/* eslint-disable @typescript-eslint/no-unused-vars */
/**
 * Intelligent A/B Testing — AI Hypothesis Generation.
 * Pattern detection from telemetry, LLM-powered hypothesis generation, auto-suggestions.
 */

import type {
  ExperimentSuggestion,
  GeneratedHypothesis,
  HypothesisInput,
  PrimaryMetric,
  ExperimentDoc,
} from './types.js';
import { createSuggestion } from './repository.js';

// ─────────────────────────────────────────────────────────────────────────────
// Pattern Detection
// ─────────────────────────────────────────────────────────────────────────────

export interface UsagePattern {
  featureName: string;
  totalUsage: number;
  uniqueUsers: number;
  adoptionRate: number; // 0–1
  trendDirection: 'up' | 'down' | 'stable';
  trendMagnitude: number; // % change
  segmentPerformance: Record<string, number>; // platform/segment -> rate
  temporalPatterns: {
    dayOfWeek: Record<string, number>;
    hourOfDay: Record<string, number>;
  };
  funnelDropOffs?: Array<{ stage: string; dropRate: number }>;
}

export interface AnomalyDetection {
  featureName: string;
  anomalyType: 'drop' | 'spike' | 'regression' | 'segment_mismatch';
  severity: 'low' | 'medium' | 'high';
  expectedValue: number;
  actualValue: number;
  deviation: number; // standard deviations from mean
  affectedSegment?: string;
}

export interface Opportunity {
  featureName: string;
  opportunityType: 'low_adoption' | 'high_dropoff' | 'competitor_gap' | 'unused_potential';
  impactScore: number; // 0–100
  currentMetric: number;
  targetMetric: number;
  estimatedLift: number;
  supportingEvidence: string[];
}

/**
 * Analyze telemetry data for usage patterns.
 * Placeholder: In production, this queries telemetry_events container.
 */
export async function detectUsagePatterns(
  productId: string,
  featureNames: string[]
): Promise<UsagePattern[]> {
  // Placeholder implementation
  // In production: query telemetry_events with aggregation queries
  return featureNames.map(name => ({
    featureName: name,
    totalUsage: Math.floor(Math.random() * 10000) + 1000,
    uniqueUsers: Math.floor(Math.random() * 5000) + 500,
    adoptionRate: Math.random() * 0.5 + 0.1,
    trendDirection: Math.random() > 0.5 ? 'up' : 'down',
    trendMagnitude: Math.random() * 0.3,
    segmentPerformance: {
      ios: Math.random() * 0.4 + 0.1,
      android: Math.random() * 0.4 + 0.1,
      web: Math.random() * 0.4 + 0.1,
    },
    temporalPatterns: {
      dayOfWeek: { Mon: 0.2, Tue: 0.25, Wed: 0.25, Thu: 0.2, Fri: 0.1 },
      hourOfDay: { '9': 0.15, '12': 0.2, '15': 0.25, '18': 0.2, '21': 0.1 },
    },
  }));
}

/**
 * Detect anomalies in feature usage.
 */
export async function detectAnomalies(
  productId: string,
  baselineDays = 30
): Promise<AnomalyDetection[]> {
  // Placeholder: In production, compare current metrics against rolling averages
  const anomalies: AnomalyDetection[] = [];

  // Simulate anomaly detection
  const randomFeatures = ['voice_dictation', 'keyboard_shortcuts', 'cloud_sync', 'analytics_view'];
  for (const feature of randomFeatures) {
    if (Math.random() > 0.7) {
      anomalies.push({
        featureName: feature,
        anomalyType: Math.random() > 0.5 ? 'drop' : 'regression',
        severity: Math.random() > 0.5 ? 'high' : 'medium',
        expectedValue: 0.25,
        actualValue: 0.15,
        deviation: 2.5,
      });
    }
  }

  return anomalies;
}

/**
 * Identify experiment opportunities from usage patterns.
 */
export function identifyOpportunities(patterns: UsagePattern[]): Opportunity[] {
  const opportunities: Opportunity[] = [];

  for (const pattern of patterns) {
    // Low adoption opportunity
    if (pattern.adoptionRate < 0.2) {
      opportunities.push({
        featureName: pattern.featureName,
        opportunityType: 'low_adoption',
        impactScore: Math.floor((0.2 - pattern.adoptionRate) * 500),
        currentMetric: pattern.adoptionRate,
        targetMetric: 0.2,
        estimatedLift: (0.2 - pattern.adoptionRate) / pattern.adoptionRate,
        supportingEvidence: [
          `Current adoption rate is ${(pattern.adoptionRate * 100).toFixed(1)}%`,
          `Trend is ${pattern.trendDirection} by ${(pattern.trendMagnitude * 100).toFixed(1)}%`,
        ],
      });
    }

    // Segment mismatch opportunity
    const segmentRates = Object.values(pattern.segmentPerformance);
    const maxSegment = Math.max(...segmentRates);
    const minSegment = Math.min(...segmentRates);
    if (maxSegment > minSegment * 2) {
      opportunities.push({
        featureName: pattern.featureName,
        opportunityType: 'unused_potential',
        impactScore: Math.floor((maxSegment - minSegment) * 100),
        currentMetric: minSegment,
        targetMetric: maxSegment,
        estimatedLift: (maxSegment - minSegment) / minSegment,
        supportingEvidence: [
          `Best performing segment: ${(maxSegment * 100).toFixed(1)}%`,
          `Worst performing segment: ${(minSegment * 100).toFixed(1)}%`,
          `Potential to lift underperforming segments to best-in-class`,
        ],
      });
    }
  }

  return opportunities.sort((a, b) => b.impactScore - a.impactScore);
}

// ─────────────────────────────────────────────────────────────────────────────
// LLM Hypothesis Generation
// ─────────────────────────────────────────────────────────────────────────────

/**
 * Generate experiment hypothesis using LLM.
 * Placeholder: In production, this calls Azure OpenAI.
 */
export async function generateHypothesis(input: HypothesisInput): Promise<GeneratedHypothesis> {
  // Build prompt for LLM
  const prompt = buildHypothesisPrompt(input);

  // Placeholder: Simulate LLM response
  // In production: const response = await callAzureOpenAI(prompt);
  return simulateLLMHypothesis(input);
}

/**
 * Build structured prompt for hypothesis generation.
 */
function buildHypothesisPrompt(input: HypothesisInput): string {
  return `
You are an expert product analyst and experimentation strategist.

Analyze this feature usage data and generate experiment hypotheses:

Feature: ${input.featureName}
Current Adoption Rate: ${(input.adoptionRate * 100).toFixed(1)}%
Baseline Rate: ${(input.baselineRate * 100).toFixed(1)}%

Segment Performance:
${Object.entries(input.segmentData)
  .map(([segment, rate]) => `- ${segment}: ${(rate * 100).toFixed(1)}%`)
  .join('\n')}

User Feedback Samples:
${input.feedbackSamples.map(f => `- "${f}"`).join('\n')}

Generate:
1. Primary hypothesis: "Changing X will improve Y because..."
2. 2-3 alternative hypotheses
3. Expected effect size (conservative)
4. Success metric recommendation
5. Risk assessment (low/medium/high)
`;
}

/**
 * Simulate LLM hypothesis response (placeholder).
 */
function simulateLLMHypothesis(input: HypothesisInput): GeneratedHypothesis {
  const effectSize = Math.abs(input.adoptionRate - input.baselineRate) * 0.5;
  const riskLevel = effectSize > 0.1 ? 'medium' : 'low';

  return {
    primary: `Redesigning the ${input.featureName} onboarding flow to highlight key benefits will increase adoption by ${(effectSize * 100).toFixed(0)}% because users currently don't understand the value proposition.`,
    alternatives: [
      `Adding tooltips and contextual help to ${input.featureName} will reduce confusion and increase usage by ${(effectSize * 0.7 * 100).toFixed(0)}%.`,
      `Simplifying the ${input.featureName} UI to match competitor patterns will improve familiarity and adoption by ${(effectSize * 0.5 * 100).toFixed(0)}%.`,
    ],
    expectedEffectSize: Math.max(effectSize, 0.05),
    successMetric: 'adoption_rate',
    riskAssessment: riskLevel,
    impactScore: Math.floor((input.baselineRate - input.adoptionRate) * 200),
    difficultyScore: Math.floor(Math.random() * 30) + 20,
    powerPrediction: 85,
  };
}

// ─────────────────────────────────────────────────────────────────────────────
// Hypothesis Ranking
// ─────────────────────────────────────────────────────────────────────────────

export interface RankedHypothesis extends GeneratedHypothesis {
  rankScore: number;
  estimatedImpact: number;
  estimatedEffort: number;
  estimatedPower: number;
}

/**
 * Rank hypotheses by expected value.
 */
export function rankHypotheses(
  hypotheses: GeneratedHypothesis[],
  baseTraffic: number
): RankedHypothesis[] {
  return hypotheses
    .map(h => {
      // Expected value calculation
      const impact = h.impactScore;
      const effort = h.difficultyScore;
      const power = h.powerPrediction;

      // Risk-adjusted expected value
      const riskMultiplier =
        h.riskAssessment === 'low' ? 1.0 : h.riskAssessment === 'medium' ? 0.8 : 0.6;

      // Rank score: higher impact, lower effort, higher power = better
      const rankScore = (impact * power * riskMultiplier) / (effort + 10);

      return {
        ...h,
        rankScore,
        estimatedImpact: impact,
        estimatedEffort: effort,
        estimatedPower: power,
      };
    })
    .sort((a, b) => b.rankScore - a.rankScore);
}

// ─────────────────────────────────────────────────────────────────────────────
// Auto-Experiment Suggestions
// ─────────────────────────────────────────────────────────────────────────────

export interface SuggestedExperiment {
  name: string;
  hypothesis: string;
  description: string;
  variants: Array<{
    name: string;
    description: string;
    flagConfig: Record<string, unknown>;
  }>;
  primaryMetric: PrimaryMetric;
  suggestedDuration: number;
  suggestedSampleSize: number;
  priority: number;
}

/**
 * Generate complete experiment suggestion from opportunity.
 */
export function generateExperimentSuggestion(
  opportunity: Opportunity,
  hypothesis: GeneratedHypothesis,
  productId: string
): Omit<ExperimentSuggestion, 'id' | 'createdAt'> {
  const variantNames = ['Control', 'Treatment A', 'Treatment B'];
  if (hypothesis.alternatives.length < 2) {
    variantNames.pop();
  }

  const suggestedVariants = variantNames.map((name, i) => ({
    name,
    description:
      i === 0
        ? 'Current implementation (control)'
        : hypothesis.alternatives[i - 1] || `Alternative approach ${i}`,
  }));

  const sampleSize = Math.ceil(
    hypothesis.expectedEffectSize > 0 ? 16 / hypothesis.expectedEffectSize ** 2 : 1000
  );

  return {
    hypothesis,
    suggestedVariants,
    suggestedMetrics: [
      {
        name: opportunity.opportunityType === 'low_adoption' ? 'adoption_rate' : 'conversion_rate',
        type: 'conversion',
        eventName: `${opportunity.featureName}_used`,
        aggregation: 'unique',
        direction: 'increase',
        minimumDetectableEffect: hypothesis.expectedEffectSize * 100,
      } as PrimaryMetric,
    ],
    suggestedDuration: Math.max(7, Math.ceil(sampleSize / 100)),
    suggestedSampleSize: sampleSize,
    priority: opportunity.impactScore,
    aiGenerated: true,
  };
}

/**
 * Generate weekly AI report with top experiment opportunities.
 */
export async function generateWeeklyReport(productId: string): Promise<{
  topOpportunities: Opportunity[];
  suggestedExperiments: Array<Omit<ExperimentSuggestion, 'id' | 'createdAt'>>;
  anomalies: AnomalyDetection[];
  generatedAt: string;
}> {
  // Detect patterns and opportunities
  const patterns = await detectUsagePatterns(productId, [
    'voice_dictation',
    'keyboard_shortcuts',
    'cloud_sync',
    'analytics_view',
    'settings_customization',
  ]);

  const opportunities = identifyOpportunities(patterns);
  const anomalies = await detectAnomalies(productId);

  // Generate hypotheses for top opportunities
  const topOpportunities = opportunities.slice(0, 5);
  const suggestedExperiments: Array<Omit<ExperimentSuggestion, 'id' | 'createdAt'>> = [];

  for (const opp of topOpportunities) {
    const hypothesisInput: HypothesisInput = {
      featureName: opp.featureName,
      adoptionRate: opp.currentMetric,
      baselineRate: opp.targetMetric,
      segmentData: {},
      feedbackSamples: opp.supportingEvidence,
    };

    const hypothesis = await generateHypothesis(hypothesisInput);
    const suggestion = generateExperimentSuggestion(opp, hypothesis, productId);
    suggestedExperiments.push(suggestion);
  }

  return {
    topOpportunities,
    suggestedExperiments,
    anomalies,
    generatedAt: new Date().toISOString(),
  };
}

// ─────────────────────────────────────────────────────────────────────────────
// AI Insights for Results
// ─────────────────────────────────────────────────────────────────────────────

export interface ExperimentInsights {
  summary: string;
  unexpectedFindings: string[];
  segmentAnalysis: Record<string, string>;
  followUpSuggestions: Array<{
    experimentName: string;
    hypothesis: string;
    priority: number;
  }>;
}

/**
 * Generate AI insights for completed experiment.
 * Placeholder: In production, calls Azure OpenAI with full experiment data.
 */
export function generateExperimentInsights(
  experiment: ExperimentDoc,
  result: {
    variantResults: Array<{
      variantName: string;
      probabilityBeatsControl: number;
      expectedLiftPercent: number;
    }>;
  },
  winnerVariantId?: string
): ExperimentInsights {
  const winner = result.variantResults.find(v => v.probabilityBeatsControl > 0.95);
  const loser = result.variantResults.find(v => v.probabilityBeatsControl < 0.05);

  const insights: ExperimentInsights = {
    summary: '',
    unexpectedFindings: [],
    segmentAnalysis: {},
    followUpSuggestions: [],
  };

  if (winner) {
    insights.summary = `The ${winner.variantName} variant significantly outperformed control with ${winner.probabilityBeatsControl.toFixed(1)}% probability of superiority and a ${winner.expectedLiftPercent.toFixed(1)}% lift. This validates the hypothesis that ${experiment.hypothesis}`;
  } else if (loser) {
    insights.summary = `The experiment did not produce a winning variant. The control performed better than expected, suggesting the current implementation is already optimized for this metric.`;
  } else {
    insights.summary = `Results were inconclusive. No variant demonstrated statistically significant improvement over control. Consider running the experiment longer or testing more distinct variations.`;
  }

  // Add unexpected findings based on result patterns
  const allPositive = result.variantResults.every(v => v.expectedLiftPercent > 0);
  const allNegative = result.variantResults.every(v => v.expectedLiftPercent < 0);

  if (allNegative) {
    insights.unexpectedFindings.push(
      'All variants underperformed control, suggesting possible confounding factors or measurement issues.'
    );
  }

  if (Math.abs(result.variantResults[0]?.expectedLiftPercent || 0) > 50) {
    insights.unexpectedFindings.push(
      'Extremely large effect size detected. Verify data quality and consider running a validation experiment.'
    );
  }

  // Generate follow-up suggestions
  if (winner) {
    insights.followUpSuggestions.push({
      experimentName: `${experiment.name} - Follow-up Validation`,
      hypothesis: `Validating the ${winner.variantName} win in a new user cohort to confirm generalizability`,
      priority: 90,
    });
  }

  insights.followUpSuggestions.push({
    experimentName: `${experiment.name} - Secondary Metrics`,
    hypothesis: `Testing the winning variant against additional success metrics to ensure no trade-offs`,
    priority: 70,
  });

  return insights;
}
