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import os
import json
import joblib
import logging
import numpy as np
from datetime import datetime
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import (precision_recall_fscore_support, roc_auc_score,
average_precision_score, confusion_matrix, precision_recall_curve)
logger = logging.getLogger('nids')
class BinaryLabelEncoder:
"""Simple encoder mapping: BENIGN -> 0, anything else -> 1.
Provides transform/inverse_transform and `classes_` compatible attribute.
"""
def __init__(self):
self.classes_ = np.array([0, 1])
def transform(self, y_series):
# accept pandas Series or array-like labels
y_str = np.array(y_series).astype(str)
return (y_str != 'BENIGN').astype(int)
def inverse_transform(self, y_arr):
y = np.array(y_arr).astype(int)
return np.where(y == 0, 'BENIGN', 'ATTACK')
def validate_and_select_features(df, features):
missing = [c for c in features if c not in df.columns]
if missing:
raise ValueError(f"Missing feature columns: {missing}")
X = df[features].copy()
# drop constant features
nunique = X.nunique()
const_cols = nunique[nunique <= 1].index.tolist()
if const_cols:
logger.info('Dropping constant columns: %s', const_cols)
X.drop(columns=const_cols, inplace=True)
return X
def train_model_cv(df, features, target='Label', n_splits=5, n_estimators=100, max_depth=None, seed=42):
"""Train RandomForest with StratifiedKFold and return best model plus metrics.
- Uses class_weight='balanced' to handle class imbalance (no SMOTE).
- Computes precision, recall, F1, PR-AUC, ROC-AUC and confusion matrices per fold.
"""
# explicit binary encoding
encoder = BinaryLabelEncoder()
y_raw = df[target].astype(str)
y = encoder.transform(y_raw)
X = validate_and_select_features(df, features)
skf = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=seed)
fold_metrics = []
models = []
# collect validation probabilities and labels across folds for PR curve and thresholding
all_val_probas = []
all_val_labels = []
X_arr = X.values
y_arr = np.asarray(y)
for fold, (train_idx, val_idx) in enumerate(skf.split(X_arr, y_arr), start=1):
clf = RandomForestClassifier(
n_estimators=n_estimators,
max_depth=(None if max_depth == 0 else max_depth),
class_weight='balanced',
random_state=seed,
n_jobs=-1,
)
clf.fit(X_arr[train_idx], y_arr[train_idx])
proba = clf.predict_proba(X_arr[val_idx])[:, 1]
preds = (proba >= 0.5).astype(int)
all_val_probas.extend(proba.tolist())
all_val_labels.extend(y_arr[val_idx].tolist())
prec, rec, f1, _ = precision_recall_fscore_support(y_arr[val_idx], preds, average='binary', zero_division=0)
pr_auc = average_precision_score(y_arr[val_idx], proba)
try:
roc = roc_auc_score(y_arr[val_idx], proba)
except Exception:
roc = float('nan')
cm = confusion_matrix(y_arr[val_idx], preds).tolist()
fold_metrics.append({
'fold': fold,
'precision': float(prec),
'recall': float(rec),
'f1': float(f1),
'pr_auc': float(pr_auc),
'roc_auc': float(roc),
'confusion_matrix': cm
})
models.append(clf)
logger.info('Fold %d metrics: prec=%.3f rec=%.3f f1=%.3f pr_auc=%.3f', fold, prec, rec, f1, pr_auc)
# pick best model by f1
best_idx = int(np.argmax([m['f1'] for m in fold_metrics]))
best_model = models[best_idx]
# aggregate metrics
agg = {}
for k in ['precision', 'recall', 'f1', 'pr_auc', 'roc_auc']:
vals = [fm[k] for fm in fold_metrics if not np.isnan(fm[k])]
agg[f'{k}_mean'] = float(np.mean(vals)) if vals else float('nan')
agg[f'{k}_std'] = float(np.std(vals)) if vals else float('nan')
results = {'folds': fold_metrics, 'aggregate': agg}
# compute overall PR curve from CV validation outputs
all_val_probas = np.array(all_val_probas)
all_val_labels = np.array(all_val_labels)
precision, recall, pr_thresholds = precision_recall_curve(all_val_labels, all_val_probas)
results['pr_curve'] = {
'precision': precision.tolist(),
'recall': recall.tolist(),
'thresholds': pr_thresholds.tolist()
}
# artifact hygiene: directories
ts = datetime.utcnow().isoformat() + 'Z'
results['timestamp'] = ts
results['seed'] = int(seed)
results['features'] = list(X.columns)
results['cv_validation_counts'] = int(len(all_val_labels))
models_dir = os.path.join('models')
metrics_dir = os.path.join('metrics')
os.makedirs(models_dir, exist_ok=True)
os.makedirs(metrics_dir, exist_ok=True)
model_path = os.path.join(models_dir, 'rf_model.joblib')
metrics_path = os.path.join(metrics_dir, 'training_metrics.json')
joblib.dump(best_model, model_path)
with open(metrics_path, 'w') as fh:
json.dump(results, fh, indent=2)
logger.info('Training complete. Metrics saved to %s, model saved to %s', metrics_path, model_path)
return best_model, results, X, y, all_val_probas, all_val_labels, encoder
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