TimeSeriesForestClassifier

class TimeSeriesForestClassifier(min_interval=3, n_estimators=200, n_jobs=1, random_state=None)

Time series forest classifier.

A time series forest is an ensemble of decision trees built on random intervals. Overview: Input n series length m. For each tree

• sample sqrt(m) intervals,

• find mean, std and slope for each interval, concatenate to form new

data set, – build decision tree on new data set.

Ensemble the trees with averaged probability estimates.

This implementation deviates from the original in minor ways. It samples intervals with replacement and does not use the splitting criteria tiny refinement described in [1]. This is an intentionally stripped down, non configurable version for use as a hive-cote component. For a configurable tree based ensemble, see sktime.classifiers.ensemble.TimeSeriesForestClassifier

Parameters

n_estimatorsint, ensemble size, optional (default = 200)

min_intervalint, minimum width of an interval, optional (default

to 3)

n_jobsint, optional (default=1)

The number of jobs to run in parallel for both fit and predict. -1 means using all processors.

random_stateint, seed for random, optional (default = none)

Attributes

n_classesint

n_intervalsint

classes_List of classes for a given problem

References

1

H.Deng, G.Runger, E.Tuv and M.Vladimir, “A time series forest for classification and feature extraction”,Information Sciences, 239, 2013 Java implementation https://github.com/uea-machine-learning/tsml/blob/master/src/main/ java/tsml/classifiers/interval_based/TSF.java Arxiv version of the paper: https://arxiv.org/abs/1302.2277

Methods

apply(X)	Apply trees in the forest to X, return leaf indices.
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.
decision_path(X)	Return the decision path in the forest.
fit(X, y)	Build a forest of trees from the training set (X, y).
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()	Get default parameters of the estimator.
predict(X)	Find predictions for all cases in X.
predict_log_proba(X)	Predict class log-probabilities for X.
predict_proba(X)	Find probability estimates for each class for all cases in X.
score(X, y[, sample_weight])	Return the mean accuracy on the given test data and labels.
set_params(**params)	Set the parameters of this estimator.
set_tags(**tag_dict)	Set dynamic tags to given values.

predict(X)

Find predictions for all cases in X. Built on top of predict_proba.

Parameters

XThe training input samples. array-like or pandas data frame.

If a Pandas data frame is passed, a check is performed that it only

has one column.

If not, an exception is thrown, since this classifier does not yet have

multivariate capability.

Returns

outputarray of shape = [n_test_instances]

predict_proba(X)

Find probability estimates for each class for all cases in X.

Parameters

XThe training input samples. array-like or sparse matrix of shape

= [n_test_instances, series_length]

If a Pandas data frame is passed (sktime format) a check is performed that it only has one column. If not, an exception is thrown, since this classifier does not yet have multivariate capability.

Returns

outputnd.array of shape = (n_instances, n_classes)

Predicted probabilities

apply(X)

Apply trees in the forest to X, return leaf indices.

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features)

The input samples. Internally, its dtype will be converted to dtype=np.float32. If a sparse matrix is provided, it will be converted into a sparse csr_matrix.

Returns

X_leavesndarray of shape (n_samples, n_estimators)

For each datapoint x in X and for each tree in the forest, return the index of the leaf x ends up in.

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

instanceobject 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.

decision_path(X)

Return the decision path in the forest.

New in version 0.18.

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features)

The input samples. Internally, its dtype will be converted to dtype=np.float32. If a sparse matrix is provided, it will be converted into a sparse csr_matrix.

Returns

indicatorsparse matrix of shape (n_samples, n_nodes)

Return a node indicator matrix where non zero elements indicates that the samples goes through the nodes. The matrix is of CSR format.

n_nodes_ptrndarray of shape (n_estimators + 1,)

The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]] gives the indicator value for the i-th estimator.

property feature_importances_

The impurity-based feature importances.

The higher, the more important the feature. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance.

Warning: impurity-based feature importances can be misleading for high cardinality features (many unique values). See sklearn.inspection.permutation_importance as an alternative.

Returns

feature_importances_ndarray of shape (n_features,)

The values of this array sum to 1, unless all trees are single node trees consisting of only the root node, in which case it will be an array of zeros.

fit(X, y)

Build a forest of trees from the training set (X, y).

Parameters

Xt: np.ndarray or pd.DataFrame

Panel training data.

ynp.ndarray

The class labels.

Returns

selfobject

An fitted instance of the classifier

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)

Get tag value from estimator class and dynamic tag overrides.

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.

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()

Get default parameters of the estimator.

Returns

paramsdict or list of dict, default = {}

Default parameters related to the estimator class

property is_fitted

Whether fit has been called.

predict_log_proba(X)

Predict class log-probabilities for X.

The predicted class log-probabilities of an input sample is computed as the log of the mean predicted class probabilities of the trees in the forest.

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features)

The input samples. Internally, its dtype will be converted to dtype=np.float32. If a sparse matrix is provided, it will be converted into a sparse csr_matrix.

Returns

pndarray of shape (n_samples, n_classes), or a list of n_outputs

such arrays if n_outputs > 1. The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.

score(X, y, sample_weight=None)

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters

Xarray-like of shape (n_samples, n_features)

Test samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True labels for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns

scorefloat

Mean accuracy of self.predict(X) wrt. y.

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.