Python absl.testing.parameterized.named_parameters() Examples
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Example #1
Source File: parameterized.py From abseil-py with Apache License 2.0 | 6 votes |
def named_parameters(*testcases): """A decorator for creating parameterized tests. See the module docstring for a usage example. For every parameter tuple passed, the first element of the tuple should be a string and will be appended to the name of the test method. Each parameter dict passed must have a value for the key "testcase_name", the string representation of that value will be appended to the name of the test method. Args: *testcases: Parameters for the decorated method, either a single iterable, or a list of tuples or dicts. Raises: NoTestsError: Raised when the decorator generates no tests. Returns: A test generator to be handled by TestGeneratorMetaclass. """ return _parameter_decorator(_NAMED, testcases)
Example #2
Source File: parameterized_test.py From abseil-py with Apache License 2.0 | 5 votes |
def test_named_test_dict_with_no_name_fails(self): with self.assertRaises(RuntimeError): class _(parameterized.TestCase): @parameterized.named_parameters( {'unused_obj': 0}, ) def test_something(self, unused_obj): pass
Example #3
Source File: actor_policy_test.py From agents with Apache License 2.0 | 5 votes |
def test_cases(): return parameterized.named_parameters({ 'testcase_name': 'SimpleNet', 'network_ctor': DummyActionNet, }, { 'testcase_name': 'DistributionNet', 'network_ctor': DummyActionDistributionNet, })
Example #4
Source File: mixture_agent_test.py From agents with Apache License 2.0 | 5 votes |
def test_cases(): return parameterized.named_parameters( { 'testcase_name': '_batch1_contextdim10_numagents2', 'batch_size': 1, 'context_dim': 10, 'num_agents': 2, }, { 'testcase_name': '_batch4_contextdim5_numagents10', 'batch_size': 4, 'context_dim': 5, 'num_agents': 10, })
Example #5
Source File: neural_linucb_agent_test.py From agents with Apache License 2.0 | 5 votes |
def test_cases(): return parameterized.named_parameters( { 'testcase_name': '_batch1_contextdim10', 'batch_size': 1, 'context_dim': 10, }, { 'testcase_name': '_batch4_contextdim5', 'batch_size': 4, 'context_dim': 5, })
Example #6
Source File: utils_test.py From agents with Apache License 2.0 | 5 votes |
def test_cases(): return parameterized.named_parameters( { 'testcase_name': '_batch1_contextdim10', 'batch_size': 1, 'context_dim': 10, }, { 'testcase_name': '_batch4_contextdim5', 'batch_size': 4, 'context_dim': 5, })
Example #7
Source File: linear_bandit_policy_test.py From agents with Apache License 2.0 | 5 votes |
def test_cases_with_decomposition(): return parameterized.named_parameters( { 'testcase_name': 'batch1', 'batch_size': 1, 'use_decomposition': False }, { 'testcase_name': 'batch4', 'batch_size': 4, 'use_decomposition': True })
Example #8
Source File: linear_bandit_policy_test.py From agents with Apache License 2.0 | 5 votes |
def test_cases(): return parameterized.named_parameters( { 'testcase_name': 'batch1UCB', 'batch_size': 1, 'exploration_strategy': linear_policy.ExplorationStrategy.optimistic, }, { 'testcase_name': 'batch4UCB', 'batch_size': 4, 'exploration_strategy': linear_policy.ExplorationStrategy.optimistic, })
Example #9
Source File: neural_linucb_policy_test.py From agents with Apache License 2.0 | 5 votes |
def test_cases(): return parameterized.named_parameters( { 'testcase_name': '_batch1_numtrainsteps0', 'batch_size': 1, 'actions_from_reward_layer': False, }, { 'testcase_name': '_batch4_numtrainsteps10', 'batch_size': 4, 'actions_from_reward_layer': True, })
Example #10
Source File: linalg_test.py From agents with Apache License 2.0 | 5 votes |
def cg_test_cases(): return parameterized.named_parameters( { 'testcase_name': '_n_1', 'n': 1, 'rhs': 1, }, { 'testcase_name': '_n_10', 'n': 10, 'rhs': 1, }, { 'testcase_name': '_n_100', 'n': 100, 'rhs': 5, })
Example #11
Source File: linalg_test.py From agents with Apache License 2.0 | 5 votes |
def test_cases(): return parameterized.named_parameters( { 'testcase_name': '_batch1_contextdim10', 'batch_size': 1, 'context_dim': 10, }, { 'testcase_name': '_batch4_contextdim5', 'batch_size': 4, 'context_dim': 5, })
Example #12
Source File: drifting_linear_environment_test.py From agents with Apache License 2.0 | 5 votes |
def test_cases(): return parameterized.named_parameters( dict(testcase_name='_observation_[5]_action_[3]_batch_1', observation_shape=[5], action_shape=[3], batch_size=1, seed=12345), dict(testcase_name='_observation_[3]_action_[5]_batch_2', observation_shape=[3], action_shape=[5], batch_size=2, seed=98765), )
Example #13
Source File: parameterized_test.py From abseil-py with Apache License 2.0 | 5 votes |
def test_named_test_with_no_name_fails(self): with self.assertRaises(RuntimeError): class _(parameterized.TestCase): @parameterized.named_parameters( (0,), ) def test_something(self, unused_obj): pass
Example #14
Source File: parameterized_test.py From abseil-py with Apache License 2.0 | 5 votes |
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
Example #15
Source File: parameterized_test.py From abseil-py with Apache License 2.0 | 5 votes |
def test_duplicate_named_test_fails(self): with self.assertRaises(parameterized.DuplicateTestNameError): class _(parameterized.TestCase): @parameterized.named_parameters( ('Interesting', 0), ('Interesting', 1), ) def test_something(self, unused_obj): pass
Example #16
Source File: parameterized_test.py From abseil-py with Apache License 2.0 | 5 votes |
def dict_decorator(key, value): """Sample implementation of a chained decorator. Sets a single field in a dict on a test with a dict parameter. Uses the exposed '_ParameterizedTestIter.testcases' field to modify arguments from previous decorators to allow decorator chains. Args: key: key to map to value: value to set Returns: The test decorator """ def decorator(test_method): # If decorating result of another dict_decorator if isinstance(test_method, abc.Iterable): actual_tests = [] for old_test in test_method.testcases: # each test is a ('test_suffix', dict) tuple new_dict = old_test[1].copy() new_dict[key] = value test_suffix = '%s_%s_%s' % (old_test[0], key, value) actual_tests.append((test_suffix, new_dict)) test_method.testcases = actual_tests return test_method else: test_suffix = ('_%s_%s') % (key, value) tests_to_make = ((test_suffix, {key: value}),) # 'test_method' here is the original test method return parameterized.named_parameters(*tests_to_make)(test_method) return decorator
Example #17
Source File: parameterized.py From abseil-py with Apache License 2.0 | 5 votes |
def __init__(self, test_class_name, new_test_name, original_test_name): super(DuplicateTestNameError, self).__init__( 'Duplicate parameterized test name in {}: generated test name {!r} ' '(generated from {!r}) already exists. Consider using ' 'named_parameters() to give your tests unique names and/or renaming ' 'the conflicting test method.'.format( test_class_name, new_test_name, original_test_name))
Example #18
Source File: test_case.py From transform with Apache License 2.0 | 5 votes |
def cross_with_function_handlers(parameters_list): """Cross named parameters with all function handlers. Takes a list of parameters suitable as an input to @named_parameters, and crosses it with the set of function handlers. A parameterized test function that uses this should have a parameter named `function_handler`. Args: parameters_list: A list of dicts. Returns: A list of dicts. """ return cross_named_parameters(parameters_list, FUNCTION_HANDLERS)
Example #19
Source File: test_case.py From transform with Apache License 2.0 | 5 votes |
def cross_named_parameters(*args): """Cross a list of lists of dicts suitable for @named_parameters. Takes a list of lists, where each list is suitable as an input to @named_parameters, and crosses them, forming a new name for each crossed test case. Args: *args: A list of lists of dicts. Returns: A list of dicts. """ def _cross_test_cases(parameters_list): """Cross a list of test case parameters.""" crossed_parameters = parameters_list[0].copy() for current_parameters in parameters_list[1:]: for name, value in current_parameters.items(): if name == 'testcase_name': crossed_parameters[name] = '{}_{}'.format( crossed_parameters[name], value) else: assert name not in crossed_parameters, name crossed_parameters[name] = value return crossed_parameters return list(map(_cross_test_cases, itertools.product(*args)))
Example #20
Source File: lax_numpy_test.py From trax with Apache License 2.0 | 5 votes |
def named_parameters(ls): """A version that allows an empty param list.""" def noop(_): def wrapper(self, *args, **kwargs): self.skipTest("Empty parameter list") return wrapper if isinstance(ls, (list, tuple)) and not ls: return noop if isinstance(ls, itertools.chain): try: first = next(ls) except StopIteration: return noop else: ls = itertools.chain([first], ls) return parameterized.named_parameters(ls) # TODO(wangpeng): Enable all disabled tests in this class
Example #21
Source File: _utils.py From tmppy with Apache License 2.0 | 5 votes |
def multiple_named_parameters(*param_lists): result = param_lists[0] for param_list in param_lists[1:]: result = [(name1 + ', ' + name2, *args1, *args2) for name1, *args1 in result for name2, *args2 in param_list] return parameterized.named_parameters(*result)
Example #22
Source File: test_trainer_utils.py From lingvo with Apache License 2.0 | 4 votes |
def MakeModelValidatorTestCase(model_classes): """Returns a TestCase that validates the training for `model_classes`. Usage in a test: MakeModelValidatorTestCase([path.to.module.MyRegisteredModel1, path.to.module.MyRegisteredModel2]) Args: model_classes: A list of model classes. """ class _ModelValidator(parameterized.TestCase, test_utils.TestCase): """TestCase template for validating training and decoding models.""" def TrainerBuilds(self, model): tmpdir = os.path.join(FLAGS.test_tmpdir, model.__name__) # Trainer should probably create these directories in the future. tf.io.gfile.makedirs(os.path.join(tmpdir, 'train')) model_params = model() cfg = model_params.Model() cfg.input = model_params.GetDatasetParams('Train') cfg.cluster.mode = 'sync' cfg.cluster.job = 'trainer_client' _ = trainer_lib.Trainer(cfg, '', tmpdir, tf_master='') def DecoderBuilds(self, model): tmpdir = os.path.join(FLAGS.test_tmpdir, model.__name__) tf.io.gfile.makedirs(tmpdir) model_params = model() cfg = model_params.Model() cfg.input = model_params.GetDatasetParams('Train') cfg.cluster.mode = 'sync' cfg.cluster.job = 'decoder' cfg.cluster.task = 0 cfg.cluster.decoder.replicas = 1 _ = trainer_lib.Decoder('train', cfg, '', tmpdir, tf_master='') # Each of these take about 10-20 seconds to run, to build the model and # execute the forward and backward pass building. @parameterized.named_parameters(_ModelTuples(model_classes)) def testTrain(self, model): self.TrainerBuilds(model) @parameterized.named_parameters(_ModelTuples(model_classes)) def testDecoder(self, model): self.DecoderBuilds(model) return _ModelValidator
Example #23
Source File: linear_bandit_agent_test.py From agents with Apache License 2.0 | 4 votes |
def test_cases(): return parameterized.named_parameters( { 'testcase_name': '_batch1_contextdim10_float32', 'batch_size': 1, 'context_dim': 10, 'exploration_policy': linear_agent.ExplorationPolicy.linear_ucb_policy, 'dtype': tf.float32, }, { 'testcase_name': '_batch4_contextdim5_float64_UCB', 'batch_size': 4, 'context_dim': 5, 'exploration_policy': linear_agent.ExplorationPolicy.linear_ucb_policy, 'dtype': tf.float64, }, { 'testcase_name': '_batch4_contextdim5_float64_TS', 'batch_size': 4, 'context_dim': 5, 'exploration_policy': linear_agent.ExplorationPolicy.linear_thompson_sampling_policy, 'dtype': tf.float64, }, { 'testcase_name': '_batch4_contextdim5_float64_decomp', 'batch_size': 4, 'context_dim': 5, 'exploration_policy': linear_agent.ExplorationPolicy.linear_ucb_policy, 'dtype': tf.float64, 'use_eigendecomp': True, })