TDMVDCClassifier

class TDMVDCClassifier(default_fc_parameters='efficient', k1=2, k2=2, feature_store_ratios=None, n_jobs=1, parallel_backend=None)

Bases: BaseClassifier

Tracking Differentiator-based Multiview Dilated Characteristics classifier.

The TDMVDCClassifier is an advanced ensemble classifier tailored for time series classification tasks. It operates by transforming the input time series data through a tracking differentiator, generating three distinct views: the original signal, the first-order differential, and the second-order differential. Each of these views is further processed using a set of dilation rates, allowing the model to capture temporal dependencies and patterns at multiple scales.

For each dilated view, the classifier extracts a comprehensive set of features using the TSFresh feature extraction framework. These features are then evaluated using ANOVA F-values to determine their relevance to the classification task. Multiple classifiers are constructed, each trained on a different proportion of the most informative features, as determined by the feature selection process. The ensemble of classifiers provides robust predictions by aggregating their outputs through a hard voting mechanism, which enhances generalization and reduces the risk of overfitting.

The TDMVDCClassifier is highly parallelized, supporting multi-threaded feature extraction and model training to efficiently handle large datasets. Its design is particularly effective for time series problems where both the original and differential characteristics of the data, as well as multiscale temporal patterns, are important for accurate classification.

Parameters:

default_fc_parametersstr, default=”efficient”

Specifies the set of TSFresh features to extract. Options include “minimal”, “efficient”, or “comprehensive”.

k1float, default=2

Filter parameter for the first tracking differentiator, controlling the generation of first-order differential series.

k2float, default=2

Filter parameter for the second tracking differentiator, controlling the generation of second-order differential series.

feature_store_ratioslist, default=None

List of feature retention ratios for different feature selectors. If None, defaults to [0.1, 0.2, 0.3, 0.4, 0.5].

n_jobsint, default=1

Number of parallel jobs to run for feature extraction and model training. “-1” uses all available processors.

parallel_backendstr, ParallelBackendBase instance or None, default=None

Specifies the parallelization backend for joblib. Options include “loky”, “multiprocessing”, “threading”, or a custom backend.

Attributes:

n_classes_int

Number of unique classes in the training data.

classes_ndarray of shape (n_classes_)

Array of class labels.

clfList_list

List of trained classifier pipelines for each feature subset.

dList_ndarray

Array of dilation rates used for multiscale feature extraction.

tsFreshListR_list

List of TSFresh feature extractors for original signal at each dilation rate.

tsFreshListF_list

List of TSFresh feature extractors for the first-order differential signal at each dilation rate.

tsFreshListS_list

List of TSFresh feature extractors for the second-order differential signal at each dilation rate.

scoreRFS_ndarray

Array of ANOVA F-values for all extracted features, used for feature selection.

Notes

Capabilities

Missing Values	No
Multithreading	Yes
Univariate	Yes
Multivariate	No
Unequal Length	No
Train Estimate	No
Contractable	No

The TDMVDCClassifier is particularly effective for time series datasets where capturing both the original and differential dynamics, as well as multiscale temporal features, is crucial for distinguishing between classes. Its ensemble approach and feature selection strategy help to mitigate overfitting and improve predictive performance on complex datasets.

For the algorithm details, see [1].

References

[1]

Changchun He, and Xin Huo. “Tracking Differentiator-based Multiview Dilated Characteristics for Time Series Classification.” in The 22nd IEEE International Conference on Industrial Informatics (INDIN2024) (2024).

Methods

clone([random_state])	Obtain a clone of the object with the same hyperparameters.
fit(X, y)	Fit time series classifier to training data.
fit_predict(X, y, **kwargs)	Fits the classifier and predicts class labels for X.
fit_predict_proba(X, y, **kwargs)	Fits the classifier and predicts class label probabilities for X.
get_class_tag(tag_name[, raise_error, ...])	Get tag value from estimator class (only class tags).
get_class_tags()	Get class tags from estimator class and all its parent classes.
get_fitted_params([deep])	Get fitted parameters.
get_params([deep])	Get parameters for this estimator.
get_tag(tag_name[, raise_error, ...])	Get tag value from estimator class.
get_tags()	Get tags from estimator.
predict(X)	Predicts class labels for time series in X.
predict_proba(X)	Predicts class label probabilities for time series in X.
reset([keep])	Reset the object to a clean post-init state.
score(X, y[, metric, use_proba, metric_params])	Scores predicted labels against ground truth labels on X.
set_params(**params)	Set the parameters of this estimator.
set_tags(**tag_dict)	Set dynamic tags to given values.

clone(random_state=None)

Obtain a clone of the object with the same hyperparameters.

A clone is a different object without shared references, in post-init state. This function is equivalent to returning sklearn.clone of self. Equal in value to type(self)(**self.get_params(deep=False)).

Parameters:

random_stateint, RandomState instance, or None, default=None

Sets the random state of the clone. If None, the random state is not set. If int, random_state is the seed used by the random number generator. If RandomState instance, random_state is the random number generator.

Returns:

estimatorobject

Instance of type(self), clone of self (see above)

fit(X, y) → BaseCollectionEstimator

Fit time series classifier to training data.

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. Other types are allowed and converted into one of the above.

Different estimators have different capabilities to handle different types of input. If self.get_tag("capability:multivariate") is False, they cannot handle multivariate series, so either n_channels == 1 is true or X is 2D of shape (n_cases, n_timepoints). If self.get_tag( "capability:unequal_length") is False, they cannot handle unequal length input. In both situations, a ValueError is raised if X has a characteristic that the estimator does not have the capability for is passed.

ynp.ndarray

1D np.array of float or str, of shape (n_cases) - class labels (ground truth) for fitting indices corresponding to instance indices in X.

Returns:

selfBaseClassifier

Reference to self.

Notes

Changes state by creating a fitted model that updates attributes ending in “_” and sets is_fitted flag to True.

fit_predict(X, y, **kwargs) → ndarray

Fits the classifier and predicts class labels for X.

fit_predict produces prediction estimates using just the train data. By default, this is through 10x cross validation, although some estimators may utilise specialist techniques such as out-of-bag estimates or leave-one-out cross-validation.

Classifiers which override _fit_predict will have the capability:train_estimate tag set to True.

Generally, this will not be the same as fitting on the whole train data then making train predictions. To do this, you should call fit(X,y).predict(X)

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. other types are allowed and converted into one of the above.

Different estimators have different capabilities to handle different types of input. If self.get_tag("capability:multivariate") is False, they cannot handle multivariate series, so either n_channels == 1 is true or X is 2D of shape (n_cases, n_timepoints). If self.get_tag( "capability:unequal_length") is False, they cannot handle unequal length input. In both situations, a ValueError is raised if X has a characteristic that the estimator does not have the capability for is passed.

ynp.ndarray

1D np.array of float or str, of shape (n_cases) - class labels (ground truth) for fitting indices corresponding to instance indices in X.

kwargsdict

key word arguments to configure the default cross validation if the base class default fit_predict is used (i.e. if function _fit_predict is not overridden. If _fit_predict is overridden, kwargs may not function as expected. If _fit_predict is not overridden, valid input is cv_size integer, which is the number of cross validation folds to use to estimate train data. If cv_size is not passed, the default is 10. If cv_size is greater than the minimum number of samples in any class, it is set to this minimum.

Returns:

predictionsnp.ndarray

shape [n_cases] - predicted class labels indices correspond to instance indices in

fit_predict_proba(X, y, **kwargs) → ndarray

Fits the classifier and predicts class label probabilities for X.

fit_predict_proba produces probability estimates using just the train data. By default, this is through 10x cross validation, although some estimators may utilise specialist techniques such as out-of-bag estimates or leave-one-out cross-validation.

Classifiers which override _fit_predict_proba will have the capability:train_estimate tag set to True.

Generally, this will not be the same as fitting on the whole train data then making train predictions. To do this, you should call fit(X,y).predict_proba(X)

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. other types are allowed and converted into one of the above.

Different estimators have different capabilities to handle different types of input. If self.get_tag("capability:multivariate") is False, they cannot handle multivariate series, so either n_channels == 1 is true or X is 2D of shape (n_cases, n_timepoints). If self.get_tag( "capability:unequal_length") is False, they cannot handle unequal length input. In both situations, a ValueError is raised if X has a characteristic that the estimator does not have the capability for is passed.

ynp.ndarray

1D np.array of float or str, of shape (n_cases) - class labels (ground truth) for fitting indices corresponding to instance indices in X.

kwargsdict

key word arguments to configure the default cross validation if the base class default fit_predict is used (i.e. if function _fit_predict is not overridden. If _fit_predict is overridden, kwargs may not function as expected. If _fit_predict is not overridden, valid input is cv_size integer, which is the number of cross validation folds to use to estimate train data. If cv_size is not passed, the default is 10. If cv_size is greater than the minimum number of samples in any class, it is set to this minimum.

Returns:

probabilitiesnp.ndarray

2D array of shape (n_cases, n_classes) - predicted class probabilities First dimension indices correspond to instance indices in X, second dimension indices correspond to class labels, (i, j)-th entry is estimated probability that i-th instance is of class j

classmethod get_class_tag(tag_name, raise_error=True, tag_value_default=None)

Get tag value from estimator class (only class tags).

Parameters:

tag_namestr

Name of tag value.

raise_errorbool, default=True

Whether a ValueError is raised when the tag is not found.

tag_value_defaultany type, default=None

Default/fallback value if tag is not found and error is not raised.

Returns:

tag_value

Value of the tag_name tag in cls. If not found, returns an error if raise_error is True, otherwise it returns tag_value_default.

Raises:

ValueError

if raise_error is True and tag_name is not in self.get_tags().keys()

Examples

>>> from aeon.classification import DummyClassifier
>>> DummyClassifier.get_class_tag("capability:multivariate")
True

classmethod get_class_tags()

Get class tags from estimator class and all its parent classes.

Returns:

collected_tagsdict

Dictionary of tag name and tag value pairs. Collected from _tags class attribute via nested inheritance. These are not overridden by dynamic tags set by set_tags or class __init__ calls.

get_fitted_params(deep=True)

Get fitted parameters.

State required:

Requires state to be “fitted”.

Parameters:

deepbool, default=True

If True, will return the fitted parameters for this estimator and contained subobjects that are estimators.

Returns:

fitted_paramsdict

Fitted parameter names mapped to their values.

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, raise_error=True, tag_value_default=None)

Get tag value from estimator class.

Includes dynamic and overridden tags.

Parameters:

tag_namestr

Name of tag to be retrieved.

raise_errorbool, default=True

Whether a ValueError is raised when the tag is not found.

tag_value_defaultany type, default=None

Default/fallback value if tag is not found and error is not raised.

Returns:

tag_value

Value of the tag_name tag in self. If not found, returns an error if raise_error is True, otherwise it returns tag_value_default.

Raises:

ValueError

if raise_error is True and tag_name is not in self.get_tags().keys()

Examples

>>> from aeon.classification import DummyClassifier
>>> d = DummyClassifier()
>>> d.get_tag("capability:multivariate")
True

get_tags()

Get tags from estimator.

Includes dynamic and overridden tags.

Returns:

collected_tagsdict

Dictionary of tag name and tag value pairs. Collected from _tags class attribute via nested inheritance and then any overridden and new tags from __init__ or set_tags.

predict(X) → ndarray

Predicts class labels for time series in X.

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i other types are allowed and converted into one of the above.

Different estimators have different capabilities to handle different types of input. If self.get_tag("capability:multivariate") is False, they cannot handle multivariate series, so either n_channels == 1 is true or X is 2D of shape (n_cases, n_timepoints). If self.get_tag( "capability:unequal_length") is False, they cannot handle unequal length input. In both situations, a ValueError is raised if X has a characteristic that the estimator does not have the capability for is passed.

Returns:

predictionsnp.ndarray

1D np.array of float, of shape (n_cases) - predicted class labels indices correspond to instance indices in X

predict_proba(X) → ndarray

Predicts class label probabilities for time series in X.

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. other types are allowed and converted into one of the above.

Different estimators have different capabilities to handle different types of input. If self.get_tag("capability:multivariate") is False, they cannot handle multivariate series, so either n_channels == 1 is true or X is 2D of shape (n_cases, n_timepoints). If self.get_tag( "capability:unequal_length") is False, they cannot handle unequal length input. In both situations, a ValueError is raised if X has a characteristic that the estimator does not have the capability for is passed.

Returns:

probabilitiesnp.ndarray

2D array of shape (n_cases, n_classes) - predicted class probabilities First dimension indices correspond to instance indices in X, second dimension indices correspond to class labels, (i, j)-th entry is estimated probability that i-th instance is of class j

reset(keep=None)

Reset the object to a clean post-init state.

After a self.reset() call, self is equal or similar in value to type(self)(**self.get_params(deep=False)), assuming no other attributes were kept using keep.

Detailed behaviour:

removes any object attributes, except:

hyper-parameters (arguments of __init__) object attributes containing double-underscores, i.e., the string “__”

runs __init__ with current values of hyperparameters (result of get_params)

Not affected by the reset are:

object attributes containing double-underscores class and object methods, class attributes any attributes specified in the keep argument

Parameters:

keepNone, str, or list of str, default=None

If None, all attributes are removed except hyperparameters. If str, only the attribute with this name is kept. If list of str, only the attributes with these names are kept.

Returns:

selfobject

Reference to self.

Raises:

TypeError

If ‘keep’ is not a string or a list of strings.

score(X, y, metric='accuracy', use_proba=False, metric_params=None) → float

Scores predicted labels against ground truth labels on X.

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. other types are allowed and converted into one of the above.

Different estimators have different capabilities to handle different types of input. If self.get_tag("capability:multivariate") is False, they cannot handle multivariate series, so either n_channels == 1 is true or X is 2D of shape (n_cases, n_timepoints). If self.get_tag( "capability:unequal_length") is False, they cannot handle unequal length input. In both situations, a ValueError is raised if X has a characteristic that the estimator does not have the capability for is passed.

ynp.ndarray

1D np.array of float or str, of shape (n_cases) - class labels (ground truth) for fitting indices corresponding to instance indices in X.

metricUnion[str, callable], default=”accuracy”,

Defines the scoring metric to test the fit of the model. For supported strings arguments, check sklearn.metrics.get_scorer_names.

use_probabool, default=False,

Argument to check if scorer works on probability estimates or not.

metric_paramsdict, default=None,

Contains parameters to be passed to the scoring function. If None, no parameters are passed.

Returns:

scorefloat

Accuracy score of predict(X) vs 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 and tag value pairs.

Returns:

selfobject

Reference to self.