IntervalSegmenter

class IntervalSegmenter(intervals=10)

Interval segmentation transformer.

Parameters

intervalsint, np.ndarray or list of np.ndarrays with one for each

column of input data.

Intervals to generate. – If int, intervals gives the number of generated intervals. – If ndarray, 2d np.ndarray [n_intervals, 2] with rows giving intervals, the first column giving start points, and the second column giving end points of intervals

Attributes

is_fitted

Whether fit has been called.

Methods

check_is_fitted()	Check if the estimator has been fitted.
clone_tags(estimator[, tag_names])	clone/mirror tags from another estimator as dynamic override.
create_test_instance()	Construct Estimator instance if possible.
fit(X[, y])	Fit transformer, generating random interval indices.
fit_transform(Z[, X])	Fit to data, then transform it.
get_class_tag(tag_name[, tag_value_default])	Get tag value from estimator class (only class tags).
get_class_tags()	Get class tags from estimator class and all its parent classes.
get_params([deep])	Get parameters for this estimator.
get_tag(tag_name[, tag_value_default, …])	Get tag value from estimator class and dynamic tag overrides.
get_tags()	Get tags from estimator class and dynamic tag overrides.
get_test_params()	Return testing parameter settings for the estimator.
set_params(**params)	Set the parameters of this estimator.
set_tags(**tag_dict)	Set dynamic tags to given values.
transform(X[, y])	Transform X, segments time-series in each column into random intervals using interval indices generated during fit.

fit(X, y=None)

Fit transformer, generating random interval indices.

Parameters

Xpandas DataFrame of shape [n_samples, n_features]

Input data

ypandas Series, shape (n_samples, …), optional

Targets for supervised learning.

Returns

selfan instance of self.

transform(X, y=None)

Transform X, segments time-series in each column into random intervals using interval indices generated during fit.

Parameters

Xnested pandas DataFrame of shape [n_samples, n_features]

Nested dataframe with time-series in cells.

Returns

Xtpandas DataFrame

Transformed pandas DataFrame with same number of rows and one column for each generated interval.

check_is_fitted()

Check if the estimator has been fitted.

Raises

NotFittedError

If the estimator has not been fitted yet.

clone_tags(estimator, tag_names=None)

clone/mirror tags from another estimator as dynamic override.

Parameters

estimatorestimator inheriting from :class:BaseEstimator

tag_namesstr or list of str, default = None

Names of tags to clone. If None then all tags in estimator are used as tag_names.

Returns

Self :

Reference to self.

Notes

Changes object state by setting tag values in tag_set from estimator as dynamic tags in self.

classmethod create_test_instance()

Construct Estimator instance if possible.

Returns

instanceinstance of the class with default parameters

Notes

get_test_params can return dict or list of dict. This function takes first or single dict that get_test_params returns, and constructs the object with that.

fit_transform(Z, X=None)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters

Zpd.DataFrame, pd.Series or np.ndarray

Data to be transformed

Xpd.Series or np.ndarray, optional (default=None)

Target values of data to be transformed.

Returns

Ztpd.DataFrame, pd.Series or np.ndarray

Transformed data.

classmethod get_class_tag(tag_name, tag_value_default=None)

Get tag value from estimator class (only class tags).

Parameters

tag_namestr

Name of tag value.

tag_value_defaultany type

Default/fallback value if tag is not found.

Returns

tag_value :

Value of the tag_name tag in self. If not found, returns tag_value_default.

classmethod get_class_tags()

Get class tags from estimator class and all its parent classes.

Returns

collected_tagsdict

Dictionary of tag name : tag value pairs. Collected from _tags class attribute via nested inheritance. NOT overridden by dynamic tags set by set_tags or mirror_tags.

get_params(deep=True)

Get parameters for this estimator.

Parameters

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

paramsdict

Parameter names mapped to their values.

get_tag(tag_name, tag_value_default=None, raise_error=True)

Get tag value from estimator class and dynamic tag overrides.

Parameters

tag_namestr

Name of tag to be retrieved

tag_value_defaultany type, optional; default=None

Default/fallback value if tag is not found

raise_errorbool

whether a ValueError is raised when the tag is not found

Returns

tag_value :

Value of the tag_name tag in self. If not found, returns an error if raise_error is True, otherwise it returns tag_value_default.

Raises

ValueError if raise_error is True i.e. if tag_name is not in self.get_tags(

).keys()

get_tags()

Get tags from estimator class and dynamic tag overrides.

Returns

collected_tagsdict

Dictionary of tag name : tag value pairs. Collected from _tags class attribute via nested inheritance and then any overrides and new tags from _tags_dynamic object attribute.

classmethod get_test_params()

Return testing parameter settings for the estimator.

Returns

paramsdict or list of dict, default = {}

Parameters to create testing instances of the class Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params

property is_fitted

Whether fit has been called.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters

**paramsdict

Estimator parameters.

Returns

selfestimator instance

Estimator instance.

set_tags(**tag_dict)

Set dynamic tags to given values.

Parameters

tag_dictdict

Dictionary of tag name : tag value pairs.

Returns

Self :

Reference to self.

Notes

Changes object state by settting tag values in tag_dict as dynamic tags in self.