src/agents/bonifacio.py

"""
Module: agents.bonifacio_agent
Description: José Bonifácio - Public Policy Agent specialized in analyzing policy effectiveness
Author: Anderson H. Silva
Date: 2025-01-24
License: Proprietary - All rights reserved
"""

import asyncio
import hashlib
from datetime import datetime, timedelta
from typing import Any, Dict, List, Optional, Tuple
from dataclasses import dataclass
from enum import Enum
import statistics

import numpy as np
import pandas as pd
from pydantic import BaseModel, Field as PydanticField

from src.agents.deodoro import BaseAgent, AgentContext, AgentMessage, AgentResponse
from src.core import get_logger
from src.core.exceptions import AgentExecutionError, DataAnalysisError


class PolicyStatus(Enum):
    """Status of policy analysis."""
    ACTIVE = "active"
    INACTIVE = "inactive"
    UNDER_REVIEW = "under_review"
    DISCONTINUED = "discontinued"
    PLANNED = "planned"


class ImpactLevel(Enum):
    """Impact level classification."""
    VERY_LOW = "very_low"
    LOW = "low"
    MEDIUM = "medium"
    HIGH = "high"
    VERY_HIGH = "very_high"


@dataclass
class PolicyIndicator:
    """Policy performance indicator."""
    
    name: str
    baseline_value: float
    current_value: float
    target_value: float
    unit: str
    data_source: str
    last_update: datetime
    statistical_significance: float
    trend: str  # "improving", "deteriorating", "stable"


@dataclass
class PolicyEvaluation:
    """Comprehensive policy evaluation result."""
    
    policy_id: str
    policy_name: str
    analysis_period: Tuple[datetime, datetime]
    status: PolicyStatus
    investment: Dict[str, float]  # planned, executed, deviation
    beneficiaries: Dict[str, Any]  # target, reached, cost_per_capita
    indicators: List[PolicyIndicator]
    effectiveness_score: Dict[str, float]  # efficacy, efficiency, effectiveness
    roi_social: float
    sustainability_score: int  # 0-100
    impact_level: ImpactLevel
    recommendations: List[Dict[str, Any]]
    evidence_sources: List[str]
    analysis_confidence: float
    hash_verification: str


class PolicyAnalysisRequest(BaseModel):
    """Request for public policy analysis."""
    
    policy_name: str = PydanticField(description="Name or description of the policy")
    policy_area: Optional[str] = PydanticField(default=None, description="Policy area (health, education, security, etc)")
    geographical_scope: Optional[str] = PydanticField(default=None, description="Geographic scope (municipal, state, federal)")
    analysis_period: Optional[Tuple[str, str]] = PydanticField(default=None, description="Analysis period (start, end)")
    budget_data: Optional[Dict[str, float]] = PydanticField(default=None, description="Budget information")
    target_indicators: Optional[List[str]] = PydanticField(default=None, description="Specific indicators to analyze")
    comparison_policies: Optional[List[str]] = PydanticField(default=None, description="Other policies to compare with")
    benchmarking_scope: str = PydanticField(default="national", description="Benchmarking scope")


class BonifacioAgent(BaseAgent):
    """
    José Bonifácio - Public Policy Agent
    
    Specialized in analyzing public policy effectiveness, efficiency, and impact.
    Evaluates institutional reforms and measures social return on investment.
    Inspired by José Bonifácio de Andrada e Silva, the "Patriarch of Independence"
    and architect of Brazilian institutional foundations.
    """
    
    def __init__(self):
        super().__init__(
            name="bonifacio",
            description="Public Policy Agent specialized in analyzing policy effectiveness and institutional reforms",
            capabilities=[
                "policy_effectiveness_analysis",
                "institutional_reform_evaluation",
                "social_roi_calculation",
                "policy_impact_assessment",
                "benchmarking_analysis",
                "cost_benefit_analysis",
                "stakeholder_impact_mapping",
                "policy_sustainability_scoring",
                "implementation_gap_analysis",
                "evidence_based_recommendations",
                "statistical_significance_testing",
                "longitudinal_policy_tracking",
                "comparative_policy_analysis",
                "resource_allocation_optimization"
            ]
        )
        self.logger = get_logger("agent.bonifacio")
        
        # Policy evaluation frameworks
        self._evaluation_frameworks = {
            "logic_model": self._apply_logic_model_framework,
            "results_chain": self._apply_results_chain_framework,
            "theory_of_change": self._apply_theory_of_change_framework,
            "cost_effectiveness": self._apply_cost_effectiveness_framework
        }
        
        # Data sources for policy analysis
        self._data_sources = [
            "Portal da Transparência", "TCU", "CGU", "IBGE",
            "IPEA", "DataSUS", "INEP", "SIAFI", "SICONV",
            "Tesouro Nacional", "CAPES", "CNJ", "CNMP"
        ]
        
        # Policy areas and their key indicators
        self._policy_indicators = {
            "education": ["literacy_rate", "school_completion", "pisa_scores", "teacher_quality"],
            "health": ["mortality_rate", "vaccination_coverage", "hospital_capacity", "health_expenditure"],
            "security": ["crime_rate", "homicide_rate", "police_effectiveness", "prison_population"],
            "social": ["poverty_rate", "inequality_index", "employment_rate", "social_mobility"],
            "infrastructure": ["road_quality", "internet_access", "urban_mobility", "housing_deficit"],
            "environment": ["deforestation_rate", "air_quality", "water_quality", "renewable_energy"]
        }
    
    async def process(
        self,
        message: AgentMessage,
        context: AgentContext,
    ) -> AgentResponse:
        """
        Process public policy analysis request.
        
        Args:
            message: Policy analysis request
            context: Agent execution context
            
        Returns:
            Comprehensive policy evaluation results
        """
        try:
            self.logger.info(
                "Processing policy analysis request",
                investigation_id=context.investigation_id,
                message_type=message.type,
            )
            
            # Parse request
            if isinstance(message.data, dict):
                request = PolicyAnalysisRequest(**message.data)
            else:
                request = PolicyAnalysisRequest(policy_name=str(message.data))
            
            # Perform comprehensive policy evaluation
            evaluation = await self._evaluate_policy(request, context)
            
            # Generate strategic recommendations
            strategic_recommendations = await self._generate_strategic_recommendations(
                evaluation, request, context
            )
            
            # Perform benchmarking analysis
            benchmarking = await self._perform_benchmarking_analysis(
                evaluation, request
            )
            
            response_data = {
                "policy_id": evaluation.policy_id,
                "timestamp": datetime.utcnow().isoformat(),
                "agent": "bonifacio",
                "analysis_type": "policy_effectiveness",
                "policy_evaluation": {
                    "policy_name": evaluation.policy_name,
                    "status": evaluation.status.value,
                    "investment": evaluation.investment,
                    "beneficiaries": evaluation.beneficiaries,
                    "effectiveness_scores": evaluation.effectiveness_score,
                    "roi_social": evaluation.roi_social,
                    "sustainability_score": evaluation.sustainability_score,
                    "impact_level": evaluation.impact_level.value,
                    "analysis_confidence": evaluation.analysis_confidence
                },
                "indicators": [
                    {
                        "name": ind.name,
                        "baseline": ind.baseline_value,
                        "current": ind.current_value,
                        "target": ind.target_value,
                        "performance_ratio": ind.current_value / ind.baseline_value if ind.baseline_value != 0 else 1.0,
                        "goal_achievement": (ind.current_value / ind.target_value * 100) if ind.target_value != 0 else 0,
                        "trend": ind.trend,
                        "significance": ind.statistical_significance
                    }
                    for ind in evaluation.indicators
                ],
                "strategic_recommendations": strategic_recommendations,
                "benchmarking": benchmarking,
                "evidence_sources": evaluation.evidence_sources,
                "hash_verification": evaluation.hash_verification
            }
            
            self.logger.info(
                "Policy analysis completed",
                investigation_id=context.investigation_id,
                policy_name=evaluation.policy_name,
                effectiveness_score=evaluation.effectiveness_score.get("effectiveness", 0),
                impact_level=evaluation.impact_level.value,
            )
            
            return AgentResponse(
                agent_name=self.name,
                response_type="policy_analysis",
                data=response_data,
                success=True,
                context=context,
            )
            
        except Exception as e:
            self.logger.error(
                "Policy analysis failed",
                investigation_id=context.investigation_id,
                error=str(e),
                exc_info=True,
            )
            
            return AgentResponse(
                agent_name=self.name,
                response_type="error",
                data={"error": str(e), "analysis_type": "policy_effectiveness"},
                success=False,
                context=context,
            )
    
    async def _evaluate_policy(
        self, 
        request: PolicyAnalysisRequest, 
        context: AgentContext
    ) -> PolicyEvaluation:
        """Perform comprehensive policy evaluation."""
        
        self.logger.info(
            "Starting policy evaluation",
            policy_name=request.policy_name,
            policy_area=request.policy_area,
        )
        
        # Generate policy ID
        policy_id = hashlib.md5(
            f"{request.policy_name}{request.policy_area}".encode()
        ).hexdigest()[:12]
        
        # Simulate comprehensive evaluation (replace with real implementation)
        await asyncio.sleep(3)  # Simulate processing time
        
        # Determine analysis period
        if request.analysis_period:
            period_start = datetime.strptime(request.analysis_period[0], "%Y-%m-%d")
            period_end = datetime.strptime(request.analysis_period[1], "%Y-%m-%d")
        else:
            period_end = datetime.utcnow()
            period_start = period_end - timedelta(days=365)  # Last year
        
        # Generate financial data
        investment_data = await self._analyze_investment_data(request, context)
        
        # Analyze beneficiaries
        beneficiary_data = await self._analyze_beneficiaries(request, context)
        
        # Evaluate indicators
        indicators = await self._evaluate_policy_indicators(request, context)
        
        # Calculate effectiveness scores
        effectiveness_scores = await self._calculate_effectiveness_scores(
            investment_data, beneficiary_data, indicators
        )
        
        # Calculate social ROI
        social_roi = await self._calculate_social_roi(
            investment_data, beneficiary_data, indicators
        )
        
        # Assess sustainability
        sustainability_score = await self._assess_policy_sustainability(
            request, investment_data, indicators
        )
        
        # Determine impact level
        impact_level = self._classify_impact_level(effectiveness_scores, social_roi)
        
        # Generate evidence hash
        evidence_hash = self._generate_evidence_hash(
            policy_id, investment_data, beneficiary_data, indicators
        )
        
        return PolicyEvaluation(
            policy_id=policy_id,
            policy_name=request.policy_name,
            analysis_period=(period_start, period_end),
            status=PolicyStatus.ACTIVE,  # Assume active for now
            investment=investment_data,
            beneficiaries=beneficiary_data,
            indicators=indicators,
            effectiveness_score=effectiveness_scores,
            roi_social=social_roi,
            sustainability_score=sustainability_score,
            impact_level=impact_level,
            recommendations=[],  # Will be filled by recommendation generator
            evidence_sources=self._data_sources,
            analysis_confidence=0.82,
            hash_verification=evidence_hash
        )
    
    async def _analyze_investment_data(
        self, 
        request: PolicyAnalysisRequest, 
        context: AgentContext
    ) -> Dict[str, float]:
        """Analyze policy investment and budget execution."""
        
        # Use provided budget data or simulate
        if request.budget_data:
            planned = request.budget_data.get("planned", 0)
            executed = request.budget_data.get("executed", 0)
        else:
            # Simulate budget data
            planned = np.random.uniform(10_000_000, 500_000_000)
            executed = planned * np.random.uniform(0.7, 1.2)  # 70-120% execution
        
        deviation = ((executed - planned) / planned) * 100 if planned > 0 else 0
        
        return {
            "planned": planned,
            "executed": executed,
            "deviation_percentage": deviation,
            "cost_per_beneficiary": executed / max(1, np.random.randint(1000, 100000))
        }
    
    async def _analyze_beneficiaries(
        self, 
        request: PolicyAnalysisRequest, 
        context: AgentContext
    ) -> Dict[str, Any]:
        """Analyze policy beneficiaries and coverage."""
        
        # Simulate beneficiary analysis
        target_population = np.random.randint(10000, 1000000)
        reached_population = int(target_population * np.random.uniform(0.6, 1.1))
        coverage_rate = (reached_population / target_population) * 100
        
        return {
            "target_population": target_population,
            "reached_population": reached_population,
            "coverage_rate": coverage_rate,
            "demographic_breakdown": {
                "urban": reached_population * 0.7,
                "rural": reached_population * 0.3,
                "vulnerable_groups": reached_population * 0.4
            }
        }
    
    async def _evaluate_policy_indicators(
        self, 
        request: PolicyAnalysisRequest, 
        context: AgentContext
    ) -> List[PolicyIndicator]:
        """Evaluate key policy performance indicators."""
        
        indicators = []
        
        # Get relevant indicators for policy area
        policy_area = request.policy_area or "social"
        relevant_indicators = self._policy_indicators.get(policy_area, ["generic_outcome"])
        
        for indicator_name in relevant_indicators[:5]:  # Limit to 5 indicators
            baseline = np.random.uniform(10, 100)
            current = baseline * np.random.uniform(0.8, 1.4)  # -20% to +40% change
            target = baseline * np.random.uniform(1.1, 1.5)   # 10-50% improvement target
            
            # Determine trend
            if current > baseline * 1.05:
                trend = "improving"
            elif current < baseline * 0.95:
                trend = "deteriorating"
            else:
                trend = "stable"
            
            indicators.append(PolicyIndicator(
                name=indicator_name,
                baseline_value=baseline,
                current_value=current,
                target_value=target,
                unit="rate" if "rate" in indicator_name else "index",
                data_source=np.random.choice(self._data_sources[:5]),
                last_update=datetime.utcnow() - timedelta(days=np.random.randint(1, 90)),
                statistical_significance=np.random.uniform(0.7, 0.95),
                trend=trend
            ))
        
        return indicators
    
    async def _calculate_effectiveness_scores(
        self,
        investment: Dict[str, float],
        beneficiaries: Dict[str, Any],
        indicators: List[PolicyIndicator]
    ) -> Dict[str, float]:
        """Calculate efficacy, efficiency, and effectiveness scores."""
        
        # Efficacy: achievement of intended results
        target_achievements = []
        for ind in indicators:
            if ind.target_value > 0:
                achievement = min(1.0, ind.current_value / ind.target_value)
                target_achievements.append(achievement)
        
        efficacy = statistics.mean(target_achievements) if target_achievements else 0.5
        
        # Efficiency: resource utilization
        budget_efficiency = 1.0 - abs(investment["deviation_percentage"]) / 100
        budget_efficiency = max(0.0, min(1.0, budget_efficiency))
        
        coverage_efficiency = min(1.0, beneficiaries["coverage_rate"] / 100)
        
        efficiency = (budget_efficiency + coverage_efficiency) / 2
        
        # Effectiveness: overall impact considering costs and benefits
        cost_effectiveness = efficacy / (investment["cost_per_beneficiary"] / 1000) if investment["cost_per_beneficiary"] > 0 else 0
        cost_effectiveness = min(1.0, cost_effectiveness)
        
        effectiveness = (efficacy * 0.4 + efficiency * 0.3 + cost_effectiveness * 0.3)
        
        return {
            "efficacy": round(efficacy, 3),
            "efficiency": round(efficiency, 3),
            "effectiveness": round(effectiveness, 3),
            "cost_effectiveness": round(cost_effectiveness, 3)
        }
    
    async def _calculate_social_roi(
        self,
        investment: Dict[str, float],
        beneficiaries: Dict[str, Any],
        indicators: List[PolicyIndicator]
    ) -> float:
        """Calculate Social Return on Investment."""
        
        # Estimate social benefits (simplified model)
        total_investment = investment["executed"]
        
        # Calculate benefits based on indicator improvements
        social_benefits = 0
        for ind in indicators:
            improvement = max(0, ind.current_value - ind.baseline_value)
            # Monetize improvement (simplified estimation)
            benefit_per_unit = np.random.uniform(100, 1000)  # R$ per unit improvement
            social_benefits += improvement * benefit_per_unit * beneficiaries["reached_population"]
        
        # Calculate ROI
        if total_investment > 0:
            social_roi = (social_benefits - total_investment) / total_investment
        else:
            social_roi = 0
        
        return round(social_roi, 3)
    
    async def _assess_policy_sustainability(
        self,
        request: PolicyAnalysisRequest,
        investment: Dict[str, float],
        indicators: List[PolicyIndicator]
    ) -> int:
        """Assess policy sustainability score (0-100)."""
        
        sustainability_factors = []
        
        # Budget sustainability
        if abs(investment["deviation_percentage"]) < 10:
            sustainability_factors.append(85)  # Good budget control
        elif abs(investment["deviation_percentage"]) < 25:
            sustainability_factors.append(65)  # Moderate control
        else:
            sustainability_factors.append(35)  # Poor control
        
        # Performance sustainability (trend analysis)
        improving_indicators = len([ind for ind in indicators if ind.trend == "improving"])
        total_indicators = len(indicators)
        
        if total_indicators > 0:
            performance_sustainability = (improving_indicators / total_indicators) * 100
            sustainability_factors.append(performance_sustainability)
        
        # Institutional capacity (simulated)
        institutional_score = np.random.uniform(50, 90)
        sustainability_factors.append(institutional_score)
        
        # Political support (simulated)
        political_score = np.random.uniform(40, 85)
        sustainability_factors.append(political_score)
        
        return int(statistics.mean(sustainability_factors))
    
    def _classify_impact_level(
        self, 
        effectiveness_scores: Dict[str, float], 
        social_roi: float
    ) -> ImpactLevel:
        """Classify policy impact level."""
        
        overall_effectiveness = effectiveness_scores["effectiveness"]
        
        if overall_effectiveness >= 0.8 and social_roi >= 2.0:
            return ImpactLevel.VERY_HIGH
        elif overall_effectiveness >= 0.7 and social_roi >= 1.0:
            return ImpactLevel.HIGH
        elif overall_effectiveness >= 0.5 and social_roi >= 0.5:
            return ImpactLevel.MEDIUM
        elif overall_effectiveness >= 0.3 and social_roi >= 0.0:
            return ImpactLevel.LOW
        else:
            return ImpactLevel.VERY_LOW
    
    async def _generate_strategic_recommendations(
        self,
        evaluation: PolicyEvaluation,
        request: PolicyAnalysisRequest,
        context: AgentContext
    ) -> List[Dict[str, Any]]:
        """Generate strategic policy recommendations."""
        
        recommendations = []
        
        # Budget recommendations
        if abs(evaluation.investment["deviation_percentage"]) > 15:
            recommendations.append({
                "area": "budget_management",
                "recommendation": "Implement enhanced budget monitoring and control mechanisms",
                "priority": "high",
                "expected_impact": 0.8,
                "implementation_timeframe": "immediate",
                "success_metrics": ["Reduce budget deviation to <10%"]
            })
        
        # Coverage recommendations
        if evaluation.beneficiaries["coverage_rate"] < 80:
            recommendations.append({
                "area": "coverage_expansion",
                "recommendation": "Expand outreach and improve access mechanisms",
                "priority": "medium",
                "expected_impact": 0.7,
                "implementation_timeframe": "short_term",
                "success_metrics": ["Increase coverage rate to >85%"]
            })
        
        # Performance recommendations
        deteriorating_indicators = [ind for ind in evaluation.indicators if ind.trend == "deteriorating"]
        if deteriorating_indicators:
            recommendations.append({
                "area": "performance_improvement",
                "recommendation": f"Address declining performance in {len(deteriorating_indicators)} key indicators",
                "priority": "high",
                "expected_impact": 0.9,
                "implementation_timeframe": "immediate",
                "success_metrics": ["Reverse negative trends in all indicators"]
            })
        
        # Sustainability recommendations
        if evaluation.sustainability_score < 70:
            recommendations.append({
                "area": "sustainability",
                "recommendation": "Strengthen institutional capacity and long-term planning",
                "priority": "medium",
                "expected_impact": 0.6,
                "implementation_timeframe": "medium_term",
                "success_metrics": ["Achieve sustainability score >75"]
            })
        
        return recommendations
    
    async def _perform_benchmarking_analysis(
        self,
        evaluation: PolicyEvaluation,
        request: PolicyAnalysisRequest
    ) -> Dict[str, Any]:
        """Perform benchmarking against similar policies."""
        
        # Simulate benchmarking data
        benchmarking = {
            "reference_policies": [
                {"name": "Similar Policy A", "effectiveness": 0.72, "roi": 1.8},
                {"name": "Similar Policy B", "effectiveness": 0.68, "roi": 1.4},
                {"name": "Best Practice Example", "effectiveness": 0.85, "roi": 2.3}
            ],
            "percentile_ranking": {
                "effectiveness": np.random.randint(40, 95),
                "efficiency": np.random.randint(35, 90),
                "roi": np.random.randint(45, 88)
            },
            "improvement_potential": {
                "effectiveness": max(0, 0.85 - evaluation.effectiveness_score["effectiveness"]),
                "roi": max(0, 2.3 - evaluation.roi_social)
            }
        }
        
        return benchmarking
    
    def _generate_evidence_hash(
        self,
        policy_id: str,
        investment: Dict[str, float],
        beneficiaries: Dict[str, Any],
        indicators: List[PolicyIndicator]
    ) -> str:
        """Generate SHA-256 hash for evidence verification."""
        
        evidence_data = f"{policy_id}{investment['executed']}{beneficiaries['reached_population']}{len(indicators)}{datetime.utcnow().date()}"
        return hashlib.sha256(evidence_data.encode()).hexdigest()
    
    # Framework application methods
    async def _apply_logic_model_framework(self, request, evaluation):
        """Apply logic model evaluation framework."""
        pass  # Implementation would depend on specific requirements
    
    async def _apply_results_chain_framework(self, request, evaluation):
        """Apply results chain evaluation framework."""
        pass  # Implementation would depend on specific requirements
    
    async def _apply_theory_of_change_framework(self, request, evaluation):
        """Apply theory of change evaluation framework."""
        pass  # Implementation would depend on specific requirements
    
    async def _apply_cost_effectiveness_framework(self, request, evaluation):
        """Apply cost-effectiveness evaluation framework."""
        pass  # Implementation would depend on specific requirements