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import numpy as np
import pandas as pd
from scipy import stats
from sklearn.metrics import mean_absolute_percentage_error
import matplotlib.pyplot as plt

class RevenueModelValidator:
    def __init__(self, historical_data, model_parameters):
        self.historical_data = historical_data
        self.model_params = model_parameters
        self.validation_results = {}

    def validate_growth_model(self):
        """
        Validate revenue growth model against historical performance
        """
        # Extract historical revenue data
        historical_revenue = self.historical_data['revenue']
        historical_periods = len(historical_revenue)

        # Apply model to historical periods
        model_predictions = self.apply_growth_model(historical_periods)

        # Calculate validation metrics
        mape = mean_absolute_percentage_error(historical_revenue, model_predictions)
        correlation = stats.pearsonr(historical_revenue, model_predictions)[0]

        # R-squared calculation
        ss_res = np.sum((historical_revenue - model_predictions) ** 2)
        ss_tot = np.sum((historical_revenue - np.mean(historical_revenue)) ** 2)
        r_squared = 1 - (ss_res / ss_tot)

        self.validation_results['model_fit'] = {
            'mape': mape,
            'correlation': correlation,
            'r_squared': r_squared,
            'validation_passed': mape < 0.15 and r_squared > 0.85
        }

        return self.validation_results

    def sensitivity_analysis(self, parameter_variations=0.1):
        """
        Test model sensitivity to parameter changes
        """
        base_result = self.apply_growth_model(12)  # 12-month projection
        sensitivity_results = {}

        for param_name, base_value in self.model_params.items():
            # Test +/- 10% parameter variation
            high_params = self.model_params.copy()
            low_params = self.model_params.copy()

            high_params[param_name] = base_value * (1 + parameter_variations)
            low_params[param_name] = base_value * (1 - parameter_variations)

            high_result = self.apply_growth_model(12, high_params)
            low_result = self.apply_growth_model(12, low_params)

            # Calculate sensitivity percentage
            sensitivity = abs(high_result[-1] - low_result[-1]) / base_result[-1]

            sensitivity_results[param_name] = {
                'sensitivity_percentage': sensitivity,
                'stability_rating': 'stable' if sensitivity < 0.2 else 'sensitive'
            }

        self.validation_results['sensitivity_analysis'] = sensitivity_results
        return sensitivity_results

    def scenario_stress_testing(self):
        """
        Test model under different market scenarios
        """
        scenarios = {
            'optimistic': {'market_growth': 1.15, 'competition_factor': 0.9},
            'base_case': {'market_growth': 1.05, 'competition_factor': 1.0},
            'pessimistic': {'market_growth': 0.95, 'competition_factor': 1.1},
            'crisis': {'market_growth': 0.85, 'competition_factor': 1.25}
        }

        scenario_results = {}

        for scenario_name, scenario_params in scenarios.items():
            # Apply scenario parameters to model
            scenario_model_params = self.model_params.copy()
            for param, multiplier in scenario_params.items():
                if param in scenario_model_params:
                    scenario_model_params[param] *= multiplier

            # Generate scenario projections
            scenario_projection = self.apply_growth_model(24, scenario_model_params)

            scenario_results[scenario_name] = {
                'final_revenue': scenario_projection[-1],
                'growth_rate': (scenario_projection[-1] / scenario_projection[0]) ** (1/2) - 1,
                'viability': scenario_projection[-1] > self.historical_data['revenue'][-1] * 1.1
            }

        self.validation_results['scenario_stress_test'] = scenario_results
        return scenario_results

# Example usage
revenue_validator = RevenueModelValidator(
    historical_data={'revenue': [1000, 1100, 1250, 1400, 1600]},
    model_parameters={'base_growth_rate': 0.12, 'market_factor': 1.05, 'competition_factor': 1.0}
)

validation_results = revenue_validator.validate_growth_model()
print(f"Model Validation Results: {validation_results}")

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class MarketPenetrationValidator:
    def __init__(self, market_data, penetration_model):
        self.market_data = market_data
        self.penetration_model = penetration_model
        self.validation_metrics = {}

    def validate_penetration_model(self):
        """
        Validate market penetration model using Bass diffusion model validation
        """
        # Historical market penetration data
        historical_penetration = self.market_data['penetration_rate']
        time_periods = range(len(historical_penetration))

        # Apply Bass diffusion model
        predicted_penetration = self.bass_diffusion_model(time_periods)

        # Calculate goodness of fit
        rmse = np.sqrt(np.mean((historical_penetration - predicted_penetration) ** 2))
        mape = np.mean(np.abs((historical_penetration - predicted_penetration) / historical_penetration))

        # Chi-square goodness of fit test
        chi2_stat, p_value = stats.chisquare(historical_penetration, predicted_penetration)

        self.validation_metrics['penetration_model'] = {
            'rmse': rmse,
            'mape': mape,
            'chi_square_p_value': p_value,
            'model_valid': p_value > 0.05 and mape < 0.20
        }

        return self.validation_metrics

    def competitive_response_simulation(self):
        """
        Simulate competitive responses and validate market share assumptions
        """
        competitive_scenarios = {
            'no_response': {'competitor_response_rate': 0.0},
            'moderate_response': {'competitor_response_rate': 0.3},
            'aggressive_response': {'competitor_response_rate': 0.7}
        }

        response_results = {}

        for scenario, params in competitive_scenarios.items():
            # Adjust penetration model for competitive response
            adjusted_penetration = self.apply_competitive_pressure(params['competitor_response_rate'])

            # Calculate market share retention
            market_share_retention = np.mean(adjusted_penetration) / np.mean(self.market_data['penetration_rate'])

            response_results[scenario] = {
                'market_share_retention': market_share_retention,
                'final_penetration': adjusted_penetration[-1],
                'sustainable': market_share_retention > 0.75
            }

        self.validation_metrics['competitive_response'] = response_results
        return response_results

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import cvxpy as cp
from scipy.optimize import minimize
import warnings

class CapitalAllocationValidator:
    def __init__(self, investment_data, allocation_model):
        self.investment_data = investment_data
        self.allocation_model = allocation_model
        self.validation_results = {}

    def validate_portfolio_optimization(self):
        """
        Validate capital allocation optimization model
        """
        # Historical investment returns
        historical_returns = self.investment_data['historical_returns']

        # Expected returns and covariance matrix
        expected_returns = np.mean(historical_returns, axis=0)
        cov_matrix = np.cov(historical_returns.T)

        # Validate optimization model
        n_assets = len(expected_returns)
        weights = cp.Variable(n_assets)

        # Optimization constraints
        constraints = [
            cp.sum(weights) == 1,
            weights >= 0,
            weights <= self.allocation_model['max_position_size']
        ]

        # Risk-adjusted return optimization
        portfolio_return = expected_returns.T @ weights
        portfolio_risk = cp.quad_form(weights, cov_matrix)

        # Sharpe ratio maximization (approximate)
        risk_free_rate = self.allocation_model['risk_free_rate']
        objective = cp.Maximize(portfolio_return - 0.5 * self.allocation_model['risk_aversion'] * portfolio_risk)

        problem = cp.Problem(objective, constraints)

        try:
            problem.solve()

            if weights.value is not None:
                optimal_weights = weights.value
                expected_return = expected_returns @ optimal_weights
                expected_risk = np.sqrt(optimal_weights @ cov_matrix @ optimal_weights)
                sharpe_ratio = (expected_return - risk_free_rate) / expected_risk

                self.validation_results['portfolio_optimization'] = {
                    'optimization_successful': True,
                    'expected_annual_return': expected_return,
                    'expected_annual_risk': expected_risk,
                    'sharpe_ratio': sharpe_ratio,
                    'diversification_ratio': 1 / np.sum(optimal_weights ** 2),
                    'model_viable': sharpe_ratio > 0.5 and expected_return > risk_free_rate * 1.5
                }
            else:
                self.validation_results['portfolio_optimization'] = {
                    'optimization_successful': False,
                    'error': 'Optimization failed to find solution'
                }

        except Exception as e:
            self.validation_results['portfolio_optimization'] = {
                'optimization_successful': False,
                'error': str(e)
            }

        return self.validation_results

    def stress_test_allocations(self):
        """
        Stress test capital allocations under different market conditions
        """
        stress_scenarios = {
            'market_crash': {'equity_shock': -0.35, 'bond_shock': -0.10, 'alternative_shock': -0.20},
            'inflation_spike': {'equity_shock': -0.15, 'bond_shock': -0.25, 'alternative_shock': 0.05},
            'liquidity_crisis': {'equity_shock': -0.20, 'bond_shock': -0.05, 'alternative_shock': -0.40},
            'growth_recession': {'equity_shock': -0.25, 'bond_shock': 0.10, 'alternative_shock': -0.15}
        }

        stress_results = {}
        base_allocation = self.allocation_model['target_allocation']

        for scenario_name, shocks in stress_scenarios.items():
            # Apply stress shocks to expected returns
            stressed_returns = []
            for i, asset_class in enumerate(['equity', 'bond', 'alternative']):
                if asset_class in shocks:
                    shock = shocks[asset_class + '_shock']
                    stressed_return = self.investment_data['expected_returns'][i] * (1 + shock)
                    stressed_returns.append(stressed_return)
                else:
                    stressed_returns.append(self.investment_data['expected_returns'][i])

            # Calculate portfolio performance under stress
            portfolio_stressed_return = np.array(base_allocation) @ np.array(stressed_returns)

            stress_results[scenario_name] = {
                'stressed_portfolio_return': portfolio_stressed_return,
                'return_vs_base': portfolio_stressed_return / (np.array(base_allocation) @ self.investment_data['expected_returns']),
                'survival_rating': 'viable' if portfolio_stressed_return > -0.15 else 'at_risk'
            }

        self.validation_results['stress_testing'] = stress_results
        return stress_results

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class OperationalValidator:
    def __init__(self, team_data, requirements_framework):
        self.team_data = team_data
        self.requirements = requirements_framework
        self.validation_results = {}

    def assess_team_capability(self):
        """
        Assess team capability to implement strategic requirements
        """
        required_skills = self.requirements['required_capabilities']
        current_skills = self.team_data['team_skills']

        capability_gaps = []
        capability_scores = {}

        for skill_area, required_level in required_skills.items():
            current_level = current_skills.get(skill_area, 0)
            gap = max(0, required_level - current_level)

            capability_gaps.append({
                'skill_area': skill_area,
                'required_level': required_level,
                'current_level': current_level,
                'gap': gap,
                'critical': gap > 2  # Significant gap if difference > 2 levels
            })

            # Calculate capability score (0-1 scale)
            capability_scores[skill_area] = min(1.0, current_level / required_level) if required_level > 0 else 1.0

        # Overall capability readiness
        overall_readiness = np.mean(list(capability_scores.values()))
        critical_gaps = sum(1 for gap in capability_gaps if gap['critical'])

        self.validation_results['team_capability'] = {
            'overall_readiness_score': overall_readiness,
            'critical_skill_gaps': critical_gaps,
            'capability_gaps': capability_gaps,
            'readiness_level': self.categorize_readiness(overall_readiness, critical_gaps)
        }

        return self.validation_results['team_capability']

    def validate_resource_allocation(self):
        """
        Validate resource allocation and availability for implementation
        """
        required_resources = self.requirements['resource_requirements']
        available_resources = self.team_data['available_resources']

        resource_utilization = {}
        resource_conflicts = []

        for resource_type, required_amount in required_resources.items():
            available_amount = available_resources.get(resource_type, 0)
            utilization_rate = required_amount / available_amount if available_amount > 0 else float('inf')

            resource_utilization[resource_type] = {
                'required': required_amount,
                'available': available_amount,
                'utilization_rate': utilization_rate,
                'over_allocated': utilization_rate > 0.85,  # Flag if >85% utilization
                'feasible': utilization_rate <= 1.0
            }

            if utilization_rate > 1.0:
                resource_conflicts.append({
                    'resource_type': resource_type,
                    'shortage': required_amount - available_amount,
                    'severity': 'critical' if utilization_rate > 1.5 else 'moderate'
                })

        # Overall resource feasibility
        feasibility_score = np.mean([
            1.0 if res['feasible'] else max(0.0, 2.0 - res['utilization_rate'])
            for res in resource_utilization.values()
        ])

        self.validation_results['resource_allocation'] = {
            'feasibility_score': feasibility_score,
            'resource_utilization': resource_utilization,
            'resource_conflicts': resource_conflicts,
            'allocation_viable': len(resource_conflicts) == 0 and feasibility_score > 0.8
        }

        return self.validation_results['resource_allocation']

    def categorize_readiness(self, overall_score, critical_gaps):
        """Categorize team readiness level"""
        if overall_score >= 0.9 and critical_gaps == 0:
            return "ready"
        elif overall_score >= 0.7 and critical_gaps <= 1:
            return "mostly_ready"
        elif overall_score >= 0.5 and critical_gaps <= 2:
            return "partially_ready"
        else:
            return "not_ready"

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import pandas as pd
from datetime import datetime, timedelta
import numpy as np

class PilotTestingFramework:
    def __init__(self, pilot_config, measurement_framework):
        self.pilot_config = pilot_config
        self.measurement_framework = measurement_framework
        self.pilot_results = {}
        self.pilot_timeline = self.generate_pilot_timeline()

    def execute_pilot_measurement_cycle(self):
        """
        Execute comprehensive pilot measurement and validation cycle
        """
        # Weekly measurement collection
        weekly_measurements = []

        for week in range(self.pilot_config['duration_weeks']):
            week_measurement = self.collect_weekly_measurements(week)
            weekly_measurements.append(week_measurement)

            # Real-time assessment and adjustment
            if week >= 2:  # Allow 2 weeks for baseline
                self.assess_pilot_performance(weekly_measurements)
                self.implement_rapid_adjustments(week_measurement)

        # Final pilot assessment
        final_assessment = self.conduct_final_pilot_assessment(weekly_measurements)
        return final_assessment

    def collect_weekly_measurements(self, week_number):
        """
        Collect comprehensive weekly pilot measurements
        """
        # Quantitative measurements
        revenue_metrics = self.measure_revenue_performance(week_number)
        efficiency_metrics = self.measure_efficiency_performance(week_number)
        resource_metrics = self.measure_resource_utilization(week_number)

        # Qualitative assessments
        team_confidence = self.assess_team_confidence(week_number)
        stakeholder_satisfaction = self.assess_stakeholder_satisfaction(week_number)
        cultural_alignment = self.assess_cultural_alignment(week_number)

        # Process effectiveness
        decision_speed = self.measure_decision_making_speed(week_number)
        communication_effectiveness = self.measure_communication_effectiveness(week_number)

        week_measurement = {
            'week': week_number,
            'timestamp': datetime.now(),
            'quantitative_metrics': {
                'revenue': revenue_metrics,
                'efficiency': efficiency_metrics,
                'resources': resource_metrics
            },
            'qualitative_metrics': {
                'team_confidence': team_confidence,
                'stakeholder_satisfaction': stakeholder_satisfaction,
                'cultural_alignment': cultural_alignment
            },
            'process_metrics': {
                'decision_speed': decision_speed,
                'communication_effectiveness': communication_effectiveness
            }
        }

        return week_measurement

    def assess_pilot_performance(self, weekly_measurements):
        """
        Assess pilot performance and identify early success/failure indicators
        """
        recent_weeks = weekly_measurements[-3:]  # Last 3 weeks

        # Trend analysis
        trends = {}
        for metric_category in ['quantitative_metrics', 'qualitative_metrics', 'process_metrics']:
            category_trends = {}
            for metric_name in recent_weeks[0][metric_category].keys():
                values = [week[metric_category][metric_name] for week in recent_weeks]

                # Calculate trend direction and magnitude
                trend_slope = np.polyfit(range(len(values)), values, 1)[0]
                trend_direction = 'improving' if trend_slope > 0.05 else 'declining' if trend_slope < -0.05 else 'stable'

                category_trends[metric_name] = {
                    'trend_direction': trend_direction,
                    'trend_magnitude': abs(trend_slope),
                    'current_value': values[-1],
                    'performance_rating': self.rate_performance(values[-1], metric_name)
                }

            trends[metric_category] = category_trends

        # Early success/failure detection
        success_indicators = self.detect_success_indicators(trends)
        failure_indicators = self.detect_failure_indicators(trends)

        pilot_status = self.determine_pilot_status(success_indicators, failure_indicators)

        self.pilot_results[f'week_{len(weekly_measurements)}_assessment'] = {
            'trends': trends,
            'success_indicators': success_indicators,
            'failure_indicators': failure_indicators,
            'pilot_status': pilot_status,
            'recommendations': self.generate_recommendations(pilot_status, trends)
        }

        return self.pilot_results[f'week_{len(weekly_measurements)}_assessment']

    def conduct_final_pilot_assessment(self, weekly_measurements):
        """
        Conduct comprehensive final pilot assessment
        """
        # Calculate overall achievement rates
        target_achievement = self.calculate_target_achievement_rates(weekly_measurements)
        resource_efficiency = self.calculate_resource_efficiency(weekly_measurements)
        stakeholder_satisfaction = self.calculate_stakeholder_satisfaction(weekly_measurements)

        # Scalability assessment
        scalability_assessment = self.assess_scalability_potential(weekly_measurements)

        # Implementation readiness evaluation
        implementation_readiness = self.evaluate_implementation_readiness(
            target_achievement, resource_efficiency, stakeholder_satisfaction, scalability_assessment
        )

        # Final recommendations
        final_recommendations = self.generate_final_recommendations(implementation_readiness)

        final_assessment = {
            'pilot_duration': len(weekly_measurements),
            'target_achievement': target_achievement,
            'resource_efficiency': resource_efficiency,
            'stakeholder_satisfaction': stakeholder_satisfaction,
            'scalability_assessment': scalability_assessment,
            'implementation_readiness': implementation_readiness,
            'final_recommendations': final_recommendations,
            'full_implementation_approval': implementation_readiness['overall_readiness_score'] > 0.8
        }

        self.pilot_results['final_assessment'] = final_assessment
        return final_assessment

    def calculate_target_achievement_rates(self, weekly_measurements):
        """Calculate overall target achievement across pilot period"""
        final_week = weekly_measurements[-1]

        achievement_rates = {}
        for metric_name in ['revenue', 'efficiency', 'quality', 'customer_satisfaction']:
            if metric_name in final_week['quantitative_metrics']:
                target = self.pilot_config['targets'][metric_name]
                actual = final_week['quantitative_metrics'][metric_name]
                achievement_rates[metric_name] = actual / target if target > 0 else 1.0

        overall_achievement_rate = np.mean(list(achievement_rates.values()))

        return {
            'individual_achievement_rates': achievement_rates,
            'overall_achievement_rate': overall_achievement_rate,
            'achievement_rating': self.rate_achievement(overall_achievement_rate)
        }

    def rate_achievement(self, achievement_rate):
        """Rate achievement level based on achievement rate"""
        if achievement_rate >= 0.9:
            return 'excellent'
        elif achievement_rate >= 0.8:
            return 'good'
        elif achievement_rate >= 0.7:
            return 'acceptable'
        elif achievement_rate >= 0.6:
            return 'concerning'
        else:
            return 'poor'