Functions — github.com/cmccomb/rust-automl

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Functions400 in github.com/cmccomb/rust-automl

Predict cluster assignments for new data using the first trained algorithm. # Errors Returns [`ModelError::NotTrained`] if the model has not been tr

src/model/clustering.rs:77

↓ 24 callersMethodadd_step

(mut self, step: PreprocessingStep)

src/settings/common.rs:236

↓ 23 callersMethodget_kfolds

(&self)

src/settings/common.rs:200

↓ 22 callersMethodtrain

Train the model using every configured algorithm.

src/model/clustering.rs:51

↓ 19 callersMethodwith_k

(mut self, k: usize)

src/settings/knn_parameters.rs:23

↓ 15 callersMethodis_empty

(&self)

src/settings/preprocessing.rs:339

↓ 14 callersMethodfit

Fit the algorithm

src/algorithms/clustering.rs:79

↓ 11 callersMethodwith_algorithm

(mut self, algorithm: KNNAlgorithmName)

src/settings/knn_parameters.rs:37

↓ 11 callersMethodwith_distance

(mut self, distance: Distance)

src/settings/knn_parameters.rs:44

↓ 11 callersMethodwith_number_of_folds

Set the number of folds for cross-validation.

src/settings/common.rs:208

↓ 10 callersFunctionclustering_testing_data

Return clustering data for tests and examples. # Returns `x` - Feature matrix containing points for clustering.

tests/fixtures/clustering_data.rs:8

↓ 10 callersMethodfit_transform

Fit preprocessing state (if required) and return a transformed copy of the training matrix. # Errors Returns an error if any preprocessing step fail

src/model/preprocessing.rs:86

↓ 10 callersFunctionregression_testing_data

Return regression data for tests and examples. # Returns `(x, y)` - Feature matrix and target vector.

tests/fixtures/regression_data.rs:8

↓ 9 callersMethodonly

( mut self, only: &RegressionAlgorithm<INPUT, OUTPUT, InputArray, OutputArray>, )

src/settings/regression_settings.rs:136

↓ 9 callersMethodshuffle_data

Enable or disable shuffling of training data.

src/settings/common.rs:215

↓ 9 callersMethodsorted_by

Set the metric used for sorting model results.

src/settings/common.rs:250

↓ 9 callersMethodtransform_training

(&self, data: &mut InputArray)

src/model/preprocessing.rs:401

↓ 8 callersMethodwith_tol

(mut self, tol: f32)

src/settings/svr_parameters.rs:35

↓ 7 callersFunctionbuild_matrix

(data: Vec<Vec<f64>>)

tests/preprocessing.rs:13

↓ 7 callersMethodpredict

(&self, x: &InputArray)

src/algorithms/classification.rs:489

↓ 7 callersMethodsupervised_mut

(&mut self)

src/settings/regression_settings.rs:312

↓ 7 callersMethodverbose

Enable or disable verbose logging.

src/settings/common.rs:222

↓ 7 callersMethodwith_weight

(mut self, weight: KNNWeightFunction)

src/settings/knn_parameters.rs:30

↓ 6 callersFunctionclassification_testing_data

Return classification data for tests and examples. # Returns `(x, y)` - Feature matrix and target vector.

tests/fixtures/classification_data.rs:8

↓ 6 callersMethodfit

( self, x: &InputArray, y: &OutputArray, settings: &ClassificationSettings,

src/algorithms/classification.rs:1084

↓ 6 callersFunctionload_csv_features

Load a CSV file and return its feature matrix. # Arguments `path` - Path to the CSV file. # Errors Returns an error if the file cannot be read, a

src/utils/io.rs:70

↓ 6 callersFunctionresolve_columns

( selector: &ColumnSelector, total_cols: usize, )

src/model/preprocessing.rs:469

↓ 6 callersMethodtransform_owned

(&self, mut data: InputArray)

src/model/preprocessing.rs:409

↓ 6 callersMethodwith_eps

(mut self, eps: f32)

src/settings/svr_parameters.rs:21

↓ 6 callersMethodwith_final_model

Choose the strategy for the final model.

src/settings/common.rs:243

↓ 6 callersMethodwith_max_iter

(mut self, max_iter: usize)

src/settings/clustering_settings.rs:68

↓ 6 callersMethodwith_multinomial_nb_settings

(mut self, settings: MultinomialNBParameters)

src/settings/classification_settings.rs:114

↓ 5 callersFunctionassert_traits

(dist: Distance, display: &str, debug: &str)

tests/distance.rs:5

↓ 5 callersMethodcv

( self, x: &InputArray, y: &OutputArray, settings: &RegressionSettings<INPUT,

src/algorithms/regression.rs:603

↓ 5 callersFunctionload_labeled_csv

Load a labeled CSV file and split features from the target column. # Arguments `path` - Path to the CSV file. `target_col` - Zero-based index of the

src/utils/io.rs:116

↓ 5 callersMethodskip

( mut self, skip: RegressionAlgorithm<INPUT, OUTPUT, InputArray, OutputArray>, )

src/settings/regression_settings.rs:126

↓ 5 callersMethodwith_bernoulli_nb_settings

(mut self, settings: BernoulliNBParameters<f64>)

src/settings/classification_settings.rs:93

↓ 5 callersMethodwith_c

(mut self, c: f32)

src/settings/svr_parameters.rs:28

↓ 5 callersMethodwith_kernel

(mut self, kernel: Kernel)

src/settings/svr_parameters.rs:42

↓ 4 callersFunctionapprox_zero

(value: INPUT)

src/model/preprocessing.rs:504

↓ 4 callersFunctioncollect_valid_values

(data: &InputArray, column: usize)

src/model/preprocessing.rs:511

↓ 4 callersMethodevaluate

Evaluate clustering results against known labels. # Arguments `truth` - Ground truth cluster labels. # Panics Panics if the model has not been trai

src/model/clustering.rs:111

↓ 4 callersFunctioninteraction_features

Generate pairwise interaction features by multiplying each combination of columns. # Arguments `x` - Input feature matrix. # Examples ``` use auto

src/utils/features.rs:62

↓ 4 callersFunctionpercentile

(values: &[INPUT], fraction: f64)

src/model/preprocessing.rs:535

↓ 4 callersFunctionpolynomial_features

Generate polynomial features up to a given order. # Arguments `x` - Input feature matrix. `order` - Highest polynomial order to generate. # Example

src/utils/features.rs:117

↓ 4 callersMethodtransform_internal

( data: &mut InputArray, params: StandardizeParams, means: &[INPUT], stds: &[I

src/model/preprocessing.rs:418

↓ 4 callersFunctionvalidate_finite

(value: f64, name: &str)

src/utils/kernels.rs:101

↓ 4 callersMethodwith_categorical_nb_settings

(mut self, settings: CategoricalNBParameters)

src/settings/classification_settings.rs:107

↓ 4 callersMethodwith_preprocessing

Specify an explicit preprocessing pipeline.

src/settings/common.rs:229

↓ 3 callersFunctiondebug_option

(x: Option<T>)

src/utils/display.rs:34

↓ 3 callersMethodget_metric

Retrieve the metric function for regression tasks. # Errors Returns [`SettingsError`] if no metric is set or if the metric is unsupported.

src/settings/regression_settings.rs:114

↓ 3 callersMethodpredict

(&self, x: &Self::InputArray)

src/algorithms/regression.rs:988

↓ 3 callersMethodpredict_array

(&self, x: &InputArray)

src/algorithms/classification.rs:385

↓ 3 callersMethodpredict_array

(&self, x: &InputArray)

src/algorithms/regression.rs:127

↓ 3 callersMethodpreprocess

Apply preprocessing to inference data. # Errors Returns an error if any preprocessing step fails to transform the data using the previously fitted s

src/model/preprocessing.rs:109

↓ 3 callersFunctionprint_settings

(label: &str, settings: &T)

examples/print_settings.rs:171

↓ 3 callersMethodselected_algorithms

(&self)

src/settings/clustering_settings.rs:113

↓ 3 callersMethodsupervised

(&self)

src/model/supervised.rs:78

↓ 3 callersFunctiontest_from_settings

(settings: RegressionSettings<f64, f64, DenseMatrix<f64>, Vec<f64>>)

tests/regression.rs:300

↓ 3 callersMethodto_parameters

(&self)

src/algorithms/regression.rs:59

↓ 3 callersFunctionvalidate_positive

(value: f64, name: &str)

src/utils/kernels.rs:112

↓ 3 callersMethodwith_min_samples

(mut self, min_samples: usize)

src/settings/clustering_settings.rs:82

↓ 2 callersMethodapply_internal

(&self, data: &InputArray)

src/model/preprocessing.rs:1531

↓ 2 callersFunctionbernoulli_threshold_classification_data

()

tests/fixtures/classification_data.rs:60

↓ 2 callersFunctionbuild_csv_reader

(path: &Path)

src/utils/io.rs:141

↓ 2 callersFunctioncolumn_min_value

( data: &InputArray, column: usize, )

src/model/preprocessing.rs:599

↓ 2 callersMethodcompute

( data: &InputArray, column: usize, params: StandardizeParams, )

src/model/preprocessing.rs:897

↓ 2 callersFunctionconvert_to_nonnegative_integer_dense_matrix

( x: &InputArray, algorithm_name: &'static str, )

src/algorithms/classification.rs:49

↓ 2 callersFunctionelementwise_multiply

Function to do element-wise multiplication of two vectors

src/utils/math.rs:6

↓ 2 callersFunctionensure_consistent_width

( row: &[f64], row_idx: usize, expected_width: &mut Option<usize>, )

src/utils/io.rs:217

↓ 2 callersMethodfit_inner

( self, x: &InputArray, y: &OutputArray, settings: &RegressionSettings<INPUT,

src/algorithms/regression.rs:465

↓ 2 callersFunctionload_breast_cancer_dataset

Load the Wisconsin Diagnostic Breast Cancer dataset from `data/breast_cancer.csv`. # Errors Returns an error if the CSV file cannot be read or parse

tests/fixtures/breast_cancer_dataset.rs:52

↓ 2 callersFunctionload_diabetes_dataset

Load the diabetes progression dataset from `data/diabetes.csv`. # Errors Returns an error if the CSV file cannot be read or parsed into numeric data

tests/fixtures/diabetes_dataset.rs:41

↓ 2 callersFunctionparse_numeric_field

(value: &str, row_idx: usize, col_idx: usize)

src/utils/io.rs:207

↓ 2 callersMethodpredict_with

Predict cluster assignments with a specific algorithm. # Errors Returns [`ModelError::NotTrained`] if the requested algorithm has not been trained.

src/model/clustering.rs:90

↓ 2 callersFunctionprint_knn_search_algorithm

(a: &KNNAlgorithmName)

src/utils/display.rs:73

↓ 2 callersFunctionprint_knn_weight_function

(f: &KNNWeightFunction)

src/utils/display.rs:49

↓ 2 callersFunctionprint_option

(x: Option<T>)

src/utils/display.rs:18

↓ 2 callersFunctionsanitize_xgboost_parameters

( params: &XGRegressorParameters, )

src/algorithms/regression.rs:271

↓ 2 callersMethodto_smartcore

Convert the enum variant into Smartcore kernel components. # Errors Returns [`Failed`] when kernel parameters are invalid (for example, NaN or non-p

src/utils/kernels.rs:56

↓ 2 callersMethodtrained_algorithm_names

(&self)

src/model/clustering.rs:65

↓ 1 callersFunctionalgorithm_to_name

( algorithm: &RegressionAlgorithm<INPUT, OUTPUT, InputArray, OutputArray>, )

src/settings/regression_settings.rs:342

↓ 1 callersFunctionbernoulli_binary_classification_data

()

tests/fixtures/classification_data.rs:40

↓ 1 callersFunctionbuild_classification_settings

()

examples/print_settings.rs:103

↓ 1 callersFunctionbuild_clustering_settings

()

examples/print_settings.rs:161

↓ 1 callersFunctionbuild_regression_settings

()

examples/print_settings.rs:18

↓ 1 callersMethodcompute_baseline

(&mut self, x: &InputArray, settings: &ClusteringSettings)

src/model/clustering.rs:219

↓ 1 callersFunctionconvert_feature_value

( value: INPUT, row: usize, col: usize, algorithm_name: &'static str, )

src/algorithms/classification.rs:71

↓ 1 callersMethodcross_validate_model

( self, x: &Self::InputArray, y: &Self::OutputArray, settings: &RegressionSett

src/algorithms/regression.rs:1015

↓ 1 callersMethodcv

( self, x: &InputArray, y: &OutputArray, settings: &ClassificationSettings,

src/algorithms/classification.rs:731

↓ 1 callersMethoddisplay_row

(&self)

src/model/clustering.rs:240

↓ 1 callersMethodemit_encoded

(&self, idx: usize, value: INPUT, buffer: &mut Vec<INPUT>)

src/model/preprocessing.rs:1284

↓ 1 callersMethodfit

( data: &mut InputArray, params: StandardizeParams, )

src/model/preprocessing.rs:322

↓ 1 callersMethodfit

( self, x: &InputArray, y: &OutputArray, settings: &RegressionSettings<INPUT,

src/algorithms/regression.rs:929

↓ 1 callersMethodfit_categorical_step

( &mut self, data: InputArray, params: &CategoricalEncoderParams, )

src/model/preprocessing.rs:226

↓ 1 callersMethodfit_column_filter_step

( &mut self, data: InputArray, params: &ColumnFilterParams, )

src/model/preprocessing.rs:246

↓ 1 callersMethodfit_impute_step

( &mut self, mut data: InputArray, params: &ImputeParams, )

src/model/preprocessing.rs:216

↓ 1 callersMethodfit_pca_step

(&mut self, data: &InputArray, n: usize)

src/model/preprocessing.rs:171

↓ 1 callersMethodfit_power_transform_step

( &mut self, mut data: InputArray, params: &PowerTransformParams, )

src/model/preprocessing.rs:236

↓ 1 callersMethodfit_scale_step

( &mut self, mut data: InputArray, params: &ScaleParams, )

src/model/preprocessing.rs:206

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