wildboar

Wildboar - a library for temporal machine learning.

Wildboar includes numerous temporal machine learning algorithms and seamlessly integrates them with scikit-learn.

Functions

iseos

Boolean indicator if a value is the end-of-sequence value.

wildboar.annotate

Annotation of time series.

See the annotation section in the User Guide for more details and examples.

Functions

motifs	Find motifs.
segment	Find change regimes in a time series.

wildboar.base

Base classes for all estimators.

Classes

BaseEstimator	Base estimator for all Wildboar estimators.
CounterfactualMixin	Mixin class for counterfactual explainer.
ExplainerMixin	Mixin class for all explainers in wildboar.

Functions

is_counterfactual	Check if estimator is a counterfactual explainer.
is_explainer	Check if estimator is an explainer.

wildboar.datasets

Dataset loading utilities.

See the dataset section in the User Guide for more details and examples.

Examples

>>> from wildboar.datasets import load_dataset
>>> X, y = load_dataset("GunPoint")
>>> X.shape
(200, 60)

Classes

Bundle	Base class for handling dataset bundles.
JSONRepository	A repository is a collection of bundles.
NpBundle	Bundle of numpy binary files.
Repository	A repository is a collection of bundles.

Functions

clear_cache	Clear the cache by deleting cached datasets.
get_bundles	Get all bundles in the repository.
get_repository	Get repository by name.
install_repository	Install repository.
list_bundles	Get a list of all bundle names in the specified repository.
list_collections	List the collections of the repository.
list_datasets	List the datasets in the repository.
list_repositories	List the key of all installed repositories.
load_dataset	Load a dataset from a repository.
load_datasets	Load all datasets as a generator.
load_gun_point	Load the GunPoint dataset.
load_synthetic_control	Load the Synthetic_Control dataset.
load_two_lead_ecg	Load the TwoLeadECG dataset.
refresh_repositories	Refresh the installed repositories.
set_cache_dir	Change the global cache directory.

wildboar.datasets.outlier

Utilities for generating synthetic outlier datasets.

See the User Guide for more details and example uses.

Functions

density_outliers	Densitiy based outlier generation.
emmott_outliers	Difficulty based outlier generation.
kmeans_outliers	K-mean outlier generation.
majority_outliers	Label the majority class as inliers.
minority_outliers	Label (a fraction of) the minority class as the outlier.

wildboar.datasets.preprocess

Utilities for preprocessing time series.

Functions

maxabs_scale	Scale each time series by its maximum absolute value.
minmax_scale	Scale x along the time dimension.
named_preprocess	Get a named preprocessor.
standardize	Scale x along the time dimension.
truncate	Truncate x to the shortest sequence.

wildboar.distance

Fast distance computations.

The wildboar.distance module includes functions for computing paired and pairwise distances between time series and between time series and subsequences.

See the User Guide for more details and examples.

Classes

KMeans	KMeans clustering with support for DTW and weighted DTW.
KMedoids	KMedoid algorithm.
KNeighborsClassifier	Classifier implementing k-nearest neighbors.
MDS	Multidimensional scaling.

Functions

argmin_distance	Find the indicies of the samples with the lowest distance in Y.
argmin_subsequence_distance	Compute the k:th closest subsequences.
distance_profile	Compute the distance profile.
matrix_profile	Compute the matrix profile.
paired_distance	Compute the distance between the i:th time series.
paired_subsequence_distance	Minimum subsequence distance between the i:th subsequence and time series.
paired_subsequence_match	Find matching subsequnces.
pairwise_distance	Compute the distance between subsequences and time series.
pairwise_subsequence_distance	Minimum subsequence distance between subsequences and time series.
subsequence_match	Find matching subsequnces.

wildboar.distance.dtw

DTW alignment and distance computations.

The wildboar.distance.dtw module implements several functions for computing DTW alignments and distances.

Functions

ddtw_distance	Compute the derivative dynamic time warping distance.
dtw_alignment	Compute the Dynamic time warping alignment matrix.
dtw_average	Compute the DTW barycenter average (DBA).
dtw_distance	Compute the dynamic time warping distance.
dtw_envelop	Compute the envelop for LB_keogh.
dtw_lb_keogh	LB_keogh lower bound.
dtw_mapping	Optimal warping path between two series or from a given alignment matrix.
jeong_weight	Weighting described by Jeong et. al. (2011)[R4bf7d056babf-1]_.
wddtw_distance	Compute the weighted derivative dynamic time warping distance.
wdtw_alignment	Weighted dynamic time warping alignment.
wdtw_distance	Compute the weighted dynamic time warping distance.

wildboar.ensemble

Ensemble methods for classification, regression and outlier detection.

Classes

BaggingClassifier	A bagging classifier.
BaggingRegressor	A bagging regressor.
BaseBagging	Base estimator for Wildboar ensemble estimators.
ElasticEnsembleClassifier	Ensemble of wildboar.distance.KNeighborsClassifier.
ExtraShapeletTreesClassifier	An ensemble of extremely random shapelet trees.
ExtraShapeletTreesRegressor	An ensemble of extremely random shapelet tree regressors.
IntervalForestClassifier	An ensemble of interval tree classifiers.
IntervalForestRegressor	An ensemble of interval tree regressors.
IsolationShapeletForest	An isolation shapelet forest.
PivotForestClassifier	An ensemble of interval tree classifiers.
ProximityForestClassifier	A forest of proximity trees.
RocketForestClassifier	An ensemble of rocket tree classifiers.
RocketForestRegressor	An ensemble of rocket tree regressors.
ShapeletForestClassifier	An ensemble of random shapelet tree classifiers.
ShapeletForestEmbedding	An ensemble of random shapelet trees.
ShapeletForestRegressor	An ensemble of random shapelet tree regressors.

wildboar.explain

Explanation methods for classifiers and regressors.

Classes

AmplitudeImportance	Compute the importance of equi-probable amplitude intervals.
FrequencyImportance	Mixin class for all explainers in wildboar.
IntervalImportance	Interval importance for time series.
ShapeletImportance	Compute the importance of shapelets.

Functions

plot_importances

Plot the importances as a boxplot.

wildboar.explain.counterfactual

Counterfactual explanations.

The wildboar.explain.counterfactual module includes numerous methods for generating counterfactual explanations.

Classes

KNeighborsCounterfactual	Fit a counterfactual explainer to a k-nearest neighbors classifier.
PrototypeCounterfactual	Model agnostic approach for constructing counterfactual explanations.
ShapeletForestCounterfactual	Counterfactual explanations for shapelet forest classifiers.

Functions

counterfactuals	Compute a single counterfactual example for each sample.
proximity	Compute the proximity of the counterfactuals.

wildboar.linear_model

Linear methods for both classification and regression.

Classes

CastorClassifier	A dictionary based method using dilated competing shapelets.
DilatedShapeletClassifier	A classifier that uses random dilated shapelets.
HydraClassifier	A Dictionary based method using convolutional kernels.
RandomShapeletClassifier	A classifier that uses random shapelets.
RandomShapeletRegressor	A regressor that uses random shapelets.
RocketClassifier	Implements the ROCKET classifier.
RocketRegressor	Implements the ROCKET regressor.

wildboar.metrics

Evaluation metrics.

Functions

compactness_score	Compute compactness score.
plausability_score	Compute plausibility score.
proximity_score	Compute proximity score.
redudancy_score	Compute the redudancy score.
relative_proximity_score	Compute relative proximity score.
silhouette_samples	Compute the Silhouette Coefficient of each samples.
silhouette_score	Compute the mean Silhouette Coefficient of all samples.
validity_score	Compute validity score.

wildboar.model_selection

Methods for model selection.

Classes

RepeatedOutlierSplit

Repeated random outlier cross-validator.

Functions

outlier_train_test_split

Outlier training and testing split from classification dataset.

wildboar.transform

Transform raw time series to tabular representations.

Classes

CastorTransform	Competing Dialated Shapelet Transform.
DerivativeTransform	Mixin class for all transformers in scikit-learn.
DiffTransform	Mixin class for all transformers in scikit-learn.
DilatedShapeletTransform	Dilated shapelet transform.
FeatureTransform	Transform a time series as a number of features.
HydraTransform	A Dictionary based method using convolutional kernels.
IntervalTransform	Embed a time series as a collection of features per interval.
MatrixProfileTransform	Matrix profile transform.
PAA	Peicewise aggregate approximation.
PivotTransform	A transform using pivot time series and sampled distance metrics.
ProximityTransform	Transform time series based on class conditional pivots.
RandomShapeletTransform	Random shapelet tranform.
RocketTransform	Transform a time series using random convolution features.
SAX	Symbolic aggregate approximation.

Functions

convolve	Apply 1D convolution over a time series.
piecewice_aggregate_approximation	Peicewise aggregate approximation.
symbolic_aggregate_approximation	Symbolic aggregate approximation.

wildboar.tree

Tree-based estimators for classification and regression.

Classes

ExtraShapeletTreeClassifier	An extra shapelet tree classifier.
ExtraShapeletTreeRegressor	An extra shapelet tree regressor.
IntervalTreeClassifier	An interval based tree classifier.
IntervalTreeRegressor	An interval based tree regressor.
PivotTreeClassifier	A tree classifier that uses pivot time series.
ProximityTreeClassifier	A classifier that uses a k-branching tree based on pivot-time series.
RocketTreeClassifier	A tree classifier that uses random convolutions as features.
RocketTreeRegressor	A tree regressor that uses random convolutions as features.
ShapeletTreeClassifier	A shapelet tree classifier.
ShapeletTreeRegressor	A shapelet tree regressor.

wildboar.utils

Utility functions.

Functions

check_array	Input validation on time-series.
check_X_y	Check both X and y.