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 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 the class log-probabilities for the provided data
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
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed topartial_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 topartial_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 inpartial_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
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed toscore
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it toscore
.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 inscore
.
- Returns:
- selfobject
The updated object.