LITETimeRegressor

class LITETimeRegressor(n_regressors=5, use_litemv=False, n_filters=32, kernel_size=40, strides=1, activation='relu', output_activation='linear', file_path='./', save_last_model=False, save_best_model=False, save_init_model=False, best_file_name='best_model', last_file_name='last_model', init_file_name='init_model', batch_size=64, use_mini_batch_size=False, n_epochs=1500, callbacks=None, random_state=None, verbose=False, loss='mean_squared_error', metrics='mean_squared_error', optimizer=None)[source]

LITETime or LITEMVTime ensemble Regressor.

Ensemble of IndividualLITETimeRegressor objects, as described in [1]_ and [2]_. For using LITEMV, simply set the use_litemv bool parameter to True.

Parameters:
n_regressorsint, default = 5,

the number of LITE or LITEMV models used for the Ensemble in order to create LITETime or LITEMVTime.

use_litemvbool, default = False

The boolean value to control which version of the network to use. If set to False, then LITE is used, if set to True then LITEMV is used. LITEMV is the same architecture as LITE but specifically designed to better handle multivariate time series.

n_filtersint or list of int32, default = 32

The number of filters used in one lite layer, if not a list, the same number of filters is used in all lite layers.

kernel_sizeint or list of int, default = 40

The head kernel size used for each lite layer, if not a list, the same is used in all lite module.

stridesint or list of int, default = 1

The strides of kernels in convolution layers for each lite layer, if not a list, the same is used in all lite layers.

activationstr or list of str, default = ‘relu’

The activation function used in each lite layer, if not a list, the same is used in all lite layers.

output_activationstr, default = “linear”,

the output activation for the regressor.

batch_sizeint, default = 64

the number of samples per gradient update.

use_mini_batch_sizebool, default = False

condition on using the mini batch size formula Wang et al.

n_epochsint, default = 1500

the number of epochs to train the model.

callbackskeras callback or list of callbacks,

default = None The default list of callbacks are set to ModelCheckpoint and ReduceLROnPlateau.

file_pathstr, default = “./”

file_path when saving model_Checkpoint callback

save_best_modelbool, default = False

Whether or not to save the best model, if the model checkpoint callback is used by default, this condition, if True, will prevent the automatic deletion of the best saved model from file and the user can choose the file name

save_last_modelbool, default = False

Whether or not to save the last model, last epoch trained, using the base class method save_last_model_to_file

save_init_modelbool, default = False

Whether to save the initialization of the model.

best_file_namestr, default = “best_model”

The name of the file of the best model, if save_best_model is set to False, this parameter is discarded

last_file_namestr, default = “last_model”

The name of the file of the last model, if save_last_model is set to False, this parameter is discarded

init_file_namestr, default = “init_model”

The name of the file of the init model, if save_init_model is set to False, this parameter is discarded.

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. Seeded random number generation can only be guaranteed on CPU processing, GPU processing will be non-deterministic.

verboseboolean, default = False

whether to output extra information

lossstr, default = “mean_squared_error”

The name of the keras training loss.

metricsstr or list[str], default=”mean_squared_error”

The evaluation metrics to use during training. If a single string metric is provided, it will be used as the only metric. If a list of metrics are provided, all will be used for evaluation.

optimizerkeras.optimizer, default = tf.keras.optimizers.Adam()

The keras optimizer used for training.

Notes

Adapted from the implementation from Ismail-Fawaz et. al https://github.com/MSD-IRIMAS/LITE

References

..[1] Ismail-Fawaz et al. LITE: Light Inception with boosTing tEchniques for Time Series Classification, IEEE International Conference on Data Science and Advanced Analytics, 2023. ..[2] Ismail-Fawaz, Ali, et al. “Look Into the LITE in Deep Learning for Time Series Classification.” arXiv preprint arXiv:2409.02869 (2024).

Examples

>>> from aeon.regression.deep_learning import LITETimeRegressor
>>> 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")
>>> ltime = LITETimeRegressor(n_epochs=20,batch_size=4)  
>>> ltime.fit(X_train, y_train)  
LITETimeRegressor(...)

Methods

clone([random_state])

Obtain a clone of the object with the same hyperparameters.

fit(X, y)

Fit time series regressor to training data.

fit_predict(X, y)

Fits the regressor and predicts class labels 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_metadata_routing()

Sklearn metadata routing.

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 target variable for time series in X.

reset([keep])

Reset the object to a clean post-init state.

score(X, y[, metric, 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)[source]

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[source]

Fit time series regressor 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, of shape (n_cases) - regression targets (ground truth) for fitting indices corresponding to instance indices in X.

Returns:
selfBaseRegressor

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) ndarray[source]

Fits the regressor 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.

Regressors 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, of shape (n_cases) - regression targets (ground truth) for fitting indices corresponding to instance indices in X.

Returns:
predictionsnp.ndarray

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

classmethod get_class_tag(tag_name, raise_error=True, tag_value_default=None)[source]

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()[source]

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)[source]

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_metadata_routing()[source]

Sklearn metadata routing.

Not supported by aeon estimators.

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.

get_tag(tag_name, raise_error=True, tag_value_default=None)[source]

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()[source]

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[source]

Predicts target variable 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 regression labels indices correspond to instance indices in X

reset(keep=None)[source]

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.

score(X, y, metric='r2', metric_params=None) float[source]

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, of shape (n_cases) - regression targets (ground truth) for fitting indices corresponding to instance indices in X.

metricUnion[str, callable], default=”r2”,

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

metric_paramsdict, default=None,

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

Returns:
scorefloat

MSE score of predict(X) vs 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_tags(**tag_dict)[source]

Set dynamic tags to given values.

Parameters:
**tag_dictdict

Dictionary of tag name and tag value pairs.

Returns:
selfobject

Reference to self.