code: def test_binary_classification_predict_proba_on_Predictor_instance():
      np.random.seed(0)
  
      (
          df_titanic_train,
          df_titanic_test,
      ) = utils.get_titanic_binary_classification_dataset()
      ml_predictor = utils.train_basic_binary_classifier(
          df_titanic_train
      )
  
      #
      predictions = ml_predictor.predict_proba(df_titanic_test)
      predictions = [pred[1] for pred in predictions]
      test_score = utils.calculate_brier_score_loss(
          df_titanic_test.survived, predictions
      )
      # Make sure our score is good, but not unreasonably good
      print(test_score)
      assert -0.16 < test_score < -0.135
  
imports: import datetime
  import os
  import random
  import sys
  from auto_ml import Predictor
  from auto_ml.utils_models import load_ml_model
  import dill
  import numpy
  from nose.tools import assert_equal, assert_not_equal, with_setup
  from sklearn.metrics import accuracy_score
  from sklearn.model_selection import train_test_split
  import utils_testing
label_desc: Use image node_id 8 for calling a global function with example usage: test_binary_classification_predict_proba_on_Predictor_instance() without return types