TimeSeriesScalerMinMax#

class tslearn.preprocessing.TimeSeriesScalerMinMax(value_range=(0.0, 1.0), per_timeseries=True, per_feature=True)[source]#

Scaler for time series datasets. Scales features values so that their span in given dimensions is between min and max where value_range=(min, max).

Parameters:

value_rangetuple (default: (0., 1.)): The minimum and maximum value for the output time series.
per_timeseries: bool (default: True): Wether the scaling should be performed per time series.
per_feature: bool (default: True): Wether the scaling should be performed per feature. Meaningless for univariate timeseries.

Notes

NaNs within a time series are ignored when calculating min and max.

Examples

>>> TimeSeriesScalerMinMax(value_range=(1., 2.)).fit_transform([[0, 3, 6]])
array([[[1. ],
        [1.5],
        [2. ]]])
>>> TimeSeriesScalerMinMax(value_range=(1., 2.)).fit_transform(
...     [[numpy.nan, 3, 6]]
... )
array([[[nan],
        [ 1.],
        [ 2.]]])
>>> TimeSeriesScalerMinMax(value_range=(1., 2.), per_timeseries=False, per_feature=False).fit_transform(
...    [[[1, 2], [2, 3]],
...    [[3, 4], [4, 5]]]
... )
array([[[1.  , 1.25],
        [1.25, 1.5 ]],

       [[1.5 , 1.75],
        [1.75, 2.  ]]])

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])	Will normalize (min-max) each of the timeseries.

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 rescaled.

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]#

Will normalize (min-max) each of the timeseries. IMPORTANT: this transformation is completely stateless, and is applied to each of the timeseries individually.

Parameters: