sklearndf.pipeline.SupervisedLearnerPipelineDF#

class sklearndf.pipeline.SupervisedLearnerPipelineDF(*, preprocessing=None)[source]#

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

Bases: LearnerPipelineDF [~T_FinalSupervisedLearnerDF], SupervisedLearnerDF
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_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_params`	Set the parameters of this estimator.
`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_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)[source]#

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_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

to_expression()#

Render this object as an expression.

Return type: Expression
Returns: the expression representing this object

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

abstract property final_estimator: T_FinalEstimatorDF#

The final estimator following the preprocessing step.

Return type: TypeVar(T_FinalEstimatorDF, bound= EstimatorDF)

abstract property final_estimator_name: str#

The name of the estimator step parameter.

Return type: str

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_: List[str]#

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

Raises: sklearn.exceptions.NotFittedError – this estimator is not fitted
Return type: List[str]

property preprocessing: Optional[sklearndf.TransformerDF]#

The preprocessing step.

Return type: Optional[TransformerDF]

property preprocessing_name: str#

The name of the preprocessing step parameter.

Return type: str

sklearndf.pipeline.RegressorPipelineDF

sklearndf.pipeline.wrapper