TimeSeriesResampler#

class tslearn.preprocessing.TimeSeriesResampler(sz: int = -1)[source]#

Resampler for time series. Resample time series so that they reach the target size.

Parameters:
szint (default: -1)

Size of the output time series. If not strictly positive, the size of the longuest timeseries in the dataset is used.

Examples

>>> TimeSeriesResampler(sz=5).fit_transform([[0, 3, 6]])
array([[[0. ],
        [1.5],
        [3. ],
        [4.5],
        [6. ]]])

Methods

fit(X[, y])

A dummy method such that it complies to the sklearn requirements.

fit_transform(X[, y])

Fit to data, then transform it.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

set_output(*[, transform])

Set output container.

set_params(**params)

Set the parameters of this estimator.

transform(X[, y])

Fit to data, then transform it.
fit(X, y=None, **kwargs)[source]#

A dummy method such that it complies to the sklearn requirements. Since this method is completely stateless, it just returns itself.

Parameters:
X

Ignored

Returns:
self
fit_transform(X, y=None, **kwargs)[source]#

Fit to data, then transform it.

Parameters:
Xarray-like of shape (n_ts, sz, d)

Time series dataset to be resampled.

Returns:
numpy.ndarray

Resampled time series dataset.

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.

set_output(*, transform=None)#

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:
transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

  • “default”: Default output format of a transformer

  • “pandas”: DataFrame output

  • “polars”: Polars output

  • None: Transform configuration is unchanged

Added in version 1.4: “polars” option was added.

Returns:
selfestimator instance

Estimator instance.

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.

transform(X, y=None, **kwargs)[source]#

Fit to data, then transform it.

Parameters:
Xarray-like of shape (n_ts, sz, d)

Time series dataset to be resampled.

Returns:
numpy.ndarray

Resampled time series dataset.

Examples using tslearn.preprocessing.TimeSeriesResampler#

k-means

k-means