pass tests pls (f7990c2d) · Commits · 钟慕尧 / deepchem

deepchem/data/datasets.py

+1 −3

Original line number	Diff line number	Diff line
		@@ -886,14 +886,12 @@ class DiskDataset(Dataset):
		n_rows = len(self.metadata_df.index)
		for i in range(n_rows):
		row = self.metadata_df.iloc[i]
		basename = row["basename"]
		X, y, w, ids = self.get_shard(i)
		n = X.shape[0]
		permutation = np.random.permutation(n)
		X, y, w, ids = (X[permutation], y[permutation], w[permutation],
		ids[permutation])
		DiskDataset.write_data_to_disk(self.data_dir, basename, tasks, X, y, w,
		ids)
		DiskDataset.write_data_to_disk(self.data_dir, "", tasks, X, y, w, ids)

		def shuffle_shards(self):
		"""Shuffles the order of the shards for this dataset."""

deepchem/models/tensorgraph/models/graph_models.py

+1 −2

Original line number	Diff line number	Diff line
		@@ -821,7 +821,6 @@ class GraphConvTensorGraph(TensorGraph):
		self.default_generator(dataset, predict=True),
		metrics,
		labels=self.my_labels,
		transformers=transformers,
		weights=[self.my_task_weights],
		per_task_metrics=per_task_metrics)

deepchem/models/tensorgraph/models/test_graph_models.py

+50 −47

Original line number	Diff line number	Diff line
		import unittest

		import numpy as np

		import deepchem
		@@ -7,7 +9,9 @@ from deepchem.models import TensorGraph
		from deepchem.molnet.load_function.delaney_datasets import load_delaney


		def get_dataset(mode='classification', featurizer='GraphConv'):
		class TestGraphModels(unittest.TestCase):

		def get_dataset(self, mode='classification', featurizer='GraphConv'):
		data_points = 10
		tasks, all_dataset, transformers = load_delaney(featurizer)
		train, valid, test = all_dataset
		@@ -25,10 +29,9 @@ def get_dataset(mode='classification', featurizer='GraphConv'):

		return tasks, ds, transformers, metric


		def test_graph_conv_model():
		tasks, dataset, transformers, metric = get_dataset('classification',
		'GraphConv')
		def test_graph_conv_model(self):
		tasks, dataset, transformers, metric = self.get_dataset(
		'classification', 'GraphConv')

		batch_size = 50
		model = GraphConvTensorGraph(
		@@ -41,9 +44,9 @@ def test_graph_conv_model():
		model = TensorGraph.load_from_dir(model.model_dir)
		scores = model.evaluate(dataset, [metric], transformers)


		def test_graph_conv_regression_model():
		tasks, dataset, transformers, metric = get_dataset('regression', 'GraphConv')
		def test_graph_conv_regression_model(self):
		tasks, dataset, transformers, metric = self.get_dataset(
		'regression', 'GraphConv')

		batch_size = 50
		model = GraphConvTensorGraph(
		@@ -56,9 +59,9 @@ def test_graph_conv_regression_model():
		model = TensorGraph.load_from_dir(model.model_dir)
		scores = model.evaluate(dataset, [metric], transformers)


		def test_graph_conv_error_bars():
		tasks, dataset, transformers, metric = get_dataset('regression', 'GraphConv')
		def test_graph_conv_error_bars(self):
		tasks, dataset, transformers, metric = self.get_dataset(
		'regression', 'GraphConv')

		batch_size = 50
		model = GraphConvTensorGraph(

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