feat: Align classification results with sklearn classification ones (#63

)

* feat: Align classification results with sklearn classification ones

* build: Bump version 1.2.3 -> 1.2.4

* docs: Add changelog

* feat: Print also task config during experiment starting phase (#62)

* fix: Add->Added in changelog

Approved By: @lorenzomammana

CHANGELOG.md

@@ -1,6 +1,11 @@
 
 # Changelog
 All notable changes to this project will be documented in this file.
+### [1.2.4]
+#### Added
+
+- Return also probabilities in Classification's module predict step and add them to `self.res`.
+
 
 ### [1.2.3]
 

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "quadra"
-version = "1.2.3"
+version = "1.2.4"
 description = "Deep Learning experiment orchestration library"
 authors = [
   { name = "Alessandro Polidori", email = "[email protected]" },
@@ -118,7 +118,7 @@ repository = "https://github.com/orobix/quadra"
 
 # Adapted from https://realpython.com/pypi-publish-python-package/#version-your-package
 [tool.bumpver]
-current_version = "1.2.3"
+current_version = "1.2.4"
 version_pattern = "MAJOR.MINOR.PATCH"
 commit_message = "build: Bump version {old_version} -> {new_version}"
 commit = true

quadra/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "1.2.3"
+__version__ = "1.2.4"
 
 
 def get_version():

quadra/modules/classification/base.py

@@ -219,7 +219,7 @@ def predict_step(self, batch: Any, batch_idx: int, dataloader_idx: int = 0) -> A
                     grayscale_cam = ndimage.zoom(grayscale_cam_low_res, zoom_factors, order=1)
         else:
             grayscale_cam = None
-        return predicted_classes, grayscale_cam
+        return predicted_classes, grayscale_cam, torch.max(probs, dim=1)[0].tolist()
 
 
 class MultilabelClassificationModule(BaseLightningModule):

quadra/tasks/classification.py

@@ -311,10 +311,12 @@ def generate_report(self) -> None:
             log.warning("There is no prediction to generate the report. Skipping report generation.")
             return
         all_outputs = [x[0] for x in predictions_outputs]
-        if not all_outputs:
+        all_probs = [x[2] for x in predictions_outputs]
+        if not all_outputs or not all_probs:
             log.warning("There is no prediction to generate the report. Skipping report generation.")
             return
         all_outputs = [item for sublist in all_outputs for item in sublist]
+        all_probs = [item for sublist in all_probs for item in sublist]
         all_targets = [target.tolist() for im, target in self.datamodule.test_dataloader()]
         all_targets = [item for sublist in all_targets for item in sublist]
 
@@ -335,16 +337,17 @@ def generate_report(self) -> None:
         output_folder_test = "test"
         test_dataloader = self.datamodule.test_dataloader()
         test_dataset = cast(ImageClassificationListDataset, test_dataloader.dataset)
-        res = pd.DataFrame(
+        self.res = pd.DataFrame(
             {
                 "sample": list(test_dataset.x),
                 "real_label": all_targets,
                 "pred_label": all_outputs,
+                "probability": all_probs,
             }
         )
         os.makedirs(output_folder_test, exist_ok=True)
         save_classification_result(
-            results=res,
+            results=self.res,
             output_folder=output_folder_test,
             confmat=self.report_confmat,
             accuracy=accuracy,

quadra/utils/utils.py

@@ -84,6 +84,7 @@ def extras(config: DictConfig) -> None:
 def print_config(
     config: DictConfig,
     fields: Sequence[str] = (
+        "task",
         "trainer",
         "model",
         "datamodule",