finish pytest markers (09585a04) · Commits · 钟慕尧 / deepchem

deepchem/data/tests/test_image_dataset.py

+1 −2

Original line number	Diff line number	Diff line
		@@ -5,10 +5,9 @@ import unittest
		import numpy as np
		import deepchem as dc
		import os
		from tensorflow.python.framework import test_util


		class TestImageDataset(test_util.TensorFlowTestCase):
		class TestImageDataset(unittest.TestCase):
		"""
		Test ImageDataset class.
		"""

deepchem/metalearning/tests/test_maml.py

+8 −3

Original line number	Diff line number	Diff line
		import unittest

		import pytest
		import numpy as np
		import tensorflow as tf
		from flaky import flaky

		import deepchem as dc

		try:
		import tensorflow as tf
		has_tensorflow = True
		except:
		has_tensorflow = False


		class TestMAML(unittest.TestCase):

		@flaky
		@pytest.mark.tensorflow
		def test_sine(self):
		"""Test meta-learning for sine function."""

deepchem/metalearning/tests/test_maml_reload.py

+11 −2

Original line number	Diff line number	Diff line
		@@ -2,8 +2,10 @@

		import deepchem as dc
		import numpy as np
		import tensorflow as tf
		import pytest

		try:
		import tensorflow as tf

		class SineLearner(dc.metalearning.MetaLearner):

		@@ -12,7 +14,8 @@ class SineLearner(dc.metalearning.MetaLearner):
		self.w1 = tf.Variable(np.random.normal(size=[1, 40], scale=1.0))
		self.w2 = tf.Variable(
		np.random.normal(size=[40, 40], scale=np.sqrt(1 / 40)))
		self.w3 = tf.Variable(np.random.normal(size=[40, 1], scale=np.sqrt(1 / 40)))
		self.w3 = tf.Variable(
		np.random.normal(size=[40, 1], scale=np.sqrt(1 / 40)))
		self.b1 = tf.Variable(np.zeros(40))
		self.b2 = tf.Variable(np.zeros(40))
		self.b3 = tf.Variable(np.zeros(1))
		@@ -38,7 +41,13 @@ class SineLearner(dc.metalearning.MetaLearner):
		x = np.random.uniform(-5.0, 5.0, (self.batch_size, 1))
		return [x, self.amplitude * np.sin(x + self.phase)]

		has_tensorflow = True

		except:
		has_tensorflow = False


		@pytest.mark.tensorflow
		def test_reload():
		"""Test that a Metalearner can be reloaded."""
		learner = SineLearner()

deepchem/metrics/tests/test_genomics.py

+7 −0

Original line number	Diff line number	Diff line
		@@ -5,7 +5,12 @@ import unittest

		import numpy as np
		import deepchem as dc
		import pytest
		try:
		import tensorflow as tf
		has_tensorflow = True
		except:
		has_tensorflow = False

		from deepchem.metrics.genomic_metrics import get_motif_scores
		from deepchem.metrics.genomic_metrics import get_pssm_scores
		@@ -54,6 +59,7 @@ class TestGenomicMetrics(unittest.TestCase):
		return dc.models.KerasModel(keras_model,
		dc.models.losses.BinaryCrossEntropy())

		@pytest.mark.tensorflow
		def test_in_silico_mutagenesis_shape(self):
		"""Test in-silico mutagenesis returns correct shape."""
		# Construct and train SequenceDNN model
		@@ -72,6 +78,7 @@ class TestGenomicMetrics(unittest.TestCase):
		mutagenesis_scores = in_silico_mutagenesis(model, sequences)
		self.assertEqual(mutagenesis_scores.shape, (1, 3, 4, 5, 1))

		@pytest.mark.tensorflow
		def test_in_silico_mutagenesis_nonzero(self):
		"""Test in-silico mutagenesis returns nonzero output."""
		# Construct and train SequenceDNN model

deepchem/models/tests/test_pretrained_keras.py

+16 −3

Original line number	Diff line number	Diff line
		import os
		import unittest
		import pytest
		import deepchem as dc
		import numpy as np
		import tensorflow as tf
		from tensorflow.keras.layers import Input, Dense
		from deepchem.models.losses import L2Loss
		from deepchem.feat.mol_graphs import ConvMol

		try:
		import tensorflow as tf
		from tensorflow.keras.layers import Input, Dense

		class MLP(dc.models.KerasModel):

		def __init__(self, n_tasks=1, feature_dim=100, hidden_layer_size=64,
		def __init__(self,
		n_tasks=1,
		feature_dim=100,
		hidden_layer_size=64,
		**kwargs):
		self.feature_dim = feature_dim
		self.hidden_layer_size = hidden_layer_size
		@@ -32,9 +37,14 @@ class MLP(dc.models.KerasModel):
		model = tf.keras.Model(inputs=[inputs], outputs=outputs)
		return model, loss, output_types

		has_tensorflow = True
		except:
		has_tensorflow = False


		class TestPretrained(unittest.TestCase):

		@pytest.mark.tensorflow
		def setUp(self):
		self.feature_dim = 2
		self.hidden_layer_size = 10
		@@ -45,6 +55,7 @@ class TestPretrained(unittest.TestCase):

		self.dataset = dc.data.NumpyDataset(X, y)

		@pytest.mark.tensorflow
		def test_load_from_pretrained(self):
		"""Tests loading pretrained model."""
		source_model = MLP(
		@@ -78,6 +89,7 @@ class TestPretrained(unittest.TestCase):
		dest_val = dest_var.numpy()
		np.testing.assert_array_almost_equal(source_val, dest_val)

		@pytest.mark.tensorflow
		def test_load_pretrained_subclassed_model(self):
		from rdkit import Chem
		bi_tasks = ['a', 'b']
		@@ -135,6 +147,7 @@ class TestPretrained(unittest.TestCase):
		dest_val = dest_var.numpy()
		np.testing.assert_array_almost_equal(source_val, dest_val)

		@pytest.mark.tensorflow
		def test_restore_equivalency(self):
		"""Test for restore based pretrained model loading."""
		source_model = MLP(

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