVE3RUVFePvtt3H9+nVYWlqyXlu4cCGWLl2K5ORkfPnllxCJRFWuj4qKwq1bt2BgYAAHBwd8+OGHyMnJwYMHD5CYmIgD+/ah6MwZoLikyrV6QiHaamsjtbyMFd+Vno4ltu3hZWSEomdDeQeyslAkkyPIzR18Hg8FMimKZHLcLS3Bhs6d0UXXDvszM2GprY1lHTqgWCbDhJho9DMyxgfWNkgqLcHnz3oEowoLAQDWlVKs/uorTHnrLSxfvhyDBw8GALz77rvYvn07bGxs8Mcff+Crr75S7s8mlUpx5cqV1/yuN086OjoICAiAl5eXclWsVCqFr68vbty4AU1NTY4zJKRmNBRL1NqqVauqrPj77LPP4OnpyVFGRF2JRCI4Ozvj999/r/LaTz/9hJiYGKSlpWHHjh2syfH/6tWrF1q3bg1NTU04OjoiJSUF7du3R0ZGBhYsWICImzdhUMvq1+oGUN319bHx4UMEZqSj7FmBcL0gH5PNzcF/dtC8gfBp72E7HR100dUFAITk5+FAViZ8oiIxLS4WZQoFsiorqnnCU5oyGSrLy3H69GlcunQJ69atQ1FREUJCQjB69Gi4urpi1apVrH3aJtLed7Xq2bMnPv30U1YsJiYGq1ev5igjQuqHeuyI2rpx4wa+/fZbVszJyQlffvklRxkRdSYQCHD06FH06dMHVlZWmDFjRpU2JiYmcHd3R3h4OGxtbVmvaT23ulUgEEAul8PIyAhxcXE4deoUvlq5EncAuBlXPbKuRC7Ho/Jy2Gjr4E7Jfz16c9u2RV8jI1zKk2B8dBT+cHGtMX+d51a0MgBWd7RD9xf2ZrxZUFjttXxGgcePH2Ptxo04f/48NDQ0UFZWBnNzc0RHR1d7TXW9loRt5cqVOHHiBOLj45Wxb775BqNGjWrRQ9VEvVGPHVFLZWVl8PPzY03sFgqFCAwMZP0PmJDn6evr4++//8bXX3+N06dPV3m9rKwM0dHRaN++fosbcnNzoVAoMHHiRIwZNQoPqtkIuFwux6p7dzHQ2ASGz+bu/Su1rAz2enqY39YaHUQiPCovR29DQxzMyoLi2SKJApm0yj17Gxpif1amciFF4rMFG7oCAUqqWexQqWDw644d+Omnn5TD0Pr6+mjdujX+/vtvAE+HEm/dulWv902e0tLSQmBgIITC//pA/p13WV5ezmFmhNSMCjuilpYvX47bt2+zYitWrICrqys3CZEmw9LSEkFBQXjnnXcQGRkJ4OkcO1dXV7i7u2PatGno1q1bve6Vnp6Ofv36wcXFBb8fPIgx7u7K196/fQsjIyMwJjoKphqaWN2xY5Xrd2WkY3hkBEZFRqCNlhbc9PUx2dwC+kIhRkZFYlRkJK7nF1S5boq5Bcw0NTE6KhIjIiPwS1oqAMDTwAAJxcUYHRWJq8+KTIZhEP8kFympqZg5cyY6duyo/Heyb98+fP/993BxcYGrq+tLrXJduXIlrKyskJeXBysrK/z000/1vrY5cXNzw/Lly1mxW7duVYkRoi5og2Kidq5evYp+/frh+R/Nbt26ITQ0VLmakZDG9s8//+DOmTN449r1Kq8xeLriVlpZCS1tbehoazdYHkXFRSgpKX3674NhwIDB9cFv4GxyEi5cuADg6XDhkiVLGiyHlkYqlaJXr16IiIhQxng8Hq5evQovLy8OMyOkKuqxI2qluLgY/v7+rKJOS0sLAQEBVNQRTonFYhTr6ED6wukOZWVlePz4MQoK8lFaVoq8PAkqKisbJIfyinIUFRVBoZCDYRRgwEAmFKKslR5rH7sXN9klr0dDQwMBAQGs1bAMw8DPzw8lJVVXSRPCJSrsiFr57LPPcP8+e9f+1atXo2vXrhxlRMhTYrEYPKEQBc9WrlZUViInNxd5+XmQy9mnEkgbqLCrqKh63xJDQ8gYhlXYtW3btt73XLBgAVxdXVl/zp8/r5J8mxMHBwd8/fXXrNi9e/fw+eefc5QRIdWjoViiNs6fP4833niDFfPy8sLly5fpDEzCOblcji2bNsEiMhJto2NQ/sKedf/i8fgwNTWFhlD1mw7I5HI8fvwYz2+u8sDJCTGWlti0ZYuyp1tTUxNTp06Fr68v+vfvD34t27SQ+pPL5ejTpw+uXbvGip8/fx6DBg3iKCtC2OhfO1ELBQUFmDlzJismEomwe/duKuqIWpBIJEjNzMRtExOU1LCnnKamFkxNTBqkqAMAoUAAY2NjAE/3wJPz+XhkY4PIZydN/KuyshIBAQEYNGgQ2rVrh6VLl+LOnTsNklNLIhAIqt0cfebMmSgsrH4rGkIaGxV2RC189NFHSEtLY8XWrVuHjtWsNCSkMZWVlWHt2rXo0KEDfvvtN5RqaOCJlRWrjVAghLGR8dOiroHngmpracHE5Glxl9u2LUqFQsTExNTYPi0tDd988w26dOmCnj174n//+x8kEkmD5tic2dnZYd26daxYamoqPv74Y44yIoSNhmIJ506cOIFRo0axYgMHDsS5c+doCIlwRqFQYN++fVi6dCnrQ8eEcePQ09QU7hcuQMjjoVWrVhCJdJ/1oTWe0ooKnHdzxbXcXPx55IgyzuPxUNevdU1NTYwcORJ+fn548803aWHSS1IoFBg8eDAuXrzIip88eRLDhw/nKCtCnqL/axJOPXnyBO+88w4r1qpVK+zcuZOKOsKZCxcuoFu3bvD19a3SkxwcGoonrVrhiZMTzMzE0OWgqAOA1A4dUG5ujpDQUGWsffv2SE1NxcaNG+Hk5FTjtZWVlThy5AhGjx6NNm3a4IMPPkBERESdBSF5is/nY+fOndDT02PFZ8+eTb2hhHP0f07Cqffffx9ZWVms2A8//AAbGxuOMiItWWJiIkaOHIlBgwYhKiqq2jZDhgxBz7598dDRCcXPVsg2tgKRCHc62aHPoEHYtGkTOnfuDA8PD+zfvx9WVlb45JNPEBsbi6ioKHz00UcwMzOr8V65ubn46aef0K1bNzg5OWH9+vWsM2VJ9dq1a4fvv/+eFcvMzMT777/PUUaEPEVDsYQzhw8frnIQ+fDhw3HixAnweFz0gZCWKisrC19++SW2b98OhUJRbZuBAwdiw4YNcHd3h0wmQ8DOnZAn3kKfqCgIa7imIcj4fFxxd4OGvT18Z85kHXdVE6lUirNnzyIwMBB//fUXKiqqX/zxLz6fj8GDB8PX1xdjx46lc2VrwDAMhg8fXuX4usOHD2P8+PEcZUVaOirsCCeys7Ph6OiI3NxcZczIyAjx8fFo06YNh5mRlqSkpATfffcd1q9fX+NGs127dsWGDRvw5ptvsj5wZGVl4UDgHhg+fIBe8QngN8KvUgWPh2uODshvZ4spvtNhbm7+0vfIy8vDH3/8gYCAAIQ+N4xbEz09PUycOBF+fn7o06cPTZF4QXp6OhwdHZGfn6+MmZqaIiEhodaeUkIaChV2pNExDINx48bh2LFjrPjevXvx1ltvcZMUaVHkcjl2796N5cuXIzMzs9o2YrEYq1atwsxaesVSUlLw5/79ME5NhWdCYoP23Mn4fIQ5dIXE2hrjp05VyXSFu3fvIjAwEHv27MHDhw/rbG9jY4Pp06fD19cXdnZ2r/385mLv3r2YPn06KzZu3DgcPnyYRh9Io6PCjjQ6+iVIuHTmzBksXrwYcXFx1b4uEomwaNEiLFq0CK1atarzfikpKTh68BBEGRnwuHUL+qWlqk4ZBSIRIrrao8yiDcZOnqTyOagKhQLBwcEICAjAH3/8gaKiojqv6dWrF/z8/DBp0iQYGRmpNJ+mhj6sEnVChR1pVOnp6XBwcEBBQYEyRsMWpDHExMRg8eLFOHfuXLWv83g8zJw5E6tWrXrp6QBZWVk4GRSEvEfp6JKcDLv0dJUMzSp4PCRZWuJOJzsYW1piuI/PKw2/vozS0lIcO3YMgYGBOHfuXI1zDv+lpaUFHx8f+Pr6YujQoS1265TqppcYGhoiISGBppeQRkWFHWk0NU00/vPPPzFu3DiOsiLNXXp6OpYvX47du3fXuJ3HsGHDsH79+lq3CKmLTCZDSEgIwkNCoJebiw4pqWibmwvBKwzPyvl8pJma4p6NNYpNTdHD2xu9e/eu10IJVcrIyMC+ffsQEBCAhISEOtubmZlh2rRp8PPzg4uLS4vrga9uQdibb76JkydPtrjvBeEOFXak0fz222+YM2cOKzZt2jTs27ePo4xIc1ZUVIT169fju+++Q1lZ9ee6Ojs7Y8OGDRgyZIjKnpuRkYHQkBA8SEqCsLQUNmlpsHgigUFJCTTk8hqvkwoEKNDVRaaJMVLatoVMJIJtp07w8vaGhYWFyvJ7FQzDICoqCoGBgfj999+Rk5NT5zVOTk7w8/PDtGnTOM+/MU2bNg379+9nxbZv345Zs2ZxlBFpaaiwI43i4cOHcHJyQnFxsTJmYWGB+Pj4Z2dfEqIaMpkM27dvx5dffonHjx9X26ZNmzZYs2YNpk+f3mBnEefl5SE2NhaxEREoLykBI5NBr6wM+pI8aMpk4DMKKHh8VAqFKDQ2QrGODnhCIbR1deHs4QFnZ2e1nLsmlUpx+vRpBAQE4Pjx46isrKy1PZ/Px5AhQ+Dn54fRo0dXOWe1uZFIJHBwcGDtz9mqVSvExcXR/pykUVBhRxqcQqHAoEGDcOnSJVacjt8hqsQwDE6ePInFixfj9u3b1bbR09PDZ599ho8//rjR9maTy+WQSCTIzs5GdnY2crKyUFleDrlMBoFQCE1tbbQ2N4dYLIZYLIaxsXGDFZuqJpFIcOjQIQQEBOD69et1ttfX18ekSZPg6+sLb2/vZjs8ScckEi5RYUca3ObNm7Fw4UJWbNasWdi+fTtHGZHmJiIiAosWLary4eFfAoEA77zzDlauXAmxWNy4ybUQSUlJ2LNnDwIDA5Gamlpne1tbW/j6+mL69Ono0KFDI2TYuGbOnIldu3axYps3b8Z7773HUUakpaDCjjSopKQkuLq6suY4WVtbIy4uDvr6+hxmRpqDlJQULF26tNZ5mqNGjcK6detgb2/fiJm1XAqFAleuXEFAQAAOHz7Mmn5RE29vb/j6+mLSpEkwMDBohCwbXkFBAZycnFhnDYtEIkRHR9MegKRBUWFHGoxcLkefPn1w7do1Vvz8+fMYNGgQR1mR5qCgoABr167Fjz/+WOPxWB4eHti4cSP69+/fuMkRpZKSEhw9ehSBgYE4f/58jauS/6WtrY3Ro0fDz88Pb7zxRqOvAla18+fP44033mDFevfujStXrjSZ4XbSBDGENJB169YxAFh/FixYwHVapAmrqKhgfvrpJ8bExKTKz9a/f6ytrZm9e/cycrmc63TJc9LS0phvv/2Wsbe3r/Hv7vk/YrGY+fjjj5mYmBiuU38t7777bpX3tmHDBq7TIs0Y9diRBpGQkAB3d3fWirkOHTogJiYGurq6HGZGmiKGYXD06FF89tlnuHv3brVt9PX18cUXX2DhwoXNfuVlU8YwDCIiIpRbpzx58qTOa1xcXJRbpzS1OZLFxcVwcXHB/fv3lTEtLS1ERkaia9euHGZGmisq7IjKSaVS9OzZE5GRkcoYj8fD1atX4eXlxWFmpCm6fv06Fi1ahJCQkGpfFwqFmD9/PlasWAFTU9NGzo68jsrKSpw6dQqBgYE4fvw4pFJpre0FAgGGDRsGX19f+Pj4QFtbu5EyfT1Xr15Fv379WEPR3bp1Q2hoaIs9qYM0IA57C0kz9dVXX1UZeli0aBHXaZEm5t69e8ykSZNqHa4bN24ck5SUxHWqRAVyc3OZX375henRo0e9hmoNDAyYOXPmMMHBwYxCoeA6/Tp9/PHHVd7DqlWruE6LNEPUY0dUKjIyEp6enpDJZMqYvb09IiMjm8yna8ItiUSC1atX4+eff66xB8fT0xPfffcd9QA3U7dv30ZgYCD27t3LWlVakw4dOii3TrG1tW2EDF9eWVkZ3N3dWXssCoVChIeHw9XVlbvESLNDhR1RmYqKCnTr1g3x8fHKmEAgwLVr19C9e3cOMyNNQUVFBX7++WesXr0a+fn51baxtbXFt99+i4kTJzbbzW3JfxQKBS5duoSAgAD8+eefKCkpqfOavn37wtfXFxMnTlS7LZVu3LiBXr16QfHc+cFOTk4IDw+HlpYWh5mRZoXbDkPSnHz++edVhhqWLVvGdVpEzSkUCubAgQOMra1tjcNuRkZGzPfff8+Ul5dznS7hSFFRERMQEMAMGjSI4fF4dQ7VamtrM1OnTmVOnTrFyGQyrtNXWrp0aZVcv/jiC6ayspI5d+4cc/PmTa5TJE0c9dgRlbh+/Tq8vLxYn0RdXFxw48YNaGpqcpgZUWfBwcFYtGgRwsLCqn1dU1MT77//PpYuXaqW56YSbqSmpmLfvn0ICAjAnTt36mxvYWGBt956C35+fnB0dGyEDGtWWVmJ7t27IzY2Vhnj8XiwtrZGSkoKAGDDhg1YtGgRVymSJo4KO/LaSktL4ebmhqSkJGVMQ0MDN2/ehLOzM4eZEXWVlJSEzz//HEePHq2xzeTJk/HNN9+gffv2jZgZaUoYhkF4eDgCAwOxf/9+SCSSOq9xc3ODn58fpk6dCjMzs0bIsqqYmBh07969xjmkZmZmyM7ObuSsSHNBhR15bR999BF+/PFHVmzNmjX44osvuEmIqK2cnBysWrUKW7duZS2weZ63tzc2btwIT0/PRs6ONGWVlZU4efIkAgMDceLEiRp/vv4lEAjw5ptvws/PDyNHjmz0xV0LFizAli1banxdIpHAyMgIcrkcEokE2dnZyM7ORk5WFirKyqCQy8EXCKClo4PW5uYQi8UQi8UwNjamUy1aOCrsyGu5fPlylSObevTogZCQkCZ/HBBRnbKyMmzatAlr165FYWFhtW3s7Oywfv16jB49mhZGkNeSm5uLAwcOICAgADdv3qyzvaGhIaZMmQJfX1/07NmzwX/+zp8/j+HDh9e6b9+VK1cgl8sRFxmJ8pISMDIZ9MrKYCCRQEMmA59hoODxIBUKUWBsjGIdHfCEQmjr6sLJ3R0uLi40faGFosKOvLKioiK4uLjgwYMHypi2tjaioqLQpUsXDjMj6kKhUGDfvn1YunRpjdtWmJiYYOXKlZg7dy5t1kpULjExEXv27MGePXuQnp5eZ3s7Ozvl1ik2NjYNktOQIUNw7ty5al8zNzeHd+/ecHV0hK5MBuvUNFhIJDAoKYGGXF7jPaUCAQp0dZFpbIxU67aQikSwtbODV58+sLCwaJD3QdQTFXbklc2bNw+//vorK/bdd9/h448/5igjok4uXLiAxYsXs04geZ6WlhY++ugjfP755zAwMGjk7EhLI5fLcfHiRQQEBODIkSMoLS2t85r+/fvD19cXEyZMQKtWrVSWy9tvv419+/axYgKBAL1794ZX9+4wLS5G50fp6FhcDMFzC9LqS87n45GpKe7aWKPY1BTdvbzg5eVFoygtBBV25JWcOXMGw4YNY8X69OmDixcv0vyOFi4xMRGffvopTp48WWOb6dOnY/Xq1bC2tm7EzAh5qqioCH/++ScCAwNx8eLFOtvr6Ohg3Lhx8PPzw8CBA1/7d1x2djZGjx6tXA1uZmYGnxEjYGlkBLvbt9EmKQkiTS0YGxu/1nMUPB6SLS1x284OxlaWGO7jA3Nz89e6J1F/VNiRl5afnw9HR0fWsIZIJEJsbCw6dOjAYWaES1lZWVi5ciV+++031rY3zxswYAA2btwId3f3Rs6OkOqlpKRg7969CAgIQHJycp3tLS0t8fbbb8PX1xddu3Z95ecqFArs378fP/74I/r37g2L0lLYR0RA9GwOKp8vgLlY/Mr3f16hSIQIe3uUtmmDsZMnNdgQM1EPVNiRl+bv74+AgABWbMuWLZg/fz5HGREulZSU4Pvvv8e6detqPBnA3t4eGzZswPDhw2lhBFFLDMMgLCwMgYGBOHDgAPLy8uq8xsPDA35+fpgyZQpat2790s9MSUnB4d9/h/69e+h4NRh8+X8reYVCDZi9wj1rIuPzEebQFRJra4yfOpWKu2aMCjvyUoKCgjB69GhWbPDgwTh79iz9D7uFkcvlCAgIwPLly5GRkVFtG7FYjK+++gqzZs2i+T2kyaioqMCJEycQEBCAU6dO1bl1ilAoxIgRI+Dr64sRI0bU63iwrKwsHAgMhOGDh+iVkABGLkeeRIJKqRR8Ph8mJibQUPG/GQWPh2uODshvZ4spvtNpWLaZosKO1Ftubi4cHR1ZG2fq6+sjLi6O5kq1MGfPnsWiRYsQFxdX7es6OjpYtGgRFi9erNJJ54Q0tsePHyu3TqlpIdDzjI2NlVun9OjRo9oPvDKZDAE7d0KeeAt9oqIgfIUFEq9KxufjirsbNOzt4TtzJn3gaoaosCP1NnnyZBw6dIgV27lzJ2bMmMFRRqSxxcbGYvHixTh79my1r/N4PMyYMQOrVq2CpaVlI2dHSMOKj49HYGAg9u7di8zMzDrbd+7cWbl1Stu2bZXxy5cvI/yfCxgQFgb9eqzOVbUCkQiXenqix6BB6Nu3b6M/nzQsKuxIvRw8eBBTpkxhxUaOHImgoCAagm0B0tPTsXz5cuzevRs1/coYOnQo1q9fT8fIkWZPLpfj/PnzCAwMxNGjR1FWVlZrex6PhwEDBsDPzw9eXl44evAgusTFo/OjR42UcVW3raxwx8kRb82YQfvcNTNU2JE6ZWVlwcHBgXUOo5GRERISEugXQjNXVFSEDRs2YOPGjTX+z8vJyQkbNmzA0KFDGzk7QrhXWFiIw4cPIzAwEJcvX66z/ZSJE9GzdWsMiIiAtoYmuPpYrODxcLGbB0x79cKEiRM5yoI0BD7XCRD1xjAM5s6dW+Vw7S1btlBR14zJZDL8+uuvsLOzw9dff11tUdemTRvs2LEDUVFRVNSRFktfXx8zZ87EpUuXcP/+faxatQodO3astq2BgQFsra3R5tYt5OXm4nF2NgqLiupcnNEQ+AyDDimpeJCUVK8VwKTpoMKO1CowMBBBQUGs2IQJEzB58mSOMiINiWEYnDhxAs7Ozpg3bx5rocy/dHV1sWrVKiQlJWHmzJm0ITUhz9ja2mL58uVISkpCSEgI5s6dyzpVxcXFBSKpFCbPhmDlCjmKi4vwOOcxcnJzUVrHkK6qtc3NhbC0FLGxsY36XNKwaCiW1CgtLQ1OTk4oKChQxszMzBAfH/9KezYR9RYZGYlFixbVuBM/n8/HO++8g5UrV9I2CYTUU3l5OY4fP46AgAB0srWFy6NHaFfDanIA0G+lDz09vUbLL862HdJdXfHuBx/Qh7RmgnrsSLUYhsHs2bNZRR0A/Prrr1TUNTOpqamYPn06PDw8aizqRo4cibi4OGzdupWKOtKkPHz4EMOGDUOnTp1gZ2eHjRs3qvT++fn52LZtm/LrmzdvYvHixQCAlStXYvv27Zg4cSJ27doFcevWsCoohIZQAwCw/ckTzEpLg19qKobev49ZaWmYfCsR1/PzVZojAJzMycGwiJt4NzGRFbd4IkF5SUmV6TaN4eHDh/Dy8oK2tjZ+/vnnRn9+c0Ub2JBqbdu2rcqWFtOnT8eYMWO4SYioXEFBAdauXYsff/wRFRUV1bZxc3PDxo0bMXDgwEbOjpDXxzAMxo4diyVLlmDSpEkoLCzE0KFD0bZtW5VNJ/m3sJszZw4AoFu3bujWrVuVdtnZ2eDJ5Wgjl0PYujWkMhk+0NXDnLIypFeU48vsbGyzskIrvVbKvR/lDAOBinYdOJKdjW/tOsFVX58VNygpASOTITs7m/WhXS6Xq6wHr6Z76evr4/vvv68y3Ye8HuqxI1Xcv38fn3zyCStmaWmJTZs2cZQRUSWpVIrNmzejY8eOWLduXbVFXdu2bbFnzx7cvHmTijrSZJ0/fx6GhoaYNGkSgKeFxNq1a/HDDz/A398fJ06cAAAUFxejXbt2AIB79+6hT58+cHd3R8+ePXHr1i0AwO7duzFp0iS88cYb6NixI7777jsAwNKlS5GYmAhXV1esWbMGly5dwoQJE6rkEhERgV0BAZgYEYEZ8XHIUyhgoK8PsVgMQ0MjCPh8GBoYIkEux4z4OHxw+xamx8WiWCaDb1wsxkRFYnRUJG4+G0UJy8+Hf3wc5icmYsjNm/jm/n0AT4vBRXdu482ImxgZGYE/s7Pwa1oaIgoL8FlyEn5OTYFEKsXchASMiozAjKgoVGRlITs7G/7+/vjkk0/Qv39/rF+/Hv3798eiRYvg7e0NZ2dnREZGYsSIEejYsSOrh23NmjXo3r07nJ2dsXXrVgDApUuXMGTIEEyaNAkDBgyo9u/H2NgYnp6e0NDQeN2/avIc6rEjLAqFAjNmzKhy5uf27dthZGTEUVZEFRiGwbFjx/DZZ5/VeNi5vr4+vvjiCyxcuBA6OjqNnCEhqpWYmAg3NzdWzM3NDbdv30aXLl2qvcbCwgLnz5+HlpYWQkND8cUXX+Do0aMAnm5QHB4eDqlUis6dO+P999/HmjVrcOfOHdy8eRPA04KmOhs3bsQ8T08Mz8jEqdwc/JyaglUd7cADoKWpCYFAAJFIBF5lJWKKinDK3QNiLS1IFQpsse8KPaEQGeXleO/2LRxxffqeEouLcdrDA3oCIUZERsC/TRtIZFI8Kq/AKY+nvYZFMhlaCYW4mpeHFR06oJOuLr66dxfdDPTxjpUDTubkIPD0adg926g4LS0NFy9eBI/Hw5kzZ6Crq4vg4GCsWbMGkydPRnh4OBQKBRwcHPDee+/h9OnTePz4McLDw1FZWQlvb2+MHDkSABAWFoZbt26hTZs2r/X3SF4OFXaE5aeffsKVK1dYsTlz5mDYsGEcZURUISwsDIsWLUJwcHC1rwuFQsybNw8rVqygOZSkWatrQ/WKigosWLAAsbGx4PP5rB7tQYMGQVdXF8DT7X6qWzVenaKiIiQlJeGH9HRsrayEgmFgqaVdY3t3fX2In503ywDY8PABIgoLwefxkPLcylm3Vvow1tAEANiJdJFeUYFOuiI8rqzAynt3MdjYBN7VfCCPKCzEvK4OAIDhpqZYGXETleXlAJ7uevD898jHxwfA0/0qu3XrBkNDQwBAq1atkJeXh3PnzuH48ePKPfwKCgpw7949AICXlxcVdRygwo4o3blzB0uWLGHF2rVrp/LJxqTx3L9/H1988QUOHjxYY5uxY8fi22+/RadOnRoxM0Ianr29vbK37V+RkZHo1q0bhEIhFM/OaH2+ePvxxx9ha2uLffv2ITs7Gz179lS+pvWs2AIAgUAAuVxerzwYhoG+vj42Dh8Ol/sP6myvw/9vltTxnMcolStwzM0dAgDO10KVr2ny/yvABDxAwTAwEGrguLsHruRJsDP9EYLz8/C5bftan8fj8SB/tpeeSCRivfbve+bz+az3z+fzIZfLwTAMVq5cCV9fX9Z1ly5dqnIv0jhojh0B8HRDWj8/P5Q/+9T2r127dtEh7k2QRCLBJ598gi5dutRY1PXo0QNXr17FkSNHqKgjzdLgwYORl5enPOO6sLAQy5Ytw7Jly2BjY4Po6GgAwJEjR5TXFBYWok2bNuDxeNizZ0+dz2jVqhWKiopqbaOvrw/9Vq0QkZ4OAJAqFLhbzzNii2VymGpqQMjj4fSTXFQ8K0ZrIpFKwTAM3jRtjQXW1rhVXFKljYe+Pk7k5AAATj/JRUdTUwiEr9bPM3jwYOzYsUO5ifmdO3eq/H+ENC7qsSMAns7/CAsLY8UWLlyI/v37c5MQeSUVFRX45ZdfsHr16hp3k2/Xrh2+/fZbTJo0ic75Jc0an8/H0aNHMW/ePCxduhTp6enYsmUL+vfvj86dO2P06NH4+++/MWTIEOU18+bNw/jx47Fv3z4MHjy4zmeYmJjA3d0dTk5OmDJlCry8vKpt9/6CBdj+/fc4ml8AORjMtrRCx3r0aI0ya413EhIwPjoKHvoGMKyjAMuuqMDnyUlQMICQx8MX7av21r1vbYPPk5Jw7HE2DIQamDZsGDS1ax4ars3w4cMRHx+PHj16gGEYmJmZ4fjx4/W6trCwEF27dkVhYSEEAgE2btyIhw8fvlIe5D+0QTFBXFwcPDw8IJVKlTE7OztER0dTV3oTwTAM/vjjD3z++ed48KD6oR5DQ0MsX74cCxYsYA2pENJS7N+/H9988w2uXLnS6IvB/vnnH9w5cwZvXLveqM+tj3O9eqLz0KEYNGgQ16kQFaCh2BausrISfn5+rKKOz+cjICCAiromIjg4GL169cLkyZOrLeo0NDTw8ccf4969e/j444+pqCMt1tSpUxEXF8fJCn+xWIxiHR1I1ex0B6lAgGIdHYjFYq5TISpCQ7Et3Jo1axAVFcWKLV68GL169eIoI1JfycnJ+Pzzz1nzg140adIkrF27Fu2rGY4hhDQesVgMnlCIAl1dmBYWcp2OUoGuLnhCYYMWdnFxcZg+fTor1rFjRxw+fLjBntmSUWHXgkVERGDNmjWsmIODA7766iuOMiL1kZubi1WrVuF///sfZM9Wsr3Iy8sLGzduZK3oI4Rwx9jYGNq6usg0Nlarwi7T5GlexsbGDfYMJycn5UIV0vBoKLaFKi8vh6+vL2u5vlAoREBAAA3VqamysjKsW7cOHTp0wObNm6st6jp27Ig//9/efcdVWfYPHP+cwUaQoYAgaspQEVDUUlwtLUszK1OzNJ8yzccyR5bbciVk05ZZar9H08c0SW2oZSVoIg4UUcCUIYIs2euM3x8aj0cOTuDA8ft+vXq94rqv+76/B9TryzW/+44///xTkjohGhCVSkWnLl1I8W6JVtkwml6tUklyy5YEhoTU2vFhwvQaxp8uUe/mzZvHiasOg541axYhISEmikjURKfT8Z///Ad/f3/eeOMNCoz8tu/i4sKHH35IXFwcQ4cOldWuQjRAQUFBVNrakubqaupQAEh1dUVja0tgYKCpQxG1SIZi70BRUVGEhYUZlHXu3JlZs2aZKCJRk99++41p06Zx6NAho9etrKyYPHkyb7zxRtWO8EKIhsnJyYk2Pj4k5eTQMisLpQk3pdApFJxu5U0bX185LtLMSI/dHaa4uJjRo0dz5S43lpaWrF27Vg5ibkDi4+MZNGgQ9913X41J3ahRozh16hRLly6VpE6IRiK0d2+KXF1J9PQ0aRwJnp4UuboS2quXSeMQtU967O4wb775JklJSQZlb731FgEBASaKSFwpMzOTefPm8eWXX9Z4XFG/fv0IDw+XYXMhGiEPDw+6hYYSXVaOR24uDjd4AsXNKiwqory8HFsbm2pbV+Xb2nLK14fuvXrh4eFRJ+8XpiMbFN9BfvvtN+677z6DsnvuuYe9e/fKxFkTKykpYfny5bzzzjsUFRUZrePv78+yZct49NFHZQ6dEI2YRqNhzVdfoT0RT+/Dh1Ff55iwm1VQUEBR8f/+HbGwsKRp06ZYqNVolEr+6NIZi/bteW7sWNS3eJSYaLhkKPYOUVBQwPPPP29QZmNjw5o1aySpMyGtVsvXX3+Nj48Pc+bMMZrUNW/enE8//ZRjx44xaNAgSeqEaOTUajWPDB5MSYsW/NWxA7pa/jtdVl5u8HVlZQVZWVnkFhSwv0N7Sj1aMHDwYEnqzJQkdneIadOmkZycbFC2ZMkSOfzdhH755Re6dOnC2LFjSU9Pr3bdxsaG2bNnk5SUxPjx4+UfYSHMiLu7O48/PYxcb2/2BXREU4tboBj7ZV2rUnIwKJBTNraUajU0b9681t4nGhYZir0D/PjjjwwcONCgrG/fvvz6668oG8h+SneSY8eOMX36dH7++Wej1xUKBWPGjOHtt9/G08QTrIUQdSs5OZktGzZim55OSHx8rcy5KyoupqAgv+rrYgcHToZ0Jd3Who1btpCamsr06dNZtmzZbb9LNDyS2Jm5vLw8AgICDHqE7O3tiY2NpU2bNiaM7M6Tnp7OnDlzWL16Nboa5tQ8+OCDhIWFERQUVM/RCSFMJSMjg+0REeSlncM/MRGfc+duayuUsvJycnNz0CkUpPv6kujvz7ncXCJ27ODChQsA+Pn5cfLkydr6CKIBkbEdM/fKK69UG+Z79913JamrR4WFhYSFhfHuu+9SUsNv4wEBAYSHhzNgwIB6jk4IYWru7u6MHjuWyMhIoq2tSPNwp21yCi2zs1HdysIKCwsyW7UitV07su3tiYyOJioqymClfd++fWvxE4iGRHrszNiWLVsYOnSoQdmAAQP48ccfZQJ+PdBoNHz11VfMnTuXzMxMo3U8PDxYuHAho0ePlkUsQgjS09OJiozkTEIC6pISWqWm4pGTi2NxMRY1bIEEUKlSkW9nx3kXZ5JbtiSrvJyEM2eIjIoiIyPDoO4DDzzADz/8gLW1dV1/HGECktiZqaysLDp27EhWVlZVmaOjI8ePH8fLy8uEkZk/vV7Pjh07mD59OvHx8Ubr2NnZ8frrrzN16lTs7OzqOUIhREOXl5dHbGwssTExlBUXo9dosC8txSE3D0uNBqVeh06hpEKtpsDZiSIbGxRqNdZ2dgSGhPDiiy+SmJho9NnNmzcnLi4O1wZytJmoXTIUa4b0ej0TJkwwSOoAPvzwQ0nq6tihQ4eYPn06v/76q9HrSqWSF154gQULFuDu7l7P0QkhGgsnJyf69u1Lr169yM3NJTMzk8zMTLIyMigrK0Or0aBSq7G0tsbP3R03Nzfc3NxwdnZGpVLRp0+fGhO7CxcuMGHCBDZu3CijN2ZIeuzM0Pr16xk5cqRB2WOPPcaWLVvkL3EdSUlJYfbs2XzzzTc11nnkkUd455136NixYz1GJoS4ExUVFbFgwQKSk5N57rnnWLlyJREREQZ11q9fz/Dhw00UoagrktiZmfPnz9OxY0fy8vKqylxcXIiLi8PNzc2EkZmn/Px8li5dynvvvUf5VZuC/iM4OJjw8HDuv//+eo5OCCEuycjIoGPHjuTm5laVOTk5ERcXJ8eKmRnZxMyM6PV6XnzxRYOkDuDTTz+VpK6WVVZWsmLFCtq1a8fSpUuNJnVeXl6sXbuWmJgYSeqEECbl7u7Op59+alCWl5fHuHHjkP4d8yKJnRlZvXo127dvNyh7+umneeqpp0wUkfnR6/V8//33BAQE8O9//5vs7OxqdZo0acKSJUtISEjg2WeflU2ghRANwrBhwxg2bJhB2bZt21i9erVpAhJ1QoZizURKSgoBAQEUFhZWlbm5uREXF4eLi4sJIzMfBw4cYOrUqezdu9fodZVKxfjx45k3bx7NmjWr5+iEEOL6srOzCQgIMNiCycHBgWPHjuHt7W3CyERtka4EM6DT6fjXv/5lkNQBrFy5UpK6WnDmzBlGjBjB3XffXWNSN2TIEOLi4vj4448lqRNCNFiurq588cUXBmUFBQX861//kiFZMyGJnRn47LPP2LVrl0HZmDFjGDRokIkiMg95eXlMmzYNf39/vv32W6N1unXrxu+//86WLVvw8/Or5wiFEOLmDR48mNGjRxuU7dq1i88++8xEEYnaJEOxjdzp06cJDAw0OKrKy8uL48eP4+joaMLIGq/y8nI++eQT3n777WoLUf7RunVrlixZwrBhw2QOnRCi0bl48SKdOnUiLS2tqszW1pbY2Fjatm1rwsjE7ZIWqRHTarWMGTOm2vmjq1atkqTuFuj1ev773//SoUMHpkyZYjSpa9q0KeHh4Zw8eZLhw4dLUieEaJSaNm3KqlWrDMpKSkp4/vnnDc6UFY2PtEqN2AcffFBtztf48ePp37+/iSJqvCIjI+nZsyfDhg3j77//rnbdwsKC1157jaSkJKZOnYqVlZUJohRCiNrTv39/XnrpJYOyP//8kw8++MBEEYnaIEOxjVR8fDydO3c22D+tTZs2xMbGYm9vb8LIGpfExETeeOMNNm/eXGOdp556iiVLlsjwhBDC7BQWFhIUFMSZM2eqyqysrDh8+DDt27c3YWTiVkmPXSOk0WgYPXq0QVKnUChYvXq1JHU3KDs7m1dffZUOHTrUmNT17NmTqKgoNm7cKEmdEMIsNWnShK+//tqgrLy8nNGjR6PRaEwUlbgdalMHIG7eO++8Q3R0tEHZ5MmT6dOnj4kiajzKysr48MMPWbx4Mfn5+Ubr/HOaxNChQ+VsXSGE2evbty+TJ0/m/fffryqLjo5m2bJlzJw506CuVqslNzeXzMxMMjMzycrIoLy0FJ1Wi1KlwsrGhmbu7ri5ueHm5oazszMqlaqeP9GdTYZiG5mjR4/SrVs3Kisrq8r8/Pw4fPgwNjY2JoysYdPpdKxfv56ZM2eSkpJitI6zszPz5s1j/PjxWFpa1nOEQghhOqWlpQQHB5OQkFBVZmFhQXR0NEFBQeTl5XH06FGOHTpEWXExeo0G+9JSHHNzsdBoUOr16BQKKtVq8p2dKbKxQaFWY21nR6cuXQgKCsLJycmEn/DOIYldI1JRUUG3bt2IjY2tKlMqlURFRXH33XebMLKGbc+ePUybNo2YmBij162srHj11Vd58803adq0af0GJ4QQDcT+/fsJDQ1Fp9NVlfXp04eJEyaQfPo0FiUleKek4pGbi2NxMRbXWD1bqVKRb2fHeWdnUrxbUmlrSxsfH0J798bDw6M+Ps4dSxK7RmTOnDksXLjQoOzNN99k8eLFJoqoYYuPj2fGjBn88MMPNdZ55plnWLRoEa1atarHyIQQomF68803Wbp0KSqVip49exLarRse5eV0OJ+BV3Y2qiuSvhulVSpJc3UlqZU3Ra6udAsNJTQ0FLVaZoPVBUnsGono6Gh69OhhsL9Qp06diI6Olq03rpKZmcn8+fNZuXJljfsx9e3bl/DwcLp27VrP0QkhRMNVXl7OfffdRwc/PzydnPA5eZIWCYk0d3HB0sLitp6tUyhI9PTkpI8Pzl6eDBw8GHd391qKXPxDErtGoLS0lJCQEOLj46vK1Go10dHRBAcHmy6wBqakpIT33nuPpUuXUlRUZLSOn58fYWFhPProo7IwQgghrpKcnMyG//s/1GfP0j4mBtuCAgDUaguaNXNFwe3/u1lga0tM+/aUtGjB408PkxGTWibbnTQCc+bMMUjqAObOnStJ3WVarZbVq1fj6+vL7NmzjSZ1zZo145NPPuHYsWMMGjRIkjohhLhKcnIy361fj1vaOXofOlyV1AFoNJUUFhTWynscSkroffgwTc+e4bv160lOTq6V54pLpMeugdu7dy99+vThyh9TSEgI+/btw+I2u8XNwc6dO5k+fTpHjx41et3GxoYpU6bw+uuv4+DgUM/RCSFE45CRkcG3a9fS9MxZesTFodDryc7OprKy4opaClxdXGpt1wCdQsG+gI5cbN2G4c89K8OytUR67Bqw4uJixowZY5DUWVlZsWbNmjs+qTt+/DgPP/ww/fv3N5rUKRQKxowZQ0JCAgsXLpSkTgghaqDRaNgeEYFt+nnuPnECpV6PAi7vEnDl6IaevIsXqa3eIKVez91xJ7A5n86OiAjZELmWSGLXgM2YMYPTp08blL399tt07NjRRBGZXnp6Oi+88AJBQUH89NNPRus88MADHDp0iK+//hovL696jlAIIRqXyMhI8tLOERIfj/qKVa8WajUODk0M6mq1GiquOPXodql1OkJOxJN77hxRUVG19tw7maw1bqB2797NihUrDMp69uzJlClTTBSRaRUVFREeHk5YWBglJSVG6wQEBBAWFsaAAQNkDp0QQtyA9PR0oiMj8U9MxMHIv612dvaUlZZRccWQrLKWT5JwLCnBLyGRA1ZW+Pj4yD53t0l67Bqg/Px8xo4da1BmY2PD6tWr77ijWTQaDStXrsTHx4cFCxYYTerc3d358ssvOXLkCA899JAkdUIIcYOi9u7FPjsbn3PnjF5XcOlUHmtra9QqNQ4OjljUwf5zvufOYZ+dTeTevbX+7DuN9Ng1QFOmTKl27NWyZcvw8fExUUT1T6/X8+OPP/L6668TFxdntI6dnR3Tp09n6tSp2Nvb13OEQgjRuOXl5XEmMZHOySkor7GOUqlU4uzkXKexKPV62iancMTFhby8PDl+7DZIj10Ds23bNr766iuDsnvvvZeXX37ZRBHVv8OHD/Pggw/yyCOPGE3qlEolL774IomJicybN0+SOiGEuAVHjx7FoqQEr+xsU4cCQMvsbNQlJQbHZoqbJ4ldA5KTk8OLL75oUNakSRO++uorlErz/1GlpqYyevRoQkJC2L17t9E6AwcOJDY2li+++ELmYQghxC3SarUcO3QI75TUWzomrC6odDpapaYSGxNT46lB4vrMP1toRCZNmkRGRoZB2XvvvUfr1q1NE1A9KSgoYNasWfj6+rJ27VqMba0YHBzMzp072b59+x29KlgI0Xio1WqCg4Or/quoqLj+TVdZtmxZrcf15ZdfYmtrS152Nh65ubf0jL8uXmRS/Ilajgw8cnIpKy4m9wbjunjxIl988UXV1wcPHmT69Om1Fs+BAwfo2rUrFhYWbNu2rdaeW5cksWsgNm3axPr16w3KBg4cWG0RhTmprKzkk08+oV27dixevJiysrJqdby8vFizZg0xMTE88MADJohSCCFuTdOmTTly5EjVf7eyse+tJHbX6+3auHEjHTp04FhcHE1rOH7RVByLi9FrNGRmZlaVXevzXJ3Yde3albCwsFqLp0WLFnz55ZeMGDGi1p5Z1ySxawAuXLjAhAkTDMqcnJxYuXKlWa7w1Ov1bN26lYCAACZOnEhWVla1Ok2aNGHx4sUkJCTw3HPP3RFD0UII87djxw7uuecegoODGTduHLrLw6Djxo0jJCSEjh07Vm11NWvWLC5evEhwcDATJ07k7NmzdO3atepZ06ZNY/Xq1QC0bt2at956i549e7Jnzx6+/vprunfvTmBgIHPnzq26Jzs7m7///pvRo0dz4tixqn3rsisqGBUby9Ajh1l+9izd9+8DoESrZcKJEww+fIiZiQn0jT5A8VWJVm5lJS/FxTHoUAyjYmNJu/xL+oyEUyw4ncSo2FgePBjN4YICJp+Mp//Bg4SfPVN1/3eZGTxx5DCDDsXwyd+nsS8t5ejRowQFBfHiiy/SuXNnysvLGTRoECEhIQQEBLB58+aq79GJEycIDg5m0aJF7NmzhyeffLLqsw4aNIjAwED69evH2bNnARgzZgyvvvoq99xzDz4+Pvz+++81/ry8vLwIDg5uVG1Q44nUTOn1el566SWyr5q8+tFHH9GiRQsTRVV3oqOj6devH0OGDCEhIaHadZVKxcsvv0xSUhJvvvkmNjY2JohSCCFu3z9JWXBwMOPHjyc7O5vly5ezZ8+eqh68jRs3ArB06VJiYmI4fPgwq1atIjs7m0WLFlX1+l29r6kxLi4uREVF4eHhwY4dO9i3bx9Hjhzh8OHD7Nt3KVH77rvvGDp0KG6urmRlZZFXWQnAxykpPODiwubgznhaW1U98z/n0/GytiKicxcecW3GeSObE3+UkkxXRwd+6BLCCA8PFv79v431i7Va/i8wkEnerXjpRBzTW7dhW5cu7MjKIreykqSSYn7PzWNjUDBbO3fhRFExmfEnyc3KIi4ujkmTJhEbG1t16lJMTAyRkZHMnDkTvV7PokWL6NChA0eOHGHWrFkGcc2fP5/evXsTGxvLhAkTeOWVVwx+Nvv37+fzzz/nrbfeuomfasMn252Y2H/+8x++//57g7KhQ4cycuRI0wRUR86ePcvMmTOrDTdf6bHHHmPp0qX4+/vXY2RCCFE3/knK/vHDDz8QGxvLPffcA0BpaSmenp4ArFu3jlWrVqHVaklJSSExMRFXV9ebet9TTz0FXNrgft++fYSEhACXNng/ffo0PXr0YMOGDSxbtoy4I0fo2rIlO3NyGObuzqHCAl729gbgEddmvHu5d+tQQSHjLp/gE+rkRFMje9jFFBQwvsOluc8DXV1ZdEVid7+zCwC+dna0trHB09oagFY2NmSUl3OwIJ/DhQU8fuQwcKmHsOPFiziUl+Pr60tgYGDVs9577z0iIiIASElJqTYn/Wp79+5lx44dAAwbNoxXX3216trgwYOBS2ev/9OTZy4ksTOhc+fOMWnSJIMyV1dXPv30U7MZgs3Ly2Px4sV8+OGHNU4c7tatG+Hh4fTp06eeoxNCiPqj1+t59NFHq21p9ffff/PJJ5+wb98+HB0deeihhyg30jOmVqurhm6BanVsbW2r3jNu3DiDIViAzMxMoqKiePLJJykqLERbWkq+lRXD3N2peRs7/TW+Mu7K1stSeekrJWCp+N8goRIF2ssvfdrdnX97t6q6dvSuNpzRaKo+D8Bvv/1GZGQk+/fvx8bGBn9/f6Pfo2vGdUW7amV1qVdSpVKZ3QpcGYo1Eb1ezwsvvMDFixcNyj///HOaN29umqBqUUVFBR988AHt2rUjPDzcaFLXqlUr1q1bx/79+yWpE0KYvXvuuYfffvuN1NRU4NIWV2lpaRQWFmJvb4+DgwNnz55l7xWnL1yZeDRv3pz09HQKCwspKipi586dRt9z3333sWHDBvLy8gBIS0sjJyeHTZs2MWHCBM6ePcu777zDZ089xdnSUnIrK+ji0ISfsi/Nd/7xiqlBnR0cqr6OuphHvkZT7X0hDg5suzxX+qecbAKbNKlWp8bviWNTdmRnk6+5NCScUV5OfnkFyqt6BgsKCnBxccHGxoYDBw5UTeVp0qQJhYWFRp/dq1cv1q1bB1xaoNi9e/cbjqsxkx47E1m1alW1Q+xHjhzJ0KFDTRRR7dDr9Xz33Xe88cYbnD592mgdR0dHZs+ezb///W+sL3fLCyGEuWvevDmffvopQ4YMobKyEgsLC1auXEmXLl3w8/MjICAAX19fevToUXXP6NGj6dSpE/feey8rVqzg9ddfp0uXLvj4+NCpUyej7wkICGDGjBn069cPnU5HkyZN+Pbbb9m4cWPVfDIrGxs0Fhb0c3bm5+wc/u3diskn4/n+wgX6Ojlhr7qUHjzj0YKpp04y+PAhujk44m5pifVVCwkmebfijYQEvr+QiaPagqW+vjf8PfG1s+NFTy9GxR5Djx47lYrn7roLSysrg3oDBgxgxYoVBAcHExQUVPXZXVxc6NKlC506dWL48OGEhoZW3TN//nzGjBnD2rVrcXZ2rlpocjNOnDhB//79ycvLY9u2bbRv354///zzpp9TnxR6Y5uGiTp19uxZOnXqRNEVy8w9PDw4fvw4zs51e2xLXYqKimLatGlVk3SvZmFhwcSJE5k9ezYuLi71HJ0QQoh/7N69m1M//8yD+/YDUK7ToVYoUCkU/JidxY6sLD5q3wGNXo9Or8dSqeRoYSELTiexObhznca2s8c9+A0YwP3331+n7zFX0mNXz3Q6HWPHjjVI6uDShpGNNan7ZwXrpk2baqzz5JNPsmTJEtq1a1ePkQkhhDDGzc2NGBsbKlUqLLRa0srKmHLqJDq9Hnu1mqU+l3rdSrRaRh87hkavx0KpYH7buv03vFKlosjGBjc3tzp9jzmTxK6erVixgt9++82g7F//+hcDBw40UUS3Licnh7fffptPPvmEystL5q/Wo0cPwsPD6dmzZz1HJ4QQoiZubm4o1Gry7exwLSigra0tWzt3qVbPQa1mS+e67aG7Ur6dHQq1ut4Tu59//pkZM2YYlPXp04cPP/ywXuOoDZLY1aPExMRqf3C8vb1Zvny5iSK6NWVlZXz00UcsWrSI/Px8o3Xatm3L0qVLeeKJJ8xmha8QQpgLZ2dnrO3sOO/sjGtBganDqXLe5VJc9T2CNWDAAAYMGFCv76wrsiq2nmi1WkaPHk1paalB+VdffYWDg4OJoro5Op2O9evX4+/vz+uvv240qXN2dub999/nxIkTPPnkk5LUCSFEA6RSqejUpQsp3i3RNpBTFbRKJcktWxIYEoJKpTJ1OI1Ww/hp3gGWL19ebVHBxIkTG83k0N9//527776bkSNHkpycXO26paUl06ZNIykpiVdfffWWzkQUQghRf4KCgqi0tSXtJjdCriuprq5obG0NNiUWN0+GYutBXFwcs2fPNihr27Yt77zzjokiunEnT55kxowZVbt9GzNixAgWL15M69at6y8wIYQQt8XJyYk2Pj4k5eTQMisLpQk3ydApFJxu5U0bX1+cnJxMFoc5kB67OlZZWcno0aMNNuhVKBSsWbMGOzs7E0Z2bRcuXGDixIkEBATUmNT16dOHAwcOsG7dOknqhBCiEQrt3ZsiV1cSLx9tZioJnp4UuboS2quXSeMwB9JjV8eWLFlCTEyMQdnUqVMNNlFsSEpKSnj//fdZunRpjbt5+/n5sWzZMgYNGiRz6IQQohHz8PCgW2go0WXleOTm4lBSUifvKSsro6y8HFsbm2pTdfJtbTnl60P3Xr3w8PCok/ffSWSD4jp0+PBhunfvjuaKI1jat2/PoUOHGtyJCzqdjm+++YbZs2eTlpZmtE6zZs2YP38+L774IhYWFvUcoRBCiLqg0WhY89VXaE/E0/vwYdRXnEdbG4qKiyko+N9iO2trGxwdHVEplWiUSv7o0hmL9u15buxY1Grpb7pdMhRbR8rLy3nuuecMkjqVSsWaNWsaXFK3e/duQkJCGDNmjNGkztrampkzZ5KUlMTLL78sSZ0QQpgRtVrNI4MHU9KiBX917ICulkdiSq7qBSwrK+XChQsUFBfzV4cOlHq0YODgwZLU1RJJ7OrIggULOH78uEHZm2++Sbdu3UwUUXXHjx9n4MCBPPDAAxw5cqTadYVCwejRo0lISGDRokWNZlsWIYQQN8fd3Z3Hnx5Grrc3+wI6oqnFLVBURp6lUSrYH9CRBDtbHJu54u7uXmvvu9PJUGwd2L9/P6Ghoeiu6M4OCgriwIEDDWIbkPPnzzNv3jxWrVplEOOV7r//fsLCwuhcjzuOCyGEMK3k5GS2bNiIbXo6IfHxtTLn7uqh2GIHB06GdCXd1oaNW7aQmprKsmXLmD59+m2/S0hiV+tKSkro3LkzCQkJVWUWFhYcPHjQ5HvzFBcXEx4eTlhYGMXFxUbrdOzYkbCwMB566CFZGCGEEHegjIwMtkdEkJd2Dv/ERHzOnbutrVDKysrIzctFp1CQ7utLor8/53JzidixgwsXLgDg6+vLqVOnausj3NFkQLuWzZo1yyCpA5g/f75JkzqtVsvXX3/N3LlzOX/+vNE67u7uvP3224wZM0bmOQghxB3M3d2d0WPHEhkZSbS1FWke7rRNTqFldjaqW1lYYWlJZqtWpLZrR7a9PZHR0URFRaHVaquqdOrUqRY/wZ1Neuxq0e+//869997Lld/S7t27ExkZaZJkSa/X89NPP/H6669Xm+/3D1tbW6ZPn860adOwt7ev5wiFEEI0ZOnp6URFRnImIQF1SQmtUlPxyMnFsbgYiysSs6tVqlTk29lx3sWZ5JYtySovJ+HMGSKjosjIyDCoGxISws8//4yLi0tdf5w7giR2taSoqIjAwEDOnDlTVWZtbc3hw4fx9/ev93iOHDnC9OnT2bVrl9HrSqWSsWPHsmDBAlq0aFHP0QkhhGhM8vLyiI2NJTYmhrLiYvQaDfalpTjk5mGp0aDU69AplFSo1RQ4O1FkY4NCrcbazo7AkBBeeOEFkpKSjD67devWHDt2TDoXaomMudWS6dOnGyR1AIsWLar3pC4tLY05c+awZs0aasrZH374YZYtW0ZAQEC9xiaEEKJxcnJyom/fvvTq1Yvc3FwyMzPJzMwkKyODsrIytBoNKrUaS2tr/NzdcXNzw83NDWdnZ1QqFSEhITUmdmfPnmX69Ol8+umn9fypzJP02NWCX375hQEDBhiU9e7dm99++w2VSlUvMRQUFLBs2TKWL19OaWmp0TpBQUGEhYXx4IMP1ktMQgghBFxakDFp0iTOnj3LqFGj+O9//0tkZKRBnV9++UXap1ogid1tunjxIp06dTLY2NfW1pbY2Fjatm1b5+/XaDSsXLmSefPmkZWVZbSOp6cnixYtYtSoUfWWaAohhBA1OX36NIGBgQabF3t5eXH8+HEcHR1NGFnjJxsU36bJkydXO60hPDy8zpM6vV5PREQEnTp14uWXXzaa1Nnb27No0SISEhIYPXq0JHVCCCEahLZt2xIWFmZQlpaWxuTJk00TkBmRHrvbEBERwWOPPWZQ9sADD/DLL7/U6R5wBw8eZNq0afz+++9Gr6tUKsaNG8e8efNwc3OrsziEEEKIW6XT6ejfvz+7d+82KI+IiGDQoEEmiqrxk8TuFuXk5NCxY0cyMzOryhwcHDh27Bje3t518s7k5GRmzpzJunXraqwzePBg3nnnHZOsxBVCCCFuRkpKCgEBARQWFlaVubm5ERcXJ9uf3CIZir1FEydONEjqAN5///06SeouXrzIjBkz8PPzqzGp69q1K3v27GHr1q2S1AkhhGgUvL29ef/99w3KMjMzmThxomkCMgPSY3cLNm7cyNNPP21Q9uijjxIREVGrQ7AVFRV89tlnvPXWW+Tk5Bit4+3tzZIlSxg+fDjKWjy0WQghhKgPer2eRx99lB07dhiUb9y4kaeeespEUTVektjdpIyMDAICAgwSLScnJ+Li4vDw8KiVd+j1ejZv3swbb7xR474/jo6OzJo1i0mTJmFtbV0r7xVCCCFMIT09nYCAAPLy8qrKXFxciIuLk7niN0m6eG6CXq/npZdeqtZ79sknn9RaUrd//3569erFk08+aTSpU6vVvPLKKyQlJTF9+nRJ6oQQQjR6LVq04OOPPzYoy8nJ4aWXXqpxs31hnCR216HT6ao2/P3mm2+IiIgwuP7kk09WG5a9FadPn2bYsGH06NGDqKgoo3WeeOIJTpw4wQcffICrq+ttv1MIIYRoKEaMGMETTzxhULZ161b+7//+z0QRNU4yFHsNO3bs4JlnnqG0tJRhw4axdetWCgoKqq43b96c48eP06xZs1t+R05ODgsXLmTFihVUVlYarXPPPfcQHh5OaGjoLb9HCCGEaOiysrLo2LGjwd6sjo6OHD9+HC8vLxNG1nhIYncN7dq14/Tp0zVe37JlC0OGDLmlZ5eXl/Pxxx+zcOFCLl68aLTOXXfdxdKlS3nyySfrdF88IYQQoqHYsmULQ4cONSjr378/P/30k7SFN+COSOy0Wm21Q4vLS0vRabUoVSqsbGxodtWhxYWFhTg5OdX4zBEjRlxzP7ma6PV6NmzYwJtvvsnZs2eN1nFycmLu3LlMmDABKyurm36HEEII0Zg9++yz1YZgP//8c8aNG2dQdivtu7mfwmTWiV1eXh5Hjx7l2KFDlBUXo9dosC8txTE3FwuNBqVej06hoFKtJt/ZmSIbGxRqNdZ2djRr0YLx48eTn59v9Nk+Pj789ttveHp63nA8f/zxB9OmTSM6OtrodUtLSyZNmsSsWbOumVQKIYQQ5iwvL4+AgADS09Oryuzs7Dh27Bht2rS5rfa9U5cuBAUFmW07a5aJXXp6OlF793ImMRGLkhK8U1LxyM3FsbgYC622xvsqVSry7ew47+zM3y08yNVqSTxzhr1RUWRkZFSrP2rUKL755pvrxpOQkMCMGTP4/vvva6wzfPhwFi9eTJs2bW7oMwohhBDm7Mcff2TgwIEGZY888gijn32Ws0lJt9y+p3i3pNLWljY+PoT27l1ru1o0FGaV2Gk0GiIjI4mOjMQ+O5t2ySl4ZWej0ulu+ln5pSWccXAgxceHbHt7IqOjiYqKQnvFH5zHHnvsmslaVlYWCxYs4PPPP0ej0Rit07t3b8LDw+nevftNxyiEEEKYs3HjxrFy5UpUKhU9e/YktFs3WlRU0D79/C2371qlkjRXV5JaeVPk6kq30FBCQ0NRq9V18Anqn9kkdhkZGWyPiCAv7Rz+iYn4nDuH8jY+Wl5eHqVlpegUCtJ9fUn09+dcbi4RO3Zw4cIFHB0d+fnnn7GysqK8vJxu3bpVnfxQWlrK+++/z5IlSwzOv7uSr68vy5YtY/DgwTIZVAghhDCisLCQPn360LVzZzydnPA5eRLPhETcXF1vOxHTKRQkenpy0scHZy9PBg4ejLu7ey1FbjpmkdglJyezZcMGbNPPExIfj0NJyW0/MzMzE63uf71zJQ4OxIeEcN7WlhKNhtmzZ7N8+XLCw8OBSxM9v/76a9atW8esWbNITU01+lxXV1fmz5/PuHHjsLCwuO04hRBCCHOVnJzMt2vXYpGSQvuYGGwvbzlmaWGJi6srtdEtUmBrS0z79pS0aMHjTw+jVatWtfBU02n0iV1ycjLfrV+PS3IK3U+cQH0L3bLGZGRmotMZjterbWw41TOUi21a49ayJaNGjTK4ftddd/H3338bfZ61tTWvvfYaM2bMwNHRsVZiFEIIIczVle27//59lF01AubQxAF7e/taeZdGqeSvjh3I9fbmiREjGnVy16hPnsjIyGDLhg04J6dwT1xcrSV1AA5NmsDl3wUUCiVOTs40b+pEaHw8TsnJxB48SPPmzQ3uqSmpe+6550hISGDx4sWS1AkhhBDXcXX77mRnj1plOPRaUFhIZQ3z12+WWqejx/E4nFNS2LJho9EFk41Fo03sNBoN2yMisE0/z90nTtzWfDpjbG1tcXNzw8XFFXd3d2wun8mq1Ovxi4zCraiYwQMHXnM/nPvuu4+YmBjWrFlDy5YtazU+IYQQwhwZa98VCgVNmzYFg8FXPRcv5lFbrb9Sr+fuuBPYnE9nR0REjYseG7pGm9hFRkaSl3aOkPj4Wu2pu5JKqcTK0tLgj1FxSQmVJcX4xxzE09mZnj17VrvP39+f7du3s2vXLrp06VInsQkhhBDmqKb23dLSEns7O4O6lZWVVFZU1Nq71TodISfiyT13rsZz2xu6RpnYpaenEx0ZiX9iYq0slLhRlRpN1YbFdgUF+Jw8SWi3btVW0YwYMYKBAwfKalchhBDiJlyvfW/i0AS12nDhYW0vFHAsKcEvIZEDe/dy/vz5Wn563WuUiV3U3r3YZ2fjc+5cvb730tYl//sj1CIhAdeiIkKv6rXbtWtXvcYlhBBCmIPrte8KFDg7O2OhtgAU2NnaYWlpWetx+J47h312NpF799b6s+tao9uNLy8vjzOJiXROTqn1eXXX888+dVVf6/W0TEoip3NnHB0dq3rz7r777nqNSwghhGjsbrR9V6tUNGvWrE5jUer1tE1O4YiLC3l5eY3q+LFGl9gdPXoUi5ISvLKz6/3djo6OVFRUoNFoUAAKpRL38xnYB+l47LHHyM7OpmfPnkyfPr3eYxNCCCEaM1O278a0zM7meEkJsbGx9O3b19Th3LBGldhptVqOHTqEd0rqLR0jcrsUQHMjvyX4Z2fTJDiYl1999ZqrZIUQQghRnanbd2NUOh2tUlOJjYmhV69ejaZ9v6k5dmvXriUkJIT8/HzGjBlDmzZtqpYDHz9+nH79+l3z/oiICN57771r1pk/fz4ff/xxtfI9e/YwZMgQyoqL8cjNvZmwr+l8eTkTTpzg/oPRPBxzkKmnTpKvqWRzZiZLzxjfl+5qHjm5lBUXs2HDBu677z4CAwP59ttvq65f+bnj4+MJCgoiODiYv/76q1Z697Zt20ZAQABKpZLjx4/f9vOEEEKIq7311lsEBATQqVMnunbtypkzZ2qs6+rqelPPzs291I7ujo42KG+/908GHz7EI4dieCU+nlKttoYn1JHUNP766y9yL+cdV7bnY8aMYdu2bTf9yIkTJ9K8eXO6du1aq6H+44Z77DZv3kxYWBi//vpr1Sa7Go2G9evX8+yzz97QMwYPHnxrUV5WXl6OXqOhaVHRTd2n1etRGVmhqtfrmRh/gmc9WvBphw4A7M3LI/8m9q7RA1Y5OZQUFrF48WLi4uIAeOaZZ7j33ntxc3Mz+Nxbt25lxIgRvPHGG8DNzcfTarVGf2Pw8/Nj06ZNjB8//oafJYQQQtyoqKgo9uzZw5EjR1Cr1aSlpWF31dYjtyMzMxO9RsO600lMvmJkrIlaTUTnS9uGTT11kvUZ5xnr6XXd59XU7t+sgrxcjmZmkJmZSbNmzW47jwEYOXIkY8eO5aWXXrrtZxlzw4ndzJkz2b17t8GExddee42wsLBqR2tpNBqmTp3Kvn37qKioYP78+QwZMoTVq1dz/PhxwsPDSUhIYOTIkajVakJDQ/n99985ePAgAEeOHKFPnz6kpaWxePFihg8fDkBWVhbf/Oc/fJ6ezgBXV15r1RqAL9JS2XrhAgpgnFdLBjdvzl8XL/JZWioOajVZFRW85+fPqydPUqzVAnrC/PzJqazATqXicTe3qth7XZ4geTC/oKpsV04On6WmUqHX4WllRbifP7YqFVvOnePTc2moAcuMDDza3lWV2Ol0Onr06MGuXbvYtGkTCQkJ9O3bl3fffRe1Ws2uXbsYO3Ys33zzDStWrKC4uJi5c+dy+vRp9Ho9c+bMoWvXrnzwwQdkZWWRnJxMu3btmDdvXrWfjUqlQqVSUVZWRmpqKra2tjf6YxVCCCGu6+jRo1haWpKSklJVVlFRwebNm/n4448pLy8nMDCQhQsXolQq0el0VacxrVixgp07d1JRUcGoUaMYOXIkAB999BHbt29HpVLRs2dP0uLiKNRoGHQohi5NHJhz110AaLRaVEolXR0cOVVcTLFWy/ykJE6XXtoOZdZddxHi4MiHyclkV1aQXFpGO1tbRnp4MC8piXyNBkulgjUBnVAoFDXem1FRztnSUjLKK5jSuhWPNmvOR3+f4XRFOUOGDGHWrFkoFIqqPOZKBw4cYOrUqRQXF9OmTRvWrFlT43FnoaGhnD17tlZ/Ple64cRu+/bteHp6GpT5+vri5+fH1q1badeuXVX5l19+SevWrfnggw8oKCjg7rvv5uGHHza4d/LkycyePbvqm3WlM2fO8Ouvv5KcnMyAAQOqErv4+HiWDRnC/alpDI89yn3OLiiBH7Oy2RzcmVKtlieOHuHuyz2KRwsL+bFLCG5WVqxKS6O7oyNTWrdGo9dTqdNxIP8i7W/gN45ujg484BIMwKepKfw34zwPqS34/Fwai9zc8LK05EhwMH9d1Zt25swZ2rZtW/X1qlWrqv4/IyOD3bt3AxjU+cfTTz9drSwqKoq1a9deM9aBAwde9/MIIYQQt8JYe/WP+Ph4NmzYcM26c+bMYc6cOdXKO/j58cRdd7E/OprPPTwAuHAhE51Ox4ULmWhR8Gt2Fve6uvJJagoPurgQ5upHRnk5L8bF8cPlwwASiktY26kTlkolQ48cZmqr1oQ6OVGk0WCtUvFe8tka700rK2Ntp0DSy8oYG3ecR5s157VWrfiwqJApCxYw/JlnWL16dbXYKyoqmDZtGhERETg5OREWFsbHH39cNTJX3244sVu3bp3RH8bMmTMZP368QdKyc+dO4uLiWLNmDQDFxcWcu2pPmpiYGB577DEAhg8fzs8//1x1beDAgajVatq2bcvFixerytu1bYu7tTWWSiUPurhwuOBSr1p/VxeslEqslEp6ODblWFERTVQqujg44GZlBUBgkybMSDiFSqHgIVdX/OzsuLSa+vpdtell5Uw6E09uZSUlWh3d7Gzp7+REgLU172ZlcX+TJrSqqMBazoEVQgghbpqjgwNqIxsSF+l0/Cs1FYBAaxuecHNn+NGj/JGby8epl3oPL2oqqbi84OJ+F2cslUqKNBoKNBpCL4/C2asvpTtReRdrvLevkzNqhQJvGxsKrpiSpdTrqSgrqzH2U6dOERsby7333gtcSvSut+agLt1wYrdp0ya8vLx4/vnnDco7d+6Mk5NTVe8TXJq79sUXX9CnTx+Dun/88YfRZ+uv2q/G6nIyZsyVe9soqL7jtJ7/pWo2V+w7183RkXWBQezJzeXVk/FMb92Gdra27MrNqfFd/1j492nGt/Smt5MT27Iu8OuFCwBMcXXlRHk5UcXFrN69m0GPP37dZwkhhBDCkFqpRGFkNay9Usmqy2etKxRKLJVK9Oj5vENHWlw+w/1K1sr/jZwZ67a51r2WyhrWk+r1aK8x916v19OlSxd+/fXXGuvUpxteFbtjxw7efvttfvrpp2rXZs6caTDe/MADD/DZZ5+hvbx65ciRI9Xu6dKlCz/88AMA//3vf28ohqTTp8kqKaFCp2NXTg7BDg6EODiwMyeHCp2OfE0lf+VfpFOTJtXuPVdWhqulJcM9PHiseXNOFRfTs2lTCjUatl5O1AB+zckhpazU4N4irRZ3S0t0ej3bsrKwsLBArVaTrtHQ0dqaF5ydUSmVFNfj8WZCCCGEuSguLUWvVKJSKNAa2ZxYpVTRtGlTFEDPpk7854qjvuKNLKi0V6txUKuJzMsDoEijQaPX39C9V7JTqyjVaFCpa+4H8/f3Jzk5uSrXKS4uJikp6ZrPrUs33GPn6elJREQEDz/8MFu3bjW41qdPH1q1alX19UsvvcTff/9NcHAwer0eX19fNm/ebHDPe++9xzPPPMPixYvp06cPDg4O143Bz9eXr6KjCb9wgQGurgRdTuAecnXl8SOHUQCveLeiuaUlZ65Ksv7Kz2fVuTTUCgUOajXL/fxRKBR80r4Db/19mo9SkrFUKulgZ89cR8N5AS+39OalEyfwsLLE386eIq2G5s2aMzfuOGdLStDpdHTx8UFlYXh+Xf/+/fnuu+9Yt24dJ06cYMmSJSxcuBAXFxcmTJjAH3/8wWeffca6desoKipiypQpHD58GK1WS79+/Vi+fLlB/Zrs3LmTCRMmkJ2dTdOmTenbt2/VMLgQQghxuw4dOsSUKVMuH615abTuo48+4s8//2T+/PloNBrUajUrVqygc+fOtGzZktTLQ6jLly9n/fr16PV6mjVrxqZNm7Czs2PJkiVs2rQJtVqN2tISSysrhrq7M+78ee52bMq8tm1RJifj4e5hcPb6RG9v3j59mkcPxaDV6+nRtClz7dtViznM1485SYksPXMGK6WSNZ063fC9//CztaNSr2fuW2+hVSqNngFvaWnJt99+y8svv0zR5URx2bJlBmsPrvTCCy+wfft2cnJy8PLy4qOPPuLxWhzxU+ivHgetJyUlJdjY2KBQKAgLCyMzM7PaKpOr7d69m1M//8yD+/bXU5Q3pqKygp+6dmVHfHxVV6yVlRXnz59vVMeQCCGEEKbQUNt3gJ097sFvwADuv/9+U4dyQ0x28sSBAweYPHkyWq0WLy+v6672BHBzcyPGxoZKlQqL+t6k8BoU1jZoXVyYOHEiLVq0ICsri2nTpklSJ4QQQtyAhtq+V6pUFNnY4HbFtmgNnckSu379+hmde3ctbm5uKNRq8u3scC0ouP4N9STfzg6FWk3v3r0ZOnRonb3n66+/5oMPPjAoGz58uMmWVAshhBC1oaG377eS2D3++OPVTudYt24dHS4fiFBXGtVZsc7Ozljb2XHe2blB/eDPu1yKy9nZuU7f8/zzz1dblSyEEEI0dubYvm/ZsqUOIrq+mzor1tRUKhWdunQhxbsl2pqWJdczrVJJcsuWBIaENJoDgoUQQoiGRNr32tMwvns3ISgoiEpbW9Ju8oDhupLq6orG1pbAwEBThyKEEEI0WtK+145Gl9g5OTnRxseHpFbe6GrhgN/boVMoON3Kmza+vrJQQgghhLgN0r7XjkaX2AGE9u5NkasriVedXVvfEjw9KXJ1JbRXL5PGIYQQQpgDad9vX6NM7Dw8POgWGspJHx8KbG1NEkO+rS2nfH3o3qsXHpcPLBZCCCHErZP2/fY1ysQOIDQ0FCcvT2Lat0dTzxMtNUolMR3a4+zpSc+ePev13UIIIYQ5k/b99jTaxE6tVvPI4MGUtGjBXx071Nt4vE6h4K+OHSj1aMHAwYNRX+P8OCGEEELcHGnfb0+jTewA3N3defzpYeR6e7MvoGOdZ/YapZJ9AR3J9fbm8aeH4e7uXqfvE0IIIe5E0r7fOpOdFVubkpOT2bJhI7bp6YTEx+NQUlLr78i3tSWmQ3tKPVrw+NPDaNWqVa2/QwghhBD/I+37zTOLxA4gIyOD7RER5KWdwz8xEZ9z51DWwkfTKRQkeHpyytcHZ09PBg4e3KgzeSGEEKIxkfb95phNYgeg0WiIjIwkOjIS++xs2ian0DI7G5VOd9PP0iqVpLq6crqVN0WurnTv1YuePXs22jF3IYQQorGS9v3GmVVi94/09HSiIiM5k5CAuqSEVqmpeOTk4lhcjIVWW+N9lSoV+XZ2nHdxJrllSzS2trTx9SW0kS55FkIIIcyJtO/XZ5aJ3T/y8vKIjY0lNiaGsuJi9BoN9qWlOOTmYanRoNTr0CmUVKjVFDg7UWRjg0KtxtrOjsCQEAIDAxvdjtNCCCGEuZP2vWZmndj9Q6vVkpubS2ZmJpmZmWRlZFBRVoZWo0GlVmNpbU0zd3fc3Nxwc3PD2dm5UR34K4QQQtyJpH2v7o5I7IQQQggh7gSNeh87IYQQQgjxP5LYCSGEEEKYCUnshBBCCCHMhCR2QgghhBBmQhI7IYQQQggzIYmdEEIIIYSZkMROCCGEEMJMSGInhBBCCGEmJLETQgghhDATktgJIYQQQpgJSeyEEEIIIcyEJHZCCCGEEGZCEjshhBBCCDMhiZ0QQgghhJmQxE4IIYQQwkxIYieEEEIIYSYksRNCCCGEMBOS2AkhhBBCmAlJ7IQQQgghzIQkdkIIIYQQZkISOyGEEEIIMyGJnRBCCCGEmZDETgghhBDCTEhiJ4QQQghhJiSxE0IIIYQwE5LYCSGEEEKYCUnshBBCCCHMxP8DRi0Ao+Y3NZYAAAAASUVORK5CYII=", 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", + "image/png": 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", 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", 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OO7mWoqIigoKCSElJMcVsbGyIj4/H39//5r+sEELUMSnshBCNhk6no1+/fsTExJhiCoWCHTt2MHDgwGtem5eXR0JCAofi4igrLsao0+FQXIJVZsalA4oNBoxKJdbOLhS2caXIzs50QHFgWBjBwcG0bt26xvvv3LmTIUOGmO2K7dWrF9HR0ajVcta7EKJxkMJOCNFoLF26lAULFpjFnn32Wd55550ar8nMzCR6zx5SkpOxKinBJy0dT60W5+JiVJWVZF/VVszD3QOlUkmlSnWppZirK2k+Hai0t6ejnx/hAwbg6elZ7bOeffZZ3nvvvSo5v/LKKzf3hYUQoo5JYSeEaBQSEhLo1asXlZWVpljXrl2Jj483HUFyJZ1OR1RUFDFRUTjm5tI5NQ3v3FxUBoNpjMFgqLGwu5JeqeSsmxsnfX0ocnOjV3g44eHhVWbiSktLCQ0N5fjx46aYlZUVMTExBAcH39L3F0KIuiCFnRDC4ioqKujVqxeJiYmmmFKpJDo6mj59+lQZn52dzdbISPLOZnBHcjJ+GRkoq/lRVtvCzjReoSDZy4tjfn64ensxcswYPDw8zMbs27ePfv36YbiigAwODmb//v1YW1vf0PcWQoi6JrtihRAWt3jxYrOiDmDevHnVFnWpqamsX7sW/ZGjDNm3j65nz1Zb1N0MpdFI17NnGbJvH7ojR1m/9j+kpqaajenTpw/z5s0ziyUkJLBkyZI6yUEIIW6FzNgJISxq//799OvXD71eb4oFBgYSExODjY2N2djU1FQ2rVtHm9Q0eh85gvqKWbPq3OiM3ZV0SiX7undD6+PD+AcewNfX1/RZeXk5vXr14tChQ6aYSqVi79699OrV67r3FkKI+iIzdkIIiyktLWX69OlmRZ1arWbt2rVVirrs7Gw2R0TgmppG36Sk6xZ1ACgUgOLKwOXY9akNBu48nIRrWhqbIzaQnf2/AtHGxoY1a9aYvYOn1+uZPn06ZWVltbq/EELUBynshBAWs3DhQo4dO2YWW7RoESEhIWYxnU7H1shI7DOz6HPkSK2XXpUKBTY2/3vvzcbGBmUtCzu4tDTbJ+kIdlmZ/BgZiU6nM30WGhrKwoULzcYfPXq0SkwIIRqSLMUKISxi9+7dDBo0yOxcuB49erB3716srKzMxu7cuZOY3/9gyL59OF3RO7Y2jEYjxZevcbC3R3EDhd1fCuzt2dG3D72HDjU7T6+yspI777yT2NhYU0yhULBr1y769+9/w88RQohbJTN2QogGV1RUxIwZM8yKur+WN68u6jIzM4mJiuKO5OQbLurgUqHl6OCAo4PDTRV1AM4lJXQ9kcz+PXvIysoyxa2srFizZo3Zblij0ciMGTMoLi6+qWcJIcStkMJOCNHg5s2bx+nTp81iS5YsoXv37lXGRu/Zg2NuLn4ZGQ2VXrW6ZGTgmJtL1FU9bLt3787rr79uFjt16lSVnbNCCNEQpLATQjSo3377jY8//tgs1q9fP+bOnVtlbF5eHinJyXROTauzI01ultJopFNqGiknTpCXl2f22dy5c+nXr59Z7KOPPuL3339vyBSFEEIKOyFEwykoKGDmzJlmMTs7O1avXo1KpaoyPiEhAauSErxzcxsqxWvqkJuLuqSkypl7KpWK1atXV+mQMXPmTAoLCxsyRSFECyeFnRCiwcydO5f09HSz2FtvvYWfn1+VsXq9nkNxcfikpZu1CbMklcGAb3o6ibGxZke0APj5+fHWW2+ZxdLS0qqdiRRCiPoihZ0Q4poGDRrErl27zGJz5szh008/ve61Bw4c4IUXXgDghx9+YNWqVWafDxkyhMcee6zaa7VaLWXFxXhqtdd9zszDhxgTH8egmP303fcnY+LjGBMfx/HiYv52MP66198IzwtaXlq4EG01eT322GMMGTLELPbFF1/g7u5OUVFRlfEzZszghx9+qPFZ48aNo3Xr1kyYMOHWExdCtAhS2AkhrmnSpEls2LDB9Ge9Xk9kZCTjx4+/5nV6vZ6ePXvy9ttvc+HCBWbNmmX2eatWrVi1alWNXSBycnIw6nS4VFMQXW1VQCCRoWE87ePL/e3aERkaRmRoGA7VLO9Wm+sNvL/nXFwMRiM5OTlVPlMqlaxatYpWrVqZxS9cuFBtIXg9Tz31FGvXrr3h64QQLZcUdkKIa5owYQLfffedqen9zp076dKlCxqNhrCwMEJDQ9lzeafojh07GD58OJMmTWLIkCHs2LGDCRMm8OSTT5p1boBLBxHfdtttvPbaazzyyCMMHDiQ22+/nfXr1wOXCrtdv//O2AMx3BcXx9rMS7tid2i1TEw4yJj4OBYkJ2O4TlFWaTDy4onj3Bt7gKePHTUdsTIkZj8fpqWiSTjIvoJ8NuVkM/5gPPfFxfJ+6hkAivV6Hj58mNFxsYyOi2V3Xh5Wej0KLvW3DQwMZOjQoaajTeLi4pg0aRJOTk5mOej1ep5//nng0qHM/v7+jBo1inPnzl0z9yFDhlQpEoUQ4lqksBNCXJO7uztdunRh9+7dAGzYsIEpU6awZcsW4uLi2LJlC88++6xp/L59+3jvvfdMy7dnz55l3bp1ZvcMDg7m7Nmzpj+npKTwxx9/8Ouvv7JgwQIAtv30EymnT7M5JJTvw8IY07Yd2spKvszI4L+BQUSGhmGlVPBj7vlr5n+6tIRHvTvwU1gPLlRUcuCKzQwuaisigkNoZ23NTm0eG4JD2BIaxpGiYuILC9mTl4eLlZofwnrwfWgYoZeLrOLSUrp07syhQ4fw8vLi22+/BWD69Ol88MEHpKen06FDB7M8vvnmG958801+/vlnEhISWLlyJdHR0Tf070IIIa5HCjshxHVpNBq++eYb9Ho933//PWPGjOHFF18kMDCQMWPGcOTIEdPY8PBw2rdvD1w6riQ+3vwdN6VSSVFREUePHjXFRo4ciVqtplOnTuTn5wOQmJDAXZ06YX15qdbFyoqDhYUcLyk2zdhF5+dztqz8mrl3tLOj0+WOE90cHcgo/18v1xFubgBE5+cTf7GQcQfjuf9gPKdKS0grK6OLgz0HCgt5KyWFgxcv4ni5N6ytWk2XTp2AS90yzpw5Q0FBAeXl5fTp0weFQsEHH3xQ5bDl119/nbvvvhtra2s8PT256667av3vQAghakN9/SFCiJZu/PjxvP7664wdO5agoCB+/PFHiouLiY+PR6VSYW9vbxr71/9vNBpZsWIFFRUVZvdau3YtnTp14qWXXjLFbGxsqjzTaDRydZ8IIzCktSvLunSpde7WV7zDp1QoMFyxcmt7xTt4Gg8PnvDxrXL9dyGh7NBqef30Ke5v587U9u2xUqnQX+4bq1Kp0Ov1XN2d0c3NjeDgYA4cOGCKFRcXExkZydKlS2+6C4YQQlyLzNgJIa7Lzc0Nf39/nnvuOSZNmkRhYSHu7u6o1Wo2btxIWVlZlWu++uoroqKizGJ33nknU6ZM4b///e91n9m9e3e2nzxJxeV3+/IrKwlp1Yp9BflklV+apcurrCS7/NozdrXR19mFH3NzKdBVApBdXk5eZSU55eXYq1SMc3dnensvjhb/byOHSm3+e7GLiws2NjbExMQA8PXXX/PAAw8wbtw4s3FJSUmsXbuW7Oxstm/ffsu5CyHElWTGTghRKxqNhjlz5nD//fej0+kYNWoUvXv3pn///rRp08ZsbEZGBk8++aRZzNnZmezsbAYMGFClS0N1evfuTdqePdx/MB61QsFEdw+mtm/Pa50789iRI+iMBtQKJa/7+eFRzYzfjeji4MAsL28eTDyEESMOKhXvdr2DU6Wl/DPlNEqFAlulkjcun7dnBKxtbavcZ/Xq1cyZM4eysjJCQkJ47LHHePDBB9myZYtp8wlcOrh42LBhDBw48Jp53XPPPcTFxVFcXIy3tzebN2+mV69et/RdhRDNm8J49fqBEELcAqPRyMiRI/n555/N4hs3brzuESlX+v333zm+bRt37/2zrlO8Zb/e2Zeu99zD0KFDazV+06ZNVc6iGzFiBFu3bpUlWSFEnZKlWCFEnfriiy+qFHVTpky5oaIOLu3GLbKzo7KWZ9E1lEqViiI7O9zd3Wt9zfjx45kyZYpZ7KeffqpyYLMQQtwqmbETQtSZM2fOEBgYaNZlwdPTk8OHD+Pq6npD9zp//jyrP/2U/n/uw60R9VvNdXJiT98+zJg9m7Zt29b6Oq1WS0BAAFlZWaZYq1atOHToEJMmTaL8qncFd+zYgYuLS12lLYRoIeQdOyFEnTAYDMycObNK66zPP//8hos6AFdXV2wdHMhydW1UhV1Wm0t53eh3cnV15fPPP2f06NGm2MWLF5k5cyZ79+6tsQOHEELcCPlJIoSoEx999FGVXZ4PP/wwo0aNuqn7qVQqAsPCSPPpgL6RFD16pZLUDh0I6tED1U0sEY8aNYqZM2eaxf744w8+/vjjukpRCNHCNY6flkKIJu3EiRPMmzfPLObj48M777xzS/cNDg6m0t6es5cPEra0dDc3dPb2BAUF3fQ93n33XXx8fMxi8+bNIzk5+VbTE0IIKeyEELdGr9czY8YMSktLzeKrVq2q0jP1RrVu3ZqOfn6c9PXBYOHdowaFglO+PnTs0oXWrVvf9H2cnJyqbJooKSlhxowZ6PX6W01TCNHCSWEnhLglK1asYO/evWaxxx9/vNZHgVxP+IABFLm5kezlVSf3u1knvLwocnMjvH//W77X0KFDeeyxx8xi0dHRvPvuu7d8byFEyya7YoUQNy0pKYmwsDCztmGdOnUiISEBBweHOnvOzp07ifn9D4bs24dTSUmd3be2Cuzt2dG3D72HDr3uocK1VVRUREhICKdOnTLFbGxsiIuLo1u3bnXyDCFEyyMzdkKIm1JZWcm0adPMijqFQsGaNWvqtKgDCA8Pp7W3F7H+/ugaeCOFTqkktps/rl5eteqYUVuOjo6sXr3a7IDi8vJypk+fTmVlZZ09RwjRskhhJ4S4KcuWLSMuLs4s9txzzxEeHl7nz1Kr1YwaM4aS9u3Z171bg71vZ1Ao2Ne9G6We7Rk5Zgxqdd2eENW/f3/mzp1rFjtw4ABvvvlmnT5HCNFyyFKsEOKGxcXF0adPH3Q6nSnm7+9PXFwcttX0UK0rqampbFq3Dte0NPokHUF9Rf/VuqZTKtnXvRtaHx/GP/AAvr6+9fKc0tJSwsLCOHbsmCmmVquJiYkhJCSkXp4phGi+pLATQtyQ8vJyevbsyeHDh00xlUrF3r17G6RBfWpqKpsjNmCfmUmPo0fr5Z27Ant7Yrv5U+rZnnGaSfVW1P1l//799OvXz2xXbGBgIDExMdjY2NTrs4UQzYssxQohbshrr71mVtQBvPzyyw1S1AH4+voyedpUVN382d6nD8e9vetsadagUHDM25sdfftg5e/P5GlT672oA+jduzcvvfSSWezQoUMsXry43p8thGheZMZOCFFrf/75J+Hh4RiuWAINDg5m//79WFtbN2guOp2OqKgoYqKicMzNpVNqGh1yc1HdxPKsXqkk3c2NU74+FLm50bt/f/r161fn79RdS0VFBb169SIxMdEUUyqVREdH06dPnwbLQwjRtElhJ4SolZKSEkJDQzlx4oQpZmVlxYEDB26pE8OtyszMJDoqipQTJ1CXlOCbno7nBS3OxcVYXePA30qVigIHB7LauJLaoQM6e3s6dulCeP/+eHp6NuA3+J+EhAR69epltiu2a9euxMfHY2dnZ5GchBBNixR2QohaefbZZ3nvvffMYkuXLuWVV16xTEJXycvLIzExkcTYWMqKizHqdDiWluKkzcNap0NpNGBQKKlQqyl0bU2RnR0KtRpbBweCevQgKCjoljpK1JWlS5eyYMECs9izzz57y+3ZhBAtgxR2Qojr2rlzJ4MHDzaL9e7dm6ioqAZdrqwNvV6PVqslJyeHnJwczmdnU1FWhl6nQ6VWY21rS1sPD9zd3XF3d8fV1RWVSmXptE10Oh39+vUjJibGFFMoFOzYsaPODkcWQjRfUtgJIa7p4sWLBAcHk5KSYorZ2toSHx/PHXfcYcHMmq+jR48SGhpKeXm5KdaxY0cSExNxdHS0YGZCiMZOdsUKIa7phRdeMCvq4NJyoRR19cff35+lS5eaxVJSUnjhhRcslJEQoqmQGTshRI22bdvGvffeaxYbMGAA27dvb1TLl82RXq9n8ODB7Nmzxyy+bds2hg8fbqGshBCNnRR2Qohq5efnExAQQEZGhilmb29PYmIinTp1smBmLcepU6cICgqi5IpDmL29vTl06BAuLi6WS0wI0WjJUqwQolrPPPOMWVEHsHz5cinqGlCnTp14++23zWJnz57l2WeftVBGQojGTmbshBBVREZGMnbsWLPYsGHD+OWXX1DUUZcHUTsGg4Hhw4fz+++/m8W3bNnCmDFjzGLV7QguLy3FoNejVKmwsbNr1DuChRC3Tgo7IYSZ3NxcAgICyMnJMcWcnJw4dOgQPj4+Fsys5UpLSyMgIICLFy+aYu7u7iQlJdGmTRvy8vJISEjgUFyc2Rl+zlotVjodSqMRg0JBpVpNgaur2Rl+gWFhBAcHN4oz/IQQt04KOyGEGY1Gw4YNG8xiq1at4qGHHrJQRgIu/Tt4+OGHzWLTpk3jvlGjSElOxqqkBJ+0dDy1N9B1w9WVNJ8OVNrb09HPj/ABAyzWdUMIUTeksBNCmERERDB58mSz2OjRo4mMjJQlWAszGo3cd999bN26FZVKRb9+/Qjv1QuvSh13ZGTgfQt9cs+6uXHycp/cXuHhhIeHN7qDp4UQtSOFnRACgOzsbLp3745WqzXFWrduTVJSksziNBJZWVkMHDiQwQMG4NW6NX7HjuGdfBL3tm1RKW9tL5xBoSDZy4tjfn64ensxcswYPDw86ihzIURDkV/JhBAYjUYeffRRs6IO4OOPP5airhGpqKjgoQcfxPbsWfy3b8e+sBCAgvx8XF1db+neSqORrmfP4qnVElvoz/r8AsZpJuHr61sXqQshGogcdyKEYO3atURGRprFJkyYgEajsVBG4mqpqalsWrcOr+wc+u7bbyrqAMrKyygpLa2T5ziVlDAgPh6XMylsWreO1NTUOrmvEKJhyFKsEC1ceno6gYGBFBQUmGLt2rXj8OHDtG3b1oKZib9kZ2ezfu1aXFLOcGdSEuj1nDt/HoPhfxskFAol7dq1RaWsm+NLDAoFewO6k39bRyZPmyrLskI0ETJjJ0QLZjQaeeSRR8yKOoDPPvtMirpGQqfTsTUyEvvMLPocOYLSaESpVOLs7Gw2zmg0kJ9fUMNdbpzSaKRP0hHssjL5MTISnU5XZ/cWQtQfKeyEaMH+/e9/88svv5jFpk6dyv3332+ZhEQVUVFR5J3NoMfRo6iv2PVqZ2uLnZ292djy8jIq67AAUxsM9DhyFG1GBtHR0XV2XyFE/ZHCTogW6vTp0zz33HNmsfbt2/P+++9bKCNxtczMTGKiorgjORmnK/rF/sXZ2RnlVUuvdf12jXNJCV1PJLN/zx6ysrLq9N5CiLonhZ0QLZDBYOChhx6iuLjYLP7FF19IB4JGJHrPHhxzc/G7qmfvX5QKBa6urqbizt7OHmsrqzrPo0tGBo65uUTt2VPn9xZC1C057kSIFuhf//oXu3btMovNmjWLe++910IZiavl5eWRkpxMaGoaymvMwllbWeHu7o7RaERZT4dIK41GOqWmcfBy+zIp/oVovGTGTogW5vjx47z88stmsdtuu40VK1ZYKCNRnYSEBKxKSvDOzb3uWAXUW1H3lw65uahLSkhMTKzX5wghbo0UdkK0IDqdjunTp1NWVmYWX7VqFa1atbJQVuJqer2eQ3Fx+KSl31SbsPqgMhjwTU8nMTYW/TX60AohLEsKOyFakOXLl7Nv3z6z2FNPPcWQIUMslFHLpFAoWLBggenPzz//PKtXrzb9WavVUlZcjOdVnUCutjojg4oGLPw8L1zK6+oOJY8//jjt2rWjZ8+eDZaLEKJ6UtgJ0UIcOnSIRYsWmcX8/PxYtmyZhTJquRwdHfnqq68ovKJ7xJVycnIw6nS4FBVd8z5rMjOorOb9O6PRiKEezp53Li7GqNORk5NjFp8yZQo//fRTnT9PCHHjZPOEEC1ARUUF06dPp7Ky0hRTKpWsXr0ae3v7a1wp6oONjQ1///vf+eSTT5g3b54pnpyczIwZM1i8eDGOpaXs017gm+xsVnS9g3knjpNUVIRKoeAhLy9K9QbOVVQwOeEgHWzt+LhbN3r/uZeJHh7szc9neZeurMvOIjo/H5VCwbyOHQl3aY3OaOTN06eJv1hIpdHIkz4+3N3GjW9zctiuvUCZwcCpkhKe8PHlbFkZv17Ixc3ams+6dccacCwtJScnh4CAAFPe4eHhnDlzpuH/QQohqpDCTogWYOnSpcTHx5vFnn/+efr162ehjMTTTz9N3759efrpp00xPz8/lEolhw4epK1Wyzc55xjXzp2jxUWcLSvnpx6Xljov6nS0Uqv5IuMs64NDcFBdOu4kX6ejp5MzL9zWkZ9zz5NaWsb3oWFklpcz9VAiP/foybc5OXjZ2rKgUyeKdDomJBxkUGtXAE6WlPBtSCj5Oh0jYg+wrEsXnvLtwTPHjrJDq2W4mxtO2jzOZ2c3/D8wIUStSGEnRDMXGxvL0qVLzWLdu3fnH//4h4UyEgBt27Zl9OjRrFq1yiw+Y8YMIr/7jvFOThy8WMibXbpQpNdxrqKc106dZJhrG/rXcNyIrVLJENdLRVpsYSH3tW2LUqHA29aW2+zsOF1SQlR+HsklJWw+d2k5tdRgILuiHIA7XVywU6mwU6mwUioZ6toGgK4ODmSUXxpjrdNV2XwjhGg8pLATohkrKytj2rRpZrsYVSoVa9aswdbW1oKZCbg0azps2DBGjBhhik2aNIlXFy3C+7bbGNamDSqFAme1Fd+H9WBXnpZVGWfZk5/HSx1vr3I/W2XNr00bubRpwwi83tmPXlf1mj1QUIj1FdcrwPRnJQrTO3tKowG99I0VotGSzRNCNGOvvvoqR44cMYstWLCAHj16WCgjcaUOHToQHh7Opk2bTLFWrVrh4+PDuvh47m/nDoC2shKj0cgIt7Y87uPD0aJLHUMcVCqKazh6pIeTE1tzz2MwGskoKyOttJSOdnb0c3FhXXYW+suF2pHrbNC4mkGhRKWWOQEhGisp7IRopqKjo3n77bfNYqGhocyfP99CGYnqzJs3j8zMTLNY//BwnOzt6ergAEBOeTl/P5TIfXFxvH7qNE/4+AAwycODqYcSeeyq4h1geBs3Otjacl98HHOOHmGJnx82SiWTPTxpZ23N2Pg4RsXF8lF62g3lW6FWY33VbO8jjzzCnXfeSWJiIt7e3mzevPmG7imEqDsKY113jBZCWFxxcTEhISGcPHnSFLO2tiY2NtZsN6NonKZOnUp5VhZLyyssnUoVv97Zl6733MPQoUMtnYoQohoyYydEM/Tyyy+bFXUAixcvlqKuCRgxYgQHDx4kqFcvKi/vdm0sKlUqiuzscHd3t3QqQogayIydEM3MH3/8UWU2pW/fvuzZswdVIysURPXOnz/P6k8/pf+f+3Cr4RBjS3gk+QTJCgWubdqgvvye3ddff023bt0snJkQ4i/yBqwQzUhhYSEzZ840i9nZ2bFmzRop6poQV1dXbB0cyHJ1bVSF3dPDh5MREsJjTz8tf5+EaKRkKVaIZuS5554jNTXVLLZs2TK6dOlioYzEzVCpVASGhZHm0wH9NY4waUh6pZLUDh0I6tFDijohGrHG8RNDCHHLfvrpJ1auXGkWGzRoEE8++aSFMhK3Ijg4mEp7e866uVk6FQDS3dzQ2dsTFBRk6VSEENcghZ0QzUBeXh6PPPKIWczR0ZEvv/wSZSOZ8RE3pnXr1nT08+Okrw8GhcKiuRgUCk75+tCxSxda19D1QgjROMhPfCGagaeeeqrKWWgrVqygY8eOFspI1IXwAQMocnMj2cvLonmc8PKiyM2N8P79LZqHEOL6pLAToonbvHkz//3vf81i99xzD7NmzbJQRqKueHp60is8nGN+fhTa29fbc/QGA2XlZVR3SEKBvT3Hu/jRu39/PD096y0HIUTdkMJOiCbs/PnzPProo2YxZ2dnVq5cicLCy3eiboSHh9Pa24tYf3909bCsXlpWRk5ODlqtlqzsbIpLSvirvNMplcR288fVy4t+/frV+bOFEHVPCjshmiij0cicOXM4f/68Wfxf//oX3t7eFspK1DW1Ws2oMWMoad+efd271fn7dhcvXgRTKWekoCCf3NxcSisr2de9G6We7Rk5Zozp3DohROMmhZ0QTdT69evNmscDjB07lqlTp1ooI1FfPDw8GKeZhNbHh70B3etl5u5KZQY9u7v4cdrZmV7h/fDw8KjX5wkh6o50nhCiCcrMzCQgIIC8vDxTrE2bNiQlJUm7p2YsNTWVzREbsM/MpMfRoziVlNzyPQsKCykuLjL9udjJiWM9epJpb8eGzZvJyclh8+bNjBw58pafJYSof1LYCdHEGI1G7rvvPrZu3WoW37BhAxMnTrRQVqKhZGdnszUykryzGdyRnIxfRgbKW/gxXlJaSn5+HgaFgswuXUi+4w4ytFoif/yRc+fOAZda0u3du7euvoIQoh7JSxNCNDFffvlllaJOo9FIUddCeHh4MH3mTKKiooixteGspwedUtPokJuLymC48RtaWZHj60t6587kOjoSFRNDdHQ0er3eNETOrhOi6ZAZOyGakNTUVAIDAy+/8H6Ju7s7SUlJtGnTxoKZCUvIzMwkOiqKlBMnUJeU4JuejucFLc7FxVhdUZhdrVKlosDBgaw2rpzx9ia3ooITKSlERUeTnZ1tNrZDhw5s376dTp061ffXEULUAZmxE6KJMBgMPPzww2ZFHcDnn38uRV0L1b59eyZMnEheXh6JiYkkxsZyqrgYo06HY2kpTto8rHU6lEYDBoWSCrWaQtfWFNnZoVCrsXVwICwsDI1GY1p2vVqnTp24/fbbG/ibCSFulszYCdFEfPzxxzz++ONmsRkzZvDll19aKCPR2Oj1erRaLTk5OeTk5HA+O5uKsjL0Oh0qtRprW1vaenjg7u6Ou7s7rq6uqFQqwsPDiY6OrvG+H3/8MXPmzGnAbyKEuFlS2AnRBJw8eZLg4GBKrtgF6e3tzeHDh3F2drZgZqI5+PPPP5k0aRLnzp1Do9Hw66+/kpWVZfrcwcGBhIQEWY4VogmQwk6IRk6v1zN48GD27NljFt+2bRvDhw+3UFaiuTEajeh0OqysrPjll1+45557zD4fMGAAO3bsQFnPZ+gJIW6N/C9UiEbuvffeq1LUzZ49W4o6UacUCgVWVlYADB8+vEqrut27d/P+++9bIjUhxA2QGTshGrGjR48SGhpKeXm5KdaxY0cSExNxdHS0YGaiubt48SLBwcGkpKSYYjY2Nhw8eJA77rjDgpkJIa5FZuyEaKR0Oh3Tp083K+oUCgWrV6+Wok7Uu1atWlXZmFNeXs706dPR6XQWykoIcT1S2AnRSP3zn/8kJibGLPbMM88wcOBAC2UkWppBgwbxzDPPmMX279/PW2+9ZZmEhBDXJUuxQjRCCQkJ9OrVi8rKSlOsa9euxMfHY2dnZ8HMRGNW3XEn5aWlGPR6lCoVNnZ21R53ci2lpaWEhIRw4sQJU8zKyoqYmBiCg4Pr+ysJIW6QFHZCNDIVFRX06tWLxMREU0ypVBIdHU2fPn0smJlorPLy8khISOBQXBxlVxxQ7KzVYqXToTQaMSgUVKrVFLi6mh1QHBgWRnBw8DXbhv3555+Eh4djuKJlWXBwMPv378fa2rohvqIQopaksBOikVmwYAFLly41i7388su88cYbFspINFaZmZlE79lDSnIyViUl+KSl46m9gZZirq6k+XSg0t6ejn5+hA8YgKenZ7XXvPzyy7z55ptmsQULFrBkyZI6/U5CiFsjhZ0Qjcj+/fvp16+fWQP2wMBAYmJisLGxsWBmojHR6XRERUURExWFY24unVPT8M7NRXXFjFpt6ZVKzrq5cdLXhyI3N3qFhxMeHo5abd5xsry8nJ49e3L48GFTTKVSsXfvXnr16nXL30kIUTeksBOikSgtLSUsLIxjx46ZYmq1mpiYGEJCQiyXmGhUsrOz2RoZSd7ZDO5ITsYvIwNlHfwYNygUJHt5cczPD1dvL0aOGYOHh4fZmPj4eHr37m22K9bf35+4uDhsbW1vOQchxK2TXbFCNBILFy40K+oAFi1aJEWdMElNTWX92rXojxxlyL59dD17tk6KOgCl0UjXs2cZsm8fuiNHWb/2P6SmppqNCQ0NZeHChWaxo0ePVokJISxHZuyEaAR2797NoEGDuPJ/jj169GDv3r2mbgCiZUtNTWXTunW0SU2j95EjqG9i2bW2dEol+7p3Q+vjw/gHHsDX19f0WWVlJXfeeSexsbGmmEKhYNeuXfTv37/echJC1I4UdkJYWFFREcHBwZw+fdoUs7GxITY2lu7du1swM9FYZGdns37tWlxSznBnUlKdzdJdi0GhYG9Ad/Jv68jkaVPNlmWTkpIICwujoqLCFOvUqRMJCQk4ODjUe25CiJrJUqwQFjZv3jyzog5gyZIlUtQJ4NJGia2RkdhnZtHnyJEGKerg0tJsn6Qj2GVl8mNkpNl7dd27d+f11183G3/q1CnmzZvXILkJIWomM3ZCWNBvv/3G3XffbRbr168fu3btuu7BsaJl2LlzJzG//8GQfftwKilp8OcX2Nuzo28feg8datb1RK/XM3DgQKKjo83G//bbbwwdOrSh0xRCXCYzdkJYSEFBATNnzjSL2dnZsXr1ainqBHDpnLqYqCjuSE62SFEH4FxSQtcTyezfs4esrCxTXKVSsXr16iqdUGbOnElhYWFDpymEuEwKOyEsZO7cuaSnp5vF3nrrLfz8/CyUkWhsovfswTE3F7+MDIvm0SUjA8fcXKL27DGL+/n58c9//tMslpaWxty5cxsyPSHEFaSwE8ICfvjhB1atWmUWGzJkCI899piFMhKNTV5eHinJyXROTWuw9+pqojQa6ZSaRsqJE+Tl5Zl99vjjjzNkyBCz2BdffMHWrVsbMkUhxGVS2AnRwC5cuMCsWbPMYq1atWLVqlUolfI/SXFJQkICViUleOfmWjoVADrk5qIuKTHrYQyX+hivWrUKR0dHs/isWbPQarUNmaIQAinshGhwTz75JNnZ2Waxd955h9tuu80yCYlGR6/XcyguDp+09JtqE1YfVAYDvunpJMbGmrW8A7jtttt49913zWJZWVk8+eSTDZmiEAIp7IRoUBs3bmTdunVmsREjRvDwww9bKCNxLYMGDWLXrl1msTlz5vDpp59e99oDBw7wwgsv3NRztVotZcXFeNZixmvm4UOMiY9jUMx++u77kzHxcYyJj+N4cTF/Oxh/U8+viecFLS8tXFjtTNzDDz/MiBEjzGJff/017dq1o6ioqMr4GTNm8MMPP1T7nIsXL9KrVy9CQkIIDAzk888/r5svIEQLoL7+ECFEXTh37hxz5swxi7m4uLBy5UoUCoWFshLXMmnSJDZs2GA65kOv1xMZGcnixYuveZ1er6dnz5707Nnzpp6bk5ODUafDpZqC6GqrAgIB+DYnhxMlxbzU8XYAzpaV1epZeqMRVS3//jkXF4PRSE5ODm3btjX7TKFQ8PnnnxMQEEB+fr4pfuHCBc6fP19lqfZa7O3t2blzJ/b29pSUlBAQEMDf/vY32rRpU+t7CNFSyYydEA3AaDTy6KOPknvV+1Iffvgh7du3t1BW4nomTJjAd999h+HycujOnTvp0qULGo2GsLAwQkND2XN5p+iOHTsYPnw4kyZNYsiQIezYsYMJEyYA8Oeff9KvXz9CQ0O56667TMeGvPbaazzyyCMMHDiQ22+/nfXr1wOXCrtdv//O2AMx3BcXx9rMS7tid2i1TEw4yJj4OBYkJ2O4zqaKSoORF08c597YAzx97KipZd2QmP18mJaKJuEg+wry2ZSTzfiD8dwXF8v7qWcAKNbrefjwYUbHxTI6LpbdeXlY6fUogMWLFxMYGMjQoUMpLi4GIC4ujnHjxtGqVSuzHAwGA08//TRGo5GFCxfi7+/PqFGjOHfuXI15q1Qq7O3tASgrK0Ov1yNHrgpRO1LYCdEAvvrqK7777juz2N/+9jemTJlimYRErbi7u9OlSxd2794NwIYNG5gyZQpbtmwhLi6OLVu28Oyzz5rG79u3j/fee6/K8m23bt3YvXs38fHxPPLII7z11lumz1JSUvjjjz/49ddfWbBgAQDbfvqJlNOn2RwSyvdhYYxp2w5tZSVfZmTw38AgIkPDsFIq+DH3/DXzP11awqPeHfgprAcXKio5cMX5ci5qKyKCQ2hnbc1ObR4bgkPYEhrGkaJi4gsL2ZOXh4uVmh/CevB9aBihlwu24tJSunTuzKFDh/Dy8uLbb78FYPr06XzwwQekpqbSsWNHszy+//57lixZws8//0xCQgIrV66scrDx1fLz8wkODsbb25sXX3wRNze3a44XQlwihZ0Q9SwjI4MnnnjCLObm5sYnn3wiS7BNgEaj4ZtvvkGv1/P9998zZswYXnzxRQIDAxkzZgxHjhwxjQ0PD692BjYvL49x48YREBDA4sWLza4ZOXIkarWaTp06mZYwExMSuKtTJ6wv75J2sbLiYGEhx0uKTTN20fn5nC0rv2buHe3s6GRvj0KhoJujAxnl/1ueHXG5UIrOzyf+YiHjDsZz/8F4TpWWkFZWRhcHew4UFvJWSgoHL17EUX3pzR1btZounToB0KNHD86cOUNBQQHl5eX06dMHhULBBx98gLW1tVkuy5YtY+jQoVhbW+Pp6cldd911zdxdXFxISEggJSWFr7/+mpycnGuOF0JcIoWdEPXIaDTyyCOPUFBQYBb/7LPPaNeunYWyEjdi/PjxbNmyhT/++IOgoCB+/PFHiouLiY+PJz4+3rRMC5iWD6+2aNEiRo0axeHDh1m9ejXl5f8ryGxsbKqMNxqNXF3yG4EhrV2JDA0jMjSMbT16MrtDh2vmbn3F8TlKhQLDFauZtld0N9F4eJju+1vPXoxt146OdvZ8FxKKn709r58+xX8yMwGwUqnQX+4bq1Kpql0mdXFxISQkxCxWVlbG5s2bb3hJ1d3dnaCgoCqzoEKI6klhJ0Q9WrlyJT///LNZbMqUKfztb3+zUEbiRrm5ueHv789zzz3HpEmTKCwsxN3dHbVazcaNGymrxSaFwsJCvL29Afjvf/973fHdu3dn+8mTVFwuGvMrKwlp1Yp9BflkXS4K8yoryS6/9oxdbfR1duHH3FwKdJUAZJeXk1dZSU55OfYqFePc3Zne3oujxf/byKFSm++7c3FxwcbGhpiYGODSbtjJkyfzwAMPmI07ceIEn332GdnZ2Wzfvr3GnHJyckxtyQoLC9m1axddu3a95e8qREsgu2KFqCdnzpyp0lrJ09OTDz74wEIZiZul0WiYM2cO999/PzqdjlGjRtG7d2/69+9fq52azz//PDNmzGDZsmX069fvuuN79+5N2p493H8wHrVCwUR3D6a2b89rnTvz2JEj6IwG1Aolr/v54VHNjN+N6OLgwCwvbx5MPIQRIw4qFe92vYNTpaX8M+U0SoUCW6WSNy63ujMC1ra2Ve6zevVq5syZQ1lZGSEhIcyZM4fp06cTERFhNqv52GOPsWnTJtNO4+qcPXuWhx9+GKPRiNFo5IknniAoKOiWvqcQLYXCKFuNhKhzBoOBoUOHsmPHDrP41q1bGTlypGWSEk3G77//zvFt27h775+WTqWKX+/sS9d77mHo0KG1Gr9161ZGjx5tFhsyZAi//fabdFoRoh7I/6qEqAcfffRRlaLu4YcflqJO1Iq7uztFdnZUXvEeXH0yGI2UlpVSefnduZpUqlQU2dnh7u5e63uPGjWKmTNnmsW2b9/Oxx9/fFO5CiGuTWbshKhjJ06cICQkhNLSUlPMx8eHQ4cO4eTkZMHMRFNx/vx5Vn/6Kf3/3IfbFUeU1AeD0cj58+fR6y8Vdfb29jg7OVe7YzvXyYk9ffswY/bsKgcUX0tBQQGBgYGkp6ebYnZ2diQkJPDggw+abSaBS2cCuri43NwXEqKFk3fshKhDer2eGTNmmBV1AKtWrZKiTtSaq6srtg4OZLm61nthV1FRYSrqAEpKSqioqKR169ZYXbVJIqvNpbxcXV1v6BnOzs6sWrWKu+++2xQrLS1lxowZREdHo2qgmUkhWgJZihWiDq1YsYK9e/eaxR5//PFav48kBFw6RiQwLIw0nw7o6/k9NLVajeKqw1V0ukpyz5+nuKTEFNMrlaR26EBQjx43VYgNGzaMxx57zCwWHR3NO++8c3OJCyGqJUuxQtSRpKQkwsLCqKioMMU6depEQkICDg4OFsxMNEV5eXms/PhjQuPi8b1G+626UFpWRn5+PkajocpndrZ2OLu4kObuzsGwUB557DFat259U88pKioiJCSEU6dOmWLW1tbExcXRvXv3m85fCPE/MmMnRB2orKxk2rRpZkWdQqFgzZo1UtSJm9K6dWs6+vlx0tcHQz13KLGztaWtmxtWaqsqn5WWlZKTm8uJDt507NLlpos6AEdHR1avXm32/l5FRQXTp0+nsrLypu8rhPgfKeyEqAPLli0jLi7OLPbcc88RHh5uoYxEcxA+YABFbm4ke3nV+7PUajVubd1wsK/6i0h6p9vJUKlIOXPmhjtHXK1///5VzneMjY3lzTffvKX7CiEukaVYIW5RXFwcffr0QXfFURH+/v7ExcVhW81BrkLciJ07dxLz+x8M2bcPpyveeatPVy7NFjs5sX/IEP6IiWH37t2MGjWK1atX43a51+xN3b+0lLCwMI4dO2aKqdVqYmJiqrQiE0LcGJmxE+IWlJeXM336dLOiTqVSsWbNGinqRJ0IDw+ntbcXsf7+6BroQF87W1vatW2LytaOYz16kqHVEh0dDVw6cDgkJOSWerfa2dmxZs0as00YOp2OadOmVTn6RAhxY6SwE+IWvPbaaxw+fNgs9vLLL9OrVy8LZSSaG7VazagxYyhp35593bvV+/t2f1Go1ZwcOIAi93Z8/9NP6PV602cZGRkMGTKExYsXm8VvRO/evXnppZfMYocOHWLx4sW3lLcQLZ0sxQpxk/7880/Cw8PN+mAGBwezf/9+rK2tLZiZaI5SU1PZtG4drmlp9Ek6gtpQdQdrXdEplezr3g2tjw/jH3iAEydO8OCDD3Kumt25gwcP5quvvqJ9+/Y3/JyKigp69epFYmKiKaZUKomOjqZPnz639B2EaKmksBPiJpSUlBAaGsqJEydMMSsrKw4cOCDNykW9SU1NZXPEBuwzM+lx9Gi9vHNXYG9PbDd/Sj3bM04zCV9fXwCys7OZOnUqv/32W5Vr3NzcWLt2LSNGjLjh5yUkJNCrVy+zXbFdu3YlPj4eOzu7m/8iQrRQshQrxE2YP3++WVEHl5ZlpagT9cnX15fJ06ai6ubP9j59OO7tXWdLswaFgmPe3uzo2wcrf38mT5tqKuoAPDw82LZtG0uXLq1yQHFubi4jR47khRdeMDvypzaCg4N59dVXzWLHjx9n/vz5N/9lhGjBZMZOiBu0c+dOBg8ebBbr3bs3UVFRqNXSpU/UP51OR1RUFDFRUTjm5tIpNY0OubmobmJ5Vq9Uku7mxilfH4rc3Ojdvz/9+vW75t/lqKgoHnjgAbPer3/p3bs369evp2PHjjf0ffr160dMTIwpplAo2LFjBwMHDryxLyRECyeFnRA34OLFiwQHB5OSkmKK2draEh8fzx133GHBzERLlJmZSXRUFCknTqAuKcE3PR3PC1qci4uxusamhkqVigIHB7LauJLaoQM6e3s6dulCeP/+eHp61urZWq2WmTNnsmXLliqfOTs7s3LlSiZMmFDr73L06FFCQ0PNdsV27NiRxMREHB0da30fIVo6KeyEuAGzZ8/ms88+M4utWLGiyoGrQjSkvLw8EhMTSYyNpay4GKNOh2NpKU7aPKx1OpRGAwaFkgq1mkLX1hTZ2aFQq7F1cCCoRw+CgoJuqqOE0Wjkww8/5Pnnn692CXb27Nm88847tX5XbsWKFTz//PNmsTlz5vDxxx/fcG5CtFRS2AlRS9u2bePee+81iw0YMIDt27ffVFN0IeqaXq9Hq9WSk5NDTk4O57OzqSgrQ6/ToVKrsba1pa2HB+7u7ri7u+Pq6lonf3fj4uLQaDScPHmyymeBgYFERETg7+9fq/wHDx7Mnj17zOK//PILd9999y3nKURLIIWdELWQn59PQEAAGRkZppi9vT2JiYl06tTJgpkJ0ThcvHiROXPm8NVXX1X5zN7eng8//JAZM2aY9YmtzqlTpwgKCqLkih2/3t7eHD58GGdn5zrPW4jmRnbFClELzzzzjFlRB7B8+XIp6oS4rFWrVvznP/9h1apV2Nvbm31WUlLCzJkzmTp1KhcvXrzmfTp16sTbb79tFjt79izPPPNMXacsRLMkM3ZCXEdkZCRjx441iw0bNoxffvnlurMPQrRER48eRaPRcOjQoSqf+fn5ERERQWhoaI3XGwwGhg8fzu+//24Wj4yM5L777qvzfIVoTqSwE+IacnNzCQgIICcnxxRzcnLi0KFD+Pj4WDAzIRq30tJS5s6dy6efflrlM2tra5YvX84TTzxR4y9HaWlpBAQEmM3wubu7k5SURJs2beotbyGaOlmKFeIaHn/8cbOiDuC9996Tok6I67Czs+OTTz5hw4YNODk5mX1WUVHBU089xbhx49BqtdVe7+Pjw3vvvWcWy8nJ4YknnqivlIVoFmTGTogaREREMHnyZLPY6NGjiYyMlCVYIW5ASkoKkydPZv/+/VU+69ChA+vWrSM8PLzKZ0ajkfvuu4+tW7eaxTds2MDEiRPNYtXtCC4vLcWg16NUqbCxs6uXHcFCNDZS2AlRjezsbLp37242m9C6dWuSkpJqfYCrEOJ/KioqmD9/PsuXL6/ymUqlYvHixbz00ksoleYLSVlZWXTv3p28vDxTrE2bNiQlJeHu7k5eXh4JCQkcioszO8PPWavFSqdDaTRiUCioVKspcHU1O8MvMCyM4ODgmzrDT4jGSgo7Ia5iNBq5//77iYyMNIuvW7euygyeEOLG/PTTT0ybNo3c3Nwqnw0bNoz//Oc/eHh4kJWVRUREBN7e3pSXl/Pggw+ajZ08eTJ/GzeOM8nJWJWU4JOWjqf2BrpuuLqS5tOBSnt7Ovr5ET5ggPzSJpoFKeyEuMqaNWuYMWOGWWzChAls2LBBlmCFqAOZmZk8+OCDbN++vcpn7dq147333uP5558nMzMTgPnz53Ps2DE2bdqESqWiX79+hPfqhVdlJXdkZOJ9C31yz7q5cfJyn9xe4eGEh4dLz2fRpElhJ8QV0tPTCQwMpKCgwBRr164dhw8fpm3bthbMTIjmRa/X88Ybb/Daa69huE5RZmVlxb59+5gyZQr977wTr9at8Tt2DK/kk3i0dUOlvLV35QwKBcleXhzz88PV24uRY8bg4eFxS/cUwlKksBPiMqPRyL333ssvv/xiFt+8eTP333+/ZZISopnbtWsXU6ZMqXIA+NUef/xxvNq1wyY9Hf/YWOwLCwGwsbGljatrneRSaG9PrL8/Je3bM04zCV9f3zq5rxANSY47EeKyf//731WKuqlTp0pRJ0Q9GjhwIAcPHmT06NE1jvHx8cHB2hqPzEzu3B9jKuoAysvLKL6i/ditcCopYUB8PC5nUti0bh2pqal1cl8hGpLM2AkBnD59mqCgIIqLi02x9u3bc/jwYdkxJ0QDyM3NpVOnThReUbTBpVchpk2ezG15+fQ4eJDWTk6cO3cOg+F/GyQUCiVt27ZFXUfHlxgUCvYGdCf/to5MnjZVlmVFkyIzdqLFMxgMPPTQQ2ZFHcAXX3whRZ0QDWTVqlVVijqVSsWYUaPwLCnBf9+flJcUo9frcHFxMRtnNBrIz8+vs1yURiN9ko5gl5XJj5GR6HS6Oru3EPVNCjvR4v3rX/9i165dZrFZs2Zx7733WigjIVqe6oqnfv364dW6Nf6xsaguH2FSkF+ArY0N9vYOZmMrKsqp1FXWWT5qg4EeR46izcggOjq6zu4rRH2Twk60aMePH+fll182i912222sWLHCQhkJ0TI99thj9OrVy/RnDw8Pwnv1wu/YMbN36gyX3x5ycnJCpTI/lqSu3yxyLimh64lk9u/ZQ1ZWVp3eW4j6IoWdaLF0Oh3Tp0+nrKzMLL5q1SpatWploayEaJlcXFzYt28fx48fZ+PGjcz+v//DvbSMDqdOXzFKgbOzMwBKhQJX19am4s7e3h5rK+s6z6tLRgaOublE7dlT5/cWoj7IKYyixVq+fDn79u0ziz311FMMGTLEQhkJ0bIpFAq6dOlC27ZtOX3sGAE5OXi2bYsRI5WVOqysrLjyiHArtRXt2rUDjCion8PDlUYjnVLTONimDXl5efLerWj0ZMZOtEiHDh1i0aJFZjE/Pz+WLVtmoYyEEH9JSEjAqqQE78ttxxQosL6qqPuL4vLn9alDbi7qkhISExPr9TlC1AUp7ESLU1FRwfTp06ms/N+L1kqlkjVr1mBvb2/BzIQQer2eQ3Fx+KSl31SbsPqgMhjwTU8nMTYW/TX60ArRGEhhJ1qcpUuXEh8fbxZ74YUXuPPOOy2UkRCWd+bMGe699166dOmCn58fy5cvr9P75+fn8+9//9v05wMHDvDCCy8A8Nprr/Hhhx8CoNVqKSsuxlOrNY1958wZxsTHMSL2AEHRUYyJj2NMfBx/1uERJ3/Zev4898Ye4LEjR8zinhcu5aW9Iq+GcubMGcLDw7G1tTX9cxKiJvKOnWhRDhw4wNKlS81i3bt35x//+IeFMhLC8oxGI+PGjePll19m0qRJFBYWcs8999ChQwc0Gk2dPOOvwu7//u//AOjZsyc9e/asMi4nJwejTodLUZEpNve225h7222cLSvjqWNH+TYk1OwavdGISlE3y7Hf5uTwpl8XQpyczOLOxcUYdTpycnLM+kbr9XpUdXQwck33cnJy4p133iEyMrJOniOaN5mxEy1GWVkZ06dPN1tKUavVrFmzBhsbGwtmJoRl/fbbb7i4uDBp0iTgUiGxbNky3n33XWbMmMEPP/wAQFFREbfddhsAp06dYsCAAYSFhdG3b1+OHj0KwOrVq5k0aRJ33303nTt3Nh0dNH/+fI4cOUJISAhLly5lx44dTJgwoUousbGxfLlmDRNjY3no8CHOVVRUm/O+/HweOnyIp48dZeqhRIp0OqYdSuT++DjGxsdxoKDANG7G4UPMOXKE4QcO8MbpS7ts9UYjzx8/xojYA4yOi2VTTjafpacTW1jAvOQTfJiWiraykkeTkrgvLpaH4uMpz84mJyeHGTNm8NxzzzF48GDeeustBg8ezPPPP0///v0JCgoiLi6OUaNG0blzZ7MZtqVLl9KrVy+CgoL49NNPAdixYwfDhw9n0qRJNW7ccnV1pU+fPlhZWd3Qv1fRMsmMnWgxFi1axJGrllfmz59Pjx49LJSREI3DkSNHCA01nwULDQ3l2LFj3HHHHdVe4+npyW+//YaNjQ3R0dG88sorbN68GYDDhw8TExNDZWUlXbt25cknn2Tp0qUcP36cAwcOAJcKmuosX76c2X36MDIzi59yz/NhWiqLO/tVOzbh4kV+CuuBu40NlQYDH/t3w1GtJrOsjCeumNk7UlTEzz164KhSMyoulhnt26PVVXK2rJyfelyaNbyo09FKrWZ3Xh6LOnWii4MD/zh1kp7OTszy7s7W8+dZ+/PP+A0cCEB6ejrbt29HoVCwbds2HBwc2LNnD0uXLkWj0RATE4PBYKB79+488cQT/Pzzz5w7d46YmBgqKiro37+/qT/uvn37OHr0KO3bt7+Bf2tCVE8KO9EiREdHV3lnKDQ0lPnz51soIyEaN8V1ljbLy8t5/PHHSUxMRKlUUl5ebvps6NChODhc6gzRvn17cnJyavXMixcvcuLECd7NyODTigoMRiNeNrY1jg9zcsL98my7EXj7TAqxhYUoFQpSS0tN40JbOeF6+Yw7P3sHMsrL6eJgz7mKcl47dZJhrm3oX80xJrGFhczu1h2AkW5uvBZ7gIrL515OmDDB7J/RmDFjAAgMDKRnz56mtmetWrUiLy+PX3/9le+//56dO3cCUFBQwKlTpwAIDw+Xok7UGSnsRLNXXFzM9OnTzU6lt7a2Zs2aNbK0IQTg7+9vmm37S1xcHD179kStVmO4vDv1yuLtvffeo2PHjnz11Vfk5OTQt29f02dXvtqgUqlqvZPUaDTi5OTE8pEjCT6dct3xdsr/vU30/flzlOgNfBcahgoI2vu/NmDWyv8VYCrFpe4Vzmorvg/rwa48LasyzrInP4+XOt5+zecpFAr0l1ufXb2D/q/vrFQqzb6/UqlEr9djNBp57bXXmDZtmtl1O3bskN34ok7JO3ai2Xv55Zc5efKkWWzx4sUEBgZaKCMhGpdhw4aRl5fHhg0bACgsLGTBggUsWLAAX19fDh48CMC3335ruqawsJD27dujUCj4z3/+c91ntGrViosXL15zjJOTE06tWhGbkQFApcHAyZKSWn2HIp0eN2sr1AoFP1/Ipfw6R6VoKysxGo2McGvL4z4+HC0qrjKmh5MTP5w/D8DPF3Lp7OaGSn1z8yHDhg3jiy++oPTyTOLx48erdL0Roi7IjJ1o1v744w8++OADs1jfvn15/vnnLZSREI2PUqlk8+bNzJ49m/nz55ORkcHHH3/M4MGD6dq1K2PHjuXHH39k+PDhpmtmz57N+PHj+eqrrxg2bNh1n9GmTRvCwsIIDAxk8uTJhIeHVzvuyccfZ+U777A5vwA9Rh7x8qZzLWa07mvXlllJSYw/GE8PJ2dcrlOA5ZSX81LyCQxGUCsUvHJ71dm6J318eenECb47l4Oz2oop996LtW3NS8PXMnLkSA4fPkzv3r0xGo20a9eO77//vlbXFhYW0q1bNwoLC1GpVCxfvpwzZ87cVB6i+VMY67prshCNRGFhIUFBQaSmpppidnZ2HDx4kC5dulgwMyEat3Xr1vHGG2+wa9euBm+h9fvvv3N82zbu3vtnlc8qdTpKiovRGww4ODhgY133vWGv5dc7+9L1nnsYOnRogz5XiBshS7Gi2XruuefMijqAZcuWSVEnxHU88MADHDp0yCJ9Ud3d3Smys6PyivPcKnU6tHl5nD9/juKSYsrKStFeuIC+ATtTVKpUFNnZ4e7u3mDPFOJmyFKsaJZ++uknVq5caRYbNGgQTz75pIUyEkLUhru7Owq1mgIHB5wuXKCo6GK176IZMaLX61ApG2bWrsDBAYVaXa+F3aFDh5g6dapZrHPnzmzcuLHenimaHynsRLOTl5fHI488YhZzdHTkyy+/RKmUSWohGjNXV1dQqThlZ0eH3PM1jrOyssZK3XC72rPauGLr4HApv3oSGBho2qgixM2S/8qJZuepp54iMzPTLLZixQo6duxooYyEELURHR3NqFGj+GHbNs54tUdfzS9iCoWSVo6taNOmzXXP2qsreqWS1A4dCOrRo87ahwlRX6SwE83K5s2b+e9//2sWu+eee5g1a5aFMhJCXM/OnTsZNmwY4eHhbNu2jYSEBEqsrLjg7W0ao1QoadXKCXd3d1q1aoWygYo6gHQ3N3T29gQFBTXYM4W4WbIUK5qN8+fP8+ijj5rFnJ2dWblyZYP9Zi+EqB2j0cj27dv5xz/+wa5du8w+KygoIDklhTZ+frTLyMDJ3gF7B4cGLeb+YlAoOOXrQ8cuXSyymUSIGyUzdqJZMBqNzJkzh/Pnzd/J+de//oX3Fb/1CyEsy2g0sm3bNvr378/QoUOrFHV/OXLsGKXt2lEYFoajo6NFijqAE15eFLm5Ed6/v0WeL8SNksJONAvr169n06ZNZrGxY8dW2WEmhLAMo9HI1q1b6du3L/feey/R0dHVjmvfvj3vvfceMTExDBg2jON+XSi0UMutAnt7jnfxo3f//nh6elokByFulBxQLJq8zMxMAgICyMvLM8XatGlDUlKSnDklhIUZjUYiIyNZvHgxcXFxNY7z9vbm5ZdfZubMmdhe7u6g0+lYs2oV+iNHGRAfj7oBz63TKZXsCgvFyt+faTNnor7JVmJCNDSZsRNNmtFo5P/+7//MijqATz75RIo6ISzIYDCwceNGQkNDuf/++2ss6nx9ffnss884efIkjz32mKmoA1Cr1YwaM4aS9u3Z170bhgZajjUoFOzr3o1Sz/aMHDNGijrRpEhhJ5q0L7/8kq1bt5rFNBoNEydOtFBGQrRser2e9evXExQUxMSJE0lISKh23O23384XX3xBcnIy//d//4eNjU214zw8PBinmYTWx4e9Ad3R1fNZlDqlkr0B3dH6+DBOMwkPD496fZ4QdU2WYkWTlZqaSmBgIBcvXjTF3N3dSUpKok2bNhbMTIiWR6fTsX79el5//XWOHz9e4zg/Pz8WLFjAlClTbmgmLDU1lc0RG7DPzKTH0aM4lZTURdpmCuztie3mT6lne8ZpJuHr61vnzxCivklhJ5okg8HA8OHD+f33383ikZGR3HfffRbKSoiWp7Kykq+++oqlS5dy8uTJGsf5+/uzYMECNBrNTR/ym52dzdbISPLOZnBHcjJ+GRko6+A/YQaFghNeXhzv4oerlxcjx4yRmTrRZElhJ5qkjz/+mMcff9wsNmPGDL788ksLZSREy1JRUcHatWt54403SElJqXFcQEAACxcuZPz48XXStUGn0xEVFUVMVBSOubl0Sk2jQ24uqpvYWKFXKkl3c+OUrw9Fbm707t+ffv36yTt1okmTwk40OSdPniQ4OJiSK5ZivL29OXz4MM7OzhbMTIjmr7y8nC+//JJly5aRlpZW47iQkBAWLVrE2LFj66VHc2ZmJtFRUaScOIG6pATf9HQ8L2hxLi7GSq+v8bpKlYoCBwey2riS2qEDOnt7OnbpQrgcaSKaCSnsRJOi1+sZPHgwe/bsMYtv27aN4cOHWygrIZq/0tJSVq5cyT//+U8yMjJqHNezZ08WLVrE6NGjG6TjS15eHomJiSTGxlJWXIxRp8OxtBQnbR7WOh1KowGDQkmFWk2ha2uK7OxQqNXYOjgQ1KMHQUFB0lFCNCtS2IkmZcWKFTz//PNmsdmzZ/PJJ59YKCMhmreSkhI+++wz3nrrLbKzs2sc16dPH1599VXuvfdei7Tw0+v1aLVacnJyyMnJ4Xx2NhVlZeh1OlRqNda2trT18MDd3R13d3dcXV3rZGlYiMZGCjvRZBw9epTQ0FDKy8tNsY4dO5KYmIijo6MFMxOi+SkqKuKTTz5h+fLlnDt3rsZx/fv3Z9GiRQwbNkx6MgvRCMgboqJJ0Ol0TJ8+3ayoUygUrF69Woo6IepQYWEhH330EStWrODChQs1jhs8eDCLFi1i8ODBUtAJ0YhIYSeahH/+85/ExMSYxZ555hkGDhxooYyEaF7y8/P54IMPePfdd6t0crnS3XffzcKFCxkwYEADZieEqC1ZihWNXkJCAr169aKystIU69q1K/Hx8djZ2VkwMyGaPq1Wy/vvv8/7779PQUFBjeNGjBjBwoULufPOOxswOyHEjZIZO9GoVVRUMG3aNLOiTqlUsmbNGinqhLgFubm5vPPOO3z44Ydm3Vuudt9997Fw4UJ69erVgNkJIW6WFHaiUVu8eDGJiYlmsXnz5tGnTx8LZSRE05aTk8OKFSv4+OOPKS4urnHcuHHjWLhwIaGhoQ2YXdNV3a7c8tJSDHo9SpUKGzs72ZUrGoQsxYpGa//+/fTr1w/9FYeNBgYGEhMTU2PDcCFE9bKysnj77bf59NNPKS0trXaMQqFg4sSJLFiwgMDAwAbOsGnKy8sjISGBQ3FxZufoOWu1WOl0KI1GDAoFlWo1Ba6uZufoBYaFERwcLOfoiTolhZ1olEpLSwkLC+PYsWOmmFqtJiYmhpCQEMslJkQTc/bsWd566y3+/e9/m+0qv5JSqWTy5MnMnz+fbt26NXCGTVNmZibRe/aQkpyMVUkJPmnpeGpvoPOFqytpPh2otLeno58f4QMGSOcLUSdkKVY0SgsXLjQr6gAWLVokRZ0QtZSamso///lPvvjiCyoqKqodo1Kp+Pvf/84rr7xC165dGzjDpunqXrWhqWl430CvWiu9HrfCQtwKC+mWlsZZNzdOXrjAVydP0is8nPDwcOlVK26JzNiJRmf37t0MGjSIK/9q9ujRg71792JlZWXBzIRo/E6fPs2yZctYvXo1Op2u2jFqtZpp06bx8ssv07lz5wbOsOnKzs5ma2QkeWczuCM5Gb+MDJR18J9Qg0JBspcXx/z8cPX2YuSYMXh4eNRBxqIlksJONCpFRUUEBwdz+vRpU8zGxobY2Fi6d+9uwcyEaNySk5N54403+M9//mP2XuqVrKyseOihh3jppZfo2LFjA2fYtKWmprI5IgL7zCx6HD2KU0lJnT+j0N6eWH9/Stq3Z5xmEr6+vnX+DNH8KS2dgBBXmjdvnllRB7BkyRIp6oSowbFjx5g6dSp33HEHq1evrraos7a25vHHH+fkyZN89tlnUtTdoNTUVDatW0frlDMMiI+vl6IOwKmkhAHx8bicSWHTunWkpqbWy3NE8yYzdqLR+O2337j77rvNYv369WPXrl1yLIAQV0lKSuL1118nIiKCmn6M29ra8uijj/LCCy/g5eXVwBk2D9nZ2axfuxaXlDPcmZRUJ0uv12NQKNgb0J382zoyedpUWZYVN0QKO9EoFBQUEBgYSHp6uilmZ2dHQkICfn5+FsxMiMYlISGBJUuWsGnTphrH2NvbM2fOHJ5//nkpCm6BTqdjzapV6I8cZUB8POpabpCok2crlewKC8XK359pM2fKhgpRa7IUKxqFuXPnmhV1AG+99ZYUdUJcFhsby/33309ISEiNRZ2DgwPz5s0jJSWF5cuXS1F3i6Kiosg7m0GPo0cbtKgDUBsM9DhyFG1GBtHR0Q36bNG0ya8AwuJ++OEHVq1aZRYbMmQIjz32mIUyEqLx2LdvH0uWLGHr1q01jmnVqhVPPfUUzzzzDG5ubg2YXfOVmZlJTFQUdyQn19s7ddfjXFJC1xPJ7Lexwc/PT865E7UiM3bCoi5cuMCsWbPMYq1atWLVqlUolfLXU7Rc0dHR3HvvvfTt27fGos7Z2ZlXX32V1NRUXn/9dSnq6lD0nj045ubil5Fh0Ty6ZGTgmJtL1J49Fs1DNB0yYycs6sknnyQ7O9ss9u6773LbbbdZJiEhLGznzp0sWbKE33//vcYxrq6uzJ07lyeeeAJnZ+cGzK5lyMvLIyU5mdDUtAbZLHEtSqORTqlpHGzThry8PGk/Jq5LpkSExWzcuJF169aZxUaOHMnMmTMtlJEQlmE0Gvnjjz8YNGgQgwcPrrGoc3Nz48033+TMmTPMnz9firp6kpCQgFVJCd65uZZOBYAOubmoS0pITEy0dCqiCZDCTlhETk4Oc+bMMYu1bt2azz//HIVCYaGshGhYRqORbdu20b9/f4YOHcquXbuqHefu7s7y5cs5c+YM8+bNo1WrVg2cacuh1+s5FBeHT1p6rduE1TeVwYBvejqJsbE1Hj4txF9kKVY0OKPRyOzZs8m96rfhDz74gPbt21soKyEajtFo5Mcff2Tx4sXs37+/xnHt27fnxRdfZNasWdjb2zdghi2XVqulrLgYT63W0qmY8byg5VRxMVqtlrZt21o6HdGIyYydaHBfffUV3333nVnsb3/7G1OmTLFMQkI0EKPRyJYtW+jZsyejR4+usajz9vbmo48+4tSpUzz99NNS1NWRxYsXExAQQGBgID179iQlJaXKmJycHIw6HSN/2XZTz/j8rPmxTf57djMmPs70fxU3OQvoXFyMUacjJyfHLP7DDz8QEBCAUqnk8OHDN3Vv0bzIjJ1oUBkZGTzxxBNmMTc3Nz755BNZghXNlsFgYPPmzSxZsoSEhIQax/n6+vLKK68wffp0bGxsGjDD5i86OpodO3Zw8OBB1Go1Z8+excHBocq4nJwcHEtLb/o5n589yyzvDqY/t1KriQwNu+n7/cVKr8extJScnBwCAgJM8a5du7Jx40Zmz559y88QzYMUdqLBGI1GHnnkEQoKCszin332Ge3atbNQVkLUH71ezzfffMPrr79OUlJSjeNuv/125s+fz9SpU7GysmrADFuO7OxsWrduberg4O3tDWBaEi8rK6N3794MHjAA56uWYT9JT+PXCxeoNBiY4tmeBy6fJ/dhWio/5eaiRMFED3dyKyq5qNMxJj6OMCcnXuvUudpcRsbFsik4BIAef+7lq8AgQp2cGBMfx1eBQSgVCl47eZJTpZfOz5t/++30cHLGSZvH+atOEZBD3MXVpLATDWblypX8/PPPZrEpU6bwt7/9zUIZCVE/dDod69ev5/XXX+f48eM1jvPz82PBggVMmTJFWkbVs7vvvptXX30Vf39/hg8fzoMPPkjHjh1555132LFjB7a2tjzxxBPs3r2bITqd6bpdeVouVFTybUgoFQYDDyQmMMTVlaPFRewvKGBzSCjWSiX5lZW4WFmxPjvLbIbur0IPIKRVKxZ39iPIsRUJFy9iBLraOxBbWEjny8vtrdRq3j6Twt1t2vC2W1eyy8uZlZTE92FhWOt0lJWVNeg/N9H0yE8S0SDOnDnD3LlzzWKenp588MEHFspIiLpXWVnJV199xdKlSzl58mSN4/z9/VmwYAEajQaVStWAGbZcrVq1Ij4+nu3bt/Pbb79x9913s2bNGhITE+nbty8ApaWlBHbrhtLJyXRdVF4+f2i17C+8tNJQpNORVlbK3vwCxrt7YH35IHWXGmZaq1uKDXNyIrawECNGHvH2Zuv583S2tyf08m7n6Lx8dmm1fJieBkC+rpIKgwGl0YD+iqJTiOpIYSfqncFg4KGHHqKoqMgsvnLlSlxdXS2UlRB1p6KigrVr1/LGG29U+0L+XwICAli4cCHjx4+Xgs4C1Go1d999N3fffTdubm48++yzjB492qyl4ZqVKzFc0eXBCDzp48M4d3eze/124eZ3zYY5ObHs9GkUCpjR3ot1WVnEFhbSw8n58jONfNatO+1tbc2uMyiUqGRmV1yH7IoV9e6jjz5ix44dZrGHH36YkSNHWiYhIepIeXk5n376KX5+fsyaNavGoi4kJIRvv/2WhIQEJk2aJEWdBRw/fpxTp04Bl973TUpK4tFHH2X79u2kp1/ayXrhwgWKSkqovKJ46ufiwsacbPJLSykoKCDhXA7FFRX0c3FhU062aZdrfmUlACqFAv11ulV0srPjTFkp5QYDjmo1He3t2HIuh7DLM4X9XFrzVVaWafzRy78UV6jVWF9V7AlxNSn9Rb06ceIE8+bNM4v5+PjwzjvvWCgjIW5dWVkZK1eu5M033yTjGr1Ee/bsyaJFixg9erTs+rawoqIinnjiCQoLCwHo0aMHTz31FMHBwdx///1UVlZiZWXFrFmzKLi8klBeUUGISkUPKys0hxIxGo20Vql4U6dnUPv2JBUVcf/BeNQKBRPdPZjavj3j2rkzOi6WPi4uNW6eUCgU+Nnb42VzqUgLa+XEdq0W78tF2+M+Piw5dYrRcbHojUbudHFhkWNnCl1b09XDw+xe27Zt4+GHH+b8+fMMGzaMIUOGVOnoI1oWhdFo4UZ4otnS6/UMGDCAvXv3msV/++03hg4daqGshLh5JSUlfPbZZ7z99ttkXTGjcrU+ffrw6quvcu+990pB14QYDAa+/fZbjsXGEr4lEmVFeY1j27Vt16AbXipVKn4YNJCREyeaHXcixNVkxk7UmxUrVlQp6h5//HEp6kSTU1RUxCeffMLy5cs5d+5cjeP69+/PokWLGDZsmBR0TYTRaGT//v1ERETwzTffUFZWxqPTp3OxlSPOF6ov7KysrBt8F3OBgwMKtRr3q971E+JqMmMn6kVSUhJhYWFUVFSYYp06dSIhIaHaQ0GFaIwKCwv56KOPWLFiBRcuXKhx3ODBg1m0aBGDBw+Wgq4JMBqNHDx4kIiICCIiIjhz5ozpM4VCwdOPPUZIRga3HTpkdp2VlTV2dnY4ONijoGH/Pb+v1/FdaipuV7QTmzx5Mi+99FKD5iEaP5mxE3WusrKSadOmmRV1CoWCNWvWSFEnmoT8/Hw++OAD3n33XfLy8mocd/fdd7Nw4UIGDBjQgNmJm3X48GFTMZecnFztGKPRSNyhQ7QJCaFDUhK2ShV2dnbY2tmhttCmF71SSaeBQ/hq+HAGDRpkkRxE0yGFnahzy5YtIy4uziz23HPPER4ebqGMhKgdrVbL+++/z/vvv1+lQ8qVRowYwcKFC7nzzjsbMDtxM44fP24q5o4cOVKra0pLSzE6OVHZvTsdcmueqW0o6W5u6OztCQoKsnQqogmQwk7Uqbi4OJYsWWIW8/f3rxITojHJzc3lnXfe4cMPP+TixYs1jrvvvvtYuHAhvXr1asDsxI1KSUkxFXMHDx6s1TV+fn5oNBo0Gg0BAQFs3LCBlOJibrugRWnBN5YMCgWnfH3o2KULrVu3tlgeoumQwk7UmfLycqZPn47uipPRVSoVa9aswVbOXhKNUE5ODitWrODjjz+muLi4xnHjxo1j4cKFhIaGNmB24kakp6ezYcMGIiIiiImJqdU1t912m6mYCwkJMXs/MnzAAL46eZJkLy+6nj1bX2lf1wkvL4rc3Bjbv7/FchBNixR2os689tprHD582Cz28ssvy+yGaHSysrJ4++23+fTTTyktLa12jEKhYMKECSxYsECWwBqprKwsNm7cSEREBFFRUbW6xsvLi0mTJqHRaOjdu3eNm108PT3pFR5OTFk5nlotTiUldZl6rRTY23O8ix+9+/fH09OzwZ8vmibZFSvqxJ9//kl4eDiGy6ewAwQHB7N//36sra0tmJkQ/3P27Fneeust/v3vf1NeXv1RFkqlksmTJzN//ny6devWwBmK68nNzWXTpk2sX7+enTt3Upv/hLm7uzNx4kQ0Gg39+vVDqaxd0yWdTseaVavQHznKgPh41Ff8fKtvOqWSXWGhWPn7M23mzAY/XkU0XfI3RdyykpISpk+fblbUWVlZsXbtWinqRKOQmprKP//5T7744guz3dpXUqlU/P3vf+eVV16ha9euDZyhuJa8vDw2b95MREQEv//+O3q9/rrXtGnThvHjx6PRaBg0aNBNtXFTq9WMGjOG9fkF7Kso587DSQ3yvp1BoWBf926UerZn7JgxUtSJGyJ/W8Qtmz9/PidOnDCLvfbaa7J8JSzu9OnTLFu2jNWrV5u9+3kltVrNtGnTePnll+ncufoWUKLhFRYWEhkZyfr16/nll1+ovNyL9VpcXFwYN24cGo2Gu+66Cysrq1vOw8PDg3GaSWxat469QJ+kI/U6c6dTKtnXvRtaHx/GaybhcVULMSGuR5ZixS3ZuXMngwcPNov17t2bqKgo+S1TWExycjJvvPEG//nPf2qc3bGysuKhhx7ipZdeomPHjg2coahOcXExP/zwAxEREfz44481LpdfydHRkbFjx6LRaBg+fDg2Njb1kltqaiqbIzZgn5lJj6NH6+WduwJ7e2K7+VPq2Z5xmkn4+vrW+TNE8yeFnbhpFy9eJDg4mJSUFFPM1taW+Ph47rjjDgtmJlqqY8eOsXTpUr7++muzVwOuZG1tzaxZs3jxxRfx8fFp4AzF1UpLS/npp5+IiIjghx9+oKQWBZOdnR333XcfGo2GESNGYGdn1wCZQnZ2NlsjI8k7m8Edycn4ZWTUydKsQaHghJcXx7v44erlxcgxY2SmTtw0mVIRN+2FF14wK+oAli5dKkWdaHBJSUm8/vrrRERE1Pgyva2tLY8++igvvPACXl5eDZyhuFJFRQW//PILERERbNmy5ZpnB/7FxsaGESNGMHnyZEaPHm2RLjYeHh5MnzmTqKgoYmxtOOvpQafUNDrk5qK6ieVZvVJJupsbp3x9KHJzo3f//vTr109WO8QtkRk7cVO2bdvGvffeaxYbMGAA27dvv6mXlIW4GQkJCbz++uts3LixxjH29vbMmTOH559/XmZBLKiyspI//viDiIgINm/eTH5+/nWvUavV3HPPPWg0GsaOHYuTk1P9J1pLmZmZREdFkXLiBOqSEnzT0/G8oMW5uBira2zuqFSpKHBwIKuNK6kdOqCzt6djly6Ey5Emoo5IYSduWH5+PgEBAWRkZJhi9vb2JCYm0qlTJwtmJlqK2NhYlixZwpYtW2oc4+DgwBNPPMHcuXNp165dA2Yn/qLX69m1axcRERFs3LiRCxeu355LpVJx1113odFoGDduHK6urg2Q6c3Ly8sjMTGRxNhYyoqLMep0OJaW4qTNw1qnQ2k0YFAoqVCrKXRtTZGdHQq1GlsHB4J69CAoKEg6Sog6JYWduGEzZsxgzZo1ZrGPP/6YOXPmWCgj0VLs37+fxYsXs3Xr1hrHtGrViqeeeopnnnkGNze3BsxOABgMBvbu3cv69evZuHEj2dnZ171GoVAwaNAgNBoN48ePp23btg2Qad3S6/VotVpycnLIycnhfHY2FWVl6HU6VGo11ra2tPXwwN3dHXd3d1xdXWV1Q9QLKezEDYmMjGTs2LFmsWHDhvHLL7/UeIK7ELcqOjqaxYsXs23bthrHODs788wzz/D000/LDEgDMxqNxMTEEBERwYYNGzhbyxZc/fr1Q6PRMGHCBNq3b1/PWQrRMkhhJ2otNzeXgIAAcnJyTDEnJycOHTokuwtFvdi1axeLFy/m999/r3GMq6src+fO5YknnsDZ2bkBs2vZjEYjBw8eNBVzV2+kqkmvXr3QaDRMnDhRfm4IUQ9k642otccff9ysqAN477335IezqFNGo5Ht27ezePFidu7cWeM4Nzc3nn/+eR577DFatWrVgBm2bElJSURERBAREVHlYPKaBAcHM3nyZCZNmsTtt99ezxkK0bLJjJ2olYiICCZPnmwWGz16NJGRkbIEK+qE0Wjk119/ZfHixdds6O7u7s4LL7zA7NmzLXLkRUt04sQJUzGXlJRUq2v8/f2ZPHkyGo1GWrQJ0YCksBPXlZ2dTffu3dFqtaZY69atSUpKku354pYZjUZ+/PFHFi9ezP79+2sc5+npybx585g1axb29vYNmGHLlJKSwoYNG1i/fj0HDx6s1TWdO3dGo9Gg0WgICAiQX/qEsABZihXXZDQaefTRR82KOri0C1aKOnErjEYjkZGRLF68mLi4uBrHeXt789JLL/Hwww9ja2vbgBm2PGfPnmXDhg1ERERcs8i+kq+vr6mYCw0NlWJOCAuTwk5c09q1a4mMjDSLTZgwAY1GY6GMRFNnMBjYvHkzS5YsISEhocZxvr6+vPLKK0yfPr3e+n+KSzPyGzduZP369ddcAr+Sl5cXEydOZPLkyfTu3VuKOQur7qiV8tJSDHo9SpUKGzs7OWqlBZGlWFGj9PR0AgMDKSgoMMXatWvH4cOHm+Q5U8Ky9Ho9GzduZMmSJdd8T+v2229n/vz5TJ06FSsrqwbMsOXIzc1l06ZNREREsHPnzhr76l6pXbt2TJw4EY1GQ3h4OEqlsgEyFdeSl5dHQkICh+LizA5HdtZqsdLpUBqNGBQKKtVqClxdzQ5HDgwLIzg4WI4GaoZkxk5Uy2g08sgjj5gVdQCfffaZFHXihuh0OtavX8/SpUs5duxYjeP8/PxYsGABU6ZMkV6Z9SAvL4/vvvuOiIgIfvvtN/TXaHv1F1dXV9MM/aBBg2SWp5HIzMwkes8eUpKTsSopwSctHU/tDbQzc3Xl4IULxERF0dHPj/ABA+TVmmZEZuxEtT777DNmz55tFps6dSpr1661UEaiqamsrOSrr75i6dKlnDx5ssZx/v7+LFiwAI1GI4VDHSssLCQyMpKIiAi2bdtGZWXlda9xdnZm3LhxaDQahg4dKrOmjYhOpyMqKoqYqCgcc3PpnJqGd24uqlrMuF5Nr1Ry1s2Nk74+FLm50Ss8nPDwcPmlqhmQwk5Ucfr0aYKCgiguLjbF2rdvz+HDh2XaXlxXRUUFa9eu5Y033rjmobUBAQEsXLiQ8ePHS0FXh4qLi9m6dSvr16/nxx9/pLy8/LrXODo6MnbsWDQaDcOHD5d3Ghuh7OxstkZGknc2gzuSk/HLyEBZB//5NigUJHt5cczPD1dvL0aOGYOHh0cdZCwsRQo7YcZgMDBkyBB27dplFv/pp5+49957LZSVaArKy8v58ssvWbZsGWlpaTWOCwkJYdGiRYwdO1be06ojZWVl/PTTT0RERPD9999TUlJy3Wvs7OwYPXo0Go2GkSNHYmdn1wCZipuRmprK5ogI7DOz6HH0KE61+Pd7owrt7Yn196ekfXvGaSbh6+tb588QDUMKO2Hmvffe49lnnzWLzZo1i3//+98Wykg0dmVlZaxcuZI333yTjIyMGsf17NmTRYsWMXr0aNlFWQcqKir49ddfWb9+PVu2bOHixYvXvcba2poRI0YwefJkRo8ejaOjYwNkKm5Famoqm9ato01qGr2PHEF9E8uutaVTKtnXvRtaHx/GP/CAFHdNlBR2wuT48eOEhIRQVlZmit12220kJiZKyyZRRUlJCZ999hlvv/02WVlZNY7r06cPr776Kvfee68UdLdIp9Pxxx9/EBERwebNm8nLy7vuNWq1muHDh6PRaBg7dqz0021CsrOzWb92LS4pZ7gzKalOll6vx6BQsDegO/m3dWTytKmyLNsEyVuSArj0H4zp06ebFXUAq1atkqJOmCkqKuKTTz5h+fLlnDt3rsZx/fv3Z9GiRQwbNkwKulug1+vZvXs369evZ9OmTeTm5l73GqVSydChQ9FoNIwbNw5XV9cGyFTUJZ1Ox9bISOwzs+hz5EiDFHUASqORPklH2GVjw4+RkUybOVM2VDQx8m9LALB8+XL27dtnFnvqqacYMmSIhTISjU1hYSEfffQRK1as4MKFCzWOGzx4MIsWLWLw4MFS0N0kg8HA3r17iYiI4JtvviE7O/u61ygUCgYOHIhGo2H8+PG0a9euATIV9SUqKoq8sxkMOXq0Xpdfq6M2GOhx5Cg7nJyIjo5m4MCBDfp8cWtkKVZw6NAhevToYXYUgp+fHwcPHpSenIL8/Hw++OAD3n333Wsu/d19990sXLiQAQMGNGB2zYfRaOTAgQNERESwYcMG0tPTa3Vdv3790Gg0TJgwgfbt29dzlqIhZGZm8vXq1dxx6DBdz561WB7HvL05HhjA3x96SM65a0Jkxq6Fq6ioYPr06WZFnVKpZPXq1VLUtXBarZb333+f999/v8pB1VcaMWIECxcu5M4772zA7JoHo9FIQkKCqZg7ffp0ra7r2bMnGo2GSZMm4ePjU89ZioYWvWcPjrm5+F1jM1JD6JKRQYanB1F79jBh4kSL5iJqTwq7Fm7p0qXEx8ebxV544QX69etnoYyEpeXm5vLuu+/ywQcfXHOn5X333cfChQvp1atXA2bXPBw5coT169cTERHBiRMnanVNcHCwqZjr1KlTPWcoLCUvL4+U5GRCU9Ma7L26miiNRjqlpnGwTRvy8vLkHNMmQgq7Fiw2NpalS5eaxbp3784//vEPC2UkLCknJ4cVK1bw8ccfmx1OfbVx48axcOFCQkNDGzC7pi85OZmIiAgiIiI4fPhwra7x9/dHo9Gg0Wi444476jlD0RgkJCRgVVKCdy02yTSEDrm5HC4pITExkUGDBlk6HVELUti1UGVlZUybNs2sX6RarWbNmjVy6nwLk5WVxdtvv82nn35KaWlptWMUCgUTJkxgwYIFBAUFNXCGTdeZM2dMxdzVM+M16dSpE5MnT0aj0RAQECAbUFoQvV7Pobg4fNLSb6pNWH1QGQz4pqeTGBtL//79pUtMEyDHvrdQixYt4siRI2ax+fPn06NHDwtlJBra2bNneeqpp+jYsSPvvvtutUWdUqlkypQpHD58mA0bNkhRVwtnz57l3XffpW/fvnTs2JGXXnrpukWdr68vL7zwAgcOHCA5OZnXX3+dwMBAKeosZNCgQVW678yZM4dPP/30utceOHCAF1544aaeq9VqKSsuxlOrve7YmYcPMSY+jkEx++m770/GxMcxJj6O48XF/O1g7X6JqC3PC1peWrgQbS3y+sttt91GUVFRlfiMGTP44Ycfqr0mPT2dwYMH061bN4KCgvjmm29uOueWTGbsWqDo6GiWL19uFgsNDWX+/PkWykg0pLS0NN58802++OILKioqqh2jUqn4+9//ziuvvELXrl0bOMOmJzs7m40bNxIREcGePXtqdU379u2ZNGkSGo2GPn36SBHXiEyaNIkNGzaYjvnQ6/VERkayePHia16n1+vp2bMnPXv2vKnn5uTkYNTpcKmmILraqoBAAL7NyeFESTEvdbwdgLNXnUVaY65GI6pa/p1zLi4Go5GcnBzatm1bq2tuhlqt5r333iMkJIRz584RFhbGyJEjcXBwqLdnNkdS2LUwxcXFTJ8+nStPubG2tmbt2rVYWVlZMDNR31JSUli2bBmrV6822wV9JbVazbRp03j55Zfp3LlzA2fYtOTm5vLtt98SERHBjh07MNRi6axdu3ZMmDABjUZD//79pVduIzVhwgR69OjBv/71L5RKJTt37qRLly5oNBry8/MxGo188MEH9O/fnx07dvDGG2/g4uJCdnY2ixcv5sMPP2Tjxo38+eefzJ07l9LSUlq3bs1XX32Fp6cnr732GmfPnuXEiROcPXuWN954g8mTJ5OTk8Ou339nZXw8ShRM9HBnWnsvdmi1fJSeRrnBQJBjKxZ37ozyGkVZpcHIiyeOk3jxIl0dHHiv6x0oFAqGxOxnvLs7u/PyeNrXl6zycr7OyqLCYGBYmzY87XsbxXo9Tx09Sk5FOQDzOt7OgNatUQCLFy/m6NGjtGvXjsjISBwcHIiLi2P27NmUlZUREhLCv//9b2xtbc3yWbhwIRs3buT222/nWieseXp6mo5VadeuHa6urmi1WinsbpD8VGlhXn75ZU6ePGkWW7x4MQEBARbKSNS35ORkHnroIfz8/Pj888+rLeqsrKz4v//7P06cOMEXX3whRV0N8vPz+fLLL7n33nvx8PDg0Ucf5Y8//rhmUefq6sqsWbP47bffyMjI4KOPPmLgwIFS1DVi7u7udOnShd27dwOwYcMGpkyZwpYtW4iLi2PLli1mPbX37dvHe++9V2X5tlu3buzevZv4+HgeeeQR3nrrLdNnKSkp/PHHH/z6668sWLAAgG0//UTK6dNsDgnl+7AwxrRth7ayki8zMvhvYBCRoWFYKRX8mHv+mvmfLi3hUe8O/BTWgwsVlRwoLDR95qK2IiI4hHbW1uzU5rEhOIQtoWEcKSomvrCQPXl5uFip+SGsB9+HhhF6ufNQcWkpXTp35tChQ3h5efHtt98CMH36dD744AMSExNxcHDg448/Nstl//79/PzzzyQkJLBy5Uqio6Nr9e/gwIEDGAwGOnToUKvx4n9kxq4F+eOPP/jggw/MYn379uX555+3UEaiPh07doylS5fy9ddf11h4WFtbM2vWLF588UU5D60GFy9eJDIykoiICLZt21bj8vWVnJ2duf/++5k8eTJDhw6V2fAmSKPR8M0339C/f3++//57/vGPf/Diiy+yZ88eVCoVycnJprHh4eHVHg6dl5fHgw8+yOnTp9HpdPj6+po+GzlyJGq1mk6dOpGfnw9AYkICd3XqhDWXZuNcrKz448IFjpcUMzHhIADlBgPu1tfe4NbRzo5Ol88h7eboQEZ5Gb241CN4hJsbANH5+cRfLGTc5ffxSvR60srKCGrlyBsphbyVksLdbdoQ6uQEgK1aTZfLx+z06NGDM2fOUFBQQHl5OX369AFg6tSpvP3228ydO9eUS3R0NOPGjcPa2hpPT0/uuuuu6/6zv3DhAtOmTWPlypXXHSuqksKuhSgsLGTmzJlmMTs7O9asWSO7nJqZpKQkXn/9dSIiImpc9rC1teXRRx/lhRdewMvLq4EzbPxKSkr44YcfiIiI4Mcff6zSQ7k6jo6OjBkzBo1Gwz333CO7y5u48ePH8/rrrzN27FiCgoL48ccfKS4uJj4+HpVKZXaAe02HuS9atIhRo0bx6KOP8ueff/LSSy+ZPqvu74fRaOTqBVa90cBAZ2cWeHkDl35uW12nd6v1FbPBSoUCwxU/Bmyv+Hmv8fDgCR9frvZdSCg7tFpeP32K+9u5M7V9e6xUKvQ6HXDpHVy9Xl/l54vRaKzyrmh1sWspLy9n3LhxvPzyy3Ke6k2StYAW4rnnniM1NdUstmzZMrp06WKhjERdS0hIYOLEiQQEBLB+/fpqizp7e3uee+45UlJSeO+996Sou0JZWRnfffcdDzzwAG3btkWj0fDtt99es6izs7NjwoQJbNy4kXPnzvHVV18xZswYKeqaATc3N/z9/XnuueeYNGkShYWFuLu7o1ar2bhxY62K/cLCQry9LxVk//3vf6873t/fnz9OnkR7sRBtXh7JWZm0Ly3lz/x8TufnUVR0kVM5OWTUcCzRjejr7MKPubkU6C69mpFdXk5eZSU55eXYq1SMc3dnensvjhb/byOH6qqC0sXFBRsbG2JiYgD4+uuvq7QUDA8PZ/PmzVRUVJCdnc327dtrzMloNDJjxgzuuusupk6desvfsaWSGbsW4KeffqoypT1o0CCefPJJC2Uk6lJcXBxLlizhu+++q3GMg4MDTzzxBHPnzpXm8FeoqKjg119/JSIigi1btlB4xbtINbG2tmbEiBFoNBruu+8+HB0dGyBTYQkajYY5c+Zw//33o9PpGDVqFL1796Z///60adPmutc///zzzJgxg2XLllWZfdLr9cTHx7N//36KiooIDAwksHt3OrVty+QjR1ApFIxycuJvzs4827YtC7Ky0HHpP9qL7ezwsrO7pe/WxcGBWV7ePJh4CCNGHFQq3u16B6dKS/lnymmUCgW2SiVv+PkBYASsr9oUAbB69WrmzJlj2jwxZ84cs8979+7NPffcQ1BQEF27djXtNK5OVFQUERERBAUFmX6e/ec//yEwMPCWvmtLozBea4uKaPLy8vIICAggMzPTFHN0dCQxMZGOHTtaMDNxq/bv38+SJUtqPBMKoFWrVjz11FM888wzuF1+t6al0+l0bN++nYiICL799lvy8vKue41arebuu+9Go9Fw//334+zs3ACZiubCaDRy+vRp9u/fb/q/uLi4KrN+d911F8P9/Oj722813kupUNLO3f2au2Lrw6939qXrPfcwdOjQBn2uuHEyY9fMPfXUU2ZFHcCKFSukqGvCoqOjWbx4Mdu2batxjLOzM8888wxPP/209Hfk0uzI7t27iYiIYNOmTZw/f+1dhXDpcOa77roLjUbDuHHjajVDIwTAuXPn2L9/PzExMaZCrjaH++bk5FAaFoZOrUZ9+X02AIVCiZWVFdbW1jg4ODR4UVepUlFkZ4e7u3uDPlfcHCnsmrHNmzdXea/jnnvuYdasWRbKSNyKXbt2sXjxYn7//fcax7i6ujJ37lyeeOKJFj+rZDAY+PPPP4mIiOCbb74hKyvrutcoFAoGDBjA5MmTGT9+vCxbi+sqKioiLi7ObDbu6veZaysnJwed0Uh5mzY4XSzC2soKK2tr1Gp1lU0VDanAwQGFWl2nhV2fPn0oLy83i+3YsQMXF5c6e0ZLJYVdM3X+/HkeffRRs5izszMrV66UE+6bEKPRyPbt21m8eDE7d+6scZybmxvPP/88jz32GK0unzvVEhmNRg4cOEBERAQbNmwgPT29VtfdeeedaDQaJk6cWO2xFULApWX8w4cPmxVxSUlJtTqcuiadO3emd+/e9O7dm549exK7dy9lt3fC5cyZukv8FmW1ccXWwQFXV9c6u+e+ffvq7F7CnBR2zZDRaGTOnDlVlpv+9a9/mXZoicbNaDTy66+/snjxYqKiomoc5+7uzgsvvMDs2bNb7OnsRqORxMREIiIiiIiI4PTp07W6rkePHkyePJmJEyeanS8mBFz6e5WSklLlvbjqeirXVtu2benTp49ZIXf1En9lZSUH8/LolpaG6hYKxmsxXD6CpDa/4uuVSlI7dCCsRw85GquJkMKuGVq/fj2bNm0yi40dO1a2jzcBRqORH3/8kcX/396dx0VV7/8Df83Crqwji+wqILKvLqjRzdxyQVIO2S0z267WzbyWXRXrK5p+LfXrr7xamZbejIO4RGqaaVnihqAgi4ob+77DsM3M+f2hTozD4ADDNryfj0ePez2cc+YzoJzXfD7vz+ezdi0uX76s8jwbGxusWLECr7/+uso1tLRdRkaGPMzdvHlTrWu8vb3BMAwiIiJodw2ioLS0VKEm7vLlyygvL+/0/QwNDREYGCgPccHBwXBwcHjiiImPjw8SExKQJxLBsaSk06+vSnVNDerr68Dn8WFmbg49Xd12z88ViSAxNIS3t7fG20K6BwU7LVNQUIAlS5YoHLOwsMCXX35JQ7B9GMdx8k3Gk5OTVZ5nZ2eHDz/8EIsWLVLaj3EguH37NliWRUxMDNLS0tS6ZuTIkYiMjATDMBg5cmQ3t5D0B/X19fK6uEdh7t69e52+n0AggJeXl0KIc3d3h/AJCwm3xczMDM4uLrhdXg770lLwNbhwhUQiQf3DdelknAyVFRUYYmkJgYrt7WQ8Hu44OsDZ1ZUmYfUjFOy0CMdxeOONN5SWb9ixYwfNZuqjZDIZDh8+jOjoaKSkpKg8z9HREStXrsSCBQsG3OK39+/fR2xsLFiWbTf0tjZ8+HAwDAOGYeDl5UUfagYwiUSC9PR0hZ64tLS0LtXFDRs2TCHE+fn5abTnPGTCBHx/+zaybG3hlpensfvyHgtwMk6G6qoqlbVzt2xtUScSYfb48RprA+l+FOy0yJ49e3Ds2DGFY48KwknfIpVKERcXh+joaKSnp6s8b9iwYVi1ahVeeumlAbXfaH5+vjzMqVtk7eDgIA9z/v7+FOYGII7jcP/+fYUQl5SU1KW6OJFIpFQX191rQtrY2CAoJASJjU2wqaiAsViskfsK+HwY6BugofGv70djUyPEDWIYGigG02pDQ9x0dUHw+PGwsbHRyOuTnkELFGuJ7OxseHl5oba2Vn7MysoK6enptP5WHyKRSBATE4P169fjxo0bKs9zcXHB6tWrMX/+/E4N5/RHxcXFiIuLA8uyOHfunMp9blsbOnQo5s2bB4ZhMGbMGApzA0xZWZlSXVxZWVmn72doaIiAgACF3jhHR8de+XslkUjw3e7dkGZkYsLVqxBqaCKFTCZDSWkpZDKp/BiPx4flkCHyyRESPh9/+PtBx90dL7/66oD5HaQt6KelBWQyGRYtWqQQ6gDg66+/plDXR7S0tOD777/H+vXrcfv2bZXnubu7Y/Xq1WAYZkDMQCsvL8ehQ4fAsix+++03tYbHhgwZIg9z48ePB19FfRDRLmKxWL4F16P/1J0B3RaBQABPT0+FEDdq1Kg+E2KEQiGemzULMVXVuNTchLFp6Rqpt+Pz+TA1MUFF5V8LJnOcDFVVVbCwsICMx8Mlj1FosBmK2bNm9ZnvB1Ef9dhpgf/85z9KEyZeeeUV7Nmzp5daRB5pbm7G3r178cknn7RbnO3p6YmoqCg8//zzWh/oqqqqcOTIEbAsi19//RWSVivsq2Jubo7w8HAwDIPQ0FB62Gg5iUSCjIwMhckN169fh1QqffLFKjg7OyvVxfWHJYKys7Nx8IcfYJ6Tg9HpGRrruausqkJDg+IQ7yAzc1wPCkSFgwOef+EFWgaon6Jg18/dvn0bPj4+ELeqwbCzs0NaWtqA33mgNzU1NWHPnj3YsGEDcnJyVJ7n6+uLqKgohIWFaXXPU21tLX766SfExMTg5MmTaG5ufuI1xsbGmDNnDhiGwaRJkwZUjeFAwnEcsrOzlerixF2oK7OwsMDo0aMRFBSE4OBgBAUFYciQIRpsdc/Kzs7GYTYWhgUFCMjM1EjNnYzjUFpSAunDIdl6Y2PcCAyEzNkZ8+bPp1DXj1Gw68ekUilCQ0Nx7tw5heMnT57E5MmTe6lVA1tjYyN27dqFjRs3Ij8/X+V5gYGBWLNmDWbMmKG1dWFisRjHjh0Dy7I4duyY0obnbTEyMsKsWbPAMAymTJkyIJd00Xbl5eVKdXHq7N2rioGBgVJdnJOTk9b9uyoqKsKx+HhU5uVjZFYWXPLzuzw029TUhNLKChS4uiJr5EjkV1Qgr6gIR48e1fqRA21Gwa4f27x5M5YvX65w7K233sKOHTt6qUUDl1gsxldffYVNmza1uyfp6NGj8dFHH2Hq1Kla9+ABHgTbEydOgGVZ/PTTT6ivr3/iNfr6+pgxYwYYhsH06dMH7ILL2qihoUGpLu7OnTudvh+fz1eqi/Pw8BgwQ/MSiQQJCQlITEjAoLIyDM/OgX1ZWad2qJDy+cgViZBhbYUifX0kJCbi/PnzkEql2Lx5M5YtW9YN74D0BAp2/VRmZib8/PwUNlF2dnZGamoqBg0a1IstG1jq6uqwY8cOfPbZZyhpZ5X48ePHY82aNZg0aZLWBbrm5mb8+uuvYFkWR44cQU1NzROv0dXVxdSpU8EwDGbOnDmg97fVFlKpFJmZmQohLjU1tUt1cU5OTgohzt/fv1/UxXW3goICnE9IwL1btyAUi+GYmwub8gqY1NdDp53vd4tAgGojIxRamCPb3h4SQ0PYOzvjsy1bkJSUJD9PT08PV69ehbu7e0+8HaJhFOz6IYlEgnHjxiExMVF+jMfj4ffff8fEiRN7sWUDR01NDbZv347Nmze3u+1QaGgo1qxZg9DQUK0KdBKJBL/99htYlsWhQ4eUFsVui1AoxLPPPguGYTB79myYmpp2f0NJt+A4Djk5OfIAl5iYiCtXrqjVQ6uKubm5QogLCgqCpaWlBlutfSorK5GamorUpCQ01teDk0gwqKEBxhWV0JVIwOdkkPH4aBYKUWNuhjoDA/CEQugbGcE7IADe3t4wMzPD2bNnERoaqnDvoKAgnD9/fsD0hmoTCnb90Pr167F69WqFY++99x62bNnSSy0aOKqqqvD5559j69at7YaZZ599FlFRUZgwYUIPtq57SaVSnDt3DizLIi4uTq26KD6fj6effhoMwyA8PJyW3+mnKioqlOri2uuhfhJ9fX2FurigoCAMGzZMqz789CSpVIqKigoUFxejuLgYpUVFaG5shFQigUAohK6+PoZYW8PKygpWVlYwNzdXqqF777338H//938Kx9avX4+VK1f24DshmkDBrp9JSUlBUFAQWlpa5Mfc3Nxw9epVGBgY9GLLtFtFRQW2bduGbdu2obq6WuV506ZNQ1RUFMaOHduDres+HMfh4sWLiImJwYEDB9qtH3yEx+NhwoQJYBgGzz//PG1n1880NDTg2rVrCiGuvbUXn4TP58PDw0OpLo5mOfctDQ0N8PX1xa1bt+THdHR0kJiYCB8fn15sGekoCnb9SHNzM4KCgpCamio/xufzcf78eYwePboXW6a9ysrKsHXrVnz++edKC0C3NnPmTERFRSEoKKgHW9c9OI5DUlISWJZFbGxsu8u1tDZmzBj5Fna2trbd3EqiCVKpFDdu3FCqi1NnbUFVHB0dleriqO63f7h48SJCQkIUFgr38fHB5cuXoaur24stIx1Bg+f9yNq1axVCHQCsWLGCQl03KCkpwebNm7F9+/Z264bmzJmDqKgo+Pn59WDrNI/jOKSmpsrDnLozFwMCAsAwDCIiImjdqz6O4zjk5eUphLgrV66grq6u0/c0MzNTqoujHtr+a8yYMfjggw+wceNG+bGUlBRER0cjOjq6F1tGOoJ67PqJy5cvY9y4cQozzLy8vJCYmAg9Pb1ebJl2KSwsxKeffoqdO3eq3Dicx+Nh7ty5WL16Nby9vXu4hZqVmZkJlmXBsmy7e9e25uXlBYZhwDAMRowY0c0tJJ1VWVmpUBeXmJiIoqKiTt9PT08P/v7+CkFu+PDhVBenZZqamhAYGIi0tDT5MYFAgISEBOTm5iI5ORnh4eEIDAzsxVaS9lCw6wcaGhrg7++v8OAVCoVITEyEr69v7zVMi+Tl5WHTpk346quvFJaQaY3P5yMyMhKrVq3CqFGjeriFmnP79m15mLt+/bpa14wcOVIe5mgJhL6nsbFRqS4uKyur0/fj8XhKdXGenp5UFzdAXL16FcHBwQpD8oaGhvLdQHR0dJCamoqRI0f2VhNJO2goth+IiopS6k1Zs2YNhToNyMnJwcaNG/HNN9+o3OZKIBDgxRdfxMqVK+Hm5tbDLdSM7OxsxMbGgmVZhfWq2jNs2DB5mPP29qaemT5CKpXi5s2bCiEuJSWlS3VxDg4OSnVxtLbgwOXn54eoqCh89NFH8mOtt3hraWnBoUOH5DNm25qV29TQAJlUCr5AAD0DgyfOyiWaQz12fdyff/6Jp556Cq1/TAEBAbhw4QJ9eu6Ce/fuYcOGDfj2228VZhi3JhQK8fLLL+Pf//53vxxyzM/Px4EDB8CyLC5evKjWNQ4ODoiIiADDMAgICKAw18s4jkN+fr5SXVx7E3mexNTUVKkuztraWoOtJtogOzsb3t7eKhccf/XVV/HZZ58hJSUF15OTFdbRM6mogI5EAj7HQcbjoUUoRLW5ucI6el7+/vDx8YGZmVkPvzPtR8GuD6urq4OPjw/u3r0rP6anp4ekpCR4eHj0Ysv6r6ysLHzyySfYt2+fyhXxdXR0sHDhQnz44Ydwdnbu4RZ2TUlJCeLi4sCyLP7880+o88/bxsYG8+bNQ2RkJEaPHg0+n98DLSVtqaqqwpUrVxSCnDpLzKiip6cHPz8/hSA3YsQICuykXfX19fD19VW5zI21tTXCw8LgZGcHHbEYDjm5sKnowM4X5ubIcbBHi6EhnF1cEDJhAmxsbLrr7Qw4NBTbh61YsUIh1AFAdHQ0hbpOuHHjBtavX4/9+/crTOVvTVdXF6+99hpWrFgBBweHHm5h55WXl+PQoUNgWRa//fabyvfX2pAhQzB37lwwDIPx48fTsEgvaGxsREpKisLkhps3b3b6fjweD+7u7gohzsvLi5apIB2WnJzcZqgTCAQYN24cQoKCMKS+Ht7JV2HXgb1qdaRSiGpqIKqpwaicHOSJRLhdXo7vb99GUEgIQkJCaKcLDaAeuz7q119/xbPPPqtwbNy4cfjjjz/oIdwB6enpWLduHViWVdl7pa+vjzfeeAMffPBBv1l/rbq6GkeOHAHLsjh16pRa9VVmZmYIDw9HZGQkQkND6RdoD5LJZG3WxakqA1CHvb29Ul2csbGxBltNBqry8nI4OjoqLPVkaWmJWc89B1szM7jcuIGht7Jga22Nrvb9yng8ZNna4oaLC8ztbDF91iwqDegiCnZ9UHV1Nby8vJCbmys/ZmBggJSUFLi4uPRiy/qPlJQUrFu3DnFxcSrPMTAwwOLFi7F8+fJ+8Yukrq4O8fHxYFkWJ06cUDnZozVjY2OEhYWBYRhMmjSJem96SFt1capqldRhYmKiVBdHQ1ekO126dAmLFi1Cenr6g9rbsDDYiMVwT0qC4cO/y5ZDLDX2AbHG0BBJ7u4QDx2KOQyti9kVFOz6oEWLFmH37t0Kxz7//HO8/fbbvdSi/iM5ORnR0dE4cuSIynOMjIzw9ttvY9myZX1+k3GxWIzjx4+DZVkcPXoUjY2NT7zGyMgIM2fORGRkJKZMmQJ9ff0eaOnAVV1drVQXV1BQ0On76erqtlkXR7WPpKdJJBLs2LEDBffvw66kFO6XL0HQqobO1NQMhhrcylLC5+OSxyhUODjg+RdeoHDXSRTs+pijR49i5syZCseefvpp/Prrr/SLvR2XL19GdHQ0jh49qvKcwYMH45///CeWLl0KkUjUg63rmKamJpw4cQIsyyI+Pr7dnS8e0dfXx3PPPQeGYfDcc8/B0NCwB1o68DQ1NSE1NVUhxKm7sHNbeDweRo4cqRDivL29qWeV9AlFRUWI2bsXpvfuwePiRYhr6wD8FRmsLK00Xhok4/FwwdMDVU7OiHz5pX4xmtLXULDrQ8rLy+Hp6amwOvzgwYORmpoKJyen3mtYH3b+/HmsXbsWJ0+eVHmOiYkJli5dinfffbfPTq1vaWnBr7/+ipiYGBw5ckStYTsdHR1MnToVkZGRmDlzJq07pmEymQy3bt1SqotTZwhcFVtbW4UQFxAQABMTEw22mhDNkEgk+G73bkgzMjHh6lUIZTJIJBJUVlVBJpNh8ODBGu2tU3htPh9/+PtBx90dL7/6KtUDdxB9t/qQd955R2nLny1btlCoa8Mff/yBtWvX4vTp0yrPMTc3x7Jly/D222/3yYenRCLB77//DpZlcejQIVRUVDzxGqFQiEmTJoFhGISFhcHU1LT7GzpAFBQUKIS4xMTELtXFGRsbK9XFDR06VIMtJqT7JCQkoDIvH09nZkL4cNarUCjEkB4Y7RDKZAjIyMTvxsY4f/48Jk6c2O2vqU0o2PURcXFx+OGHHxSOTZs2DYsWLeqlFvU9HMfht99+w9q1a3H27FmV54lEIixfvhyLFy/uc71YMpkM586dA8uyiIuLQ0lJyROv4fP5CA0NRWRkJMLDw2FhYdEDLdVuNTU1SnVx+fn5nb6frq4ufH19FYKci4sLlU+QfqmgoACJCQkYmZUF41Y7TvQkE7EYbreycFlPDy4uLjRZqANoKLYPKCkpgYeHB8rKyuTHTE1NkZ6eTp/w8SDQnTp1CmvXrkVCQoLK86ysrPD+++/jrbfegpGRUQ+2sH0cx+HixYtgWRYHDhxQq7Cex+Nh/PjxYBgGc+fOhZWVVQ+0VDs1Nze3WRfXlV99bdXF6enpabDVhPSeuNhYlF28iKevJIHfixFBxuPht8AAiMaOxdx583qtHf0N9dj1Mo7j8OabbyqEOgD44osvBnyo4zgOP//8M9auXYtLly6pPM/GxgYrVqzA66+/3mcmDXAch+TkZLAsC5ZlkZOTo9Z1Y8aMAcMwmDdvXr9ZU68vkclkuH37tkKIu3r1apfq4mxsbDB69Gh5iAsMDOyTQ/uEaEJlZSXuZWXBLzunV0MdAPA5DsOzc3DNwgKVlZV9tka6r6Fg18u+//57paU5wsPDMX/+/N5pUB/AcRzi4+MRHR3d7ob1dnZ2+PDDD7Fo0aI+saQHx3G4fv26PMzduXNHrev8/f3BMAwiIiKonrKDCgsLleriqqurO30/Y2NjBAYGKvTGUcAmA0lKSgp0xGLYPdbZ0Fvsy8qQJhYjNTUVTz31VG83p1+gYNeL8vPzldamE4lE2LFjx4Dcy1Emk+Hw4cOIjo5GSkqKyvMcHR2xcuVKLFiwoE8Mf2VmZsrDnLpLX3h6eiIyMhIRERG06LSaampqkJSUpBDk8vLyOn0/HR0dpbo4V1dXqosjA5ZUKsX15GQ45OSqvU1YdxPIZHDMzUVqUhJtf6gmCna9hOM4vPbaa0q9C19++WWfXzRX06RSKeLi4hAdHY309HSV5w0bNgyrVq3CSy+9BB0dnR5sobI7d+7Iw1xqaqpa17i5uYFhGDAMg1GjRnVzC/u35uZmXL9+XSHEZWZmdqkuzs3NTSHE+fj49IkPBoT0FRUVFWisr4eNGjP0e5JNeQXu1NejoqICQ4YM6e3m9HkU7HrJrl27cOLECYVj8+fPR3h4eC+1qOdJJBKwLIt169a129Pl4uKC1atXY/78+b26nlF2djZiY2PBsmy7Q8StOTs7IzIyEgzDwNvbe0D2xD4Jx3Ft1sU1NTV1+p7W1tZKdXG0NAzpC4RCITw9PeV/vnz5cocXpN60aRM++OADjbZr165dWLx4MVa99x5M6+o6dY9LVVX4b2EBPnfX7AdXk/p6cBIJiouL1Qp2VVVViI2NxRtvvAEAuHLlCliWxaeffqqR9ly+fBmLFy9GSkoKDh8+jBkzZmjkvppCs2J7wf379+Hl5YW6Vv94bGxskJaWBnNz815sWc9oaWnB/v37sX79emRlZak8z93dHatXrwbDML3W/V5QUIADBw6AZVlcuHBBrWvs7e0REREBhmEQGBhIYe4xRUVFSnVxVVVVnb7f4MGD26yLo+876YtEIpHSZLmeuIdUKm339+jkyZNRUFAAP3t7rKl78m43bemuYAcAp8aOgduUKXjmmWcAtP9+7t+/j7lz5+LKlSsabwcA5OXloaysDFu2bEFERESfC3bUY9fDZDIZFi5cqBDqgAeflrQ91DU3N2Pfvn345JNPcPfuXZXneXp6IioqCs8//3yvBLqSkhIcPHgQMTEx+PPPP9Ua/rOxscG8efPAMAzGjBlDdVoP1dbWKtXF5ebmdvp+Ojo68PHxQVBQkDzEubm5Ud0N6deOHz+OtWvXorGxEcHBwdi5cyf4fD7eeOMNJCUlobGxEYsXL8aSJUuwatUqVFVVwdfXFyEhIXj//fcVQszy5cvh6emJV155BU5OTnj11Vdx4sQJREdHIycnBzt27EBjYyPCwsKwdu1aAEBZWRnu3r2Lv8+fj8N79gBOzg+ONzdj6Y0bEMukGG9qhpiiQlweMxZiqRT/unkT+U2N8Bw0CAlVVTjuH6DwnipaWvDvW7dQ0NQIE6EONrq6wk5fHytu3YShQICsejGKm5uwydUN3xXkI6OuHpNFFlj+8LUPFhdhf2EhmmUyTLKwwBg3N2Rcv45ly5YhODgYly5dQmJiIubOnYuCggI0NTVh7dq1CA8Px6pVq5CRkQFfX1/MmzcPISEh+OKLLxAXF4eysjIsXLgQ2dnZMDc3x7fffgsnJye88sorMDExwaVLl1BeXo5du3apnKxhZ2cHOzu7Pvt7noJdD9u+fTt+//13hWOLFi3C9OnTe6dBPaCpqQl79uzBhg0b2l32w9fXF1FRUQgLC+vxfzAVFRU4dOgQWJbFmTNnIFOjcFgkEmHu3LlgGAYTJkwY8OGipaVFqS4uIyOjS3Vxrq6uSnVxfWEGNCGd9SiUAQ+WN1q3bh22bNmC33//Hfr6+nj77bcRGxuLyMhIbNy4Eebm5mhubpYvhbR+/Xp8+eWXuHbtGoAHvVPtsbCwwPnz55GRkYGdO3fiwoUL4PF4mD17Ni5cuICxY8fi4MGDCA8Ph5O9PQqrqlDZ0gIzHR18kZODSRYWeMXWFmxRofye3xcWwE5fDztGjUJCZSXiiouVXvfznGwEmhjjdTsPHCstxbq7d7BzlAcAoF4qxX+9vRFfUoI3M9Jx2NcPQ3R1MTXpCl61tUNFSzPOVlQi1scXPAD/yMiAdUEBDFxGID09Hfv27cPXX38NAPjuu+9gbm6O6upqjB49GnPmzMH69etx8+ZNedht/cz9+OOPMWHCBPz0009gWRb//Oc/ER8fL//ZXLx4EWfOnHnizkZ9GQW7HnTr1i2sWLFC4ZiDgwO2bNnSSy3qXo2Njdi1axc2btzY7qr+gYGBWLNmDWbMmNGjw2fV1dU4cuQIWJbFqVOnIJFInniNmZkZwsPDwTAMnn766QG7hyHHcbhz545SXVxjY2On72llZaVUF0frVhFtY2pqKg9lAPDTTz8hNTUVY8aMAQA0NDTIl9jZv38/vvnmG0ilUuTk5CArKwuiDm7pNe/hwr6nT5/GhQsXEBDwoGetrq4Od+7cwdixY8GyLDZt2oTrSUkIsrfHqfJyRFhbI7m2BosdHAAAz4mGYPPDEJlcU4s37OwAACFmZjBt4/dgUk0N3noY5KaLRFh/96/ln54xf7B7jquREZwMDGD78MOao4EBipqacKWmGldrazDn2lUAgFgqhWdtDewlEri6usLb21t+r61bt8qDWU5OjtK2nI87d+4cjh8/DgCIiIjAu+++K//arFmzAAABAQFPDMx92cB8KvUCqVSKV155BQ0NDQrHd+/eDWNj415qVfcQi8X46quvsGnTJhQWFqo8b/To0fjoo48wderUHgt0dXV18k9qP//8s1oL1w4ePBhhYWGIjIzEpEmTOlzorA2Ki4uRmJioEOQqKys7fb9BgwYp1cXZ2dlRXRwZcDiOw4wZM7B7926F43fv3sV//vMfXLhwASYmJpg6dWqbE4qEQqHCCMPj5zxatJ3jOLzxxhtYs2aNwteLi4tx/vx5zJ07F3V1dZCKxajS00OEtTVUd7Zz7fypba3/ZevyH/yJD0CX99foDB88SB++KGNtjbcdHOVfuzp8OHKFQoVF6H/77TckJCTg4sWLMDAwwMiRIzs86ar175xHs+QFAgGkUmmH7tOXULDrIZs3b1Yqvl+yZIm8EFQb1NXVYceOHfjss8/a3QM1JCQEH330ESZNmtQjD/KGhgYcO3YMLMvi2LFjSuG6LYaGhpg1axYYhsHUqVMH1PBfXV2dUl2cujtntEUoFMLb21se4IKCguDu7j7gh64JAR4Mx7777rvIzc2Fvb09ysvL0dDQgNraWgwaNAjGxsa4f/8+zp07J7/mUfAQCASwtLREQUEBamtrwePxcOrUKXmvXGt/+9vfwDAM3nnnHZiZmSEvLw8GBgaIi4vDP/7xD2zduhUx338PyZkzWPH996hoaYa/8WCcKCvFy0Nt8XOryRp+xsb4uawMfsbGOF9Vieo2RjsCjI1xtLQUi+zscKK8DN4d2Ld7jIkplt68gZeGDoWJUAdFTU0ol0ig+9jyRDU1NbCwsICBgQEuX76MW7duAXjwYby2trbNe48fPx779+/H8uXLERcXh+DgYLXb1V9QsOsB6enpiIqKUjg2fPhw/O///m8vtUizampqsH37dmzevBnl5eUqzwsNDcWaNWsQGhra7YGuqakJJ0+eBMuyiI+PV5qs0hZ9fX1Mnz4dDMPgueee61P7zXaXlpYWpKWlKdXFqVNjqIqLi4vC5AZfX18YGBhosNWEaA9LS0vs2LEDYWFhaGlpgY6ODr7++mv4+/vDzc0Nnp6ecHV1xdixY+XXLFiwAF5eXnj66aexfft2fPDBB/D394eLiwu8vLzafB1PT0+sWLECoaGhkMlkGDx4MGJiYhAbGyufRDHE2ho3LSwQam6Ok2XleNvBEUtvZOJISQmeMjPDIMGDyPCizVD86+YNzLqajCBjE1jr6kL/sbrodxwc8eGtWzhSUiyfPKEuVyMjvG5rh7+nXgcHDkYCAcL9/eD82FInU6ZMwfbt2+Hr6wsfHx/5e7ewsIC/vz+8vLwQGRmJkJAQ+TUff/wxXnnlFezdu1c+eaKjMjIyMHnyZFRWVuLo0aNwd3fHn3/+2eH7dBda7qSbtbS0YMyYMUhOTpYf4/F4+PPPPxX+svVHVVVV+Pzzz7F169Z2h+WeffZZREVFYcKECd3anpaWFvz6669gWRZHjhxRa2spHR0dTJ06FQzDYNasWRjcgU+V/Q3Hcbh7965CiEtOTu5SXZylpaVSXZy2z+4mRFulpaXh+IEDmHH2D+hIpWiSySDk8SDg8fBzWSmOl5bic/dRkHAcZBwHXT4fKbW1+J87t3HI16/b2tUiEODoUxMxfd48hTUASduox66bbdiwQSHUAcC//vWvfh3qKioqsG3bNmzbtq3d8DRt2jRERUUpfNLUNKlUit9//x0sy+LgwYOoUGPFdIFAgEmTJoFhGISFhWltgX5JSYlSXZw63x9VjIyMlOri7O3tqS6OEC1hZWUFnlCIaiMjiGpqkNfYiGU3b0DGcRgkFGKjy4NeN7FUigXXr0PCcdDh8/Dx8BHd2q5qIyPwhEJYWVl16+toC+qx60bJyckYPXq0wmxLd3d3JCcn98uarbKyMmzduhWff/65yvoFAJg5cyaioqIQFBTULe2QyWQ4d+4cWJZFXFxcu/V8j/D5fISGhoJhGISHh3d4ZllfV19fj+TkZIUQ15VZXQKBQKEuLjg4mOriCNFyUqkU/9m2DbZXr8GrD80Kve7shHxfXyx+990e/R108uRJpZUsJk6ciP/3//5fj7WhM6jHrps0NTVhwYIFCqFOIBDgu+++63ehrqSkBJs3b8b27dtRX696RfI5c+YgKioKfn6a75LnOA6XLl0Cy7I4cOBAu8untDZ+/HgwDIO5c+fC2tpa4+3qDRKJRKkuLj09vUt1ccOHD1cIcX5+flQXR8gAIxAI4OXvj2vl5RiVkwNBF36naIqUz0e2vT38AwJ6/IPllClTMGXKlB59TU2gYNdNPv74Y6SlpSkc+/e//91tvVjdobCwEJ9++il27typciYpj8fD3LlzsXr1aoW1hTSB4zgkJyeDZVnExsYiOztbretGjx4NhmEwb9482D1ca6m/4jgO9+7dU6qLU2dmrypDhgzB6NGj5RMcgoKCYGFhocFWE0L6Kx8fHyQmJCBPJIKjGqMhHcHhwXJYEokEhgYG0NHReeI1uSIRJIaGGn++aDMKdt3g4sWL2LRpk8IxHx8fpZmxfVVeXh42bdqEr776SuWaQHw+H5GRkVi1ahVGjdLcvoAcxyEtLQ0sy4JlWdy+fVut6/z8/BAZGYmIiAg4OTlprD09rbS0VKkurr2Zxk9iaGioVBfn4OBAdXGEkDaZmZnB2cUFt8vLYV9aCr4Gq7Xq6+pQU1sDABDX18NCJIJuO+FOxuPhjqMDnF1dtbYWujtQsNMwsViMBQsWKAyL6ejoYO/evX1+YducnBxs3LgR33zzjcqFewUCAV588UWsXLkSbm5uGnvtGzduyMNcZmamWtd4enqCYRgwDAMXFxeNtaWn1NfX4+rVqwoh7t69e52+n0AggJeXl1Jd3EDdHYMQ0jkhEybg+9u3kWVrC7e8PI3dt/VzhQOHqqpKDBENUflB85atLepEIsweP15jbRgI6De+hq1atUq+SOIjH3/8cZ/uRr537x42bNiAb7/9Fi0tLW2eIxQK8fLLL+Pf//43RozQzAyou3fvgmVZxMTEIDU1Va1rXF1d5WHOw8NDI+3oCRKJBBkZGQohLi0trUurmw8bNkypLq71quyEENIZNjY2CAoJQWJjE2wqKmAsFmvkvnr6+mhs+mt5JYlEgpraWpi0sftStaEhbrq6IHj8eNjY2Gjk9QcKmhWrQWfPnkVoaKjCseDgYCQkJPTJXpPbt2/jk08+wd69e1UGDB0dHSxcuBAffvghnJ2du/yaOTk5iI2NBcuy8g2an8TZ2Vke5nx8fPr8MCLHcbh//75SXZy4C78cRSKRQogLCgrSupm9hJC+QyKR4LvduyHNyMSEq1ch1MBECg4PVldoaWk9IsSDhYUF9FqNaEn4fPzh7wcdd3e8/OqrffL52ZdRsNOQ2tpa+Pj4KAyl6evr4+rVqxg5cmQvtkzZzZs3sX79enz//fcqZ1Lq6uritddew4oVK+DwcBPoziosLMSBAwcQExOjtK2aKnZ2doiIiEBkZCQCAwP7dJgrKytTqosra7X9TkcZGhoiICBAHuCCg4Ph5OTUp78HhBDtU1RUhJi9+2B6/x7GpqVrpN5OIpGgtLQUXKsdZgUCIYYMGQI+jwcZj4cLnh6ocnJG5Msvac1qBj2JYrCGvP/++0r1UevXr+9ToS49PR3r1q0Dy7JQlef19fXxxhtv4IMPPoCtrW2nX6ukpAQHDx4Ey7L4448/VL5ea9bW1pg3bx4YhsHYsWPBf2yLmr5ALBYr1cXdvXu30/fj8/lKdXGjRo2iT6iEkF5nbW2NOUwEDv7wAy4AGJ2e0eWeO6FQiMHGxqip+Wtxe6lUgpqaGgwyM8Mlj1GocHDA80wEhbpOoh47DTh58iSmTp2qcGzChAn47bff+sSCrqmpqYiOjkZcXJzKcwwMDLB48WIsX7680/+YKioqcPjwYbAsizNnzqhVPyYSiTB37lwwDIMJEyb0ie/XI1KpVKku7vr1612qi3N2dlaqixsIe9ISQvqv7OxsHGZjYVhQgIDMzC7X3HEAysvL0dz816oL9cbGuD9+PJrt7DGHiYCjo2MXWz1wUbDroqqqKnh6eiosmGtoaIjU1FQMHz68F1v2YOeL6OhoHDlyROU5RkZGePvtt7Fs2TJYWlp2+DWqq6vx448/gmVZ/PLLLwoLMqtiamqK8PBwMAyDv/3tb32id4rjOGRnZysMqSYlJbW7IPOTWFhYKNXFDXlsE2tCCOkPioqKcCw+HpV5+RiZlQWX/PwuDc1KpFKUlpZACqDA1RVZI0eitK4O773/vsYm6A1Uvf9E7eeWLl2qtAvCZ5991quh7vLly4iOjsbRo0dVnjN48GD885//xNKlSztchF9XV4ejR48iJiYGJ06cULnW3eOvFxYWBoZh8Oyzz/b60i/l5eVKdXGlpaWdvp+BgQECAgLkNXHBwcFwdnamujhCiFawtrbGgldfRUJCAhL19ZBnY43h2TmwLyvr1A4VPB0d1IwciZtDh6Js0CAkJCbi/PnzEDc3Y8+ePd3wDgYO6rHrgvj4eMyePVvh2KRJk/DLL7/0ygP9/PnziI6OxokTJ1SeY2JigqVLl+Ldd9/t0IKPDQ0NOH78OFiWxdGjR9Xa+cDQ0BAzZ84EwzCYNm1ar22l1tDQoFQXd+fOnU7fj8/nw9PTU6E3zsPDo0/0PBJCSHcrKCjA+YQE3Lt1C0KxGI65ubApr4BJfT102ilVaREIUG1khEILc2Tb20NiaIjs/HwcPHwYRUVF8vPi4+Mxc+bMnngrWomCXSeVlZXB09MTxcXF8mPGxsa4fv16l2eRdtQff/yBtWvX4vTp0yrPMTMzw7Jly/DOO+/AxMRErfs2NTXh5MmTYFkW8fHxqKure+I1enp6mD59OiIjI/Hcc8/1eP2YVCpFZmamQohLTU3tUl2ck5OTQojz9/enujhCyIBXWVmJ1NRUpCYlobG+HpxEgkENDTCuqISuRAI+J4OMx0ezUIgaczPUGRiAJxRC38gI3gEB8Pb2Rm1tLTw9PVFbWyu/r5WVFdLT02mrw06iYNdJDMMgNjZW4dju3buxcOHCHnl9juPw22+/Ye3atTh79qzK80QiEZYvX47Fixdj8ODBT7xvS0sLTp8+DZZlcfjwYVRXVz/xGh0dHUyZMgUMw2DWrFkwbmOxye7AcRxyc3MVQtyVK1e6VBdnbm6uVBfXmdpDQggZKKRSKSoqKlBcXIzi4mKUFhWhubERUokEAqEQuvr6GGJtDSsrK1hZWcHc3Fxhotzu3buxaNEihXtGRkbihx9+6Om3ohUo2HUCy7KIjIxUODZjxgzEx8d3+xAsx3E4deoU1q5di4SEBJXnWVlZ4f3338dbb731xN4lqVSKs2fPgmVZHDx4UK29SQUCASZNmgSGYRAWFtYj+/hVVFTI6+Ie/W/rHtOO0tfXh7+/v0KQGzZsGNXFEUJID+I4DjNnzsSxY8cUjrMsi4iIiF5qVf9Fwa6DioqK4OHhgYqKCvkxMzMzpKend+u2JxzH4eeff8batWtx6dIllefZ2NhgxYoVeP3119vdXkomkyEhIQEsyyIuLk6tgMTj8RAaGgqGYRAeHt6tMzwbGhpw7do1hd6427dvd/p+fD4fHh4eCpMbPD09odPOBtSEEEJ6RmFhITw8PFBZWSk/ZmFhgfT0dFhZWfViy/ofCnYdwHEcwsLCEB8fr3D8hx9+UOrB0+RrxsfHIzo6GklJSSrPs7Ozw4cffohFixapnKTAcRwuX76MmJgYHDhwQGk2ryrjx48HwzCYO3dutywYKZVKcePGDaW6OHWWTlHF0dFRqS5u0KBBGmw1IYQQTfrhhx8wf/58hWOzZs3CkSNHaCSlAyjYPYFMJkNTUxMMDAzw3Xff4ZVXXlH4+ty5cxEbG6vxv3QymQyHDx9GdHQ0UlJSVJ7n6OiIlStXYsGCBdDT01P6OsdxuHr1KliWRWxsLO7fv6/W6wcHB4NhGMybNw/29vadfRttticvL0+pLk6diRmqmJmZKdXF0Sc8QgjpXziOQ0REhNJi+t999x1efvllNDQ0QE9Pr0/uStSXULBrx/Hjx/Hiiy+ioaEBERER+PHHH1FTUyP/uqWlJdLS0jQ6JCmVShEXF4fo6Gikp6erPG/YsGFYtWoVXnrppTaHE9PS0hATEwOWZdUewvTz8wPDMIiIiICzs3On30NrlZWVuHLlikKQaz2tvaP09PSU6uKGDx9On+YIIUQLlJaWwsPDQ2FdUWNjY8yePRuxsbEwMDDA999/j+nTp/diK/s2CnbtGDFiRLvrnR0+fBhhYWEaeS2JRAKWZbFu3TrcuHFD5XkuLi5YvXo15s+fr7Ru2s2bN8GyLFiWRUZGhlqv6+HhAYZhwDAMXF1du/QeGhsbFeriEhMTcevWrU7fj8fjYdSoUQohzsvLi+riCCFEix05cgRz5sxR+fXhw4d3qeZa2w2IYNfWVOymhgbIpFLwBQLoGRgoTcWura1td6bnCy+8gP3793e5bS0tLdi/fz/Wr1+PrKwslee5u7tj9erVYBhGYZr43bt35WGuvSHb1lxdXeVhzsPDo1PtlslkbdbFtbS0dOp+AODg4CAfSg0ODkZAQIBaS7QQQgjRLi+88AJiYmJUfr2qqgomJiader73pT3Ju4NWL5VfWVmJlJQUXE9OVlg80aSiAgYSCfgcBxmPhxahEDfNzZHUavHEIUOHwsTEROU6bleuXEF+fj5sbW071bbm5mbs27cPn3zyCe7evavyPE9PT0RFReH555+X/2XMzc1FbGwsWJZFYmKiWq/n5OQkD3O+vr4dGrrkOA75+flKdXGtF5TsKFNTU6W6uO6YmEEIIaR/yc/Pb3eyIPCg3KilpaVTz3cvf3/4+Pj0yDJdvUEre+wKCgpw/tw53MvKgo5YDIecXNhUdGC7E3Nz3B1qgwqpFFn37uHc+fNt1oX9/e9/x759+zrUtqamJuzZswcbNmxATk6OyvN8fX0RFRWFsLAw8Pl8FBYW4sCBA2BZFufPn1frtezs7BAREQGGYRAUFKR2mKuqqlKqiyssLFTr2rbo6enBz89PIciNGDGC6uIIIYQoeemll/Df//63za9ZW1tj/Lhx8PXyglFLS6ee7zkO9mgxNISziwtCJkzo1qXKeoNW9dhJJJIHGxQnJGBQWRn8snNg14ENinWkUohqaiCqqYFDZgbuGRvDwsUFI+bPl29Q3Hprqo70WDU2NmLXrl3YuHFju8uMBAYGYs2aNZgxYwbKysrw1VdfgWVZnD17FupkcCsrK8ybNw8Mw2DcuHFPnD3U1NSElJQUhRB38+ZNtd/X43g8Htzd3ZXq4nR1dTt9T0IIIQNH60mKjwgEAowbNw4hQUEQ1dXB7coVjKit69TzfVRODvJEItwuL8f3t28jKCQEISEhWrPft9b02BUVFeFYfDwq8/IxMisLLvn54HfhrVVWVqKhsQEyHg8Frq7IGjkS+RUViD9+HCUlJTAxMcEvv/yC4ODgdu8jFovx1VdfYdOmTe32eo0ePRofffQRgoODceTIEbAsizNnzqi1x6lIJMLzzz8PhmEwceJElfUDMpkMN2/eVAhxKSkpXaqLs7OzUwhxAQEBPbalGCGEEO1z+fJlTJ48WV4KZWlpiVnPPQdbMzO43LiBobduwUhfH2amXRtKlfF4yLK1xQ0XF5jb2WL6rFlaURKkFcEuOzsbh1kWhgWFCMjMhLFY3OV7FhcXQyr7K1SJjY2RGRCAQkNDNEilWL16NaytrVFUVISTJ0/Cy8sL/v7+8vPr6uqwc+dOfPrppygpKVH5OiEhIVi+fDlqamoQGxuLX375Ra2gZWpqijlz5iAyMhJPP/10mzNF26qLa+uTkLpMTEwUauKCgoIwdOjQTt+PEEIIaUtRURGioqJw6tQpzJs9GzZiMdyTkmD48Bkm4As0tl5pjaEhktzdIR46FHOYCDg6Omrkvr2l3we77OxsHPzhB1hk5yA4IwNCNbtln6SouBgymWJvmdDAADfHhaDK2QnPv/ACmpubMWHCBPl2XCzLYtq0adi+fTs2b96MsrIylfefMGECnnrqKaSlpeHnn39GU1PTE9s0ePBgzJ49GwzDYPLkyQrDm9XV1Up1cQUFBZ1894Curm6bdXG0MCQhhJCekJ2djZh9+2By5w5cz5+HoNUIliaDHQBI+Hxc8hiFCgcHPP/CC/063PXrYFdUVISYvXtheu8+xqand2no9XFisRhV1dUAOPB4fJiamsJAXx8yHg8XPD1Q6eiEQ0d/wrlz5+TXGBsbg8/no6qqSuV9vb29YWFhgYsXL6KhoeGJ7TA0NMSMGTPAMAymTZsGAwMDNDU1ITU1VSHEtbf23ZPweDyMHDlSIcR5e3tTXRwhhJBe8fjzvamhAVVVVeA4GQAeTE1M2t0PvTMePd+rnJwR+fJL/XZYtt8GO4lEgu9274Y0IxMTrl7VWE9da1KZDBKJBLq6umg9f1PC5+OUxyhcb2jAnn371KqDGzp0KKqqqiBWY5hYT08P06dPB8MwmD59OvLz85GYmCgPcdeuXUNzc3On35etra1SXZyJiUmn70cIIYRoiqrnO4cHS4UJhUIIumn0SMLn4w9/P+i4u+PlV1/tlxMq+m2wO3v2LBJPn8HTly5ppKauI8QNDciXSXH56adxJjERf/75p8pzhUKhWpvZ6+joYPLkyZg8eTJEIhHS0tLkuzd0pS7O2NhYvuDvo9q4zq69RwghhHS33ny+A0C1oSF+HzMawc88g4kTJ/b463dV/4uieLBOXWJCAkZmZfX4D725pQVVVVUwAgeXGzfQFBSErKwslfufthfqBAIB/Pz84OjoKN+O69ixY51um66uLnx9fRV2b3B1daW6OEIIIf1Cbz7fHzERi+F2KwuX9fTg4uLS79a565fB7vy5cxhUVgaXdtaD6w4cgPLy8of/Dxh66xaK7OwQMm4cDh46pNY9eDyevOCzqKgIV65cwZUrVzrVnrbq4vT09Dp1L0IIIaS39dbz/XGu+fnIt7FGwrlzmDtvXq+2paP6XbCrrKzEvaws+GXnaHSyhDokLS0PCzcf4HMc7G/fRrmfX7vbjwEPAh3HceA4TmXvXntsbGwwevRoeYgLDAykujhCCCFaozef74/jcxyGZ+fgmoUFKisr+9X2Y/0u2KWkpEBHLIZdO0uJdBehjg4AHh712AGAKDcXhl5e8PHxwR9//KHy2o6UMg4ePFihLi44OJjq4gghhGi13ny+t8W+rAxpYjFSU1Px1FNP9XZz1Navgp1UKsX15GQ45OSqvY2IJvEAGBoaKMxsFchksMvOhr+XF/78888OBTjgwaQJHx8fhRDn5uZGdXGEEEIGjN5+vrdFIJPBMTcXqUlJGD9+vMpdnfqaDqWHvXv3IiAgANXV1XjllVfg7OwsnxyQlpaG0NDQdq+Pj4/H1q1b2z3n448/xhdffKF0/Pfff0dYWBga6+thU1HRkWa3q7CpCf/IyMAzVxIxLekK/nXzBqolLThUXIyN9+4qnW9kNEjpmHlhIYz09WFhYfHE13NycoKZmRlsbW2xa9cuLFmyBImJidi+fTsWLFgAd3f3Doe6o0ePwtPTE3w+H2lpaR26lhBCCOkO27dvh6+vr/w/Nzc3CAQC1NXVKZ1bUVGh9vO9WSbD+rt38MyVRDyXnISXr6cis417dsWh4mKUNzfDpvxBuyraaNeSJUtgaWmJwMBAjb52V6ndY3fo0CF8+umnOHPmjLy2SyKR4IcffsBLL72k1j1mzZrVuVY+1NTUBE4igWkHf4BSjoOAx1M6znEclmRm4CWbodgxahQA4FxlJarbmcmqIxTK6+UeMaqqgvDhpIjWu02IRCKEhIQo1MXt3LkTAPDhhx926D0ADz7RtPWJwc3NDXFxcXjrrbc6fE9CCCGkOyxZsgRLliyR/3nhwoUIDw/HoEHKHSTFxcUKz3dVz20A+Oz+PTRIZTgZEAghj4f7DQ3Ib2rUaNsPFRfDc9AgONfXg5NIUFxcjCFDhiicM3/+fLz66qt48803NfraXaV2sFu5ciVOnz6t8Mbee+89fPrpp/j73/+ucK5EIsG//vUvXLhwAc3Nzfj4448RFhaGb7/9Fmlpafjss89w69YtzJ8/H0KhECEhITh79qx8dui1a9cwceJE5OXl4ZNPPkFkZCQAoLS0FPu+/x5fFhRgikiE9xydAABf5eXix5IS8AC8YWePWZaWuFRVhZ15uTAWClHa3IytbiPx7o0bqJdKAXD41G0kyluaYSQQYE6rbUnGPyyQvFL919pxv5aXY2duLpo5GWz19LDS0hKClhacrq3Fd5WV0OHxoHsgDjbDhyE9Pf2vb65QiC1btiAuLg4xMTEoLCzE5s2bIRQK8euvv+LVV1/Fvn37sH37dtTX12PNmjW4c+cOOI5DVFQUAgMDsW3bNpSWliI7OxsjRozARx99pPSzEQgEEAgEaGxsRG5ursZX4yaEEEK64pdffsHly5dx8OBBXL9+Xel5JxAI8PupU0i6exc5jY0YbmCAOUMs8T9376KJk8HdyAjrXFwh5TgcKSnBb0HBED4Mfk4GBnAyMACgOg/8t7AAn7s/6MB5JzMDf7cZitGmpgi+eAHhVlY4V1kJcx0d7BzlgXOVlUirq8U7NzIxWCDEm8FBKC4uhqenp8J7CgkJwf3793vy26gWtYPdsWPHlAr4XV1d4ebmhh9//BEjRoyQH9+1axecnJywbds21NTUYPTo0Zg2bZrCtUuXLsXq1asRFhaGVatWKXzt3r17OHPmDLKzszFlyhR5sMvMzMSmsDA8k5uHyNQU/M3cAnwAP5eW4ZCvHxqkUjyfcg2jH/YoptTW4mf/AFjp6eGbvDwEm5hgmZMTJByHFpkMl6ur4G5k9MT3HmRijEkWvgCAHbk5ON3UhCl8Af5bVYVPrK1hp6uLa76+uPRYb1pRURGGDx8u//M333yj8LXTp08DgMI5jzAMo3Ts/Pnz2Lt3b7ttnT59+hPfDyGEENIb3N3dlY4xDIMXIiIgqK1FRnU1tg4dCh0eDwtv3sD7Q4ZglL4+tpSWYU92NkItLWGjpwejNkavrtfWqswDqlRJJHjKzBwfOg/D+zdv4pfyMoRZWmFvQQHWDB8OVyMjXKqoRGknVrPoLWoXc+3fv7/N4ytXrsSGDRsUjp06dQpffvklfH19MXHiRNTX1yP/sTVpkpKSMHv2bACQB7dHpk+fDqFQiOHDhyvsuzpi+HBY6+tDl8/HsxYWuFpTg6SaGkwWWUCPz4epjg7Gmpji+sOuXH9jY1g9XNfNe/BgHC0twbbsbNwRi2EgEODBaGrbXb2tFTQ24eXrqZiRnIQDRcXIrK4GBw6e+vrYXFqKozU1EDQ3Q5/2ViWEEEI6TEcoBF8qRYihIXR4PNRJpWjhOIzS1wcATB48CJerKsFxnMqndnt5QBUjgQBjTU0BAJ6DBiG/sUnpHF2JBM2Nmh3q7U5qB7u4uDjs2bNH6bifnx/MzMzkvU/Ag9q1r776CteuXcO1a9eQk5ODYcOGqbz34zNJ21tkt/XaNm39cLlWxw1aTUIIMjHBfm8fWOnq4t0bmThdXo4RhobIrH9yvd66u3fwup09jvoHYJmTI1oetmGZSITXLCxQ2NKC/zl9Guifu7MRQgghvUpPRwfgOOg9fG4//jR9FOgcDAxQ0NQEsRp7tD/KAwIeD63n2TbL/rq7Tqs6Pj6PB2kbz3E+J4NUja1B+wq1g93x48cRHR2NEydOKH1t5cqV+Oyzz+R/njRpEnbu3Anpw2/8tWvXlK7x9/fHTz/9BAA4cOCAWm24fecOSsViNMtk+LW8HL7GxggwNsap8nI0y2SolrTgUnUVvAYPVro2v7ERIl1dRNrYYLalJW7W12OcqSlqJRL8WFIiP+9MeTlyGhsUrq2TSmGtqwsZx+FoaSl0dHQgFApRIJHAQ18fr5mbQ8Dno76Xtj8hhBBC+jOJTAa0ClmDBQLo8Hi48bCn7Ex9PUabmcNIIMBsS0v877278hB2WyzGn5WVKvPAUD093BaLIeE4lDU342rtk/dfNxIIHtbkAzIeHwJh/1kdTu2W2traIj4+HtOmTcOPP/6o8LWJEyfC0dFR/uc333wTd+/eha+vLziOg6urKw49tuXW1q1b8eKLL+KTTz7BxIkTYWxs/MQ2uLm6YndiIj4rKcEUkQg+DwPcVJEIc65dBQ/APx0cYamri3uPhaxL1dX4Jj8PQh4PxkIhtriNBI/Hw3/cR2Ht3Tv4PCcbunw+RhkNwhoTxZq3xfYOeDMjAzZ6uhhpNAh1Ugksh1hiTXoa7ovFkMlk8HdxgUBHR+G6yZMn4+DBg9i/fz8yMjKwYcMGrFu3DhYWFvjHP/6BP/74Azt37sT+/ftRV1eHZcuW4erVq5BKpQgNDcWWLVsUzlfl1KlT+Mc//oGysjKYmpriqaeewnfffffE7ychhBDSXd5++20cPnwYdnZ2Cse//vprfPHFFwrPu/Fjx+JgWhpMjI1hY/1gb9bPBg3Gx3duo6myEu5Gg7DAwQEAsNzJGf977y6evXIFhgI+zHV0sGrYcLgZGbWZBwDgKTNzzEhOwghDQ7i3sWzZ48KtrPBh1i0MFgjxvpsbdB8OCbf22muv4dixYygvL4ednR0+//xzzJkzp4vfta7jcR1dUVdDxGIxDAwMwOPx8Omnn6K4uFih168tp0+fxs2TJ/HshYs91Er1NLc040RgII5nZuLMmTMAHgwnFxYW9qttSAghhJDe0Fef7wBwauwYuE2Zgmeeeaa3m6KWXutbvHz5MpYuXQqpVAo7O7snzvYEACsrKyQZGKBFIICOGuPrPYWnbwCphQWWLFmCoUOHorS0FMuXL6dQRwghhKihrz7fWwQC1BkYwKrVsmh9Xa8Fu9DQ0DZr79pjZWUFnlCIaiMjiGqePEbeU6qNjMATCjFhwgSEh4d32+vs2bMH27ZtUzgWGRnZqcWOCSGEkL6iLz/f9x44gL0HD0LYqs5u//79GPVwY4O+pv9UAwIwNzeHvpERCs3N+9QPvtDiQbvMzc279XUWLlyIhQsXdutrEEIIIT2tLz/f33zmb1j87rvauVdsbxMIBPDy90eOgz2kHdxPtbtI+Xxk29vDOyCg3/zQCSGEkL6Enu+a0ze+ex3g4+ODFkND5IlEvd0UAECuSASJoSG8vb17uymEEEJIv0XPd83od8HOzMwMzi4uuO3oAJmKDYJ7iozHwx1HBzi7utJECUIIIaQL6PmuGf0u2AFAyIQJqBOJkPXY3rU97ZatLepEIoSMH9+r7SCEEEK0AT3fu65fBjsbGxsEhYTghosLagwNe6UN1YaGuOnqguDx42FjY9MrbSCEEEK0CT3fu65fBjsACAkJgZmdLZLc3SHp4UJLCZ+PpFHuMLe1xbhx43r0tQkhhBBtRs/3rum3wU4oFOK5WbMgHjoUlzxG9dh4vIzHwyWPUWiwGYrps2YprGtDCCGEkK6h53vX9NtgBwDW1taYw0SgwsEBFzw9uj3ZS/h8XPD0QIWDA+YwEbC2tu7W1yOEEEIGInq+d16v7RWrSdnZ2TjMxsKwoAABmZkwFos1/hrVhoZIGuWOBpuhmMNEwNHRUeOvQQghhJC/0PO947Qi2AFAUVERjsXHozIvHyOzsuCSnw++Bt6ajMfDLVtb3HR1gbmtLabPmtWvkzwhhBDSn9DzvWO0JtgBgEQiQUJCAhITEjCorAzDs3NgX1YGgUzW4XtJ+XzkikS44+iAOpEIwePHY9y4cf12zJ0QQgjpr+j5rj6tCnaPFBQU4HxCAu7dugWhWAzH3FzYlFfApL4eOlKpyutaBAJUGxmh0MIc2fb2kBgawtnVFSH9dMozIYQQok3o+f5kWhnsHqmsrERqaipSk5LQWF8PTiLBoIYGGFdUQlciAZ+TQcbjo1koRI25GeoMDMATCqFvZATvgAB4e3v3uxWnCSGEEG1Hz3fVtDrYPSKVSlFRUYHi4mIUFxejtKgIzY2NkEokEAiF0NXXxxBra1hZWcHKygrm5ub9asNfQgghZCCi57uyARHsCCGEEEIGgn69jh0hhBBCCPkLBTtCCCGEEC1BwY4QQgghREtQsCOEEEII0RIU7AghhBBCtAQFO0IIIYQQLUHBjhBCCCFES1CwI4QQQgjREhTsCCGEEEK0BAU7QgghhBAtQcGOEEIIIURLULAjhBBCCNESFOwIIYQQQrQEBTtCCCGEEC1BwY4QQgghREtQsCOEEEII0RIU7AghhBBCtAQFO0IIIYQQLUHBjhBCCCFES1CwI4QQQgjREhTsCCGEEEK0BAU7QgghhBAtQcGOEEIIIURLULAjhBBCCNESFOwIIYQQQrQEBTtCCCGEEC1BwY4QQgghREv8f+wsZuLoZ9Q0AAAAAElFTkSuQmCC", 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", + "image/png": 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", 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" ] @@ -13854,12 +13626,12 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 40, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -13901,13 +13673,13 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
FeatureUnion(transformer_list=[('featureunion',\n",
-       "                                FeatureUnion(transformer_list=[('binarizer',\n",
-       "                                                                Binarizer(threshold=0.1286154935127))])),\n",
-       "                               ('passthrough', Passthrough())])
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" + "
FeatureUnion(transformer_list=[('featureunion',\n",
+       "                                FeatureUnion(transformer_list=[('kbinsdiscretizer',\n",
+       "                                                                KBinsDiscretizer(encode='onehot-dense',\n",
+       "                                                                                 n_bins=9,\n",
+       "                                                                                 strategy='uniform')),\n",
+       "                                                               ('quantiletransformer',\n",
+       "                                                                QuantileTransformer(n_quantiles=697))])),\n",
+       "                               ('passthrough', Passthrough())])
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" ], "text/plain": [ "FeatureUnion(transformer_list=[('featureunion',\n", - " FeatureUnion(transformer_list=[('binarizer',\n", - " Binarizer(threshold=0.1286154935127))])),\n", + " FeatureUnion(transformer_list=[('kbinsdiscretizer',\n", + " KBinsDiscretizer(encode='onehot-dense',\n", + " n_bins=9,\n", + " strategy='uniform')),\n", + " ('quantiletransformer',\n", + " QuantileTransformer(n_quantiles=697))])),\n", " ('passthrough', Passthrough())])" ] }, - "execution_count": 60, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -14354,13 +14138,13 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
FeatureUnion(transformer_list=[('featureunion',\n",
+       "
FeatureUnion(transformer_list=[('featureunion',\n",
        "                                FeatureUnion(transformer_list=[('quantiletransformer',\n",
-       "                                                                QuantileTransformer(n_quantiles=98,\n",
-       "                                                                                    output_distribution='normal'))])),\n",
-       "                               ('passthrough', Passthrough())])
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QuantileTransformer(n_quantiles=842)
Passthrough()
" ], "text/plain": [ "FeatureUnion(transformer_list=[('featureunion',\n", " FeatureUnion(transformer_list=[('quantiletransformer',\n", - " QuantileTransformer(n_quantiles=98,\n", - " output_distribution='normal'))])),\n", + " QuantileTransformer(n_quantiles=842))])),\n", " ('passthrough', Passthrough())])" ] }, - "execution_count": 61, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -14802,13 +14583,13 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
FeatureUnion(transformer_list=[('featureunion',\n",
+       "
FeatureUnion(transformer_list=[('featureunion',\n",
        "                                FeatureUnion(transformer_list=[('estimatortransformer-1',\n",
-       "                                                                EstimatorTransformer(estimator=BernoulliNB(alpha=0.0316290799363))),\n",
+       "                                                                EstimatorTransformer(estimator=LogisticRegression(C=3553.613707181859,\n",
+       "                                                                                                                  max_iter=1000,\n",
+       "                                                                                                                  n_jobs=1,\n",
+       "                                                                                                                  solver='saga'))),\n",
        "                                                               ('estimatortransformer-2',\n",
-       "                                                                EstimatorTransformer(estimator=GaussianNB()))])),\n",
-       "                               ('passthrough', Passthrough())])
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LogisticRegression(C=3553.613707181859, max_iter=1000, n_jobs=1, solver='saga')
LogisticRegression(C=3553.613707181859, max_iter=1000, n_jobs=1, solver='saga')
GaussianNB()
GaussianNB()
MultinomialNB(alpha=0.0128552259108, fit_prior=False)
MultinomialNB(alpha=0.0128552259108, fit_prior=False)
Passthrough()
" ], "text/plain": [ "FeatureUnion(transformer_list=[('featureunion',\n", " FeatureUnion(transformer_list=[('estimatortransformer-1',\n", - " EstimatorTransformer(estimator=BernoulliNB(alpha=0.0316290799363))),\n", + " EstimatorTransformer(estimator=LogisticRegression(C=3553.613707181859,\n", + " max_iter=1000,\n", + " n_jobs=1,\n", + " solver='saga'))),\n", " ('estimatortransformer-2',\n", - " EstimatorTransformer(estimator=GaussianNB()))])),\n", + " EstimatorTransformer(estimator=GaussianNB())),\n", + " ('estimatortransformer-3',\n", + " EstimatorTransformer(estimator=MultinomialNB(alpha=0.0128552259108,\n", + " fit_prior=False)))])),\n", " ('passthrough', Passthrough())])" ] }, - "execution_count": 62, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -15252,13 +15051,13 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
Pipeline(steps=[('robustscaler',\n",
-       "                 RobustScaler(quantile_range=(0.1503060406741,\n",
-       "                                              0.8118816788829))),\n",
+       "
Pipeline(steps=[('normalizer', Normalizer(norm='max')),\n",
        "                ('featureunion-1',\n",
-       "                 FeatureUnion(transformer_list=[('skiptransformer',\n",
-       "                                                 SkipTransformer()),\n",
+       "                 FeatureUnion(transformer_list=[('featureunion',\n",
+       "                                                 FeatureUnion(transformer_list=[('columnonehotencoder',\n",
+       "                                                                                 ColumnOneHotEncoder())])),\n",
        "                                                ('passthrough',\n",
        "                                                 Passthrough())])),\n",
        "                ('featureunion-2',\n",
@@ -15675,15 +15473,15 @@
        "                                                 SkipTransformer()),\n",
        "                                                ('passthrough',\n",
        "                                                 Passthrough())])),\n",
-       "                ('sgdclassifier',\n",
-       "                 SGDClassifier(alpha=0.0002054334005, eta0=0.5702721028736,\n",
-       "                               l1_ratio=0.984925401959, loss='modified_huber',\n",
-       "                               n_jobs=1, penalty='elasticnet'))])
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Normalizer(norm='max')
FeatureUnion(transformer_list=[('featureunion',\n",
+       "                                FeatureUnion(transformer_list=[('columnonehotencoder',\n",
+       "                                                                ColumnOneHotEncoder())])),\n",
+       "                               ('passthrough', Passthrough())])
ColumnOneHotEncoder()
Passthrough()
FeatureUnion(transformer_list=[('skiptransformer', SkipTransformer()),\n",
+       "                               ('passthrough', Passthrough())])
SkipTransformer()
Passthrough()
BaggingClassifier(bootstrap_features=True, max_features=0.6083887402217,\n",
+       "                  max_samples=0.440010144908, n_estimators=24, n_jobs=1,\n",
+       "                  oob_score=True)
" ], "text/plain": [ - "Pipeline(steps=[('robustscaler',\n", - " RobustScaler(quantile_range=(0.1503060406741,\n", - " 0.8118816788829))),\n", + "Pipeline(steps=[('normalizer', Normalizer(norm='max')),\n", " ('featureunion-1',\n", - " FeatureUnion(transformer_list=[('skiptransformer',\n", - " SkipTransformer()),\n", + " FeatureUnion(transformer_list=[('featureunion',\n", + " FeatureUnion(transformer_list=[('columnonehotencoder',\n", + " ColumnOneHotEncoder())])),\n", " ('passthrough',\n", " Passthrough())])),\n", " ('featureunion-2',\n", @@ -15714,13 +15514,14 @@ " SkipTransformer()),\n", " ('passthrough',\n", " Passthrough())])),\n", - " ('sgdclassifier',\n", - " SGDClassifier(alpha=0.0002054334005, eta0=0.5702721028736,\n", - " l1_ratio=0.984925401959, loss='modified_huber',\n", - " n_jobs=1, penalty='elasticnet'))])" + " ('baggingclassifier',\n", + " BaggingClassifier(bootstrap_features=True,\n", + " max_features=0.6083887402217,\n", + " max_samples=0.440010144908, n_estimators=24,\n", + " n_jobs=1, oob_score=True))])" ] }, - "execution_count": 63, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -15785,13 +15586,13 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
Pipeline(steps=[('passthrough', Passthrough()),\n",
+       "
Pipeline(steps=[('passthrough', Passthrough()),\n",
        "                ('variancethreshold',\n",
-       "                 VarianceThreshold(threshold=0.0033508168395)),\n",
+       "                 VarianceThreshold(threshold=0.0014368451974)),\n",
        "                ('featureunion-1',\n",
-       "                 FeatureUnion(transformer_list=[('skiptransformer',\n",
-       "                                                 SkipTransformer()),\n",
+       "                 FeatureUnion(transformer_list=[('featureunion',\n",
+       "                                                 FeatureUnion(transformer_list=[('powertransformer',\n",
+       "                                                                                 PowerTransformer()),\n",
+       "                                                                                ('nystroem',\n",
+       "                                                                                 Nystroem(gamma=0.8842695866347,\n",
+       "                                                                                          kernel='sigmoid',\n",
+       "                                                                                          n_components=7))])),\n",
        "                                                ('passthrough',\n",
-       "                                                 Passthrough())])),\n",
-       "                ('featureunion-2',\n",
+       "                                                 Passth...\n",
        "                 FeatureUnion(transformer_list=[('featureunion',\n",
        "                                                 FeatureUnion(transformer_list=[('estimatortransformer',\n",
-       "                                                                                 EstimatorTransformer(estimator=AdaBoostClassifier(algorithm='SAMME',\n",
-       "                                                                                                                                   learning_rate=0.0473135874378,\n",
-       "                                                                                                                                   n_estimators=436)))])),\n",
+       "                                                                                 EstimatorTransformer(cross_val_predict_cv=5,\n",
+       "                                                                                                      estimator=BaggingClassifier(bootstrap=False,\n",
+       "                                                                                                                                  max_features=0.2031842311627,\n",
+       "                                                                                                                                  max_samples=0.4743985327407,\n",
+       "                                                                                                                                  n_estimators=89,\n",
+       "                                                                                                                                  n_jobs=1)))])),\n",
        "                                                ('passthrough',\n",
        "                                                 Passthrough())])),\n",
-       "                ('linearsvc', LinearSVC(C=0.012266617842, penalty='l1'))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
BaggingClassifier(bootstrap=False, max_features=0.2031842311627,\n",
+       "                  max_samples=0.4743985327407, n_estimators=89, n_jobs=1)
BaggingClassifier(bootstrap=False, max_features=0.2031842311627,\n",
+       "                  max_samples=0.4743985327407, n_estimators=89, n_jobs=1)
Passthrough()
BernoulliNB(alpha=4.2777686142181)
" ], "text/plain": [ "Pipeline(steps=[('passthrough', Passthrough()),\n", " ('variancethreshold',\n", - " VarianceThreshold(threshold=0.0033508168395)),\n", + " VarianceThreshold(threshold=0.0014368451974)),\n", " ('featureunion-1',\n", - " FeatureUnion(transformer_list=[('skiptransformer',\n", - " SkipTransformer()),\n", + " FeatureUnion(transformer_list=[('featureunion',\n", + " FeatureUnion(transformer_list=[('powertransformer',\n", + " PowerTransformer()),\n", + " ('nystroem',\n", + " Nystroem(gamma=0.8842695866347,\n", + " kernel='sigmoid',\n", + " n_components=7))])),\n", " ('passthrough',\n", - " Passthrough())])),\n", - " ('featureunion-2',\n", + " Passth...\n", " FeatureUnion(transformer_list=[('featureunion',\n", " FeatureUnion(transformer_list=[('estimatortransformer',\n", - " EstimatorTransformer(estimator=AdaBoostClassifier(algorithm='SAMME',\n", - " learning_rate=0.0473135874378,\n", - " n_estimators=436)))])),\n", + " EstimatorTransformer(cross_val_predict_cv=5,\n", + " estimator=BaggingClassifier(bootstrap=False,\n", + " max_features=0.2031842311627,\n", + " max_samples=0.4743985327407,\n", + " n_estimators=89,\n", + " n_jobs=1)))])),\n", " ('passthrough',\n", " Passthrough())])),\n", - " ('linearsvc', LinearSVC(C=0.012266617842, penalty='l1'))])" + " ('bernoullinb', BernoulliNB(alpha=4.2777686142181))])" ] }, - "execution_count": 76, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -16269,7 +16100,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -16304,7 +16135,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -16324,20 +16155,22 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Generation: 100%|██████████| 5/5 [00:55<00:00, 11.02s/it]\n" + "Generation: : 8it [01:44, 13.07s/it]\n", + "/home/perib/miniconda3/envs/myenv/lib/python3.10/site-packages/sklearn/preprocessing/_data.py:2785: UserWarning: n_quantiles (911) is greater than the total number of samples (284). n_quantiles is set to n_samples.\n", + " warnings.warn(\n" ] }, { "data": { "text/html": [ - "
TPOTEstimator(classification=True, cv=5, early_stop=2, generations=5,\n",
-       "              max_eval_time_mins=10, n_jobs=4,\n",
+       "
TPOTEstimator(classification=True, cv=5, early_stop=2, max_time_mins=10,\n",
+       "              n_jobs=4,\n",
        "              scorers=['roc_auc_ovr',\n",
-       "                       <function complexity_scorer at 0x73f2b05de710>],\n",
+       "                       <function complexity_scorer at 0x78eb3afa4160>],\n",
        "              scorers_weights=[1.0, -1.0],\n",
-       "              search_space=<tpot2.search_spaces.pipelines.sequential.SequentialPipeline object at 0x73f3bd5db070>,\n",
-       "              verbose=2)
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