Crate sklears_feature_selection 

Feature selection algorithms

This module provides algorithms for selecting relevant features from data, compatible with scikit-learn’s feature_selection module.

Re-exports

pub use automl::analyze_and_recommend;

pub use automl::comprehensive_automl;

pub use automl::quick_automl;

pub use automl::AdvancedHyperparameterOptimizer;

pub use automl::AutoMLBenchmark;

pub use automl::AutoMLError;

pub use automl::AutoMLFactory;

pub use automl::AutoMLFactoryConfig;

pub use automl::AutoMLMethod;

pub use automl::AutoMLResults;

pub use automl::AutoMLSummary;

pub use automl::AutomatedFeatureSelectionPipeline;

pub use automl::ComputationalBudget;

pub use automl::DataAnalyzer;

pub use automl::DataCharacteristics;

pub use automl::HyperparameterOptimizer;

pub use automl::MethodSelector;

pub use automl::PipelineConfig;

pub use automl::PipelineOptimizer;

pub use automl::PreprocessingIntegration;

pub use automl::TargetType;

pub use automl::ValidationStrategy;

pub use benchmark::BenchmarkConfig;

pub use benchmark::BenchmarkDataset;

pub use benchmark::BenchmarkSuiteResults;

pub use benchmark::BenchmarkableMethod;

pub use benchmark::FeatureSelectionBenchmark;

pub use benchmark::RandomSelectionMethod;

pub use benchmark::UnivariateFilterMethod;

pub use crate::filter::CompressedSensingAlgorithm;

pub use crate::filter::CompressedSensingSelector;

pub use crate::filter::CorrelationThreshold;

pub use crate::filter::GenericUnivariateSelect;

pub use crate::filter::HighDimensionalInference;

pub use crate::filter::ImbalancedDataSelector;

pub use crate::filter::ImbalancedStrategy;

pub use crate::filter::InferenceMethod;

pub use crate::filter::KnockoffSelector;

pub use crate::filter::KnockoffType;

pub use crate::filter::RReliefF;

pub use crate::filter::Relief;

pub use crate::filter::ReliefF;

pub use crate::filter::SelectFdr;

pub use crate::filter::SelectFpr;

pub use crate::filter::SelectFwe;

pub use crate::filter::SelectKBest;

pub use crate::filter::SelectKBestParallel;

pub use crate::filter::SelectPercentile;

pub use crate::filter::SureIndependenceScreening;

pub use crate::filter::VarianceThreshold;

pub use crate::regularization_selectors::ElasticNetSelector;

pub use crate::regularization_selectors::LassoSelector;

pub use crate::regularization_selectors::RidgeSelector;

pub use crate::domain_specific::AdvancedNLPFeatureSelector;

pub use crate::domain_specific::BioinformaticsFeatureSelector;

pub use crate::domain_specific::FinanceFeatureSelector;

pub use crate::domain_specific::GraphFeatureSelector;

pub use crate::domain_specific::ImageFeatureSelector;

pub use crate::domain_specific::MultiModalFeatureSelector;

pub use crate::domain_specific::TextFeatureSelector;

pub use crate::domain_specific::TimeSeriesSelector;

pub use crate::domain_benchmark::run_quick_benchmark;

pub use crate::domain_benchmark::BenchmarkConfig as DomainBenchmarkConfig;

pub use crate::domain_benchmark::BenchmarkResult;

pub use crate::domain_benchmark::BenchmarkSuite;

pub use crate::domain_benchmark::BenchmarkSummary;

pub use crate::domain_benchmark::DomainBenchmarkFramework;

pub use crate::ml_based::AttentionFeatureSelector;

pub use crate::ml_based::MetaLearningFeatureSelector;

pub use crate::ml_based::NeuralFeatureSelector;

pub use crate::ml_based::RLFeatureSelector;

pub use crate::evaluation::ComparativeAnalysis;

pub use crate::evaluation::FeatureInteractionAnalysis;

pub use crate::evaluation::FeatureSetDiversityMeasures;

pub use crate::evaluation::FeatureSetVisualization;

pub use crate::evaluation::NestedCVResults;

pub use crate::evaluation::NestedCrossValidation;

pub use crate::evaluation::PowerAnalysis;

pub use crate::evaluation::QualityAssessment;

pub use crate::evaluation::RedundancyMeasures;

pub use crate::evaluation::RelevanceScoring;

pub use crate::evaluation::StabilityMeasures;

pub use crate::evaluation::StratifiedKFold;

pub use crate::statistical_tests::chi2;

pub use crate::statistical_tests::f_classif;

pub use crate::statistical_tests::f_oneway;

pub use crate::statistical_tests::f_regression;

pub use crate::statistical_tests::kruskal_wallis;

pub use crate::statistical_tests::mann_whitney_u;

pub use crate::statistical_tests::mutual_info_classif;

pub use crate::statistical_tests::mutual_info_regression;

pub use crate::statistical_tests::r_regression;

pub use crate::multi_label::AggregateMethod;

pub use crate::multi_label::LabelSpecificSelector;

pub use crate::multi_label::MultiLabelFeatureSelector;

pub use crate::multi_label::MultiLabelStrategy;

pub use crate::multi_label::MultiLabelTarget;

pub use crate::bayesian::BayesianInferenceMethod;

pub use crate::bayesian::BayesianModelAveraging;

pub use crate::bayesian::BayesianVariableSelector;

pub use crate::bayesian::PriorType;

pub use crate::spectral::GraphConstructionMethod;

pub use crate::spectral::KernelFeatureSelector;

pub use crate::spectral::KernelType;

pub use crate::spectral::LaplacianScoreSelector;

pub use crate::spectral::ManifoldFeatureSelector;

pub use crate::spectral::ManifoldMethod;

pub use crate::spectral::SpectralFeatureSelector;

pub use crate::optimization::ADMMFeatureSelector;

pub use crate::optimization::ConvexFeatureSelector;

pub use crate::optimization::IntegerProgrammingFeatureSelector;

pub use crate::optimization::ProximalGradientSelector;

pub use crate::optimization::SemidefiniteFeatureSelector;

pub use crate::parallel::ParallelCorrelationComputer;

pub use crate::parallel::ParallelFeatureEvaluator;

pub use crate::parallel::ParallelFeatureRanker;

pub use crate::parallel::ParallelSelectionUtils;

pub use crate::parallel::ParallelUnivariateRegressionScorer;

pub use crate::parallel::ParallelUnivariateScorer;

pub use crate::parallel::ParallelVarianceComputer;

pub use crate::plugin::ComputationalComplexity;

pub use crate::plugin::FeatureSelectionPlugin;

pub use crate::plugin::LoggingMiddleware;

pub use crate::plugin::MemoryComplexity;

pub use crate::plugin::PerformanceMetrics;

pub use crate::plugin::PerformanceMiddleware;

pub use crate::plugin::PipelineResult;

pub use crate::plugin::PluginContext;

pub use crate::plugin::PluginMetadata;

pub use crate::plugin::PluginPipeline;

pub use crate::plugin::PluginRegistry;

pub use crate::plugin::PluginResult;

pub use crate::plugin::StepResult as PluginStepResult;

pub use crate::pipeline::BinningStrategy;

pub use crate::pipeline::FeatureSelectionPipeline;

pub use crate::pipeline::OptimizationConfiguration;

pub use crate::pipeline::PipelineConfiguration;

pub use crate::pipeline::PreprocessingStep;

pub use crate::pipeline::SelectionMethod;

pub use crate::pipeline::Trained;

pub use crate::pipeline::Untrained;

pub use crate::type_safe::data_states;

pub use crate::type_safe::selection_types;

pub use crate::type_safe::FeatureIndex;

pub use crate::type_safe::FeatureMask;

pub use crate::performance::SIMDStats;

pub use crate::fluent_api::presets;

pub use crate::fluent_api::FeatureSelectionBuilder;

pub use crate::fluent_api::FluentConfig;

pub use crate::fluent_api::FluentSelectionResult;

pub use crate::fluent_api::SelectionStep;

pub use crate::fluent_api::StepResult;

pub use crate::comprehensive_benchmark::quick_benchmark;

pub use crate::comprehensive_benchmark::BenchmarkConfiguration;

pub use crate::comprehensive_benchmark::BenchmarkDataset as ComprehensiveBenchmarkDataset;

pub use crate::comprehensive_benchmark::BenchmarkMethod;

pub use crate::comprehensive_benchmark::BenchmarkMetric;

pub use crate::comprehensive_benchmark::ComprehensiveBenchmarkResults;

pub use crate::comprehensive_benchmark::ComprehensiveBenchmarkSuite;

pub use crate::comprehensive_benchmark::DatasetDomain;

pub use crate::comprehensive_benchmark::DatasetMetadata;

pub use crate::comprehensive_benchmark::DetailedMethodResult;

pub use crate::comprehensive_benchmark::MethodCategory;

pub use crate::comprehensive_benchmark::TaskType;

pub use base::*;

pub use filter::*;

Modules

automl

AutoML Feature Selection Module

base

Base classes for feature selection

bayesian

Bayesian feature selection algorithms

benchmark

Benchmarking framework for feature selection methods

comprehensive_benchmark

Comprehensive Benchmarking Framework for Feature Selection

domain_benchmark

Benchmarking framework for domain-specific feature selection methods

domain_specific

Domain-specific feature selection modules.

evaluation

Evaluation metrics for feature selection quality assessment

filter

Filter-based feature selection methods

fluent_api

Fluent API for Feature Selection Configuration

group_selection

Group-based feature selection methods

hierarchical

Hierarchical feature selection methods

ml_based

Machine learning-based feature selection methods

multi_label

Multi-label feature selection algorithms

optimization

Optimization-based feature selection algorithms

parallel

Parallel feature evaluation utilities

performance

Performance Optimizations for Feature Selection

pipeline

Pipeline Integration Framework

plugin

Modular Plugin Architecture for Feature Selection

regularization_selectors

Regularization-based feature selection methods

spectral

Spectral feature selection algorithms

statistical_tests

Statistical tests for feature selection

streaming

Streaming and online feature selection methods

type_safe

Type-Safe Feature Selection Framework

Macros

impl_type_safe_selector

Implementation macro for type-safe selectors

plugin_pipeline

Helper macro for creating plugin pipelines

register_plugin

Helper macro for easy plugin registration