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.

Attributes:
partial_fit

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

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)  # doctest: +ELLIPSIS
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_params([deep]) Get parameters for this estimator.
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.
fit(X, y)[source]

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

Parameters:
X : array-like, shape (n_ts, sz, d)

Training data.

y : array-like, shape (n_ts, ) or (n_ts, dim_y)

Target values.

Returns:
TimeSeriesMLPClassifier

The fitted estimator

get_params(deep=True)[source]

Get parameters for this estimator.

Parameters:
deep : bool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:
params : dict

Parameter names mapped to their values.

partial_fit[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.

y : array-like of shape (n_samples,)

The target values.

classes : array 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:
self : returns a trained MLP model.
predict(X)[source]

Predict the class labels for the provided data

Parameters:
X : array-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:
X : array-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:
X : array-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:
X : array-like of shape (n_samples, n_features)

Test samples.

y : array-like of shape (n_samples,) or (n_samples, n_outputs)

True labels for X.

sample_weight : array-like of shape (n_samples,), default=None

Sample weights.

Returns:
score : float

Mean accuracy of self.predict(X) wrt. 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:
**params : dict

Estimator parameters.

Returns:
self : estimator instance

Estimator instance.