"""
Regression Analyzer Agent

Performs regression analysis to model relationships between variables
and make continuous value predictions.
"""

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


class RegressionAnalyzerAgent(BaseAgent):
    """
    Performs regression analysis.

    Supports:
    - Linear regression
    - Polynomial regression
    - Ridge and Lasso regression
    - Multiple regression
    - Time series regression
    - Model diagnostics and validation
    """

    def __init__(self):
        super().__init__(
            name='regression-analyzer',
            description='Perform regression analysis and predictions',
            category='data',
            version='1.0.0',
            tags=['regression', 'machine-learning', 'prediction', 'statistics']
        )

    async def execute(self, params: Dict[str, Any]) -> Dict[str, Any]:
        """
        Perform regression analysis.

        Args:
            params: {
                'data_source': str,
                'target_variable': str,
                'features': List[str],
                'model_type': 'linear|polynomial|ridge|lasso|elasticnet|auto',
                'options': {
                    'degree': int,  # For polynomial regression
                    'alpha': float,  # For regularization
                    'train_test_split': float,
                    'cross_validation': int,
                    'include_diagnostics': bool,
                    'feature_scaling': bool
                },
                'prediction_data': List[Dict[str, Any]]  # Optional
            }

        Returns:
            {
                'status': 'success|failed',
                'model_type': str,
                'predictions': List[Dict[str, Any]],
                'model_performance': Dict[str, Any],
                'coefficients': Dict[str, float],
                'diagnostics': Dict[str, Any],
                'execution_time_seconds': float,
                'insights': List[str]
            }
        """
        data_source = params.get('data_source')
        target_variable = params.get('target_variable')
        features = params.get('features', [])
        model_type = params.get('model_type', 'auto')
        options = params.get('options', {})
        prediction_data = params.get('prediction_data', [])

        self.logger.info(
            f"Performing {model_type} regression for '{target_variable}'"
        )

        # Mock regression analysis
        num_predictions = len(prediction_data) if prediction_data else 100
        predictions = self._generate_regression_predictions(num_predictions)

        return {
            'status': 'success',
            'data_source': data_source,
            'target_variable': target_variable,
            'model_type': 'linear' if model_type == 'auto' else model_type,
            'features_used': len(features) or 8,
            'execution_time_seconds': 3.2,
            'training_samples': 35000,
            'test_samples': 15000,
            'predictions': predictions[:20],  # First 20 predictions
            'total_predictions': num_predictions,
            'model_performance': {
                'r_squared': 0.84,
                'adjusted_r_squared': 0.83,
                'rmse': 2345.67,
                'mae': 1876.43,
                'mape': 8.5,  # Mean Absolute Percentage Error
                'mse': 5501568.89
            },
            'coefficients': {
                'intercept': 1234.56,
                'feature_1_age': 234.12,
                'feature_2_experience': 567.89,
                'feature_3_education': 345.67,
                'feature_4_location': -123.45,
                'feature_5_skills': 456.78,
                'feature_6_tenure': 189.34,
                'feature_7_performance': 678.90,
                'feature_8_certifications': 234.56
            },
            'feature_statistics': {
                'most_influential': 'feature_7_performance',
                'least_influential': 'feature_4_location',
                'correlation_with_target': {
                    'feature_1': 0.72,
                    'feature_2': 0.68,
                    'feature_3': 0.65,
                    'feature_7': 0.81
                }
            },
            'diagnostics': {
                'residual_mean': 0.003,
                'residual_std': 2340.5,
                'normality_test_p_value': 0.23,
                'homoscedasticity_test_p_value': 0.18,
                'durbin_watson': 1.98,
                'vif_scores': {
                    'feature_1': 1.3,
                    'feature_2': 2.1,
                    'feature_3': 1.7,
                    'feature_7': 1.5
                },
                'outliers_detected': 45,
                'influential_points': 12
            } if options.get('include_diagnostics') else {},
            'cross_validation': {
                'mean_r_squared': 0.83,
                'std_r_squared': 0.04,
                'fold_scores': [0.82, 0.85, 0.83, 0.82, 0.84]
            } if options.get('cross_validation') else {},
            'residual_analysis': {
                'residual_distribution': 'approximately_normal',
                'heteroscedasticity': 'not_detected',
                'autocorrelation': 'not_detected',
                'patterns': 'random'
            } if options.get('include_diagnostics') else {},
            'prediction_intervals': {
                'confidence_level': 0.95,
                'average_interval_width': 4680.5
            },
            'insights': [
                'Model explains 84% of variance in target variable',
                'Performance rating is strongest predictor',
                'All assumptions of linear regression are met',
                'Low multicollinearity (VIF < 3 for all features)',
                'Residuals follow normal distribution (p > 0.05)'
            ],
            'recommendations': [
                'Model is suitable for production deployment',
                'Monitor predictions for drift over time',
                'Consider feature engineering for location variable',
                'Collect more data to improve accuracy further',
                'Implement regular retraining schedule'
            ],
            'equation': (
                f'{target_variable} = 1234.56 + 234.12*age + 567.89*experience + '
                '345.67*education + 456.78*skills + 678.90*performance'
            ),
            'model_metadata': {
                'training_date': '2025-11-16',
                'model_version': '1.1.0',
                'framework': 'scikit-learn',
                'regularization_alpha': options.get('alpha', 0.0)
            }
        }

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

        if 'target_variable' not in params:
            self.logger.error("Missing required field: target_variable")
            return False

        valid_models = ['linear', 'polynomial', 'ridge', 'lasso', 'elasticnet', 'auto']
        model_type = params.get('model_type', 'auto')

        if model_type not in valid_models:
            self.logger.error(f"Invalid model type: {model_type}")
            return False

        return True

    def _generate_regression_predictions(self, count: int) -> List[Dict[str, Any]]:
        """Generate mock regression predictions."""
        predictions = []
        base_value = 50000

        for i in range(min(count, 20)):
            actual = base_value + (i * 1000)
            predicted = actual + ((i % 3 - 1) * 500)  # Add some variance

            predictions.append({
                'id': i,
                'predicted_value': round(predicted, 2),
                'actual_value': round(actual, 2) if i < 10 else None,
                'residual': round(actual - predicted, 2) if i < 10 else None,
                'confidence_interval_lower': round(predicted * 0.92, 2),
                'confidence_interval_upper': round(predicted * 1.08, 2),
                'prediction_interval_lower': round(predicted * 0.85, 2),
                'prediction_interval_upper': round(predicted * 1.15, 2)
            })

        return predictions
