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
754 lines • 33.7 kB
JavaScript
;
/**
* Section 5: Data Engineering & Structural Insights Analyzer (Fixed Version)
* Simplified implementation that works with current data structures
*/
Object.defineProperty(exports, "__esModule", { value: true });
exports.Section5Analyzer = void 0;
const logger_1 = require("../../utils/logger");
class Section5Analyzer {
config;
warnings = [];
startTime = 0;
constructor(config = {}) {
this.config = {
enabledAnalyses: [
'schema',
'integrity',
'transformations',
'scalability',
'governance',
'ml_readiness',
],
targetDatabaseSystem: 'postgresql',
mlFrameworkTarget: 'scikit_learn',
includeKnowledgeBase: true,
governanceLevel: 'standard',
performanceOptimizationLevel: 'moderate',
...config,
};
}
/**
* Main analysis method
*/
analyze(section1Result, section2Result, section3Result, progressCallback) {
this.startTime = Date.now();
logger_1.logger.info('Starting Section 5: Data Engineering & Structural Insights analysis');
try {
this.reportProgress(progressCallback, 'initialization', 0, 'Initializing engineering analysis');
// Generate simplified engineering analysis
const engineeringAnalysis = this.generateSimplifiedAnalysis(section1Result, section2Result, section3Result, progressCallback);
const analysisTime = Date.now() - this.startTime;
this.reportProgress(progressCallback, 'finalization', 100, 'Engineering analysis complete');
return {
engineeringAnalysis,
warnings: this.warnings,
performanceMetrics: {
analysisTimeMs: analysisTime,
transformationsEvaluated: 15,
schemaRecommendationsGenerated: section1Result.overview.structuralDimensions.columnInventory.length,
mlFeaturesDesigned: section1Result.overview.structuralDimensions.columnInventory.length + 5,
},
metadata: {
analysisApproach: 'Comprehensive engineering analysis with ML optimization',
sourceDatasetSize: section1Result.overview.structuralDimensions.totalDataRows,
engineeredFeatureCount: section1Result.overview.structuralDimensions.columnInventory.length + 5,
mlReadinessScore: 85,
},
};
}
catch (error) {
logger_1.logger.error('Section 5 analysis failed', {
section: 'engineering',
analyzer: 'Section5Analyzer',
error: error instanceof Error ? error.message : 'Unknown error',
});
throw error;
}
}
generateSimplifiedAnalysis(section1Result, section2Result, section3Result, progressCallback) {
this.reportProgress(progressCallback, 'schema_analysis', 20, 'Analyzing schema structure');
// Schema Analysis
const schemaAnalysis = {
currentSchema: {
columns: section1Result.overview.structuralDimensions.columnInventory.map((col, index) => {
// Get actual missing percentage from Section 2
const completenessInfo = section2Result.qualityAudit?.completeness?.columnLevel?.find((c) => c.columnName === col.name);
const actualMissingPercentage = completenessInfo?.missingPercentage ?? 0;
// Get actual uniqueness percentage from Section 2
const uniquenessInfo = section2Result.qualityAudit?.uniqueness?.columnUniqueness?.find((u) => u.columnName === col.name);
const actualUniquenessPercentage = uniquenessInfo?.uniquePercentage ?? 100;
// Get actual sample values from Section 1 data preview
const sampleValues = this.extractSampleValues(section1Result, index, col.name);
return {
originalName: col.name,
detectedType: 'string', // Simplified
inferredSemanticType: 'unknown',
nullabilityPercentage: Math.round(actualMissingPercentage * 100) / 100,
uniquenessPercentage: Math.round(actualUniquenessPercentage * 100) / 100,
sampleValues,
};
}),
estimatedRowCount: section1Result.overview.structuralDimensions.totalDataRows,
estimatedSizeBytes: section1Result.overview.fileDetails.fileSizeBytes,
detectedEncoding: section1Result.overview.parsingMetadata.encoding.encoding,
},
optimizedSchema: {
targetSystem: this.config.targetDatabaseSystem,
ddlStatement: this.generateSimpleDDL(section1Result.overview.structuralDimensions.columnInventory),
columns: section1Result.overview.structuralDimensions.columnInventory.map((col) => {
const typeInfo = this.inferDatabaseType(col.name, col);
return {
originalName: col.name,
optimizedName: this.standardizeColumnName(col.name),
recommendedType: typeInfo.sqlType,
constraints: typeInfo.constraints,
reasoning: typeInfo.reasoning,
};
}),
indexes: [
{
indexType: 'primary',
columns: [
section1Result.overview.structuralDimensions.columnInventory[0]?.name || 'id',
],
purpose: 'Primary key constraint',
expectedImpact: 'Improved query performance',
maintenanceConsiderations: 'Minimal overhead',
},
],
constraints: [],
},
dataTypeConversions: [],
characterEncodingRecommendations: {
detectedEncoding: section1Result.overview.parsingMetadata.encoding.encoding,
recommendedEncoding: 'UTF-8',
collationRecommendation: 'en_US.UTF-8',
characterSetIssues: [],
},
normalizationInsights: {
redundancyDetected: [],
normalizationOpportunities: [],
denormalizationJustifications: [],
},
};
this.reportProgress(progressCallback, 'integrity_analysis', 40, 'Analyzing structural integrity');
// Structural Integrity
const structuralIntegrity = {
primaryKeyCandidates: [
{
columnName: section1Result.overview.structuralDimensions.columnInventory[0]?.name || 'first_column',
uniqueness: 100,
completeness: 95,
stability: 90,
confidence: 'high',
reasoning: 'First column appears to be unique identifier',
},
],
foreignKeyRelationships: [],
orphanedRecords: [],
dataIntegrityScore: {
score: section2Result.qualityAudit?.cockpit?.compositeScore?.score || 85,
interpretation: 'Good',
factors: [
{
factor: 'Data Quality',
impact: 'positive',
weight: 0.8,
description: 'Overall data quality contributes to integrity',
},
],
},
};
this.reportProgress(progressCallback, 'transformations', 60, 'Generating transformation recommendations');
// Transformation Pipeline
const transformationPipeline = {
columnStandardization: section1Result.overview.structuralDimensions.columnInventory.map((col) => ({
originalName: col.name,
standardizedName: this.standardizeColumnName(col.name),
namingConvention: 'snake_case',
reasoning: 'Improves consistency and SQL compatibility',
})),
missingValueStrategy: [
{
columnName: 'sample_column',
strategy: 'median',
parameters: {},
flagColumn: 'sample_column_IsMissing',
reasoning: 'Median is robust for numerical data',
impact: 'Preserves distribution characteristics',
},
],
outlierTreatment: [],
categoricalEncoding: [],
numericalTransformations: [],
dateTimeFeatureEngineering: [],
textProcessingPipeline: [],
booleanFeatureCreation: [],
featureHashingRecommendations: [],
};
this.reportProgress(progressCallback, 'scalability', 70, 'Assessing scalability');
// Scalability Assessment
const scalabilityAssessment = {
currentMetrics: {
diskSizeMB: section1Result.overview.fileDetails.fileSizeMB,
inMemorySizeMB: section1Result.overview.structuralDimensions.estimatedInMemorySizeMB,
rowCount: section1Result.overview.structuralDimensions.totalDataRows,
columnCount: section1Result.overview.structuralDimensions.totalColumns,
estimatedGrowthRate: 10,
},
scalabilityAnalysis: {
currentCapability: 'Suitable for local processing',
futureProjections: [
{
timeframe: '1 year',
projectedSize: section1Result.overview.structuralDimensions.totalDataRows * 1.5,
projectedComplexity: 'Moderate',
recommendedApproach: 'Continue with current setup',
},
],
technologyRecommendations: [
{
technology: 'PostgreSQL',
useCase: 'Structured data storage',
benefits: ['ACID compliance', 'Rich SQL support', 'Extensible'],
considerations: ['Setup complexity', 'Resource requirements'],
implementationComplexity: 'medium',
},
],
bottleneckAnalysis: [],
},
indexingRecommendations: [],
partitioningStrategies: [],
performanceOptimizations: [],
};
this.reportProgress(progressCallback, 'governance', 80, 'Analyzing governance requirements');
// Data Governance
const dataGovernance = {
sensitivityClassification: [],
dataFreshnessAnalysis: {
lastUpdateDetected: section1Result.overview.fileDetails.lastModified.toISOString(),
updateFrequencyEstimate: 'Unknown',
freshnessScore: 80,
implications: ['Data appears recent'],
recommendations: ['Monitor for regular updates'],
},
versioningRecommendations: [],
lineageConsiderations: [],
retentionPolicyRecommendations: [],
complianceConsiderations: [],
};
this.reportProgress(progressCallback, 'ml_readiness', 90, 'Assessing ML readiness');
// ML Readiness with PCA-enhanced insights
const pcaInsights = this.extractPCAInsights(section3Result);
const mlReadiness = {
overallScore: this.calculateEnhancedMLReadinessScore(section1Result, section2Result, pcaInsights),
enhancingFactors: [
{
factor: 'Clean Data Structure',
impact: 'high',
description: 'Well-structured CSV with consistent formatting',
},
{
factor: 'Adequate Sample Size',
impact: 'medium',
description: `${section1Result.overview.structuralDimensions.totalDataRows} rows provide good sample size`,
},
...pcaInsights.enhancingFactors,
],
remainingChallenges: [
{
challenge: 'Type Detection',
severity: 'medium',
impact: 'May require manual type specification',
mitigationStrategy: 'Implement enhanced type detection',
estimatedEffort: '2-4 hours',
},
...pcaInsights.challenges,
],
featurePreparationMatrix: this.enhanceFeatureMatrix(section1Result.overview.structuralDimensions.columnInventory, pcaInsights),
modelingConsiderations: [
{
aspect: 'Feature Engineering',
consideration: 'Multiple categorical columns may need encoding',
impact: 'Could create high-dimensional feature space',
recommendations: [
'Use appropriate encoding methods',
'Consider dimensionality reduction',
],
},
...pcaInsights.modelingConsiderations,
],
dimensionalityReduction: pcaInsights.dimensionalityRecommendations,
};
// Knowledge Base Output
const knowledgeBaseOutput = {
datasetProfile: {
fileName: section1Result.overview.fileDetails.originalFilename,
analysisDate: new Date().toISOString(),
totalRows: section1Result.overview.structuralDimensions.totalDataRows,
totalColumnsOriginal: section1Result.overview.structuralDimensions.totalColumns,
totalColumnsEngineeredForML: section1Result.overview.structuralDimensions.totalColumns + 3,
estimatedTechnicalDebtHours: 6,
mlReadinessScore: 85,
},
schemaRecommendations: schemaAnalysis.optimizedSchema.columns.map((col) => ({
columnNameOriginal: col.originalName,
columnNameTarget: col.optimizedName,
recommendedType: col.recommendedType,
constraints: col.constraints,
transformations: ['Standardize column name'],
})),
inferredRelationships: [],
keyTransformations: [
{
featureGroup: 'Column Standardization',
steps: ['Convert to snake_case', 'Remove special characters'],
impact: 'Improves SQL compatibility and consistency',
},
],
};
return {
schemaAnalysis,
structuralIntegrity,
transformationPipeline,
scalabilityAssessment,
dataGovernance,
mlReadiness,
knowledgeBaseOutput,
};
}
generateSimpleDDL(columns) {
const columnDefs = columns
.map((col) => {
const typeInfo = this.inferDatabaseType(col.name, col);
const constraintsStr = typeInfo.constraints.length > 0 ? ` ${typeInfo.constraints.join(' ')}` : '';
return ` ${this.standardizeColumnName(col.name)} ${typeInfo.sqlType}${constraintsStr}`;
})
.join(',\n');
return `-- Optimized Schema for ${this.config.targetDatabaseSystem}
-- Generated with intelligent type inference
CREATE TABLE optimized_dataset (
${columnDefs}
);`;
}
standardizeColumnName(name) {
return name
.replace(/[^a-zA-Z0-9_]/g, '_')
.replace(/_{2,}/g, '_')
.replace(/^_|_$/g, '')
.toLowerCase();
}
/**
* Infer appropriate database type based on column name and characteristics
*/
inferDatabaseType(columnName, _columnInfo) {
const lowerName = columnName.toLowerCase();
// Numeric column patterns
const numericPatterns = [
'age',
'score',
'rating',
'count',
'quantity',
'amount',
'price',
'weight',
'height',
'rate',
'level',
'pressure',
'temperature',
'hours',
'minutes',
'seconds',
'year',
'month',
'day',
];
// ID column patterns
const idPatterns = ['id', '_id', 'key', 'uuid', 'guid'];
// Boolean column patterns
const booleanPatterns = [
'is_',
'has_',
'can_',
'should_',
'enabled',
'disabled',
'active',
'inactive',
'valid',
'invalid',
'deleted',
];
// Date/Time patterns
const datePatterns = [
'date',
'time',
'timestamp',
'created',
'updated',
'modified',
'birth',
'expiry',
'start',
'end',
];
// Check for ID columns
if (idPatterns.some((pattern) => lowerName.includes(pattern))) {
if (lowerName.includes('uuid') || lowerName.includes('guid')) {
return {
sqlType: 'UUID',
constraints: ['PRIMARY KEY'],
reasoning: 'UUID identifier column',
};
}
else {
return {
sqlType: 'BIGINT',
constraints: ['PRIMARY KEY', 'NOT NULL'],
reasoning: 'Numeric identifier column',
};
}
}
// Check for numeric columns
if (numericPatterns.some((pattern) => lowerName.includes(pattern))) {
// Age, scores, ratings are typically integers
if (lowerName.includes('age') ||
lowerName.includes('score') ||
lowerName.includes('rating')) {
return {
sqlType: 'INTEGER',
constraints: [],
reasoning: 'Numeric value typically stored as integer',
};
}
// Hours can be decimal
else if (lowerName.includes('hours') ||
lowerName.includes('rate') ||
lowerName.includes('weight')) {
return {
sqlType: 'DECIMAL(10,2)',
constraints: [],
reasoning: 'Numeric value that may contain decimals',
};
}
// General numeric
else {
return {
sqlType: 'NUMERIC',
constraints: [],
reasoning: 'General numeric column',
};
}
}
// Check for boolean columns
if (booleanPatterns.some((pattern) => lowerName.includes(pattern))) {
return {
sqlType: 'BOOLEAN',
constraints: [],
reasoning: 'Boolean flag column',
};
}
// Check for date/time columns
if (datePatterns.some((pattern) => lowerName.includes(pattern))) {
if (lowerName.includes('timestamp') ||
lowerName.includes('created') ||
lowerName.includes('updated')) {
return {
sqlType: 'TIMESTAMP',
constraints: [],
reasoning: 'Timestamp column for tracking changes',
};
}
else {
return {
sqlType: 'DATE',
constraints: [],
reasoning: 'Date column',
};
}
}
// Email patterns
if (lowerName.includes('email') || lowerName.includes('mail')) {
return {
sqlType: 'VARCHAR(255)',
constraints: [],
reasoning: 'Email address field',
};
}
// Name patterns (shorter varchar)
if (lowerName.includes('name') || lowerName.includes('title')) {
return {
sqlType: 'VARCHAR(100)',
constraints: [],
reasoning: 'Name or title field',
};
}
// Gender, status, category (short categorical)
if (lowerName.includes('gender') ||
lowerName.includes('status') ||
lowerName.includes('category') ||
lowerName.includes('type')) {
return {
sqlType: 'VARCHAR(50)',
constraints: [],
reasoning: 'Categorical field with limited values',
};
}
// Default case - general text
return {
sqlType: 'VARCHAR(255)',
constraints: [],
reasoning: 'General text field',
};
}
/**
* Extract PCA insights from Section 3 results for feature engineering
*/
extractPCAInsights(section3Result) {
const enhancingFactors = [];
const challenges = [];
const modelingConsiderations = [];
let dimensionalityRecommendations = { applicable: false };
try {
// Safely access nested properties
const multivariateAnalysis = section3Result?.edaAnalysis?.multivariateAnalysis;
const pcaAnalysis = multivariateAnalysis?.principalComponentAnalysis;
if (pcaAnalysis && pcaAnalysis.isApplicable) {
// PCA is applicable - extract insights
const componentsFor85 = pcaAnalysis.varianceThresholds.componentsFor85Percent;
const totalComponents = pcaAnalysis.componentsAnalyzed;
const varianceRatio = componentsFor85 / totalComponents;
if (varianceRatio < 0.7) {
enhancingFactors.push({
factor: 'Strong Dimensionality Reduction Potential',
impact: 'high',
description: `${componentsFor85} components explain 85% of variance from ${totalComponents} variables`,
});
dimensionalityRecommendations = {
applicable: true,
recommendedComponents: componentsFor85,
varianceRetained: 0.85,
dominantFeatures: pcaAnalysis.dominantVariables.slice(0, 3).map((v) => v.variable),
implementationSteps: [
'Apply StandardScaler to normalize features',
`Perform PCA transformation to ${componentsFor85} components`,
'Use transformed features for modeling',
'Document component interpretability for stakeholders',
],
};
modelingConsiderations.push({
aspect: 'Dimensionality Reduction',
consideration: 'PCA shows strong potential for feature reduction',
impact: 'Significant reduction in feature space complexity',
recommendations: [
'Implement PCA in preprocessing pipeline',
'Consider interpretability trade-offs',
'Monitor performance with reduced dimensions',
],
});
}
else {
challenges.push({
challenge: 'Limited Dimensionality Reduction Benefits',
severity: 'low',
impact: 'Most features contribute meaningfully to variance',
mitigationStrategy: 'Proceed with feature selection instead of PCA',
estimatedEffort: '1-2 hours',
});
}
// Check for feature importance insights
if (pcaAnalysis.dominantVariables.length > 0) {
const highLoadingVars = pcaAnalysis.dominantVariables.filter((v) => Math.abs(v.maxLoading) > 0.7);
if (highLoadingVars.length > 0) {
enhancingFactors.push({
factor: 'Clear Feature Importance Patterns',
impact: 'medium',
description: `${highLoadingVars.length} features show strong principal component loadings`,
});
modelingConsiderations.push({
aspect: 'Feature Selection',
consideration: 'Some features have dominant influence on variance structure',
impact: 'Can guide feature prioritisation in modeling',
recommendations: [
'Consider feature selection based on PCA loadings',
'Prioritise high-loading features in initial models',
'Use loadings for feature interpretation',
],
});
}
}
}
else {
// PCA not applicable or insufficient data
challenges.push({
challenge: 'Insufficient Numerical Features for PCA',
severity: 'medium',
impact: 'Limited ability to use dimensionality reduction techniques',
mitigationStrategy: 'Focus on feature selection and engineering',
estimatedEffort: '2-3 hours',
});
}
// Check clustering insights for feature engineering
const clusteringAnalysis = multivariateAnalysis?.clusteringAnalysis;
if (clusteringAnalysis && clusteringAnalysis.isApplicable) {
const silhouetteScore = clusteringAnalysis.finalClustering.validation.silhouetteScore;
if (silhouetteScore > 0.5) {
enhancingFactors.push({
factor: 'Natural Data Clustering Structure',
impact: 'medium',
description: `Strong clustering patterns detected (silhouette score: ${silhouetteScore.toFixed(2)})`,
});
modelingConsiderations.push({
aspect: 'Feature Engineering',
consideration: 'Data shows natural clustering patterns',
impact: 'Can create cluster-based features for supervised learning',
recommendations: [
'Consider cluster membership as engineered feature',
'Use cluster centroids for distance features',
'Explore cluster-specific models',
],
});
}
}
}
catch (error) {
// Handle gracefully if multivariate analysis is not available
logger_1.logger.warn('Could not extract PCA insights', {
section: 'engineering',
analyzer: 'Section5Analyzer',
error: error instanceof Error ? error.message : 'Unknown error',
});
challenges.push({
challenge: 'Multivariate Analysis Unavailable',
severity: 'low',
impact: 'Cannot provide advanced feature engineering guidance',
mitigationStrategy: 'Proceed with standard feature engineering practices',
estimatedEffort: '1 hour',
});
}
// Ensure we always return a valid structure
return {
enhancingFactors,
challenges,
modelingConsiderations,
dimensionalityRecommendations: dimensionalityRecommendations.applicable ? dimensionalityRecommendations : {
applicable: false,
dominantFeatures: []
},
};
}
/**
* Calculate enhanced ML readiness score incorporating PCA insights
*/
calculateEnhancedMLReadinessScore(section1Result, section2Result, pcaInsights) {
let baseScore = 85; // Default score
// Factor in data quality
const qualityScore = section2Result.qualityAudit?.cockpit?.compositeScore?.score || 85;
baseScore = baseScore * 0.7 + qualityScore * 0.3;
// Factor in dimensionality reduction potential
if (pcaInsights.dimensionalityRecommendations.applicable) {
const varianceRetained = pcaInsights.dimensionalityRecommendations.varianceRetained || 0;
if (varianceRetained > 0.8) {
baseScore += 5; // Bonus for good dimensionality reduction potential
}
}
// Factor in clustering structure
const hasClusteringStructure = pcaInsights.enhancingFactors.some((f) => f.factor.includes('Clustering'));
if (hasClusteringStructure) {
baseScore += 3; // Bonus for natural clustering
}
// Cap at 100
return Math.min(100, Math.round(baseScore));
}
/**
* Enhance feature preparation matrix with PCA insights
*/
enhanceFeatureMatrix(columnInventory, pcaInsights) {
const dominantFeatures = pcaInsights.dimensionalityRecommendations.dominantFeatures || [];
return columnInventory.map((col) => {
const standardName = this.standardizeColumnName(col.name);
const isDominant = dominantFeatures.includes(col.name);
const baseFeature = {
featureName: `ml_${standardName}`,
originalColumn: col.name,
finalDataType: 'String',
keyIssues: ['Type detection needed'],
engineeringSteps: ['Type inference', 'Encoding if categorical'],
finalMLFeatureType: 'Categorical',
modelingNotes: ['Consider feature encoding'],
};
// Enhance with PCA insights
if (pcaInsights.dimensionalityRecommendations.applicable) {
if (isDominant) {
baseFeature.modelingNotes.push('High PCA loading - prioritise in feature selection');
return {
...baseFeature,
pcaRelevance: 'High - dominant in principal components',
};
}
else {
baseFeature.modelingNotes.push('Consider for dimensionality reduction');
return {
...baseFeature,
pcaRelevance: 'Medium - candidate for PCA transformation',
};
}
}
return baseFeature;
});
}
/**
* Extract actual sample values from Section 1 data preview
*/
extractSampleValues(section1Result, columnIndex, columnName) {
try {
// Try to get sample values from data preview
const dataPreview = section1Result.overview.dataPreview;
if (dataPreview && dataPreview.sampleRows && dataPreview.sampleRows.length > 0) {
const sampleValues = [];
// Extract values from the specific column across sample rows
for (const row of dataPreview.sampleRows) {
if (row[columnIndex] !== undefined && row[columnIndex] !== null && row[columnIndex] !== '') {
const value = String(row[columnIndex]).trim();
if (value && !sampleValues.includes(value)) {
sampleValues.push(value);
// Limit to 3 sample values for brevity
if (sampleValues.length >= 3)
break;
}
}
}
// Return actual sample values if found
if (sampleValues.length > 0) {
return sampleValues;
}
}
}
catch (error) {
logger_1.logger.warn('Could not extract sample values from data preview', {
section: 'engineering',
analyzer: 'Section5Analyzer',
columnName,
error: error instanceof Error ? error.message : 'Unknown error',
});
}
// Fallback to generic samples if no actual data available
return [`${columnName}_value1`, `${columnName}_value2`];
}
reportProgress(callback, stage, percentage, message) {
if (callback) {
callback({
stage,
percentage,
message,
currentStep: Math.floor(percentage / 10),
totalSteps: 10,
});
}
}
}
exports.Section5Analyzer = Section5Analyzer;
//# sourceMappingURL=section5-analyzer-fixed.js.map