Expand description
Linear models for sklears
This crate provides implementations of linear models including:
- Linear Regression (OLS, Ridge, Lasso)
- Logistic Regression
- Generalized Linear Models
These implementations leverage scirs2’s linear algebra and optimization capabilities.
Re-exports§
pub use builder_enhancements::EnhancedLinearRegressionBuilder;pub use builder_enhancements::ModelPreset;pub use builder_enhancements::ModelValidation;pub use builder_enhancements::ValidationConfig;pub use coordinate_descent::CoordinateDescentSolver;pub use coordinate_descent::ValidationInfo;pub use early_stopping::train_validation_split;pub use early_stopping::EarlyStopping;pub use early_stopping::EarlyStoppingCallback;pub use early_stopping::EarlyStoppingConfig;pub use early_stopping::StoppingCriterion;pub use errors::ConfigurationError;pub use errors::ConfigurationErrorKind;pub use errors::ConvergenceInfo;pub use errors::CrossValidationError;pub use errors::CrossValidationErrorKind;pub use errors::DataError;pub use errors::DataErrorKind;pub use errors::ErrorBuilder;pub use errors::ErrorSeverity;pub use errors::FeatureError;pub use errors::FeatureErrorKind;pub use errors::FoldInfo;pub use errors::LinearModelError;pub use errors::MatrixError;pub use errors::MatrixErrorKind;pub use errors::MatrixInfo;pub use errors::NumericalError;pub use errors::NumericalErrorKind;pub use errors::OptimizationError;pub use errors::OptimizationErrorKind;pub use errors::ResourceError;pub use errors::ResourceErrorKind;pub use errors::ResourceInfo;pub use errors::StateError;pub use errors::StateErrorKind;pub use lars::Lars;pub use lars::LarsConfig;pub use lasso_cv::LassoCV;pub use lasso_cv::LassoCVConfig;pub use lasso_lars::LassoLars;pub use lasso_lars::LassoLarsConfig;pub use linear_regression::LinearRegression;pub use linear_regression::LinearRegressionConfig;pub use omp::OrthogonalMatchingPursuit;pub use omp::OrthogonalMatchingPursuitConfig;pub use optimizer::FistaOptimizer;pub use optimizer::LbfgsOptimizer;pub use optimizer::NesterovAcceleratedGradient;pub use optimizer::ProximalGradientOptimizer;pub use optimizer::SagOptimizer;pub use optimizer::SagaOptimizer;pub use ridge_classifier::RidgeClassifier;pub use ridge_classifier::RidgeClassifierConfig;pub use ridge_cv::RidgeCV;pub use ridge_cv::RidgeCVConfig;pub use modular_framework::create_modular_linear_regression;pub use modular_framework::BayesianPredictionProvider;pub use modular_framework::CompositeObjective;pub use modular_framework::LinearPredictionProvider;pub use modular_framework::ModularConfig;pub use modular_framework::ModularFramework;pub use modular_framework::ModularLinearModel;pub use modular_framework::Objective;pub use modular_framework::ObjectiveData;pub use modular_framework::ObjectiveMetadata;pub use modular_framework::OptimizationResult;pub use modular_framework::OptimizationSolver;pub use modular_framework::PredictionProvider;pub use modular_framework::PredictionWithConfidence;pub use modular_framework::PredictionWithUncertainty;pub use modular_framework::ProbabilisticPredictionProvider;pub use modular_framework::SolverInfo;pub use modular_framework::SolverRecommendations;pub use solver::Solver;pub use loss_functions::AbsoluteLoss;pub use loss_functions::EpsilonInsensitiveLoss;pub use loss_functions::HingeLoss;pub use loss_functions::HuberLoss;pub use loss_functions::LogisticLoss;pub use loss_functions::LossFactory;pub use loss_functions::QuantileLoss;pub use loss_functions::SquaredHingeLoss;pub use loss_functions::SquaredLoss;pub use regularization_schemes::CompositeRegularization;pub use regularization_schemes::ElasticNetRegularization;pub use regularization_schemes::GroupLassoRegularization;pub use regularization_schemes::L1Regularization;pub use regularization_schemes::L2Regularization;pub use regularization_schemes::RegularizationFactory;pub use modular_framework::Regularization;pub use solver_implementations::BacktrackingConfig;pub use solver_implementations::CoordinateDescentConfig;pub use solver_implementations::CoordinateDescentResult;pub use solver_implementations::GradientDescentConfig;pub use solver_implementations::GradientDescentResult;pub use solver_implementations::GradientDescentSolver;pub use solver_implementations::LineSearchConfig;pub use solver_implementations::ProximalGradientConfig;pub use solver_implementations::ProximalGradientResult;pub use solver_implementations::ProximalGradientSolver;pub use solver_implementations::SolverFactory;pub use type_safety::problem_type;pub use type_safety::solver_capability;pub use type_safety::ComputationalComplexity;pub use type_safety::ConfigurationHints;pub use type_safety::ConfigurationValidator;pub use type_safety::FeatureValidator;pub use type_safety::FixedSizeOps;pub use type_safety::L1Scheme;pub use type_safety::L2Scheme;pub use type_safety::LargeLinearRegression;pub use type_safety::MediumLinearRegression;pub use type_safety::MemoryRequirements;pub use type_safety::RegularizationConstraint;pub use type_safety::RegularizationScheme;pub use type_safety::SmallLinearRegression;pub use type_safety::SolverConstraint;pub use type_safety::Trained;pub use type_safety::TypeSafeConfig;pub use type_safety::TypeSafeFit;pub use type_safety::TypeSafeLinearModel;pub use type_safety::TypeSafeModelBuilder;pub use type_safety::TypeSafePredict;pub use type_safety::TypeSafeSolverSelector;pub use type_safety::Untrained;pub use large_scale_variational_inference::ARDConfiguration;pub use large_scale_variational_inference::LargeScaleVariationalConfig;pub use large_scale_variational_inference::LargeScaleVariationalRegression;pub use large_scale_variational_inference::LearningRateDecay;pub use large_scale_variational_inference::PriorConfiguration;pub use large_scale_variational_inference::VariationalPosterior;pub use uncertainty_quantification::CalibrationMetrics;pub use uncertainty_quantification::UncertaintyCapable;pub use uncertainty_quantification::UncertaintyConfig;pub use uncertainty_quantification::UncertaintyMethod;pub use uncertainty_quantification::UncertaintyQuantifier;pub use uncertainty_quantification::UncertaintyResult;pub use crate::utils::accurate_condition_number;pub use crate::utils::adaptive_least_squares;pub use crate::utils::condition_number;pub use crate::utils::diagnose_numerical_stability;pub use crate::utils::enhanced_ridge_regression;pub use crate::utils::orthogonal_mp;pub use crate::utils::orthogonal_mp_gram;pub use crate::utils::qr_ridge_regression;pub use crate::utils::rank_revealing_qr;pub use crate::utils::ridge_regression;pub use crate::utils::solve_with_iterative_refinement;pub use crate::utils::stable_normal_equations;pub use crate::utils::stable_ridge_regression;pub use crate::utils::svd_ridge_regression;pub use crate::utils::NumericalDiagnostics;
Modules§
- builder_
enhancements - Enhanced builder patterns for linear models
- coordinate_
descent - Coordinate Descent solver for Lasso and ElasticNet regression
- early_
stopping - Early stopping utilities for linear models
- errors
- Domain-specific error types for linear models
- irls
- Iteratively Reweighted Least Squares (IRLS) for robust regression
- large_
scale_ variational_ inference - Large-Scale Variational Inference for Linear Models
- lars
- Least Angle Regression (LARS) implementation
- lasso_
cv - Lasso regression with built-in cross-validation
- lasso_
lars - Lasso LARS (Least Angle Regression with Lasso modification) implementation
- linear_
regression - Linear Regression implementation
- loss_
functions - Pluggable Loss Functions for Linear Models
- modular_
framework - Modular Framework for Linear Models
- omp
- Orthogonal Matching Pursuit (OMP) implementation
- optimizer
- Optimization algorithms for linear models
- regularization_
schemes - Composable Regularization Schemes for Linear Models
- ridge_
classifier - Ridge Classifier
- ridge_
cv - Ridge regression with built-in cross-validation
- solver
- Solver types for linear models
- solver_
implementations - Trait-based Solver Implementations
- type_
safety - Type Safety Enhancements for Linear Models
- uncertainty_
quantification - Uncertainty Quantification for Linear Models
- utils
- Utility functions for linear models
Enums§
- Penalty
- Penalty types for regularized models