:rotating_light: fix lint error (cf797e14) · Commits · 钟慕尧 / deepchem

deepchem/data/tests/test_datasets.py

+0 −10

Original line number	Diff line number	Diff line
		@@ -782,26 +782,16 @@ def test_to_str():
		ref_str = '<NumpyDataset X.shape: (5, 3), y.shape: (5,), w.shape: (5,), ids: [0 1 2 3 4], task_names: [0]>'
		assert str(dataset) == ref_str

		# Test id shrinkage
		dc.utils.set_print_threshold(10)
		dataset = dc.data.NumpyDataset(
		X=np.random.rand(50, 3), y=np.random.rand(50,), ids=np.arange(50))
		ref_str = '<NumpyDataset X.shape: (50, 3), y.shape: (50,), w.shape: (50,), ids: [0 1 2 ... 47 48 49], task_names: [0]>'
		assert str(dataset) == ref_str

		# Test task shrinkage
		dataset = dc.data.NumpyDataset(
		X=np.random.rand(50, 3), y=np.random.rand(50, 20), ids=np.arange(50))
		ref_str = '<NumpyDataset X.shape: (50, 3), y.shape: (50, 20), w.shape: (50, 1), ids: [0 1 2 ... 47 48 49], task_names: [ 0 1 2 ... 17 18 19]>'
		assert str(dataset) == ref_str

		# Test max print size
		dc.utils.set_max_print_size(25)
		dataset = dc.data.NumpyDataset(
		X=np.random.rand(50, 3), y=np.random.rand(50,), ids=np.arange(50))
		ref_str = '<NumpyDataset X.shape: (50, 3), y.shape: (50,), w.shape: (50,), task_names: [0]>'
		assert str(dataset) == ref_str


		class TestDatasets(unittest.TestCase):
		"""

+1 −1

Original line number	Diff line number	Diff line
		@@ -23,7 +23,7 @@ class TestReload(unittest.TestCase):
		# Load MUV dataset
		logger.info("About to featurize compounds")
		featurizer = dc.feat.CircularFingerprint(size=1024)
		raw_dataset = dc.utils.save.load_from_disk(dataset_file)
		raw_dataset = dc.utils.data_utils.load_from_disk(dataset_file)
		MUV_tasks = [
		'MUV-692', 'MUV-689', 'MUV-846', 'MUV-859', 'MUV-644', 'MUV-548',
		'MUV-852', 'MUV-600', 'MUV-810', 'MUV-712', 'MUV-737', 'MUV-858',

+1 −1

Original line number	Diff line number	Diff line
		@@ -15,7 +15,7 @@ from sklearn.linear_model import ElasticNetCV
		from deepchem.models import Model
		from deepchem.data import Dataset
		from deepchem.trans import Transformer
		from deepchem.utils.save import load_from_disk, save_to_disk
		from deepchem.utils.data_utils import load_from_disk, save_to_disk

		NON_WEIGHTED_MODELS = [
		LogisticRegression, PLSRegression, GaussianProcessRegressor, ElasticNetCV,

+2 −2

Original line number	Diff line number	Diff line
		@@ -70,7 +70,7 @@ def load_bace_regression(featurizer='ECFP',

		dataset_file = os.path.join(data_dir, "bace.csv")
		if not os.path.exists(dataset_file):
		deepchem.utils.download_url(url=BACE_URL, dest_dir=data_dir)
		deepchem.utils.data_utils.download_url(url=BACE_URL, dest_dir=data_dir)

		if featurizer == 'ECFP':
		featurizer = deepchem.feat.CircularFingerprint(size=1024)
		@@ -174,7 +174,7 @@ def load_bace_classification(featurizer='ECFP',

		dataset_file = os.path.join(data_dir, "bace.csv")
		if not os.path.exists(dataset_file):
		deepchem.utils.download_url(url=BACE_URL, dest_dir=data_dir)
		deepchem.utils.data_utils.download_url(url=BACE_URL, dest_dir=data_dir)

		if featurizer == 'ECFP':
		featurizer = deepchem.feat.CircularFingerprint(size=1024)

+8 −4

Original line number	Diff line number	Diff line
		@@ -48,9 +48,11 @@ def load_bbbc001(split='index',
		labels_file = os.path.join(data_dir, "BBBC001_v1_counts.txt")

		if not os.path.exists(dataset_file):
		deepchem.utils.download_url(url=BBBC1_IMAGE_URL, dest_dir=data_dir)
		deepchem.utils.data_utils.download_url(
		url=BBBC1_IMAGE_URL, dest_dir=data_dir)
		if not os.path.exists(labels_file):
		deepchem.utils.download_url(url=BBBC1_LABEL_URL, dest_dir=data_dir)
		deepchem.utils.data_utils.download_url(
		url=BBBC1_LABEL_URL, dest_dir=data_dir)
		# Featurize Images into NumpyArrays
		loader = deepchem.data.ImageLoader()
		dataset = loader.featurize(dataset_file, in_memory=False)
		@@ -130,9 +132,11 @@ def load_bbbc002(split='index',
		labels_file = os.path.join(data_dir, "BBBC002_v1_counts.txt")

		if not os.path.exists(dataset_file):
		deepchem.utils.download_url(url=BBBC2_IMAGE_URL, dest_dir=data_dir)
		deepchem.utils.data_utils.download_url(
		url=BBBC2_IMAGE_URL, dest_dir=data_dir)
		if not os.path.exists(labels_file):
		deepchem.utils.download_url(url=BBBC2_LABEL_URL, dest_dir=data_dir)
		deepchem.utils.data_utils.download_url(
		url=BBBC2_LABEL_URL, dest_dir=data_dir)
		# Featurize Images into NumpyArrays
		loader = deepchem.data.ImageLoader()
		dataset = loader.featurize(dataset_file, in_memory=False)