Loading deepchem/trans/tests/test_transformers.py +22 −24 Original line number Diff line number Diff line Loading @@ -29,6 +29,18 @@ class TestTransformers(unittest.TestCase): def setUp(self): super(TestTransformers, self).setUp() self.current_dir = os.path.dirname(os.path.abspath(__file__)) ''' init to load the MNIST data for DataTransforms Tests ''' mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) # extracting validation set of MNIST for testing the DataTransforms valid = dc.data.NumpyDataset(mnist.validation.images, mnist.validation.labels) # extract only the images (no need of the labels) data = (valid.X)[0] # reshaping the vector to image data = np.reshape(data, (28, 28)) self.d = data def test_y_log_transformer(self): """Tests logarithmic data transformer.""" Loading Loading @@ -486,56 +498,42 @@ class TestTransformers(unittest.TestCase): self.assertEqual(new_train.y.shape, train.y.shape) self.assertEqual(new_train.X.shape[-1], fp_size) def __init__(self, d): ''' init to load the MNIST data for DataTransforms Tests ''' mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) # extracting validation set of MNIST for testing the DataTransforms valid = dc.data.NumpyDataset(mnist.validation.images, mnist.validation.labels) # extract only the images (no need of the labels) data = (valid.X)[0] # reshaping the vector to image data = np.reshape(data, (28, 28)) self.d = data def test_blurring(): def test_blurring(self): # Check Blurring dt = DataTransforms(self.d) blurred = dt.gaussian_blur(sigma=1.5) check_blur = scipy.ndimage.gaussian_filter(data, 1.5) check_blur = scipy.ndimage.gaussian_filter(self.d, 1.5) assert np.allclose(check_blur, blurred) def test_rotation(): def test_rotation(self): # Check rotation dt = DataTransforms(self.d) angles = [0, 5, 10, 90] for ang in angles: rotate = dt.rotate(ang) check_rotate = scipy.ndimage.rotate(data, ang) check_rotate = scipy.ndimage.rotate(self.d, ang) assert np.allclose(rotate, check_rotate) # Some more test cases for flip rotate = dt.rotate(-90) check_rotate = scipy.ndimage.rotate(data, 270) check_rotate = scipy.ndimage.rotate(self.d, 270) assert np.allclose(rotate, check_rotate) def test_flipping(): def test_flipping(self): # Check flip dt = DataTransforms(self.d) flip_lr = dt.flip(direction="lr") flip_ud = dt.flip(direction="ud") check_lr = np.fliplr(data) check_ud = np.flipud(data) check_lr = np.fliplr(self.d) check_ud = np.flipud(self.d) assert np.allclose(flip_ud, check_ud) assert np.allclose(flip_lr, check_lr) def test_scaling(): def test_scaling(self): # Check Scales dt = DataTransforms(self.d) h = 150 w = 150 scale = scipy.misc.imresize(data, (h, w)) scale = scipy.misc.imresize(self.d, (h, w)) check_scale = dt.scale(h, w) np.allclose(scale, check_scale) Loading
deepchem/trans/tests/test_transformers.py +22 −24 Original line number Diff line number Diff line Loading @@ -29,6 +29,18 @@ class TestTransformers(unittest.TestCase): def setUp(self): super(TestTransformers, self).setUp() self.current_dir = os.path.dirname(os.path.abspath(__file__)) ''' init to load the MNIST data for DataTransforms Tests ''' mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) # extracting validation set of MNIST for testing the DataTransforms valid = dc.data.NumpyDataset(mnist.validation.images, mnist.validation.labels) # extract only the images (no need of the labels) data = (valid.X)[0] # reshaping the vector to image data = np.reshape(data, (28, 28)) self.d = data def test_y_log_transformer(self): """Tests logarithmic data transformer.""" Loading Loading @@ -486,56 +498,42 @@ class TestTransformers(unittest.TestCase): self.assertEqual(new_train.y.shape, train.y.shape) self.assertEqual(new_train.X.shape[-1], fp_size) def __init__(self, d): ''' init to load the MNIST data for DataTransforms Tests ''' mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) # extracting validation set of MNIST for testing the DataTransforms valid = dc.data.NumpyDataset(mnist.validation.images, mnist.validation.labels) # extract only the images (no need of the labels) data = (valid.X)[0] # reshaping the vector to image data = np.reshape(data, (28, 28)) self.d = data def test_blurring(): def test_blurring(self): # Check Blurring dt = DataTransforms(self.d) blurred = dt.gaussian_blur(sigma=1.5) check_blur = scipy.ndimage.gaussian_filter(data, 1.5) check_blur = scipy.ndimage.gaussian_filter(self.d, 1.5) assert np.allclose(check_blur, blurred) def test_rotation(): def test_rotation(self): # Check rotation dt = DataTransforms(self.d) angles = [0, 5, 10, 90] for ang in angles: rotate = dt.rotate(ang) check_rotate = scipy.ndimage.rotate(data, ang) check_rotate = scipy.ndimage.rotate(self.d, ang) assert np.allclose(rotate, check_rotate) # Some more test cases for flip rotate = dt.rotate(-90) check_rotate = scipy.ndimage.rotate(data, 270) check_rotate = scipy.ndimage.rotate(self.d, 270) assert np.allclose(rotate, check_rotate) def test_flipping(): def test_flipping(self): # Check flip dt = DataTransforms(self.d) flip_lr = dt.flip(direction="lr") flip_ud = dt.flip(direction="ud") check_lr = np.fliplr(data) check_ud = np.flipud(data) check_lr = np.fliplr(self.d) check_ud = np.flipud(self.d) assert np.allclose(flip_ud, check_ud) assert np.allclose(flip_lr, check_lr) def test_scaling(): def test_scaling(self): # Check Scales dt = DataTransforms(self.d) h = 150 w = 150 scale = scipy.misc.imresize(data, (h, w)) scale = scipy.misc.imresize(self.d, (h, w)) check_scale = dt.scale(h, w) np.allclose(scale, check_scale)
