Create metrics_utils.py

metrics_utils.py

@@ -0,0 +1,65 @@
+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:
+    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)
+
+    report_dict = classification_report(
+        y_true,
+        y_pred,
+        target_names=class_names,
+        zero_division=0,
+        output_dict=True,
+    )
+
+    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_df = pd.DataFrame(cm, index=class_names, columns=class_names)
+
+    return {
+        "accuracy": round(float(acc), 4),
+        "f1_macro": round(float(f1_macro), 4),
+        "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