tslearn.neural_network.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[, classes])

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)

Predict the class log-probabilities for the provided data

predict_proba(X)

Predict the class probabilities for the provided data

score(X, y[, sample_weight])

Return the mean accuracy on the given test data and labels.

set_params(**params)

Set the parameters of this estimator.

set_partial_fit_request(*[, classes])

Request metadata passed to the partial_fit method.

set_score_request(*[, sample_weight])

Request metadata 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()[source]

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)[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.

partial_fit(X, y, classes=None)[source]

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

Parameters:
X{array-like, sparse matrix} of shape (n_samples, n_features)

The input data.

yarray-like of shape (n_samples,)

The target values.

classesarray of shape (n_classes,), default=None

Classes across all calls to partial_fit. Can be obtained via np.unique(y_all), where y_all is the target vector of the entire dataset. This argument is required for the first call to partial_fit and can be omitted in the subsequent calls. Note that y doesn’t need to contain all labels in classes.

Returns:
selfobject

Trained MLP model.

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)[source]

Predict the class log-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 log-probabilities

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)[source]

Return the mean accuracy on the given test 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_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_partial_fit_request(*, classes: bool | None | str = '$UNCHANGED$') TimeSeriesMLPClassifier[source]

Request metadata passed to the partial_fit 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 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.

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

Metadata routing for classes parameter in partial_fit.

Returns:
selfobject

The updated object.

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

New 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:
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.