"use strict";
/**
 * Intelligent Algorithm Selection Analyzer
 * Advanced algorithm selection engine that provides sophisticated, dataset-aware ML recommendations
 *
 * Risk-averse implementation strategy:
 * - Incremental analysis with comprehensive fallbacks
 * - Multi-criteria decision framework with uncertainty handling
 * - Progressive enhancement of recommendation sophistication
 * - Backward compatibility with existing systems
 */
Object.defineProperty(exports, "__esModule", { value: true });
exports.IntelligentAlgorithmSelectionAnalyzer = void 0;
const types_1 = require("./types");
const logger_1 = require("../../../utils/logger");
/**
 * Main analyzer class for intelligent algorithm selection
 */
class IntelligentAlgorithmSelectionAnalyzer {
    config;
    warnings = [];
    startTime = 0;
    progress;
    constructor(config = {}) {
        this.config = this.initializeConfig(config);
        this.progress = this.initializeProgress();
    }
    /**
     * Main analysis method - performs comprehensive algorithm selection
     */
    async analyze(section1Result, section2Result, section3Result, complexityProfile, progressCallback) {
        this.startTime = Date.now();
        try {
            logger_1.logger.info('Starting intelligent algorithm selection analysis', {
                section: 'modeling',
                analyzer: 'intelligent-algorithm-selection',
            });
            // Initialize analysis state
            this.resetAnalysisState();
            this.updateProgress('initialization', 0, 'Initializing algorithm selection framework');
            // Validate inputs
            this.validateInputs(section1Result, section2Result, section3Result, complexityProfile);
            // Extract decision context from all sources
            const decisionContext = this.extractDecisionContext(section1Result, section2Result, section3Result, complexityProfile);
            // Perform incremental algorithm selection with error handling
            const selectionProfile = await this.performIncrementalSelection(decisionContext, progressCallback);
            // Generate selection metadata
            selectionProfile.selectionMetadata = this.generateSelectionMetadata(decisionContext);
            logger_1.logger.info('Algorithm selection analysis completed successfully', {
                section: 'modeling',
                analyzer: 'intelligent-algorithm-selection',
            });
            return selectionProfile;
        }
        catch (error) {
            const selectionError = this.handleAnalysisError(error);
            logger_1.logger.error('Algorithm selection analysis failed', {
                section: 'modeling',
                analyzer: 'intelligent-algorithm-selection',
                error: selectionError.message,
            });
            throw selectionError;
        }
    }
    /**
     * Performs incremental algorithm selection with comprehensive error handling
     */
    async performIncrementalSelection(decisionContext, 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: Generate candidate algorithms
            this.updateProgress('algorithm_discovery', (currentStep / totalSteps) * 100, 'Discovering candidate algorithms');
            const candidateAlgorithms = await this.generateCandidateAlgorithms(decisionContext);
            currentStep++;
            progressCallback?.(this.progress);
            // Step 2: Perform multi-criteria evaluation
            this.updateProgress('evaluation', (currentStep / totalSteps) * 100, 'Evaluating algorithm suitability');
            profile.selectedAlgorithms = await this.performMultiCriteriaEvaluation(candidateAlgorithms, decisionContext);
            currentStep++;
            progressCallback?.(this.progress);
            // Step 3: Generate performance predictions
            this.updateProgress('performance_prediction', (currentStep / totalSteps) * 100, 'Predicting algorithm performance');
            profile.performancePredictions = await this.generatePerformancePredictions(profile.selectedAlgorithms, decisionContext);
            currentStep++;
            progressCallback?.(this.progress);
            // Step 4: Assess risks and mitigation strategies
            this.updateProgress('risk_assessment', (currentStep / totalSteps) * 100, 'Assessing implementation risks');
            profile.riskAssessment = await this.performRiskAssessment(profile.selectedAlgorithms, decisionContext);
            currentStep++;
            progressCallback?.(this.progress);
            // Step 5: Generate ensemble recommendations
            this.updateProgress('ensemble_analysis', (currentStep / totalSteps) * 100, 'Analyzing ensemble opportunities');
            profile.ensembleRecommendations = await this.generateEnsembleRecommendations(profile.selectedAlgorithms, decisionContext);
            currentStep++;
            progressCallback?.(this.progress);
            // Step 6: Create hyperparameter optimization guidance
            this.updateProgress('hyperparameter_guidance', (currentStep / totalSteps) * 100, 'Generating hyperparameter guidance');
            profile.hyperparameterGuidance = await this.generateHyperparameterGuidance(profile.selectedAlgorithms, decisionContext);
            currentStep++;
            progressCallback?.(this.progress);
            // Step 7: Develop implementation strategy
            this.updateProgress('implementation_planning', (currentStep / totalSteps) * 100, 'Planning implementation strategy');
            profile.implementationStrategy = await this.developImplementationStrategy(profile.selectedAlgorithms, decisionContext);
            currentStep++;
            progressCallback?.(this.progress);
            // Step 8: Generate comprehensive selection reasoning
            this.updateProgress('reasoning_generation', (currentStep / totalSteps) * 100, 'Generating selection reasoning');
            profile.selectionReasoning = await this.generateSelectionReasoning(profile.selectedAlgorithms, candidateAlgorithms, decisionContext);
            currentStep++;
            this.updateProgress('finalization', 100, 'Algorithm selection complete');
            progressCallback?.(this.progress);
            return profile;
        }
        catch (error) {
            throw this.handleAnalysisError(error);
        }
    }
    /**
     * Generate candidate algorithms based on dataset characteristics and task requirements
     */
    async generateCandidateAlgorithms(decisionContext) {
        const candidates = [];
        try {
            // Determine task type and generate appropriate candidates
            const taskType = this.inferTaskType(decisionContext);
            switch (taskType) {
                case 'supervised_classification':
                    candidates.push(...this.generateClassificationCandidates(decisionContext));
                    break;
                case 'supervised_regression':
                    candidates.push(...this.generateRegressionCandidates(decisionContext));
                    break;
                case 'unsupervised_clustering':
                    candidates.push(...this.generateClusteringCandidates(decisionContext));
                    break;
                case 'time_series_forecasting':
                    candidates.push(...this.generateTimeSeriesCandidates(decisionContext));
                    break;
                case 'anomaly_detection':
                    candidates.push(...this.generateAnomalyDetectionCandidates(decisionContext));
                    break;
                default:
                    // Generate general-purpose candidates
                    candidates.push(...this.generateGeneralCandidates(decisionContext));
            }
            // Filter candidates based on constraints
            return this.filterCandidatesByConstraints(candidates, decisionContext);
        }
        catch (error) {
            this.addWarning('algorithm_discovery', 'high', `Candidate generation failed: ${error.message}`, 'algorithm_discovery', 'Limited algorithm recommendations', 'Using fallback algorithm set');
            // Return basic fallback candidates
            return this.getFallbackCandidates(decisionContext);
        }
    }
    /**
     * Perform multi-criteria evaluation of candidate algorithms
     */
    async performMultiCriteriaEvaluation(candidates, decisionContext) {
        const recommendations = [];
        try {
            for (const candidate of candidates) {
                const recommendation = await this.evaluateCandidate(candidate, decisionContext);
                recommendations.push(recommendation);
            }
            // Sort by overall suitability score
            recommendations.sort((a, b) => b.suitabilityScore - a.suitabilityScore);
            // Return top recommendations (configurable limit)
            const maxRecommendations = this.config.selectionCriteria.primaryCriteria.length * 2;
            return recommendations.slice(0, Math.min(maxRecommendations, 10));
        }
        catch (error) {
            this.addWarning('evaluation', 'high', `Multi-criteria evaluation failed: ${error.message}`, 'evaluation', 'May affect recommendation quality', 'Using simplified evaluation metrics');
            // Fallback to basic scoring
            return this.performBasicEvaluation(candidates, decisionContext);
        }
    }
    /**
     * Generate performance predictions for selected algorithms
     */
    async generatePerformancePredictions(algorithms, decisionContext) {
        const predictions = [];
        try {
            for (const algorithm of algorithms) {
                const prediction = await this.predictAlgorithmPerformance(algorithm, decisionContext);
                predictions.push(prediction);
            }
            return predictions;
        }
        catch (error) {
            this.addWarning('performance_prediction', 'medium', `Performance prediction failed: ${error.message}`, 'performance_prediction', 'Limited performance insights', 'Using theoretical performance estimates');
            // Fallback to theoretical predictions
            return this.generateTheoreticalPredictions(algorithms, decisionContext);
        }
    }
    // Helper methods
    /**
     * Initialize configuration with defaults
     */
    initializeConfig(config) {
        return {
            selectionCriteria: {
                primaryCriteria: ['performance', 'interpretability', 'implementation'],
                weights: {
                    performance: 0.4,
                    interpretability: 0.3,
                    implementation: 0.2,
                    maintenance: 0.1,
                    robustness: 0.0,
                    scalability: 0.0,
                },
                weightingStrategy: 'adaptive',
                tradeoffAcceptance: {
                    performanceForInterpretability: 0.7,
                    implementationForPerformance: 0.6,
                    robustnessForComplexity: 0.8,
                    customTradeoffs: [],
                },
            },
            performancePrediction: {
                predictionMethods: [
                    {
                        method: 'meta_learning',
                        weight: 0.5,
                        applicabilityConditions: ['sufficient_similar_datasets'],
                        fallbackMethods: ['theoretical_bounds'],
                    },
                ],
                confidenceRequirements: {
                    minimumConfidence: 0.6,
                    criticalMetrics: ['primary_metric'],
                    uncertaintyTolerance: 0.3,
                    conservatismLevel: 0.7,
                },
                benchmarkingStrategy: {
                    includeSimilarDatasets: true,
                    includeIndustryBenchmarks: true,
                    includeTheoreticalLimits: false,
                    similarityThreshold: 0.7,
                },
                uncertaintyHandling: {
                    propagationMethod: 'monte_carlo',
                    samplingSize: 1000,
                    confidenceLevel: 0.95,
                    uncertaintyVisualization: false,
                },
            },
            riskTolerance: {
                overallRiskTolerance: 'medium',
                categorySpecificTolerance: [],
                riskMitigationPreference: {
                    preventive: 0.7,
                    reactive: 0.3,
                    riskTransfer: 0.1,
                    riskAcceptance: 0.2,
                },
                contingencyPlanning: {
                    planningHorizon: 6,
                    scenarioPlanning: true,
                    contingencyBudget: 15,
                    decisionFramework: ['performance_threshold', 'time_constraint', 'resource_availability'],
                },
            },
            implementationConstraints: {
                timeConstraints: {
                    maxImplementationTime: 30,
                    phaseDeadlines: [],
                    timeBuffers: 20,
                    accelerationOptions: [],
                },
                resourceConstraints: {
                    budgetConstraints: {
                        totalBudget: 10000,
                        budgetPhasing: [],
                        contingencyPercentage: 15,
                        flexibilityRanges: [],
                    },
                    humanResourceConstraints: {
                        availableRoles: [],
                        skillConstraints: [],
                        trainingBudget: 2000,
                        externalConsultingBudget: 5000,
                    },
                    technicalResourceConstraints: {
                        computingResources: {
                            cpuHours: 100,
                            memoryGB: 16,
                            storageGB: 100,
                            networkBandwidth: 100,
                            cloudBudget: 1000,
                        },
                        softwareLicenses: {
                            availableLicenses: [],
                            licenseBudget: 1000,
                            procurementTime: 7,
                            restrictions: [],
                        },
                        dataAccessConstraints: {
                            dataAvailability: [],
                            privacyConstraints: [],
                            dataGovernance: [],
                            dataQuality: [],
                        },
                        securityConstraints: {
                            securityRequirements: [],
                            complianceFrameworks: [],
                            securityBudget: 2000,
                            securityTimeline: 14,
                        },
                    },
                    externalResourceConstraints: {
                        vendorConstraints: [],
                        partnerConstraints: [],
                        regulatoryConstraints: [],
                        marketConstraints: [],
                    },
                },
                technicalConstraints: {
                    platformConstraints: [],
                    integrationConstraints: [],
                    performanceConstraints: [],
                    scalabilityConstraints: [],
                },
                organizationalConstraints: {
                    culturalConstraints: [],
                    processConstraints: [],
                    governanceConstraints: [],
                    changeManagementConstraints: [],
                },
            },
            businessRequirements: {
                businessObjectives: [],
                stakeholderRequirements: [],
                constraintCompliance: [],
                valueRequirements: {
                    valueMetrics: [],
                    roiRequirements: {
                        minimumROI: 2.0,
                        timeframe: 12,
                        calculationMethod: 'net_present_value',
                        includedCosts: ['development', 'deployment', 'maintenance'],
                        includedBenefits: ['efficiency_gains', 'accuracy_improvements'],
                    },
                    timeToValue: {
                        targetTime: 6,
                        valueMilestones: [],
                        accelerationOptions: [],
                    },
                    sustainabilityRequirements: {
                        environmentalImpact: {
                            carbonFootprint: {
                                targetReduction: 10,
                                measurement: 'co2_equivalent',
                                offsetting: false,
                                reporting: ['quarterly'],
                            },
                            resourceUsage: {
                                efficiency: [],
                                renewable: [],
                                conservation: [],
                            },
                            wasteGeneration: {
                                wasteTypes: [],
                                reductionTargets: [],
                                disposalMethods: [],
                            },
                        },
                        socialImpact: {
                            communityBenefit: [],
                            employmentImpact: {
                                jobCreation: 0,
                                skillDevelopment: [],
                                retention: {
                                    targetRetention: 90,
                                    strategies: [],
                                    measurement: 'annual_turnover',
                                    incentives: [],
                                },
                            },
                            diversityInclusion: {
                                targets: [],
                                initiatives: [],
                                measurement: [],
                            },
                        },
                        economicSustainability: {
                            longTermViability: {
                                planningHorizon: 5,
                                viabilityMetrics: [],
                                scenarios: [],
                                adaptationStrategy: [],
                            },
                            stakeholderValue: {
                                stakeholders: [],
                                valueDistribution: [],
                                balancingStrategy: [],
                            },
                            riskManagement: {
                                riskTypes: [],
                                mitigation: [],
                                monitoring: [],
                            },
                        },
                    },
                },
            },
            computationalBudget: {
                totalBudget: {
                    computeHours: 100,
                    memoryGBHours: 1000,
                    storageGB: 100,
                    networkBandwidth: 1000,
                    specializedResources: [],
                },
                allocationStrategy: {
                    strategy: 'adaptive',
                    parameters: {},
                    reallocationTriggers: ['performance_threshold', 'time_pressure'],
                    buffers: [],
                },
                optimizationPriorities: [
                    {
                        aspect: 'algorithm_selection',
                        priority: 1,
                        budgetAllocation: 40,
                        optimization: ['multi_criteria_optimization', 'parallel_evaluation'],
                    },
                ],
                resourceManagement: {
                    monitoring: {
                        metrics: ['cpu_usage', 'memory_usage', 'execution_time'],
                        frequency: 'real_time',
                        alerting: [],
                        reporting: [],
                    },
                    optimization: {
                        techniques: [],
                        automation: [],
                        costOptimization: [],
                    },
                    scaling: {
                        scalingPolicies: [],
                        scalingTriggers: [],
                        scalingLimits: [],
                    },
                    cleanup: {
                        policies: [],
                        automation: [],
                        verification: [],
                    },
                },
            },
            ...config,
        };
    }
    /**
     * Initialize progress tracking
     */
    initializeProgress() {
        return {
            phase: 'initialization',
            progress: 0,
            currentOperation: 'Initializing',
            estimatedTimeRemaining: 0,
            completedComponents: [],
            errorCount: 0,
            warningCount: 0,
            algorithmsEvaluated: 0,
            selectionConfidence: 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.implementationConstraints.timeConstraints.maxImplementationTime * 1000;
        const elapsed = Date.now() - this.startTime;
        const estimatedTotal = elapsed / (progress / 100);
        return Math.max(0, estimatedTotal - elapsed);
    }
    /**
     * Calculate total analysis steps
     */
    calculateTotalSteps() {
        return 8; // Fixed number of major steps in the analysis pipeline
    }
    /**
     * Validate inputs before analysis
     */
    validateInputs(section1Result, section2Result, section3Result, complexityProfile) {
        if (!section1Result?.overview) {
            throw new types_1.SelectionError('Invalid Section 1 result', 'data_error', 'validation', false);
        }
        if (!section2Result) {
            throw new types_1.SelectionError('Invalid Section 2 result', 'data_error', 'validation', false);
        }
        if (!section3Result) {
            throw new types_1.SelectionError('Invalid Section 3 result', 'data_error', 'validation', false);
        }
        if (!complexityProfile) {
            throw new types_1.SelectionError('Invalid complexity profile', 'data_error', 'validation', false);
        }
    }
    /**
     * Extract decision context from all input sources
     */
    extractDecisionContext(section1Result, section2Result, section3Result, complexityProfile) {
        const overview = section1Result.overview;
        return {
            datasetSize: overview.structuralDimensions.totalDataRows,
            featureCount: overview.structuralDimensions.totalColumns,
            dataQuality: this.assessDataQuality(section2Result),
            complexity: complexityProfile,
            businessContext: this.config.businessRequirements,
            constraints: this.config.implementationConstraints,
            section1Result,
            section2Result,
            section3Result,
        };
    }
    /**
     * 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.SelectionError) {
            return error;
        }
        const errorMessage = error instanceof Error ? error.message : 'Unknown error occurred';
        return new types_1.SelectionError(`Algorithm selection failed: ${errorMessage}`, 'computational_error', 'general', false);
    }
    /**
     * Generate selection metadata
     */
    generateSelectionMetadata(decisionContext) {
        return {
            selectionTimestamp: new Date(),
            selectionVersion: '1.0.0',
            computationTime: Math.max(1, Date.now() - this.startTime),
            dataCharacteristics: {
                size: decisionContext?.datasetSize || 0,
                features: decisionContext?.featureCount || 0,
                complexity: decisionContext?.complexity?.overallComplexityScore || 50,
                quality: decisionContext?.dataQuality || 0.8,
                domain: 'unknown',
                taskType: 'unknown',
            },
            selectionConfidence: 0.8,
            assumptions: [
                'Standard ML workflow assumptions',
                'Typical business requirements',
                'Common computational constraints',
            ],
            limitations: [
                'Limited historical performance data',
                'Simplified complexity assessment',
                'Generic recommendation framework',
            ],
            reproducibilityInfo: {
                deterministicSelection: true,
                softwareVersions: [],
                configurationParameters: this.config,
                environmentInfo: {
                    platform: 'Node.js',
                    architecture: 'Unknown',
                    runtime: 'TypeScript',
                    dependencies: [],
                },
            },
        };
    }
    // Placeholder methods for incremental implementation
    inferTaskType(decisionContext) {
        // Placeholder - will be enhanced incrementally
        return 'supervised_classification';
    }
    generateClassificationCandidates(decisionContext) {
        // Placeholder - basic candidate for testing
        return [
            {
                name: 'Logistic Regression',
                family: 'linear_models',
                category: 'classical_ml',
                complexity: 'simple',
                interpretability: 'high',
                applicabilityConditions: ['classification_task'],
                constraints: [],
            },
        ];
    }
    generateRegressionCandidates(_decisionContext) {
        // Placeholder
        return [];
    }
    generateClusteringCandidates(_decisionContext) {
        // Placeholder
        return [];
    }
    generateTimeSeriesCandidates(_decisionContext) {
        // Placeholder
        return [];
    }
    generateAnomalyDetectionCandidates(_decisionContext) {
        // Placeholder
        return [];
    }
    generateGeneralCandidates(decisionContext) {
        // Placeholder - basic candidate for testing
        return [
            {
                name: 'Random Forest',
                family: 'ensemble_methods',
                category: 'classical_ml',
                complexity: 'moderate',
                interpretability: 'medium',
                applicabilityConditions: ['general_purpose'],
                constraints: [],
            },
        ];
    }
    filterCandidatesByConstraints(candidates, _decisionContext) {
        // Placeholder
        return candidates;
    }
    getFallbackCandidates(_decisionContext) {
        // Placeholder - basic fallback algorithms
        return [];
    }
    async evaluateCandidate(candidate, decisionContext) {
        // Placeholder - will implement comprehensive evaluation
        return {
            algorithmName: candidate.name,
            algorithmFamily: 'linear_models',
            category: 'classical_ml',
            suitabilityScore: 75,
            complexity: 'moderate',
            interpretability: 'medium',
            scoringBreakdown: {
                dataFitScore: 75,
                performanceScore: 70,
                interpretabilityScore: 80,
                implementationScore: 85,
                robustnessScore: 70,
                scalabilityScore: 65,
                maintenanceScore: 80,
                technicalScore: 72,
                businessScore: 78,
                overallScore: 75,
                weightingStrategy: {
                    weights: {
                        dataFit: this.config.selectionCriteria.weights.performance * 0.3,
                        performance: this.config.selectionCriteria.weights.performance,
                        interpretability: this.config.selectionCriteria.weights.interpretability,
                        implementation: this.config.selectionCriteria.weights.implementation,
                        robustness: this.config.selectionCriteria.weights.robustness,
                        scalability: this.config.selectionCriteria.weights.scalability,
                        maintenance: this.config.selectionCriteria.weights.maintenance,
                    },
                    justification: 'Default weighting strategy',
                    adaptiveWeighting: false,
                    uncertaintyHandling: {
                        method: 'conservative',
                        uncertaintyPenalty: 0.1,
                        confidenceThreshold: 0.6,
                    },
                },
                confidenceInterval: [70, 80],
            },
            datasetFitAnalysis: {
                complexityAlignment: {
                    intrinsicComplexityFit: 0.7,
                    nonLinearityHandling: 0.6,
                    interactionCapturing: 0.5,
                    manifoldLearning: 0.4,
                    overallAlignment: 0.6,
                },
                featureCompatibility: {
                    numericFeatureHandling: {
                        nativeSupport: true,
                        preprocessingRequired: false,
                        performanceImpact: 'none',
                        implementationComplexity: 'simple',
                        recommendations: [],
                    },
                    categoricalFeatureHandling: {
                        nativeSupport: false,
                        preprocessingRequired: true,
                        performanceImpact: 'minimal',
                        implementationComplexity: 'simple',
                        recommendations: ['One-hot encoding', 'Label encoding'],
                    },
                    textFeatureHandling: {
                        nativeSupport: false,
                        preprocessingRequired: true,
                        performanceImpact: 'moderate',
                        implementationComplexity: 'complex',
                        recommendations: ['TF-IDF vectorization', 'Word embeddings'],
                    },
                    temporalFeatureHandling: {
                        nativeSupport: false,
                        preprocessingRequired: true,
                        performanceImpact: 'minimal',
                        implementationComplexity: 'moderate',
                        recommendations: ['Feature extraction', 'Time-based features'],
                    },
                    mixedTypeHandling: {
                        nativeSupport: false,
                        preprocessingRequired: true,
                        performanceImpact: 'moderate',
                        implementationComplexity: 'moderate',
                        recommendations: ['Preprocessing pipeline'],
                    },
                    highDimensionalitySupport: {
                        nativeSupport: true,
                        preprocessingRequired: false,
                        performanceImpact: 'moderate',
                        implementationComplexity: 'simple',
                        recommendations: [],
                    },
                },
                sizeSuitability: {
                    optimalSampleSize: {
                        minimum: 100,
                        recommended: 1000,
                        optimal: 10000,
                    },
                    currentDatasetFit: {
                        fit: 'adequate',
                        confidenceImpact: 0.8,
                        recommendations: [],
                    },
                    scalabilityCharacteristics: {
                        horizontalScalability: 'good',
                        verticalScalability: 'fair',
                        distributedSupport: false,
                        streamingCapability: false,
                        incrementalLearning: false,
                    },
                    memoryEfficiency: {
                        memoryComplexity: 'O(n)',
                        inMemoryRequirement: true,
                        diskBasedSupport: false,
                        streamingSupport: false,
                        memoryOptimizations: [],
                    },
                    computationalScaling: {
                        trainingComplexity: 'O(n)',
                        predictionComplexity: 'O(1)',
                        parallelizability: {
                            trainingParallelization: true,
                            predictionParallelization: true,
                            gpuSupport: false,
                            tpuSupport: false,
                            distributedTraining: false,
                        },
                        hardwareOptimizations: [],
                    },
                },
                distributionCompatibility: {
                    gaussianAssumptions: true,
                    robustToOutliers: false,
                    skewnessHandling: {
                        leftSkewTolerance: 0.3,
                        rightSkewTolerance: 0.3,
                        transformationRecommendations: ['Log transformation', 'Box-Cox'],
                        nativeSkewHandling: false,
                    },
                    multimodalSupport: false,
                    distributionFreeApproach: false,
                },
                noiseRobustness: {
                    noiseResistance: 0.5,
                    outlierRobustness: 0.3,
                    labelNoiseHandling: 0.4,
                    featureNoiseHandling: 0.5,
                    robustnessMechanisms: [],
                },
                sparsityHandling: {
                    sparseSupport: false,
                    sparseOptimizations: false,
                    sparsityThreshold: 0.5,
                    densificationRequired: true,
                    sparsityStrategies: [],
                },
            },
            expectedPerformance: {
                accuracy: 0.75,
                precision: 0.74,
                recall: 0.76,
                f1Score: 0.75,
                confidenceInterval: [0.7, 0.8],
                performanceFactors: [],
            },
            computationalRequirements: {
                trainingRequirements: {
                    cpu: {
                        cores: 1,
                        architecture: ['x86_64'],
                        specialInstructions: [],
                        utilization: 0.8,
                    },
                    memory: {
                        minimum: 256,
                        recommended: 512,
                        scaling: 'linear',
                        pattern: 'in_memory',
                    },
                    timeComplexity: 'O(n)',
                    parallelization: {
                        dataParallelism: true,
                        modelParallelism: false,
                        maxWorkers: 4,
                        efficiency: 0.8,
                    },
                },
                predictionRequirements: {
                    cpu: {
                        cores: 1,
                        architecture: ['x86_64'],
                        specialInstructions: [],
                        utilization: 0.1,
                    },
                    memory: {
                        minimum: 64,
                        recommended: 128,
                        scaling: 'constant',
                        pattern: 'in_memory',
                    },
                    timeComplexity: 'O(1)',
                    parallelization: {
                        dataParallelism: true,
                        modelParallelism: false,
                        maxWorkers: 8,
                        efficiency: 0.9,
                    },
                },
                storageRequirements: {
                    modelSize: 10,
                    temporaryStorage: 100,
                    dataFormat: ['pickle', 'joblib'],
                    compressionSupport: true,
                },
                networkRequirements: {
                    bandwidth: 1,
                    latency: 100,
                    reliability: 0.99,
                    distributedSupport: false,
                },
            },
            implementationDetails: {
                frameworks: [
                    {
                        framework: 'scikit-learn',
                        supportLevel: 'native',
                        maturity: 'mature',
                        documentation: 'excellent',
                        communitySize: 'large',
                    },
                ],
                libraries: [
                    {
                        library: 'scikit-learn',
                        version: '>=0.24.0',
                        purpose: 'Core implementation',
                        alternatives: ['statsmodels'],
                        pros: ['Extensive documentation', 'Large community'],
                        cons: ['Limited deep learning support'],
                    },
                ],
                dependencies: [
                    {
                        dependency: 'numpy',
                        version: '>=1.19.0',
                        optional: false,
                        purpose: 'Numerical computations',
                        installationComplexity: 'simple',
                    },
                ],
                configuration: {
                    requiredParameters: [],
                    optionalParameters: [],
                    tuningPriority: [],
                    bestPractices: [],
                },
                deployment: {
                    deploymentPatterns: [
                        {
                            pattern: 'batch',
                            suitability: 0.9,
                            considerations: ['Offline processing'],
                            implementation: ['Scheduled jobs'],
                        },
                    ],
                    infrastructureRequirements: [],
                    scalingConsiderations: [],
                    maintenanceRequirements: [],
                },
            },
            riskFactors: [],
            limitations: [],
            mitigationStrategies: [],
            businessAlignment: {
                businessObjectives: [],
                stakeholderRequirements: [],
                constraintCompliance: [],
                valueProposition: {
                    primaryValue: 'Baseline performance',
                    quantifiableVBenefits: [],
                    qualitativeBenefits: ['Interpretability', 'Fast training'],
                    investmentRequired: {
                        initialInvestment: 1000,
                        ongoingCosts: 100,
                        resourceRequirements: ['Data scientist'],
                        timeInvestment: 5,
                    },
                    roi: {
                        expectedROI: 2.0,
                        paybackPeriod: 3,
                        netPresentValue: 5000,
                        riskAdjustedROI: 1.8,
                        assumptions: ['Standard adoption curve'],
                    },
                },
            },
            advancedFeatures: [],
            customizationOptions: [],
        };
    }
    async performBasicEvaluation(_candidates, _decisionContext) {
        // Placeholder
        return [];
    }
    async predictAlgorithmPerformance(algorithm, decisionContext) {
        // Placeholder
        return {
            algorithmName: algorithm.algorithmName,
            expectedMetrics: {
                primaryMetric: {
                    metricName: 'accuracy',
                    expectedValue: 0.75,
                    confidenceInterval: [0.7, 0.8],
                    predictionMethod: 'theoretical_bounds',
                    reliability: 0.7,
                },
                secondaryMetrics: [],
                businessMetrics: [],
                robustnessMetrics: [],
            },
            confidenceIntervals: {
                methodology: 'bootstrap',
                confidenceLevel: 0.95,
                intervalWidth: 0.1,
                asymmetry: 0,
            },
            performanceFactors: [],
            scenarioAnalysis: {
                bestCase: {
                    scenario: 'Optimal conditions',
                    probability: 0.2,
                    expectedPerformance: 0.85,
                    keyFactors: ['High quality data', 'Optimal hyperparameters'],
                    implications: ['Excellent results possible'],
                },
                expectedCase: {
                    scenario: 'Standard conditions',
                    probability: 0.6,
                    expectedPerformance: 0.75,
                    keyFactors: ['Typical data quality', 'Default parameters'],
                    implications: ['Good baseline performance'],
                },
                worstCase: {
                    scenario: 'Challenging conditions',
                    probability: 0.2,
                    expectedPerformance: 0.65,
                    keyFactors: ['Poor data quality', 'Suboptimal setup'],
                    implications: ['May need additional preprocessing'],
                },
                sensitivityAnalysis: [],
            },
            benchmarkComparison: {
                similarDatasets: [],
                industryBenchmarks: [],
                theoreticalLimits: [],
                relativeRanking: {
                    algorithmRankings: [],
                    rankingCriteria: [],
                    rankingConfidence: 0.7,
                    rankingStability: 0.8,
                },
            },
        };
    }
    async generateTheoreticalPredictions(_algorithms, _decisionContext) {
        // Placeholder
        return [];
    }
    async performRiskAssessment(_algorithms, _decisionContext) {
        // Placeholder
        return {
            overallRiskLevel: 'medium',
            riskCategories: [],
            mitigationPlan: {
                primaryMitigations: [],
                secondaryMitigations: [],
                preventiveMeasures: [],
                contingencyPlans: [],
            },
            contingencyStrategies: [],
            monitoringPlan: {
                monitoringMetrics: [],
                alertThresholds: [],
                reviewSchedule: {
                    frequency: 'monthly',
                    scope: ['Performance', 'Risk indicators'],
                    participants: ['Data scientist', 'Project manager'],
                    deliverables: ['Risk assessment report'],
                },
                escalationProcedures: [],
            },
        };
    }
    async generateEnsembleRecommendations(_algorithms, _decisionContext) {
        // Placeholder
        return [];
    }
    async generateHyperparameterGuidance(_algorithms, _decisionContext) {
        // Placeholder
        return {
            optimizationStrategy: {
                primaryMethod: 'grid_search',
                fallbackMethods: ['random_search'],
                multiPhaseStrategy: [],
                earlyStoppingCriteria: [],
                warmStartingStrategy: {
                    strategy: 'default_parameters',
                    sourceData: [],
                    transferability: 0.5,
                    adaptation: [],
                },
            },
            parameterImportance: [],
            searchSpaceDefinition: {
                continuousParameters: [],
                discreteParameters: [],
                categoricalParameters: [],
                conditionalParameters: [],
                constraints: [],
            },
            optimizationBudget: {
                totalEvaluations: 100,
                parallelEvaluations: 4,
                timeLimit: 60,
                computeResources: {
                    cpuHours: 10,
                    memoryGB: 8,
                    storageGB: 50,
                    networkBandwidth: 100,
                },
                adaptiveBudgetAllocation: true,
            },
            multiObjectiveConsiderations: {
                objectives: [],
                tradeoffAnalysis: {
                    objectivePairs: [],
                    tradeoffStrength: 0.5,
                    dominantObjectives: [],
                    compromiseSolutions: [],
                },
                paretoOptimization: {
                    enabled: false,
                    frontierSize: 10,
                    diversityMaintenance: true,
                    convergenceMetrics: [],
                },
                scalarization: {
                    method: 'weighted_sum',
                    parameters: {},
                    adaptiveWeights: false,
                    robustness: 0.7,
                },
            },
            transferLearningOpportunities: [],
        };
    }
    async developImplementationStrategy(_algorithms, _decisionContext) {
        // Placeholder
        return {
            phaseAPproach: [],
            riskMitigation: {
                identifiedRisks: [],
                mitigationStrategies: [],
                contingencyPlans: [],
                monitoring: {
                    monitoringPoints: [],
                    escalationProcedures: [],
                    reviewFrequency: 'weekly',
                    responsibilityMatrix: [],
                },
            },
            resourcePlanning: {
                humanResources: [],
                technicalResources: [],
                budgetEstimate: {
                    totalCost: 10000,
                    costBreakdown: [],
                    confidenceLevel: 0.7,
                    contingencyFactor: 0.2,
                    costRange: [8000, 12000],
                },
                resourceTimeline: {
                    milestones: [],
                    criticalPath: [],
                    buffers: [],
                    dependencies: [],
                },
            },
            timeline: {
                totalDuration: 30,
                phases: [],
                criticalMilestones: [],
                buffers: [],
                parallelActivities: [],
            },
            qualityAssurance: {
                qualityStandards: [],
                testingStrategy: {
                    testingPhases: [],
                    testingTypes: [],
                    automationLevel: 0.8,
                    coverageTargets: [],
                },
                reviewProcesses: [],
                validationCriteria: [],
            },
            rolloutStrategy: {
                rolloutPhases: [],
                rolloutCriteria: [],
                rollbackPlan: {
                    triggers: ['Performance degradation', 'System failure'],
                    procedures: ['Revert to previous model', 'Notify stakeholders'],
                    timeframe: 4,
                    dataProtection: ['Backup current model', 'Preserve training data'],
                    communication: ['Status update', 'Impact assessment'],
                },
                userAcceptance: {
                    userGroups: [],
                    acceptanceCriteria: [],
                    training: {
                        trainingModules: [],
                        deliveryMethods: ['Online training', 'Documentation'],
                        timeline: 5,
                        assessment: ['Knowledge quiz', 'Practical demonstration'],
                    },
                    support: {
                        supportChannels: [],
                        escalationProcedures: [],
                        knowledgeBase: [],
                        feedbackMechanisms: [],
                    },
                },
            },
        };
    }
    async generateSelectionReasoning(_selectedAlgorithms, _candidateAlgorithms, _decisionContext) {
        // Placeholder
        return {
            selectionCriteria: [],
            decisionTree: {
                decision: 'Algorithm selection based on multi-criteria analysis',
                reasoning: 'Evaluated candidates against performance, interpretability, and implementation criteria',
                alternatives: [],
                confidence: 0.8,
            },
            alternativeConsiderations: [],
            selectionConfidence: {
                overallConfidence: 0.8,
                confidenceFactors: [],
                uncertaintySource: [],
                robustnessToAssumptions: 0.7,
            },
            assumptions: [],
            sensitivityAnalysis: {
                parameterSensitivity: [],
                assumptionSensitivity: [],
                dataCharacteristicSensitivity: [],
            },
        };
    }
    assessDataQuality(_section2Result) {
        // Placeholder - will implement actual quality assessment
        return 0.8;
    }
}
exports.IntelligentAlgorithmSelectionAnalyzer = IntelligentAlgorithmSelectionAnalyzer;
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