facet.selection.base.CandidateEstimatorDF#

class facet.selection.base.CandidateEstimatorDF(candidate=None, candidate_name=None)[source]#

A trivial wrapper for classifiers, regressors and transformers, acting like a pipeline with a single step.

Used in conjunction with MultiEstimatorParameterSpace to evaluate multiple competing models: the candidate parameter determines the estimator to be used and is used to include multiple estimators as part of the parameter space that is searched during model tuning.

Bases

RegressorDF, TransformerDF, ClassifierDF

Metaclasses

ABCMeta

Parameters

candidate (Union[ClassifierDF, RegressorDF, TransformerDF, None]) – the current estimator candidate; usually not specified on class creation but set as a parameter during multi-estimator model selection
candidate_name (Optional[str]) – a name for the estimator candidate; usually not specified on class creation but set as a parameter during multi-estimator model selection

Method summary

`clone`	See `sklearndf.EstimatorDF.clone()`
`decision_function`	Compute the decision function for the given inputs.
`fit`	Fit this estimator using the given inputs.
`fit_transform`	See `sklearndf.TransformerDF.fit_transform()`
`get_params`	See `sklearndf.EstimatorDF.get_params()`
`inverse_transform`	Inverse-transform the given inputs.
`predict`	Predict outputs for the given inputs.
`predict_log_proba`	Predict class log-probabilities for the given inputs.
`predict_proba`	Predict class probabilities for the given inputs.
`score`	Score this learner using the given inputs and outputs.
`set_output`	See `sklearn.utils.set_output()`
`set_params`	See `sklearndf.EstimatorDF.set_params()`
`to_expression`	See `sklearndf.EstimatorDF.to_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.
`PARAM_CANDIDATE`	name of the candidate parameter
`PARAM_CANDIDATE_NAME`	name of the candidate_name parameter
`classes_`	See `sklearndf.ClassifierDF.classes_`
`feature_names_in_`	See `sklearndf.EstimatorDF.feature_names_in_`
`feature_names_original_`	See `sklearndf.TransformerDF.feature_names_original_`
`feature_names_out_`	See `sklearndf.TransformerDF.feature_names_out_`
`is_fitted`	`True` if this object is fitted, `False` otherwise.
`n_features_in_`	See `sklearndf.EstimatorDF.n_features_in_`
`n_outputs_`	See `sklearndf.EstimatorDF.n_outputs_`
`native_estimator`	See `sklearndf.EstimatorDF.native_estimator`
`output_names_`	See `sklearndf.SupervisedLearnerDF.output_names_`
`candidate`	The currently selected estimator candidate.
`candidate_name`	The name of the candidate, used for more readable summary reports of model tuning results.

Definitions

clone()#: See sklearndf.EstimatorDF.clone()

decision_function(X, **predict_params)[source]#

Compute the decision function 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

a data frame with observations as rows and classes as columns, and values as the raw values predicted per observation and class; for multi-output classifiers, a list of one observation/class data frames per output

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

Fit this estimator using the given inputs.

Parameters

X (Union[Series, DataFrame]) – 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

CandidateEstimatorDF

Returns

self

fit_transform(X, y=None, **fit_params)#: See sklearndf.TransformerDF.fit_transform()

get_params(deep=True)#: See sklearndf.EstimatorDF.get_params()

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[Series, DataFrame]) – input data frame with observations as rows and features as columns
Return type: DataFrame
Returns: the reverse-transformed inputs

predict(X, **predict_params)[source]#

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

predict_log_proba(X, **predict_params)[source]#

Predict class log-probabilities 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[DataFrame, List[DataFrame]]

Returns

a data frame with observations as rows and classes as columns, and values as log-probabilities per observation and class; for multi-output classifiers, a list of one observation/class data frames per output

predict_proba(X, **predict_params)[source]#

Predict class probabilities 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[DataFrame, List[DataFrame]]

Returns

a data frame with observations as rows and classes as columns, and values as probabilities per observation and class; for multi-output classifiers, a list of one observation/class data frames per output

score(X, y, 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 (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_output(*, transform=None)#: See sklearn.utils.set_output()

set_params(**params)#: See sklearndf.EstimatorDF.set_params()

to_expression()#: See sklearndf.EstimatorDF.to_expression()

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[Series, DataFrame]) – input data frame with observations as rows and features as columns
Return type: DataFrame
Returns: the transformed inputs

PARAM_CANDIDATE = 'candidate'#: name of the candidate parameter

PARAM_CANDIDATE_NAME = 'candidate_name'#: name of the candidate_name parameter

candidate: Optional[Union[sklearndf.ClassifierDF, sklearndf.RegressorDF, sklearndf.TransformerDF]]#: The currently selected estimator candidate.

candidate_name: Optional[str]#: The name of the candidate, used for more readable summary reports of model tuning results.

property classes_: Union[numpy.ndarray[Any, numpy.dtype[Any]], List[numpy.ndarray[Any, numpy.dtype[Any]]]]#: See sklearndf.ClassifierDF.classes_

property feature_names_in_: pandas.Index#: See sklearndf.EstimatorDF.feature_names_in_

property feature_names_original_: pandas.Series#: See sklearndf.TransformerDF.feature_names_original_

property feature_names_out_: pandas.Index#: See sklearndf.TransformerDF.feature_names_out_

property is_fitted: bool#

True if this object is fitted, False otherwise.

Return type: bool

property n_features_in_: int#: See sklearndf.EstimatorDF.n_features_in_

property n_outputs_: int#: See sklearndf.EstimatorDF.n_outputs_

property native_estimator: sklearn.base.BaseEstimator#: See sklearndf.EstimatorDF.native_estimator

property output_names_: List[str]#: See sklearndf.SupervisedLearnerDF.output_names_

facet.selection.base.BaseParameterSpace

facet.simulation