TimeSeriesSVRTslearn

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

Time Series Suppoer Vector Regressor, from tslearn.

Direct interface to tslearn.svm.svm.TimeSeriesSVR.

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.SVR. 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’. If gamma is ‘auto’ then:

for ‘gak’ kernel, it is computed based on a sampling of the training set tslearn.metrics.gamma_soft_dtw
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’.

shrinkingboolean, optional (default=True)

Whether to use the shrinking heuristic.

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.

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.

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.
sample_weightarray-like, shape = [n_samples]: Individual weights for each sample
svm_estimator_sklearn.svm.SVR: The underlying sklearn estimator

Methods

`check_is_fitted`([method_name])	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`fit`(X, y)	Fit time series regressor to training data.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from parent classes.
`get_config`()	Get config flags for self.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get a dict of parameters values for this object.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`is_composite`()	Check if the object is composed of other BaseObjects.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`predict`(X)	Predicts labels for sequences in X.
`reset`()	Reset the object to a clean post-init state.
`save`([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
`score`(X, y[, multioutput])	Scores predicted labels against ground truth labels on X.
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**params)	Set the parameters of this object.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.