sklearndf.pipeline.SupervisedLearnerPipelineDF

class sklearndf.pipeline.SupervisedLearnerPipelineDF(*, preprocessing=None)

A data frame enabled pipeline with an optional preprocessing step and a mandatory supervised learner step.

Bases:

SupervisedLearnerDF, LearnerPipelineDF [~T_FinalSupervisedLearnerDF]

Generic types:

~T_FinalSupervisedLearnerDF(bound= SupervisedLearnerDF)

Metaclasses:

ABCMeta

Parameters:

preprocessing (Optional[TransformerDF]) – the preprocessing step in the pipeline (default: None)

Method summary

clone	Make an unfitted clone of this estimator.
fit	Fit this pipeline using the given inputs.
get_metadata_routing	See sklearn.utils.get_metadata_routing()
get_params	Get the parameters for this estimator.
predict	Predict outputs for the given inputs.
preprocess	Preprocess the given feature values using this pipeline's preprocessing step.
score	Score this learner using the given inputs and outputs.
set_fit_request	See sklearn.utils.RequestMethod.__get__.<locals>.func()
set_params	Set the parameters of this estimator.
set_score_request	See sklearn.utils.RequestMethod.__get__.<locals>.func()
to_expression	Render this object as an expression.

Attribute summary

COL_FEATURE	Name assigned to an Index or a Series with the names of the features used to fit a EstimatorDF.
feature_names_in_	The pandas column index with the names of the features used to fit this estimator.
final_estimator	The final estimator following the preprocessing step.
final_estimator_name	The name of the estimator step parameter.
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 supervised learner was fitted to.
preprocessing	The preprocessing step.
preprocessing_name	The name of the preprocessing step parameter.

Definitions

clone()

Make an unfitted clone of this estimator.

Return type:

SupervisedLearnerPipelineDF

Returns:

the unfitted clone

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

Fit this pipeline using the given inputs.

Parameters:

• X (Union[DataFrame, Series]) – input data frame with observations as rows and features as columns

• y (Union[Series, DataFrame, None]) – an optional series or data frame with one or more outputs

• sample_weight (Optional[Series]) – sample weights for observations, to be passed to the final estimator (optional)

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

Return type:

SupervisedLearnerPipelineDF

Returns:

self

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

predict(X, **predict_params)

Predict outputs for the given inputs.

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

Parameters:

• X (Union[Series, DataFrame]) – input data frame with observations as rows and features as columns

• predict_params (Any) – optional keyword parameters as required by specific learner implementations

Return type:

Union[Series, DataFrame]

Returns:

predictions per observation as a series, or as a data frame in case of multiple outputs

preprocess(X)

Preprocess the given feature values using this pipeline’s preprocessing step.

If the pipeline has no preprocessing step, return the input unchanged.

Parameters:

X (DataFrame) – input data frame with observations as rows and features as columns

Return type:

DataFrame

Returns:

the preprocessed input data frame

score(X, y=None, sample_weight=None)

Score this learner using the given inputs and outputs.

Parameters:

• X (Union[Series, DataFrame]) – data frame with observations as rows and features as columns

• y (Optional[Series]) – a series or data frame with the true outputs per observation

• sample_weight (Optional[Any]) – optional series of scalar weights, for calculating the resulting score as the weighted mean of the scores for the individual predictions

Return type:

float

Returns:

the score

set_fit_request()

See sklearn.utils.RequestMethod.__get__.<locals>.func()

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:

SupervisedLearnerPipelineDF

Returns:

self

set_score_request()

See sklearn.utils.RequestMethod.__get__.<locals>.func()

to_expression()

Render this object as an expression.

Return type:

Expression

Returns:

the expression representing this object

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_().

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

abstract property final_estimator: SupervisedLearnerPipelineDF

The final estimator following the preprocessing step.

abstract property final_estimator_name: str

The name of the estimator step parameter.

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]

The name(s) of the output(s) this supervised learner was fitted to.

Raises:

sklearn.exceptions.NotFittedError – this estimator is not fitted

property preprocessing: TransformerDF | None

The preprocessing step.

property preprocessing_name: str

The name of the preprocessing step parameter.