code: def test_default_configuration_predict_proba_individual(self):
      # Leave this additional test here
      for i in range(2):
          predictions, targets = _test_classifier_predict_proba(
              LibSVM_SVC,
              sparse=True,
              dataset="digits",
              train_size_maximum=500,
          )
          self.assertAlmostEqual(
              5.273502056835706,
              sklearn.metrics.log_loss(targets, predictions),
          )
  
      for i in range(2):
          predictions, targets = _test_classifier_predict_proba(
              LibSVM_SVC, sparse=True, dataset="iris"
          )
          self.assertAlmostEqual(
              0.8408320837510618,
              sklearn.metrics.log_loss(targets, predictions),
          )
  
      # 2 class
      for i in range(2):
          X_train, Y_train, X_test, Y_test = get_dataset(dataset="iris")
          remove_training_data = Y_train == 2
          remove_test_data = Y_test == 2
          X_train = X_train[~remove_training_data]
          Y_train = Y_train[~remove_training_data]
          X_test = X_test[~remove_test_data]
          Y_test = Y_test[~remove_test_data]
          ss = sklearn.preprocessing.StandardScaler()
          X_train = ss.fit_transform(X_train)
          configuration_space = (
              LibSVM_SVC.get_hyperparameter_search_space()
          )
          default = configuration_space.get_default_configuration()
  
          cls = LibSVM_SVC(
              random_state=1,
              **{
                  hp_name: default[hp_name]
                  for hp_name in default
                  if default[hp_name] is not None
              },
          )
  
          cls = cls.fit(X_train, Y_train)
          prediction = cls.predict_proba(X_test)
          self.assertAlmostEqual(
              sklearn.metrics.log_loss(Y_test, prediction),
              0.6927962762794081,
              places=4,
          )
  
file_path: auto-sklearn/test/test_pipeline/components/classification/test_libsvm_svc.py
imports: import sklearn.metrics
  import sklearn.svm
  from autosklearn.pipeline.components.classification.libsvm_svc import LibSVM_SVC
  from autosklearn.pipeline.util import _test_classifier_predict_proba, get_dataset
  from .test_base import BaseClassificationComponentTest
label_desc: Use image node_id 1 for calling the LibSVM_SVCComponentTest obj's underlying member method code with example usage: obj.test_default_configuration_predict_proba_individual() without return types