TSFreshFeatureExtractor#

class TSFreshFeatureExtractor(default_fc_parameters: dict | None = None, fill_na_value: float = -100, n_jobs: int = 1, **kwargs)[source]#

Bases: BaseTimeSeriesFeatureExtractor

Class to hold tsfresh features extraction from tsfresh.

Notes

tsfresh should be installed separately using pip install tsfresh.

Init TSFreshFeatureExtractor with given parameters.

Parameters:

default_fc_parameters (dict | None) – Dict with names of features. .. Examples: blue-yonder/tsfresh
fill_na_value (float) – Value to fill the NaNs in the resulting dataframe.
n_jobs (int) – The number of processes to use for parallelization.

Methods

`dump`(path, args, *kwargs)	Save the object.
`fit`(x[, y])	Fit the feature extractor.
`fit_transform`(x[, y])	Fit the feature extractor and extract features from the input data.
`load`(path, args, *kwargs)	Load the object.
`set_params`(**params)	Return new object instance with modified parameters.
`to_dict`()	Collect all information about etna object in dict.
`transform`(x)	Extract tsfresh features from the input data.

Attributes

This class stores its __init__ parameters as attributes.

dump(path: str, *args, **kwargs)[source]#

Save the object.

Parameters:: path (str) –

fit(x: List[ndarray], y: ndarray | None = None) → TSFreshFeatureExtractor[source]#

Fit the feature extractor.

Parameters:

x (List[ndarray]) –
y (ndarray | None) –

Return type:

TSFreshFeatureExtractor

fit_transform(x: List[ndarray], y: ndarray | None = None) → ndarray[source]#

Fit the feature extractor and extract features from the input data.

Parameters:

x (List[ndarray]) – Array with time series.
y (ndarray | None) – Array of class labels.

Returns:

Transformed input data.

Return type:

ndarray

static load(path: str, *args, **kwargs)[source]#

Load the object.

Parameters:: path (str) –

set_params(**params: dict) → Self[source]#

Return new object instance with modified parameters.

Method also allows to change parameters of nested objects within the current object. For example, it is possible to change parameters of a model in a Pipeline.

Nested parameters are expected to be in a <component_1>.<...>.<parameter> form, where components are separated by a dot.

Parameters:: **params (dict) – Estimator parameters
Returns:: New instance with changed parameters
Return type:: Self

Examples

>>> from etna.pipeline import Pipeline
>>> from etna.models import NaiveModel
>>> from etna.transforms import AddConstTransform
>>> model = model=NaiveModel(lag=1)
>>> transforms = [AddConstTransform(in_column="target", value=1)]
>>> pipeline = Pipeline(model, transforms=transforms, horizon=3)
>>> pipeline.set_params(**{"model.lag": 3, "transforms.0.value": 2})
Pipeline(model = NaiveModel(lag = 3, ), transforms = [AddConstTransform(in_column = 'target', value = 2, inplace = True, out_column = None, )], horizon = 3, )

to_dict()[source]#: Collect all information about etna object in dict.

transform(x: List[ndarray]) → ndarray[source]#

Extract tsfresh features from the input data.

Parameters:: x (List[ndarray]) – Array with time series.
Returns:: Transformed input data.
Return type:: ndarray