ContinuousIntervalTree¶
- class ContinuousIntervalTree(max_depth: int = 9223372036854775807, thresholds: int = 20, random_state: int | RandomState | None = None)[source]¶
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 continous 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 metadata routing of this object.
get_params
([deep])Get parameters for this estimator.
predict
(X)Predict for all cases in X.
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_score_request
(*[, sample_weight])Request metadata passed to the
score
method.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 “_”.
- 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_.
- tree_node_splits_and_gain() tuple[list[int], list[float]] [source]¶
Recursively find the split and information gain for each tree node.
- get_metadata_routing()[source]¶
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns:
- routingMetadataRequest
A
MetadataRequest
encapsulating routing information.
- get_params(deep=True)[source]¶
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.
- score(X, y, sample_weight=None)[source]¶
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)
w.r.t. y.
- set_params(**params)[source]¶
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 [source]¶
Request metadata passed to the
score
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed toscore
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it toscore
.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.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
Pipeline
. Otherwise it has no effect.- Parameters:
- sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
sample_weight
parameter inscore
.
- Returns:
- selfobject
The updated object.