TimeSeriesSVR#

class tslearn.svm.TimeSeriesSVR(C=1.0, kernel='gak', degree=3, gamma='auto', coef0=0.0, tol=0.001, epsilon=0.1, shrinking=True, cache_size=200, n_jobs=None, verbose=0, max_iter=-1, random_state=None)[source]#

Time-series specific Support Vector Regressor.

Parameters:

Cfloat, optional (default=1.0)

Penalty parameter C of the error term.

kernelstring, optional (default=’gak’)

Specifies the kernel type to be used in the algorithm. It must be one of ‘gak’ or a kernel accepted by sklearn.svm.SVC. If none is given, ‘gak’ will be used. If a callable is given it is used to pre-compute the kernel matrix from data matrices; that matrix should be an array of shape (n_samples, n_samples).

degreeint, optional (default=3)

Degree of the polynomial kernel function (‘poly’). Ignored by all other kernels.

gammafloat, optional (default=’auto’)

Kernel coefficient for ‘gak’, ‘rbf’, ‘poly’ and ‘sigmoid’. For ‘gak’ kernel, a RuntimeError is raised at fit time when value is close to 0 and therefore not compatible with ‘gak’ kernel.

If gamma is ‘auto’ then:

for ‘gak’ kernel, it is computed based on a sampling of the training set (cf tslearn.metrics.gamma_soft_dtw). A RuntimeError is raised at fit time when computed value is close to 0 and therefore not compatible with ‘gak’ kernel.
for other kernels (eg. ‘rbf’), 1/n_features will be used.

coef0float, optional (default=0.0)

Independent term in kernel function. It is only significant in ‘poly’ and ‘sigmoid’.

tolfloat, optional (default=1e-3)

Tolerance for stopping criterion.

epsilonfloat, optional (default=0.1)

Epsilon in the epsilon-SVR model. It specifies the epsilon-tube within which no penalty is associated in the training loss function with points predicted within a distance epsilon from the actual value.

shrinkingboolean, optional (default=True)

Whether to use the shrinking heuristic.

cache_sizefloat, optional (default=200.0)

Specify the size of the kernel cache (in MB).

n_jobsint or None, optional (default=None)

The number of jobs to run in parallel for GAK cross-similarity matrix computations. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See scikit-learns’ Glossary for more details.

verboseint, default: 0

Enable verbose output. Note that this setting takes advantage of a per-process runtime setting in libsvm that, if enabled, may not work properly in a multithreaded context.

max_iterint, optional (default=-1)

Hard limit on iterations within solver, or -1 for no limit.

random_stateint, RandomState instance or None, optional (default=None)

The seed of the pseudo random number generator to use when shuffling the data. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

Attributes:

support_array-like, shape = [n_SV]: Indices of support vectors.
support_vectors_array of shape [n_SV, sz, d]: Support vectors in tslearn dataset format
dual_coef_array, shape = [1, n_SV]: Coefficients of the support vector in the decision function.
coef_array, shape = [1, n_features]: Weights assigned to the features (coefficients in the primal problem). This is only available in the case of a linear kernel. coef_ is readonly property derived from dual_coef_ and support_vectors_.
intercept_array, shape = [1]: Constants in decision function.
svm_estimator_sklearn.svm.SVR: The underlying sklearn estimator

References

Fast Global Alignment Kernels. Marco Cuturi. ICML 2011.

Examples

>>> from tslearn.generators import random_walk_blobs
>>> X, y = random_walk_blobs(n_ts_per_blob=10, sz=64, d=2, n_blobs=2)
>>> import numpy
>>> y = y.astype(float) + numpy.random.randn(20) * .1
>>> reg = TimeSeriesSVR(kernel="gak", gamma="auto")
>>> reg.fit(X, y).predict(X).shape
(20,)
>>> sv = reg.support_vectors_
>>> sv.shape
(..., 64, 2)
>>> sv.shape[0] <= 20
True

Methods

`fit`(X, y[, sample_weight])	Fit the SVM model according to the given training data.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)	Predict class for a given set of time series.
`score`(X, y[, sample_weight])	Return coefficient of determination on test data.
`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_score_request`(*[, sample_weight])	Configure whether metadata should be requested to be passed to the `score` method.

fit(X, y, sample_weight=None)[source]#

Fit the SVM model according to the given training data.

Parameters:

Xarray-like of shape=(n_ts, sz, d): Time series dataset.
yarray-like of shape=(n_ts, ): Time series labels.
sample_weightarray-like of shape (n_samples,), default=None: Per-sample weights. Rescale C per sample. Higher weights force the classifier to put more emphasis on these points.

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.

predict(X)[source]#

Predict class for a given set of time series.

Parameters:

Xarray-like of shape=(n_ts, sz, d): Time series dataset.

Returns:

array of shape=(n_ts, ) or (n_ts, dim_output), depending on the shape
of the target vector provided at training time.: Predicted targets

score(X, y, sample_weight=None)#

Return coefficient of determination on test data.

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

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

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

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.