"use strict";
/**
 * Unsupervised Learning Analyzer - Enhanced unsupervised learning and synthetic target generation
 * Addresses GitHub issue #22: Never return "0 modeling tasks" - always find opportunities
 */
Object.defineProperty(exports, "__esModule", { value: true });
exports.UnsupervisedAnalyzer = void 0;
class UnsupervisedAnalyzer {
    /**
     * Generate comprehensive unsupervised learning recommendations when no obvious targets exist
     */
    async analyzeUnsupervisedOpportunities(section1Result, section2Result, section3Result, section5Result) {
        // Use EDA column analysis for detailed type information, fall back to basic inventory
        const edaUnivariate = section3Result.edaAnalysis?.univariateAnalysis;
        let edaColumns = [];
        // Handle different EDA result structures (array vs object with separate arrays)
        if (Array.isArray(edaUnivariate)) {
            edaColumns = edaUnivariate;
        }
        else if (edaUnivariate && typeof edaUnivariate === 'object') {
            // Handle test mock structure with separate arrays
            const numerical = edaUnivariate.numericalAnalysis || [];
            const categorical = edaUnivariate.categoricalAnalysis || [];
            const dateTime = edaUnivariate.dateTimeAnalysis || [];
            const boolean = edaUnivariate.booleanAnalysis || [];
            const text = edaUnivariate.textAnalysis || [];
            edaColumns = [
                ...numerical.map((col) => ({ ...col, detectedDataType: 'numerical_float' })),
                ...categorical.map((col) => ({ ...col, detectedDataType: 'categorical' })),
                ...dateTime.map((col) => ({ ...col, detectedDataType: 'date_time' })),
                ...boolean.map((col) => ({ ...col, detectedDataType: 'boolean' })),
                ...text.map((col) => ({ ...col, detectedDataType: 'text_general' })),
            ];
        }
        // Fall back to basic columns if no EDA data available
        if (edaColumns.length === 0) {
            edaColumns = section1Result.overview.structuralDimensions.columnInventory.map(col => ({
                columnName: col.name,
                detectedDataType: 'categorical', // Default assumption
                uniqueValues: 10, // Default assumption
            }));
        }
        const basicColumns = section1Result.overview.structuralDimensions.columnInventory;
        const qualityScores = section2Result.qualityAudit?.cockpit?.compositeScore?.score || 0.5;
        const correlations = section3Result.edaAnalysis?.bivariateAnalysis?.numericalVsNumerical;
        // Generate all types of recommendations
        const syntheticTargets = await this.generateSyntheticTargets(edaColumns, section2Result, section3Result, section5Result);
        const unsupervisedApproaches = await this.generateUnsupervisedRecommendations(edaColumns, section2Result, section3Result);
        const fileSizeMB = section1Result.overview.fileDetails?.fileSizeMB || 1; // Safe fallback for missing fileDetails
        const autoMLRecommendations = await this.generateAutoMLRecommendations(edaColumns, qualityScores, fileSizeMB);
        const featureEngineeringRecipes = await this.generateFeatureEngineeringRecipes(edaColumns, section3Result, section5Result);
        const deploymentConsiderations = await this.generateDeploymentConsiderations(edaColumns, fileSizeMB);
        return {
            syntheticTargets,
            unsupervisedApproaches,
            autoMLRecommendations,
            featureEngineeringRecipes,
            deploymentConsiderations,
        };
    }
    /**
     * Generate synthetic target variable recommendations
     */
    async generateSyntheticTargets(columns, qualityResult, edaResult, engineeringResult) {
        const targets = [];
        // 1. Clustering-based targets
        targets.push(...this.generateClusteringBasedTargets(columns, edaResult));
        // 2. Outlier-based targets
        targets.push(...this.generateOutlierBasedTargets(columns, edaResult));
        // 3. Composite targets from feature engineering
        targets.push(...this.generateCompositeTargets(columns, edaResult));
        // 4. Temporal targets (if temporal data exists)
        targets.push(...this.generateTemporalTargets(columns, edaResult));
        // 5. Domain-derived targets
        targets.push(...this.generateDomainDerivedTargets(columns, qualityResult));
        return targets.sort((a, b) => b.feasibilityScore - a.feasibilityScore);
    }
    /**
     * Generate clustering-based synthetic targets
     */
    generateClusteringBasedTargets(columns, edaResult) {
        const targets = [];
        // Find suitable columns for clustering
        const numericalColumns = columns.filter(col => col.detectedDataType === 'numerical_float' || col.detectedDataType === 'numerical_integer');
        const categoricalColumns = columns.filter(col => col.detectedDataType === 'categorical' && col.uniqueValues <= 50);
        if (numericalColumns.length >= 2 || categoricalColumns.length >= 1) {
            targets.push({
                targetName: 'customer_segment',
                targetType: 'clustering_based',
                description: 'Customer segmentation based on behavioral and demographic features',
                businessValue: 'Enables targeted marketing campaigns, personalized product recommendations, and customer lifetime value analysis',
                technicalImplementation: 'K-Means clustering with optimal K selection using elbow method and silhouette analysis',
                sourceColumns: [...numericalColumns.slice(0, 4).map(c => c.columnName), ...categoricalColumns.slice(0, 2).map(c => c.columnName)],
                expectedCardinality: 5,
                feasibilityScore: 85,
                codeExample: this.generateClusteringCode(numericalColumns, categoricalColumns),
                validationStrategy: 'Silhouette analysis, Davies-Bouldin index, business interpretation validation',
                useCases: [
                    'Marketing campaign targeting',
                    'Product recommendation systems',
                    'Customer service optimization',
                    'Pricing strategy development',
                ],
            });
        }
        if (categoricalColumns.some(col => col.columnName.toLowerCase().includes('country'))) {
            targets.push({
                targetName: 'market_tier',
                targetType: 'clustering_based',
                description: 'Geographic market tiers based on customer density and characteristics',
                businessValue: 'Market prioritization, resource allocation, and expansion planning',
                technicalImplementation: 'Hierarchical clustering of geographic regions based on customer metrics',
                sourceColumns: ['country', ...numericalColumns.slice(0, 2).map(c => c.columnName)],
                expectedCardinality: 3,
                feasibilityScore: 75,
                codeExample: this.generateGeographicClusteringCode(),
                validationStrategy: 'Geographic coherence validation, business metric correlation',
                useCases: [
                    'Market expansion strategy',
                    'Regional sales team allocation',
                    'Localized marketing campaigns',
                    'Supply chain optimization',
                ],
            });
        }
        return targets;
    }
    /**
     * Generate outlier-based synthetic targets
     */
    generateOutlierBasedTargets(columns, edaResult) {
        const targets = [];
        const numericalColumns = columns.filter(col => col.detectedDataType === 'numerical_float' || col.detectedDataType === 'numerical_integer');
        if (numericalColumns.length >= 2) {
            targets.push({
                targetName: 'anomaly_score',
                targetType: 'outlier_based',
                description: 'Anomaly detection score for identifying unusual records',
                businessValue: 'Fraud detection, data quality monitoring, and outlier investigation',
                technicalImplementation: 'Isolation Forest algorithm to assign anomaly scores to each record',
                sourceColumns: numericalColumns.map(c => c.columnName),
                feasibilityScore: 80,
                codeExample: this.generateAnomalyDetectionCode(numericalColumns),
                validationStrategy: 'Manual inspection of high-scoring anomalies, domain expert validation',
                useCases: [
                    'Fraud detection systems',
                    'Data quality monitoring',
                    'Outlier investigation',
                    'Rare event detection',
                ],
            });
            targets.push({
                targetName: 'data_quality_flag',
                targetType: 'outlier_based',
                description: 'Binary flag indicating records with potential data quality issues',
                businessValue: 'Automated data quality assessment and cleaning prioritization',
                technicalImplementation: 'Binary classification based on anomaly score threshold',
                sourceColumns: numericalColumns.map(c => c.columnName),
                expectedCardinality: 2,
                feasibilityScore: 75,
                codeExample: this.generateDataQualityFlagCode(numericalColumns),
                validationStrategy: 'Precision/recall analysis against manually identified quality issues',
                useCases: [
                    'Automated data cleaning',
                    'Data quality dashboards',
                    'ETL pipeline monitoring',
                    'Data validation workflows',
                ],
            });
        }
        return targets;
    }
    /**
     * Generate composite synthetic targets
     */
    generateCompositeTargets(columns, edaResult) {
        const targets = [];
        // Look for email columns to create email domain quality target
        const emailColumns = columns.filter(col => col.columnName.toLowerCase().includes('email'));
        if (emailColumns.length > 0) {
            targets.push({
                targetName: 'email_domain_quality',
                targetType: 'composite',
                description: 'Classification of email domains as corporate vs personal',
                businessValue: 'Lead quality assessment, B2B vs B2C customer segmentation',
                technicalImplementation: 'Rule-based classification using domain patterns and known corporate/personal email providers',
                sourceColumns: [emailColumns[0].columnName],
                expectedCardinality: 2,
                feasibilityScore: 90,
                codeExample: this.generateEmailDomainQualityCode(emailColumns[0].columnName),
                validationStrategy: 'Manual validation against known corporate domains, accuracy testing',
                useCases: [
                    'Lead scoring systems',
                    'B2B marketing targeting',
                    'Customer acquisition cost optimization',
                    'Sales pipeline qualification',
                ],
            });
        }
        // Look for name columns to create completeness score
        const nameColumns = columns.filter(col => col.columnName.toLowerCase().includes('name') ||
            col.columnName.toLowerCase().includes('first') ||
            col.columnName.toLowerCase().includes('last'));
        if (nameColumns.length > 0) {
            targets.push({
                targetName: 'profile_completeness_score',
                targetType: 'composite',
                description: 'Percentage score of profile completeness based on filled important fields',
                businessValue: 'User engagement optimization, onboarding funnel analysis',
                technicalImplementation: 'Weighted percentage of non-null values across important profile fields',
                sourceColumns: columns.slice(0, 8).map(c => c.columnName),
                feasibilityScore: 85,
                codeExample: this.generateProfileCompletenessCode(columns),
                validationStrategy: 'Correlation analysis with user engagement metrics',
                useCases: [
                    'User onboarding optimization',
                    'Engagement prediction models',
                    'Profile completion campaigns',
                    'Data collection prioritization',
                ],
            });
        }
        return targets;
    }
    /**
     * Generate temporal synthetic targets
     */
    generateTemporalTargets(columns, edaResult) {
        const targets = [];
        const dateColumns = columns.filter(col => col.detectedDataType === 'date_time' ||
            col.columnName.toLowerCase().includes('date') ||
            col.columnName.toLowerCase().includes('time'));
        if (dateColumns.length > 0) {
            targets.push({
                targetName: 'customer_lifetime_days',
                targetType: 'temporal',
                description: 'Days since customer first interaction or subscription',
                businessValue: 'Customer lifetime value analysis, churn prediction, retention modeling',
                technicalImplementation: 'Calculate days between first recorded date and reference date',
                sourceColumns: [dateColumns[0].columnName],
                feasibilityScore: 80,
                codeExample: this.generateCustomerLifetimeCode(dateColumns[0].columnName),
                validationStrategy: 'Business logic validation, outlier analysis',
                useCases: [
                    'Customer lifetime value models',
                    'Churn prediction systems',
                    'Retention campaign targeting',
                    'Customer journey analysis',
                ],
            });
            targets.push({
                targetName: 'subscription_quarter',
                targetType: 'temporal',
                description: 'Quarter of year when customer subscribed (seasonal analysis)',
                businessValue: 'Seasonal trend analysis, marketing campaign timing optimization',
                technicalImplementation: 'Extract quarter from subscription date',
                sourceColumns: [dateColumns[0].columnName],
                expectedCardinality: 4,
                feasibilityScore: 75,
                codeExample: this.generateSeasonalityCode(dateColumns[0].columnName),
                validationStrategy: 'Seasonal pattern validation, business cycle correlation',
                useCases: [
                    'Seasonal marketing campaigns',
                    'Resource planning',
                    'Budget allocation',
                    'Trend analysis',
                ],
            });
        }
        return targets;
    }
    /**
     * Generate domain-derived synthetic targets
     */
    generateDomainDerivedTargets(columns, qualityResult) {
        const targets = [];
        // Look for high-cardinality categorical columns that could be grouped
        const highCardinalityColumns = columns.filter(col => col.detectedDataType === 'categorical' &&
            col.uniqueValues > 20 &&
            col.uniqueValues < 1000);
        if (highCardinalityColumns.length > 0) {
            const col = highCardinalityColumns[0];
            targets.push({
                targetName: `${col.columnName}_category`,
                targetType: 'domain_derived',
                description: `Grouped categories from high-cardinality ${col.columnName} field`,
                businessValue: 'Simplify analysis by grouping related categories, enable pattern recognition',
                technicalImplementation: 'Frequency-based grouping: top N categories + "Other" group',
                sourceColumns: [col.columnName],
                expectedCardinality: Math.min(10, Math.ceil(col.uniqueValues / 10)),
                feasibilityScore: 70,
                codeExample: this.generateCategoryGroupingCode(col.columnName),
                validationStrategy: 'Business logic review of groupings, frequency distribution analysis',
                useCases: [
                    'Simplified reporting',
                    'Category-based analysis',
                    'Market segmentation',
                    'Product grouping',
                ],
            });
        }
        return targets;
    }
    /**
     * Generate enhanced unsupervised learning recommendations
     */
    async generateUnsupervisedRecommendations(columns, qualityResult, edaResult) {
        const recommendations = [];
        const numericalColumns = columns.filter(col => col.detectedDataType === 'numerical_float' || col.detectedDataType === 'numerical_integer');
        const categoricalColumns = columns.filter(col => col.detectedDataType === 'categorical');
        // 1. Clustering recommendations
        recommendations.push(...this.generateClusteringRecommendations(numericalColumns, categoricalColumns));
        // 2. Dimensionality reduction recommendations
        if (numericalColumns.length >= 4) {
            recommendations.push(...this.generateDimensionalityReductionRecommendations(numericalColumns));
        }
        // 3. Association rule mining (for categorical data)
        if (categoricalColumns.length >= 3) {
            recommendations.push(...this.generateAssociationMiningRecommendations(categoricalColumns));
        }
        // 4. Anomaly detection recommendations
        if (numericalColumns.length >= 2) {
            recommendations.push(...this.generateAnomalyDetectionRecommendations(numericalColumns));
        }
        return recommendations;
    }
    /**
     * Generate clustering recommendations
     */
    generateClusteringRecommendations(numericalColumns, categoricalColumns) {
        const recommendations = [];
        if (numericalColumns.length >= 2) {
            // K-Means recommendation
            recommendations.push({
                approach: 'clustering',
                algorithmName: 'K-Means Clustering',
                description: 'Partition data into K clusters based on feature similarity',
                businessValue: 'Customer segmentation, market analysis, behavioral grouping',
                technicalDetails: {
                    inputFeatures: numericalColumns.slice(0, 6).map(c => c.columnName),
                    preprocessing: ['StandardScaler normalization', 'Handle missing values', 'Remove outliers'],
                    hyperparameters: [
                        {
                            parameterName: 'n_clusters',
                            description: 'Number of clusters',
                            defaultValue: 5,
                            recommendedRange: '3-8 (use elbow method)',
                            tuningStrategy: 'Grid search with silhouette score',
                            importance: 'critical',
                        },
                        {
                            parameterName: 'random_state',
                            description: 'Random seed for reproducibility',
                            defaultValue: 42,
                            recommendedRange: 'Any integer',
                            tuningStrategy: 'Fixed for reproducibility',
                            importance: 'important',
                        },
                    ],
                    computationalComplexity: 'O(n * k * i * d) where n=samples, k=clusters, i=iterations, d=dimensions',
                    memoryRequirements: 'Low - scales linearly with data size',
                    optimalDataSize: '1K-1M records',
                },
                codeImplementation: this.generateKMeansImplementation(numericalColumns),
                evaluationMetrics: ['Silhouette Score', 'Davies-Bouldin Index', 'Calinski-Harabasz Index', 'Inertia'],
                interpretationGuidance: [
                    'Analyze cluster centroids to understand group characteristics',
                    'Profile each cluster with business metrics',
                    'Validate clusters make business sense',
                    'Check cluster stability with different random seeds',
                ],
                scalabilityNotes: [
                    'Use MiniBatch K-Means for datasets > 100K records',
                    'Consider feature selection for high-dimensional data',
                    'Parallel processing available for large datasets',
                ],
            });
            // DBSCAN recommendation
            recommendations.push({
                approach: 'clustering',
                algorithmName: 'DBSCAN',
                description: 'Density-based clustering that finds clusters of varying shapes and identifies outliers',
                businessValue: 'Anomaly detection, flexible cluster shapes, automatic outlier identification',
                technicalDetails: {
                    inputFeatures: numericalColumns.slice(0, 6).map(c => c.columnName),
                    preprocessing: ['StandardScaler normalization', 'Handle missing values'],
                    hyperparameters: [
                        {
                            parameterName: 'eps',
                            description: 'Maximum distance between samples in neighborhood',
                            defaultValue: 0.5,
                            recommendedRange: 'Use k-distance graph to determine',
                            tuningStrategy: 'Grid search or k-distance analysis',
                            importance: 'critical',
                        },
                        {
                            parameterName: 'min_samples',
                            description: 'Minimum samples in neighborhood to form cluster',
                            defaultValue: 5,
                            recommendedRange: '2 * dimensions',
                            tuningStrategy: 'Start with 2*dimensions, adjust based on results',
                            importance: 'critical',
                        },
                    ],
                    computationalComplexity: 'O(n log n) with spatial indexing',
                    memoryRequirements: 'Moderate - requires distance matrix',
                    optimalDataSize: '1K-100K records',
                },
                codeImplementation: this.generateDBSCANImplementation(numericalColumns),
                evaluationMetrics: ['Silhouette Score', 'Number of clusters found', 'Noise points ratio'],
                interpretationGuidance: [
                    'Noise points (label -1) are potential outliers',
                    'Clusters can have irregular shapes',
                    'No need to specify number of clusters in advance',
                ],
                scalabilityNotes: [
                    'Memory usage increases with dataset size',
                    'Consider sampling for very large datasets',
                    'Use approximate nearest neighbor algorithms for speedup',
                ],
            });
        }
        return recommendations;
    }
    /**
     * Generate dimensionality reduction recommendations
     */
    generateDimensionalityReductionRecommendations(numericalColumns) {
        const recommendations = [];
        // PCA recommendation
        recommendations.push({
            approach: 'dimensionality_reduction',
            algorithmName: 'Principal Component Analysis (PCA)',
            description: 'Linear dimensionality reduction using orthogonal transformation',
            businessValue: 'Feature reduction, visualization, noise reduction, data compression',
            technicalDetails: {
                inputFeatures: numericalColumns.map(c => c.columnName),
                preprocessing: ['StandardScaler normalization', 'Handle missing values'],
                hyperparameters: [
                    {
                        parameterName: 'n_components',
                        description: 'Number of components to keep',
                        defaultValue: 'auto',
                        recommendedRange: 'Explain 95% of variance',
                        tuningStrategy: 'Cumulative explained variance analysis',
                        importance: 'critical',
                    },
                ],
                computationalComplexity: 'O(n * d^2) where n=samples, d=dimensions',
                memoryRequirements: 'Moderate - requires covariance matrix',
                optimalDataSize: '1K-1M records',
            },
            codeImplementation: this.generatePCAImplementation(numericalColumns),
            evaluationMetrics: ['Explained Variance Ratio', 'Cumulative Variance Explained'],
            interpretationGuidance: [
                'Components are linear combinations of original features',
                'First component explains most variance',
                'Use feature loadings to interpret components',
            ],
            scalabilityNotes: [
                'Incremental PCA available for large datasets',
                'Randomized PCA for faster computation',
                'Sparse PCA for interpretable components',
            ],
        });
        // t-SNE recommendation (for visualization)
        if (numericalColumns.length >= 3) {
            recommendations.push({
                approach: 'dimensionality_reduction',
                algorithmName: 't-SNE',
                description: 'Non-linear dimensionality reduction for visualization',
                businessValue: 'Data visualization, cluster visualization, pattern discovery',
                technicalDetails: {
                    inputFeatures: numericalColumns.slice(0, 10).map(c => c.columnName),
                    preprocessing: ['StandardScaler normalization', 'Handle missing values', 'Consider PCA preprocessing'],
                    hyperparameters: [
                        {
                            parameterName: 'perplexity',
                            description: 'Balance attention between local and global aspects',
                            defaultValue: 30,
                            recommendedRange: '5-50 (depends on dataset size)',
                            tuningStrategy: 'Try multiple values, visualize results',
                            importance: 'critical',
                        },
                        {
                            parameterName: 'n_iter',
                            description: 'Number of iterations for optimization',
                            defaultValue: 1000,
                            recommendedRange: '1000-5000',
                            tuningStrategy: 'Increase until convergence',
                            importance: 'important',
                        },
                    ],
                    computationalComplexity: 'O(n^2) - computationally expensive',
                    memoryRequirements: 'High - quadratic with number of samples',
                    optimalDataSize: '100-10K records (sample larger datasets)',
                },
                codeImplementation: this.generateTSNEImplementation(numericalColumns),
                evaluationMetrics: ['Visual clustering quality', 'KL divergence', 'Trustworthiness'],
                interpretationGuidance: [
                    'Use primarily for visualization, not feature extraction',
                    'Distances in t-SNE space may not reflect original distances',
                    'Multiple runs may produce different results',
                ],
                scalabilityNotes: [
                    'Sample large datasets to <10K records',
                    'Use PCA preprocessing for high dimensions',
                    'Consider UMAP as faster alternative',
                ],
            });
        }
        return recommendations;
    }
    /**
     * Generate association mining recommendations
     */
    generateAssociationMiningRecommendations(categoricalColumns) {
        const recommendations = [];
        recommendations.push({
            approach: 'association_mining',
            algorithmName: 'Apriori Algorithm',
            description: 'Find frequent itemsets and association rules in categorical data',
            businessValue: 'Market basket analysis, recommendation systems, cross-selling opportunities',
            technicalDetails: {
                inputFeatures: categoricalColumns.filter(c => c.uniqueValues <= 100).map(c => c.columnName),
                preprocessing: ['One-hot encoding', 'Handle missing values', 'Binary transformation'],
                hyperparameters: [
                    {
                        parameterName: 'min_support',
                        description: 'Minimum support threshold for frequent itemsets',
                        defaultValue: 0.01,
                        recommendedRange: '0.001-0.1',
                        tuningStrategy: 'Start high, lower until meaningful patterns emerge',
                        importance: 'critical',
                    },
                    {
                        parameterName: 'min_confidence',
                        description: 'Minimum confidence for association rules',
                        defaultValue: 0.5,
                        recommendedRange: '0.3-0.9',
                        tuningStrategy: 'Domain-specific threshold selection',
                        importance: 'critical',
                    },
                ],
                computationalComplexity: 'Exponential in worst case, depends on data density',
                memoryRequirements: 'High - stores all frequent itemsets',
                optimalDataSize: '1K-100K transactions',
            },
            codeImplementation: this.generateAprioriImplementation(categoricalColumns),
            evaluationMetrics: ['Support', 'Confidence', 'Lift', 'Conviction'],
            interpretationGuidance: [
                'High lift values indicate strong associations',
                'Support shows frequency of itemset occurrence',
                'Confidence measures rule reliability',
            ],
            scalabilityNotes: [
                'Use FP-Growth for larger datasets',
                'Sample data if memory constraints exist',
                'Filter rare items to improve performance',
            ],
        });
        return recommendations;
    }
    /**
     * Generate anomaly detection recommendations
     */
    generateAnomalyDetectionRecommendations(numericalColumns) {
        const recommendations = [];
        recommendations.push({
            approach: 'anomaly_detection',
            algorithmName: 'Isolation Forest',
            description: 'Unsupervised anomaly detection using random forest principles',
            businessValue: 'Fraud detection, quality control, outlier identification, system monitoring',
            technicalDetails: {
                inputFeatures: numericalColumns.map(c => c.columnName),
                preprocessing: ['Handle missing values', 'Optional: StandardScaler'],
                hyperparameters: [
                    {
                        parameterName: 'contamination',
                        description: 'Expected proportion of outliers',
                        defaultValue: 0.1,
                        recommendedRange: '0.01-0.2',
                        tuningStrategy: 'Domain knowledge or exploratory analysis',
                        importance: 'critical',
                    },
                    {
                        parameterName: 'n_estimators',
                        description: 'Number of isolation trees',
                        defaultValue: 100,
                        recommendedRange: '50-200',
                        tuningStrategy: 'Balance performance vs accuracy',
                        importance: 'important',
                    },
                ],
                computationalComplexity: 'O(n log n) for training, O(log n) for prediction',
                memoryRequirements: 'Low - tree-based algorithm',
                optimalDataSize: '1K-1M records',
            },
            codeImplementation: this.generateIsolationForestImplementation(numericalColumns),
            evaluationMetrics: ['Anomaly Score', 'Precision', 'Recall', 'F1-Score (if labels available)'],
            interpretationGuidance: [
                'Negative scores indicate anomalies',
                'Score magnitude indicates anomaly strength',
                'Validate results with domain expertise',
            ],
            scalabilityNotes: [
                'Scales well to large datasets',
                'Parallel training available',
                'Real-time scoring possible',
            ],
        });
        return recommendations;
    }
    /**
     * Generate AutoML platform recommendations
     */
    async generateAutoMLRecommendations(columns, qualityScore, fileSizeMB) {
        const recommendations = [];
        const numericalColumns = columns.filter(col => col.detectedDataType === 'numerical_float' || col.detectedDataType === 'numerical_integer');
        const categoricalColumns = columns.filter(col => col.detectedDataType === 'categorical');
        const highCardinalityColumns = categoricalColumns.filter(col => col.uniqueValues > 50);
        // H2O AutoML recommendation
        recommendations.push({
            platform: 'H2O_AutoML',
            suitabilityScore: this.calculateH2OSuitability(numericalColumns, categoricalColumns, fileSizeMB),
            strengths: [
                'Excellent handling of mixed data types',
                'Automatic feature engineering',
                'Built-in model interpretation',
                'Scalable to large datasets',
                'Free and open source',
            ],
            limitations: [
                'Requires Java runtime',
                'Learning curve for beginners',
                'Limited deep learning options',
            ],
            dataRequirements: [
                'Minimum 1000 rows recommended',
                'Handles missing values automatically',
                'Automatic encoding of categorical variables',
            ],
            estimatedCost: 'Free (open source)',
            setupComplexity: 'moderate',
            codeExample: this.generateH2OAutoMLCode(columns),
            configurationRecommendations: {
                max_models: fileSizeMB > 100 ? 10 : 20,
                seed: 42,
                exclude_algos: highCardinalityColumns.length > 0 ? ['DeepLearning'] : [],
                max_runtime_secs: fileSizeMB > 50 ? 7200 : 3600,
                stopping_metric: 'AUTO',
                stopping_tolerance: 0.001,
            },
        });
        // AutoGluon recommendation
        recommendations.push({
            platform: 'AutoGluon',
            suitabilityScore: this.calculateAutoGluonSuitability(numericalColumns, categoricalColumns, fileSizeMB),
            strengths: [
                'State-of-the-art ensemble methods',
                'Excellent text feature handling',
                'Multi-modal learning capabilities',
                'Neural network options',
                'Easy to use Python API',
            ],
            limitations: [
                'Higher computational requirements',
                'Longer training times',
                'Memory intensive',
            ],
            dataRequirements: [
                'Works well with smaller datasets',
                'Automatic feature preprocessing',
                'Handles text and categorical data excellently',
            ],
            estimatedCost: 'Free (open source)',
            setupComplexity: 'simple',
            codeExample: this.generateAutoGluonCode(columns),
            configurationRecommendations: {
                presets: fileSizeMB > 50 ? 'medium_quality_faster_train' : 'best_quality',
                time_limit: fileSizeMB > 50 ? 3600 : 7200,
                eval_metric: 'auto',
                auto_stack: true,
            },
        });
        return recommendations.sort((a, b) => b.suitabilityScore - a.suitabilityScore);
    }
    /**
     * Generate feature engineering recipes
     */
    async generateFeatureEngineeringRecipes(columns, edaResult, engineeringResult) {
        const recipes = [];
        // Temporal feature engineering
        const dateColumns = columns.filter(col => col.detectedDataType === 'date_time' ||
            col.columnName.toLowerCase().includes('date'));
        if (dateColumns.length > 0) {
            recipes.push({
                recipeName: 'Temporal Feature Extraction',
                description: 'Extract meaningful time-based features from date columns',
                applicableColumns: dateColumns.map(c => c.columnName),
                businessRationale: 'Time-based patterns often drive business outcomes (seasonality, trends, cycles)',
                codeImplementation: this.generateTemporalFeatureCode(dateColumns),
                expectedImpact: 'Improved model performance through temporal pattern recognition',
                prerequisites: ['Valid date format', 'Reasonable date range'],
                riskFactors: ['Time zone considerations', 'Missing date handling'],
            });
        }
        // Geographic feature engineering
        const countryColumns = columns.filter(col => col.columnName.toLowerCase().includes('country') ||
            col.columnName.toLowerCase().includes('region'));
        if (countryColumns.length > 0) {
            recipes.push({
                recipeName: 'Geographic Feature Enrichment',
                description: 'Create derived geographic features from location data',
                applicableColumns: countryColumns.map(c => c.columnName),
                businessRationale: 'Geographic patterns influence customer behavior, market dynamics, and operational efficiency',
                codeImplementation: this.generateGeographicFeatureCode(countryColumns),
                expectedImpact: 'Enhanced geographic insights and regional pattern recognition',
                prerequisites: ['Standardized country/region names', 'Geographic reference data'],
                riskFactors: ['Data quality in geographic fields', 'Changing geographic boundaries'],
            });
        }
        // Text feature engineering
        const textColumns = columns.filter(col => col.detectedDataType === 'text_general' || col.detectedDataType === 'text_address' ||
            (col.detectedDataType === 'categorical' && col.uniqueValues > 100));
        if (textColumns.length > 0) {
            recipes.push({
                recipeName: 'Text Feature Engineering',
                description: 'Extract patterns and features from high-cardinality text columns',
                applicableColumns: textColumns.map(c => c.columnName),
                businessRationale: 'Text data contains rich patterns that can improve predictive accuracy',
                codeImplementation: this.generateTextFeatureCode(textColumns),
                expectedImpact: 'Better handling of unstructured text information',
                prerequisites: ['Clean text data', 'Consistent formatting'],
                riskFactors: ['High dimensionality', 'Overfitting risk'],
            });
        }
        return recipes;
    }
    /**
     * Generate deployment considerations
     */
    async generateDeploymentConsiderations(columns, fileSizeMB) {
        const considerations = [];
        // Data pipeline considerations
        considerations.push({
            aspect: 'data_pipeline',
            requirements: [
                'Real-time preprocessing for all input features',
                'Encoding dictionaries for categorical variables',
                'Missing value imputation strategies',
                'Data validation and quality checks',
            ],
            recommendations: [
                'Use pipeline objects for consistent preprocessing',
                'Version control preprocessing steps',
                'Implement data quality monitoring',
                'Cache frequently used transformations',
            ],
            riskFactors: [
                'Data drift affecting preprocessing',
                'Missing values in production data',
                'Categorical values not seen in training',
            ],
            codeTemplates: this.generateDataPipelineTemplates(columns),
        });
        // Monitoring considerations
        considerations.push({
            aspect: 'monitoring',
            requirements: [
                `Monitor distribution drift for ${columns.length} features`,
                'Track prediction confidence scores',
                'Alert on unusual input patterns',
                'Performance metric tracking',
            ],
            recommendations: [
                'Implement statistical drift detection',
                'Set up automated retraining triggers',
                'Monitor model performance degradation',
                'Log all predictions for audit trail',
            ],
            riskFactors: [
                'Concept drift affecting model accuracy',
                'Data quality degradation over time',
                'Unexpected input combinations',
            ],
        });
        // API schema considerations
        considerations.push({
            aspect: 'api_schema',
            requirements: [
                'Input validation for all features',
                'Standardized response format',
                'Error handling for invalid inputs',
                'Documentation and examples',
            ],
            recommendations: [
                'Use JSON schema validation',
                'Provide clear error messages',
                'Include confidence scores in responses',
                'Support batch and single predictions',
            ],
            riskFactors: [
                'Breaking changes in API schema',
                'Performance bottlenecks',
                'Security vulnerabilities',
            ],
            codeTemplates: this.generateAPISchemaTemplates(columns),
        });
        return considerations;
    }
    // Helper methods for code generation
    generateClusteringCode(numericalColumns, categoricalColumns) {
        return `from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.metrics import silhouette_score
import pandas as pd

# Prepare features
features = ${JSON.stringify([...numericalColumns.slice(0, 4).map(c => c.columnName), ...categoricalColumns.slice(0, 2).map(c => c.columnName)])}

# Encode categorical variables
le = LabelEncoder()
df_encoded = df.copy()
for col in ${JSON.stringify(categoricalColumns.slice(0, 2).map(c => c.columnName))}:
    df_encoded[col + '_encoded'] = le.fit_transform(df[col].astype(str))

# Scale numerical features
scaler = StandardScaler()
X = scaler.fit_transform(df_encoded[features])

# Find optimal number of clusters
silhouette_scores = []
K_range = range(2, 9)
for k in K_range:
    kmeans = KMeans(n_clusters=k, random_state=42)
    cluster_labels = kmeans.fit_predict(X)
    silhouette_avg = silhouette_score(X, cluster_labels)
    silhouette_scores.append(silhouette_avg)

# Train final model with optimal K
optimal_k = K_range[silhouette_scores.index(max(silhouette_scores))]
kmeans = KMeans(n_clusters=optimal_k, random_state=42)
df['customer_segment'] = kmeans.fit_predict(X)`;
    }
    generateGeographicClusteringCode() {
        return `import pandas as pd
from sklearn.cluster import AgglomerativeClustering
from sklearn.preprocessing import StandardScaler

# Aggregate metrics by country
country_metrics = df.groupby('country').agg({
    'customer_id': 'count',
    'company': 'nunique'
}).rename(columns={'customer_id': 'customer_count', 'company': 'company_count'})

# Scale features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(country_metrics)

# Hierarchical clustering
clustering = AgglomerativeClustering(n_clusters=3, linkage='ward')
country_metrics['market_tier'] = clustering.fit_predict(X_scaled)

# Map back to original data
df = df.merge(country_metrics[['market_tier']], left_on='country', right_index=True)`;
    }
    generateAnomalyDetectionCode(numericalColumns) {
        return `from sklearn.ensemble import IsolationForest
from sklearn.preprocessing import StandardScaler

# Prepare numerical features
features = ${JSON.stringify(numericalColumns.map(c => c.columnName))}
X = df[features].fillna(df[features].median())

# Scale features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# Fit Isolation Forest
iso_forest = IsolationForest(contamination=0.1, random_state=42)
df['anomaly_score'] = iso_forest.fit_predict(X_scaled)
df['anomaly_score_raw'] = iso_forest.decision_function(X_scaled)

# Convert to positive scale (higher = more anomalous)
df['anomaly_score_normalized'] = (df['anomaly_score_raw'] - df['anomaly_score_raw'].min()) / (df['anomaly_score_raw'].max() - df['anomaly_score_raw'].min())`;
    }
    generateDataQualityFlagCode(numericalColumns) {
        return `from sklearn.ensemble import IsolationForest

# Use anomaly detection for data quality assessment
features = ${JSON.stringify(numericalColumns.map(c => c.columnName))}
X = df[features].fillna(df[features].median())

iso_forest = IsolationForest(contamination=0.05, random_state=42)
anomaly_scores = iso_forest.decision_function(X)

# Create binary quality flag (1 = good quality, 0 = potential issues)
threshold = np.percentile(anomaly_scores, 5)  # Bottom 5% flagged as quality issues
df['data_quality_flag'] = (anomaly_scores > threshold).astype(int)`;
    }
    generateEmailDomainQualityCode(emailColumn) {
        return `import re

def classify_email_domain(email):
    if pd.isna(email):
        return 'unknown'
    
    # Extract domain
    domain_match = re.search(r'@([^.]+\\.[^.]+)$', str(email).lower())
    if not domain_match:
        return 'invalid'
    
    domain = domain_match.group(1)
    
    # Personal email providers
    personal_domains = {
        'gmail.com', 'yahoo.com', 'hotmail.com', 'outlook.com', 
        'aol.com', 'icloud.com', 'live.com', 'msn.com'
    }
    
    if domain in personal_domains:
        return 'personal'
    else:
        return 'corporate'

df['email_domain_quality'] = df['${emailColumn}'].apply(classify_email_domain)
df['is_corporate_email'] = (df['email_domain_quality'] == 'corporate').astype(int)`;
    }
    generateProfileCompletenessCode(columns) {
        const importantColumns = columns.slice(0, 8).map(c => c.columnName);
        return `# Define important fields and their weights
important_fields = ${JSON.stringify(importantColumns)}
field_weights = {field: 1.0 for field in important_fields}

def calculate_completeness_score(row):
    total_weight = sum(field_weights.values())
    weighted_score = 0
    
    for field, weight in field_weights.items():
        if pd.notna(row[field]) and str(row[field]).strip() != '':
            weighted_score += weight
    
    return (weighted_score / total_weight) * 100

df['profile_completeness_score'] = df.apply(calculate_completeness_score, axis=1)`;
    }
    generateCustomerLifetimeCode(dateColumn) {
        return `import pandas as pd
from datetime import datetime

# Convert to datetime
df['${dateColumn}'] = pd.to_datetime(df['${dateColumn}'])

# Calculate customer lifetime in days
reference_date = pd.Timestamp.now()
df['customer_lifetime_days'] = (reference_date - df['${dateColumn}']).dt.days

# Handle negative values (future dates)
df['customer_lifetime_days'] = df['customer_lifetime_days'].clip(lower=0)`;
    }
    generateSeasonalityCode(dateColumn) {
        return `# Extract temporal features
df['${dateColumn}'] = pd.to_datetime(df['${dateColumn}'])
df['subscription_quarter'] = df['${dateColumn}'].dt.quarter
df['subscription_month'] = df['${dateColumn}'].dt.month
df['subscription_dayofweek'] = df['${dateColumn}'].dt.dayofweek
df['is_weekend_signup'] = df['subscription_dayofweek'].isin([5, 6]).astype(int)`;
    }
    generateCategoryGroupingCode(columnName) {
        return `# Group high-cardinality categories
def group_categories(series, top_n=10):
    value_counts = series.value_counts()
    top_categories = value_counts.head(top_n).index.tolist()
    
    def categorize(value):
        if pd.isna(value):
            return 'Missing'
        elif value in top_categories:
            return value
        else:
            return 'Other'
    
    return series.apply(categorize)

df['${columnName}_category'] = group_categories(df['${columnName}'], top_n=8)`;
    }
    generateKMeansImplementation(numericalColumns) {
        return {
            framework: 'scikit-learn',
            importStatements: [
                'from sklearn.cluster import KMeans',
                'from sklearn.preprocessing import StandardScaler',
                'from sklearn.metrics import silhouette_score',
                'import numpy as np',
                'import pandas as pd',
                'import matplotlib.pyplot as plt'
            ],
            preprocessingCode: [
                `features = ${JSON.stringify(numericalColumns.slice(0, 6).map(c => c.columnName))}`,
                'X = df[features].fillna(df[features].median())',
                'scaler = StandardScaler()',
                'X_scaled = scaler.fit_transform(X)'
            ],
            mainImplementation: [
                '# Find optimal number of clusters using elbow method',
                'inertias = []',
                'silhouette_scores = []',
                'K_range = range(2, 11)',
                '',
                'for k in K_range:',
                '    kmeans = KMeans(n_clusters=k, random_state=42, n_init=10)',
                '    cluster_labels = kmeans.fit_predict(X_scaled)',
                '    inertias.append(kmeans.inertia_)',
                '    silhouette_scores.append(silhouette_score(X_scaled, cluster_labels))',
                '',
                '# Select optimal K based on silhouette score',
                'optimal_k = K_range[np.argmax(silhouette_scores)]',
                '',
                '# Train final model',
                'final_kmeans = KMeans(n_clusters=optimal_k, random_state=42, n_init=10)',
                'cluster_labels = final_kmeans.fit_predict(X_scaled)',
                'df["cluster"] = cluster_labels'
            ],
            evaluationCode: [
                'silhouette_avg = silhouette_score(X_scaled, cluster_labels)',
                'print(f"Silhouette Score: {silhouette_avg:.3f}")',
                '',
                '# Cluster analysis',
                'cluster_summary = df.groupby("cluster")[features].mean()',
                'print("\\nCluster Centroids:")',
                'print(cluster_summary)'
            ],
            visualizationCode: [
                'plt.figure(figsize=(12, 4))',
                '',
                '# Elbow curve',
                'plt.subplot(1, 2, 1)',
                'plt.plot(K_range, inertias, "bo-")',
                'plt.xlabel("Number of Clusters (K)")',
                'plt.ylabel("Inertia")',
                'plt.title("Elbow Method For Optimal K")',
                '',
                '# Silhouette scores',
                'plt.subplot(1, 2, 2)',
                'plt.plot(K_range, silhouette_scores, "ro-")',
                'plt.xlabel("Number of Clusters (K)")',
                'plt.ylabel("Silhouette Score")',
                'plt.title("Silhouette Analysis")',
                'plt.tight_layout()',
                'plt.show()'
            ]
        };
    }
    generateDBSCANImplementation(numericalColumns) {
        return {
            framework: 'scikit-learn',
            importStatements: [
                'from sklearn.cluster import DBSCAN',
                'from sklearn.preprocessing import StandardScaler',
                'from sklearn.metrics import silhouette_score',
                'from sklearn.neighbors import NearestNeighbors',
                'import numpy as np',
                'import pandas as pd',
                'import matplotlib.pyplot as plt'
            ],
            preprocessingCode: [
                `features = ${JSON.stringify(numericalColumns.slice(0, 6).map(c => c.columnName))}`,
                'X = df[features].fillna(df[features].median())',
                'scaler = StandardScaler()',
                'X_scaled = scaler.fit_transform(X)'
            ],
            mainImplementation: [
                '# Determine optimal eps using k-distance graph',
                'k = 4  # Typically 2 * dimensions',
                'neigh = NearestNeighbors(n_neighbors=k)',
                'neigh.fit(X_scaled)',
                'distances, indices = neigh.kneighbors(X_scaled)',
                'distances = np.sort(distances[:, k-1], axis=0)',
                '',
                '# Use knee point as eps (simplified approach)',
                'eps = np.percentile(distances, 90)',
                '',
                '# Apply DBSCAN',
                'dbscan = DBSCAN(eps=eps, min_samples=k)',
                'cluster_labels = dbscan.fit_predict(X_scaled)',
                'df["cluster"] = cluster_labels'
            ],
            evaluationCode: [
                'n_clusters = len(set(cluster_labels)) - (1 if -1 in cluster_labels else 0)',
                'n_noise = list(cluster_labels).count(-1)',
                'print(f"Number of clusters: {n_clusters}")',
                'print(f"Number of noise points: {n_noise}")',
                '',
                'if n_clusters > 1:',
                '    silhouette_avg = silhouette_score(X_scaled, cluster_labels)',
                '    print(f"Silhouette Score: {silhouette_avg:.3f}")'
            ],
            visualizationCode: [
                'plt.figure(figsize=(10, 4))',
                '',
                '# K-distance graph',
                'plt.subplot(1, 2, 1)',
                'plt.plot(distances)',
                'plt.xlabel("Points")',
                'plt.ylabel("4th Nearest Neighbor Distance")',
                'plt.title("K-Distance Graph")',
                'plt.axhline(y=eps, color="r", linestyle="--", label=f"eps={eps:.3f}")',
                'plt.legend()',
                '',
                '# Cluster distribution',
                'plt.subplot(1, 2, 2)',
                'unique, counts = np.unique(cluster_labels, return_counts=True)',
                'plt.bar(unique, counts)',
                'plt.xlabel("Cluster Label (-1 = Noise)")',
                'plt.ylabel("Count")',
                'plt.title("Cluster Distribution")',
                'plt.tight_layout()',
                'plt.show()'
            ]
        };
    }
    generatePCAImplementation(numericalColumns) {
        return {
            framework: 'scikit-learn',
            importStatements: [
                'from sklearn.decomposition import PCA',
                'from sklearn.preprocessing import StandardScaler',
                'import numpy as np',
                'import pandas as pd',
                'import matplotlib.pyplot as plt'
            ],
            preprocessingCode: [
                `features = ${JSON.stringify(numericalColumns.map(c => c.columnName))}`,
                'X = df[features].fillna(df[features].median())',
                'scaler = StandardScaler()',
                'X_scaled = scaler.fit_transform(X)'
            ],
            mainImplementation: [
                '# Fit PCA to determine optimal number of components',
                'pca_full = PCA()',
                'pca_full.fit(X_scaled)',
                '',
                '# Find number of components for 95% variance',
                'cumsum = np.cumsum(pca_full.explained_variance_ratio_)',
                'n_components = np.argmax(cumsum >= 0.95) + 1',
                '',
                '# Apply PCA with optimal components',
                'pca = PCA(n_components=n_components)',
                'X_pca = pca.fit_transform(X_scaled)',
                '',
                '# Create component dataframe',
                'pca_columns = [f"PC{i+1}" for i in range(n_components)]',
                'df_pca = pd.DataFrame(X_pca, columns=pca_columns, index=df.index)',
                'df = pd.concat([df, df_pca], axis=1)'
            ],
            evaluationCode: [
                'print(f"Original features: {len(features)}")',
                'print(f"PCA components: {n_components}")',
                'print(f"Variance explained: {cumsum[n_components-1]:.3f}")',
                '',
                '# Feature importance in components',
                'feature_importance = pd.DataFrame(',
                '    pca.components_[:3].T,',
                '    columns=[f"PC{i+1}" for i in range(min(3, n_components))],',
                '    index=features',
                ')',
                'print("\\nTop features by component:")',
                'print(feature_importance.abs().sort_values("PC1", ascending=False).head())'
            ],
            visualizationCode: [
                'plt.figure(figsize=(12, 4))',
                '',
                '# Explained variance',
                'plt.subplot(1, 2, 1)',
                'plt.plot(range(1, len(pca_full.explained_variance_ratio_) + 1),',
                '         np.cumsum(pca_full.explained_variance_ratio_), "bo-")',
                'plt.axhline(y=0.95, color="r", linestyle="--", label="95% Variance")',
                'plt.xlabel("Number of Components")',
                'plt.ylabel("Cumulative Explained Variance")',
                'plt.title("PCA Explained Variance")',
                'plt.legend()',
                '',
                '# First two components scatter',
                'if n_components >= 2:',
                '    plt.subplot(1, 2, 2)',
                '    plt.scatter(X_pca[:, 0], X_pca[:, 1], alpha=0.6)',
                '    plt.xlabel(f"PC1 ({pca.explained_variance_ratio_[0]:.2%} variance)")',
                '    plt.ylabel(f"PC2 ({pca.explained_variance_ratio_[1]:.2%} variance)")',
                '    plt.title("PCA Projection")',
                '',
                'plt.tight_layout()',
                'plt.show()'
            ]
        };
    }
    generateTSNEImplementation(numericalColumns) {
        return {
            framework: 'scikit-learn',
            importStatements: [
                'from sklearn.manifold import TSNE',
                'from sklearn.preprocessing import StandardScaler',
                'from sklearn.decomposition import PCA',
                'import numpy as np',
                'import pandas as pd',
                'import matplotlib.pyplot as plt'
            ],
            preprocessingCode: [
                `features = ${JSON.stringify(numericalColumns.slice(0, 10).map(c => c.columnName))}`,
                'X = df[features].fillna(df[features].median())',
                'scaler = StandardScaler()',
                'X_scaled = scaler.fit_transform(X)',
                '',
                '# Pre-reduce dimensions with PCA if high-dimensional',
                'if X_scaled.shape[1] > 50:',
                '    pca = PCA(n_components=50)',
                '    X_scaled = pca.fit_transform(X_scaled)'
            ],
            mainImplementation: [
                '# Apply t-SNE with different perplexity values',
                'perplexities = [5, 30, 50]',
                'tsne_results = {}',
                '',
                'for perp in perplexities:',
                '    if len(X_scaled) < perp * 3:  # Skip if insufficient data',
                '        continue',
                '        ',
                '    tsne = TSNE(n_components=2, perplexity=perp, ',
                '                random_state=42, n_iter=1000)',
                '    X_tsne = tsne.fit_transform(X_scaled)',
                '    ',
                '    tsne_results[perp] = {',
                '        "coordinates": X_tsne,',
                '        "kl_divergence": tsne.kl_divergence_',
                '    }',
                '',
                '# Use best perplexity result',
                'best_perp = min(tsne_results.keys())',
                'df["tsne_x"] = tsne_results[best_perp]["coordinates"][:, 0]',
                'df["tsne_y"] = tsne_results[best_perp]["coordinates"][:, 1]'
            ],
            evaluationCode: [
                'for perp, result in tsne_results.items():',
                '    print(f"Perplexity {perp}: KL divergence = {result[\\"kl_divergence\\"]:.3f}")',
                '',
                'print(f"\\nUsing perplexity {best_perp} for final embedding")'
            ],
            visualizationCode: [
                'fig, axes = plt.subplots(1, len(tsne_results), figsize=(5*len(tsne_results), 4))',
                'if len(tsne_results) == 1:',
                '    axes = [axes]',
                '',
                'for i, (perp, result) in enumerate(tsne_results.items()):',
                '    coords = result["coordinates"]',
                '    axes[i].scatter(coords[:, 0], coords[:, 1], alpha=0.6)',
                '    axes[i].set_title(f"t-SNE (perplexity={perp})")',
                '    axes[i].set_xlabel("t-SNE 1")',
                '    axes[i].set_ylabel("t-SNE 2")',
                '',
                'plt.tight_layout()',
                'plt.show()'
            ]
        };
    }
    generateAprioriImplementation(categoricalColumns) {
        return {
            framework: 'pandas',
            importStatements: [
                'import pandas as pd',
                'from itertools import combinations',
                'from collections import defaultdict',
                'import numpy as np'
            ],
            preprocessingCode: [
                `# Select categorical columns with reasonable cardinality`,
                `cat_columns = ${JSON.stringify(categoricalColumns.filter(c => c.uniqueValues <= 100).map(c => c.columnName))}`,
                '',
                '# Create transaction format',
                'transactions = []',
                'for idx, row in df.iterrows():',
                '    transaction = []',
                '    for col in cat_columns:',
                '        if pd.notna(row[col]):',
                '            transaction.append(f"{col}_{row[col]}")',
                '    transactions.append(transaction)'
            ],
            mainImplementation: [
                '# Simplified Apriori implementation',
                'def get_support(itemset, transactions):',
                '    count = 0',
                '    for transaction in transactions:',
                '        if set(itemset).issubset(set(transaction)):',
                '            count += 1',
                '    return count / len(transactions)',
                '',
                '# Find frequent items',
                'min_support = 0.01',
                'all_items = set(item for transaction in transactions for item in transaction)',
                '',
                '# 1-itemsets',
                'frequent_1 = {}',
                'for item in all_items:',
                '    support = get_support([item], transactions)',
                '    if support >= min_support:',
                '        frequent_1[frozenset([item])] = support',
                '',
                '# 2-itemsets',
                'frequent_2 = {}',
                'for item1, item2 in combinations(frequent_1.keys(), 2):',
                '    itemset = item1.union(item2)',
                '    support = get_support(list(itemset), transactions)',
                '    if support >= min_support:',
                '        frequent_2[itemset] = support',
                '',
                '# Generate association rules',
                'min_confidence = 0.5',
                'rules = []',
                '',
                'for itemset, support in frequent_2.items():',
                '    items = list(itemset)',
                '    if len(items) == 2:',
                '        # A -> B',
                '        antecedent = frozenset([items[0]])',
                '        consequent = frozenset([items[1]])',
                '        confidence = support / frequent_1[antecedent]',
                '        lift = confidence / frequent_1[consequent]',
                '        ',
                '        if confidence >= min_confidence:',
                '            rules.append({',
                '                "antecedent": items[0],',
                '                "consequent": items[1],',
                '                "support": support,',
                '                "confidence": confidence,',
                '                "lift": lift',
                '            })',
                '',
                'rules_df = pd.DataFrame(rules)',
                'rules_df = rules_df.sort_values("lift", ascending=False)'
            ],
            evaluationCode: [
                'print(f"Frequent 1-itemsets: {len(frequent_1)}")',
                'print(f"Frequent 2-itemsets: {len(frequent_2)}")',
                'print(f"Association rules found: {len(rules)}")',
                '',
                'if len(rules) > 0:',
                '    print("\\nTop association rules by lift:")',
                '    print(rules_df.head(10))'
            ]
        };
    }
    generateIsolationForestImplementation(numericalColumns) {
        return {
            framework: 'scikit-learn',
            importStatements: [
                'from sklearn.ensemble import IsolationForest',
                'from sklearn.preprocessing import StandardScaler',
                'import numpy as np',
                'import pandas as pd',
                'import matplotlib.pyplot as plt'
            ],
            preprocessingCode: [
                `features = ${JSON.stringify(numericalColumns.map(c => c.columnName))}`,
                'X = df[features].fillna(df[features].median())',
                '',
                '# Optional: scale features (not required for Isolation Forest)',
                '# scaler = StandardScaler()',
                '# X_scaled = scaler.fit_transform(X)'
            ],
            mainImplementation: [
                '# Fit Isolation Forest',
                'contamination = 0.1  # Expected proportion of anomalies',
                'iso_forest = IsolationForest(',
                '    contamination=contamination,',
                '    random_state=42,',
                '    n_estimators=100',
                ')',
                '',
                '# Predict anomalies (-1 for anomaly, 1 for normal)',
                'anomaly_labels = iso_forest.fit_predict(X)',
                'anomaly_scores = iso_forest.decision_function(X)',
                '',
                '# Add results to dataframe',
                'df["anomaly_label"] = anomaly_labels',
                'df["anomaly_score"] = anomaly_scores',
                '',
                '# Convert scores to 0-1 scale (higher = more anomalous)',
                'df["anomaly_score_norm"] = (',
                '    (df["anomaly_score"] - df["anomaly_score"].min()) /',
                '    (df["anomaly_score"].max() - df["anomaly_score"].min())',
                ')',
                'df["anomaly_score_norm"] = 1 - df["anomaly_score_norm"]  # Flip scale'
            ],
            evaluationCode: [
                'n_anomalies = (anomaly_labels == -1).sum()',
                'anomaly_rate = n_anomalies / len(df)',
                'print(f"Anomalies detected: {n_anomalies} ({anomaly_rate:.2%})")',
                '',
                '# Top anomalies',
                'top_anomalies = df[df["anomaly_label"] == -1].nlargest(5, "anomaly_score_norm")',
                'print("\\nTop 5 anomalies:")',
                'print(top_anomalies[features + ["anomaly_score_norm"]])'
            ],
            visualizationCode: [
                'plt.figure(figsize=(12, 4))',
                '',
                '# Anomaly score distribution',
                'plt.subplot(1, 2, 1)',
                'plt.hist(df["anomaly_score_norm"], bins=30, alpha=0.7)',
                'plt.axvline(df[df["anomaly_label"] == -1]["anomaly_score_norm"].min(),',
                '           color="r", linestyle="--", label="Anomaly Threshold")',
                'plt.xlabel("Anomaly Score (Normalized)")',
                'plt.ylabel("Frequency")',
                'plt.title("Anomaly Score Distribution")',
                'plt.legend()',
                '',
                '# Feature comparison: normal vs anomalies',
                'if len(features) >= 2:',
                '    plt.subplot(1, 2, 2)',
                '    normal_points = df[df["anomaly_label"] == 1]',
                '    anomaly_points = df[df["anomaly_label"] == -1]',
                '    ',
                '    plt.scatter(normal_points[features[0]], normal_points[features[1]],',
                '               alpha=0.6, label="Normal", s=20)',
                '    plt.scatter(anomaly_points[features[0]], anomaly_points[features[1]],',
                '               alpha=0.8, label="Anomaly", s=50, color="red")',
                '    plt.xlabel(features[0])',
                '    plt.ylabel(features[1])',
                '    plt.title("Normal vs Anomalous Points")',
                '    plt.legend()',
                '',
                'plt.tight_layout()',
                'plt.show()'
            ]
        };
    }
    generateH2OAutoMLCode(columns) {
        return `import h2o
from h2o.automl import H2OAutoML

# Initialize H2O
h2o.init()

# Convert to H2O frame
h2o_df = h2o.H2OFrame(df)

# Define target variable (use one of the synthetic targets)
target = "customer_segment"  # Or any synthetic target you created
features = h2o_df.columns
features.remove(target)

# Split data
train, test = h2o_df.split_frame(ratios=[0.8], seed=42)

# Run AutoML
aml = H2OAutoML(
    max_models=20,
    seed=42,
    max_runtime_secs=3600,
    exclude_algos=["DeepLearning"],  # Exclude if high cardinality issues
    sort_metric="AUC"  # Adjust based on problem type
)

aml.train(x=features, y=target, training_frame=train)

# Get leaderboard
print(aml.leaderboard.head())

# Best model performance
best_model = aml.leader
performance = best_model.model_performance(test)
print(performance)`;
    }
    generateAutoGluonCode(columns) {
        return `from autogluon.tabular import TabularPredictor

# Prepare data with synthetic target
target = "customer_segment"  # Or any synthetic target you created

# Train AutoGluon model
predictor = TabularPredictor(
    label=target,
    eval_metric="accuracy",  # Adjust based on problem type
    path="./autogluon_models"
)

# Fit the model
predictor.fit(
    train_data=df,
    presets="best_quality",  # Options: fast, good, best
    time_limit=3600,  # 1 hour
    auto_stack=True
)

# Evaluate model
test_performance = predictor.evaluate(df_test)
print(f"Test performance: {test_performance}")

# Feature importance
feature_importance = predictor.feature_importance(df)
print("Feature importance:")
print(feature_importance.head(10))`;
    }
    generateTemporalFeatureCode(dateColumns) {
        return [
            `# Temporal feature engineering for ${dateColumns[0].columnName}`,
            `df['${dateColumns[0].columnName}'] = pd.to_datetime(df['${dateColumns[0].columnName}'])`,
            '',
            '# Extract temporal components',
            `df['year'] = df['${dateColumns[0].columnName}'].dt.year`,
            `df['month'] = df['${dateColumns[0].columnName}'].dt.month`,
            `df['quarter'] = df['${dateColumns[0].columnName}'].dt.quarter`,
            `df['day_of_week'] = df['${dateColumns[0].columnName}'].dt.dayofweek`,
            `df['day_of_year'] = df['${dateColumns[0].columnName}'].dt.dayofyear`,
            `df['week_of_year'] = df['${dateColumns[0].columnName}'].dt.isocalendar().week`,
            '',
            '# Business calendar features',
            `df['is_weekend'] = df['day_of_week'].isin([5, 6]).astype(int)`,
            `df['is_month_start'] = df['${dateColumns[0].columnName}'].dt.is_month_start.astype(int)`,
            `df['is_month_end'] = df['${dateColumns[0].columnName}'].dt.is_month_end.astype(int)`,
            `df['is_quarter_start'] = df['${dateColumns[0].columnName}'].dt.is_quarter_start.astype(int)`,
            `df['is_quarter_end'] = df['${dateColumns[0].columnName}'].dt.is_quarter_end.astype(int)`,
            '',
            '# Cyclical encoding for circular features',
            'df["month_sin"] = np.sin(2 * np.pi * df["month"] / 12)',
            'df["month_cos"] = np.cos(2 * np.pi * df["month"] / 12)',
            'df["day_of_week_sin"] = np.sin(2 * np.pi * df["day_of_week"] / 7)',
            'df["day_of_week_cos"] = np.cos(2 * np.pi * df["day_of_week"] / 7)',
        ];
    }
    generateGeographicFeatureCode(countryColumns) {
        return [
            `# Geographic feature engineering for ${countryColumns[0].columnName}`,
            '',
            '# Country-level aggregations',
            'country_stats = df.groupby("country").agg({',
            '    "customer_id": "count",',
            '    "company": "nunique"',
            '}).rename(columns={',
            '    "customer_id": "country_customer_count",',
            '    "company": "country_company_count"',
            '})',
            '',
            'df = df.merge(country_stats, on="country", how="left")',
            '',
            '# Customer density features',
            'df["customer_density_rank"] = df["country_customer_count"].rank(method="dense")',
            'df["is_high_density_country"] = (df["customer_density_rank"] > df["customer_density_rank"].quantile(0.8)).astype(int)',
            '',
            '# Geographic groupings (simplified example)',
            'developed_countries = ["USA", "Canada", "UK", "Germany", "France", "Japan", "Australia"]',
            'df["is_developed_country"] = df["country"].isin(developed_countries).astype(int)',
            '',
            '# Market size categories',
            'df["market_size"] = pd.cut(',
            '    df["country_customer_count"],',
            '    bins=[0, 10, 50, 200, float("inf")],',
            '    labels=["Small", "Medium", "Large", "Huge"]',
            ')',
        ];
    }
    generateTextFeatureCode(textColumns) {
        return [
            `# Text feature engineering for high-cardinality columns`,
            '',
            '# Length-based features',
            ...textColumns.map(col => `df['${col.columnName}_length'] = df['${col.columnName}'].str.len().fillna(0)`),
            '',
            '# Word count features',
            ...textColumns.map(col => `df['${col.columnName}_word_count'] = df['${col.columnName}'].str.split().str.len().fillna(0)`),
            '',
            '# Pattern-based features',
            ...textColumns.map(col => [
                `df['${col.columnName}_has_numbers'] = df['${col.columnName}'].str.contains(r'\\d', na=False).astype(int)`,
                `df['${col.columnName}_has_special_chars'] = df['${col.columnName}'].str.contains(r'[^a-zA-Z0-9\\s]', na=False).astype(int)`,
                `df['${col.columnName}_is_uppercase'] = df['${col.columnName}'].str.isupper().fillna(False).astype(int)`,
            ]).flat(),
            '',
            '# Frequency-based encoding',
            ...textColumns.map(col => [
                `${col.columnName}_counts = df['${col.columnName}'].value_counts()`,
                `df['${col.columnName}_frequency'] = df['${col.columnName}'].map(${col.columnName}_counts).fillna(0)`,
                `df['${col.columnName}_frequency_rank'] = df['${col.columnName}_frequency'].rank(method='dense')`,
            ]).flat(),
        ];
    }
    generateDataPipelineTemplates(columns) {
        return [
            '# Data pipeline template',
            'from sklearn.pipeline import Pipeline',
            'from sklearn.compose import ColumnTransformer',
            'from sklearn.preprocessing import StandardScaler, OneHotEncoder',
            'from sklearn.impute import SimpleImputer',
            '',
            '# Define column types',
            `numerical_features = ${JSON.stringify(columns.filter(c => c.detectedDataType === 'numerical_float' || c.detectedDataType === 'numerical_integer').map(c => c.columnName))}`,
            `categorical_features = ${JSON.stringify(columns.filter(c => c.detectedDataType === 'categorical').map(c => c.columnName))}`,
            '',
            '# Create preprocessing pipelines',
            'numerical_pipeline = Pipeline([',
            '    ("imputer", SimpleImputer(strategy="median")),',
            '    ("scaler", StandardScaler())',
            '])',
            '',
            'categorical_pipeline = Pipeline([',
            '    ("imputer", SimpleImputer(strategy="constant", fill_value="missing")),',
            '    ("encoder", OneHotEncoder(handle_unknown="ignore", sparse=False))',
            '])',
            '',
            '# Combine preprocessing steps',
            'preprocessor = ColumnTransformer([',
            '    ("num", numerical_pipeline, numerical_features),',
            '    ("cat", categorical_pipeline, categorical_features)',
            '])',
            '',
            '# Full pipeline with model',
            'full_pipeline = Pipeline([',
            '    ("preprocessor", preprocessor),',
            '    ("classifier", YourModel())  # Replace with your model',
            '])',
        ];
    }
    generateAPISchemaTemplates(columns) {
        const schemaFields = columns.slice(0, 10).map(col => {
            const fieldType = col.detectedDataType === 'numerical_float' || col.detectedDataType === 'numerical_integer' ? 'number' : 'string';
            return `    "${col.columnName}": {"type": "${fieldType}", "required": true}`;
        });
        return [
            '# API schema template',
            '{',
            '  "input_schema": {',
            '    "type": "object",',
            '    "properties": {',
            ...schemaFields,
            '    }',
            '  },',
            '  "output_schema": {',
            '    "type": "object",',
            '    "properties": {',
            '      "prediction": {"type": "number"},',
            '      "confidence": {"type": "number", "minimum": 0, "maximum": 1},',
            '      "segment": {"type": "string"},',
            '      "prediction_timestamp": {"type": "string", "format": "date-time"}',
            '    }',
            '  },',
            '  "preprocessing_steps": [',
            '    "validate_input",',
            '    "handle_missing_values",',
            '    "encode_categorical_variables",',
            '    "scale_numerical_features"',
            '  ]',
            '}',
        ];
    }
    // Helper methods for suitability scoring
    calculateH2OSuitability(numericalColumns, categoricalColumns, fileSizeMB) {
        let score = 70; // Base score
        // Bonus for mixed data types (H2O handles well)
        if (numericalColumns.length > 0 && categoricalColumns.length > 0)
            score += 15;
        // Bonus for medium-large datasets
        if (fileSizeMB > 10)
            score += 10;
        if (fileSizeMB > 100)
            score += 5;
        // Penalty for very high cardinality
        const highCardColumns = categoricalColumns.filter(col => col.uniqueValues > 1000);
        score -= highCardColumns.length * 5;
        return Math.min(100, Math.max(0, score));
    }
    calculateAutoGluonSuitability(numericalColumns, categoricalColumns, fileSizeMB) {
        let score = 75; // Base score (good default performance)
        // Bonus for text-heavy data
        const textColumns = categoricalColumns.filter(col => col.uniqueValues > 100);
        score += textColumns.length * 5;
        // Penalty for very large files (memory intensive)
        if (fileSizeMB > 100)
            score -= 10;
        if (fileSizeMB > 500)
            score -= 15;
        // Bonus for complex mixed data
        if (numericalColumns.length > 5 && categoricalColumns.length > 3)
            score += 10;
        return Math.min(100, Math.max(0, score));
    }
}
exports.UnsupervisedAnalyzer = UnsupervisedAnalyzer;
//# sourceMappingURL=unsupervised-analyzer.js.map