Update metrics_utils.py

metrics_utils.py

@@ -1,26 +1,26 @@
-import os
-from typing import Dict, List
-
-import matplotlib.pyplot as plt
-import pandas as pd
-from sklearn.metrics import (
-    accuracy_score,
-    f1_score,
-    classification_report,
-    confusion_matrix,
-)
-
-from config import FIGURE_DIR
-
-
 def compute_classification_metrics(y_true, y_pred, class_names: List[str]) -> Dict:
+    labels = list(range(len(class_names)))
+
     acc = accuracy_score(y_true, y_pred)
-    f1_macro = f1_score(y_true, y_pred, average="macro", zero_division=0)
-    f1_weighted = f1_score(y_true, y_pred, average="weighted", zero_division=0)
+    f1_macro = f1_score(
+        y_true,
+        y_pred,
+        labels=labels,
+        average="macro",
+        zero_division=0,
+    )
+    f1_weighted = f1_score(
+        y_true,
+        y_pred,
+        labels=labels,
+        average="weighted",
+        zero_division=0,
+    )
 
     report_dict = classification_report(
         y_true,
         y_pred,
+        labels=labels,
         target_names=class_names,
         zero_division=0,
         output_dict=True,
@@ -29,7 +29,11 @@ def compute_classification_metrics(y_true, y_pred, class_names: List[str]) -> Di
     report_df = pd.DataFrame(report_dict).transpose().reset_index()
     report_df = report_df.rename(columns={"index": "classe"})
 
-    cm = confusion_matrix(y_true, y_pred, labels=list(range(len(class_names))))
+    cm = confusion_matrix(
+        y_true,
+        y_pred,
+        labels=labels,
+    )
     cm_df = pd.DataFrame(cm, index=class_names, columns=class_names)
 
     return {
@@ -38,28 +42,4 @@ def compute_classification_metrics(y_true, y_pred, class_names: List[str]) -> Di
         "f1_weighted": round(float(f1_weighted), 4),
         "classification_report": report_df,
         "confusion_matrix": cm_df,
-    }
-
-
-def save_confusion_matrix_figure(cm_df: pd.DataFrame, model_name: str) -> str:
-    fig_path = os.path.join(FIGURE_DIR, f"{model_name}_confusion_matrix.png")
-
-    fig_width = max(8, min(20, 0.45 * len(cm_df.columns)))
-    fig_height = max(6, min(20, 0.45 * len(cm_df.index)))
-
-    plt.figure(figsize=(fig_width, fig_height))
-    plt.imshow(cm_df.values, interpolation="nearest")
-    plt.title("Matrice de confusion")
-    plt.colorbar()
-
-    tick_marks = range(len(cm_df.columns))
-    plt.xticks(tick_marks, cm_df.columns, rotation=90, fontsize=7)
-    plt.yticks(tick_marks, cm_df.index, fontsize=7)
-
-    plt.xlabel("Classe prédite")
-    plt.ylabel("Classe réelle")
-    plt.tight_layout()
-    plt.savefig(fig_path, dpi=200)
-    plt.close()
-
-    return fig_path
+    }