pipeline multiclass

Author: joshuawe

plotsandgraphs/binary_classifier.py

@@ -580,6 +580,8 @@ def plot_pr_curve(
 
     # Save the figure if save_fig_path is specified
     if save_fig_path:
-        plt.savefig(save_fig_path, bbox_inches="tight")
+        path = Path(save_fig_path)
+        path.parent.mkdir(parents=True, exist_ok=True)
+        fig.savefig(save_fig_path, bbox_inches="tight")
 
     return fig

plotsandgraphs/multiclass_classifier.py

@@ -267,7 +267,7 @@ def auroc_metric_function(y_true, y_score, average, multi_class):
     return fig, fig_aurocs
 
 
-def plot_y_prob_histogram(
+def plot_y_score_histogram(
     y_true: np.ndarray, y_score: Optional[np.ndarray] = None, save_fig_path: Optional[str]=None
 ) -> Figure:
     """

plotsandgraphs/pipeline.py

@@ -1,10 +1,18 @@
+from typing import Literal, Union
+from pathlib import Path
+from tqdm.auto import tqdm
+
 from . import binary_classifier as bc
+from . import multiclass_classifier as mc
 
-from tqdm.auto import tqdm
+
+
+FILE_ENDINGS = Literal['pdf', 'png', 'jpg', 'jpeg', 'svg']
 
 
 
-def binary_classifier(y_true, y_score, save_fig_path=None, plot_kwargs={}):
+def binary_classifier(y_true, y_score, save_fig_path=None, plot_kwargs={}, file_type:FILE_ENDINGS='png'):
+    
 
     # Create new tqdm instance
     tqdm_instance = tqdm(total=6, desc='Binary classifier metrics', leave=True)
@@ -14,32 +22,91 @@ def binary_classifier(y_true, y_score, save_fig_path=None, plot_kwargs={}):
 
     # 1) Plot ROC curve
     roc_kwargs = plot_kwargs.get('roc', {})
-    bc.plot_roc_curve(y_true, y_score, save_fig_path=save_fig_path, **roc_kwargs)
+    save_path = get_file_path(save_fig_path, 'roc_curve', file_type)
+    bc.plot_roc_curve(y_true, y_score, save_fig_path=save_path, **roc_kwargs)
     tqdm_instance.update()
 
     # 2) Plot precision-recall curve
     pr_kwargs = plot_kwargs.get('pr', {})
-    bc.plot_pr_curve(y_true, y_score, save_fig_path=save_fig_path, **pr_kwargs)
+    save_path = get_file_path(save_fig_path, 'pr_curve', file_type)
+    bc.plot_pr_curve(y_true, y_score, save_fig_path=save_path, **pr_kwargs)
     tqdm_instance.update()
 
     # 3) Plot calibration curve
     cal_kwargs = plot_kwargs.get('cal', {})
-    bc.plot_calibration_curve(y_true, y_score, save_fig_path=save_fig_path, **cal_kwargs)
+    save_path = get_file_path(save_fig_path, 'calibration_curve', file_type)
+    bc.plot_calibration_curve(y_true, y_score, save_fig_path=save_path, **cal_kwargs)
     tqdm_instance.update()
 
     # 3) Plot confusion matrix
     cm_kwargs = plot_kwargs.get('cm', {})
-    bc.plot_confusion_matrix(y_true, y_score, save_fig_path=save_fig_path, **cm_kwargs)
+    save_path = get_file_path(save_fig_path, 'confusion_matrix', file_type)
+    bc.plot_confusion_matrix(y_true, y_score, save_fig_path=save_path, **cm_kwargs)
     tqdm_instance.update()
 
     # 5) Plot classification report
     cr_kwargs = plot_kwargs.get('cr', {})
-    bc.plot_classification_report(y_true, y_score, save_fig_path=save_fig_path, **cr_kwargs)
+    save_path = get_file_path(save_fig_path, 'classification_report', file_type)
+    bc.plot_classification_report(y_true, y_score, save_fig_path=save_path, **cr_kwargs)
+    tqdm_instance.update()
+    
+    # 6) Plot y_score histogram
+    hist_kwargs = plot_kwargs.get('hist', {})
+    save_path = get_file_path(save_fig_path, 'y_score_histogram', file_type)
+    bc.plot_y_score_histogram(y_true, y_score, save_fig_path=save_path, **hist_kwargs)
+    tqdm_instance.update()
+    
+    return
+
+
+
+
+def multiclass_classifier(y_true, y_score, save_fig_path=None, plot_kwargs={}):
+    
+    # Create new tqdm instance
+    tqdm_instance = tqdm(total=6, desc='Binary classifier metrics', leave=True)
+    
+    # Update tqdm instance
     tqdm_instance.update()
 
+    # 1) Plot ROC curve
+    roc_kwargs = plot_kwargs.get('roc', {})
+    mc.plot_roc_curve(y_true, y_score, save_fig_path=save_fig_path, **roc_kwargs)
+    tqdm_instance.update()
+    
+    # 2) Plot precision-recall curve
+    # pr_kwargs = plot_kwargs.get('pr', {})
+    # mc.plot_pr_curve(y_true, y_score, save_fig_path=save_fig_path, **pr_kwargs)
+    # tqdm_instance.update()
+    
+    # 3) Plot calibration curve
+    # cal_kwargs = plot_kwargs.get('cal', {})
+    # mc.plot_calibration_curve(y_true, y_score, save_fig_path=save_fig_path, **cal_kwargs)
+    # tqdm_instance.update()
+    
+    # 3) Plot confusion matrix
+    # cm_kwargs = plot_kwargs.get('cm', {})
+    # mc.plot_confusion_matrix(y_true, y_score, save_fig_path=save_fig_path, **cm_kwargs)
+    # tqdm_instance.update()
+    
+    # 5) Plot classification report
+    # cr_kwargs = plot_kwargs.get('cr', {})
+    # mc.plot_classification_report(y_true, y_score, save_fig_path=save_fig_path, **cr_kwargs)
+    # tqdm_instance.update()
+    
     # 6) Plot y_score histogram
     hist_kwargs = plot_kwargs.get('hist', {})
-    bc.plot_y_score_histogram(y_true, y_score, save_fig_path=save_fig_path, **hist_kwargs)
+    mc.plot_y_score_histogram(y_true, y_score, save_fig_path=save_fig_path, **hist_kwargs)
     tqdm_instance.update()
 
-    return
+    return
+
+
+
+def get_file_path(save_fig_path: Union[Path,None, str], name:str, ending:str):
+    if save_fig_path is None:
+        return None
+    else:
+        result = Path(save_fig_path) / f"{name}.{ending}"
+        print(result)
+        return str(result)

tests/test_binary_classifier.py

@@ -4,7 +4,7 @@
 import pytest
 import plotsandgraphs.binary_classifier as binary
 
-TEST_RESULTS_PATH = Path(r"tests\test_results")
+TEST_RESULTS_PATH = Path(r"tests\test_results\binary_classifier")
 
 
 @pytest.fixture(scope="module")