InceptionTimeRegressor

class InceptionTimeRegressor(n_regressors=5, n_filters=32, n_conv_per_layer=3, kernel_size=40, use_max_pooling=True, max_pool_size=3, strides=1, dilation_rate=1, padding='same', activation='relu', use_bias=False, use_residual=True, use_bottleneck=True, bottleneck_size=32, depth=6, use_custom_filters=False, output_activation='linear', file_path='./', save_last_model=False, save_best_model=False, best_file_name='best_model', last_file_name='last_model', batch_size=64, use_mini_batch_size=False, n_epochs=1500, callbacks=None, random_state=None, verbose=False, loss='mse', optimizer=None)[source]

InceptionTime ensemble regressor.

Ensemble of IndividualInceptionRegressor, as described in [1]_. This ensemble regressor is adapted from the classier InceptionTime

Parameters:
n_regressorsint, default = 5,

the number of Inception models used for the Ensemble in order to create InceptionTime.

depthint, default = 6,

the number of inception modules used

n_filtersint or list of int32, default = 32,

the number of filters used in one inception module, if not a list, the same number of filters is used in all inception modules

n_conv_per_layerint or list of int, default = 3,

the number of convolution layers in each inception module, if not a list, the same number of convolution layers is used in all inception modules

kernel_sizeint or list of int, default = 40,

the head kernel size used for each inception module, if not a list, the same is used in all inception modules

use_max_poolingbool or list of bool, default = True,

conditioning whether or not to use max pooling layer in inception modules,if not a list, the same is used in all inception modules

max_pool_sizeint or list of int, default = 3,

the size of the max pooling layer, if not a list, the same is used in all inception modules

stridesint or list of int, default = 1,

the strides of kernels in convolution layers for each inception module, if not a list, the same is used in all inception modules

dilation_rateint or list of int, default = 1,

the dilation rate of convolutions in each inception module, if not a list, the same is used in all inception modules

paddingstr or list of str, default = “same”,

the type of padding used for convoltuon for each inception module, if not a list, the same is used in all inception modules

activationstr or list of str, default = “relu”,

the activation function used in each inception module, if not a list, the same is used in all inception modules

use_biasbool or list of bool, default = False,

condition whether or not convolutions should use bias values in each inception module, if not a list, the same is used in all inception modules

use_residualbool, default = True,

condition whether or not to use residual connections all over Inception

use_bottleneckbool, default = True,

condition whether or not to use bottlenecks all over Inception

bottleneck_sizeint, default = 32,

the bottleneck size in case use_bottleneck = True

use_custom_filtersbool, default = False,

condition on whether or not to use custom filters in the first inception module

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.

callbackscallable or None, default
ReduceOnPlateau and ModelCheckpoint

list of tf.keras.callbacks.Callback objects.

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 modelcheckpoint 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

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

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

optimizerkeras optimizer, default = Adam
losskeras loss,

default = mean_squared_error

will be set to accuracy as default if None
Attributes:
is_fitted

Whether fit has been called.

Notes

..[1] Fawaz et al. InceptionTime: Finding AlexNet for Time Series regression, Data Mining and Knowledge Discovery, 34, 2020

..[2] Ismail-Fawaz et al. Deep Learning For Time Series regression Using New Hand-Crafted Convolution Filters, 2022 IEEE International Conference on Big Data.

Adapted from the implementation from Fawaz et. al https://github.com/hfawaz/InceptionTime/blob/master/regressors/inception.py

and Ismail-Fawaz et al. https://github.com/MSD-IRIMAS/CF-4-TSC

Examples

>>> from aeon.regression.deep_learning import InceptionTimeRegressor
>>> from aeon.testing.data_generation import make_example_3d_numpy
>>> X, y = make_example_3d_numpy(n_cases=10, n_channels=1, n_timepoints=12,
...                              return_y=True, regression_target=True,
...                              random_state=0)
>>> inctime = InceptionTimeRegressor(n_epochs=20,batch_size=4)  
>>> inctime.fit(X, y)  
InceptionTimeRegressor(...)

Methods

check_is_fitted()

Check if the estimator has been fitted.

clone()

Obtain a clone of the object with same hyper-parameters.

clone_tags(estimator[, tag_names])

Clone/mirror tags from another estimator as dynamic override.

create_test_instance([parameter_set])

Construct Estimator instance if possible.

create_test_instances_and_names([parameter_set])

Create list of all test instances and a list of names for them.

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[, 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_fitted_params([deep])

Get fitted parameters.

get_metadata_routing()

Get metadata routing of this object.

get_param_defaults()

Get parameter defaults for the object.

get_param_names()

Get parameter names for the object.

get_params([deep])

Get parameters for this estimator.

get_tag(tag_name[, tag_value_default, ...])

Get tag value from estimator class.

get_tags()

Get tags from estimator class.

get_test_params([parameter_set])

Return testing parameter settings for the estimator.

is_composite()

Check if the object is composite.

load_from_path(serial)

Load object from file location.

load_from_serial(serial)

Load object from serialized memory container.

predict(X)

Predicts target variable for time series in X.

reset()

Reset the object to a clean post-init state.

save([path])

Save serialized self to bytes-like object or to (.zip) file.

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

Scores predicted labels against ground truth labels on X.

set_params(**params)

Set the parameters of this object.

set_score_request(*[, metric, metric_params])

Request metadata passed to the score method.

set_tags(**tag_dict)

Set dynamic tags to given values.

classmethod get_test_params(parameter_set='default')[source]

Return testing parameter settings for the estimator.

Parameters:
parameter_setstr, default=”default”

Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set. For regressors, a “default” set of parameters should be provided for general testing, and a “results_comparison” set for comparing against previously recorded results if the general set does not produce suitable probabilities to compare against.

Returns:
paramsdict or list of dict, default=[None]

Parameters to create testing instances of the class. Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params.

check_is_fitted()[source]

Check if the estimator has been fitted.

Raises:
NotFittedError

If the estimator has not been fitted yet.

clone()[source]

Obtain a clone of the object with same hyper-parameters.

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)).

Returns:
instance of type(self), clone of self (see above)
clone_tags(estimator, tag_names=None)[source]

Clone/mirror tags from another estimator as dynamic override.

Parameters:
estimatorobject

Estimator 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(parameter_set='default')[source]

Construct Estimator instance if possible.

Parameters:
parameter_setstr, default=”default”

Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:
instanceinstance 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.

classmethod create_test_instances_and_names(parameter_set='default')[source]

Create list of all test instances and a list of names for them.

Parameters:
parameter_setstr, default=”default”

Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:
objslist of instances of cls

i-th instance is cls(**cls.get_test_params()[i]).

nameslist of str, same length as objs

i-th element is name of i-th instance of obj in tests convention is {cls.__name__}-{i} if more than one instance otherwise {cls.__name__}.

parameter_setstr, default=”default”

Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

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, tag_value_default=None)[source]

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.

See also

get_tag

Get a single tag from an object.

get_tags

Get all tags from an object.

get_class_tag

Get a single tag from a class.

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 : tag value pairs. Collected from _tags class attribute via nested inheritance. NOT overridden by dynamic tags set by set_tags or mirror_tags.

get_fitted_params(deep=True)[source]

Get fitted parameters.

State required:

Requires state to be “fitted”.

Parameters:
deepbool, default=True

Whether to return fitted parameters of components.

  • If True, will return a dict of parameter name : value for this object, including fitted parameters of fittable components (= BaseEstimator-valued parameters).

  • If False, will return a dict of parameter name : value for this object, but not include fitted parameters of components.

Returns:
fitted_paramsdict with str-valued keys

Dictionary of fitted parameters, paramname : paramvalue keys-value pairs include:

  • always: all fitted parameters of this object, as via get_param_names values are fitted parameter value for that key, of this object

  • if deep=True, also contains keys/value pairs of component parameters parameters of components are indexed as [componentname]__[paramname] all parameters of componentname appear as paramname with its value

  • if deep=True, also contains arbitrary levels of component recursion, e.g., [componentname]__[componentcomponentname]__[paramname], etc.

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.

classmethod get_param_defaults()[source]

Get parameter defaults for the object.

Returns:
default_dict: dict with str keys

keys are all parameters of cls that have a default defined in __init__ values are the defaults, as defined in __init__.

classmethod get_param_names()[source]

Get parameter names for the object.

Returns:
param_names: list of str, alphabetically sorted list of parameter names of cls
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, tag_value_default=None, raise_error=True)[source]

Get tag value from estimator class.

Uses dynamic tag overrides.

Parameters:
tag_namestr

Name of tag to be retrieved.

tag_value_defaultany type, default=None

Default/fallback value if tag is not found.

raise_errorbool

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

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 i.e. if tag_name is not in self.get_tags(
).keys()

See also

get_tags

Get all tags from an object.

get_clas_tags

Get all tags from a class.

get_class_tag

Get a single tag from a class.

Examples

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

Get tags from estimator class.

Includes the dynamic tag overrides.

Returns:
dict

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.

See also

get_tag

Get a single tag from an object.

get_class_tags

Get all tags from a class.

get_class_tag

Get a single tag from a class.

Examples

>>> from aeon.classification import DummyClassifier
>>> d = DummyClassifier()
>>> tags = d.get_tags()
is_composite()[source]

Check if the object is composite.

A composite object is an object which contains objects, as parameters. Called on an instance, since this may differ by instance.

Returns:
composite: bool

Whether self contains a parameter which is BaseObject.

property is_fitted[source]

Whether fit has been called.

classmethod load_from_path(serial)[source]

Load object from file location.

Parameters:
serialobject

Result of ZipFile(path).open(“object).

Returns:
deserialized self resulting in output at path, of cls.save(path)
classmethod load_from_serial(serial)[source]

Load object from serialized memory container.

Parameters:
serialobject

First element of output of cls.save(None).

Returns:
deserialized self resulting in output serial, of cls.save(None).
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()[source]

Reset the object to a clean post-init state.

Equivalent to sklearn.clone but overwrites self. After self.reset() call, self is equal in value to type(self)(**self.get_params(deep=False))

Detail 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 hyper-parameters (result of get_params)

Not affected by the reset are: object attributes containing double-underscores class and object methods, class attributes

save(path=None)[source]

Save serialized self to bytes-like object or to (.zip) file.

Behaviour: if path is None, returns an in-memory serialized self if path is a file location, stores self at that location as a zip file

saved files are zip files with following contents: _metadata - contains class of self, i.e., type(self) _obj - serialized self. This class uses the default serialization (pickle).

Parameters:
pathNone or file location (str or Path).

if None, self is saved to an in-memory object if file location, self is saved to that file location. If:

path=”estimator” then a zip file estimator.zip will be made at cwd. path=”/home/stored/estimator” then a zip file estimator.zip will be stored in /home/stored/.

Returns:
if path is None - in-memory serialized self
if path is file location - ZipFile with reference to the file.
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 object.

The method works on simple estimators as well as on nested objects. The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:
**paramsdict

BaseObject parameters

Returns:
selfreference to self (after parameters have been set)
set_score_request(*, metric: bool | None | str = '$UNCHANGED$', metric_params: bool | None | str = '$UNCHANGED$') InceptionTimeRegressor[source]

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see 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.

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:
metricstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for metric parameter in score.

metric_paramsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for metric_params parameter in score.

Returns:
selfobject

The updated object.

set_tags(**tag_dict)[source]

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 setting tag values in tag_dict as dynamic tags in self.