WeightedEnsembleClassifier

WeightedEnsembleClassifier

class WeightedEnsembleClassifier(classifiers, weights=None, cv=None, metric=None, metric_type='point', random_state=None)[source]

Weighted ensemble of classifiers with fittable ensemble weight.

Produces a probabilistic prediction which is the weighted average of predictions of individual classifiers. Classifier with name name has ensemble weight in weights_[name]. weights_ is fitted in fit, if weights is a scalar, otherwise fixed.

If weights is a scalar, empirical training loss is computed for each classifier. In this case, ensemble weights of classifier is empirical loss, to the power of weights (a scalar).

The evaluation for the empirical training loss can be selected through the metric and metric_type parameters.

The in-sample empirical training loss is computed in-sample or out-of-sample, depending on the cv parameter. None = in-sample; other = cross-validated oos.

Parameters:

classifiersdict or None, default=None

Parameters for the ShapeletTransformClassifier module. If None, uses the default parameters with a 2 hour transform contract.

weightsfloat, or iterable of float, optional, default=None

if float, ensemble weight for classifier i will be train score to this power if iterable of float, must be equal length as classifiers

ensemble weight for classifier i will be weights[i]

if None, ensemble weights are equal (uniform average)

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

determines whether in-sample or which cross-validated predictions used in fit 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

metricsklearn metric for computing training score, default=accuracy_score

only used if weights is a float

metric_typestr, one of “point” or “proba”, default=”point”

type of sklearn metric, point prediction (“point”) or probabilistic (“proba”) if “point”, most probable class is passed as y_pred if “proba”, probability of most probable class is passed as y_pred

random_stateint or None, default=None

Seed for random number generation.

Attributes:

classifiers_list of tuples (str, classifier) of sktime classifiers

clones of classifies in classifiers which are fitted in the ensemble is always in (str, classifier) format, even if classifiers is just a list strings not passed in classifiers are replaced by unique generated strings i-th classifier in classifier_ is clone of i-th in classifier

weights_dict with str being classifier names as in classifiers_

value at key is ensemble weights of classifier with name key ensemble weights are fitted in fit if weights is a scalar

Examples

>>> from sktime.classification.ensemble import WeightedEnsembleClassifier
>>> from sktime.classification.dummy import DummyClassifier
>>> from sktime.classification.kernel_based import RocketClassifier
>>> from sktime.datasets import load_unit_test
>>> X_train, y_train = load_unit_test(split="train")
>>> X_test, y_test = load_unit_test(split="test")
>>> clf = WeightedEnsembleClassifier(
...     [DummyClassifier(), RocketClassifier(num_kernels=100)],
...     weights=2,
... )
>>> clf.fit(X_train, y_train)
WeightedEnsembleClassifier(...)
>>> y_pred = clf.predict(X_test)

Methods

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.
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 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)	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, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
score(X, y)	Scores predicted labels against ground truth labels on X.
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.