snowflake.ml.modeling.feature_selection.SequentialFeatureSelector¶

class snowflake.ml.modeling.feature_selection.SequentialFeatureSelector(*, estimator, n_features_to_select='auto', tol=None, direction='forward', scoring=None, cv=5, n_jobs=None, input_cols: Optional[Union[str, Iterable[str]]] = None, output_cols: Optional[Union[str, Iterable[str]]] = None, label_cols: Optional[Union[str, Iterable[str]]] = None, passthrough_cols: Optional[Union[str, Iterable[str]]] = None, drop_input_cols: Optional[bool] = False, sample_weight_col: Optional[str] = None)¶

Bases: BaseTransformer

Transformer that performs Sequential Feature Selection For more details on this class, see sklearn.feature_selection.SequentialFeatureSelector

input_cols: Optional[Union[str, List[str]]]

A string or list of strings representing column names that contain features. If this parameter is not specified, all columns in the input DataFrame except the columns specified by label_cols, sample_weight_col, and passthrough_cols parameters are considered input columns. Input columns can also be set after initialization with the set_input_cols method.

label_cols: Optional[Union[str, List[str]]]

This parameter is optional and will be ignored during fit. It is present here for API consistency by convention.

output_cols: Optional[Union[str, List[str]]]

A string or list of strings representing column names that will store the output of predict and transform operations. The length of output_cols must match the expected number of output columns from the specific predictor or transformer class used. If you omit this parameter, output column names are derived by adding an OUTPUT_ prefix to the label column names for supervised estimators, or OUTPUT_<IDX>for unsupervised estimators. These inferred output column names work for predictors, but output_cols must be set explicitly for transformers. In general, explicitly specifying output column names is clearer, especially if you don’t specify the input column names. To transform in place, pass the same names for input_cols and output_cols. be set explicitly for transformers. Output columns can also be set after initialization with the set_output_cols method.

sample_weight_col: Optional[str]

A string representing the column name containing the sample weights. This argument is only required when working with weighted datasets. Sample weight column can also be set after initialization with the set_sample_weight_col method.

passthrough_cols: Optional[Union[str, List[str]]]

A string or a list of strings indicating column names to be excluded from any operations (such as train, transform, or inference). These specified column(s) will remain untouched throughout the process. This option is helpful in scenarios requiring automatic input_cols inference, but need to avoid using specific columns, like index columns, during training or inference. Passthrough columns can also be set after initialization with the set_passthrough_cols method.

drop_input_cols: Optional[bool], default=False

If set, the response of predict(), transform() methods will not contain input columns.

estimator: estimator instance

An unfitted estimator.

n_features_to_select: “auto”, int or float, default=”auto”

If “auto”, the behaviour depends on the tol parameter:

• if tol is not None, then features are selected while the score change does not exceed tol.

• otherwise, half of the features are selected.

If integer, the parameter is the absolute number of features to select. If float between 0 and 1, it is the fraction of features to select.

tol: float, default=None

If the score is not incremented by at least tol between two consecutive feature additions or removals, stop adding or removing.

tol can be negative when removing features using direction=”backward”. It can be useful to reduce the number of features at the cost of a small decrease in the score.

tol is enabled only when n_features_to_select is “auto”.

direction: {‘forward’, ‘backward’}, default=’forward’

Whether to perform forward selection or backward selection.

scoring: str or callable, default=None

A single str (see scoring_parameter) or a callable (see scoring) to evaluate the predictions on the test set.

NOTE that when using a custom scorer, it should return a single value.

If None, the estimator’s score method is used.

cv: int, cross-validation generator or an iterable, default=None

Determines the cross-validation splitting strategy. Possible inputs for cv are:

• None, to use the default 5-fold cross validation,

• integer, to specify the number of folds in a (Stratified)KFold,

• CV splitter,

• An iterable yielding (train, test) splits as arrays of indices.

For integer/None inputs, if the estimator is a classifier and y is either binary or multiclass, StratifiedKFold is used. In all other cases, KFold is used. These splitters are instantiated with shuffle=False so the splits will be the same across calls.

Refer User Guide for the various cross-validation strategies that can be used here.

n_jobs: int, default=None

Number of jobs to run in parallel. When evaluating a new feature to add or remove, the cross-validation procedure is parallel over the folds. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

Methods

fit(dataset)	Learn the features to select from X For more details on this function, see sklearn.feature_selection.SequentialFeatureSelector.fit
get_input_cols()	Input columns getter.
get_label_cols()	Label column getter.
get_output_cols()	Output columns getter.
get_params([deep])	Get parameters for this transformer.
get_passthrough_cols()	Passthrough columns getter.
get_sample_weight_col()	Sample weight column getter.
get_sklearn_args([default_sklearn_obj, ...])	Get sklearn keyword arguments.
set_drop_input_cols([drop_input_cols])
set_input_cols(input_cols)	Input columns setter.
set_label_cols(label_cols)	Label column setter.
set_output_cols(output_cols)	Output columns setter.
set_params(**params)	Set the parameters of this transformer.
set_passthrough_cols(passthrough_cols)	Passthrough columns setter.
set_sample_weight_col(sample_weight_col)	Sample weight column setter.
to_sklearn()	Get sklearn.feature_selection.SequentialFeatureSelector object.
transform(dataset)	Reduce X to the selected features For more details on this function, see sklearn.feature_selection.SequentialFeatureSelector.transform

Attributes

model_signatures

Returns model signature of current class.