KaneAI - World’s First E2E Software Testing Agent.Book a Demo Join TestMu Conference

How to use test_env_spec method in tox

Best Python code snippet using tox_python

test_gaussian_cnn_baseline.py

Source:test_gaussian_cnn_baseline.py

1import pickle2from unittest import mock3import akro4import numpy as np5import pytest6import tensorflow as tf7from garage.envs.env_spec import EnvSpec8from garage.tf.baselines import GaussianCNNBaseline9from garage.tf.optimizers import LbfgsOptimizer10from tests.fixtures import TfGraphTestCase11def get_train_test_data():12    matrices = [13        np.linspace(i - 0.5, i + 0.5, 300).reshape((10, 10, 3))14        for i in range(110)15    ]16    data = [np.sin(matrices[i]) for i in range(100)]17    obs = [{'observations': [x], 'returns': [np.mean(x)]} for x in data]18    observations = np.concatenate([p['observations'] for p in obs])19    returns = np.concatenate([p['returns'] for p in obs])20    returns = returns.reshape((-1, 1))21    paths = {'observations': [np.sin(matrices[i]) for i in range(100, 110)]}22    expected = [[np.mean(x)] for x in paths['observations']]23    return (obs, observations, returns), (paths, expected)24test_env_spec = EnvSpec(observation_space=akro.Box(low=-1,25                                                   high=1,26                                                   shape=(10, 10, 3)),27                        action_space=None)28class TestGaussianCNNBaseline(TfGraphTestCase):29    @pytest.mark.large30    def test_fit_normalized(self):31        gcr = GaussianCNNBaseline(env_spec=test_env_spec,32                                  filters=((3, (3, 3)), (6, (3, 3))),33                                  strides=(1, 1),34                                  padding='SAME',35                                  hidden_sizes=(32, ),36                                  adaptive_std=False,37                                  use_trust_region=True)38        train_data, test_data = get_train_test_data()39        train_paths, observations, returns = train_data40        for _ in range(20):41            gcr.fit(train_paths)42        test_paths, expected = test_data43        prediction = gcr.predict(test_paths)44        average_error = 0.045        for i, exp in enumerate(expected):46            average_error += np.abs(exp - prediction[i])47        average_error /= len(expected)48        assert average_error <= 0.149        x_mean = self.sess.run(gcr._networks['default'].x_mean)50        x_mean_expected = np.mean(observations, axis=0, keepdims=True)51        x_std = self.sess.run(gcr._networks['default'].x_std)52        x_std_expected = np.std(observations, axis=0, keepdims=True)53        assert np.allclose(x_mean, x_mean_expected)54        assert np.allclose(x_std, x_std_expected)55        y_mean = self.sess.run(gcr._networks['default'].y_mean)56        y_mean_expected = np.mean(returns, axis=0, keepdims=True)57        y_std = self.sess.run(gcr._networks['default'].y_std)58        y_std_expected = np.std(returns, axis=0, keepdims=True)59        assert np.allclose(y_mean, y_mean_expected)60        assert np.allclose(y_std, y_std_expected)61    @pytest.mark.large62    def test_fit_unnormalized(self):63        gcr = GaussianCNNBaseline(env_spec=test_env_spec,64                                  filters=((3, (3, 3)), (6, (3, 3))),65                                  strides=(1, 1),66                                  padding='SAME',67                                  hidden_sizes=(32, ),68                                  adaptive_std=True,69                                  normalize_inputs=False,70                                  normalize_outputs=False)71        train_data, test_data = get_train_test_data()72        train_paths, _, _ = train_data73        for _ in range(20):74            gcr.fit(train_paths)75        test_paths, expected = test_data76        prediction = gcr.predict(test_paths)77        average_error = 0.078        for i, exp in enumerate(expected):79            average_error += np.abs(exp - prediction[i])80        average_error /= len(expected)81        assert average_error <= 0.182        x_mean = self.sess.run(gcr._networks['default'].x_mean)83        x_mean_expected = np.zeros_like(x_mean)84        x_std = self.sess.run(gcr._networks['default'].x_std)85        x_std_expected = np.ones_like(x_std)86        assert np.array_equal(x_mean, x_mean_expected)87        assert np.array_equal(x_std, x_std_expected)88        y_mean = self.sess.run(gcr._networks['default'].y_mean)89        y_mean_expected = np.zeros_like(y_mean)90        y_std = self.sess.run(gcr._networks['default'].y_std)91        y_std_expected = np.ones_like(y_std)92        assert np.allclose(y_mean, y_mean_expected)93        assert np.allclose(y_std, y_std_expected)94    @pytest.mark.large95    def test_fit_smaller_subsample_factor(self):96        gcr = GaussianCNNBaseline(env_spec=test_env_spec,97                                  filters=((3, (3, 3)), (6, (3, 3))),98                                  strides=(1, 1),99                                  padding='SAME',100                                  hidden_sizes=(32, ),101                                  subsample_factor=0.9,102                                  adaptive_std=False)103        train_data, test_data = get_train_test_data()104        train_paths, _, _ = train_data105        for _ in range(20):106            gcr.fit(train_paths)107        test_paths, expected = test_data108        prediction = gcr.predict(test_paths)109        average_error = 0.0110        for i, exp in enumerate(expected):111            average_error += np.abs(exp - prediction[i])112        average_error /= len(expected)113        assert average_error <= 0.1114    @pytest.mark.large115    def test_fit_without_trusted_region(self):116        gcr = GaussianCNNBaseline(env_spec=test_env_spec,117                                  filters=((3, (3, 3)), (6, (3, 3))),118                                  strides=(1, 1),119                                  padding='SAME',120                                  hidden_sizes=(32, ),121                                  adaptive_std=False,122                                  use_trust_region=False)123        train_data, test_data = get_train_test_data()124        train_paths, _, _ = train_data125        for _ in range(20):126            gcr.fit(train_paths)127        test_paths, expected = test_data128        prediction = gcr.predict(test_paths)129        average_error = 0.0130        for i, exp in enumerate(expected):131            average_error += np.abs(exp - prediction[i])132        average_error /= len(expected)133        assert average_error <= 0.1134    @mock.patch('tests.garage.tf.baselines.'135                'test_gaussian_cnn_baseline.'136                'LbfgsOptimizer')137    def test_optimizer_args(self, mock_lbfgs):138        lbfgs_args = dict(max_opt_itr=25)139        gcr = GaussianCNNBaseline(env_spec=test_env_spec,140                                  filters=((3, (3, 3)), (6, (3, 3))),141                                  strides=(1, 1),142                                  padding='SAME',143                                  hidden_sizes=(32, ),144                                  optimizer=LbfgsOptimizer,145                                  optimizer_args=lbfgs_args,146                                  use_trust_region=True)147        assert mock_lbfgs.return_value is gcr._optimizer148        mock_lbfgs.assert_called_with(max_opt_itr=25)149    def test_is_pickleable(self):150        gcr = GaussianCNNBaseline(env_spec=test_env_spec,151                                  filters=((3, (3, 3)), (6, (3, 3))),152                                  strides=(1, 1),153                                  padding='SAME',154                                  hidden_sizes=(32, ),155                                  adaptive_std=False,156                                  use_trust_region=False)157        with tf.compat.v1.variable_scope('GaussianCNNBaseline', reuse=True):158            bias = tf.compat.v1.get_variable(159                'dist_params/mean_network/hidden_0/bias')160        bias.load(tf.ones_like(bias).eval())161        _, test_data = get_train_test_data()162        test_paths, _ = test_data163        result1 = gcr.predict(test_paths)164        h = pickle.dumps(gcr)165        with tf.compat.v1.Session(graph=tf.Graph()):166            gcr_pickled = pickle.loads(h)167            result2 = gcr_pickled.predict(test_paths)168            assert np.array_equal(result1, result2)169    def test_param_values(self):170        gcb = GaussianCNNBaseline(env_spec=test_env_spec,171                                  filters=((3, (3, 3)), (6, (3, 3))),172                                  strides=(1, 1),173                                  padding='SAME',174                                  hidden_sizes=(32, ),175                                  adaptive_std=False,176                                  use_trust_region=False)177        new_gcb = GaussianCNNBaseline(env_spec=test_env_spec,178                                      filters=((3, (3, 3)), (6, (3, 3))),179                                      strides=(1, 1),180                                      padding='SAME',181                                      hidden_sizes=(32, ),182                                      adaptive_std=False,183                                      use_trust_region=False,184                                      name='GaussianCNNBaseline2')185        # Manual change the parameter of GaussianCNNBaseline186        with tf.compat.v1.variable_scope('GaussianCNNBaseline', reuse=True):187            bias_var = tf.compat.v1.get_variable(188                'dist_params/mean_network/hidden_0/bias')189        bias_var.load(tf.ones_like(bias_var).eval())190        old_param_values = gcb.get_param_values()191        new_param_values = new_gcb.get_param_values()192        assert not np.array_equal(old_param_values, new_param_values)193        new_gcb.set_param_values(old_param_values)194        new_param_values = new_gcb.get_param_values()195        assert np.array_equal(old_param_values, new_param_values)196    def test_get_params(self):197        gcb = GaussianCNNBaseline(env_spec=test_env_spec,198                                  filters=((3, (3, 3)), (6, (3, 3))),199                                  strides=(1, 1),200                                  padding='SAME',201                                  hidden_sizes=(32, ),202                                  adaptive_std=False,203                                  use_trust_region=False)204        params_interal = gcb.get_params()205        trainable_params = tf.compat.v1.trainable_variables(206            scope='GaussianCNNBaseline')...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.