cleverly.learners.CorrelationScreener#

class cleverly.learners.CorrelationScreener(threshold: float = 0.1, min_retain: int | None = None)[source]#

scikit-learn selector wrapping screen_by_correlation().

Usable inside a Pipeline, which is how the treatment-model screen is applied:

>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.pipeline import make_pipeline
>>> model = make_pipeline(CorrelationScreener(), LogisticRegression())
Parameters:
thresholdfloat

Absolute correlation a covariate must reach to be retained.

min_retainint or None

Smallest number of covariates to keep, whatever the threshold rejects. None keeps only what clears it.

Methods#

CorrelationScreener.fit(X: ndarray[tuple[Any, ...], dtype[float64]], y: ndarray[tuple[Any, ...], dtype[float64]], sample_weight: ndarray[tuple[Any, ...], dtype[float64]] | None = None) → CorrelationScreener[source]#

Fit this object to training data.

Parameters:
Xndarray

(n, p) covariates.

yndarray

(n,) target the correlation is taken against.

sample_weightndarray or None

(n,) weights, or None for equal weights.

Returns:
CorrelationScreener

This object, fitted, as scikit-learn expects.

CorrelationScreener.set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → CorrelationScreener#

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in fit.

Returns:
selfobject

The updated object.