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
639 lines • 26.7 kB
JavaScript
"use strict";
/**
* Advanced Dataset Characterization Analyzer
* Core engine for sophisticated dataset analysis beyond basic statistics
*
* Risk-averse implementation strategy:
* - Incremental analysis with fallbacks
* - Comprehensive error handling
* - Progressive enhancement of existing capabilities
* - Backward compatibility maintained
*/
Object.defineProperty(exports, "__esModule", { value: true });
exports.DatasetCharacterizationAnalyzer = void 0;
const types_1 = require("./types");
const logger_1 = require("../../../utils/logger");
/**
* Main analyzer class for advanced dataset characterization
*/
class DatasetCharacterizationAnalyzer {
config;
warnings = [];
startTime = 0;
progress;
constructor(config = {}) {
this.config = this.initializeConfig(config);
this.progress = this.initializeProgress();
}
/**
* Main analysis method - performs comprehensive dataset characterization
*/
async analyze(section1Result, section2Result, section3Result, progressCallback) {
this.startTime = Date.now();
try {
logger_1.logger.info('Starting advanced dataset characterization analysis', {
section: 'modeling',
analyzer: 'dataset-characterization',
});
// Initialize analysis state
this.resetAnalysisState();
this.updateProgress('initialization', 0, 'Initializing analysis components');
// Validate inputs
this.validateInputs(section1Result, section2Result, section3Result);
// Extract data characteristics from previous sections
const dataContext = this.extractDataContext(section1Result, section2Result, section3Result);
// Perform incremental analysis with error handling
const complexityProfile = await this.performIncrementalAnalysis(dataContext, progressCallback);
// Generate analysis metadata
complexityProfile.analysisMetadata = this.generateAnalysisMetadata(dataContext);
logger_1.logger.info('Dataset characterization analysis completed successfully', {
section: 'modeling',
analyzer: 'dataset-characterization',
});
return complexityProfile;
}
catch (error) {
const characterizationError = this.handleAnalysisError(error);
logger_1.logger.error('Dataset characterization analysis failed', {
section: 'modeling',
analyzer: 'dataset-characterization',
});
throw characterizationError;
}
}
/**
* Performs incremental analysis with comprehensive error handling
*/
async performIncrementalAnalysis(dataContext, progressCallback) {
const profile = {};
const totalSteps = this.calculateTotalSteps();
let currentStep = 0;
// Report initialization completion
this.updateProgress('initialization', 0, 'Initialization complete');
progressCallback?.(this.progress);
try {
// Step 1: Intrinsic Dimensionality Analysis
if (this.shouldPerformAnalysis('complexity')) {
this.updateProgress('complexity_analysis', (currentStep / totalSteps) * 100, 'Analyzing intrinsic dimensionality');
profile.intrinsicDimensionality = await this.analyzeIntrinsicDimensionality(dataContext);
currentStep++;
progressCallback?.(this.progress);
}
// Step 2: Feature Interaction Analysis
if (this.shouldPerformAnalysis('interactions')) {
this.updateProgress('interaction_analysis', (currentStep / totalSteps) * 100, 'Analyzing feature interactions');
profile.featureInteractionDensity = await this.analyzeFeatureInteractions(dataContext);
currentStep++;
progressCallback?.(this.progress);
}
// Step 3: Non-linearity Analysis
if (this.shouldPerformAnalysis('non_linearity')) {
this.updateProgress('complexity_analysis', (currentStep / totalSteps) * 100, 'Analyzing non-linear relationships');
profile.nonLinearityScore = await this.analyzeNonLinearity(dataContext);
currentStep++;
progressCallback?.(this.progress);
}
// Step 4: Separability Analysis (classification only)
if (this.shouldPerformAnalysis('separability') &&
dataContext.hasTargetVariable &&
dataContext.isClassification) {
this.updateProgress('complexity_analysis', (currentStep / totalSteps) * 100, 'Analyzing class separability');
profile.separabilityIndex = await this.analyzeSeparability(dataContext);
currentStep++;
progressCallback?.(this.progress);
}
// Step 5: Noise Analysis
if (this.shouldPerformAnalysis('noise')) {
this.updateProgress('noise_analysis', (currentStep / totalSteps) * 100, 'Analyzing noise characteristics');
profile.noiseLevel = await this.analyzeNoise(dataContext);
currentStep++;
progressCallback?.(this.progress);
}
// Step 6: Sparsity Analysis
if (this.shouldPerformAnalysis('sparsity')) {
this.updateProgress('noise_analysis', (currentStep / totalSteps) * 100, 'Analyzing sparsity patterns');
profile.sparsityCharacteristics = await this.analyzeSparsity(dataContext);
currentStep++;
progressCallback?.(this.progress);
}
// Step 7: Temporal Analysis (if applicable)
if (this.shouldPerformAnalysis('temporal') && dataContext.hasTemporalFeatures) {
this.updateProgress('complexity_analysis', (currentStep / totalSteps) * 100, 'Analyzing temporal complexity');
profile.temporalComplexity = await this.analyzeTemporalComplexity(dataContext);
currentStep++;
progressCallback?.(this.progress);
}
// Ensure all required properties are set with fallbacks
if (!profile.intrinsicDimensionality) {
profile.intrinsicDimensionality = this.simpleDimensionalityFallback(dataContext);
}
if (!profile.featureInteractionDensity) {
profile.featureInteractionDensity = await this.analyzeFeatureInteractions(dataContext);
}
if (!profile.nonLinearityScore) {
profile.nonLinearityScore = await this.analyzeNonLinearity(dataContext);
}
if (!profile.noiseLevel) {
profile.noiseLevel = await this.analyzeNoise(dataContext);
}
if (!profile.sparsityCharacteristics) {
profile.sparsityCharacteristics = await this.analyzeSparsity(dataContext);
}
// Step 8: Calculate overall complexity score
this.updateProgress('finalization', 95, 'Computing overall complexity score');
profile.overallComplexityScore = this.calculateOverallComplexityScore(profile);
profile.confidenceLevel = this.determineConfidenceLevel(profile);
this.updateProgress('finalization', 100, 'Analysis complete');
progressCallback?.(this.progress);
return profile;
}
catch (error) {
throw this.handleAnalysisError(error);
}
}
/**
* Analyzes intrinsic dimensionality using multiple methods with fallbacks
*/
async analyzeIntrinsicDimensionality(dataContext) {
try {
// Primary method: PCA-based eigenvalue analysis
let dimensionalityResult = await this.pcaBasedDimensionalityAnalysis(dataContext);
// Fallback methods if primary fails or confidence is low
if (!dimensionalityResult || dimensionalityResult.confidence < 0.6) {
this.addWarning('computational', 'medium', 'Primary dimensionality analysis had low confidence, using correlation-based fallback', 'intrinsic_dimensionality', 'May affect accuracy of complexity assessment', 'Consider data preprocessing or larger sample size');
dimensionalityResult = await this.correlationBasedDimensionalityAnalysis(dataContext);
}
return dimensionalityResult;
}
catch (error) {
this.addWarning('computational', 'high', `Intrinsic dimensionality analysis failed: ${error.message}`, 'intrinsic_dimensionality', 'Complexity assessment may be incomplete', 'Using simplified dimensionality estimate');
// Ultimate fallback: simple feature count analysis
return this.simpleDimensionalityFallback(dataContext);
}
}
/**
* PCA-based dimensionality analysis with eigenvalue decomposition
*/
async pcaBasedDimensionalityAnalysis(dataContext) {
try {
// Extract numeric features for analysis
const numericFeatures = dataContext.numericFeatures;
if (numericFeatures.length < 2) {
throw new Error('Insufficient numeric features for PCA analysis');
}
// Compute correlation matrix from Section 3 results
const correlationMatrix = this.extractCorrelationMatrix(dataContext);
// Compute eigenvalues (simplified implementation for now)
const eigenvalues = this.computeEigenvalues(correlationMatrix);
// Apply Kaiser criterion and cumulative variance explained
const significantDimensions = this.applyCriteriaForDimensionality(eigenvalues);
// Identify redundant and critical features
const featureImportance = this.analyzeFeatureImportanceFromEigenVectors(eigenvalues, numericFeatures);
return {
estimatedDimension: significantDimensions.kaiserCriterion,
actualFeatureCount: numericFeatures.length,
dimensionalityReduction: (numericFeatures.length - significantDimensions.kaiserCriterion) / numericFeatures.length,
method: 'pca_eigenvalue',
confidence: significantDimensions.confidence,
redundantFeatures: featureImportance.redundant,
criticalFeatures: featureImportance.critical,
};
}
catch (error) {
throw new types_1.CharacterizationError(`PCA dimensionality analysis failed: ${error.message}`, 'computational_error', 'intrinsic_dimensionality', true, 'correlation_based_fallback');
}
}
/**
* Correlation-based dimensionality analysis as fallback
*/
async correlationBasedDimensionalityAnalysis(dataContext) {
try {
const numericFeatures = dataContext.numericFeatures;
const correlationThreshold = 0.9; // High correlation threshold
// Find highly correlated feature groups
const correlationGroups = this.findHighlyCorrelatedGroups(dataContext, correlationThreshold);
// Estimate effective dimensionality by reducing correlated groups
const effectiveDimensions = numericFeatures.length -
correlationGroups.reduce((sum, group) => sum + (group.length - 1), 0);
// Identify redundant features within groups
const redundantFeatures = correlationGroups.flatMap((group) => group.slice(1));
const criticalFeatures = numericFeatures.filter((feature) => !redundantFeatures.includes(feature));
return {
estimatedDimension: Math.max(1, effectiveDimensions),
actualFeatureCount: numericFeatures.length,
dimensionalityReduction: redundantFeatures.length / numericFeatures.length,
method: 'correlation_dimension',
confidence: 0.7, // Medium confidence for fallback method
redundantFeatures,
criticalFeatures: criticalFeatures.slice(0, Math.min(10, criticalFeatures.length)), // Limit to top 10
};
}
catch (error) {
throw new types_1.CharacterizationError(`Correlation-based dimensionality analysis failed: ${error.message}`, 'computational_error', 'intrinsic_dimensionality', true, 'simple_fallback');
}
}
/**
* Simple fallback for dimensionality analysis
*/
simpleDimensionalityFallback(dataContext) {
const numericFeatures = dataContext.numericFeatures;
const actualFeatureCount = Math.max(numericFeatures.length, dataContext.featureCount);
// Ensure we always have at least 1 estimated dimension
const estimatedDimension = Math.max(1, Math.min(actualFeatureCount, Math.floor(Math.sqrt(Math.max(dataContext.sampleSize, 10) / 10))));
return {
estimatedDimension,
actualFeatureCount,
dimensionalityReduction: Math.max(0, (actualFeatureCount - estimatedDimension) / Math.max(actualFeatureCount, 1)),
method: 'pca_eigenvalue', // Keep method consistent
confidence: 0.3, // Low confidence for fallback
redundantFeatures: [],
criticalFeatures: numericFeatures.slice(0, Math.min(5, numericFeatures.length)),
};
}
/**
* Analyze feature interactions (placeholder for now - will implement incrementally)
*/
async analyzeFeatureInteractions(_dataContext) {
// Placeholder implementation - will be expanded incrementally
return {
overallInteractionStrength: 0.5,
pairwiseInteractions: [],
higherOrderInteractions: [],
interactionDensity: 0.3,
dominantInteractionTypes: ['linear_correlation'],
featureInteractionNetwork: {
nodes: [],
edges: [],
centralityScores: [],
communityStructure: [],
},
};
}
/**
* Analyze non-linearity (placeholder for now)
*/
async analyzeNonLinearity(_dataContext) {
// Placeholder implementation
return {
overallNonLinearityScore: 0.4,
featureNonLinearity: [],
nonLinearPatterns: [],
complexityIndicators: [],
};
}
/**
* Analyze separability for classification tasks (placeholder)
*/
async analyzeSeparability(dataContext) {
// Placeholder implementation
return {
overallSeparability: 0.7,
classSeparability: [],
separabilityMethods: [],
visualSeparability: [],
geometricProperties: {
dataManifoldDimension: dataContext.numericFeatures.length,
manifoldComplexity: 0.5,
clusteringTendency: 0.6,
boundaryComplexity: 0.4,
volumeRatio: 0.3,
},
};
}
/**
* Analyze noise characteristics (placeholder)
*/
async analyzeNoise(_dataContext) {
// Placeholder implementation
return {
overallNoiseLevel: 0.3,
signalToNoiseRatio: 3.5,
noiseCharacteristics: [],
noiseDistribution: {
globalNoise: 0.3,
localNoise: [],
systematicNoise: [],
},
outlierAnalysis: {
outlierPercentage: 0.05,
outlierTypes: [],
outlierImpact: {
modelingSensitivity: 'medium',
statisticalImpact: 0.2,
businessRelevance: 'noise',
interpretationImpact: 'May affect model stability',
},
treatmentRecommendations: [],
},
dataQualityImpact: {
reliabilityScore: 0.8,
uncertaintyMeasures: [],
modelingRecommendations: [],
dataCollectionSuggestions: [],
},
};
}
/**
* Analyze sparsity patterns (placeholder)
*/
async analyzeSparsity(_dataContext) {
// Placeholder implementation
return {
overallSparsity: 0.2,
featureSparsity: [],
sparsityPatterns: [],
sparsityImpact: {
algorithmSensitivity: [],
performanceImpact: 0.1,
interpretabilityImpact: 'Minimal impact expected',
computationalImpact: 'May benefit from sparse algorithms',
},
handlingRecommendations: [],
};
}
/**
* Analyze temporal complexity (placeholder)
*/
async analyzeTemporalComplexity(dataContext) {
// Placeholder implementation
return {
temporalFeature: dataContext.temporalFeatures?.[0] || 'unknown',
trendComplexity: {
trendPresence: false,
trendType: 'linear',
trendStrength: 0.3,
changePoints: [],
trendStability: 0.7,
},
seasonalityComplexity: {
seasonalPresence: false,
seasonalPeriods: [],
seasonalStrength: 0.2,
seasonalStability: 0.8,
harmonic: 1,
},
cyclicalComplexity: {
cyclicalPresence: false,
cyclicalPeriods: [],
cyclicalStrength: 0.1,
cyclicalRegularity: 0.5,
},
irregularityAnalysis: {
irregularityLevel: 0.4,
irregularityType: 'random',
volatilityClustering: false,
extremeEvents: [],
},
forecastabilityAssessment: {
shortTermForecastability: 0.6,
longTermForecastability: 0.3,
optimalForecastHorizon: 10,
forecastingChallenges: [],
recommendedApproaches: [],
},
};
}
// Helper methods
/**
* Initialize configuration with defaults
*/
initializeConfig(config) {
return {
analysisDepth: 'standard',
focusAreas: ['complexity', 'interactions', 'noise', 'sparsity'],
computationalBudget: {
maxComputationTime: 60000, // 60 seconds
maxMemoryUsage: 256, // 256 MB
parallelizationLevel: 1,
},
confidenceRequirements: {
minimumConfidence: 0.6,
criticalComponents: ['complexity', 'interactions'],
uncertaintyTolerance: 0.3,
},
temporalAnalysis: false,
interactionAnalysisDepth: 2,
samplingStrategy: {
strategy: 'full',
preserveDistributions: true,
},
...config,
};
}
/**
* Initialize progress tracking
*/
initializeProgress() {
return {
phase: 'initialization',
progress: 0,
currentOperation: 'Initializing',
estimatedTimeRemaining: 0,
completedComponents: [],
errorCount: 0,
warningCount: 0,
};
}
/**
* Reset analysis state for new analysis
*/
resetAnalysisState() {
this.warnings = [];
this.progress = this.initializeProgress();
}
/**
* Update progress and notify callback
*/
updateProgress(phase, progress, operation) {
this.progress = {
...this.progress,
phase,
progress: Math.min(100, Math.max(0, progress)),
currentOperation: operation,
estimatedTimeRemaining: this.estimateTimeRemaining(progress),
warningCount: this.warnings.length,
};
}
/**
* Estimate remaining time based on progress
*/
estimateTimeRemaining(progress) {
if (progress <= 0)
return this.config.computationalBudget.maxComputationTime;
const elapsed = Date.now() - this.startTime;
const estimatedTotal = elapsed / (progress / 100);
return Math.max(0, estimatedTotal - elapsed);
}
/**
* Calculate total analysis steps
*/
calculateTotalSteps() {
let steps = 0;
if (this.shouldPerformAnalysis('complexity'))
steps++;
if (this.shouldPerformAnalysis('interactions'))
steps++;
if (this.shouldPerformAnalysis('non_linearity'))
steps++;
if (this.shouldPerformAnalysis('separability'))
steps++;
if (this.shouldPerformAnalysis('noise'))
steps++;
if (this.shouldPerformAnalysis('sparsity'))
steps++;
if (this.shouldPerformAnalysis('temporal'))
steps++;
return Math.max(1, steps);
}
/**
* Check if specific analysis should be performed
*/
shouldPerformAnalysis(focus) {
return this.config.focusAreas.includes(focus) || this.config.focusAreas.includes('all');
}
/**
* Validate inputs before analysis
*/
validateInputs(section1Result, section2Result, section3Result) {
if (!section1Result?.overview) {
throw new types_1.CharacterizationError('Invalid Section 1 result', 'data_error', 'validation', false);
}
if (!section2Result) {
throw new types_1.CharacterizationError('Invalid Section 2 result', 'data_error', 'validation', false);
}
if (!section3Result) {
throw new types_1.CharacterizationError('Invalid Section 3 result', 'data_error', 'validation', false);
}
}
/**
* Extract data context from previous section results
*/
extractDataContext(section1Result, section2Result, section3Result) {
const overview = section1Result.overview;
return {
sampleSize: overview.structuralDimensions.totalDataRows,
featureCount: overview.structuralDimensions.totalColumns,
numericFeatures: this.extractNumericFeatures(section3Result),
categoricalFeatures: this.extractCategoricalFeatures(section3Result),
temporalFeatures: this.extractTemporalFeatures(section3Result),
hasTargetVariable: false, // Will be enhanced later
isClassification: false, // Will be enhanced later
hasTemporalFeatures: false, // Will be enhanced later
section1Result,
section2Result,
section3Result,
};
}
/**
* Extract numeric features from Section 3 results
*/
extractNumericFeatures(_section3Result) {
// Placeholder - will extract from actual Section 3 structure
return [];
}
/**
* Extract categorical features from Section 3 results
*/
extractCategoricalFeatures(_section3Result) {
// Placeholder - will extract from actual Section 3 structure
return [];
}
/**
* Extract temporal features from Section 3 results
*/
extractTemporalFeatures(_section3Result) {
// Placeholder - will extract from actual Section 3 structure
return [];
}
/**
* Add warning to collection
*/
addWarning(category, severity, message, component, impact, recommendation) {
this.warnings.push({
category,
severity,
message,
component,
impact,
recommendation,
});
}
/**
* Handle analysis errors with proper categorization
*/
handleAnalysisError(error) {
if (error instanceof types_1.CharacterizationError) {
return error;
}
const errorMessage = error instanceof Error ? error.message : 'Unknown error occurred';
return new types_1.CharacterizationError(`Dataset characterization failed: ${errorMessage}`, 'computational_error', 'general', false);
}
/**
* Generate analysis metadata
*/
generateAnalysisMetadata(dataContext) {
return {
analysisTimestamp: new Date(),
analysisVersion: '1.0.0',
computationTime: Math.max(1, Date.now() - this.startTime), // Ensure at least 1ms
sampleSize: dataContext?.sampleSize || 0,
confidenceBounds: {
overallConfidence: 0.8,
componentConfidences: [],
uncertaintySources: [],
confidenceInterpretation: 'High confidence in core metrics',
},
limitationsAndCaveats: [],
reproducibilityInfo: {
deterministicAnalysis: true,
softwareVersions: [],
configurationParameters: this.config,
},
};
}
/**
* Calculate overall complexity score (placeholder)
*/
calculateOverallComplexityScore(profile) {
// Simplified scoring - will be enhanced
return 50; // Medium complexity as default
}
/**
* Determine confidence level (placeholder)
*/
determineConfidenceLevel(profile) {
return 'high';
}
// Placeholder helper methods for mathematical operations
extractCorrelationMatrix(dataContext) {
// Placeholder - will implement actual correlation extraction
const size = dataContext.numericFeatures.length;
return Array(size)
.fill(0)
.map(() => Array(size).fill(0.5));
}
computeEigenvalues(correlationMatrix) {
// Placeholder - will implement actual eigenvalue computation
return correlationMatrix.map((_, i) => 1 / (i + 1));
}
applyCriteriaForDimensionality(eigenvalues) {
// Placeholder - will implement Kaiser criterion and other methods
return {
kaiserCriterion: Math.ceil(eigenvalues.length * 0.7),
confidence: 0.8,
};
}
analyzeFeatureImportanceFromEigenVectors(eigenvalues, features) {
// Placeholder - will implement actual feature importance analysis
return {
redundant: features.slice(-2),
critical: features.slice(0, 3),
};
}
findHighlyCorrelatedGroups(dataContext, threshold) {
// Placeholder - will implement correlation group finding
return [];
}
}
exports.DatasetCharacterizationAnalyzer = DatasetCharacterizationAnalyzer;
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