`tslearn.svm`.TimeSeriesSVC¶

class tslearn.svm.TimeSeriesSVC(C=1.0, kernel='gak', degree=3, gamma='auto', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, n_jobs=None, verbose=0, max_iter=-1, decision_function_shape='ovr', random_state=None)[source]¶

Time-series specific Support Vector Classifier.

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’. 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)
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.

probabilityboolean, optional (default=False)

Whether to enable probability estimates. This must be enabled prior to calling fit, and will slow down that method. Also, probability estimates are not guaranteed to match predict output. See our dedicated user guide section for more details.

tolfloat, optional (default=1e-3)

Tolerance for stopping criterion.

cache_sizefloat, optional (default=200.0)

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

class_weight{dict, ‘balanced’}, optional

Set the parameter C of class i to class_weight[i]*C for SVC. If not given, all classes are supposed to have weight one. The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y))

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.

decision_function_shape‘ovo’, ‘ovr’, default=’ovr’

Whether to return a one-vs-rest (‘ovr’) decision function of shape (n_samples, n_classes) as all other classifiers, or the original one-vs-one (‘ovo’) decision function of libsvm which has shape (n_samples, n_classes * (n_classes - 1) / 2).

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.
n_support_array-like, dtype=int32, shape = [n_class]: Number of support vectors for each class.
support_vectors_list of arrays of shape [n_SV, sz, d]: List of support vectors in tslearn dataset format, one array per class
dual_coef_array, shape = [n_class-1, n_SV]: Coefficients of the support vector in the decision function. For multiclass, coefficient for all 1-vs-1 classifiers. The layout of the coefficients in the multiclass case is somewhat non-trivial. See the section about multi-class classification in the SVM section of the User Guide of sklearn for details.
coef_array, shape = [n_class-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 a readonly property derived from dual_coef_ and support_vectors_.
intercept_array, shape = [n_class * (n_class-1) / 2]: Constants in decision function.
svm_estimator_sklearn.svm.SVC: 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)
>>> clf = TimeSeriesSVC(kernel="gak", gamma="auto", probability=True)
>>> clf.fit(X, y).predict(X).shape
(20,)
>>> sv = clf.support_vectors_
>>> len(sv)  # should be equal to the nr of classes in the clf problem
2
>>> sv[0].shape  
(..., 64, 2)
>>> sv_sum = sum([sv_i.shape[0] for sv_i in sv])
>>> sv_sum == clf.svm_estimator_.n_support_.sum()
True
>>> clf.decision_function(X).shape
(20,)
>>> clf.predict_log_proba(X).shape
(20, 2)
>>> clf.predict_proba(X).shape
(20, 2)

Methods

`decision_function`(X)	Evaluates the decision function for the samples in X.
`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.
`predict_log_proba`(X)	Predict class log-probabilities for a given set of time series.
`predict_proba`(X)	Predict class probability for a given set of time series.
`score`(X, y[, sample_weight])	Return the mean accuracy on the given test data and labels.
`set_fit_request`(*[, sample_weight])	Request metadata passed to the `fit` method.
`set_params`(**params)	Set the parameters of this estimator.
`set_score_request`(*[, sample_weight])	Request metadata passed to the `score` method.

decision_function(X)[source]¶

Evaluates the decision function for the samples in X.

Parameters:

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

Returns:

ndarray of shape (n_samples, n_classes * (n_classes-1) / 2): Returns the decision function of the sample for each class in the model. If decision_function_shape=’ovr’, the shape is (n_samples, n_classes).

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()[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.

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, n_classes), depending on the shape
of the label vector provided at training time.: Index of the cluster each sample belongs to or class probability matrix, depending on what was provided at training time.

predict_log_proba(X)[source]¶

Predict class log-probabilities for a given set of time series.

Note that probability estimates are not guaranteed to match predict output. See our dedicated user guide section for more details.

Parameters:

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

Returns:

array of shape=(n_ts, n_classes),: Class probability matrix.

predict_proba(X)[source]¶

Predict class probability for a given set of time series.

Note that probability estimates are not guaranteed to match predict output. See our dedicated user guide section for more details.

Parameters:

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

Returns:

array of shape=(n_ts, n_classes),: Class probability matrix.

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_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → TimeSeriesSVC[source]¶

Request metadata passed to the fit 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 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.

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 fit.

Returns:

selfobject: The updated object.

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_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → TimeSeriesSVC[source]¶

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.

Examples using `tslearn.svm.TimeSeriesSVC`¶

SVM and GAK

tslearn.svm.TimeSeriesSVC¶

Examples using tslearn.svm.TimeSeriesSVC¶

`tslearn.svm`.TimeSeriesSVC¶

Examples using `tslearn.svm.TimeSeriesSVC`¶