old_contents: """ Test Binary Relevance Model
  """
  import unittest
  
  import numpy as np
  from numpy.testing import assert_array_equal
  from sklearn import datasets
  from sklearn.model_selection import train_test_split
  import sklearn.linear_model
  
  from libact.base.dataset import Dataset
  from libact.models import LogisticRegression
  from libact.models.multilabel import BinaryRelevance
  
  
  class BinaryRelevanceTestCase(unittest.TestCase):
  
      def setUp(self):
          X, Y = datasets.make_multilabel_classification(random_state=1126)
          self.X_train, self.X_test, self.Y_train, self.Y_test = \
              train_test_split(X, Y, test_size=0.3, random_state=1126)
  
      def test_binary_relevance_lr(self):
          br = BinaryRelevance(base_clf=LogisticRegression(random_state=1126))
          br.train(Dataset(self.X_train, self.Y_train))
  
          br_pred_train = br.predict(self.X_train).astype(int)
          br_pred_test = br.predict(self.X_test).astype(int)
  
          for i in range(np.shape(self.Y_train)[1]):
              clf = sklearn.linear_model.LogisticRegression(random_state=1126)
              clf.fit(self.X_train, self.Y_train[:, i])
  
              assert_array_equal(clf.predict(self.X_train).astype(int),
                                 br_pred_train[:, i])
              assert_array_equal(clf.predict(self.X_test).astype(int),
                                 br_pred_test[:, i])
  
          self.assertEqual(
              np.mean(np.abs(self.Y_test - br_pred_test).mean(axis=1)),
              br.score(Dataset(self.X_test, self.Y_test), 'hamming'))
  
          self.assertRaises(NotImplementedError,
                  lambda: br.score(Dataset(self.X_test, self.Y_test),
                                   criterion='not_exist'))
  
  if __name__ == '__main__':
      unittest.main()
  
new_contents: """ Test Binary Relevance Model
  """
  import unittest
  
  import numpy as np
  from numpy.testing import assert_array_equal
  from sklearn import datasets
  from sklearn.model_selection import train_test_split
  import sklearn.linear_model
  
  from libact.base.dataset import Dataset
  from libact.models import LogisticRegression
  from libact.models.multilabel import BinaryRelevance
  
  
  class BinaryRelevanceTestCase(unittest.TestCase):
  
      def setUp(self):
          X, Y = datasets.make_multilabel_classification(random_state=1126)
          self.X_train, self.X_test, self.Y_train, self.Y_test = \
              train_test_split(X, Y, test_size=0.3, random_state=1126)
  
      def test_binary_relevance_lr(self):
          br = BinaryRelevance(base_clf=LogisticRegression(random_state=1126))
          br.train(Dataset(self.X_train, self.Y_train))
  
          br_pred_train = br.predict(self.X_train).astype(int)
          br_pred_test = br.predict(self.X_test).astype(int)
  
          br_pred_proba_train = br.predict_proba(self.X_train).astype(float)
          br_pred_proba_test = br.predict_proba(self.X_test).astype(float)
  
          for i in range(np.shape(self.Y_train)[1]):
              clf = sklearn.linear_model.LogisticRegression(random_state=1126)
              clf.fit(self.X_train, self.Y_train[:, i])
  
              assert_array_equal(clf.predict(self.X_train).astype(int),
                                 br_pred_train[:, i])
              assert_array_equal(clf.predict(self.X_test).astype(int),
                                 br_pred_test[:, i])
  
              assert_array_equal(clf.predict_proba(self.X_train)[:, 1].astype(float),
                                 br_pred_proba_train[:, i])
              assert_array_equal(clf.predict_proba(self.X_test)[:, 1].astype(float),
                                 br_pred_proba_test[:, i])
  
          self.assertEqual(
              np.mean(np.abs(self.Y_test - br_pred_test).mean(axis=1)),
              br.score(Dataset(self.X_test, self.Y_test), 'hamming'))
  
          self.assertRaises(NotImplementedError,
                  lambda: br.score(Dataset(self.X_test, self.Y_test),
                                   criterion='not_exist'))
  
  if __name__ == '__main__':
      unittest.main()
  
subject: add predict_proba test for binary_relevance