API CheatSheet

The following APIs are applicable for all detector models for ease of use.

  • pythresh.thresholders.base.BaseDetector.eval(): evaluate a single outlier or multiple outlier detection likelihood score sets

Key Attributes of a fitted model:

  • pythresh.thresholds.base.BaseThresholder.thresh_: threshold value from scores normalize between 0 and 1

  • pythresh.thresholders.base.BaseDetector.confidence_interval_: Return the lower and upper confidence interval of the contamination level. Only applies to the COMB thresholder

  • pythresh.thresholders.base.BaseDetector.dscores_: 1D array of the TruncatedSVD decomposed decision scores if multiple outlier detector score sets are passed

  • pythresh.thresholders.mixmod.MIXMOD.mixture_: fitted mixture model class of the selected model used for thresholding. Only applies to MIXMOD. Attributes include: components, weights, params. Functions include: fit, loglikelihood, pdf, and posterior.

See base class definition below:

pythresh.thresholds.base module

class pythresh.thresholds.base.BaseThresholder[source]

Bases: object

Abstract class for all outlier detection thresholding algorithms.

thresh_
Type:

threshold value that separates inliers from outliers

confidence_interval_
Type:

lower and upper confidence interval of the contamination level

dscores_
Type:

1D array of decomposed decision scores

abstract eval(decision)[source]

Outlier/inlier evaluation process for decision scores.

Parameters:

decision (np.array or list of shape (n_samples)) – or np.array of shape (n_samples, n_detectors) which are the decision scores from a outlier detection.

Returns:

outlier_labels – For each observation, tells whether or not it should be considered as an outlier according to the fitted model. 0 stands for inliers and 1 for outliers.

Return type:

numpy array of shape (n_samples,)