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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TypeScript
/**
* 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
*/
import type { Section1Result } from '../../overview/types';
import type { Section2Result } from '../../quality/types';
import type { Section3Result } from '../../eda/types';
import type { DatasetComplexityProfile } from '../advanced-characterization/types';
import type { AlgorithmSelectionProfile, AlgorithmSelectionConfig, SelectionProgress } from './types';
/**
* Main analyzer class for intelligent algorithm selection
*/
export declare class IntelligentAlgorithmSelectionAnalyzer {
private config;
private warnings;
private startTime;
private progress;
constructor(config?: Partial<AlgorithmSelectionConfig>);
/**
* Main analysis method - performs comprehensive algorithm selection
*/
analyze(section1Result: Section1Result, section2Result: Section2Result, section3Result: Section3Result, complexityProfile: DatasetComplexityProfile, progressCallback?: (progress: SelectionProgress) => void): Promise<AlgorithmSelectionProfile>;
/**
* Performs incremental algorithm selection with comprehensive error handling
*/
private performIncrementalSelection;
/**
* Generate candidate algorithms based on dataset characteristics and task requirements
*/
private generateCandidateAlgorithms;
/**
* Perform multi-criteria evaluation of candidate algorithms
*/
private performMultiCriteriaEvaluation;
/**
* Generate performance predictions for selected algorithms
*/
private generatePerformancePredictions;
/**
* Initialize configuration with defaults
*/
private initializeConfig;
/**
* Initialize progress tracking
*/
private initializeProgress;
/**
* Reset analysis state for new analysis
*/
private resetAnalysisState;
/**
* Update progress and notify callback
*/
private updateProgress;
/**
* Estimate remaining time based on progress
*/
private estimateTimeRemaining;
/**
* Calculate total analysis steps
*/
private calculateTotalSteps;
/**
* Validate inputs before analysis
*/
private validateInputs;
/**
* Extract decision context from all input sources
*/
private extractDecisionContext;
/**
* Add warning to collection
*/
private addWarning;
/**
* Handle analysis errors with proper categorization
*/
private handleAnalysisError;
/**
* Generate selection metadata
*/
private generateSelectionMetadata;
private inferTaskType;
private generateClassificationCandidates;
private generateRegressionCandidates;
private generateClusteringCandidates;
private generateTimeSeriesCandidates;
private generateAnomalyDetectionCandidates;
private generateGeneralCandidates;
private filterCandidatesByConstraints;
private getFallbackCandidates;
private evaluateCandidate;
private performBasicEvaluation;
private predictAlgorithmPerformance;
private generateTheoreticalPredictions;
private performRiskAssessment;
private generateEnsembleRecommendations;
private generateHyperparameterGuidance;
private developImplementationStrategy;
private generateSelectionReasoning;
private assessDataQuality;
}
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