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2a3c0e5 28c1a1b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | from sklearn.metrics import average_precision_score, make_scorer, precision_score, recall_score, f1_score, roc_auc_score
from sklearn.model_selection import StratifiedKFold, cross_validate
def score_classification(name, model_or_class, X, y, model_params=None, cv=5):
if callable(model_or_class):
model = model_or_class(**(model_params or {}))
else:
model = model_or_class
scoring = {
'accuracy': 'accuracy',
'precision': make_scorer(precision_score, zero_division=0),
'recall': make_scorer(recall_score, zero_division=0),
'f1': make_scorer(f1_score, zero_division=0),
'roc_auc': make_scorer(roc_auc_score),
'pr_auc': make_scorer(average_precision_score)
}
results_cv = cross_validate(
model,
X,
y,
cv=StratifiedKFold(n_splits=cv, shuffle=True, random_state=42),
scoring=scoring,
return_train_score=True
)
print(f"📊 Model: {name}")
print(f"→ Accuracy | Train: {results_cv['train_accuracy'].mean():.3f} | Test: {results_cv['test_accuracy'].mean():.3f}")
print(f"→ Precision | Train: {results_cv['train_precision'].mean():.3f} | Test: {results_cv['test_precision'].mean():.3f}")
print(f"→ Recall | Train: {results_cv['train_recall'].mean():.3f} | Test: {results_cv['test_recall'].mean():.3f}")
print(f"→ F1-score | Train: {results_cv['train_f1'].mean():.3f} | Test: {results_cv['test_f1'].mean():.3f}")
print(f"→ ROC-AUC-score | Train: {results_cv['train_roc_auc'].mean():.3f} | Test: {results_cv['test_roc_auc'].mean():.3f}")
print(f"→ PR-AUC-score | Train: {results_cv['train_pr_auc'].mean():.3f} | Test: {results_cv['test_pr_auc'].mean():.3f}")
print(f"→ Train Time: {(results_cv['fit_time'].mean() * 1000):.2f} ms")
print(f"→ Predict Time: {(results_cv['score_time'].mean() * 1000):.2f} ms")
return {
"Model": name,
"Train": {
"Accuracy": round(results_cv["train_accuracy"].mean(), 3),
"Precision": round(results_cv["train_precision"].mean(), 3),
"Recall": round(results_cv["train_recall"].mean(), 3),
"F1": round(results_cv["train_f1"].mean(), 3),
"ROC-AUC": round(results_cv["train_roc_auc"].mean(), 3),
"PR-AUC": round(results_cv["train_pr_auc"].mean(), 3),
},
"Test": {
"Accuracy": round(results_cv["test_accuracy"].mean(), 3),
"Precision": round(results_cv["test_precision"].mean(), 3),
"Recall": round(results_cv["test_recall"].mean(), 3),
"F1": round(results_cv["test_f1"].mean(), 3),
"ROC-AUC": round(results_cv["test_roc_auc"].mean(), 3),
"PR-AUC": round(results_cv["test_pr_auc"].mean(), 3),
},
"Times (ms)": {
"Train": round(results_cv["fit_time"].mean() * 1000, 2),
"Predict": round(results_cv["score_time"].mean() * 1000, 2),
}
}
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