Common

Core APIs

Recipe

Recipe(self, *steps)

Attributes

Name Description
output_format The output format to use for transform

Methods

Name Description
fit Fit a recipe.
fit_transform Fit and transform in one step.
get_params Get parameters for this recipe.
is_fitted Check if this recipe has already been fit.
set_output Set output type returned by transform.
set_params Set the parameters of this recipe.
to_dask_dataframe Transform X and return a dask.dataframe.DataFrame.
to_dask_dmatrix Transform X and return a xgboost.dask.DMatrix
to_dmatrix Transform X and return a xgboost.DMatrix
to_ibis Transform X and return an ibis table.
to_numpy Transform X and return a numpy.ndarray.
to_pandas Transform X and return a pandas.DataFrame.
to_polars Transform X and return a polars.DataFrame.
to_pyarrow Transform X and return a pyarrow.Table.
to_pyarrow_batches Transform X and return a pyarrow.RecordBatchReader.
transform Transform the data.

fit

fit(X, y=None)

Fit a recipe.

Parameters

Name Type Description Default
X table - like Training data. required
y column - like Training targets. None

Returns

Name Type Description
self Returns the same instance.

fit_transform

fit_transform(X, y=None)

Fit and transform in one step.

Parameters

Name Type Description Default
X table - like Training data. required
y column - like Training targets. None

Returns

Name Type Description
Xt Transformed training data.

get_params

get_params(deep=True)

Get parameters for this recipe.

Returns the parameters given in the constructor as well as the steps contained within the steps of the Recipe.

Parameters

Name Type Description Default
deep bool If True, will return the parameters for this recipe and contained steps. True

Returns

Name Type Description
params mapping of string to any Parameter names mapped to their values.

Notes

Derived from [1]_.

References

.. [1] https://github.com/scikit-learn/scikit-learn/blob/ee5a1b6/sklearn/utils/metaestimators.py#L30-L50

is_fitted

is_fitted()

Check if this recipe has already been fit.

set_output

set_output(transform=None)

Set output type returned by transform.

Parameters

Name Type Description Default
transform ('default', 'pandas') Configure output of transform and fit_transform. - "default": Default output format of a transformer - "pandas": Pandas dataframe - "polars": Polars dataframe - "pyarrow": PyArrow table - None: Transform configuration is unchanged "default"

set_params

set_params(**params)

Set the parameters of this recipe.

Valid parameter keys can be listed with get_params(). Note that you can directly set the parameters of the steps contained in steps.

Parameters

Name Type Description Default
**params dict Parameters of this recipe or parameters of steps contained in steps. Parameters of the steps may be set using its name and the parameter name separated by a ’__’. {}

Returns

Name Type Description
self object Recipe class instance.

Notes

Derived from [1]_ and [2]_.

References

.. [1] https://github.com/scikit-learn/scikit-learn/blob/ff1c6f3/sklearn/utils/metaestimators.py#L51-L70 .. [2] https://github.com/scikit-learn/scikit-learn/blob/74016ab/sklearn/base.py#L214-L256

to_dask_dataframe

to_dask_dataframe(X, categories=False)

Transform X and return a dask.dataframe.DataFrame.

Parameters

Name Type Description Default
X table - like The input data to transform. required
categories bool Whether to return any categorical columns as dask categorical series. If False (the default) these columns will be returned as numeric columns containing only their integral categorical codes. False

to_dask_dmatrix

to_dask_dmatrix(X)

Transform X and return a xgboost.dask.DMatrix

Parameters

Name Type Description Default
X table - like The input data to transform. required

to_dmatrix

to_dmatrix(X)

Transform X and return a xgboost.DMatrix

Parameters

Name Type Description Default
X table - like The input data to transform. required

to_ibis

to_ibis(X)

Transform X and return an ibis table.

Parameters

Name Type Description Default
X table - like The input data to transform. required

to_numpy

to_numpy(X)

Transform X and return a numpy.ndarray.

Parameters

Name Type Description Default
X table - like The input data to transform. required

to_pandas

to_pandas(X, categories=False)

Transform X and return a pandas.DataFrame.

Parameters

Name Type Description Default
X table - like The input data to transform. required
categories bool Whether to return any categorical columns as pandas categorical series. If False (the default) these columns will be returned as numeric columns containing only their integral categorical codes. False

to_polars

to_polars(X)

Transform X and return a polars.DataFrame.

Parameters

Name Type Description Default
X table - like The input data to transform. required

to_pyarrow

to_pyarrow(X, categories=False)

Transform X and return a pyarrow.Table.

Parameters

Name Type Description Default
X table - like The input data to transform. required
categories bool Whether to return any categorical columns as dictionary-encoded columns. If False (the default) these columns will be returned as numeric columns containing only their integral categorical codes. False

to_pyarrow_batches

to_pyarrow_batches(X, categories=False)

Transform X and return a pyarrow.RecordBatchReader.

Parameters

Name Type Description Default
X table - like The input data to transform. required
categories bool Whether to return any categorical columns as dictionary-encoded columns. If False (the default) these columns will be returned as numeric columns containing only their integral categorical codes. False

transform

transform(X)

Transform the data.

Parameters

Name Type Description Default
X table - like Data to transform. required

Returns

Name Type Description
Xt Transformed data.
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