ContinuousIntervalTree¶

class ContinuousIntervalTree(max_depth: int = 9223372036854775807, thresholds: int = 20, random_state: int | RandomState | None = None)[source]¶

Bases: ClassifierMixin, BaseEstimator

Continuous interval tree (CIT) vector classifier (aka Time Series Tree).

The Time Series Tree described in the Time Series Forest (TSF) [1]. A simple information gain based tree for continuous attributes using a bespoke margin gain metric for tie breaking.

Implemented as a bade classifier for interval based time series classifiers such as CanonicalIntervalForest and DrCIF.

Parameters:

max_depthint, default=sys.maxsize: Maximum depth for the tree.
thresholdsint, default=20: Number of thresholds to split continuous attributes on at tree nodes.
random_stateint, RandomState instance or None, default=None: If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

Attributes:

classes_list: The unique class labels in the training set.
n_classes_int: The number of unique classes in the training set.
n_cases_int: The number of train cases in the training set.
n_atts_int: The number of attributes in the training set.

See also

CanonicalIntervalForest
DrCIF

Notes

For the Java version, see tsml.

References

[1]

H.Deng, G.Runger, E.Tuv and M.Vladimir, “A time series forest for classification and feature extraction”,Information Sciences, 239, 2013

Examples

>>> from aeon.classification.sklearn import ContinuousIntervalTree
>>> from aeon.datasets import load_unit_test
>>> X_train, y_train = load_unit_test(split="train")
>>> X_test, y_test = load_unit_test(split="test")
>>> clf = ContinuousIntervalTree()
>>> clf.fit(X_train, y_train)
ContinuousIntervalTree(...)
>>> y_pred = clf.predict(X_test)

Methods

`fit`(X, y)	Fit a tree on cases (X,y), where y is the target variable.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)	Predict for all cases in X.
`predict_proba`(X)	Probability estimates for each class for all cases in X.
`score`(X, y[, sample_weight])	Return accuracy on provided data and labels.
`set_params`(**params)	Set the parameters of this estimator.
`set_score_request`(*[, sample_weight])	Configure whether metadata should be requested to be passed to the `score` method.
`tree_node_splits_and_gain`()	Recursively find the split and information gain for each tree node.

fit(X, y)[source]¶

Fit a tree on cases (X,y), where y is the target variable.

Build an information gain based tree for continuous attributes using the margin gain metric for ties.

Parameters:

X2d ndarray or DataFrame of shape = [n_cases, n_attributes]: The training data.
yarray-like, shape = [n_cases]: The class labels.

Returns:

self: Reference to self.

Notes

Changes state by creating a fitted model that updates attributes ending in “_”.

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.

predict(X)[source]¶

Predict for all cases in X. Built on top of predict_proba.

Parameters:

X2d ndarray or DataFrame of shape = [n_cases, n_attributes]: The data to make predictions for.

Returns:

yarray-like, shape = [n_cases]: Predicted class labels.

predict_proba(X)[source]¶

Probability estimates for each class for all cases in X.

Parameters:

X2d ndarray or DataFrame of shape = [n_cases, n_attributes]: The data to make predictions for.

Returns:

yarray-like, shape = [n_cases, n_classes_]: Predicted probabilities using the ordering in classes_.

score(X, y, sample_weight=None)¶

Return accuracy on provided 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) w.r.t. 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_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → ContinuousIntervalTree¶

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to score.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in score.

Returns:

selfobject: The updated object.

tree_node_splits_and_gain() → tuple[list[int], list[float]][source]¶: Recursively find the split and information gain for each tree node.