Crate scirs2_transform 

Data transformation module for SciRS2

This module provides utilities for transforming data in ways that are useful for machine learning and data analysis. The main functionalities include:

• Data normalization and standardization
• Feature engineering
• Dimensionality reduction

Re-exports

pub use decomposition::DictionaryLearning;

pub use decomposition::NMF;

pub use encoding::BinaryEncoder;

pub use encoding::EncodedOutput;

pub use encoding::FrequencyEncoder;

pub use encoding::OneHotEncoder;

pub use encoding::OrdinalEncoder;

pub use encoding::SparseMatrix;

pub use encoding::TargetEncoder;

pub use encoding::WOEEncoder;

pub use error::Result;

pub use error::TransformError;

pub use features::binarize;

pub use features::discretize_equal_frequency;

pub use features::discretize_equal_width;

pub use features::log_transform;

pub use features::power_transform;

pub use features::PolynomialFeatures;

pub use features::PowerTransformer;

pub use impute::DistanceMetric;

pub use impute::ImputeStrategy;

pub use impute::IterativeImputer;

pub use impute::KNNImputer;

pub use impute::MissingIndicator;

pub use impute::SimpleImputer;

pub use impute::WeightingScheme;

pub use normalize::normalize_array;

pub use normalize::normalize_vector;

pub use normalize::NormalizationMethod;

pub use normalize::Normalizer;

pub use pipeline::make_column_transformer;

pub use pipeline::make_pipeline;

pub use pipeline::ColumnTransformer;

pub use pipeline::Pipeline;

pub use pipeline::RemainderOption;

pub use pipeline::Transformer;

pub use reduction::trustworthiness;

pub use reduction::AffinityMethod;

pub use reduction::Isomap;

pub use reduction::SpectralEmbedding;

pub use reduction::TruncatedSVD;

pub use reduction::LDA;

pub use reduction::LLE;

pub use reduction::PCA;

pub use reduction::TSNE;

pub use reduction::UMAP;

pub use scaling::MaxAbsScaler;

pub use scaling::QuantileTransformer;

pub use selection::MutualInfoSelector;

pub use selection::RecursiveFeatureElimination;

pub use selection::VarianceThreshold;

pub use time_series::FourierFeatures;

pub use time_series::LagFeatures;

pub use time_series::TimeSeriesFeatures;

pub use time_series::WaveletFeatures;

pub use graph::adjacency_to_edge_list;

pub use graph::edge_list_to_adjacency;

pub use graph::ActivationType;

pub use graph::DeepWalk;

pub use graph::GraphAutoencoder;

pub use graph::LaplacianType;

pub use graph::Node2Vec;

pub use image::resize_images;

pub use image::rgb_to_grayscale;

pub use image::BlockNorm;

pub use image::HOGDescriptor;

pub use image::ImageNormMethod;

pub use image::ImageNormalizer;

pub use image::PatchExtractor;

pub use optimization_config::AdaptiveParameterTuner;

pub use optimization_config::AdvancedConfigOptimizer;

pub use optimization_config::AutoTuner;

pub use optimization_config::ConfigurationPredictor;

pub use optimization_config::DataCharacteristics;

pub use optimization_config::OptimizationConfig;

pub use optimization_config::OptimizationReport;

pub use optimization_config::PerformanceMetric;

pub use optimization_config::SystemMonitor;

pub use optimization_config::SystemResources;

pub use optimization_config::TransformationRecommendation;

pub use out_of_core::csv_chunks;

pub use out_of_core::ChunkedArrayReader;

pub use out_of_core::ChunkedArrayWriter;

pub use out_of_core::OutOfCoreConfig;

pub use out_of_core::OutOfCoreNormalizer;

pub use out_of_core::OutOfCoreTransformer;

pub use performance::EnhancedPCA;

pub use performance::EnhancedStandardScaler;

pub use streaming::OutlierMethod;

pub use streaming::StreamingFeatureSelector;

pub use streaming::StreamingMinMaxScaler;

pub use streaming::StreamingOutlierDetector;

pub use streaming::StreamingPCA;

pub use streaming::StreamingQuantileTracker;

pub use streaming::StreamingStandardScaler;

pub use streaming::StreamingTransformer;

pub use streaming::WindowedStreamingTransformer;

pub use text::CountVectorizer;

pub use text::HashingVectorizer;

pub use text::StreamingCountVectorizer;

pub use text::TfidfVectorizer;

pub use utils::ArrayMemoryPool;

pub use utils::DataChunker;

pub use utils::PerfUtils;

pub use utils::ProcessingStrategy;

pub use utils::StatUtils;

pub use utils::TypeConverter;

pub use utils::ValidationUtils;

pub use auto_feature_engineering::AdvancedMetaLearningSystem;

pub use auto_feature_engineering::AutoFeatureEngineer;

pub use auto_feature_engineering::DatasetMetaFeatures;

pub use auto_feature_engineering::EnhancedMetaFeatures;

pub use auto_feature_engineering::MultiObjectiveRecommendation;

pub use auto_feature_engineering::TransformationConfig;

pub use auto_feature_engineering::TransformationType;

pub use quantum_optimization::AdvancedQuantumMetrics;

pub use quantum_optimization::AdvancedQuantumOptimizer;

pub use quantum_optimization::AdvancedQuantumParams;

pub use quantum_optimization::QuantumHyperparameterTuner;

pub use quantum_optimization::QuantumInspiredOptimizer;

pub use quantum_optimization::QuantumParticle;

pub use quantum_optimization::QuantumTransformationOptimizer;

pub use neuromorphic_adaptation::AdvancedNeuromorphicMetrics;

pub use neuromorphic_adaptation::AdvancedNeuromorphicProcessor;

pub use neuromorphic_adaptation::NeuromorphicAdaptationNetwork;

pub use neuromorphic_adaptation::NeuromorphicMemorySystem;

pub use neuromorphic_adaptation::NeuromorphicTransformationSystem;

pub use neuromorphic_adaptation::SpikingNeuron;

pub use neuromorphic_adaptation::SystemState;

pub use neuromorphic_adaptation::TransformationEpisode;

Modules

auto_feature_engineering

Automated feature engineering with meta-learning Automated feature engineering with meta-learning

decomposition

Matrix decomposition techniques Matrix decomposition techniques

encoding

Categorical data encoding utilities Categorical data encoding utilities

error

Error handling for the transformation module Error types for the data transformation module

features

Feature engineering techniques Feature engineering utilities

graph

Graph embedding transformers Graph embedding transformers for graph-based feature extraction

image

Image processing transformers Image processing transformers for feature extraction

impute

Missing value imputation utilities Missing value imputation utilities

neuromorphic_adaptation

Neuromorphic computing integration for real-time adaptation Neuromorphic computing integration for real-time transformation adaptation

normalize

Basic normalization methods for data Data normalization and standardization utilities

optimization_config

Optimization configuration and auto-tuning system Optimization configuration and auto-tuning system

out_of_core

Out-of-core processing for large datasets Out-of-core processing for large datasets

performance

Performance optimizations and enhanced implementations Performance optimizations and enhanced implementations

pipeline

Pipeline API for chaining transformations Pipeline API for chaining transformations

quantum_optimization

Quantum-inspired optimization for data transformations Quantum-inspired optimization for data transformations

reduction

Dimensionality reduction algorithms Dimensionality reduction techniques

scaling

Advanced scaling and transformation methods Advanced scaling and transformation methods

selection

Feature selection utilities Feature selection utilities

streaming

Streaming transformations for continuous data Streaming transformations for continuous data processing

text

Text processing transformers Text processing transformers for feature extraction

time_series

Time series feature extraction Time series feature extraction

utils

Utility functions and helpers for data transformation Utility functions and helpers for data transformation