sklearndf.wrapper.numpy.TransformerNPDF

class sklearndf.wrapper.numpy.TransformerNPDF(delegate, column_names=None)

Adapter class that delegates to TransformerDF and accepts numpy arrays in addition to pandas data frames and series.

Converts all numpy arrays to pandas series or data frames before deferring to the delegate estimator, and passes through pandas objects unchanged.

For use in meta-estimators that internally hand numpy arrays on to sub-estimators.

Bases:

TransformerDF, EstimatorNPDF [~T_DelegateTransformerDF]

Generic types:

~T_DelegateTransformerDF(bound= TransformerDF)

Metaclasses:

ABCMeta

Parameters:

• delegate (EstimatorNPDF) – the sklearndf estimator to invoke after transforming the incoming numpy arrays to pandas data frames or series

• column_names (Union[Sequence[str], Callable[[], Sequence[str]], None]) – optional column names to use for the pandas data frame derived from the features numpy array; passed either as a sequence of strings, or as a function that dynamically provides the column names

Method summary

clone	Make an unfitted clone of this estimator.
fit	Fit this estimator using the given inputs.
fit_transform	Fit this transformer using the given inputs, then transform the inputs.
get_metadata_routing	See sklearn.utils.get_metadata_routing()
get_params	Get the parameters for this estimator.
inverse_transform	Inverse-transform the given inputs.
set_output	See sklearn.utils.set_output()
set_params	Set the parameters of this estimator.
to_expression	Render this object as an expression.
transform	Transform the given inputs.

Attribute summary

COL_FEATURE	Name assigned to an Index or a Series with the names of the features used to fit a EstimatorDF.
COL_FEATURE_ORIGINAL	Name assigned to a Series with the original feature names before transformation.
feature_names_in_	The pandas column index with the names of the features used to fit this estimator.
feature_names_original_	A pandas series, mapping the output features resulting from the transformation to the original input features.
feature_names_out_	A pandas column index with the names of the features produced by this transformer
is_fitted	True if this object is fitted, False otherwise.
n_features_in_	The number of features used to fit this estimator.
n_outputs_	The number of outputs used to fit this estimator.
native_estimator	The native estimator underlying this estimator.
output_names_	The name(s) of the output(s) this estimator was fitted to, or None if this estimator was not fitted to any outputs.
delegate	The sklearndf estimator to invoke after transforming the incoming numpy arrays to pandas data frames or series.
column_names	Column names to use for the pandas data frame derived from the features numpy array.

Definitions

clone()

Make an unfitted clone of this estimator.

Return type:

TransformerNPDF

Returns:

the unfitted clone

fit(X, y=None, **fit_params)

Fit this estimator using the given inputs.

Parameters:

• X (Union[ndarray[tuple[int, ...], dtype[Any]], spmatrix, Series, DataFrame]) – input data frame with observations as rows and features as columns

• y (Union[ndarray[tuple[int, ...], dtype[Any]], Series, DataFrame, None]) – an optional series or data frame with one or more outputs

• fit_params (Any) – additional keyword parameters as required by specific estimator implementations

Return type:

TransformerNPDF

Returns:

self

fit_transform(X, y=None, **fit_params)

Fit this transformer using the given inputs, then transform the inputs.

Parameters:

• X (Union[ndarray[tuple[int, ...], dtype[Any]], DataFrame]) – input data frame with observations as rows and features as columns

• y (Union[ndarray[tuple[int, ...], dtype[Any]], Series, DataFrame, None]) – an optional series or data frame with one or more outputs

• fit_params (Any) – additional keyword parameters as required by specific transformer implementations

Return type:

DataFrame

Returns:

the transformed inputs

get_metadata_routing()

See sklearn.utils.get_metadata_routing()

get_params(deep=True)

Get the parameters for this estimator.

Parameters:

deep (bool) – if True, return the parameters for this estimator, and for any sub-estimators contained in this estimator

Return type:

Mapping[str, Any]

Returns:

a mapping of parameter names to their values

inverse_transform(X)

Inverse-transform the given inputs.

The inputs must have the same features as the inputs used to fit this transformer. The features can be provided in any order since they are identified by their column names.

Parameters:

X (Union[ndarray[tuple[int, ...], dtype[Any]], DataFrame]) – input data frame with observations as rows and features as columns

Return type:

DataFrame

Returns:

the reverse-transformed inputs

set_output(*, transform=None)

See sklearn.utils.set_output()

set_params(**params)

Set the parameters of this estimator.

Valid parameter keys can be obtained by calling get_params().

Parameters:

params (Any) – the estimator parameters to set

Return type:

TransformerNPDF

Returns:

self

to_expression()

Render this object as an expression.

Return type:

Expression

Returns:

the expression representing this object

transform(X)

Transform the given inputs.

The inputs must have the same features as the inputs used to fit this transformer. The features can be provided in any order since they are identified by their column names.

Parameters:

X (Union[ndarray[tuple[int, ...], dtype[Any]], DataFrame]) – input data frame with observations as rows and features as columns

Return type:

DataFrame

Returns:

the transformed inputs

COL_FEATURE = 'feature'

Name assigned to an Index or a Series with the names of the features used to fit a EstimatorDF.

See feature_names_in_() and feature_names_original_().

COL_FEATURE_ORIGINAL = 'feature_original'

Name assigned to a Series with the original feature names before transformation.

See feature_names_original_().

column_names: Union[Sequence[str], Callable[[], Sequence[str]], None]

Column names to use for the pandas data frame derived from the features numpy array.

delegate: TypeVar(T_DelegateTransformerDF, bound= TransformerDF)

The sklearndf estimator to invoke after transforming the incoming numpy arrays to pandas data frames or series.

property feature_names_in_: Index

The pandas column index with the names of the features used to fit this estimator.

Raises:

AttributeError – this estimator is not fitted

property feature_names_original_: Series

A pandas series, mapping the output features resulting from the transformation to the original input features.

The index of the resulting series consists of the names of the output features; the corresponding values are the names of the original input features.

Raises:

AttributeError – this transformer is not fitted

property feature_names_out_: Index

A pandas column index with the names of the features produced by this transformer

Raises:

AttributeError – this transformer is not fitted

property is_fitted: bool

True if this object is fitted, False otherwise.

property n_features_in_: int

The number of features used to fit this estimator.

Raises:

AttributeError – this estimator is not fitted

property n_outputs_: int

The number of outputs used to fit this estimator.

Raises:

AttributeError – this estimator is not fitted

property native_estimator: BaseEstimator

The native estimator underlying this estimator.

This can be another estimator that this estimator delegates to, otherwise the native estimator is self.

property output_names_: list[str] | None

The name(s) of the output(s) this estimator was fitted to, or None if this estimator was not fitted to any outputs.

Raises:

AttributeError – this estimator is not fitted