code: def test_prediction_proba(self):
      y_test_predicted = self.clf.predict_proba(self.X_test)
      assert y_test_predicted.min() >= 0
      assert y_test_predicted.max() <= 1
  
      # check performance
      assert (
          roc_auc_score(self.y_test, y_test_predicted[:, 1])
          >= self.roc_floor
      )
  
      # check shape of integrity
      n_classes = len(np.unique(self.y_train))
      assert_equal(
          y_test_predicted.shape, (self.X_test.shape[0], n_classes)
      )
  
      # check probability sum is 1
      y_test_predicted_sum = np.sum(y_test_predicted, axis=1)
      assert_allclose(
          np.ones(
              [
                  self.X_test.shape[0],
              ]
          ),
          y_test_predicted_sum,
      )
  
imports: import os
  import sys
  import unittest
  import numpy
  from sklearn.model_selection import train_test_split
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.linear_model import LogisticRegression
  from sklearn.ensemble import GradientBoostingClassifier
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.datasets import load_breast_cancer
  from numpy.testing import assert_allclose
  from numpy.testing import assert_equal
  from numpy.testing import assert_raises
  from sklearn.metrics import roc_auc_score
  from sklearn.metrics import accuracy_score
  from combo.models.classifier_comb import SimpleClassifierAggregator
  from combo.utils.data import evaluate_print
label_desc: Use image node_id 5 for calling the TestMedian obj's underlying member method code with example usage: obj.test_prediction_proba() without return types