scirs2_transform/
lib.rs

1#![allow(deprecated)]
2//! Data transformation module for SciRS2
3//!
4//! This module provides utilities for transforming data in ways that are useful
5//! for machine learning and data analysis. The main functionalities include:
6//!
7//! - Data normalization and standardization
8//! - Feature engineering
9//! - Dimensionality reduction
10
11#![warn(missing_docs)]
12#![allow(clippy::too_many_arguments)]
13
14/// Error handling for the transformation module
15pub mod error;
16
17/// Basic normalization methods for data
18pub mod normalize;
19
20/// Feature engineering techniques
21pub mod features;
22
23/// Dimensionality reduction algorithms
24pub mod reduction;
25
26/// Matrix decomposition techniques
27pub mod decomposition;
28
29/// Advanced scaling and transformation methods
30pub mod scaling;
31
32/// Missing value imputation utilities
33pub mod impute;
34
35/// Categorical data encoding utilities
36pub mod encoding;
37
38/// Feature selection utilities
39pub mod selection;
40
41/// Time series feature extraction
42pub mod time_series;
43
44/// Pipeline API for chaining transformations
45pub mod pipeline;
46
47/// SIMD-accelerated normalization operations
48#[cfg(feature = "simd")]
49pub mod normalize_simd;
50
51/// SIMD-accelerated feature engineering operations
52#[cfg(feature = "simd")]
53pub mod features_simd;
54
55/// SIMD-accelerated scaling operations
56#[cfg(feature = "simd")]
57pub mod scaling_simd;
58
59/// Out-of-core processing for large datasets
60pub mod out_of_core;
61
62/// Streaming transformations for continuous data
63pub mod streaming;
64
65/// Text processing transformers
66pub mod text;
67
68/// Image processing transformers
69pub mod image;
70
71/// Utility functions and helpers for data transformation
72pub mod utils;
73
74/// Test module for advanced implementations
75#[cfg(test)]
76mod advanced_test;
77/// Performance optimizations and enhanced implementations
78pub mod performance;
79
80/// Optimization configuration and auto-tuning system
81pub mod optimization_config;
82
83/// Graph embedding transformers
84pub mod graph;
85
86/// GPU-accelerated transformations
87#[cfg(feature = "gpu")]
88pub mod gpu;
89
90/// Distributed processing for multi-node transformations
91#[cfg(feature = "distributed")]
92pub mod distributed;
93
94/// Automated feature engineering with meta-learning
95pub mod auto_feature_engineering;
96
97/// Quantum-inspired optimization for data transformations
98pub mod quantum_optimization;
99
100/// Neuromorphic computing integration for real-time adaptation
101pub mod neuromorphic_adaptation;
102
103/// Production monitoring with drift detection
104#[cfg(feature = "monitoring")]
105pub mod monitoring;
106
107// Re-export important types and functions
108pub use decomposition::{DictionaryLearning, NMF};
109pub use encoding::{
110    BinaryEncoder, EncodedOutput, FrequencyEncoder, OneHotEncoder, OrdinalEncoder, SparseMatrix,
111    TargetEncoder, WOEEncoder,
112};
113pub use error::{Result, TransformError};
114pub use features::{
115    binarize, discretize_equal_frequency, discretize_equal_width, log_transform, power_transform,
116    PolynomialFeatures, PowerTransformer,
117};
118pub use impute::{
119    DistanceMetric, ImputeStrategy, IterativeImputer, KNNImputer, MissingIndicator, SimpleImputer,
120    WeightingScheme,
121};
122pub use normalize::{normalize_array, normalize_vector, NormalizationMethod, Normalizer};
123pub use pipeline::{
124    make_column_transformer, make_pipeline, ColumnTransformer, Pipeline, RemainderOption,
125    Transformer,
126};
127pub use reduction::{
128    trustworthiness, AffinityMethod, Isomap, SpectralEmbedding, TruncatedSVD, LDA, LLE, PCA, TSNE,
129    UMAP,
130};
131pub use scaling::{MaxAbsScaler, QuantileTransformer};
132pub use selection::{MutualInfoSelector, RecursiveFeatureElimination, VarianceThreshold};
133pub use time_series::{FourierFeatures, LagFeatures, TimeSeriesFeatures, WaveletFeatures};
134
135#[cfg(feature = "simd")]
136pub use normalize_simd::{
137    simd_l2_normalize_1d, simd_maxabs_normalize_1d, simd_minmax_normalize_1d,
138    simd_normalize_adaptive, simd_normalize_batch, simd_normalizearray, simd_zscore_normalize_1d,
139    AdaptiveBlockSizer,
140};
141
142#[cfg(feature = "simd")]
143pub use features_simd::{
144    simd_binarize, simd_polynomial_features_optimized, simd_power_transform, SimdPolynomialFeatures,
145};
146
147#[cfg(feature = "simd")]
148pub use scaling_simd::{SimdMaxAbsScaler, SimdRobustScaler, SimdStandardScaler};
149
150pub use graph::{
151    adjacency_to_edge_list, edge_list_to_adjacency, ActivationType, DeepWalk, GraphAutoencoder,
152    LaplacianType, Node2Vec,
153};
154pub use image::{
155    resize_images, rgb_to_grayscale, BlockNorm, HOGDescriptor, ImageNormMethod, ImageNormalizer,
156    PatchExtractor,
157};
158pub use optimization_config::{
159    AdaptiveParameterTuner, AdvancedConfigOptimizer, AutoTuner, ConfigurationPredictor,
160    DataCharacteristics, OptimizationConfig, OptimizationReport, PerformanceMetric, SystemMonitor,
161    SystemResources, TransformationRecommendation,
162};
163pub use out_of_core::{
164    csv_chunks, ChunkedArrayReader, ChunkedArrayWriter, OutOfCoreConfig, OutOfCoreNormalizer,
165    OutOfCoreTransformer,
166};
167pub use performance::{EnhancedPCA, EnhancedStandardScaler};
168pub use streaming::{
169    OutlierMethod, StreamingFeatureSelector, StreamingMinMaxScaler, StreamingOutlierDetector,
170    StreamingPCA, StreamingQuantileTracker, StreamingStandardScaler, StreamingTransformer,
171    WindowedStreamingTransformer,
172};
173pub use text::{CountVectorizer, HashingVectorizer, StreamingCountVectorizer, TfidfVectorizer};
174pub use utils::{
175    ArrayMemoryPool, DataChunker, PerfUtils, ProcessingStrategy, StatUtils, TypeConverter,
176    ValidationUtils,
177};
178
179// GPU acceleration exports
180#[cfg(feature = "gpu")]
181pub use gpu::{GpuMatrixOps, GpuPCA, GpuTSNE};
182
183// Distributed processing exports
184#[cfg(feature = "distributed")]
185pub use distributed::{
186    AutoScalingConfig, CircuitBreaker, ClusterHealthSummary, DistributedConfig,
187    DistributedCoordinator, DistributedPCA, EnhancedDistributedCoordinator, NodeHealth, NodeInfo,
188    NodeStatus, PartitioningStrategy,
189};
190
191// Automated feature engineering exports
192pub use auto_feature_engineering::{
193    AdvancedMetaLearningSystem, AutoFeatureEngineer, DatasetMetaFeatures, EnhancedMetaFeatures,
194    MultiObjectiveRecommendation, TransformationConfig, TransformationType,
195};
196
197// Quantum optimization exports
198pub use quantum_optimization::{
199    AdvancedQuantumMetrics, AdvancedQuantumOptimizer, AdvancedQuantumParams,
200    QuantumHyperparameterTuner, QuantumInspiredOptimizer, QuantumParticle,
201    QuantumTransformationOptimizer,
202};
203
204// Neuromorphic computing exports
205pub use neuromorphic_adaptation::{
206    AdvancedNeuromorphicMetrics, AdvancedNeuromorphicProcessor, NeuromorphicAdaptationNetwork,
207    NeuromorphicMemorySystem, NeuromorphicTransformationSystem, SpikingNeuron, SystemState,
208    TransformationEpisode,
209};
210
211// Production monitoring exports
212#[cfg(feature = "monitoring")]
213pub use monitoring::{
214    AlertConfig, AlertType, DriftDetectionResult, DriftMethod, PerformanceMetrics,
215    TransformationMonitor,
216};
scirs2_transform/lib.rs

scirs2_transform/
lib.rs
