"""
Anomaly Detector Agent

Detects anomalies and outliers in data using statistical methods
and machine learning algorithms.
"""

from typing import Any, Dict, List
from agents.base import BaseAgent


class AnomalyDetectorAgent(BaseAgent):
    """
    Detects anomalies and outliers in data.

    Supports:
    - Statistical methods (Z-score, IQR, isolation forest)
    - Time series anomaly detection
    - Multivariate anomaly detection
    - Pattern-based anomaly detection
    - Real-time anomaly detection
    - Customizable sensitivity thresholds
    """

    def __init__(self):
        super().__init__(
            name='anomaly-detector',
            description='Detect anomalies and outliers in data',
            category='data',
            version='1.0.0',
            tags=['anomaly-detection', 'outliers', 'machine-learning', 'monitoring']
        )

    async def execute(self, params: Dict[str, Any]) -> Dict[str, Any]:
        """
        Detect anomalies in dataset.

        Args:
            params: {
                'data_source': str,
                'columns': List[str],
                'method': 'zscore|iqr|isolation_forest|lof|dbscan|autoencoder',
                'sensitivity': float,  # 0.0 to 1.0
                'options': {
                    'time_series': bool,
                    'multivariate': bool,
                    'seasonal': bool,
                    'window_size': int,
                    'threshold': float,
                    'min_samples': int
                },
                'action': 'flag|remove|replace'
            }

        Returns:
            {
                'status': 'success|failed',
                'anomalies_detected': int,
                'anomaly_percentage': float,
                'total_rows': int,
                'method_used': str,
                'sensitivity': float,
                'anomalies': List[Dict[str, Any]],
                'execution_time_seconds': float,
                'statistics': Dict[str, Any],
                'visualizations': List[str],
                'recommendations': List[str]
            }
        """
        data_source = params.get('data_source')
        columns = params.get('columns', [])
        method = params.get('method', 'zscore')
        sensitivity = params.get('sensitivity', 0.95)
        options = params.get('options', {})

        self.logger.info(
            f"Detecting anomalies in '{data_source}' using {method} method"
        )

        # Mock anomaly detection
        total_rows = 50000
        anomalies_count = int(total_rows * 0.02)  # 2% anomalies

        anomalies = self._generate_mock_anomalies(anomalies_count, method)

        return {
            'status': 'success',
            'data_source': data_source,
            'method_used': method,
            'sensitivity': sensitivity,
            'total_rows': total_rows,
            'anomalies_detected': anomalies_count,
            'anomaly_percentage': round((anomalies_count / total_rows) * 100, 2),
            'columns_analyzed': len(columns) or 8,
            'execution_time_seconds': 7.2,
            'anomalies': anomalies[:10],  # Return first 10 for preview
            'anomaly_types': {
                'point_anomalies': 756,
                'contextual_anomalies': 189,
                'collective_anomalies': 55
            },
            'severity_distribution': {
                'high': 145,
                'medium': 423,
                'low': 432
            },
            'statistics': {
                'mean_anomaly_score': 0.82,
                'max_anomaly_score': 0.98,
                'threshold_used': 0.75,
                'false_positive_estimate': 0.05
            },
            'affected_columns': {
                'revenue': 345,
                'user_count': 234,
                'transaction_amount': 189,
                'response_time': 232
            },
            'temporal_distribution': {
                'weekday_anomalies': 678,
                'weekend_anomalies': 322,
                'night_hours_anomalies': 234
            } if options.get('time_series') else {},
            'visualizations': [
                'anomaly_scatter_plot.png',
                'anomaly_timeline.png',
                'feature_importance.png'
            ],
            'insights': [
                f'Detected {anomalies_count} anomalies ({round((anomalies_count / total_rows) * 100, 2)}% of data)',
                'Most anomalies found in revenue and transaction amount columns',
                'Higher anomaly rate during night hours',
                'Seasonal pattern detected in anomaly occurrence'
            ],
            'recommendations': [
                'Investigate high-severity anomalies in revenue data',
                'Review data collection process for transaction amounts',
                'Consider implementing real-time anomaly alerts',
                'Adjust sensitivity threshold if false positives are high',
                'Monitor anomaly trends over time for pattern changes'
            ],
            'action_taken': params.get('action', 'flag')
        }

    def validate_params(self, params: Dict[str, Any]) -> bool:
        """Validate anomaly detection parameters."""
        if 'data_source' not in params:
            self.logger.error("Missing required field: data_source")
            return False

        valid_methods = ['zscore', 'iqr', 'isolation_forest', 'lof', 'dbscan', 'autoencoder']
        method = params.get('method', 'zscore')

        if method not in valid_methods:
            self.logger.error(f"Invalid method: {method}")
            return False

        sensitivity = params.get('sensitivity', 0.95)
        if not 0.0 <= sensitivity <= 1.0:
            self.logger.error("Sensitivity must be between 0.0 and 1.0")
            return False

        return True

    def _generate_mock_anomalies(
        self,
        count: int,
        method: str
    ) -> List[Dict[str, Any]]:
        """Generate mock anomaly records."""
        anomalies = []
        for i in range(min(count, 10)):
            anomalies.append({
                'row_index': 1234 + i * 500,
                'column': 'revenue',
                'value': 15000 + i * 1000,
                'expected_value': 5000,
                'anomaly_score': 0.85 + i * 0.01,
                'severity': 'high' if i < 3 else 'medium' if i < 7 else 'low',
                'type': 'point_anomaly',
                'timestamp': f'2025-11-{16-i:02d}T{10+i:02d}:00:00Z',
                'explanation': f'Value significantly higher than expected range'
            })
        return anomalies
