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How to use assert_in_range method in Testify

Best Python code snippet using Testify_python

dnn_benchmark_test.py

Source:dnn_benchmark_test.py

...76    input_fn = test_data.iris_input_logistic_fn77    steps = 40078    metrics = classifier.fit(input_fn=input_fn, steps=steps).evaluate(79        input_fn=input_fn, steps=1)80    estimator_test_utils.assert_in_range(steps, steps + 5, 'global_step',81                                         metrics)82    estimator_test_utils.assert_in_range(0.9, 1.0, 'accuracy', metrics)83    estimator_test_utils.assert_in_range(0.0, 0.3, 'loss', metrics)84    self._report_metrics(metrics)85  def benchmarkLogisticMatrixDataLabels1D(self):86    def _input_fn():87      iris = test_data.prepare_iris_data_for_logistic_regression()88      return {89          'feature': constant_op.constant(90              iris.data, dtype=dtypes.float32)91      }, constant_op.constant(92          iris.target, shape=(100,), dtype=dtypes.int32)93    classifier = dnn.DNNClassifier(94        feature_columns=(feature_column.real_valued_column(95            'feature', dimension=4),),96        hidden_units=(3, 3),97        config=run_config.RunConfig(tf_random_seed=1))98    steps = 100099    metrics = classifier.fit(input_fn=_input_fn, steps=steps).evaluate(100        input_fn=_input_fn, steps=1)101    estimator_test_utils.assert_in_range(steps, steps + 5, 'global_step',102                                         metrics)103    estimator_test_utils.assert_in_range(0.9, 1.0, 'accuracy', metrics)104    self._report_metrics(metrics)105  def benchmarkLogisticNpMatrixData(self):106    classifier = dnn.DNNClassifier(107        feature_columns=(feature_column.real_valued_column(108            '', dimension=4),),109        hidden_units=(3, 3),110        config=run_config.RunConfig(tf_random_seed=1))111    iris = test_data.prepare_iris_data_for_logistic_regression()112    train_x = iris.data113    train_y = iris.target114    steps = 100115    metrics = classifier.fit(x=train_x, y=train_y, steps=steps).evaluate(116        x=train_x, y=train_y, steps=1)117    estimator_test_utils.assert_in_range(steps, steps + 5, 'global_step',118                                         metrics)119    estimator_test_utils.assert_in_range(0.8, 1.0, 'accuracy', metrics)120    self._report_metrics(metrics)121  def benchmarkLogisticTensorData(self):122    def _input_fn(num_epochs=None):123      features = {124          'age':125              input_lib.limit_epochs(126                  constant_op.constant(((.8,), (0.2,), (.1,))),127                  num_epochs=num_epochs),128          'language':129              sparse_tensor.SparseTensor(130                  values=input_lib.limit_epochs(131                      ('en', 'fr', 'zh'), num_epochs=num_epochs),132                  indices=((0, 0), (0, 1), (2, 0)),133                  dense_shape=(3, 2))134      }135      return features, constant_op.constant(136          ((1,), (0,), (0,)), dtype=dtypes.int32)137    lang_column = feature_column.sparse_column_with_hash_bucket(138        'language', hash_bucket_size=20)139    classifier = dnn.DNNClassifier(140        feature_columns=(feature_column.embedding_column(141            lang_column, dimension=1),142                         feature_column.real_valued_column('age')),143        hidden_units=(3, 3),144        config=run_config.RunConfig(tf_random_seed=1))145    steps = 100146    metrics = classifier.fit(input_fn=_input_fn, steps=steps).evaluate(147        input_fn=_input_fn, steps=1)148    estimator_test_utils.assert_in_range(steps, steps + 5, 'global_step',149                                         metrics)150    estimator_test_utils.assert_in_range(0.9, 1.0, 'accuracy', metrics)151    estimator_test_utils.assert_in_range(0.0, 0.3, 'loss', metrics)152    self._report_metrics(metrics)153    self._report_predictions(154        classifier=classifier,155        input_fn=functools.partial(_input_fn, num_epochs=1),156        iters=metrics['global_step'],157        n_examples=3,158        n_classes=2,159        expected_classes=(1, 0, 0))160  def benchmarkLogisticFloatLabel(self):161    def _input_fn(num_epochs=None):162      features = {163          'age':164              input_lib.limit_epochs(165                  constant_op.constant(((50,), (20,), (10,))),166                  num_epochs=num_epochs),167          'language':168              sparse_tensor.SparseTensor(169                  values=input_lib.limit_epochs(170                      ('en', 'fr', 'zh'), num_epochs=num_epochs),171                  indices=((0, 0), (0, 1), (2, 0)),172                  dense_shape=(3, 2))173      }174      return features, constant_op.constant(175          ((0.8,), (0.,), (0.2,)), dtype=dtypes.float32)176    lang_column = feature_column.sparse_column_with_hash_bucket(177        'language', hash_bucket_size=20)178    n_classes = 2179    classifier = dnn.DNNClassifier(180        n_classes=n_classes,181        feature_columns=(feature_column.embedding_column(182            lang_column, dimension=1),183                         feature_column.real_valued_column('age')),184        hidden_units=(3, 3),185        config=run_config.RunConfig(tf_random_seed=1))186    steps = 1000187    metrics = classifier.fit(input_fn=_input_fn, steps=steps).evaluate(188        input_fn=_input_fn, steps=1)189    estimator_test_utils.assert_in_range(steps, steps + 5, 'global_step',190                                         metrics)191    # Prediction probabilities mirror the labels column, which proves that the192    # classifier learns from float input.193    self._report_metrics(metrics)194    self._report_predictions(195        classifier=classifier,196        input_fn=functools.partial(_input_fn, num_epochs=1),197        iters=metrics['global_step'],198        n_examples=3,199        n_classes=n_classes,200        expected_probabilities=((0.2, 0.8), (1., 0.), (0.8, 0.2)),201        expected_classes=(1, 0, 0))202  def benchmarkMultiClassMatrixData(self):203    """Tests multi-class classification using matrix data as input."""204    classifier = dnn.DNNClassifier(205        n_classes=3,206        feature_columns=(feature_column.real_valued_column(207            'feature', dimension=4),),208        hidden_units=(3, 3),209        config=run_config.RunConfig(tf_random_seed=1))210    input_fn = test_data.iris_input_multiclass_fn211    steps = 500212    metrics = classifier.fit(input_fn=input_fn, steps=steps).evaluate(213        input_fn=input_fn, steps=1)214    estimator_test_utils.assert_in_range(steps, steps + 5, 'global_step',215                                         metrics)216    estimator_test_utils.assert_in_range(0.9, 1.0, 'accuracy', metrics)217    estimator_test_utils.assert_in_range(0.0, 0.4, 'loss', metrics)218    self._report_metrics(metrics)219if __name__ == '__main__':...

numrangeregex.py

Source:numrangeregex.py

...108        print("usage: numrageregex.py min max")109        sys.exit(1)110    min_, max_ = sys.argv[1:]111    print(generate_numeric_range_regex(int(min_), int(max_)))112# def assert_in_range(regex, min_, max_, lower, upper):113#     import re114#     pattern = re.compile(regex)115#     for i in xrange(lower, upper):116#         match = pattern.match(str(i))117#         if min_ <= i <= max_:118#             if match is None:119#                 print "ERROR", min_, max_, i120#                 return121#         else:122#             if match is not None:123#                 print "ERROR", min_, max_, i124#                 return125# def test_random_numbers():126#     import random127#     while True:128#         min_1 = random.randint(0, 999999)129#         max_1 = random.randint(min_1, 999999)130#         regex_1 = generate_numeric_range_regex(min_1, max_1)131#         min_2 = random.randint(0, 999)132#         max_2 = random.randint(10000, 999999)133#         regex_2 = generate_numeric_range_regex(min_2, max_2)134#         try:135#             assert_in_range(regex_1, min_1, max_1, 0, 1999999)136#             assert_in_range(regex_2, min_2, max_2, 0, 1999999)137#         except Exception, e:138#             print e139# if __name__ == "__main__":140#     regex = generate_numeric_range_regex(927, 496952)141#     print regex142#     assert_in_range(regex, 927, 496952, 0, 999999)143#     regex = generate_numeric_range_regex(0, 111111)144#     print regex145#     assert_in_range(regex, 0, 111111, 0, 999999)...

test_rl.py

Source:test_rl.py

1import numpy as np2import pytest3from rl2048.env import Env20484from rl2048.exp_buffer import ExperineReplayBuffer5def assert_in_range(a, l, h):6    assert np.max(a) <= h7    assert np.min(a) >= l8@pytest.mark.parametrize('reward_mode', ['dense', 'valid'])9def test_random_moves(reward_mode):10    env = Env2048()11    env.step_limit = 10012    exp_buffer = ExperineReplayBuffer()13    exp_buffer.size = 1014    for i in range(100):15        state = env.reset()16        while not env.done:17            action = np.random.randint(0, 4)18            next_state, reward, done = env.execute(action)19            exp_buffer.add(state, action, reward, env.done, next_state)20            assert_in_range(state, -1, 1)21            assert_in_range(next_state, -1, 1)22            assert_in_range(action, 0, 3)23            assert_in_range(reward, -1, 1)24    state, action, reward, finished, next_state = exp_buffer.sample(100)25    assert_in_range(state, -1, 1)26    assert_in_range(action, 0, 3)27    assert_in_range(reward, -1, 1)28    assert_in_range(finished, 0, 1)...

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