First support model numbers (few epochs, bad numbers) (57a12b0c) · Commits · 钟慕尧 / deepchem

deepchem/models/tf_keras_models/support_classifier.py

+21 −4

Original line number	Diff line number	Diff line
		@@ -149,7 +149,8 @@ class SupportGenerator(object):
		n_neg: int
		Number of negative samples.
		n_trials: int
		Number of support sets to sample from dataset.
		Number of passes over tasks to make. In total, n_tasks*n_trials
		support sets will be sampled by algorithm.
		replace: bool
		Whether to use sampling with or without replacement.
		"""
		@@ -334,10 +335,9 @@ class SupportGraphClassifier(Model):
		# Create different support sets
		for (task, support) in SupportGenerator(dataset, range(self.n_tasks),
		n_pos, n_neg, n_trials_per_epoch, replace):
		print("Sampled Support set")
		print("sampled support for task %s" % str(task))
		# Get batch to try it out on
		test = get_task_test(dataset, self.test_batch_size, task, replace)
		print("Obtained batch")
		feed_dict = self.construct_feed_dict(test, support)
		# Train on support set, batch pair
		self.sess.run(self.train_op, feed_dict=feed_dict)
		@@ -440,6 +440,12 @@ class SupportGraphClassifier(Model):

		TODO(rbharath): Does not currently support any transforms.
		TODO(rbharath): Only for 1 task at a time currently. Is there a better way?
		Parameters
		----------
		support: deepchem.datasets.Dataset
		The support dataset
		test: deepchem.datasets.Dataset
		The test dataset
		"""
		y_preds = []
		for (X_batch, y_batch, w_batch, ids_batch) in test.iterbatches(
		@@ -460,6 +466,10 @@ class SupportGraphClassifier(Model):
		# Get scores
		pred, scores = self.sess.run([self.pred_op, self.scores_op], feed_dict=feed_dict)
		y_pred_batch = np.round(scores)
		########################################################### DEBUG
		# Remove padded elements
		y_pred_batch = y_pred_batch[:n_samples]
		########################################################### DEBUG
		return y_pred_batch

		def predict_proba_on_batch(self, support, test_batch):
		@@ -472,6 +482,10 @@ class SupportGraphClassifier(Model):
		# Get scores
		pred, scores = self.sess.run([self.pred_op, self.scores_op], feed_dict=feed_dict)
		y_pred_batch = to_one_hot(np.round(pred))
		########################################################### DEBUG
		# Remove padded elements
		y_pred_batch = y_pred_batch[:n_samples]
		########################################################### DEBUG
		return y_pred_batch

		def evaluate(self, dataset, test_tasks, metric, n_pos=1,
		@@ -523,7 +537,10 @@ class SupportGraphClassifier(Model):
		print("Keeping support datapoints for eval.")
		task_dataset = get_task_dataset(dataset, task)
		y_pred = self.predict_proba(support, task_dataset)

		######################################################### DEBUG
		#print("task_dataset.y.shape, y_pred.shape, task_dataset.w.shape")
		#print(task_dataset.y.shape, y_pred.shape, task_dataset.w.shape)
		######################################################### DEBUG
		task_scores[task].append(metric.compute_metric(
		task_dataset.y, y_pred, task_dataset.w))

examples/low_data/tox_siamese_one_fold.py

+8 −1

Original line number	Diff line number	Diff line
		@@ -77,11 +77,18 @@ with tf.Session() as sess:
		learning_rate=1e-3, learning_rate_decay_time=1000,
		optimizer_type="adam", beta1=.9, beta2=.999, verbosity="high")

		############################################################ DEBUG
		print("FIT")
		############################################################ DEBUG
		model.fit(train_dataset, nb_epoch=1, n_trials_per_epoch=10, n_pos=n_pos,
		n_neg=n_neg, replace=False)
		model.save()

		############################################################ DEBUG
		print("EVAL")
		############################################################ DEBUG
		scores = model.evaluate(test_dataset, range(len(test_dataset.get_task_names())),
		metric, n_pos=n_pos, n_neg=n_neg, replace=replace)
		metric, n_pos=n_pos, n_neg=n_neg, replace=replace,
		n_trials=n_trials)
		print("Scores")
		print(scores)

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