Create anomaly_detection.py

blockchain_integration/pi_network/pibrain/security/anomaly_detection.py

@@ -0,0 +1,64 @@
+# anomaly_detection.py
+
+import os
+import sys
+import logging
+import pandas as pd
+import numpy as np
+from sklearn.ensemble import IsolationForest
+from sklearn.svm import OneClassSVM
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
+from typing import Any, Dict, List, Optional
+
+_LOGGER = logging.getLogger(__name__)
+
+class AnomalyDetector:
+    """Anomaly detector class."""
+
+    def __init__(self, model_type: str, threshold: float):
+        self.model_type = model_type
+        self.threshold = threshold
+        self.model = None
+
+    def train(self, data: pd.DataFrame) -> None:
+        """Train the anomaly detector model."""
+        if self.model_type == 'isolation_forest':
+            self.model = IsolationForest(contamination=self.threshold)
+        elif self.model_type == 'one_class_svm':
+            self.model = OneClassSVM(kernel='rbf', gamma=0.1, nu=self.threshold)
+        else:
+            raise ValueError(f'Invalid model type: {self.model_type}')
+        self.model.fit(data)
+
+    def predict(self, data: pd.DataFrame) -> np.ndarray:
+        """Predict anomalies in the data."""
+        return self.model.predict(data)
+
+    def evaluate(self, data: pd.DataFrame, labels: np.ndarray) -> Dict[str, float]:
+        """Evaluate the anomaly detector model."""
+        predictions = self.predict(data)
+        accuracy = accuracy_score(labels, predictions)
+        precision = precision_score(labels, predictions)
+        recall = recall_score(labels, predictions)
+        f1 = f1_score(labels, predictions)
+        return {'accuracy': accuracy, 'precision': precision, 'recall': recall, 'f1': f1}
+
+def load_data(file_path: str) -> pd.DataFrame:
+    """Load data from a CSV file."""
+    return pd.read_csv(file_path)
+
+def main():
+    logging.basicConfig(level=logging.INFO)
+    data_file = 'data.csv'
+    model_type = 'isolation_forest'
+    threshold = 0.1
+    detector = AnomalyDetector(model_type, threshold)
+    data = load_data(data_file)
+    detector.train(data)
+    predictions = detector.predict(data)
+    labels = np.array([0] * len(data))  # assume all data is normal
+    evaluation = detector.evaluate(data, labels)
+    _LOGGER.info(f'Evaluation results: {evaluation}')
+
+if __name__ == '__main__':
+    main()