Some fixes to tests (45153ea2) · Commits · 钟慕尧 / deepchem

deepchem/models/test/init.py

+2 −2

Original line number	Diff line number	Diff line
		@@ -73,14 +73,14 @@ class TestAPI(unittest.TestCase):
		evaluator = Evaluator(model, train_dataset, transformers, verbose=True)
		with tempfile.NamedTemporaryFile() as train_csv_out:
		with tempfile.NamedTemporaryFile() as train_stats_out:
		_, _ = evaluator.compute_model_performance(
		_, _, _ = evaluator.compute_model_performance(
		metrics, train_csv_out, train_stats_out)

		# Eval model on test
		evaluator = Evaluator(model, test_dataset, transformers, verbose=True)
		with tempfile.NamedTemporaryFile() as test_csv_out:
		with tempfile.NamedTemporaryFile() as test_stats_out:
		_, _ = evaluator.compute_model_performance(
		_, _, _ = evaluator.compute_model_performance(
		metrics, test_csv_out, test_stats_out)

		def _featurize_train_test_split(self, splittype, compound_featurizers,

+6 −2

Original line number	Diff line number	Diff line
		@@ -67,7 +67,7 @@ class Evaluator(object):
		colnames = ["task_name"] + [metric.name for metric in metrics]
		performance_df = pd.DataFrame(columns=colnames)

		ys, y_preds, ws = [], [], []
		nonempty_tasks, ys, y_preds, ws = [], [], [], []
		for i, task_name in enumerate(self.task_names):
		y = pred_y_df[task_name].values
		y_pred = pred_y_df["%s_pred" % task_name].values
		@@ -82,6 +82,7 @@ class Evaluator(object):
		# Sometimes all samples have zero weight. In this case, continue.
		if not len(y):
		continue
		nonempty_tasks.append(task_name)
		ys.append(y)
		y_preds.append(y_pred)
		ws.append(w)
		@@ -99,8 +100,11 @@ class Evaluator(object):
		all_scores = np.array(all_scores)
		# If there are any singletask_metrics
		if all_scores.shape[0] > 0:
		nonzero_ind = 0
		for i, task_name in enumerate(self.task_names):
		performance_df.loc[i] = [task_name] + list(all_scores[i])
		if task_name in nonempty_tasks:
		performance_df.loc[i] = [task_name] + list(all_scores[:, nonzero_ind])
		nonzero_ind += 1

		log("Saving predictions to %s" % csv_out, self.verbose)
		pred_y_df.to_csv(csv_out)