Fixed failing test cases (10471ebf) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorgraph/tests/test_tensor_graph.py

+7 −7

Original line number	Diff line number	Diff line
		@@ -33,7 +33,7 @@ class TestTensorGraph(unittest.TestCase):
		tg = dc.models.TensorGraph(learning_rate=0.1)
		tg.add_output(output)
		tg.set_loss(loss)
		tg.fit(dataset, nb_epoch=10)
		tg.fit(dataset, nb_epoch=1000)
		prediction = np.squeeze(tg.predict_proba_on_batch(X))
		assert_true(np.all(np.isclose(prediction, y, atol=0.4)))

		@@ -73,7 +73,7 @@ class TestTensorGraph(unittest.TestCase):

		tg.fit_generator(
		databag.iterbatches(
		epochs=10, batch_size=tg.batch_size, pad_batches=True))
		epochs=1000, batch_size=tg.batch_size, pad_batches=True))
		prediction = tg.predict_proba_on_generator(databag.iterbatches())
		for i in range(2):
		y_real = ys[i].X
		@@ -93,7 +93,7 @@ class TestTensorGraph(unittest.TestCase):
		tg = dc.models.TensorGraph(learning_rate=0.1)
		tg.add_output(dense)
		tg.set_loss(loss)
		tg.fit(dataset, nb_epoch=10)
		tg.fit(dataset, nb_epoch=1000)
		prediction = np.squeeze(tg.predict_proba_on_batch(X))
		assert_true(np.all(np.isclose(prediction, y, atol=3.0)))

		@@ -132,7 +132,7 @@ class TestTensorGraph(unittest.TestCase):

		tg.fit_generator(
		databag.iterbatches(
		epochs=200, batch_size=tg.batch_size, pad_batches=True))
		epochs=1000, batch_size=tg.batch_size, pad_batches=True))
		prediction = tg.predict_proba_on_generator(databag.iterbatches())
		for i in range(2):
		y_real = ys[i].X
		@@ -154,9 +154,9 @@ class TestTensorGraph(unittest.TestCase):
		tg = dc.models.TensorGraph(learning_rate=1.0, use_queue=False)
		tg.add_output(output)
		tg.set_loss(loss)
		tg.fit(dataset, nb_epoch=10)
		tg.fit(dataset, nb_epoch=1000)
		prediction = np.squeeze(tg.predict_proba_on_batch(X))
		assert_true(np.all(np.isclose(prediction, y, atol=0.2)))
		assert_true(np.all(np.isclose(prediction, y, atol=0.4)))

		def test_tensorboard(self):
		n_data_points = 20
		@@ -177,7 +177,7 @@ class TestTensorGraph(unittest.TestCase):
		model_dir='/tmp/tensorgraph')
		tg.add_output(output)
		tg.set_loss(loss)
		tg.fit(dataset, nb_epoch=10)
		tg.fit(dataset, nb_epoch=1000)
		files_in_dir = os.listdir(tg.model_dir)
		event_file = list(filter(lambda x: x.startswith("events"), files_in_dir))
		assert_true(len(event_file) > 0)

deepchem/utils/test/test_generator_evaluator.py

+6 −6

Original line number	Diff line number	Diff line
		@@ -16,8 +16,8 @@ class TestGeneratorEvaluator(TestCase):
		n_data_points = 20
		n_features = 2

		X = np.ones(shape=(n_data_points / 2, n_features)) * -1
		X1 = np.ones(shape=(n_data_points / 2, n_features))
		X = np.ones(shape=(n_data_points // 2, n_features)) * -1
		X1 = np.ones(shape=(n_data_points // 2, n_features))
		X = np.concatenate((X, X1))
		class_1 = np.array([[0.0, 1.0] for x in range(int(n_data_points / 2))])
		class_0 = np.array([[1.0, 0.0] for x in range(int(n_data_points / 2))])
		@@ -54,7 +54,7 @@ class TestGeneratorEvaluator(TestCase):

		tg.fit_generator(
		databag.iterbatches(
		epochs=10, batch_size=tg.batch_size, pad_batches=True))
		epochs=1000, batch_size=tg.batch_size, pad_batches=True))
		metric = dc.metrics.Metric(
		dc.metrics.roc_auc_score, np.mean, mode="classification")

		@@ -105,7 +105,7 @@ class TestGeneratorEvaluator(TestCase):

		tg.fit_generator(
		databag.iterbatches(
		epochs=10, batch_size=tg.batch_size, pad_batches=True))
		epochs=1000, batch_size=tg.batch_size, pad_batches=True))
		metric = dc.metrics.Metric(
		dc.metrics.roc_auc_score, np.mean, mode="classification")

		@@ -157,7 +157,7 @@ class TestGeneratorEvaluator(TestCase):

		tg.fit_generator(
		databag.iterbatches(
		epochs=10, batch_size=tg.batch_size, pad_batches=True))
		epochs=1000, batch_size=tg.batch_size, pad_batches=True))
		metric = [
		dc.metrics.Metric(
		dc.metrics.mean_absolute_error, np.mean, mode="regression"),
		@@ -203,7 +203,7 @@ class TestGeneratorEvaluator(TestCase):

		tg.fit_generator(
		databag.iterbatches(
		epochs=10, batch_size=tg.batch_size, pad_batches=True))
		epochs=1000, batch_size=tg.batch_size, pad_batches=True))
		metric = [
		dc.metrics.Metric(
		dc.metrics.mean_absolute_error, np.mean, mode="regression"),

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