FeatureFilter#

class scikitplot.feature_selection.FeatureFilter(policy=None, *, drop=('constant', 'identifier_like'), keep_roles=('feature',), keep=(), on_mismatch='raise')[source]#

Drop features whose assessments are true, and columns with non-feature roles.

Parameters:

policyFeaturePolicy, mapping, path or None, default=None: Profiling policy.
dropsequence of str, default=(“constant”, “identifier_like”): A feature is dropped when any of these assessments is "true". unknown never drops.
keep_rolessequence of str, default=(“feature”,): Roles that survive; every other role (target, identifier, drop …) is removed so it cannot leak into the model.
keepsequence of str, default=(): Columns kept regardless of their assessments (not of their role).
on_mismatch{“raise”, “warn”, “ignore”}, default=”raise”: As in FeatureProfiler.

Attributes:

schema_FeatureSchema: Fitted schema.
support_ndarray of bool: Kept columns.
report_pandas.DataFrame: One row per column: kept and the reasons for dropping.
feature_names_in_, n_features_in_: As in scikit-learn.

Parameters:

policy (Any)
drop (Sequence[str])
keep_roles (Sequence[str])
keep (Sequence[str])
on_mismatch (str)

Examples

>>> import pandas as pd
>>> from scikitplot.feature_selection import FeatureFilter
>>> X = pd.DataFrame(
...     {"id": [f"r{i}" for i in range(30)], "c": [1] * 30, "x": list(range(30))}
... )
>>> f = FeatureFilter().fit(X)
>>> f.get_feature_names_out().tolist()
['x']
>>> f.report_.loc["id", "reasons"]
('identifier_like',)

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

Profile X and decide which columns to keep.

Parameters:

Xpandas.DataFrame: Training data.
yignored: Present for API consistency.

Returns:

selfFeatureFilter: The fitted filter.

Raises:

ValueError: For unknown assessment or role names, keep columns absent from X, or when every column would be dropped.

Parameters:

X (DataFrame)
y (Any)

Return type:

FeatureFilter

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

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

Xarray-like of shape (n_samples, n_features): Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None: Target values (None for unsupervised transformations).
**fit_paramsdict: Additional fit parameters. Pass only if the estimator accepts additional params in its fit method.

Returns:

X_newndarray array of shape (n_samples, n_features_new): Transformed array.

get_feature_lineage(input_features=None)[source]#

Return the exact subset lineage of this step.

Parameters:

input_featuresarray-like of str or None, default=None: Must equal feature_names_in_ when given.

Returns:

FeatureMapping: The mapping.

Parameters:

input_features (Any)

Return type:

FeatureMapping

get_feature_names_out(input_features=None)[source]#

Return the kept column names.

Parameters:

input_featuresarray-like of str or None, default=None: Must equal feature_names_in_ when given.

Returns:

ndarray of str: Kept names.

Parameters:

input_features (Any)

Return type:

ndarray

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

get_support(indices=False)[source]#

Return the mask (or indices) of kept input columns.

Parameters:

indicesbool, default=False: Return integer positions instead of a mask.

Returns:

ndarray: Boolean mask or integer positions.

Parameters:

indices (bool)

Return type:

ndarray

set_output(*, transform=None)[source]#

Set output container.

Refer to the user guide for more details and Introducing the set_output API for an example on how to use the API.

Parameters:

transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

"default": Default output format of a transformer
"pandas": DataFrame output
"polars": Polars output
None: Transform configuration is unchanged

Added in version 1.4: "polars" option was added.

Returns:

selfestimator instance: Estimator instance.

set_params(**params)[source]#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters: