"""
Clustering Agent

Performs data clustering using various algorithms to identify
natural groupings and segments in data.
"""

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


class ClusteringAgent(BaseAgent):
    """
    Performs data clustering and segmentation.

    Supports:
    - K-means clustering
    - DBSCAN (density-based)
    - Hierarchical clustering
    - Gaussian mixture models
    - Optimal cluster number detection
    - Cluster profiling and interpretation
    """

    def __init__(self):
        super().__init__(
            name='clustering-agent',
            description='Perform data clustering and segmentation',
            category='data',
            version='1.0.0',
            tags=['clustering', 'segmentation', 'machine-learning', 'unsupervised']
        )

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

        Args:
            params: {
                'data_source': str,
                'features': List[str],
                'algorithm': 'kmeans|dbscan|hierarchical|gmm|auto',
                'num_clusters': int,  # Optional, auto-detected if not provided
                'options': {
                    'normalize': bool,
                    'auto_detect_clusters': bool,
                    'include_cluster_profiles': bool,
                    'visualization': bool,
                    'pca_reduction': bool
                }
            }

        Returns:
            {
                'status': 'success|failed',
                'algorithm_used': str,
                'num_clusters': int,
                'cluster_assignments': List[int],
                'cluster_profiles': List[Dict[str, Any]],
                'metrics': Dict[str, float],
                'execution_time_seconds': float,
                'insights': List[str],
                'visualizations': List[str]
            }
        """
        data_source = params.get('data_source')
        features = params.get('features', [])
        algorithm = params.get('algorithm', 'auto')
        num_clusters = params.get('num_clusters')
        options = params.get('options', {})

        self.logger.info(
            f"Performing {algorithm} clustering on '{data_source}'"
        )

        # Mock clustering analysis
        if options.get('auto_detect_clusters') or not num_clusters:
            num_clusters = 5  # Optimal number detected

        cluster_profiles = self._generate_cluster_profiles(num_clusters)

        return {
            'status': 'success',
            'data_source': data_source,
            'algorithm_used': 'kmeans' if algorithm == 'auto' else algorithm,
            'features_used': len(features) or 8,
            'num_clusters': num_clusters,
            'total_data_points': 25000,
            'execution_time_seconds': 3.4,
            'cluster_sizes': [6500, 5200, 4800, 4300, 4200],
            'cluster_profiles': cluster_profiles if options.get('include_cluster_profiles') else [],
            'cluster_metrics': {
                'silhouette_score': 0.68,
                'davies_bouldin_index': 0.82,
                'calinski_harabasz_score': 3245.6,
                'inertia': 12450.3
            },
            'optimal_clusters_analysis': {
                'elbow_method_suggestion': 5,
                'silhouette_method_suggestion': 5,
                'gap_statistic_suggestion': 4,
                'recommended_clusters': 5
            } if options.get('auto_detect_clusters') else {},
            'feature_importance': {
                'purchase_frequency': 0.22,
                'average_order_value': 0.19,
                'recency': 0.18,
                'category_preference': 0.15,
                'engagement_score': 0.13,
                'tenure': 0.13
            },
            'cluster_separation': {
                'well_separated': True,
                'overlap_percentage': 8.5,
                'minimum_distance': 2.34,
                'maximum_distance': 8.92
            },
            'dimensionality_reduction': {
                'pca_applied': True,
                'components': 3,
                'explained_variance': 0.85
            } if options.get('pca_reduction') else {},
            'insights': [
                f'Identified {num_clusters} distinct customer segments',
                'Cluster 1 (VIP Customers) shows highest value and engagement',
                'Cluster 3 (At-Risk) needs immediate retention focus',
                'Purchase frequency is strongest clustering feature',
                'High silhouette score (0.68) indicates good clustering quality'
            ],
            'cluster_names_suggested': [
                'VIP Customers',
                'Regular Buyers',
                'At-Risk Customers',
                'New Customers',
                'Occasional Shoppers'
            ],
            'recommendations': [
                'Create targeted marketing campaigns for each segment',
                'Focus retention efforts on At-Risk cluster (4800 customers)',
                'Upsell premium features to VIP cluster',
                'Implement reactivation campaigns for Occasional Shoppers',
                'Personalize onboarding for New Customers cluster'
            ],
            'visualizations': [
                'cluster_scatter_plot.png',
                'cluster_profiles_radar.png',
                'elbow_curve.png',
                'silhouette_plot.png',
                'pca_projection.png'
            ] if options.get('visualization') else []
        }

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

        valid_algorithms = ['kmeans', 'dbscan', 'hierarchical', 'gmm', 'auto']
        algorithm = params.get('algorithm', 'auto')

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

        return True

    def _generate_cluster_profiles(self, num_clusters: int) -> List[Dict[str, Any]]:
        """Generate mock cluster profiles."""
        profiles = []
        cluster_names = [
            'VIP Customers',
            'Regular Buyers',
            'At-Risk Customers',
            'New Customers',
            'Occasional Shoppers'
        ]

        for i in range(num_clusters):
            profiles.append({
                'cluster_id': i,
                'cluster_name': cluster_names[i] if i < len(cluster_names) else f'Cluster {i}',
                'size': 6500 - (i * 300),
                'percentage': round((6500 - (i * 300)) / 25000 * 100, 1),
                'characteristics': {
                    'avg_purchase_frequency': 8.5 - (i * 1.5),
                    'avg_order_value': 125.0 - (i * 15),
                    'avg_recency_days': 15 + (i * 10),
                    'avg_engagement_score': 0.85 - (i * 0.15),
                    'avg_tenure_months': 24 - (i * 4)
                },
                'top_categories': [
                    'Electronics', 'Books', 'Home'
                ][0:3-i] if i < 3 else ['Misc'],
                'recommended_actions': [
                    'Upsell premium features',
                    'Maintain engagement with loyalty rewards',
                    'Implement retention campaign',
                    'Optimize onboarding experience',
                    'Reactivation email campaign'
                ][i]
            })

        return profiles
