Fix index out of bounds error during synthetic data testing

Corrected an indexing error in `train_hybrid.py` by using `enumerate` to ensure accurate mapping of detections to the test dataset, resolving an `IndexError` when processing synthetic data.

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attached_assets/Pasted--python-train-hybrid-py-test-WARNING-Extended-Isolation-Forest-not-available-using-standard-IF--1764006995649_1764006995649.txt

@@ -0,0 +1,75 @@
+ python train_hybrid.py --test
+[WARNING] Extended Isolation Forest not available, using standard IF
+
+======================================================================
+  IDS HYBRID ML TEST - SYNTHETIC DATA
+======================================================================
+INFO:dataset_loader:Creating sample dataset (10000 samples)...
+INFO:dataset_loader:Sample dataset created: 10000 rows
+INFO:dataset_loader:Attack distribution:
+attack_type
+normal         8981
+brute_force     273
+suspicious      258
+ddos            257
+port_scan       231
+Name: count, dtype: int64
+
+[TEST] Created synthetic dataset: 10000 samples
+  Normal:  8,981 (89.8%)
+  Attacks: 1,019 (10.2%)
+
+[TEST] Training on 6,281 normal samples...
+[HYBRID] Training hybrid model on 6281 logs...
+[HYBRID] Extracted features for 100 unique IPs
+[HYBRID] Pre-training Isolation Forest for feature selection...
+[HYBRID] Generated 3 pseudo-anomalies from pre-training IF
+[HYBRID] Feature selection: 25 → 18 features
+[HYBRID] Selected features: total_packets, conn_count, time_span_seconds, conn_per_second, hour_of_day... (+13 more)
+[HYBRID] Normalizing features...
+[HYBRID] Training Extended Isolation Forest (contamination=0.03)...
+/opt/ids/python_ml/venv/lib64/python3.11/site-packages/sklearn/ensemble/_iforest.py:307: UserWarning: max_samples (256) is greater than the total number of samples (100). max_samples will be set to n_samples for estimation.
+  warn(
+[HYBRID] Generating pseudo-labels from Isolation Forest...
+[HYBRID] ⚠  IF found only 3 anomalies (need 10)
+[HYBRID] Applying ADAPTIVE percentile fallback...
+[HYBRID]   Trying 5% percentile → 5 anomalies
+[HYBRID]   Trying 10% percentile → 10 anomalies
+[HYBRID] ✅ Success with 10% percentile
+[HYBRID] Pseudo-labels: 10 anomalies, 90 normal
+[HYBRID] Training ensemble classifier (DT + RF + XGBoost)...
+[HYBRID] Class distribution OK: [0 1] (counts: [90 10])
+[HYBRID] Ensemble .fit() completed successfully
+[HYBRID] ✅ Ensemble verified: produces 2 class probabilities
+[HYBRID] Ensemble training completed and verified!
+[HYBRID] Models saved to models
+[HYBRID] Ensemble classifier included
+[HYBRID] ✅ Training completed successfully! 10/100 IPs flagged as anomalies
+[HYBRID] ✅ Ensemble classifier verified and ready for production
+[DETECT] Ensemble classifier available - computing hybrid score...
+[DETECT] IF scores: min=0.0, max=100.0, mean=57.6
+[DETECT] Ensemble scores: min=86.9, max=97.2, mean=92.1
+[DETECT] Combined scores: min=54.3, max=93.1, mean=78.3
+[DETECT] ✅ Hybrid scoring active: 40% IF + 60% Ensemble
+
+[TEST] Detection results:
+  Total detections: 100
+  High confidence:   0
+  Medium confidence: 85
+  Low confidence:    15
+
+[TEST] Top 5 detections:
+  1. 192.168.0.24: risk=93.1, type=suspicious, confidence=medium
+  2. 192.168.0.27: risk=92.7, type=suspicious, confidence=medium
+  3. 192.168.0.88: risk=92.5, type=suspicious, confidence=medium
+  4. 192.168.0.70: risk=92.3, type=suspicious, confidence=medium
+  5. 192.168.0.4: risk=91.4, type=suspicious, confidence=medium
+
+❌ Error: index 7000 is out of bounds for axis 0 with size 3000
+Traceback (most recent call last):
+  File "/opt/ids/python_ml/train_hybrid.py", line 361, in main
+    test_on_synthetic(args)
+  File "/opt/ids/python_ml/train_hybrid.py", line 283, in test_on_synthetic
+    y_pred[i] = 1
+    ~~~~~~^^^
+IndexError: index 7000 is out of bounds for axis 0 with size 3000

python_ml/train_hybrid.py

@@ -277,10 +277,10 @@ def test_on_synthetic(args):
     y_true = test_df['is_attack'].values
     y_pred = np.zeros(len(test_df), dtype=int)
     
-    # Map detections to test_df rows
+    # Map detections to test_df rows (use enumerate for correct indexing)
-    for i, row in test_df.iterrows():
+    for idx, (_, row) in enumerate(test_df.iterrows()):
         if row['source_ip'] in detected_ips:
-            y_pred[i] = 1
+            y_pred[idx] = 1
     
     validator = ValidationMetrics()
     metrics = validator.calculate(y_true, y_pred)