MapperPipeline

class gtda.mapper.pipeline.MapperPipeline(**kwargs)[source]

Subclass of sklearn.pipeline.Pipeline to deal with pipelines generated by make_mapper_pipeline.

The set_params method is modified from the corresponding method in sklearn.pipeline.Pipeline to allow for simple access to the parameters involved in the definition of the Mapper algorithm, without the need to interface with the nested structure of the Pipeline objects generated by make_mapper_pipeline. The convenience method get_mapper_params shows which parameters can be set. See the Examples below.

Examples

>>> from sklearn.cluster import DBSCAN
>>> from sklearn.decomposition import PCA
>>> from gtda.mapper import make_mapper_pipeline, CubicalCover
>>> filter_func = PCA(n_components=2)
>>> cover = CubicalCover()
>>> clusterer = DBSCAN()
>>> pipe = make_mapper_pipeline(filter_func=filter_func,
...                             cover=cover,
...                             clusterer=clusterer)
>>> print(pipe.get_mapper_params()["clusterer__eps"])
0.5
>>> pipe.set_params(clusterer___eps=0.1)
>>> print(pipe.get_mapper_params()["clusterer__eps"])
0.1
__init__(steps, *, memory=None, verbose=False)

Initialize self. See help(type(self)) for accurate signature.

decision_function(X)

Apply transforms, and decision_function of the final estimator

Parameters

X (iterable) – Data to predict on. Must fulfill input requirements of first step of the pipeline.

Returns

y_score

Return type

array-like of shape (n_samples, n_classes)

fit(X, y=None, **fit_params)

Fit the model

Fit all the transforms one after the other and transform the data, then fit the transformed data using the final estimator.

Parameters
  • X (iterable) – Training data. Must fulfill input requirements of first step of the pipeline.

  • y (iterable, default=None) – Training targets. Must fulfill label requirements for all steps of the pipeline.

  • **fit_params (dict of string -> object) – Parameters passed to the fit method of each step, where each parameter name is prefixed such that parameter p for step s has key s__p.

Returns

self – This estimator

Return type

Pipeline

fit_predict(X, y=None, **fit_params)

Applies fit_predict of last step in pipeline after transforms.

Applies fit_transforms of a pipeline to the data, followed by the fit_predict method of the final estimator in the pipeline. Valid only if the final estimator implements fit_predict.

Parameters
  • X (iterable) – Training data. Must fulfill input requirements of first step of the pipeline.

  • y (iterable, default=None) – Training targets. Must fulfill label requirements for all steps of the pipeline.

  • **fit_params (dict of string -> object) – Parameters passed to the fit method of each step, where each parameter name is prefixed such that parameter p for step s has key s__p.

Returns

y_pred

Return type

array-like

fit_transform(X, y=None, **fit_params)

Fit the model and transform with the final estimator

Fits all the transforms one after the other and transforms the data, then uses fit_transform on transformed data with the final estimator.

Parameters
  • X (iterable) – Training data. Must fulfill input requirements of first step of the pipeline.

  • y (iterable, default=None) – Training targets. Must fulfill label requirements for all steps of the pipeline.

  • **fit_params (dict of string -> object) – Parameters passed to the fit method of each step, where each parameter name is prefixed such that parameter p for step s has key s__p.

Returns

Xt – Transformed samples

Return type

array-like of shape (n_samples, n_transformed_features)

get_mapper_params(deep=True)[source]

Get all Mapper parameters for this estimator.

Parameters

deep (boolean, optional, default: True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

params – Parameter names mapped to their values.

Return type

mapping of string to any

get_params(deep=True)

Get parameters for this estimator.

Parameters

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

params – Parameter names mapped to their values.

Return type

mapping of string to any

property inverse_transform

Apply inverse transformations in reverse order

All estimators in the pipeline must support inverse_transform.

Parameters

Xt (array-like of shape (n_samples, n_transformed_features)) – Data samples, where n_samples is the number of samples and n_features is the number of features. Must fulfill input requirements of last step of pipeline’s inverse_transform method.

Returns

Xt

Return type

array-like of shape (n_samples, n_features)

predict(X, **predict_params)

Apply transforms to the data, and predict with the final estimator

Parameters
  • X (iterable) – Data to predict on. Must fulfill input requirements of first step of the pipeline.

  • **predict_params (dict of string -> object) –

    Parameters to the predict called at the end of all transformations in the pipeline. Note that while this may be used to return uncertainties from some models with return_std or return_cov, uncertainties that are generated by the transformations in the pipeline are not propagated to the final estimator.

    New in version 0.20.

Returns

y_pred

Return type

array-like

predict_log_proba(X)

Apply transforms, and predict_log_proba of the final estimator

Parameters

X (iterable) – Data to predict on. Must fulfill input requirements of first step of the pipeline.

Returns

y_score

Return type

array-like of shape (n_samples, n_classes)

predict_proba(X)

Apply transforms, and predict_proba of the final estimator

Parameters

X (iterable) – Data to predict on. Must fulfill input requirements of first step of the pipeline.

Returns

y_proba

Return type

array-like of shape (n_samples, n_classes)

score(X, y=None, sample_weight=None)

Apply transforms, and score with the final estimator

Parameters
  • X (iterable) – Data to predict on. Must fulfill input requirements of first step of the pipeline.

  • y (iterable, default=None) – Targets used for scoring. Must fulfill label requirements for all steps of the pipeline.

  • sample_weight (array-like, default=None) – If not None, this argument is passed as sample_weight keyword argument to the score method of the final estimator.

Returns

score

Return type

float

score_samples(X)

Apply transforms, and score_samples of the final estimator.

Parameters

X (iterable) – Data to predict on. Must fulfill input requirements of first step of the pipeline.

Returns

y_score

Return type

ndarray of shape (n_samples,)

set_params(**kwargs)[source]

Set the Mapper parameters.

Valid parameter keys can be listed with get_mapper_params.

Returns

Return type

self

property transform

Apply transforms, and transform with the final estimator

This also works where final estimator is None: all prior transformations are applied.

Parameters

X (iterable) – Data to transform. Must fulfill input requirements of first step of the pipeline.

Returns

Xt

Return type

array-like of shape (n_samples, n_transformed_features)