ProximityTree

ProximityTree

class ProximityTree(random_state=None, distance_measure=None, max_depth=inf, is_leaf=<function pure>, verbosity=0, n_jobs=1, n_stump_evaluations=5)[source]

Proximity Tree class.

A decision tree which uses distance measures to partition data.

Parameters:

random_state: int or np.RandomState, default=0

random seed for the random number generator

distance_measure: ``None`` (default) or str; if str, one of

euclidean, dtw, ddtw, wdtw, wddtw, msm, lcss, erp distance measure to use if None, selects distances randomly from the list of available distances

max_depth: int or math.inf, default=math.inf

maximum depth of the tree

is_leaffunction, default=pure

decide when to mark a node as a leaf node

verbosity: 0 or 1

number reflecting the verbosity of logging 0 = no logging, 1 = verbose logging

n_jobs: int or None, default=1

number of parallel threads to use while building

n_stump_evaluations: number of stump evaluations to do if find_stump method is None

Attributes:

is_fitted

Whether fit has been called.

Examples

>>> from sktime.classification.distance_based import ProximityTree
>>> from sktime.datasets import load_unit_test
>>> X_train, y_train = load_unit_test(split="train", return_X_y=True)
>>> X_test, y_test = load_unit_test(split="test", return_X_y=True)
>>> clf = ProximityTree(max_depth=2, n_stump_evaluations=1)
>>> clf.fit(X_train, y_train)
ProximityTree(...)
>>> 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.
find_stump()	Find the best stump.
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_exemplars()	Extract exemplars from a dataframe and class value list.
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 a dict of parameters values for this object.
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 composed of other BaseObjects.
load_from_path(serial)	Load object from file location.
load_from_serial(serial)	Load object from serialized memory container.
pick_distance_measure()	Pick a distance measure.
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(**params)	Set the parameters of this object.
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.