sklearndf.SupervisedLearnerDF

class sklearndf.SupervisedLearnerDF

Base class for augmented scikit-learn supervised learners.

Provides enhanced support for data frames.

Bases:

LearnerDF

Metaclasses:

ABCMeta

Method summary

clone	Make an unfitted clone of this estimator.
fit	Fit this estimator 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.
score	Score this learner using the given inputs and outputs.
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.
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.

Definitions

clone()

Make an unfitted clone of this estimator.

Return type:

SupervisedLearnerDF

Returns:

the unfitted clone

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

Fit this estimator 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

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

Return type:

SupervisedLearnerDF

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

abstract 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

abstract score(X, y, 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 (Series) – a series or data frame with the true outputs per observation

• sample_weight (Optional[Series]) – 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:

SupervisedLearnerDF

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 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