`wildboar.ensemble._ensemble`#

Module Contents#

Classes#

`ShapeletForestMixin`
`BaseShapeletForestClassifier`	Base class for shapelet forest classifiers.
`ShapeletForestClassifier`	An ensemble of random shapelet tree classifiers.
`ExtraShapeletTreesClassifier`	An ensemble of extremely random shapelet trees for time series regression.
`BaseShapeletForestRegressor`	Base class for shapelet forest regressors.
`ShapeletForestRegressor`	An ensemble of random shapelet regression trees.
`ExtraShapeletTreesRegressor`	An ensemble of extremely random shapelet trees for time series regression.
`ShapeletForestEmbedding`	An ensemble of random shapelet trees
`IsolationShapeletForest`	A isolation shapelet forest.

class wildboar.ensemble._ensemble.ShapeletForestMixin#

apply(x)#

decision_function(x)#

class wildboar.ensemble._ensemble.BaseShapeletForestClassifier(base_estimator, *, estimator_params=tuple(), oob_score=False, n_estimators=100, max_depth=None, min_samples_split=2, n_shapelets=10, min_shapelet_size=0, max_shapelet_size=1, metric='euclidean', metric_params=None, bootstrap=True, warm_start=False, n_jobs=None, random_state=None)#

Bases: ShapeletForestMixin, sklearn.ensemble.BaggingClassifier

Base class for shapelet forest classifiers.

Warning

This class should not be used directly. Use derived classes instead.

predict(X, check_input=True)#

Predict class for X.

The predicted class of an input sample is computed as the class with the highest mean predicted probability. If base estimators do not implement a predict_proba method, then it resorts to voting.

Parameters:: X ({array-like, sparse matrix} of shape (n_samples, n_features)) – The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.
Returns:: y – The predicted classes.
Return type:: ndarray of shape (n_samples,)

predict_proba(x, check_input=True)#

Predict class probabilities for X.

The predicted class probabilities of an input sample is computed as the mean predicted class probabilities of the base estimators in the ensemble. If base estimators do not implement a predict_proba method, then it resorts to voting and the predicted class probabilities of an input sample represents the proportion of estimators predicting each class.

Parameters:: X ({array-like, sparse matrix} of shape (n_samples, n_features)) – The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.
Returns:: p – The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.
Return type:: ndarray of shape (n_samples, n_classes)

predict_log_proba(x, check_input=True)#

Predict class log-probabilities for X.

The predicted class log-probabilities of an input sample is computed as the log of the mean predicted class probabilities of the base estimators in the ensemble.

Parameters:: X ({array-like, sparse matrix} of shape (n_samples, n_features)) – The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.
Returns:: p – The class log-probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.
Return type:: ndarray of shape (n_samples, n_classes)

fit(x, y, sample_weight=None, check_input=True)#: Fit a random shapelet forest classifier

class wildboar.ensemble._ensemble.ShapeletForestClassifier(*, n_estimators=100, n_shapelets=10, max_depth=None, min_samples_split=2, min_shapelet_size=0, max_shapelet_size=1, metric='euclidean', metric_params=None, oob_score=False, bootstrap=True, warm_start=False, n_jobs=None, random_state=None)#

Bases: BaseShapeletForestClassifier

An ensemble of random shapelet tree classifiers.

Examples

>>> from wildboar.ensemble import ShapeletForestClassifier
>>> from wildboar.datasets import load_synthetic_control
>>> x, y = load_synthetic_control()
>>> f = ShapeletForestClassifier(n_estimators=100, metric='scaled_euclidean')
>>> f.fit(x, y)
>>> y_hat = f.predict(x)

class wildboar.ensemble._ensemble.ExtraShapeletTreesClassifier(*, n_estimators=100, max_depth=None, min_samples_split=2, min_shapelet_size=0, max_shapelet_size=1, metric='euclidean', metric_params=None, oob_score=False, bootstrap=True, warm_start=False, n_jobs=None, random_state=None)#

Bases: BaseShapeletForestClassifier

An ensemble of extremely random shapelet trees for time series regression.

Examples

>>> from wildboar.ensemble import ExtraShapeletTreesClassifier
>>> from wildboar.datasets import load_synthetic_control
>>> x, y = load_synthetic_control()
>>> f = ExtraShapeletTreesClassifier(n_estimators=100, metric='scaled_euclidean')
>>> f.fit(x, y)
>>> y_hat = f.predict(x)

class wildboar.ensemble._ensemble.BaseShapeletForestRegressor(base_estimator, *, estimator_params=tuple(), oob_score=False, n_estimators=100, max_depth=None, min_samples_split=2, n_shapelets=10, min_shapelet_size=0.0, max_shapelet_size=1.0, metric='euclidean', metric_params=None, bootstrap=True, warm_start=False, n_jobs=None, random_state=None)#

Bases: ShapeletForestMixin, sklearn.ensemble.BaggingRegressor

Base class for shapelet forest regressors.

Warning

This class should not be used directly. Use derived classes instead.

predict(x, check_input=True)#

Predict regression target for X.

The predicted regression target of an input sample is computed as the mean predicted regression targets of the estimators in the ensemble.

Parameters:: X ({array-like, sparse matrix} of shape (n_samples, n_features)) – The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.
Returns:: y – The predicted values.
Return type:: ndarray of shape (n_samples,)

fit(x, y, sample_weight=None, check_input=True)#: Fit a random shapelet forest regressor

class wildboar.ensemble._ensemble.ShapeletForestRegressor(*, n_estimators=100, n_shapelets=10, max_depth=None, min_samples_split=2, min_shapelet_size=0, max_shapelet_size=1, metric='euclidean', metric_params=None, oob_score=False, bootstrap=True, warm_start=False, n_jobs=None, random_state=None)#

Bases: BaseShapeletForestRegressor

An ensemble of random shapelet regression trees.

Examples

>>> from wildboar.ensemble import ShapeletForestRegressor
>>> from wildboar.datasets import load_synthetic_control
>>> x, y = load_synthetic_control()
>>> f = ShapeletForestRegressor(n_estimators=100, metric='scaled_euclidean')
>>> f.fit(x, y)
>>> y_hat = f.predict(x)

class wildboar.ensemble._ensemble.ExtraShapeletTreesRegressor(*, n_estimators=100, max_depth=None, min_samples_split=2, min_shapelet_size=0, max_shapelet_size=1, metric='euclidean', metric_params=None, oob_score=False, bootstrap=True, warm_start=False, n_jobs=None, random_state=None)#

Bases: BaseShapeletForestRegressor

An ensemble of extremely random shapelet trees for time series regression.

Examples

>>> from wildboar.ensemble import ExtraShapeletTreesRegressor
>>> from wildboar.datasets import load_synthetic_control
>>> x, y = load_synthetic_control()
>>> f = ExtraShapeletTreesRegressor(n_estimators=100, metric='scaled_euclidean')
>>> f.fit(x, y)
>>> y_hat = f.predict(x)

class wildboar.ensemble._ensemble.ShapeletForestEmbedding(n_estimators=100, *, n_shapelets=1, max_depth=5, min_samples_split=2, min_shapelet_size=0, max_shapelet_size=1, metric='euclidean', metric_params=None, bootstrap=True, warm_start=False, n_jobs=None, sparse_output=True, random_state=None)#

Bases: BaseShapeletForestRegressor

An ensemble of random shapelet trees

An unsupervised transformation of a time series dataset to a high-dimensional sparse representation. A time series i indexed by the leaf that it falls into. This leads to a binary coding of a time series with as many ones as trees in the forest.

The dimensionality of the resulting representation is <= n_estimators * 2^max_depth

fit(x, y=None, sample_weight=None, check_input=True)#: Fit a random shapelet forest regressor

fit_transform(x, y=None, sample_weight=None, check_input=True)#

transform(x)#

class wildboar.ensemble._ensemble.IsolationShapeletForest(*, n_estimators=100, bootstrap=False, n_jobs=None, min_shapelet_size=0, max_shapelet_size=1, min_samples_split=2, max_samples='auto', contamination='auto', contamination_set='training', warm_start=False, metric='euclidean', metric_params=None, random_state=None)#

Bases: ShapeletForestMixin, sklearn.base.OutlierMixin, sklearn.ensemble._bagging.BaseBagging

A isolation shapelet forest.

New in version 0.3.5.

offset_#

The offset for computing the final decision

Type:: float

Examples

>>> from wildboar.ensemble import IsolationShapeletForest
>>> from wildboar.datasets import load_two_lead_ecg
>>> from model_selection.outlier import train_test_split
>>> from sklearn.metrics import balanced_accuracy_score
>>> x, y = load_two_lead_ecg("two_lead_ecg")
>>> x_train, x_test, y_train, y_test = train_test_split(x, y, 1, test_size=0.2, anomalies_train_size=0.05)
>>> f = IsolationShapeletForest(n_estimators=100, contamination=balanced_accuracy_score)
>>> f.fit(x_train, y_train)
>>> y_pred = f.predict(x_test)
>>> balanced_accuracy_score(y_test, y_pred)

Or using default offset threshold

>>> from wildboar.ensemble import IsolationShapeletForest
>>> from wildboar.datasets import load_two_lead_ecg
>>> from model_selection.outlier import train_test_split
>>> from sklearn.metrics import balanced_accuracy_score
>>> f = IsolationShapeletForest()
>>> x, y = load_two_lead_ecg("two_lead_ecg")
>>> x_train, x_test, y_train, y_test = train_test_split(x, y, 1, test_size=0.2, anomalies_train_size=0.05)
>>> f.fit(x_train)
>>> y_pred = f.predict(x_test)
>>> balanced_accuracy_score(y_test, y_pred)

fit(x, y=None, sample_weight=None, check_input=True)#

Build a Bagging ensemble of estimators from the training set (X, y).

Parameters:

X ({array-like, sparse matrix} of shape (n_samples, n_features)) – The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.
y (array-like of shape (n_samples,)) – The target values (class labels in classification, real numbers in regression).
sample_weight (array-like of shape (n_samples,), default=None) – Sample weights. If None, then samples are equally weighted. Note that this is supported only if the base estimator supports sample weighting.

Returns:

self – Fitted estimator.

Return type:

object

predict(x)#

decision_function(x)#

score_samples(x)#

wildboar.ensemble._ensemble#

Module Contents#

Classes#

`wildboar.ensemble._ensemble`#