sklearndf.wrapper.numpy.TransformerNPDF#

class sklearndf.wrapper.numpy.TransformerNPDF(delegate, column_names=None)[source]#

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

EstimatorNPDF [~T_DelegateTransformerDF], TransformerDF

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_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
Return type

TransformerNPDF

Returns

self

fit_transform(X, y=None, **fit_params)[source]#

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

Parameters
Return type

DataFrame

Returns

the transformed inputs

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)[source]#

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[Any, 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)[source]#

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[Any, dtype[Any]], DataFrame]) – input data frame with observations as rows and features as columns

Return type

DataFrame

Returns

the transformed inputs

property feature_names_in_: pandas.Index#

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

Raises

AttributeError – this estimator is not fitted

Return type

Index

property feature_names_original_: pandas.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

Return type

Series

property feature_names_out_: pandas.Index#

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

Raises

AttributeError – this transformer is not fitted

Return type

Index

property is_fitted: bool#

True if this object is fitted, False otherwise.

Return type

bool

property n_features_in_: int#

The number of features used to fit this estimator.

Raises

AttributeError – this estimator is not fitted

Return type

int

property n_outputs_: int#

The number of outputs used to fit this estimator.

Raises

AttributeError – this estimator is not fitted

Return type

int

property native_estimator: sklearn.base.BaseEstimator#

The native estimator underlying this estimator.

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

Return type

BaseEstimator

property output_names_: Optional[List[str]]#

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

Return type

Optional[List[str]]