tslearn.neural_network.TimeSeriesMLPRegressor

class tslearn.neural_network.TimeSeriesMLPRegressor(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)

A Multi-Layer Perceptron regressor for time series.

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

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

Notes

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

Examples

>>> mlp = TimeSeriesMLPRegressor(hidden_layer_sizes=(64, 64),
...                               random_state=0)
>>> mlp.fit(X=[[1, 2, 3], [1, 1.2, 3.2], [3, 2, 1]],
...         y=[0, 0, 1])  
TimeSeriesMLPRegressor(...)
>>> [c.shape for c in mlp.coefs_]
[(3, 64), (64, 64), (64, 1)]
>>> [c.shape for c in mlp.intercepts_]
[(64,), (64,), (1,)]

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)	Update the model with a single iteration over the given data.
predict(X)	Predict the target for the provided data
score(X, y[, sample_weight])	Return the coefficient of determination of the prediction.
set_params(**params)	Set the parameters of this estimator.
set_score_request(*[, sample_weight])	Request metadata passed to the score method.

fit(X, y)

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:

TimeSeriesMLPRegressor

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)

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.

yndarray of shape (n_samples,)

The target values.

Returns:

selfobject

Trained MLP model.

predict(X)

Predict the target for the provided data

Parameters:

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

Test samples.

Returns:

array, shape = (n_ts, ) or (n_ts, dim_y)

Array of predicted targets

score(X, y, sample_weight=None)

Return the coefficient of determination of the prediction.

The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)** 2).sum() and \(v\) is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.

Parameters:

Xarray-like of shape (n_samples, n_features)

Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True values for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns:

scorefloat

\(R^2\) of self.predict(X) w.r.t. y.

Notes

The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score(). This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

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_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → TimeSeriesMLPRegressor

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.