Overfit AttnLSTM test runs to completion, fails due to accuracy (d848a1c0) · Commits · 钟慕尧 / deepchem

deepchem/models/tests/test_overfit.py

+3 −2

Original line number	Diff line number	Diff line
		@@ -792,8 +792,9 @@ class TestOverfitAPI(test_util.TensorFlowTestCase):

		# Eval model on train
		scores = model.evaluate(dataset, range(n_tasks),
		[classification_metric], n_trials=1)
		classification_metric, n_trials=1)
		print("scores")
		print(scores)

		assert scores[classification_metric.name] > .9
		# Measure performance on 0-th task.
		assert scores[0] > .9

deepchem/models/tf_keras_models/support_classifier.py

+15 −17

Original line number	Diff line number	Diff line
		@@ -373,6 +373,8 @@ class SupportGraphClassifier(Model):
		print("scores")
		print(scores)
		y_pred_batch = np.round(scores)
		print("y_pred_batch")
		print(y_pred_batch)
		########################################################## DEBUG
		return y_pred_batch

		@@ -388,11 +390,13 @@ class SupportGraphClassifier(Model):
		############################################### DEBUG
		print("scores.shape")
		print(scores.shape)
		y_pred_batch = scores
		y_pred_batch = to_one_hot(np.round(scores))
		print("y_pred_batch")
		print(y_pred_batch)
		############################################### DEBUG
		return y_pred_batch

		def evaluate(self, dataset, test_tasks, metrics, n_pos=1,
		def evaluate(self, dataset, test_tasks, metric, n_pos=1,
		n_neg=9, n_trials=1000):
		"""Evaluate performance of dataset on test_tasks according to metrics

		@@ -403,24 +407,18 @@ class SupportGraphClassifier(Model):
		is averaged and returned.
		"""
		# Get batches
		task_scores = {(task, metric.name): []
		for task in test_tasks for metric in metrics}
		task_scores = {task: [] for task in test_tasks}
		for (task, support) in SupportGenerator(dataset, test_tasks,
		n_pos, n_neg, n_trials):
		print("Sampled Support set.")
		task_dataset = get_task_dataset_minus_support(dataset, support, task)
		y_pred = self.predict_proba(support, task_dataset)

		#TODO(rbharath): Fix this up so numbers are meaningfully averaged across trials.
		for metric in metrics:
		task_scores[(task, metric.name)].append(metric.compute_metric(
		task_scores[task].append(metric.compute_metric(
		task_dataset.y, y_pred, task_dataset.w))

		## Join information for all tasks.
		#joint_task_scores = {}
		#for metric in metrics:
		# task_array = np.zeros(len(n_tasks))
		# for task in test_tasks:
		# task_array[task] = np.mean(np.array(task_scores[(task, metric.name)]))
		# joint_task_scores[metric.name] = task_array
		#return joint_task_scores
		# Join information for all tasks.
		mean_task_scores = {}
		for task in test_tasks:
		mean_task_scores[task] = np.mean(np.array(task_scores[task]))
		return mean_task_scores

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