datapilot-cli
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Enterprise-grade streaming multi-format data analysis with comprehensive statistical insights and intelligent relationship detection - supports CSV, JSON, Excel, TSV, Parquet - memory-efficient, cross-platform
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JavaScript
"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: [],