TimeSeriesMLPClassifier#

class tslearn.neural_network.TimeSeriesMLPClassifier(hidden_layer_sizes=(100,), activation='relu', *, solver='adam', alpha=0.0001, batch_size='auto', learning_rate='constant', learning_rate_init=0.001, power_t=0.5, max_iter=200, shuffle=True, random_state=None, tol=0.0001, verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-08, n_iter_no_change=10, max_fun=15000)[source]#

A Multi-Layer Perceptron classifier for time series.

This class mainly reshapes data so that it can be fed to scikit-learn’s MLPClassifier.

It accepts the exact same hyper-parameters as MLPClassifier, check scikit-learn docs for a list of parameters and attributes.

Notes

This method requires a dataset of equal-sized time series.

Examples

>>> from tslearn.generators import random_walk_blobs
>>> X, y = random_walk_blobs(n_ts_per_blob=30, sz=16, d=2, n_blobs=3,
...                          random_state=0)
>>> mlp = TimeSeriesMLPClassifier(hidden_layer_sizes=(64, 64),
...                               random_state=0)
>>> mlp.fit(X, y)
TimeSeriesMLPClassifier(...)
>>> [c.shape for c in mlp.coefs_]
[(32, 64), (64, 64), (64, 3)]
>>> [c.shape for c in mlp.intercepts_]
[(64,), (64,), (3,)]

Methods

fit(X, y)

Fit the model using X as training data and y as target values

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

partial_fit(X, y, *args, **kwargs)

Update the model with a single iteration over the given data.

predict(X)

Predict the class labels for the provided data

predict_log_proba(X)

Return the log of probability estimates.

predict_proba(X)

Predict the class probabilities for the provided data

score(X, y[, sample_weight])

Return accuracy on provided data and labels.

set_fit_request(*[, sample_weight])

Configure whether metadata should be requested to be passed to the fit method.

set_params(**params)

Set the parameters of this estimator.

set_partial_fit_request(*[, classes, ...])

Configure whether metadata should be requested to be passed to the partial_fit method.

set_score_request(*[, sample_weight])

Configure whether metadata should be requested to be passed to the score method.
fit(X, y)[source]#

Fit the model using X as training data and y as target values

Parameters:
Xarray-like, shape (n_ts, sz, d)

Training data.

yarray-like, shape (n_ts, ) or (n_ts, dim_y)

Target values.

Returns:
TimeSeriesMLPClassifier

The fitted estimator

get_metadata_routing()#

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)#

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.

partial_fit(X, y, *args, **kwargs)[source]#

Update the model with a single iteration over the given data.

Parameters:
Xarray-like, shape (n_ts, sz, d)

The input data.

yarray-like, shape (n_ts, ) or (n_ts, dim_y)
Target values.
*args, **kwargsarguments for the underlying
MLPClassifier’s method from scikit-learn
Returns:
TimeSeriesMLPClassifier

The fitted estimator

predict(X)[source]#

Predict the class labels for the provided data

Parameters:
Xarray-like, shape (n_ts, sz, d)

Test samples.

Returns:
array, shape = (n_ts, )

Array of predicted class labels

predict_log_proba(X)#

Return the log of probability estimates.

Parameters:
Xndarray of shape (n_samples, n_features)

The input data.

Returns:
log_y_probndarray of shape (n_samples, n_classes)

The predicted log-probability of the sample for each class in the model, where classes are ordered as they are in self.classes_. Equivalent to log(predict_proba(X)).

predict_proba(X)[source]#

Predict the class probabilities for the provided data

Parameters:
Xarray-like, shape (n_ts, sz, d)

Test samples.

Returns:
array, shape = (n_ts, n_classes)

Array of predicted class probabilities

score(X, y, sample_weight=None)#

Return accuracy on provided 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_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → TimeSeriesMLPClassifier#

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit 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 fit.

  • 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.

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

Metadata routing for sample_weight parameter in fit.

Returns:
selfobject

The updated object.

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_partial_fit_request(*, classes: bool | None | str = '$UNCHANGED$', sample_weight: bool | None | str = '$UNCHANGED$') → TimeSeriesMLPClassifier#

Configure whether metadata should be requested to be passed to the partial_fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to partial_fit 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 partial_fit.

  • 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.

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

Metadata routing for classes parameter in partial_fit.

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

Metadata routing for sample_weight parameter in partial_fit.

Returns:
selfobject

The updated object.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → TimeSeriesMLPClassifier#

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the 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.

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

Metadata routing for sample_weight parameter in score.

Returns:
selfobject

The updated object.