sklearndf.regression.ElasticNetDF#

class sklearndf.regression.ElasticNetDF(alpha=1.0, *, l1_ratio=0.5, fit_intercept=True, precompute=False, max_iter=1000, copy_X=True, tol=0.0001, warm_start=False, positive=False, random_state=None, selection='cyclic')[source]#

Linear regression with combined L1 and L2 priors as regularizer.

Note

This class is a wrapper around class sklearn.linear_model.ElasticNet. It provides enhanced support for pandas data frames, and otherwise delegates all attribute access and method calls to an associated ElasticNet instance.

Bases: RegressorWrapperDF [ElasticNet]
Metaclasses: EstimatorWrapperDFMeta, ABCMeta

Method summary

`clone`	Make an unfitted clone of this estimator.
`fit`	Fit this estimator using the given inputs.
`from_fitted`	Make a new wrapped DF estimator, delegating to a given native estimator that has already been fitted.
`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.
`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`.
`COL_PREDICTION`	Name of `Series` objects containing the predictions of single-output learners.
`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 that this wrapper delegates to.
`output_names_`	The name(s) of the output(s) this supervised learner was fitted to.
`sparse_coef_`	See documentation of `sklearn.linear_model.ElasticNet`.

Definitions

clone()#

Make an unfitted clone of this estimator.

Return type: ElasticNetDF
Returns: the unfitted clone

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

ElasticNetDF

Returns

self

classmethod from_fitted(estimator, features_in, n_outputs)#

Make a new wrapped DF estimator, delegating to a given native estimator that has already been fitted.

Parameters

estimator (ElasticNet) – the fitted native estimator to use as the delegate
features_in (Index) – the column names of X used for fitting the estimator
n_outputs (int) – the number of outputs in y used for fitting the estimator

Return type

ElasticNetDF

Returns

the wrapped data frame estimator

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

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: ElasticNetDF
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

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: sklearndf.wrapper.T_NativeEstimator#

The native estimator that this wrapper delegates to.

Return type: TypeVar(T_NativeEstimator, bound= 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 sparse_coef_#: See documentation of sklearn.linear_model.ElasticNet.

sklearndf.regression.ElasticNetCVDF

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