DetectorPipeline

class DetectorPipeline(steps)[source]

Pipeline for time series anomaly, changepoint detection, segmentation.

Parameters:

stepslist of sktime transformers and detectors, or: list of tuples (str, estimator) of sktime transformers or detectors. The list must contain exactly one forecaster. These are “blueprint” transformers resp forecasters, detector/transformer states do not change when fit is called.

Attributes:

steps_list of tuples (str, estimator) of sktime transformers or detectors: clones of estimators in steps which are fitted in the pipeline is always in (str, estimator) format, even if steps is just a list strings not passed in steps are replaced by unique generated strings i-th transformer in steps_ is clone of i-th in steps
estimator_estimator, reference to the first non-transformer in steps_: Return reference to the detector in the pipeline.

Examples

>>> import numpy as np
>>> import pandas as pd
>>> from sktime.detection.lof import SubLOF
>>> from sktime.transformations.detrend import Detrender
>>>
>>> n = 100
>>> x = pd.Series(np.linspace(0, 5, n) + np.random.normal(0, 0.1, size=n))
>>> x.at[50] = 100
>>>
>>> pipeline = Detrender() * SubLOF(n_neighbors=5, window_size=5, novelty=True)
>>> pipeline.fit(x)
DetectorPipeline(...)
>>> y_hat = pipeline.transform(x)

Methods

`change_points_to_segments`(y_sparse[, start, end])	Convert an series of change point indexes to segments.
`check_is_fitted`([method_name])	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`dense_to_sparse`(y_dense)	Convert the dense output from an detector to a sparse format.
`fit`(X[, y])	Fit to training data.
`fit_predict`(X[, y])	Fit to data, then predict it.
`fit_transform`(X[, y])	Fit to data, then transform it.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from parent classes.
`get_config`()	Get config flags for self.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get parameters of estimator.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`is_composite`()	Check if the object is composite.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`predict`(X)	Create labels on test/deployment data.
`predict_points`(X)	Predict changepoints/anomalies on test/deployment data.
`predict_scores`(X)	Return scores for predicted labels on test/deployment data.
`predict_segments`(X)	Predict segments on test/deployment data.
`reset`()	Reset the object to a clean post-init state.
`save`([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
`segments_to_change_points`(y_sparse)	Convert segments to change points.
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**kwargs)	Set the parameters of estimator.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.
`sparse_to_dense`(y_sparse, index)	Convert the sparse output from an detector to a dense format.
`transform`(X)	Create labels on test/deployment data.
`transform_scores`(X)	Return scores for predicted labels on test/deployment data.
`update`(X[, y])	Update model with new data and optional ground truth labels.
`update_predict`(X[, y])	Update model with new data and create labels for it.