ertk.sklearn.utils.GridSearchVal

class ertk.sklearn.utils.GridSearchVal(estimator, param_grid, *, scoring=None, n_jobs=None, refit=False, cv=None, verbose=0, pre_dispatch='2*n_jobs', error_score=nan, return_train_score=False)

Bases: GridSearchCV

Grid-search but using a single validation set. Simple hack of GridSearchCV to avoid retraining on train + val data.

__init__(estimator, param_grid, *, scoring=None, n_jobs=None, refit=False, cv=None, verbose=0, pre_dispatch='2*n_jobs', error_score=nan, return_train_score=False)

Methods

__init__(estimator, param_grid, *[, ...])

fit(X[, y, groups])

Run fit with all sets of parameters.

set_fit_request(*[, groups])

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

Inherited Methods

decision_function(X)

Call decision_function on the estimator with the best found parameters.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

inverse_transform(X)

Call inverse_transform on the estimator with the best found params.

predict(X)

Call predict on the estimator with the best found parameters.

predict_log_proba(X)

Call predict_log_proba on the estimator with the best found parameters.

predict_proba(X)

Call predict_proba on the estimator with the best found parameters.

score(X[, y])

Return the score on the given data, if the estimator has been refit.

score_samples(X)

Call score_samples on the estimator with the best found parameters.

set_params(**params)

Set the parameters of this estimator.

transform(X)

Call transform on the estimator with the best found parameters.

Attributes

classes_

Class labels.

n_features_in_

Number of features seen during fit.
fit(X, y=None, *, groups=None, **fit_params)

Run fit with all sets of parameters.

Parameters:
Xarray-like of shape (n_samples, n_features) or (n_samples, n_samples)

Training vectors, where n_samples is the number of samples and n_features is the number of features. For precomputed kernel or distance matrix, the expected shape of X is (n_samples, n_samples).

yarray-like of shape (n_samples, n_output) or (n_samples,), default=None

Target relative to X for classification or regression; None for unsupervised learning.

**paramsdict of str -> object

Parameters passed to the fit method of the estimator, the scorer, and the CV splitter.

If a fit parameter is an array-like whose length is equal to num_samples then it will be split by cross-validation along with X and y. For example, the sample_weight parameter is split because len(sample_weights) = len(X). However, this behavior does not apply to groups which is passed to the splitter configured via the cv parameter of the constructor. Thus, groups is used to perform the split and determines which samples are assigned to the each side of the a split.

Returns:
selfobject

Instance of fitted estimator.

set_fit_request(*, groups: bool | None | str = '$UNCHANGED$') → GridSearchVal

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.

New in version 1.3.

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

Metadata routing for groups parameter in fit.

Returns:
selfobject

The updated object.