Python absl.testing.parameterized.TestCase() Examples

def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
    tf.test.TestCase.__init__(self, methodName)
    dnn_testing_utils_v1.BaseDNNRegressorEvaluateTest.__init__(
        self, _dnn_regressor_fn, fc_impl=feature_column) 

def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
    tf.test.TestCase.__init__(self, methodName)
    dnn_testing_utils.BaseDNNModelFnTest.__init__(
        self, dnn.dnn_model_fn_v2, fc_impl=feature_column_v2) 

def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
    tf.test.TestCase.__init__(self, methodName)
    dnn_testing_utils.BaseDNNLogitFnTest.__init__(
        self, dnn.dnn_logit_fn_builder_v2, fc_impl=feature_column_v2) 

def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
    tf.test.TestCase.__init__(self, methodName)
    dnn_testing_utils.BaseDNNWarmStartingTest.__init__(
        self, _dnn_classifier_fn, _dnn_regressor_fn, fc_impl=feature_column_v2) 

def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
    tf.test.TestCase.__init__(self, methodName)
    dnn_testing_utils.BaseDNNClassifierEvaluateTest.__init__(
        self, _dnn_classifier_fn, fc_impl=feature_column_v2) 

def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
    tf.test.TestCase.__init__(self, methodName)
    dnn_testing_utils.BaseDNNClassifierPredictTest.__init__(
        self, _dnn_classifier_fn, fc_impl=feature_column_v2) 

def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
    tf.test.TestCase.__init__(self, methodName)
    dnn_testing_utils.BaseDNNRegressorEvaluateTest.__init__(
        self, _dnn_regressor_fn, fc_impl=feature_column_v2) 

def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
    tf.test.TestCase.__init__(self, methodName)
    dnn_testing_utils.BaseDNNRegressorPredictTest.__init__(
        self, _dnn_regressor_fn, fc_impl=feature_column_v2) 

def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
    tf.test.TestCase.__init__(self, methodName)
    dnn_testing_utils.BaseDNNRegressorTrainTest.__init__(
        self, _dnn_regressor_fn, fc_impl=feature_column_v2) 

def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
    tf.test.TestCase.__init__(self, methodName)
    dnn_testing_utils_v1.BaseDNNModelFnTest.__init__(
        self, dnn._dnn_model_fn, fc_impl=feature_column) 

def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
    tf.test.TestCase.__init__(self, methodName)
    dnn_testing_utils_v1.BaseDNNLogitFnTest.__init__(
        self, dnn.dnn_logit_fn_builder, fc_impl=feature_column) 

def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
    tf.test.TestCase.__init__(self, methodName)
    dnn_testing_utils_v1.BaseDNNClassifierEvaluateTest.__init__(
        self, _dnn_classifier_fn, fc_impl=feature_column) 

def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
    tf.test.TestCase.__init__(self, methodName)
    dnn_testing_utils_v1.BaseDNNClassifierPredictTest.__init__(
        self, _dnn_classifier_fn, fc_impl=feature_column) 

def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
    tf.test.TestCase.__init__(self, methodName)
    dnn_testing_utils_v1.BaseDNNClassifierTrainTest.__init__(
        self, _dnn_classifier_fn, fc_impl=feature_column) 

def run_lagrange_multiplier_test(global_objective,
                                 objective_kwargs,
                                 data_builder,
                                 test_object):
  """Runs a test for the Lagrange multiplier update of `global_objective`.

  The test checks that the constraint for `global_objective` is satisfied on
  the first label of the data produced by `data_builder` but not the second.

  Args:
    global_objective: One of the global objectives.
    objective_kwargs: A dictionary of keyword arguments to pass to
      `global_objective`. Must contain an entry for the constraint argument
      of `global_objective`, e.g. 'target_rate' or 'target_precision'.
    data_builder: A function  which returns tensors corresponding to labels,
      logits, and label priors.
    test_object: An instance of tf.test.TestCase.
  """
  # Construct global objective kwargs from a copy of `objective_kwargs`.
  kwargs = dict(objective_kwargs)
  targets, logits, priors = data_builder()
  kwargs['labels'] = targets
  kwargs['logits'] = logits
  kwargs['label_priors'] = priors

  loss, output_dict = global_objective(**kwargs)
  lambdas = tf.squeeze(output_dict['lambdas'])
  opt = tf.train.GradientDescentOptimizer(learning_rate=0.1)
  update_op = opt.minimize(loss, var_list=[output_dict['lambdas']])

  with test_object.test_session() as session:
    tf.global_variables_initializer().run()
    lambdas_before = session.run(lambdas)
    session.run(update_op)
    lambdas_after = session.run(lambdas)
    test_object.assertLess(lambdas_after[0], lambdas_before[0])
    test_object.assertGreater(lambdas_after[1], lambdas_before[1]) 

def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
    tf.test.TestCase.__init__(self, methodName)
    dnn_testing_utils_v1.BaseDNNRegressorPredictTest.__init__(
        self, _dnn_regressor_fn, fc_impl=feature_column) 

def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
    tf.test.TestCase.__init__(self, methodName)
    dnn_testing_utils_v1.BaseDNNModelFnTest.__init__(
        self, dnn._dnn_model_fn, fc_impl=feature_column_v2) 

def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
    tf.test.TestCase.__init__(self, methodName)
    dnn_testing_utils_v1.BaseDNNLogitFnTest.__init__(
        self, dnn.dnn_logit_fn_builder, fc_impl=feature_column_v2) 

def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
    tf.test.TestCase.__init__(self, methodName)
    dnn_testing_utils_v1.BaseDNNWarmStartingTest.__init__(
        self, _dnn_classifier_fn, _dnn_regressor_fn, fc_impl=feature_column_v2) 

def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
    tf.test.TestCase.__init__(self, methodName)
    dnn_testing_utils_v1.BaseDNNClassifierEvaluateTest.__init__(
        self, _dnn_classifier_fn, fc_impl=feature_column_v2) 

def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
    tf.test.TestCase.__init__(self, methodName)
    dnn_testing_utils_v1.BaseDNNClassifierPredictTest.__init__(
        self, _dnn_classifier_fn, fc_impl=feature_column_v2) 

def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
    tf.test.TestCase.__init__(self, methodName)
    dnn_testing_utils_v1.BaseDNNRegressorEvaluateTest.__init__(
        self, _dnn_regressor_fn, fc_impl=feature_column_v2) 

def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
    tf.test.TestCase.__init__(self, methodName)
    dnn_testing_utils_v1.BaseDNNRegressorPredictTest.__init__(
        self, _dnn_regressor_fn, fc_impl=feature_column_v2) 

def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
    tf.test.TestCase.__init__(self, methodName)
    dnn_testing_utils_v1.BaseDNNRegressorTrainTest.__init__(
        self, _dnn_regressor_fn, fc_impl=feature_column_v2) 

def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
    tf.test.TestCase.__init__(self, methodName)
    BaseRNNClassificationIntegrationTest.__init__(self, _rnn_classifier_fn) 

def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
    tf.test.TestCase.__init__(self, methodName)
    BaseRNNClassificationIntegrationTest.__init__(self,
                                                  _rnn_classifier_dropout_fn) 

def run_lagrange_multiplier_test(global_objective,
                                 objective_kwargs,
                                 data_builder,
                                 test_object):
  """Runs a test for the Lagrange multiplier update of `global_objective`.

  The test checks that the constraint for `global_objective` is satisfied on
  the first label of the data produced by `data_builder` but not the second.

  Args:
    global_objective: One of the global objectives.
    objective_kwargs: A dictionary of keyword arguments to pass to
      `global_objective`. Must contain an entry for the constraint argument
      of `global_objective`, e.g. 'target_rate' or 'target_precision'.
    data_builder: A function  which returns tensors corresponding to labels,
      logits, and label priors.
    test_object: An instance of tf.test.TestCase.
  """
  # Construct global objective kwargs from a copy of `objective_kwargs`.
  kwargs = dict(objective_kwargs)
  targets, logits, priors = data_builder()
  kwargs['labels'] = targets
  kwargs['logits'] = logits
  kwargs['label_priors'] = priors

  loss, output_dict = global_objective(**kwargs)
  lambdas = tf.squeeze(output_dict['lambdas'])
  opt = tf.train.GradientDescentOptimizer(learning_rate=0.1)
  update_op = opt.minimize(loss, var_list=[output_dict['lambdas']])

  with test_object.test_session() as session:
    tf.global_variables_initializer().run()
    lambdas_before = session.run(lambdas)
    session.run(update_op)
    lambdas_after = session.run(lambdas)
    test_object.assertLess(lambdas_after[0], lambdas_before[0])
    test_object.assertGreater(lambdas_after[1], lambdas_before[1]) 

def test_duplicate_dict_named_test_fails(self):
    with self.assertRaises(parameterized.DuplicateTestNameError):

      class _(parameterized.TestCase):

        @parameterized.named_parameters(
            {'testcase_name': 'Interesting', 'unused_obj': 0},
            {'testcase_name': 'Interesting', 'unused_obj': 1},
        )
        def test_dict_something(self, unused_obj):
          pass 

def _run_quantiles_combiner_test(test: absltest.TestCase,
                                 q_combiner: quantiles_util.QuantilesCombiner,
                                 batches: List[List[np.ndarray]],
                                 expected_result: np.ndarray):
  """Tests quantiles combiner."""
  summaries = [q_combiner.add_input(q_combiner.create_accumulator(),
                                    batch) for batch in batches]
  result = q_combiner.extract_output(q_combiner.merge_accumulators(summaries))
  test.assertEqual(result.dtype, expected_result.dtype)
  test.assertEqual(result.size, expected_result.size)
  for i in range(expected_result.size):
    test.assertAlmostEqual(result[i], expected_result[i]) 

def _assert_buckets_almost_equal(test: parameterized.TestCase,
                                 a: List[Tuple[float, float, float]],
                                 b: List[Tuple[float, float, float]]):
  """Check if the histogram buckets are almost equal."""
  test.assertEqual(len(a), len(b))
  for i in range(len(a)):
    test.assertAlmostEqual(a[i].low_value, b[i].low_value)
    test.assertAlmostEqual(a[i].high_value, b[i].high_value)
    test.assertAlmostEqual(a[i].sample_count, b[i].sample_count)