wildboar.model_selection.outlier

Module Contents

Classes

RepeatedOutlierSplit

Repeated random outlier cross-validator

Functions

threshold_score(y_true, score, score_f)	Compute the performance of using the i:th score
train_test_split(x, y, normal_class[, test_size, ...])	Training and testing split from classification dataset

class wildboar.model_selection.outlier.RepeatedOutlierSplit(n_splits=None, *, test_size=0.2, n_outlier=0.05, shuffle=True, random_state=None)

Repeated random outlier cross-validator

Yields indicies that split the dataset into training and test sets.

Note

Contrary to other cross-validation strategies, the random outlier cross-validator does not ensure that all folds will be different. Instead, the inlier samples are shuffled and new outlier samples are inserted in the training and test sets repeatedly.

Parameters:

• n_splits (int, optional) –

  The maximum number of splits.

  • if None, the number of splits is determined by the number of outliers as, total_n_outliers/(n_inliers * n_outliers)

  • if int, the number of splits is an upper-bound

• test_size (float, optional) – The size of the test set.

• n_outlier (float, optional) – The fraction of outliers in the training and test sets.

• shuffle (bool, optional) – Shuffle the training indicies in each iteration.

• random_state (int or RandomState, optional) – The psudo-random number generator

__repr__()

Return repr(self).

get_n_splits(X, y, groups=None)

Returns the number of splitting iterations in the cross-validator :param X: The samples :type X: object :param y: The labels :type y: object :param groups: Always ignored, exists for compatibility. :type groups: object

Returns:

n_splits – Returns the number of splitting iterations in the cross-validator.

Return type:

int

split(x, y, groups=None)

Return training and test indicies

Parameters:

• x (object) – Always ignored, exists for compatibility.

• y (object) – The labels

• groups (object, optional) – Always ignored, exists for compatibility.

Yields:

train_idx, test_idx (ndarray) – The training and test indicies

wildboar.model_selection.outlier.threshold_score(y_true, score, score_f)

Compute the performance of using the i:th score

The scores are typically computed using an outlier detection algorithm

Parameters:

• y_true (array-like) – The true labels

• score (array-like) – The scores

• score_f (callable) – Function for estimating the performance of the i:th scoring

Returns:

score – performance for each score as threshold

Return type:

ndarray

See also

wildboar.ensemble.IsolationShapeletForest

an isolation forest for time series

Examples

Setting the offset that maximizes balanced accuracy of a shapelet isolation forest

>>> from wildboar.ensemble import IsolationShapeletForest
>>> from wildboar.datasets import load_two_lead_ecg
>>> from sklearn.metrics import balanced_accuracy_score
>>> x, y = load_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()
>>> f.fit(x_train)
>>> scores = f.score_samples(x_train)
>>> perf = threshold_score(y_train, scores, balanced_accuracy_score)
>>> f.offset_ = score[np.argmax(perf)]

wildboar.model_selection.outlier.train_test_split(x, y, normal_class, test_size=0.2, anomalies_train_size=0.05, random_state=None)

Training and testing split from classification dataset

Parameters:

• x (array-like of shape (n_samples, n_timestep) or (n_samples, n_dim, n_timestep)) – Input data samples

• y (array-like of shape (n_samples,)) – Input class label

• normal_class (int) – Class label that should be considered as the normal class

• test_size (float) – Size of the test set

• anomalies_train_size (float) – Contamination of anomalies in the training dataset

• random_state (int or RandomState) – Psudo random state used for stable results.

Returns:

• x_train (array-like) – Training samples

• x_test (array-like) – Test samples

• y_train (array-like) – Training labels (either 1 or -1, where 1 denotes normal and -1 anomalous)

• y_test (array-like) – Test labels (either 1 or -1, where 1 denotes normal and -1 anomalous)

Examples

>>> from wildboar.datasets import load_two_lead_ecg
>>> x, y = load_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
... )