تقييم النماذج

import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import (
    cross_val_score, StratifiedKFold, learning_curve, GridSearchCV
)
from sklearn.metrics import roc_auc_score, roc_curve
from sklearn.datasets import make_classification
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt

print("=" * 55)
print("تقييم النماذج: الدليل الكامل")
print("=" * 55)

X, y = make_classification(
    n_samples=1500, n_features=15, n_informative=8,
    n_classes=2, random_state=42
)

# ─────────────────────────────────────────
# 1. K-Fold Cross-Validation
# ─────────────────────────────────────────
print("\n1. Stratified K-Fold Cross-Validation (k=10):")
model = RandomForestClassifier(n_estimators=100, random_state=42, n_jobs=-1)
cv = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)

for metric in ["accuracy", "precision", "recall", "f1", "roc_auc"]:
    scores = cross_val_score(model, X, y, cv=cv, scoring=metric, n_jobs=-1)
    print(f"  {metric:12s}: {scores.mean():.3f} ± {scores.std():.3f}  "
          f"[{scores.min():.3f} - {scores.max():.3f}]")

# ─────────────────────────────────────────
# 2. Learning Curves
# ─────────────────────────────────────────
print("\n2. Learning Curves:")
train_sizes, train_scores, val_scores = learning_curve(
    model, X, y,
    train_sizes=np.linspace(0.1, 1.0, 10),
    cv=5, scoring="accuracy", n_jobs=-1
)

print("  حجم التدريب | Train Acc | Val Acc | فجوة Overfit")
print("  " + "-" * 52)
for i, size in enumerate(train_sizes):
    t_mean = train_scores[i].mean()
    v_mean = val_scores[i].mean()
    gap    = t_mean - v_mean
    print(f"  {int(size):11d} | {t_mean:.3f}     | {v_mean:.3f}   | {gap:.3f}")

# ─────────────────────────────────────────
# 3. Hyperparameter Tuning (Grid Search)
# ─────────────────────────────────────────
print("\n3. Grid Search لإيجاد أفضل المعاملات:")
param_grid = {
    "n_estimators": [50, 100],
    "max_depth": [None, 5, 10],
    "min_samples_leaf": [1, 5],
}
gs = GridSearchCV(
    RandomForestClassifier(random_state=42, n_jobs=-1),
    param_grid, cv=5, scoring="roc_auc", n_jobs=-1, verbose=0
)
gs.fit(X, y)

print(f"  أفضل معاملات: {gs.best_params_}")
print(f"  أفضل AUC-ROC: {gs.best_score_:.4f}")

# ─────────────────────────────────────────
# 4. AUC-ROC Analysis
# ─────────────────────────────────────────
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, stratify=y, random_state=42
)

best_model = gs.best_estimator_
best_model.fit(X_train, y_train)
y_proba = best_model.predict_proba(X_test)[:, 1]

auc = roc_auc_score(y_test, y_proba)
print(f"\n4. AUC-ROC على Test Set: {auc:.4f}")
print("  (1.0 = مثالي | 0.5 = عشوائي | > 0.9 = ممتاز)")