Remove buggy and uncessary crossval class (#41)

The functionally is very simple and should, IMO, not be automatic.

Signed-off-by: Matthias Kümmmerer <[email protected]>

CHANGELOG.md

@@ -3,7 +3,7 @@
 * 0.2.22 (dev):
   * Bugfix: The NUSEF dataset scaled some fixations not correctly to image coordinates. Also, we now account for some typos in the
     dataset source data.
-  * Feature: CrossvalMultipleRegularizations, CrossvalGoldMultipleRegularizations and GeneralMixtureKernelDensityEstimator in baseline utils (names might change!)
+  * Feature: CrossvalMultipleRegularizations and GeneralMixtureKernelDensityEstimator in baseline utils (names might change!)
   * Feature: DVAAwareScanpathModel
   * Feature: ShuffledBaselineModel is now much more efficient and able to handle large numbers of stimuli.
     hence, ShuffledSimpleBaselineModel is not necessary anymore and a deprecated alias to ShuffledBaselineModel

pysaliency/baseline_utils.py

@@ -417,16 +417,6 @@ def score(self, log_bandwidth, *args, **kwargs):
         return val
 
 
-class CrossvalGoldMultipleRegularizations(CrossvalMultipleRegularizations):
-    def __init__(self, stimuli, fixations, regularization_models, n_jobs=None, verbose=False):
-        if fixations.subject_count > 1:
-            crossvalidation_factory = ScikitLearnImageSubjectCrossValidationGenerator
-        else:
-            crossvalidation_factory = ScikitLearnWithinImageCrossValidationGenerator
-
-        super().__init__(stimuli, fixations, regularization_models, crossvalidation_factory=crossvalidation_factory, n_jobs=n_jobs, verbose=verbose)
-
-
 # baseline models
 
 class GoldModel(Model):