Pipeline¶

class darts.dataprocessing.pipeline.Pipeline(transformers, copy=False, verbose=None, n_jobs=None)[source]¶

Bases: object

Pipeline to combine multiple data transformers, chaining them together.

Parameters

transformers (Sequence[BaseDataTransformer]) – Sequence of data transformers.
copy (bool) – If set makes a (deep) copy of each data transformer before adding them to the pipeline
n_jobs (Optional[int]) – The number of jobs to run in parallel. Parallel jobs are created only when a Sequence[TimeSeries] is passed as input to a method, parallelising operations regarding different TimeSeries. Defaults to 1 (sequential). Setting the parameter to -1 means using all the available processors. Note: for a small amount of data, the parallelisation overhead could end up increasing the total required amount of time. Note: this parameter will overwrite the value set in each single transformer. Leave this parameter set to None for keeping the original transformers’ configurations.
verbose (Optional[bool]) – Whether to print progress of the operations. Note: this parameter will overwrite the value set in each single transformer. Leave this parameter set to None for keeping the transformers configurations.

Examples

>>> import numpy as np
>>> from darts import TimeSeries
>>> from darts.datasets import AirPassengersDataset
>>> from darts.dataprocessing.transformers import Scaler, MissingValuesFiller
>>> from darts.dataprocessing.pipeline import Pipeline
>>> values = np.arange(start=0, stop=12.5, step=2.5)
>>> values[1:3] = np.nan
>>> series = series.from_values(values)
>>> pipeline = Pipeline([MissingValuesFiller(), Scaler()])
>>> series_transformed = pipeline.fit_transform(series)
<TimeSeries (DataArray) (time: 5, component: 1, sample: 1)>
array([[[0.  ]],
    [[0.25]],
    [[0.5 ]],
    [[0.75]],
    [[1.  ]]])
Coordinates:
* time       (time) int64 0 1 2 3 4
* component  (component) object '0'
Dimensions without coordinates: sample

Methods

`fit`(data)	Fit all fittable transformers in pipeline.
`fit_transform`(data)	For each data transformer in the pipeline, first fit the data if transformer is fittable then transform data using fitted transformer.
`inverse_transform`(data[, partial])	For each data transformer in the pipeline, inverse-transform data.
`invertible`()	Returns whether the pipeline is invertible or not.
`transform`(data)	For each data transformer in pipeline transform data.

fit(data)[source]¶

Fit all fittable transformers in pipeline.

Parameters: data (Union[TimeSeries, Sequence[TimeSeries]]) – (Sequence of) TimeSeries to fit on.

fit_transform(data)[source]¶

For each data transformer in the pipeline, first fit the data if transformer is fittable then transform data using fitted transformer. The transformed data is then passed to next transformer.

Parameters: data (Union[TimeSeries, Sequence[TimeSeries]]) – (Sequence of) TimeSeries to fit and transform on.
Returns: Transformed data.
Return type: Union[TimeSeries, Sequence[TimeSeries]]

inverse_transform(data, partial=False)[source]¶

For each data transformer in the pipeline, inverse-transform data. Then inverse transformed data is passed to the next transformer. Transformers are traversed in reverse order. Raises value error if not all of the transformers are invertible and partial is set to False. Set partial to True for inverting only the InvertibleDataTransformer in the pipeline.

Parameters

data (Union[TimeSeries, Sequence[TimeSeries]]) – (Sequence of) TimeSeries to be inverse transformed.
partial (bool) – If set to True, the inverse transformation is applied even if the pipeline is not fully invertible, calling inverse_transform() only on the `InvertibleDataTransformer`s

Returns

Inverse transformed data.

Return type

Union[TimeSeries, Sequence[TimeSeries]]

invertible()[source]¶

Returns whether the pipeline is invertible or not. A pipeline is invertible if all transformers in the pipeline are themselves invertible.

Returns: True if the pipeline is invertible, False otherwise
Return type: bool

transform(data)[source]¶

For each data transformer in pipeline transform data. Then transformed data is passed to next transformer.

Parameters: data (Union[TimeSeries, Sequence[TimeSeries]]) – (Sequence of) TimeSeries to be transformed.
Returns: Transformed data.
Return type: Union[TimeSeries, Sequence[TimeSeries]]

Window Transformer

Datasets