feat: update ROC and Precision-Recall curve functions to include best model visualization

app/training/generate_figures.py

@@ -154,33 +154,32 @@ def fig_confusion_matrix(results: dict, y_true: np.ndarray, y_pred: np.ndarray)
     print("  ✓ confusion_matrix.png")
 
 
-def fig_roc_comparison(results: dict) -> None:
-    """All models ROC curves overlaid."""
+def fig_roc_comparison(results: dict, y_true: np.ndarray, y_prob: np.ndarray) -> None:
+    """ROC curve for best model + reference diagonal."""
     fig, ax = plt.subplots(figsize=(8, 6.5))
-    colors = plt.cm.plasma(np.linspace(0.15, 0.85, 10))
-
     best = results.get("_best_model", "XGBoost")
-    items = [(k, v) for k, v in results.items() if not k.startswith("_") and isinstance(v, dict)]
-    items.sort(key=lambda x: x[1].get("roc_auc", 0), reverse=True)
-
-    for idx, (name, data) in enumerate(items):
-        y_true = np.array(data["y_true"])
-        y_prob = np.array(data["y_prob"])
-        fpr, tpr, _ = roc_curve(y_true, y_prob)
-        roc_auc = auc(fpr, tpr)
-        lw = 3 if name == best else 1.5
-        ls = "-" if name == best else "--"
-        ax.plot(
-            fpr, tpr,
-            color=colors[idx],
-            lw=lw, ls=ls,
-            label=f"{name} (AUC = {roc_auc:.4f})",
+    fpr, tpr, _ = roc_curve(y_true, y_prob)
+    roc_auc = auc(fpr, tpr)
+
+    ax.plot(fpr, tpr, color=PALETTE["primary"], lw=3,
+            label=f"{best} (AUC = {roc_auc:.4f})")
+    ax.fill_between(fpr, tpr, alpha=0.15, color=PALETTE["primary"])
+    ax.plot([0, 1], [0, 1], "k:", alpha=0.5, lw=1.5, label="Rastgele / Random")
+
+    # Add other models as AUC markers
+    for name, data in results.items():
+        if name.startswith("_") or not isinstance(data, dict) or name == best:
+            continue
+        auc_v = data.get("roc_auc", 0)
+        ax.annotate(
+            f"{name}: AUC={auc_v:.3f}",
+            xy=(0.45, 0.05 + 0.04 * list(results.keys()).index(name)),
+            fontsize=8, alpha=0.7,
         )
 
-    ax.plot([0, 1], [0, 1], "k:", alpha=0.3, lw=1)
     ax.set_xlabel("Yanlış Pozitif Oranı / False Positive Rate")
     ax.set_ylabel("Doğru Pozitif Oranı / True Positive Rate")
-    ax.set_title("ROC Eğrileri — Model Karşılaştırması", fontsize=13, fontweight="bold")
+    ax.set_title("ROC Eğrisi — En İyi Model (5-fold CV)", fontsize=13, fontweight="bold")
     ax.legend(loc="lower right", framealpha=0.85)
     ax.set_xlim([0, 1])
     ax.set_ylim([0, 1.02])
@@ -189,32 +188,23 @@ def fig_roc_comparison(results: dict) -> None:
     print("  ✓ roc_curves_comparison.png")
 
 
-def fig_pr_curves(results: dict) -> None:
-    """Precision-Recall curves — critical for imbalanced classes."""
+def fig_pr_curves(results: dict, y_true: np.ndarray, y_prob: np.ndarray) -> None:
+    """Precision-Recall curve for best model."""
     fig, ax = plt.subplots(figsize=(8, 6.5))
-    colors = plt.cm.plasma(np.linspace(0.15, 0.85, 10))
-
     best = results.get("_best_model", "XGBoost")
-    items = [(k, v) for k, v in results.items() if not k.startswith("_") and isinstance(v, dict)]
-    items.sort(key=lambda x: x[1].get("roc_auc", 0), reverse=True)
+    prec, rec, _ = precision_recall_curve(y_true, y_prob)
+    ap = average_precision_score(y_true, y_prob)
+    baseline = y_true.mean()
 
-    for idx, (name, data) in enumerate(items):
-        y_true = np.array(data["y_true"])
-        y_prob = np.array(data["y_prob"])
-        prec, rec, _ = precision_recall_curve(y_true, y_prob)
-        ap = average_precision_score(y_true, y_prob)
-        lw = 3 if name == best else 1.5
-        ls = "-" if name == best else "--"
-        ax.plot(
-            rec, prec,
-            color=colors[idx],
-            lw=lw, ls=ls,
-            label=f"{name} (AP = {ap:.4f})",
-        )
+    ax.plot(rec, prec, color=PALETTE["primary"], lw=3,
+            label=f"{best} (AP = {ap:.4f})")
+    ax.fill_between(rec, prec, alpha=0.15, color=PALETTE["primary"])
+    ax.axhline(baseline, color="k", linestyle=":", alpha=0.5,
+               label=f"Baseline = {baseline:.3f}")
 
     ax.set_xlabel("Duyarlılık / Recall")
     ax.set_ylabel("Kesinlik / Precision")
-    ax.set_title("Precision-Recall Eğrileri", fontsize=13, fontweight="bold")
+    ax.set_title("Precision-Recall Eğrisi — En İyi Model", fontsize=13, fontweight="bold")
     ax.legend(loc="lower left", framealpha=0.85)
     ax.set_xlim([0, 1])
     ax.set_ylim([0, 1.02])