SklearnClassifierPipeline

class SklearnClassifierPipeline(classifier, transformers)

Pipeline of transformers and a classifier.

The SklearnClassifierPipeline chains transformers and an single classifier.

Similar to ClassifierPipeline, but uses a tabular sklearn classifier.

The pipeline is constructed with a list of sktime transformers, plus a classifier,

i.e., transformers following the BaseTransformer interface, classifier follows the scikit-learn classifier interface.

The transformer list can be unnamed - a simple list of transformers -

or string named - a list of pairs of string, estimator.

For a list of transformers trafo1, trafo2, …, trafoN and a classifier clf,

the pipeline behaves as follows:

fit(X, y) - changes styte by running trafo1.fit_transform on X,

them trafo2.fit_transform on the output of trafo1.fit_transform, etc sequentially, with trafo[i] receiving the output of trafo[i-1], and then running clf.fit with X the output of trafo[N] converted to numpy, and y identical with the input to self.fit. X is converted to numpyflat mtype if X is of Panel scitype; X is converted to numpy2D mtype if X is of Table scitype.

predict(X) - result is of executing trafo1.transform, trafo2.transform, etc

with trafo[i].transform input = output of trafo[i-1].transform, then running clf.predict on the numpy converted output of trafoN.transform, and returning the output of clf.predict. Output of trasfoN.transform is converted to numpy, as in fit.

predict_proba(X) - result is of executing trafo1.transform, trafo2.transform,

etc, with trafo[i].transform input = output of trafo[i-1].transform, then running clf.predict_proba on the output of trafoN.transform, and returning the output of clf.predict_proba. Output of trasfoN.transform is converted to numpy, as in fit.

get_params, set_params uses sklearn compatible nesting interface

if list is unnamed, names are generated as names of classes if names are non-unique, f”_{str(i)}” is appended to each name string

where i is the total count of occurrence of a non-unique string inside the list of names leading up to it (inclusive)

SklearnClassifierPipeline can also be created by using the magic multiplication

between sktime transformers and sklearn classifiers,

and my_trafo1, my_trafo2 inherit from BaseTransformer, then, for instance, my_trafo1 * my_trafo2 * my_clf will result in the same object as obtained from the constructor SklearnClassifierPipeline(classifier=my_clf, transformers=[t1, t2])

magic multiplication can also be used with (str, transformer) pairs,

as long as one element in the chain is a transformer

Parameters

classifiersklearn classifier, i.e., inheriting from sklearn ClassifierMixin

this is a “blueprint” classifier, state does not change when fit is called

transformerslist of sktime transformers, or

list of tuples (str, transformer) of sktime transformers these are “blueprint” transformers, states do not change when fit is called

Attributes

classifier_sklearn classifier, clone of classifier in classifier

this clone is fitted in the pipeline when fit is called

transformers_list of tuples (str, transformer) of sktime transformers

clones of transformers in transformers which are fitted in the pipeline is always in (str, transformer) format, even if transformers is just a list strings not passed in transformers are unique generated strings i-th transformer in transformers_ is clone of i-th in transformers

Examples

>>> from sklearn.neighbors import KNeighborsClassifier
>>> from sktime.transformations.series.exponent import ExponentTransformer
>>> from sktime.transformations.series.summarize import SummaryTransformer
>>> from sktime.datasets import load_unit_test
>>> from sktime.classification.compose import SklearnClassifierPipeline
>>> X_train, y_train = load_unit_test(split="train")
>>> X_test, y_test = load_unit_test(split="test")
>>> t1 = ExponentTransformer()
>>> t2 = SummaryTransformer()
>>> pipeline = SklearnClassifierPipeline(KNeighborsClassifier(), [t1, t2])
>>> pipeline = pipeline.fit(X_train, y_train)
>>> y_pred = pipeline.predict(X_test)

Alternative construction via dunder method: >>> pipeline = t1 * t2 * KNeighborsClassifier()

Methods

check_is_fitted()	Check if the estimator has been fitted.
clone()	Obtain a clone of the object with same hyper-parameters.
clone_tags(estimator[, tag_names])	clone/mirror tags from another estimator as dynamic override.
create_test_instance([parameter_set])	Construct Estimator instance if possible.
create_test_instances_and_names([parameter_set])	Create list of all test instances and a list of names for them.
fit(X, y)	Fit time series classifier to training data.
fit_predict(X, y[, cv, change_state])	Fit and predict labels for sequences in X.
fit_predict_proba(X, y[, cv, change_state])	Fit and predict labels probabilities for sequences in X.
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_fitted_params()	Get fitted parameters.
get_param_defaults()	Get parameter defaults for the object.
get_param_names()	Get parameter names for the object.
get_params([deep])	Get parameters of estimator in transformers.
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([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)	Predicts labels for sequences in X.
predict_proba(X)	Predicts labels probabilities for sequences in X.
reset()	Reset the object to a clean post-init state.
save([path])	Save serialized self to bytes-like object or to (.zip) file.
score(X, y)	Scores predicted labels against ground truth labels on X.
set_params(**kwargs)	Set the parameters of estimator in transformers.
set_tags(**tag_dict)	Set dynamic tags to given values.

clone()

Obtain a clone of the object with same hyper-parameters.

A clone is a different object without shared references, in post-init state. This function is equivalent to returning sklearn.clone of self. Equal in value to type(self)(**self.get_params(deep=False)).

Returns

instance of type(self), clone of self (see above)

get_params(deep=True)

Get parameters of estimator in transformers.

Parameters

deepboolean, optional, default=True

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

Returns

paramsmapping of string to any

Parameter names mapped to their values.

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(parameter_set='default')

Construct Estimator instance if possible.

Parameters

parameter_setstr, default=”default”

Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

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.

classmethod create_test_instances_and_names(parameter_set='default')

Create list of all test instances and a list of names for them.

Parameters

parameter_setstr, default=”default”

Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns

objslist of instances of cls

i-th instance is cls(**cls.get_test_params()[i])

nameslist of str, same length as objs

i-th element is name of i-th instance of obj in tests convention is {cls.__name__}-{i} if more than one instance otherwise {cls.__name__}

parameter_setstr, default=”default”

Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

fit(X, y)

Fit time series classifier to training data.

Parameters

X3D np.array (any number of dimensions, equal length series)

of shape [n_instances, n_dimensions, series_length]

or 2D np.array (univariate, equal length series)

of shape [n_instances, series_length]

or pd.DataFrame with each column a dimension, each cell a pd.Series

(any number of dimensions, equal or unequal length series)

or of any other supported Panel mtype

for list of mtypes, see datatypes.SCITYPE_REGISTER for specifications, see examples/AA_datatypes_and_datasets.ipynb

y1D np.array of int, of shape [n_instances] - class labels for fitting

indices correspond to instance indices in X

Returns

selfReference to self.

Notes

Changes state by creating a fitted model that updates attributes ending in “_” and sets is_fitted flag to True.

fit_predict(X, y, cv=None, change_state=True) → numpy.ndarray

Fit and predict labels for sequences in X.

Convenience method to produce in-sample predictions and cross-validated out-of-sample predictions.

Writes to self, if change_state=True:

Sets self.is_fitted to True. Sets fitted model attributes ending in “_”.

Does not update state if change_state=False.

Parameters

X3D np.array (any number of dimensions, equal length series)

of shape [n_instances, n_dimensions, series_length]

or 2D np.array (univariate, equal length series)

of shape [n_instances, series_length]

or pd.DataFrame with each column a dimension, each cell a pd.Series

(any number of dimensions, equal or unequal length series)

or of any other supported Panel mtype

for list of mtypes, see datatypes.SCITYPE_REGISTER for specifications, see examples/AA_datatypes_and_datasets.ipynb

y1D np.array of int, of shape [n_instances] - class labels for fitting

indices correspond to instance indices in X

cvNone, int, or sklearn cross-validation object, optional, default=None

None : predictions are in-sample, equivalent to fit(X, y).predict(X) cv : predictions are equivalent to fit(X_train, y_train).predict(X_test)

where multiple X_train, y_train, X_test are obtained from cv folds returned y is union over all test fold predictions cv test folds must be non-intersecting

intequivalent to cv=KFold(cv, shuffle=True, random_state=x),

i.e., k-fold cross-validation predictions out-of-sample random_state x is taken from self if exists, otherwise x=None

change_statebool, optional (default=True)

if False, will not change the state of the classifier,

i.e., fit/predict sequence is run with a copy, self does not change

if True, will fit self to the full X and y,

end state will be equivalent to running fit(X, y)

Returns

y1D np.array of int, of shape [n_instances] - predicted class labels

indices correspond to instance indices in X if cv is passed, -1 indicates entries not seen in union of test sets

fit_predict_proba(X, y, cv=None, change_state=True) → numpy.ndarray

Fit and predict labels probabilities for sequences in X.

Convenience method to produce in-sample predictions and cross-validated out-of-sample predictions.

Parameters

X3D np.array (any number of dimensions, equal length series)

of shape [n_instances, n_dimensions, series_length]

or 2D np.array (univariate, equal length series)

of shape [n_instances, series_length]

or pd.DataFrame with each column a dimension, each cell a pd.Series

(any number of dimensions, equal or unequal length series)

or of any other supported Panel mtype

for list of mtypes, see datatypes.SCITYPE_REGISTER for specifications, see examples/AA_datatypes_and_datasets.ipynb

y1D np.array of int, of shape [n_instances] - class labels for fitting

indices correspond to instance indices in X

cvNone, int, or sklearn cross-validation object, optional, default=None

None : predictions are in-sample, equivalent to fit(X, y).predict(X) cv : predictions are equivalent to fit(X_train, y_train).predict(X_test)

where multiple X_train, y_train, X_test are obtained from cv folds returned y is union over all test fold predictions cv test folds must be non-intersecting

int : equivalent to cv=Kfold(int), i.e., k-fold cross-validation predictions

change_statebool, optional (default=True)

if False, will not change the state of the classifier,

i.e., fit/predict sequence is run with a copy, self does not change

if True, will fit self to the full X and y,

end state will be equivalent to running fit(X, y)

Returns

y2D array of shape [n_instances, n_classes] - predicted class probabilities

1st dimension indices correspond to instance indices in X 2nd dimension indices correspond to possible labels (integers) (i, j)-th entry is predictive probability that i-th instance is of class j

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_fitted_params()

Get fitted parameters.

State required:

Requires state to be “fitted”.

Returns

fitted_paramsdict of fitted parameters, keys are str names of parameters

parameters of components are indexed as [componentname]__[paramname]

classmethod get_param_defaults()

Get parameter defaults for the object.

Returns

default_dict: dict with str keys

keys are all parameters of cls that have a default defined in __init__ values are the defaults, as defined in __init__

classmethod get_param_names()

Get parameter names for the object.

Returns

param_names: list of str, alphabetically sorted list of parameter names of cls

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.

is_composite()

Check if the object is composite.

A composite object is an object which contains objects, as parameters. Called on an instance, since this may differ by instance.

Returns

composite: bool, whether self contains a parameter which is BaseObject

property is_fitted

Whether fit has been called.

classmethod load_from_path(serial)

Load object from file location.

Parameters

serialresult of ZipFile(path).open(“object)

Returns

deserialized self resulting in output at path, of cls.save(path)

classmethod load_from_serial(serial)

Load object from serialized memory container.

Parameters

serial1st element of output of cls.save(None)

Returns

deserialized self resulting in output serial, of cls.save(None)

predict(X) → numpy.ndarray

Predicts labels for sequences in X.

Parameters

X3D np.array (any number of dimensions, equal length series)

of shape [n_instances, n_dimensions, series_length]

or 2D np.array (univariate, equal length series)

of shape [n_instances, series_length]

or pd.DataFrame with each column a dimension, each cell a pd.Series

(any number of dimensions, equal or unequal length series)

or of any other supported Panel mtype

for list of mtypes, see datatypes.SCITYPE_REGISTER for specifications, see examples/AA_datatypes_and_datasets.ipynb

Returns

y1D np.array of int, of shape [n_instances] - predicted class labels

indices correspond to instance indices in X

predict_proba(X) → numpy.ndarray

Predicts labels probabilities for sequences in X.

Parameters

X3D np.array (any number of dimensions, equal length series)

of shape [n_instances, n_dimensions, series_length]

or 2D np.array (univariate, equal length series)

of shape [n_instances, series_length]

or pd.DataFrame with each column a dimension, each cell a pd.Series

(any number of dimensions, equal or unequal length series)

or of any other supported Panel mtype

for list of mtypes, see datatypes.SCITYPE_REGISTER for specifications, see examples/AA_datatypes_and_datasets.ipynb

Returns

y2D array of shape [n_instances, n_classes] - predicted class probabilities

1st dimension indices correspond to instance indices in X 2nd dimension indices correspond to possible labels (integers) (i, j)-th entry is predictive probability that i-th instance is of class j

reset()

Reset the object to a clean post-init state.

Equivalent to sklearn.clone but overwrites self. After self.reset() call, self is equal in value to type(self)(**self.get_params(deep=False))

Detail behaviour: removes any object attributes, except:

hyper-parameters = arguments of __init__ object attributes containing double-underscores, i.e., the string “__”

runs __init__ with current values of hyper-parameters (result of get_params)

Not affected by the reset are: object attributes containing double-underscores class and object methods, class attributes

save(path=None)

Save serialized self to bytes-like object or to (.zip) file.

Behaviour: if path is None, returns an in-memory serialized self if path is a file location, stores self at that location as a zip file

saved files are zip files with following contents: _metadata - contains class of self, i.e., type(self) _obj - serialized self. This class uses the default serialization (pickle).

Parameters

pathNone or file location (str or Path)

if None, self is saved to an in-memory object if file location, self is saved to that file location. If:

path=”estimator” then a zip file estimator.zip will be made at cwd. path=”/home/stored/estimator” then a zip file estimator.zip will be stored in /home/stored/.

Returns

if path is None - in-memory serialized self

if path is file location - ZipFile with reference to the file

score(X, y) → float

Scores predicted labels against ground truth labels on X.

Parameters

X3D np.array (any number of dimensions, equal length series)

of shape [n_instances, n_dimensions, series_length]

or 2D np.array (univariate, equal length series)

of shape [n_instances, series_length]

or pd.DataFrame with each column a dimension, each cell a pd.Series

(any number of dimensions, equal or unequal length series)

or of any other supported Panel mtype

for list of mtypes, see datatypes.SCITYPE_REGISTER for specifications, see examples/AA_datatypes_and_datasets.ipynb

y1D np.ndarray of int, of shape [n_instances] - class labels (ground truth)

indices correspond to instance indices in X

Returns

float, accuracy score of predict(X) vs y

set_params(**kwargs)

Set the parameters of estimator in transformers.

Valid parameter keys can be listed with get_params().

Returns

selfreturns an instance of self.

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.

classmethod get_test_params(parameter_set='default')

Return testing parameter settings for the estimator.

Parameters

parameter_setstr, default=”default”

Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set. For classifiers, a “default” set of parameters should be provided for general testing, and a “results_comparison” set for comparing against previously recorded results if the general set does not produce suitable probabilities to compare against.

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.