odes-exp03-orffff.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "23239e1e-3500-4e02-90a8-12f9565be6b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import arviz as az\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import pymc as pm\n",
    "from pymc.ode import DifferentialEquation\n",
    "\n",
    "from itertools import product\n",
    "import os.path as osp\n",
    "from scipy.optimize import leastsq\n",
    "from scipy.integrate import solve_ivp\n",
    "from scipy.integrate import odeint\n",
    "import math\n",
    "import time\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "az.style.use(\"arviz-darkgrid\")\n",
    "from IPython.display import display as print\n",
    "\n",
    "from scipy.integrate import odeint\n",
    "\n",
    "from datetime import datetime\n",
    "from core_lib import MyDataset,get_format_time,get_dcdts_for_scipy_odeint, plot_dataset,r2_loss,get_predict_ks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2c4a6925-fd16-4e0a-a051-52104233c3f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# simulator function\n",
    "def competition_model(rng, t_eval, y0,  ks, k_kinetics, size=None):\n",
    "    # print(y0)\n",
    "    y = odeint(get_dcdts_for_scipy_odeint(), y0=y0, t=t_eval, args=(ks, k_kinetics))\n",
    "    return y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "17b2bf0d-b268-4fc2-b959-661c45d2a3c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def get_model(dataset, t_eval, k_kinetics, k_sigma_priors=0.01, kf_type=0):\n",
    "    df = dataset.get_df()\n",
    "    times = df['time'].values\n",
    "    cct_names, error_names = dataset.get_var_col_names()\n",
    "    ccts = dataset.get_cct()\n",
    "    \n",
    "    mcmc_model = pm.Model()\n",
    "    params_n = 11\n",
    "\n",
    "    parames =[]\n",
    "    c0 = []\n",
    "    # c0 = dataset.get_cct()[0]\n",
    "    with mcmc_model:\n",
    "        for ki in range(1, params_n + 1):\n",
    "            if kf_type == 0:\n",
    "                p_dense = pm.HalfNormal(f\"k{ki}\", sigma=k_sigma_priors)\n",
    "            else:\n",
    "                p_dense = pm.Normal(f\"k{ki}\",mu=0, sigma=k_sigma_priors)\n",
    "            parames.append(p_dense)\n",
    "        \n",
    "        for c_name in cct_names:\n",
    "            _maxx = df[c_name].values.max()\n",
    "            c0.append(pm.HalfNormal(f\"{c_name}_s\", sigma=_maxx))\n",
    "    \n",
    "    with mcmc_model:\n",
    "        sim = pm.Simulator(\"sim\", competition_model, params=(t_eval, c0, parames, k_kinetics), epsilon=1, observed=ccts)\n",
    "        # sim = pm.Simulator(\"sim\", competition_model, params=(t_eval,), epsilon=1, observed=ccts)\n",
    "        \n",
    "    return mcmc_model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1f378d84-f7c0-4d76-b168-1a41f554d200",
   "metadata": {},
   "outputs": [],
   "source": [
    "k_kinetics = np.repeat(1, 11).astype(np.uint8)\n",
    "# ks = np.random.random(11)/100 # 先验k\n",
    "ks = np.array([0.00071942, 0.00269696, 0.00498945, 0.00444931, 0.00571299, 0.00801272, 0.00131931, 0.00319959, 0.00415571, 0.00228432, 0.00177611])\n",
    "#  =======================================================\n",
    "\n",
    "t_eval = np.arange(0, 150, 5)\n",
    "dataset = MyDataset(\"dataset/data.csv\")\n",
    "df = dataset.get_df()\n",
    "cct_names, error_names = dataset.get_var_col_names()\n",
    "\n",
    "c0 = df[cct_names].iloc[0].values\n",
    "dataset.set_as_sim_dataset(get_dcdts_for_scipy_odeint(), t_eval, c0, args=(ks, k_kinetics))\n",
    "df = dataset.get_df()\n",
    "\n",
    "mcmc_model = get_model(dataset, t_eval, k_kinetics, k_sigma_priors=0.01, kf_type=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0af881ba-3791-41a4-9853-b1572c2a331d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00071942, 0.00269696, 0.00498945, 0.00444931, 0.00571299,\n",
       "       0.00801272, 0.00131931, 0.00319959, 0.00415571, 0.00228432,\n",
       "       0.00177611])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([1.16666667e+00, 2.39880952e+00, 3.11000000e-02, 6.22850000e+01,\n",
       "       7.11000000e-05, 7.94863498e+00, 6.85792330e-02, 7.23100000e-02,\n",
       "       9.85666700e-03, 8.32000000e-03])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/90/l1bfwzjx4ms6v5_k7236h2n40000gn/T/ipykernel_15192/1967272327.py:37: UserWarning: The figure layout has changed to tight\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2100x800 with 10 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 811,
       "width": 2111
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     },
     "output_type": "display_data"
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       "<title>k8</title>\n",
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       "<text text-anchor=\"middle\" x=\"2758.69\" y=\"-198.73\" font-family=\"Times,serif\" font-size=\"14.00\">k8</text>\n",
       "<text text-anchor=\"middle\" x=\"2758.69\" y=\"-183.73\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"2758.69\" y=\"-168.73\" font-family=\"Times,serif\" font-size=\"14.00\">HalfNormal</text>\n",
       "</g>\n",
       "<!-- k8&#45;&gt;sim -->\n",
       "<g id=\"edge8\" class=\"edge\">\n",
       "<title>k8&#45;&gt;sim</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M2717.37,-160.3C2709.14,-156.15 2700.35,-152.42 2691.69,-149.95 2464.24,-85.17 1733.57,-77.98 1489.17,-77.42\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1489.43,-73.92 1479.42,-77.4 1489.42,-80.92 1489.43,-73.92\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.graphs.Digraph at 0x145aca940>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(ks, c0)\n",
    "plot_dataset(dataset)\n",
    "pm.model_to_graphviz(mcmc_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35c3447e-0edd-41bb-91fd-91c891a3ddf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "with mcmc_model:\n",
    "    idata_lv = pm.sample_smc(cores=4, progressbar=False)\n",
    "# 本次实验要证明是否有bug，是否可重现当时的结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db74a58c-114a-4c26-a4be-d7395f9c8069",
   "metadata": {},
   "outputs": [],
   "source": [
    "az.plot_trace(idata_lv, kind=\"rank_vlines\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c434a03e-bec5-43ee-ab4c-0665c6a2709b",
   "metadata": {},
   "outputs": [],
   "source": [
    "az.summary(idata_lv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25ba8da8-2a6f-4f25-a4cc-794aaef8186b",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "ks_predict = get_predict_ks(idata_lv)\n",
    "k_kinetics = np.repeat(1, 11).astype(np.uint8)\n",
    "t_eval = np.arange(0, 150, 5)\n",
    "\n",
    "dataset_new = MyDataset(\"dataset/data.csv\")\n",
    "cct_names, error_names = dataset_new.get_var_col_names()\n",
    "dataset_new.set_as_sim_dataset(get_dcdts_for_scipy_odeint(), t_eval, dataset_new.get_cct()[0], args=(ks_predict, k_kinetics))\n",
    "\n",
    "print(\"r2_loss\", r2_loss(dataset_new.get_cct(), dataset.get_cct()))\n",
    "plot_dataset(dataset, dataset_new)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "daa5d69a-d1bc-417f-9bbc-42a5eba81d3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "az.plot_posterior(idata_lv);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7a919ad-12d3-4828-ad68-461a13d020b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot results\n",
    "_, ax = plt.subplots(figsize=(14, 6))\n",
    "posterior = idata_lv.posterior.stack(samples=(\"draw\", \"chain\"))\n",
    "ax.plot(observed[:, 0], \"o\", label=\"prey\", c=\"C0\", mec=\"k\")\n",
    "ax.plot(observed[:, 1], \"o\", label=\"predator\", c=\"C1\", mec=\"k\")\n",
    "\n",
    "y_posterior = ( posterior[\"y0_1\"].mean(), posterior[\"y0_2\"].mean())\n",
    "ax.plot(competition_model(None, y_posterior, posterior[\"a\"].mean(), posterior[\"b\"].mean()), linewidth=3)\n",
    "for i in np.random.randint(0, size, 10):\n",
    "    sim = competition_model(None, (posterior[\"y0_1\"][i], posterior[\"y0_2\"][i]), posterior[\"a\"][i], posterior[\"b\"][i])\n",
    "    ax.plot(sim[:, 0], alpha=0.1, c=\"C0\")\n",
    "    ax.plot(sim[:, 1], alpha=0.1, c=\"C1\")\n",
    "ax.set_xlabel(\"time\")\n",
    "ax.set_ylabel(\"population\")\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2110e80-52cf-41cc-8ed6-3e7ec2ba257b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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