TapNetRegressor#

class TapNetRegressor(n_epochs=2000, batch_size=16, dropout=0.5, filter_sizes=(256, 256, 128), kernel_size=(8, 5, 3), dilation=1, layers=(500, 300), use_rp=True, activation=None, rp_params=(-1, 3), use_bias=True, use_att=True, use_lstm=True, use_cnn=True, random_state=None, padding='same', loss='mean_squared_error', optimizer=None, metrics=None, callbacks=None, verbose=False)[source]#

Time series attentional prototype network (TapNet), as described in [1].

TapNet was initially proposed for multivariate time series classification. The is an adaptation for time series regression. TapNet comprises these components: random dimension permutation, multivariate time series encoding, and attentional prototype learning.

Parameters:

filter_sizesarray of int, default = (256, 256, 128): sets the kernel size argument for each convolutional block. Controls number of convolutional filters and number of neurons in attention dense layers.
kernel_sizearray of int, default = (8, 5, 3): controls the size of the convolutional kernels
layersarray of int, default = (500, 300): size of dense layers
n_epochsint, default = 2000: number of epochs to train the model
batch_sizeint, default = 16: number of samples per update
dropoutfloat, default = 0.5: dropout rate, in the range [0, 1)
dilationint, default = 1: dilation value
activationstr, default = “sigmoid”: activation function for the last output layer
lossstr, default = “mean_squared_error”: loss function for the classifier
optimizerstr or None, default = “Adam(lr=0.01)”: gradient updating function for the classifier
use_biasbool, default = True: whether to use bias in the output dense layer
use_rpbool, default = True: whether to use random projections
use_attbool, default = True: whether to use self attention
use_lstmbool, default = True: whether to use an LSTM layer
use_cnnbool, default = True: whether to use a CNN layer
verbosebool, default = False: whether to output extra information
random_stateint or None, default = None: seed for random

Attributes:

is_fitted: Whether fit has been called.

Notes

The Implementation of TapNet found at https://github.com/kdd2019-tapnet/tapnet Currently does not implement custom distance matrix loss function or class based self attention.

References

[1]

Zhang et al. Tapnet: Multivariate time series classification with

attentional prototypical network, Proceedings of the AAAI Conference on Artificial Intelligence 34(4), 6845-6852, 2020

Methods

`build_model`(input_shape, **kwargs)	Construct a complied, un-trained, keras model that is ready for training.
`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.
`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_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 labels for sequences 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.
`save_last_model_to_file`([file_path])	Save the last epoch of the trained deep learning model.
`score`(X, y)	Scores predicted labels against ground truth labels on X.
`set_params`(**params)	Set the parameters of this object.
`set_tags`(**tag_dict)	Set dynamic tags to given values.
`summary`()	Summary function to return the losses/metrics for model fit.

build_model(input_shape, **kwargs)[source]#

Construct a complied, un-trained, keras model that is ready for training.

In aeon, time series are stored in numpy arrays of shape (d,m), where d is the number of dimensions, m is the series length. Keras/tensorflow assume data is in shape (m,d). This method also assumes (m,d). Transpose should happen in fit.

Parameters:

input_shapetuple: The shape of the data fed into the input layer, should be (m, d)

Returns:

output: a compiled Keras model

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)[source]#

Fit time series regressor to training data.

Parameters:

X3D np.array (any number of channels, equal length series)

of shape (n_instances, n_channels, n_timepoints)

or 2D np.array (univariate, equal length series): of shape (n_instances, n_timepoints)
or list of numpy arrays (any number of channels, unequal length series): of shape [n_instances], 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.

y1D np.array of float, of shape (n_instances) - regression labels for fitting

indices correspond to instance indices in X

Returns:

selfReference to self.

Notes

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

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_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()
>>> tags = d.get_tags()

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 classifiers, 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={}: 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.

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 labels for sequences in X.

Parameters:

X3D np.array (any number of channels, equal length series)

of shape (n_instances, n_channels, n_timepoints)

or 2D np.array (univariate, equal length series): of shape (n_instances, n_timepoints)
or list of numpy arrays (any number of channels, unequal length series): of shape [n_instances], 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.

Returns:

y1D np.array of float, of shape (n_instances) - 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.

save_last_model_to_file(file_path='./')[source]#

Save the last epoch of the trained deep learning model.

Parameters:

file_pathstr, default = “./”: The directory where the model will be saved

Returns:

None

score(X, y) → float[source]#

Scores predicted labels against ground truth labels on X.

Parameters:

X3D np.array (any number of channels, equal length series)

of shape (n_instances, n_channels, n_timepoints)

or 2D np.array (univariate, equal length series): of shape (n_instances, n_timepoints)
or list of numpy arrays (any number of channels, unequal length series): of shape [n_instances], 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.

y1D np.array of float, of shape (n_instances) - regression labels (ground

truth)

indices correspond to instance indices in X

Returns:

float, R-squared 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_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 settting tag values in tag_dict as dynamic tags in self.

summary()[source]#

Summary function to return the losses/metrics for model fit.

Returns:

history: dict or None,: Dictionary containing model’s train/validation losses and metrics