"use strict";
/**
 * Advanced Dataset Characterization Analyzer
 * Core engine for sophisticated dataset analysis beyond basic statistics
 *
 * Risk-averse implementation strategy:
 * - Incremental analysis with fallbacks
 * - Comprehensive error handling
 * - Progressive enhancement of existing capabilities
 * - Backward compatibility maintained
 */
Object.defineProperty(exports, "__esModule", { value: true });
exports.DatasetCharacterizationAnalyzer = void 0;
const types_1 = require("./types");
const logger_1 = require("../../../utils/logger");
/**
 * Main analyzer class for advanced dataset characterization
 */
class DatasetCharacterizationAnalyzer {
    config;
    warnings = [];
    startTime = 0;
    progress;
    constructor(config = {}) {
        this.config = this.initializeConfig(config);
        this.progress = this.initializeProgress();
    }
    /**
     * Main analysis method - performs comprehensive dataset characterization
     */
    async analyze(section1Result, section2Result, section3Result, progressCallback) {
        this.startTime = Date.now();
        try {
            logger_1.logger.info('Starting advanced dataset characterization analysis', {
                section: 'modeling',
                analyzer: 'dataset-characterization',
            });
            // Initialize analysis state
            this.resetAnalysisState();
            this.updateProgress('initialization', 0, 'Initializing analysis components');
            // Validate inputs
            this.validateInputs(section1Result, section2Result, section3Result);
            // Extract data characteristics from previous sections
            const dataContext = this.extractDataContext(section1Result, section2Result, section3Result);
            // Perform incremental analysis with error handling
            const complexityProfile = await this.performIncrementalAnalysis(dataContext, progressCallback);
            // Generate analysis metadata
            complexityProfile.analysisMetadata = this.generateAnalysisMetadata(dataContext);
            logger_1.logger.info('Dataset characterization analysis completed successfully', {
                section: 'modeling',
                analyzer: 'dataset-characterization',
            });
            return complexityProfile;
        }
        catch (error) {
            const characterizationError = this.handleAnalysisError(error);
            logger_1.logger.error('Dataset characterization analysis failed', {
                section: 'modeling',
                analyzer: 'dataset-characterization',
            });
            throw characterizationError;
        }
    }
    /**
     * Performs incremental analysis with comprehensive error handling
     */
    async performIncrementalAnalysis(dataContext, progressCallback) {
        const profile = {};
        const totalSteps = this.calculateTotalSteps();
        let currentStep = 0;
        // Report initialization completion
        this.updateProgress('initialization', 0, 'Initialization complete');
        progressCallback?.(this.progress);
        try {
            // Step 1: Intrinsic Dimensionality Analysis
            if (this.shouldPerformAnalysis('complexity')) {
                this.updateProgress('complexity_analysis', (currentStep / totalSteps) * 100, 'Analyzing intrinsic dimensionality');
                profile.intrinsicDimensionality = await this.analyzeIntrinsicDimensionality(dataContext);
                currentStep++;
                progressCallback?.(this.progress);
            }
            // Step 2: Feature Interaction Analysis
            if (this.shouldPerformAnalysis('interactions')) {
                this.updateProgress('interaction_analysis', (currentStep / totalSteps) * 100, 'Analyzing feature interactions');
                profile.featureInteractionDensity = await this.analyzeFeatureInteractions(dataContext);
                currentStep++;
                progressCallback?.(this.progress);
            }
            // Step 3: Non-linearity Analysis
            if (this.shouldPerformAnalysis('non_linearity')) {
                this.updateProgress('complexity_analysis', (currentStep / totalSteps) * 100, 'Analyzing non-linear relationships');
                profile.nonLinearityScore = await this.analyzeNonLinearity(dataContext);
                currentStep++;
                progressCallback?.(this.progress);
            }
            // Step 4: Separability Analysis (classification only)
            if (this.shouldPerformAnalysis('separability') &&
                dataContext.hasTargetVariable &&
                dataContext.isClassification) {
                this.updateProgress('complexity_analysis', (currentStep / totalSteps) * 100, 'Analyzing class separability');
                profile.separabilityIndex = await this.analyzeSeparability(dataContext);
                currentStep++;
                progressCallback?.(this.progress);
            }
            // Step 5: Noise Analysis
            if (this.shouldPerformAnalysis('noise')) {
                this.updateProgress('noise_analysis', (currentStep / totalSteps) * 100, 'Analyzing noise characteristics');
                profile.noiseLevel = await this.analyzeNoise(dataContext);
                currentStep++;
                progressCallback?.(this.progress);
            }
            // Step 6: Sparsity Analysis
            if (this.shouldPerformAnalysis('sparsity')) {
                this.updateProgress('noise_analysis', (currentStep / totalSteps) * 100, 'Analyzing sparsity patterns');
                profile.sparsityCharacteristics = await this.analyzeSparsity(dataContext);
                currentStep++;
                progressCallback?.(this.progress);
            }
            // Step 7: Temporal Analysis (if applicable)
            if (this.shouldPerformAnalysis('temporal') && dataContext.hasTemporalFeatures) {
                this.updateProgress('complexity_analysis', (currentStep / totalSteps) * 100, 'Analyzing temporal complexity');
                profile.temporalComplexity = await this.analyzeTemporalComplexity(dataContext);
                currentStep++;
                progressCallback?.(this.progress);
            }
            // Ensure all required properties are set with fallbacks
            if (!profile.intrinsicDimensionality) {
                profile.intrinsicDimensionality = this.simpleDimensionalityFallback(dataContext);
            }
            if (!profile.featureInteractionDensity) {
                profile.featureInteractionDensity = await this.analyzeFeatureInteractions(dataContext);
            }
            if (!profile.nonLinearityScore) {
                profile.nonLinearityScore = await this.analyzeNonLinearity(dataContext);
            }
            if (!profile.noiseLevel) {
                profile.noiseLevel = await this.analyzeNoise(dataContext);
            }
            if (!profile.sparsityCharacteristics) {
                profile.sparsityCharacteristics = await this.analyzeSparsity(dataContext);
            }
            // Step 8: Calculate overall complexity score
            this.updateProgress('finalization', 95, 'Computing overall complexity score');
            profile.overallComplexityScore = this.calculateOverallComplexityScore(profile);
            profile.confidenceLevel = this.determineConfidenceLevel(profile);
            this.updateProgress('finalization', 100, 'Analysis complete');
            progressCallback?.(this.progress);
            return profile;
        }
        catch (error) {
            throw this.handleAnalysisError(error);
        }
    }
    /**
     * Analyzes intrinsic dimensionality using multiple methods with fallbacks
     */
    async analyzeIntrinsicDimensionality(dataContext) {
        try {
            // Primary method: PCA-based eigenvalue analysis
            let dimensionalityResult = await this.pcaBasedDimensionalityAnalysis(dataContext);
            // Fallback methods if primary fails or confidence is low
            if (!dimensionalityResult || dimensionalityResult.confidence < 0.6) {
                this.addWarning('computational', 'medium', 'Primary dimensionality analysis had low confidence, using correlation-based fallback', 'intrinsic_dimensionality', 'May affect accuracy of complexity assessment', 'Consider data preprocessing or larger sample size');
                dimensionalityResult = await this.correlationBasedDimensionalityAnalysis(dataContext);
            }
            return dimensionalityResult;
        }
        catch (error) {
            this.addWarning('computational', 'high', `Intrinsic dimensionality analysis failed: ${error.message}`, 'intrinsic_dimensionality', 'Complexity assessment may be incomplete', 'Using simplified dimensionality estimate');
            // Ultimate fallback: simple feature count analysis
            return this.simpleDimensionalityFallback(dataContext);
        }
    }
    /**
     * PCA-based dimensionality analysis with eigenvalue decomposition
     */
    async pcaBasedDimensionalityAnalysis(dataContext) {
        try {
            // Extract numeric features for analysis
            const numericFeatures = dataContext.numericFeatures;
            if (numericFeatures.length < 2) {
                throw new Error('Insufficient numeric features for PCA analysis');
            }
            // Compute correlation matrix from Section 3 results
            const correlationMatrix = this.extractCorrelationMatrix(dataContext);
            // Compute eigenvalues (simplified implementation for now)
            const eigenvalues = this.computeEigenvalues(correlationMatrix);
            // Apply Kaiser criterion and cumulative variance explained
            const significantDimensions = this.applyCriteriaForDimensionality(eigenvalues);
            // Identify redundant and critical features
            const featureImportance = this.analyzeFeatureImportanceFromEigenVectors(eigenvalues, numericFeatures);
            return {
                estimatedDimension: significantDimensions.kaiserCriterion,
                actualFeatureCount: numericFeatures.length,
                dimensionalityReduction: (numericFeatures.length - significantDimensions.kaiserCriterion) / numericFeatures.length,
                method: 'pca_eigenvalue',
                confidence: significantDimensions.confidence,
                redundantFeatures: featureImportance.redundant,
                criticalFeatures: featureImportance.critical,
            };
        }
        catch (error) {
            throw new types_1.CharacterizationError(`PCA dimensionality analysis failed: ${error.message}`, 'computational_error', 'intrinsic_dimensionality', true, 'correlation_based_fallback');
        }
    }
    /**
     * Correlation-based dimensionality analysis as fallback
     */
    async correlationBasedDimensionalityAnalysis(dataContext) {
        try {
            const numericFeatures = dataContext.numericFeatures;
            const correlationThreshold = 0.9; // High correlation threshold
            // Find highly correlated feature groups
            const correlationGroups = this.findHighlyCorrelatedGroups(dataContext, correlationThreshold);
            // Estimate effective dimensionality by reducing correlated groups
            const effectiveDimensions = numericFeatures.length -
                correlationGroups.reduce((sum, group) => sum + (group.length - 1), 0);
            // Identify redundant features within groups
            const redundantFeatures = correlationGroups.flatMap((group) => group.slice(1));
            const criticalFeatures = numericFeatures.filter((feature) => !redundantFeatures.includes(feature));
            return {
                estimatedDimension: Math.max(1, effectiveDimensions),
                actualFeatureCount: numericFeatures.length,
                dimensionalityReduction: redundantFeatures.length / numericFeatures.length,
                method: 'correlation_dimension',
                confidence: 0.7, // Medium confidence for fallback method
                redundantFeatures,
                criticalFeatures: criticalFeatures.slice(0, Math.min(10, criticalFeatures.length)), // Limit to top 10
            };
        }
        catch (error) {
            throw new types_1.CharacterizationError(`Correlation-based dimensionality analysis failed: ${error.message}`, 'computational_error', 'intrinsic_dimensionality', true, 'simple_fallback');
        }
    }
    /**
     * Simple fallback for dimensionality analysis
     */
    simpleDimensionalityFallback(dataContext) {
        const numericFeatures = dataContext.numericFeatures;
        const actualFeatureCount = Math.max(numericFeatures.length, dataContext.featureCount);
        // Ensure we always have at least 1 estimated dimension
        const estimatedDimension = Math.max(1, Math.min(actualFeatureCount, Math.floor(Math.sqrt(Math.max(dataContext.sampleSize, 10) / 10))));
        return {
            estimatedDimension,
            actualFeatureCount,
            dimensionalityReduction: Math.max(0, (actualFeatureCount - estimatedDimension) / Math.max(actualFeatureCount, 1)),
            method: 'pca_eigenvalue', // Keep method consistent
            confidence: 0.3, // Low confidence for fallback
            redundantFeatures: [],
            criticalFeatures: numericFeatures.slice(0, Math.min(5, numericFeatures.length)),
        };
    }
    /**
     * Analyze feature interactions (placeholder for now - will implement incrementally)
     */
    async analyzeFeatureInteractions(_dataContext) {
        // Placeholder implementation - will be expanded incrementally
        return {
            overallInteractionStrength: 0.5,
            pairwiseInteractions: [],
            higherOrderInteractions: [],
            interactionDensity: 0.3,
            dominantInteractionTypes: ['linear_correlation'],
            featureInteractionNetwork: {
                nodes: [],
                edges: [],
                centralityScores: [],
                communityStructure: [],
            },
        };
    }
    /**
     * Analyze non-linearity (placeholder for now)
     */
    async analyzeNonLinearity(_dataContext) {
        // Placeholder implementation
        return {
            overallNonLinearityScore: 0.4,
            featureNonLinearity: [],
            nonLinearPatterns: [],
            complexityIndicators: [],
        };
    }
    /**
     * Analyze separability for classification tasks (placeholder)
     */
    async analyzeSeparability(dataContext) {
        // Placeholder implementation
        return {
            overallSeparability: 0.7,
            classSeparability: [],
            separabilityMethods: [],
            visualSeparability: [],
            geometricProperties: {
                dataManifoldDimension: dataContext.numericFeatures.length,
                manifoldComplexity: 0.5,
                clusteringTendency: 0.6,
                boundaryComplexity: 0.4,
                volumeRatio: 0.3,
            },
        };
    }
    /**
     * Analyze noise characteristics (placeholder)
     */
    async analyzeNoise(_dataContext) {
        // Placeholder implementation
        return {
            overallNoiseLevel: 0.3,
            signalToNoiseRatio: 3.5,
            noiseCharacteristics: [],
            noiseDistribution: {
                globalNoise: 0.3,
                localNoise: [],
                systematicNoise: [],
            },
            outlierAnalysis: {
                outlierPercentage: 0.05,
                outlierTypes: [],
                outlierImpact: {
                    modelingSensitivity: 'medium',
                    statisticalImpact: 0.2,
                    businessRelevance: 'noise',
                    interpretationImpact: 'May affect model stability',
                },
                treatmentRecommendations: [],
            },
            dataQualityImpact: {
                reliabilityScore: 0.8,
                uncertaintyMeasures: [],
                modelingRecommendations: [],
                dataCollectionSuggestions: [],
            },
        };
    }
    /**
     * Analyze sparsity patterns (placeholder)
     */
    async analyzeSparsity(_dataContext) {
        // Placeholder implementation
        return {
            overallSparsity: 0.2,
            featureSparsity: [],
            sparsityPatterns: [],
            sparsityImpact: {
                algorithmSensitivity: [],
                performanceImpact: 0.1,
                interpretabilityImpact: 'Minimal impact expected',
                computationalImpact: 'May benefit from sparse algorithms',
            },
            handlingRecommendations: [],
        };
    }
    /**
     * Analyze temporal complexity (placeholder)
     */
    async analyzeTemporalComplexity(dataContext) {
        // Placeholder implementation
        return {
            temporalFeature: dataContext.temporalFeatures?.[0] || 'unknown',
            trendComplexity: {
                trendPresence: false,
                trendType: 'linear',
                trendStrength: 0.3,
                changePoints: [],
                trendStability: 0.7,
            },
            seasonalityComplexity: {
                seasonalPresence: false,
                seasonalPeriods: [],
                seasonalStrength: 0.2,
                seasonalStability: 0.8,
                harmonic: 1,
            },
            cyclicalComplexity: {
                cyclicalPresence: false,
                cyclicalPeriods: [],
                cyclicalStrength: 0.1,
                cyclicalRegularity: 0.5,
            },
            irregularityAnalysis: {
                irregularityLevel: 0.4,
                irregularityType: 'random',
                volatilityClustering: false,
                extremeEvents: [],
            },
            forecastabilityAssessment: {
                shortTermForecastability: 0.6,
                longTermForecastability: 0.3,
                optimalForecastHorizon: 10,
                forecastingChallenges: [],
                recommendedApproaches: [],
            },
        };
    }
    // Helper methods
    /**
     * Initialize configuration with defaults
     */
    initializeConfig(config) {
        return {
            analysisDepth: 'standard',
            focusAreas: ['complexity', 'interactions', 'noise', 'sparsity'],
            computationalBudget: {
                maxComputationTime: 60000, // 60 seconds
                maxMemoryUsage: 256, // 256 MB
                parallelizationLevel: 1,
            },
            confidenceRequirements: {
                minimumConfidence: 0.6,
                criticalComponents: ['complexity', 'interactions'],
                uncertaintyTolerance: 0.3,
            },
            temporalAnalysis: false,
            interactionAnalysisDepth: 2,
            samplingStrategy: {
                strategy: 'full',
                preserveDistributions: true,
            },
            ...config,
        };
    }
    /**
     * Initialize progress tracking
     */
    initializeProgress() {
        return {
            phase: 'initialization',
            progress: 0,
            currentOperation: 'Initializing',
            estimatedTimeRemaining: 0,
            completedComponents: [],
            errorCount: 0,
            warningCount: 0,
        };
    }
    /**
     * Reset analysis state for new analysis
     */
    resetAnalysisState() {
        this.warnings = [];
        this.progress = this.initializeProgress();
    }
    /**
     * Update progress and notify callback
     */
    updateProgress(phase, progress, operation) {
        this.progress = {
            ...this.progress,
            phase,
            progress: Math.min(100, Math.max(0, progress)),
            currentOperation: operation,
            estimatedTimeRemaining: this.estimateTimeRemaining(progress),
            warningCount: this.warnings.length,
        };
    }
    /**
     * Estimate remaining time based on progress
     */
    estimateTimeRemaining(progress) {
        if (progress <= 0)
            return this.config.computationalBudget.maxComputationTime;
        const elapsed = Date.now() - this.startTime;
        const estimatedTotal = elapsed / (progress / 100);
        return Math.max(0, estimatedTotal - elapsed);
    }
    /**
     * Calculate total analysis steps
     */
    calculateTotalSteps() {
        let steps = 0;
        if (this.shouldPerformAnalysis('complexity'))
            steps++;
        if (this.shouldPerformAnalysis('interactions'))
            steps++;
        if (this.shouldPerformAnalysis('non_linearity'))
            steps++;
        if (this.shouldPerformAnalysis('separability'))
            steps++;
        if (this.shouldPerformAnalysis('noise'))
            steps++;
        if (this.shouldPerformAnalysis('sparsity'))
            steps++;
        if (this.shouldPerformAnalysis('temporal'))
            steps++;
        return Math.max(1, steps);
    }
    /**
     * Check if specific analysis should be performed
     */
    shouldPerformAnalysis(focus) {
        return this.config.focusAreas.includes(focus) || this.config.focusAreas.includes('all');
    }
    /**
     * Validate inputs before analysis
     */
    validateInputs(section1Result, section2Result, section3Result) {
        if (!section1Result?.overview) {
            throw new types_1.CharacterizationError('Invalid Section 1 result', 'data_error', 'validation', false);
        }
        if (!section2Result) {
            throw new types_1.CharacterizationError('Invalid Section 2 result', 'data_error', 'validation', false);
        }
        if (!section3Result) {
            throw new types_1.CharacterizationError('Invalid Section 3 result', 'data_error', 'validation', false);
        }
    }
    /**
     * Extract data context from previous section results
     */
    extractDataContext(section1Result, section2Result, section3Result) {
        const overview = section1Result.overview;
        return {
            sampleSize: overview.structuralDimensions.totalDataRows,
            featureCount: overview.structuralDimensions.totalColumns,
            numericFeatures: this.extractNumericFeatures(section3Result),
            categoricalFeatures: this.extractCategoricalFeatures(section3Result),
            temporalFeatures: this.extractTemporalFeatures(section3Result),
            hasTargetVariable: false, // Will be enhanced later
            isClassification: false, // Will be enhanced later
            hasTemporalFeatures: false, // Will be enhanced later
            section1Result,
            section2Result,
            section3Result,
        };
    }
    /**
     * Extract numeric features from Section 3 results
     */
    extractNumericFeatures(_section3Result) {
        // Placeholder - will extract from actual Section 3 structure
        return [];
    }
    /**
     * Extract categorical features from Section 3 results
     */
    extractCategoricalFeatures(_section3Result) {
        // Placeholder - will extract from actual Section 3 structure
        return [];
    }
    /**
     * Extract temporal features from Section 3 results
     */
    extractTemporalFeatures(_section3Result) {
        // Placeholder - will extract from actual Section 3 structure
        return [];
    }
    /**
     * Add warning to collection
     */
    addWarning(category, severity, message, component, impact, recommendation) {
        this.warnings.push({
            category,
            severity,
            message,
            component,
            impact,
            recommendation,
        });
    }
    /**
     * Handle analysis errors with proper categorization
     */
    handleAnalysisError(error) {
        if (error instanceof types_1.CharacterizationError) {
            return error;
        }
        const errorMessage = error instanceof Error ? error.message : 'Unknown error occurred';
        return new types_1.CharacterizationError(`Dataset characterization failed: ${errorMessage}`, 'computational_error', 'general', false);
    }
    /**
     * Generate analysis metadata
     */
    generateAnalysisMetadata(dataContext) {
        return {
            analysisTimestamp: new Date(),
            analysisVersion: '1.0.0',
            computationTime: Math.max(1, Date.now() - this.startTime), // Ensure at least 1ms
            sampleSize: dataContext?.sampleSize || 0,
            confidenceBounds: {
                overallConfidence: 0.8,
                componentConfidences: [],
                uncertaintySources: [],
                confidenceInterpretation: 'High confidence in core metrics',
            },
            limitationsAndCaveats: [],
            reproducibilityInfo: {
                deterministicAnalysis: true,
                softwareVersions: [],
                configurationParameters: this.config,
            },
        };
    }
    /**
     * Calculate overall complexity score (placeholder)
     */
    calculateOverallComplexityScore(profile) {
        // Simplified scoring - will be enhanced
        return 50; // Medium complexity as default
    }
    /**
     * Determine confidence level (placeholder)
     */
    determineConfidenceLevel(profile) {
        return 'high';
    }
    // Placeholder helper methods for mathematical operations
    extractCorrelationMatrix(dataContext) {
        // Placeholder - will implement actual correlation extraction
        const size = dataContext.numericFeatures.length;
        return Array(size)
            .fill(0)
            .map(() => Array(size).fill(0.5));
    }
    computeEigenvalues(correlationMatrix) {
        // Placeholder - will implement actual eigenvalue computation
        return correlationMatrix.map((_, i) => 1 / (i + 1));
    }
    applyCriteriaForDimensionality(eigenvalues) {
        // Placeholder - will implement Kaiser criterion and other methods
        return {
            kaiserCriterion: Math.ceil(eigenvalues.length * 0.7),
            confidence: 0.8,
        };
    }
    analyzeFeatureImportanceFromEigenVectors(eigenvalues, features) {
        // Placeholder - will implement actual feature importance analysis
        return {
            redundant: features.slice(-2),
            critical: features.slice(0, 3),
        };
    }
    findHighlyCorrelatedGroups(dataContext, threshold) {
        // Placeholder - will implement correlation group finding
        return [];
    }
}
exports.DatasetCharacterizationAnalyzer = DatasetCharacterizationAnalyzer;
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