"""
Retention Analyzer Agent

Analyzes user retention patterns, calculates retention rates,
and identifies factors that influence user retention.
"""

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


class RetentionAnalyzerAgent(BaseAgent):
    """
    Analyzes user retention patterns.

    Supports:
    - Classic retention analysis
    - Rolling retention
    - Bracket retention
    - Feature-based retention
    - Cohort retention comparison
    - Churn prediction
    """

    def __init__(self):
        super().__init__(
            name='retention-analyzer',
            description='Analyze user retention patterns and trends',
            category='data',
            version='1.0.0',
            tags=['retention', 'user-behavior', 'engagement', 'analytics']
        )

    async def execute(self, params: Dict[str, Any]) -> Dict[str, Any]:
        """
        Analyze user retention.

        Args:
            params: {
                'data_source': str,
                'retention_type': 'classic|rolling|bracket',
                'time_period': {
                    'start_date': str,
                    'end_date': str,
                    'granularity': 'daily|weekly|monthly'
                },
                'cohort_period': 'daily|weekly|monthly',
                'return_periods': List[int],  # e.g., [1, 7, 14, 30, 60, 90]
                'options': {
                    'segment_by': List[str],
                    'include_feature_usage': bool,
                    'include_revenue_retention': bool,
                    'compare_cohorts': bool
                }
            }

        Returns:
            {
                'status': 'success|failed',
                'retention_type': str,
                'overall_retention': Dict[str, float],
                'cohort_retention': List[Dict[str, Any]],
                'retention_curve': List[float],
                'insights': List[str],
                'recommendations': List[str],
                'execution_time_seconds': float
            }
        """
        data_source = params.get('data_source')
        retention_type = params.get('retention_type', 'classic')
        time_period = params.get('time_period', {})
        return_periods = params.get('return_periods', [1, 7, 14, 30, 60, 90])
        options = params.get('options', {})

        self.logger.info(
            f"Analyzing {retention_type} retention from '{data_source}'"
        )

        # Mock retention analysis
        return {
            'status': 'success',
            'data_source': data_source,
            'retention_type': retention_type,
            'execution_time_seconds': 5.7,
            'time_period': {
                'start_date': time_period.get('start_date', '2025-01-01'),
                'end_date': time_period.get('end_date', '2025-11-16'),
                'granularity': time_period.get('granularity', 'daily')
            },
            'total_users_analyzed': 125000,
            'active_users': 28500,
            'overall_retention': {
                'day_1': 0.65,
                'day_7': 0.42,
                'day_14': 0.35,
                'day_30': 0.28,
                'day_60': 0.22,
                'day_90': 0.18
            },
            'retention_curve': [1.0, 0.65, 0.52, 0.42, 0.35, 0.28, 0.22, 0.18],
            'retention_metrics': {
                'average_retention_day_30': 0.28,
                'median_retention_day_30': 0.26,
                'retention_rate_trend': 'stable',
                'month_over_month_change': 0.02
            },
            'cohort_retention': [
                {
                    'cohort': 'October 2025',
                    'size': 12000,
                    'day_1': 0.68,
                    'day_7': 0.45,
                    'day_30': 0.32,
                    'day_90': 0.21
                },
                {
                    'cohort': 'September 2025',
                    'size': 11500,
                    'day_1': 0.64,
                    'day_7': 0.41,
                    'day_30': 0.28,
                    'day_90': 0.18
                },
                {
                    'cohort': 'August 2025',
                    'size': 10800,
                    'day_1': 0.62,
                    'day_7': 0.39,
                    'day_30': 0.25,
                    'day_90': 0.16
                }
            ] if options.get('compare_cohorts') else [],
            'segment_analysis': {
                'power_users': {
                    'count': 8500,
                    'day_30_retention': 0.72,
                    'definition': 'Users with 10+ sessions in first week'
                },
                'casual_users': {
                    'count': 45000,
                    'day_30_retention': 0.18,
                    'definition': 'Users with 1-3 sessions in first week'
                },
                'dormant_users': {
                    'count': 71500,
                    'day_30_retention': 0.05,
                    'definition': 'Users with no activity in last 30 days'
                }
            } if options.get('segment_by') else {},
            'feature_retention': {
                'feature_a_users': {
                    'retention_day_30': 0.45,
                    'vs_non_users': 0.17
                },
                'feature_b_users': {
                    'retention_day_30': 0.52,
                    'vs_non_users': 0.24
                },
                'feature_c_users': {
                    'retention_day_30': 0.38,
                    'vs_non_users': 0.10
                }
            } if options.get('include_feature_usage') else {},
            'revenue_retention': {
                'paying_users_day_30': 0.68,
                'free_users_day_30': 0.22,
                'mrr_retention': 0.85,
                'gross_revenue_retention': 0.92,
                'net_revenue_retention': 1.08
            } if options.get('include_revenue_retention') else {},
            'critical_periods': {
                'highest_churn': 'Day 1-3',
                'churn_rate_day_1_3': 0.35,
                'stabilization_point': 'Day 30',
                'long_term_retention': 0.18
            },
            'visualizations': [
                'retention_curve.png',
                'cohort_retention_heatmap.png',
                'segment_comparison.png',
                'feature_impact_analysis.png'
            ],
            'insights': [
                'Day 1 retention of 65% is above industry average',
                'Significant drop-off between Day 1 and Day 7 (35% churn)',
                'Retention stabilizes after Day 30 at around 18%',
                'Power users show 3x better retention than casual users',
                'Feature B adoption correlates with 52% higher retention',
                'Recent cohorts showing improving retention trends (+2% MoM)'
            ],
            'recommendations': [
                'Focus on improving Day 1-7 experience (35% churn)',
                'Implement targeted campaigns to activate casual users',
                'Drive adoption of Feature B (shows 24% retention lift)',
                'Create re-engagement campaigns for Day 30+ users',
                'Personalize onboarding based on user segment',
                'Monitor power user behaviors for replication'
            ],
            'benchmarks': {
                'industry_average_day_30': 0.25,
                'top_quartile_day_30': 0.40,
                'your_position': 'above_average'
            }
        }

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

        valid_retention_types = ['classic', 'rolling', 'bracket']
        retention_type = params.get('retention_type', 'classic')

        if retention_type not in valid_retention_types:
            self.logger.error(f"Invalid retention type: {retention_type}")
            return False

        return True
