PR Updates (b6eb7473) · Commits · 钟慕尧 / deepchem

contrib/autoencoder_models/test_tensorflowEncoders.py

+3 −4

Original line number	Diff line number	Diff line
		from unittest import TestCase

		from nose.tools import assert_equals
		from nose.plugins.attrib import attr
		import pytest
		from rdkit import Chem

		import deepchem as dc
		@@ -12,7 +11,7 @@ from deepchem.models.autoencoder_models.autoencoder import TensorflowMoleculeEnc

		class TestTensorflowEncoders(TestCase):

		@pytest.mark.slow('slow')
		@pytest.mark.slow
		def test_fit(self):
		tf_enc = TensorflowMoleculeEncoder.zinc_encoder()

		@@ -36,4 +35,4 @@ class TestTensorflowEncoders(TestCase):
		tf_de = TensorflowMoleculeDecoder.zinc_decoder()
		one_hot_decoded = tf_de.predict_on_batch(prediction)
		decoded_smiles = featurizer.untransform(one_hot_decoded)
		assert_equals(len(decoded_smiles), len(smiles))
		assert len(decoded_smiles) == len(smiles)

+1 −1

Original line number	Diff line number	Diff line
		@@ -148,7 +148,7 @@ class TestBindingPocket(unittest.TestCase):

		assert len(pockets) < len(all_pockets)

		@pytest.mark.skip(reson="Unknown")
		@pytest.mark.skip(reason="Unknown")
		def test_rf_convex_find_pockets(self):
		"""Test that filter with pre-trained RF models works."""
		if sys.version_info >= (3, 0):

+1 −37

Original line number	Diff line number	Diff line
		@@ -5,10 +5,9 @@ __author__ = "Bharath Ramsundar"
		__copyright__ = "Copyright 2016, Stanford University"
		__license__ = "MIT"

		import unittest
		import os

		import sys
		import unittest

		import pytest

		@@ -106,38 +105,3 @@ class TestDocking(unittest.TestCase):
		return

		assert score.shape == (1,)

		'''
		@pytest.mark.slow("slow")
		def test_vina_grid_dnn_docker_dock(self):
		"""Test that VinaGridDNNDocker can dock."""
		current_dir = os.path.dirname(os.path.realpath(__file__))
		protein_file = os.path.join(current_dir, "1jld_protein.pdb")
		ligand_file = os.path.join(current_dir, "1jld_ligand.sdf")

		docker = dc.dock.VinaGridDNNDocker(exhaustiveness=1, detect_pockets=False)
		(score, (protein_docked, ligand_docked)) = docker.dock(
		protein_file, ligand_file)

		# Check returned files exist
		assert score.shape == (1,)
		assert os.path.exists(protein_docked)
		assert os.path.exists(ligand_docked)

		@pytest.mark.slow('slow')
		def test_pocket_vina_grid_dnn_docker_dock(self):
		"""Test that VinaGridDNNDocker can dock."""
		if sys.version_info >= (3, 0):
		return

		current_dir = os.path.dirname(os.path.realpath(__file__))
		protein_file = os.path.join(current_dir, "1jld_protein.pdb")
		ligand_file = os.path.join(current_dir, "1jld_ligand.sdf")

		docker = dc.dock.VinaGridDNNDocker(exhaustiveness=1, detect_pockets=True)
		(score, (protein_docked, ligand_docked)) = docker.dock(
		protein_file, ligand_file, dry_run=True)

		# Check returned files exist
		assert score.shape == (1,)
		'''

+2 −2

Original line number	Diff line number	Diff line
		@@ -66,7 +66,7 @@ class TestAtomicConv(unittest.TestCase):
		atomic_convnet.fit(train, nb_epoch=300)
		assert np.allclose(labels, atomic_convnet.predict(train), atol=0.01)

		@pytest.mark.slow("slow")
		@pytest.mark.slow
		def test_atomic_conv_variable(self):
		"""A simple test that initializes and fits an AtomicConvModel on variable input size."""
		# For simplicity, let's assume both molecules have same number of
		@@ -102,7 +102,7 @@ class TestAtomicConv(unittest.TestCase):
		train = NumpyDataset(features, labels)
		atomic_convnet.fit(train, nb_epoch=1)

		@pytest.mark.slow("slow")
		@pytest.mark.slow
		def test_atomic_conv_with_feat(self):
		"""A simple test for running an atomic convolution on featurized data."""
		dir_path = os.path.dirname(os.path.realpath(__file__))

+9 −12

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		@@ -7,20 +7,17 @@ __copyright__ = "Copyright 2016, Stanford University"
		__license__ = "MIT"

		import os
		import tempfile

		import numpy as np
		import unittest
		import sklearn
		import shutil
		import pytest
		import tensorflow as tf
		import deepchem as dc
		import scipy.io
		from deepchem.models.optimizers import Adam, ExponentialDecay
		from tensorflow.python.framework import test_util
		from flaky import flaky
		from sklearn.ensemble import RandomForestClassifier
		from sklearn.ensemble import RandomForestRegressor
		from flaky import flaky
		import pytest
		from tensorflow.python.framework import test_util

		import deepchem as dc
		from deepchem.models.optimizers import Adam


		class TestOverfit(test_util.TensorFlowTestCase):
		@@ -573,7 +570,7 @@ class TestOverfit(test_util.TensorFlowTestCase):
		scores = model.evaluate(dataset, [regression_metric])
		assert scores[regression_metric.name] < .2

		@pytest.mark.slow('slow')
		@pytest.mark.slow
		def test_DAG_singletask_regression_overfit(self):
		"""Test DAG regressor multitask overfits tiny data."""
		np.random.seed(123)
		@@ -691,7 +688,7 @@ class TestOverfit(test_util.TensorFlowTestCase):

		assert scores[regression_metric.name] > .8

		@pytest.mark.slow("slow")
		@pytest.mark.slow
		def test_MPNN_singletask_regression_overfit(self):
		"""Test MPNN overfits tiny data."""
		np.random.seed(123)