import numpy as np
from sklearn.ensemble import IsolationForest
from sklearn.preprocessing import StandardScaler

class AnomalyDetector:
    def __init__(self):
        self.model = IsolationForest(contamination=0.1, random_state=42)
        self.scaler = StandardScaler()

    def detect(self, data):
        # Select numeric columns
        numeric_columns = data.select_dtypes(include=[np.number]).columns
        X = data[numeric_columns]

        # Scale the data
        X_scaled = self.scaler.fit_transform(X)

        # Fit the model and predict
        self.model.fit(X_scaled)
        anomaly_labels = self.model.predict(X_scaled)

        # Create a DataFrame with anomaly information
        anomaly_data = data.copy()
        anomaly_data['is_anomaly'] = anomaly_labels == -1

        # Calculate anomaly scores
        anomaly_scores = self.model.decision_function(X_scaled)
        anomaly_data['anomaly_score'] = anomaly_scores

        # Sort by anomaly score (most anomalous first)
        anomaly_data = anomaly_data.sort_values('anomaly_score')

        # Return summary of anomalies
        n_anomalies = anomaly_data['is_anomaly'].sum()
        summary = f"Detected {n_anomalies} anomalies out of {len(data)} data points."
        
        return summary, anomaly_data[anomaly_data['is_anomaly']]