def making_prediction(test_dataset, ggnn, sess, opt, original=False):
# For debugging purpose
nodes_representation = ggnn.nodes_representation
graph_representation = ggnn.graph_representation
logits = ggnn.logits
softmax_values = ggnn.softmax_values
attention_scores = ggnn.attention_scores
batch_iterator = ThreadedIterator(test_dataset.make_minibatch_iterator(),
max_queue_size=5)
correct_labels = []
predictions = []
attention_scores_data = []
softmax_values_data = []
print("--------------------------------------")
print('Computing training accuracy...')
for step, batch_data in enumerate(batch_iterator):
# print(batch_data["labels"])
print(batch_data['labels'])
softmax_values_data, attention_scores_data = sess.run(
[softmax_values, attention_scores],
feed_dict={
ggnn.placeholders["initial_node_representation"]:
batch_data["initial_representations"],
ggnn.placeholders["num_vertices"]:
batch_data["num_vertices"],
ggnn.placeholders["adjacency_matrix"]:
batch_data['adjacency_matrix'],
ggnn.placeholders["labels"]:
batch_data['labels']
})
correct_labels.extend(np.argmax(batch_data['labels'], axis=1))
argmax = np.argmax(softmax_values_data, axis=1)
predictions.extend(np.argmax(softmax_values_data, axis=1))
print("Probability : " + str(softmax_values_data))
print("Probability max : " +
str(np.argmax(softmax_values_data, axis=1)))
print("Correct class " + str(correct_labels))
print("Predicted class : " + str(predictions))
scaled_attention_scores_path, raw_attention_scores_path, raw_attention_scores_dict = generate_attention_scores(
opt.test_file, attention_scores_data[0])
prediction_results = {}
prediction_results[
"scaled_attention_scores_path"] = scaled_attention_scores_path
prediction_results["raw_attention_scores_path"] = raw_attention_scores_path
prediction_results["raw_attention_scores_dict"] = raw_attention_scores_dict
prediction_results["softmax_values_data"] = softmax_values_data
prediction_results["predicted_label"] = argmax[0]
prediction_results["correct_label"] = np.argmax(batch_data['labels'],
axis=1)
return prediction_results
def making_prediction(test_dataset, ggnn, sess, opt):
# For debugging purpose
nodes_representation = ggnn.nodes_representation
graph_representation = ggnn.graph_representation
logits = ggnn.logits
softmax_values = ggnn.softmax_values
attention_scores = ggnn.attention_scores
batch_iterator = ThreadedIterator(test_dataset.make_minibatch_iterator(), max_queue_size=5)
correct_labels = []
predictions = []
print("--------------------------------------")
print('Computing training accuracy...')
for step, batch_data in enumerate(batch_iterator):
# print(batch_data["labels"])
print(batch_data['labels'])
softmax_values_data, attention_scores_data = sess.run(
[softmax_values, attention_scores],
feed_dict={
ggnn.placeholders["initial_node_representation"]: batch_data["initial_representations"],
ggnn.placeholders["num_vertices"]: batch_data["num_vertices"],
ggnn.placeholders["adjacency_matrix"]: batch_data['adjacency_matrix'],
ggnn.placeholders["labels"]: batch_data['labels']
}
)
# print(attention_scores_data)
# print(len(attention_scores_data[0]))
correct_labels.extend(np.argmax(batch_data['labels'],axis=1))
argmax = np.argmax(softmax_values_data,axis=1)
predictions.extend(np.argmax(softmax_values_data,axis=1))
print("Probability : " + str(softmax_values_data))
print("Probability max : " + str(np.argmax(softmax_values_data,axis=1)))
print("Correct class " + str(correct_labels[0]))
print("Predicted class : " + str(predictions[0]))
attention_path, raw_attention_score_dict = generate_attention_scores(opt, attention_scores_data[0])
generate_subtree(opt, opt.stmt_ids_path, raw_attention_score_dict)
print(attention_path)
print(opt.pb_path)
generate_visualization(opt.pb_path,attention_path)
return softmax_values_data, argmax, str(correct_labels[0]), str(predictions[0])
def making_prediction(graph_path, opt, ggnn, sess):
# For debugging purpose
softmax_values = ggnn.softmax_values
attention_scores = ggnn.attention_scores
opt.test_graph_path = graph_path
test_dataset = MonoLanguageProgramData(opt, False, False, True)
batch_iterator = ThreadedIterator(test_dataset.make_minibatch_iterator(), max_queue_size=5)
for step, batch_data in enumerate(batch_iterator):
# print(batch_data["labels"])
print(batch_data['labels'])
softmax_values_data, attention_scores_data = sess.run(
[softmax_values, attention_scores],
feed_dict={
ggnn.placeholders["initial_node_representation"]: batch_data["initial_representations"],
ggnn.placeholders["num_vertices"]: batch_data["num_vertices"],
ggnn.placeholders["adjacency_matrix"]: batch_data['adjacency_matrix'],
ggnn.placeholders["labels"]: batch_data['labels']
}
)
predicted_label = np.argmax(softmax_values_data,axis=1)
print("Probability : " + str(softmax_values_data))
print("Probability max : " + str(np.argmax(softmax_values_data,axis=1)))
return predicted_label[0]
def main(opt):
with open(opt.pretrained_embeddings_url, 'rb') as fh:
embeddings, embed_lookup = pickle.load(fh,encoding='latin1')
opt.pretrained_embeddings = embeddings
opt.pretrained_embed_lookup = embed_lookup
print("Finished loading pretrained embeddings......")
checkfile = os.path.join(opt.model_path, 'cnn_tree.ckpt')
ckpt = tf.train.get_checkpoint_state(opt.model_path)
test_dataset = MonoLanguageProgramData(opt, False, True)
opt.n_edge_types = test_dataset.n_edge_types
print("Num edge types : " + str(opt.n_edge_types))
ggnn = DenseGGNNModel(opt)
# For debugging purpose
nodes_representation = ggnn.nodes_representation
graph_representation = ggnn.graph_representation
logits = ggnn.logits
softmax_values = ggnn.softmax_values
saver = tf.train.Saver(save_relative_paths=True, max_to_keep=5)
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
if ckpt and ckpt.model_checkpoint_path:
print("Continue training with old model")
print("Checkpoint path : " + str(ckpt.model_checkpoint_path))
saver.restore(sess, ckpt.model_checkpoint_path)
for i, var in enumerate(saver._var_list):
print('Var {}: {}'.format(i, var))
correct_labels = []
predictions = []
print('Computing training accuracy...')
batch_iterator = ThreadedIterator(test_dataset.make_minibatch_iterator(), max_queue_size=5)
for step, batch_data in enumerate(batch_iterator):
# print(batch_data["labels"])
softmax_values_data = sess.run(
[softmax_values],
feed_dict={
ggnn.placeholders["initial_node_representation"]: batch_data["initial_representations"],
ggnn.placeholders["num_vertices"]: batch_data["num_vertices"],
ggnn.placeholders["adjacency_matrix"]: batch_data['adjacency_matrix'],
ggnn.placeholders["labels"]: batch_data['labels']
}
)
correct_labels.extend(np.argmax(batch_data['labels'],axis=1))
predictions.extend(np.argmax(softmax_values_data[0],axis=1))
print("Num target : " + str(len(correct_labels)))
# print(correct_labels)
# print(predictions)
target_names = [str(i) for i in range(1,11)]
print('Accuracy:', accuracy_score(correct_labels, predictions))
print(classification_report(correct_labels, predictions, target_names=target_names))
print(confusion_matrix(correct_labels, predictions))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--workers',
type=int,
help='number of data loading workers',
default=2)
parser.add_argument('--train_batch_size',
type=int,
default=10,
help='input batch size')
parser.add_argument('--test_batch_size',
type=int,
default=5,
help='input batch size')
parser.add_argument('--state_dim',
type=int,
default=30,
help='GGNN hidden state dimension size')
parser.add_argument('--node_dim',
type=int,
default=100,
help='node dimension size')
parser.add_argument('--hidden_layer_size',
type=int,
default=200,
help='size of hidden layer')
parser.add_argument('--num_hidden_layer',
type=int,
default=1,
help='number of hidden layer')
parser.add_argument('--n_steps',
type=int,
default=10,
help='propogation steps number of GGNN')
parser.add_argument('--lr',
type=float,
default=0.001,
help='learning rate')
parser.add_argument('--cuda', action='store_true', help='enables cuda')
parser.add_argument('--verbal',
type=bool,
default=True,
help='print training info or not')
parser.add_argument('--manualSeed', type=int, help='manual seed')
parser.add_argument(
'--test_file',
default="program_data/test_data/5/100_dead_code_1.java",
help="test program")
parser.add_argument('--n_classes',
type=int,
default=10,
help='manual seed')
parser.add_argument('--path',
default="program_data/github_java_sort_function_babi",
help='program data')
parser.add_argument('--model_path',
default="model",
help='path to save the model')
parser.add_argument('--n_hidden',
type=int,
default=50,
help='number of hidden layers')
parser.add_argument('--size_vocabulary',
type=int,
default=59,
help='maximum number of node types')
parser.add_argument('--log_path',
default="logs/",
help='log path for tensorboard')
parser.add_argument(
'--aggregation',
type=int,
default=1,
choices=range(0, 4),
help=
'0 for max pooling, 1 for attention with sum pooling, 2 for attention with max pooling, 3 for attention with average pooling'
)
parser.add_argument('--distributed_function',
type=int,
default=0,
choices=range(0, 2),
help='0 for softmax, 1 for sigmoid')
parser.add_argument(
'--pretrained_embeddings_url',
default="embedding/fast_pretrained_vectors.pkl.gz",
help=
'pretrained embeddings url, there are 2 objects in this file, the first object is the embedding matrix, the other is the lookup dictionary'
)
parser.add_argument('argv', nargs="+", help='filenames')
opt = parser.parse_args()
print(opt)
opt.model_path = os.path.join(
opt.model_path, "sum_softmax" + "_hidden_layer_size_" +
str(opt.hidden_layer_size) + "_num_hidden_layer_" +
str(opt.num_hidden_layer)) + "_node_dim_" + str(opt.node_dim)
if len(opt.argv) == 1:
opt.test_file = opt.argv[0]
# Create model path folder if not exists
if not os.path.exists(opt.model_path):
print("Cannot find path : " + opt.model_path)
generate_files(opt, opt.test_file)
# if not os.path.exists(opt.pretrained_embeddings_url):
# fetch_data_from_github(opt.pretrained_embeddings_url)
with gzip.open(opt.pretrained_embeddings_url, 'rb') as fh:
embeddings, embed_lookup = pickle.load(fh, encoding='latin1')
opt.pretrained_embeddings = embeddings
opt.pretrained_embed_lookup = embed_lookup
checkfile = os.path.join(opt.model_path, 'cnn_tree.ckpt')
# for f in ['checkpoint', 'cnn_tree.ckpt.index', 'cnn_tree.ckpt.meta', 'cnn_tree.ckpt.data-00000-of-00001']:
# filename = os.path.join(opt.model_path, f)
# if not os.path.exists(filename):
# fetch_data_from_github(filename)
ckpt = tf.train.get_checkpoint_state(opt.model_path)
test_dataset = MonoLanguageProgramData(opt, False, False, True)
# opt.n_edge_types = test_dataset.n_edge_types
opt.n_edge_types = 7
ggnn = DenseGGNNModel(opt)
# For debugging purpose
nodes_representation = ggnn.nodes_representation
graph_representation = ggnn.graph_representation
logits = ggnn.logits
softmax_values = ggnn.softmax_values
attention_scores = ggnn.attention_scores
saver = tf.train.Saver(save_relative_paths=True, max_to_keep=5)
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
if ckpt and ckpt.model_checkpoint_path:
print("Continue training with old model")
print("Checkpoint path : " + str(ckpt.model_checkpoint_path))
saver.restore(sess, ckpt.model_checkpoint_path)
for i, var in enumerate(saver._var_list):
print('Var {}: {}'.format(i, var))
correct_labels = []
predictions = []
print('Computing training accuracy...')
batch_iterator = ThreadedIterator(
test_dataset.make_minibatch_iterator(), max_queue_size=5)
for step, batch_data in enumerate(batch_iterator):
# print(batch_data["labels"])
softmax_values_data, attention_scores_data = sess.run(
[softmax_values, attention_scores],
feed_dict={
ggnn.placeholders["initial_node_representation"]:
batch_data["initial_representations"],
ggnn.placeholders["num_vertices"]:
batch_data["num_vertices"],
ggnn.placeholders["adjacency_matrix"]:
batch_data['adjacency_matrix'],
ggnn.placeholders["labels"]:
batch_data['labels']
})
print(softmax_values_data)
# print(attention_scores_data)
# print(len(attention_scores_data[0]))
correct_labels.extend(np.argmax(batch_data['labels'], axis=1))
predictions.extend(np.argmax(softmax_values_data, axis=1))
print("Num target : " + str(len(correct_labels)))
print("True label : " + str(correct_labels[0]))
print("Predicted label : " + str(predictions[0]))
attention_path = generate_attention_scores(opt,
attention_scores_data[0])
print(attention_path)
print(opt.pb_path)
generate_visualization(opt.pb_path, attention_path)
def main(opt):
opt.model_path = os.path.join(opt.model_path, form_model_path(opt))
checkfile = os.path.join(opt.model_path, 'cnn_tree.ckpt')
ckpt = tf.train.get_checkpoint_state(opt.model_path)
print("The model path : " + str(checkfile))
print("Loss : " + str(opt.loss))
if ckpt and ckpt.model_checkpoint_path:
print("Continue training with old model : " + str(checkfile))
print("Loading vocabs.........")
node_type_lookup, node_token_lookup, subtree_lookup = load_vocabs(opt)
opt.node_type_lookup = node_type_lookup
opt.node_token_lookup = node_token_lookup
opt.subtree_lookup = subtree_lookup
if opt.task == 1:
train_dataset = CodeClassificationData(opt, True, False, False)
if opt.task == 0:
val_opt = copy.deepcopy(opt)
val_opt.node_token_lookup = node_token_lookup
validation_dataset = CodeClassificationData(val_opt, False, False,
True)
print("Initializing tree caps model...........")
corder = CorderModel(opt)
print("Finished initializing corder model...........")
loss_node = corder.loss
optimizer = RAdamOptimizer(opt.lr)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
training_point = optimizer.minimize(loss_node)
saver = tf.train.Saver(save_relative_paths=True, max_to_keep=5)
init = tf.global_variables_initializer()
# best_f1_score = get_best_f1_score(opt)
# print("Best f1 score : " + str(best_f1_score))
with tf.Session() as sess:
sess.run(init)
if ckpt and ckpt.model_checkpoint_path:
print("Continue training with old model")
print("Checkpoint path : " + str(ckpt.model_checkpoint_path))
saver.restore(sess, ckpt.model_checkpoint_path)
for i, var in enumerate(saver._var_list):
print('Var {}: {}'.format(i, var))
if opt.task == 1:
for epoch in range(1, opt.epochs + 1):
train_batch_iterator = ThreadedIterator(
train_dataset.make_minibatch_iterator(),
max_queue_size=opt.worker)
train_accs = []
for train_step, train_batch_data in enumerate(
train_batch_iterator):
print("--------------------------")
# print(train_batch_data["batch_subtrees_ids"])
logging.info(str(train_batch_data["batch_subtree_id"]))
_, err = sess.run(
[training_point, corder.loss],
feed_dict={
corder.placeholders["node_types"]:
train_batch_data["batch_node_types"],
corder.placeholders["node_tokens"]:
train_batch_data["batch_node_tokens"],
corder.placeholders["children_indices"]:
train_batch_data["batch_children_indices"],
corder.placeholders["children_node_types"]:
train_batch_data["batch_children_node_types"],
corder.placeholders["children_node_tokens"]:
train_batch_data["batch_children_node_tokens"],
corder.placeholders["labels"]:
train_batch_data["batch_subtree_id"],
corder.placeholders["dropout_rate"]:
0.3
})
logging.info("Training at epoch " + str(epoch) +
" and step " + str(train_step) +
" with loss " + str(err))
print("Epoch:", epoch, "Step:", train_step,
"Training loss:", err)
if train_step % opt.checkpoint_every == 0 and train_step > 0:
saver.save(sess, checkfile)
print('Checkpoint saved, epoch:' + str(epoch) +
', step: ' + str(train_step) + ', loss: ' +
str(err) + '.')
if opt.task == 0:
validation_batch_iterator = ThreadedIterator(
validation_dataset.make_minibatch_iterator(),
max_queue_size=opt.worker)
for val_step, val_batch_data in enumerate(
validation_batch_iterator):
scores = sess.run(
[corder.code_vector],
feed_dict={
corder.placeholders["node_types"]:
val_batch_data["batch_node_types"],
corder.placeholders["node_tokens"]:
val_batch_data["batch_node_tokens"],
corder.placeholders["children_indices"]:
val_batch_data["batch_children_indices"],
corder.placeholders["children_node_types"]:
val_batch_data["batch_children_node_types"],
corder.placeholders["children_node_tokens"]:
val_batch_data["batch_children_node_tokens"],
corder.placeholders["dropout_rate"]:
0.0
})
for i, vector in enumerate(scores[0]):
file_name = "analysis/rosetta_sampled_softmax_train.csv"
with open(file_name, "a") as f:
vector_score = []
for score in vector:
vector_score.append(str(score))
# print(val_batch_data["batch_file_path"])
line = str(val_batch_data["batch_file_path"]
[i]) + "," + " ".join(vector_score)
f.write(line)
f.write("\n")
def main(opt):
with open(opt.pretrained_embeddings_url, 'rb') as fh:
embeddings, embed_lookup = pickle.load(fh,encoding='latin1')
opt.pretrained_embeddings = embeddings
opt.pretrained_embed_lookup = embed_lookup
checkfile = os.path.join(opt.model_path, 'cnn_tree.ckpt')
ckpt = tf.train.get_checkpoint_state(opt.model_path)
train_dataset = MonoLanguageProgramData(opt, True, False)
test_dataset = MonoLanguageProgramData(opt, False, True)
opt.n_edge_types = train_dataset.n_edge_types
ggnn = DenseGGNNModel(opt)
# For debugging purpose
nodes_representation = ggnn.nodes_representation
graph_representation = ggnn.graph_representation
logits = ggnn.logits
softmax_values = ggnn.softmax_values
attention_scores = ggnn.attention_scores
loss_node = ggnn.loss
optimizer = tf.train.AdamOptimizer(opt.lr)
training_point = optimizer.minimize(loss_node)
saver = tf.train.Saver(save_relative_paths=True, max_to_keep=5)
init = tf.global_variables_initializer()
# with open("model_selection.txt","r") as f:
with tf.Session() as sess:
sess.run(init)
print("List of available devices..........")
print(tf.test.gpu_device_name())
if ckpt and ckpt.model_checkpoint_path:
print("Continue training with old model")
print("Checkpoint path : " + str(ckpt.model_checkpoint_path))
saver.restore(sess, ckpt.model_checkpoint_path)
for i, var in enumerate(saver._var_list):
print('Var {}: {}'.format(i, var))
best_accuracy = opt.best_accuracy
for epoch in range(1, opt.epochs + 1):
train_batch_iterator = ThreadedIterator(train_dataset.make_minibatch_iterator(), max_queue_size=5)
for train_step, train_batch_data in enumerate(train_batch_iterator):
# print(batch_data["labels"])
_ , err, softmax_values_data, attention_scores_data = sess.run(
[training_point, loss_node, softmax_values, attention_scores],
feed_dict={
ggnn.placeholders["initial_node_representation"]: train_batch_data["initial_representations"],
ggnn.placeholders["num_vertices"]: train_batch_data["num_vertices"],
ggnn.placeholders["adjacency_matrix"]: train_batch_data['adjacency_matrix'],
ggnn.placeholders["labels"]: train_batch_data['labels']
}
)
print("Epoch:", epoch, "Step:",train_step,"Loss:", err, "Best Accuracy:", best_accuracy)
if train_step % opt.checkpoint_every == 0:
# saver.save(sess, checkfile)
# print('Checkpoint saved, epoch:' + str(epoch) + ', step: ' + str(step) + ', loss: ' + str(err) + '.')
# Validating
#--------------------------------------
print("Validating.......")
correct_labels = []
predictions = []
test_batch_iterator = ThreadedIterator(test_dataset.make_minibatch_iterator(), max_queue_size=5)
for test_step, test_batch_data in enumerate(test_batch_iterator):
softmax_values_data = sess.run(
[softmax_values],
feed_dict={
ggnn.placeholders["initial_node_representation"]: test_batch_data["initial_representations"],
ggnn.placeholders["num_vertices"]: test_batch_data["num_vertices"],
ggnn.placeholders["adjacency_matrix"]: test_batch_data['adjacency_matrix'],
ggnn.placeholders["labels"]: test_batch_data['labels']
}
)
correct_labels.extend(np.argmax(test_batch_data['labels'],axis=1))
predictions.extend(np.argmax(softmax_values_data[0],axis=1))
print("Num target : " + str(len(correct_labels)))
print(correct_labels)
print(predictions)
target_names = [str(i) for i in range(1,11)]
accuracy = float(accuracy_score(correct_labels, predictions))
print('Accuracy:', accuracy)
print(classification_report(correct_labels, predictions, target_names=target_names))
print(confusion_matrix(correct_labels, predictions))
if accuracy > best_accuracy:
best_accuracy = accuracy
saver.save(sess, checkfile)
print('Checkpoint saved, epoch:' + str(epoch) + ', step: ' + str(train_step) + ', loss: ' + str(err) + '.')
def main(opt):
opt.model_path = os.path.join(opt.model_path, form_model_path(opt))
checkfile = os.path.join(opt.model_path, 'cnn_tree.ckpt')
ckpt = tf.train.get_checkpoint_state(opt.model_path)
if ckpt and ckpt.model_checkpoint_path:
print("Continue training with old model : " + str(checkfile))
print("Loading vocabs.........")
train_label_lookup, node_type_lookup, node_token_lookup, val_label_lookup = load_vocabs(opt)
opt.label_lookup = train_label_lookup
opt.label_size = len(train_label_lookup.keys())
opt.node_type_lookup = node_type_lookup
opt.node_token_lookup = node_token_lookup
if opt.task == 1:
train_dataset = MethodNamePredictionData(opt, opt.train_path, True, False, False)
val_opt = copy.deepcopy(opt)
val_opt.label_lookup = val_label_lookup
val_opt.num_labels = len(val_label_lookup.keys())
val_opt.node_token_lookup = node_token_lookup
validation_dataset = MethodNamePredictionData(val_opt, opt.val_path, False, False, True)
print("Initializing tree caps model...........")
treecaps = TreeCapsModel(opt)
# network.init_net_treecaps(30,30)
print("Finished initializing tree caps model...........")
code_caps = treecaps.code_caps
loss_node = treecaps.loss
softmax_values = treecaps.softmax_values
logits = treecaps.logits
optimizer = RAdamOptimizer(opt.lr)
# optimizer = tf.compat.v1.train.AdamOptimizer(opt.lr)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
training_point = optimizer.minimize(loss_node)
saver = tf.train.Saver(save_relative_paths=True, max_to_keep=5)
init = tf.global_variables_initializer()
best_f1_score = get_best_f1_score(opt)
print("Best f1 score : " + str(best_f1_score))
num_caps_top_a = int(opt.num_conv*opt.output_size/opt.num_channel)*opt.top_a
with tf.Session() as sess:
sess.run(init)
if ckpt and ckpt.model_checkpoint_path:
print("Continue training with old model")
print("Checkpoint path : " + str(ckpt.model_checkpoint_path))
saver.restore(sess, ckpt.model_checkpoint_path)
for i, var in enumerate(saver._var_list):
print('Var {}: {}'.format(i, var))
validation_batch_iterator = ThreadedIterator(validation_dataset.make_minibatch_iterator(), max_queue_size=5)
# f1_scores_of_val_data = []
all_predicted_labels = []
all_ground_truth_labels = []
for val_step, val_batch_data in enumerate(validation_batch_iterator):
alpha_IJ_shape = (opt.batch_size, int(num_caps_top_a/opt.top_a*val_batch_data["batch_node_types"].shape[1]), num_caps_top_a)
alpha_IJ = np.zeros(alpha_IJ_shape)
scores, alpha_IJ_scores= sess.run(
[logits, treecaps.alpha_IJ],
feed_dict={
treecaps.placeholders["node_types"]: val_batch_data["batch_node_types"],
treecaps.placeholders["node_tokens"]: val_batch_data["batch_node_tokens"],
treecaps.placeholders["children_indices"]: val_batch_data["batch_children_indices"],
treecaps.placeholders["children_node_types"]: val_batch_data["batch_children_node_types"],
treecaps.placeholders["children_node_tokens"]: val_batch_data["batch_children_node_tokens"],
treecaps.placeholders["labels"]: val_batch_data["batch_labels"],
treecaps.placeholders["alpha_IJ"]: alpha_IJ,
treecaps.placeholders["is_training"]: False
}
)
alpha_IJ_scores = np.reshape(alpha_IJ_scores, (opt.batch_size, val_batch_data["batch_node_types"].shape[1], 8, opt.top_a, 8))
alpha_IJ_scores = np.sum(alpha_IJ_scores, axis=2)
alpha_IJ_scores = np.sum(alpha_IJ_scores, axis=3)
alpha_IJ_scores = np.squeeze(alpha_IJ_scores, axis=0)
alpha_IJ_scores = np.transpose(alpha_IJ_scores)
predictions = np.argmax(scores, axis=1)
ground_truths = np.argmax(val_batch_data['batch_labels'], axis=1)
predicted_labels = []
for prediction in predictions:
predicted_labels.append(train_label_lookup.inverse[prediction])
ground_truth_labels = []
for ground_truth in ground_truths:
ground_truth_labels.append(
val_label_lookup.inverse[ground_truth])
f1_score = evaluation.calculate_f1_scores(predicted_labels, ground_truth_labels)
print(ground_truth_labels)
print(predicted_labels)
print("F1:", f1_score, "Step:", val_step)
if f1_score > 0:
node_types = val_batch_data["batch_node_types"][0]
node_tokens_text = val_batch_data["batch_node_tokens_text"][0]
node_indexes = val_batch_data["batch_node_indexes"][0]
file_path = val_batch_data["batch_file_path"][0]
file_path_splits = file_path.split("/")
file_path_splits[1] = "java-small"
file_path_splits[len(file_path_splits) - 1] = file_path_splits[len(file_path_splits) - 1].replace(".pkl",".java")
file_path = "/".join(file_path_splits)
analysis_folder = os.path.join("analysis", "_".join(file_path_splits[-2:]).replace(".java",""))
try:
from pathlib import Path
Path(analysis_folder).mkdir(parents=True, exist_ok=True)
except Exception as e:
print(e)
count = 0
for capsule in alpha_IJ_scores:
# print(val_batch_data["batch_node_indexes"])
connection_strength = capsule
all_tuples = []
for i, node_index in enumerate(node_indexes):
tuple_of_info = []
tuple_of_info.append(str(node_indexes[i]))
tuple_of_info.append(str(node_types[i]))
tuple_of_info.append(str(connection_strength[i]))
tuple_of_info.append(node_tokens_text[i])
tuple_of_info = tuple(tuple_of_info)
# print(tuple_of_info)
all_tuples.append(tuple_of_info)
all_tuples = sorted(all_tuples, key=lambda x: x[2])
all_tuples.reverse()
with open(os.path.join(analysis_folder, "Group_" + str(count) + ".txt"), "w") as f:
for t in all_tuples:
line = ";".join(list(t))
f.write(line)
f.write("\n")
with open(os.path.join(analysis_folder, "result.txt"), "w") as f1:
f1.write("Predicted : " + str(predicted_labels[0]))
f1.write("\n")
f1.write("Ground truth : " + str(ground_truth_labels[0]))
f1.write("\n")
import shutil
try:
print("Trying to copy original source file....")
shutil.copy(file_path, analysis_folder)
except Exception as e:
print(e)
count += 1
def run_epoch(self, epoch_name: str, epoch_num, data, is_training: bool):
loss = 0
mean_edge_loss = 0
mean_node_loss = 0
mean_kl_loss = 0
mean_qed_loss = 0
node_loss_error = -10000000
node_pred_error = 0
start_time = time.time()
processed_graphs = 0
if is_training and self.params['num_teacher_forcing'] >= epoch_num:
teacher_forcing = True
else:
teacher_forcing = False
batch_iterator = ThreadedIterator(
self.make_minibatch_iterator(data, is_training),
max_queue_size=self.params['batch_size']
) # self.params['batch_size'])
for step, batch_data in enumerate(batch_iterator):
num_graphs = batch_data[self.placeholders['num_graphs']]
processed_graphs += num_graphs
batch_data[self.placeholders['is_generative']] = False
batch_data[self.placeholders[
'use_teacher_forcing_nodes']] = teacher_forcing
batch_data[
self.placeholders['z_prior']] = utils.generate_std_normal(
self.params['batch_size'],
batch_data[self.placeholders['num_vertices']],
self.params['hidden_size_encoder'])
if is_training:
batch_data[self.placeholders[
'out_layer_dropout_keep_prob']] = self.params[
'out_layer_dropout_keep_prob']
fetch_list = [
self.ops['loss'], self.ops['train_step'],
self.ops["edge_loss"], self.ops['kl_loss'],
self.ops['node_symbol_prob'],
self.placeholders['node_symbols'],
self.ops['qed_computed_values'],
self.placeholders['target_values'],
self.ops['total_qed_loss'], self.ops['mean'],
self.ops['logvariance'], self.ops['grads'],
self.ops['mean_edge_loss'],
self.ops['mean_node_symbol_loss'],
self.ops['mean_kl_loss'], self.ops['mean_total_qed_loss'],
self.ops['grads2'], self.ops['node_loss_error'],
self.ops['node_pred_error']
]
else:
batch_data[
self.placeholders['out_layer_dropout_keep_prob']] = 1.0
fetch_list = [
self.ops['loss'], self.ops['mean_edge_loss'],
self.ops['mean_node_symbol_loss'],
self.ops['mean_kl_loss'], self.ops['mean_total_qed_loss'],
self.ops['sampled_atoms'], self.ops['node_loss_error'],
self.ops['node_pred_error']
]
result = self.sess.run(fetch_list, feed_dict=batch_data)
batch_loss = result[0]
loss += batch_loss * num_graphs
if is_training:
mean_edge_loss += result[12] * num_graphs
mean_node_loss += result[13] * num_graphs
mean_kl_loss += result[14] * num_graphs
mean_qed_loss += result[15] * num_graphs
node_loss_error = max(node_loss_error, np.max(result[17]))
node_pred_error += result[18]
else:
mean_edge_loss += result[1] * num_graphs
mean_node_loss += result[2] * num_graphs
mean_kl_loss += result[3] * num_graphs
mean_qed_loss += result[4] * num_graphs
node_loss_error = max(node_loss_error, np.max(result[6]))
node_pred_error += result[7]
print(
"Running %s, batch %i (has %i graphs). Total loss: %.4f. Edge loss: %.4f. Node loss: %.4f. KL loss: %.4f. Property loss: %.4f. Node error: %.4f. Node pred: %.4f."
% (epoch_name, step, num_graphs, loss / processed_graphs,
mean_edge_loss / processed_graphs, mean_node_loss /
processed_graphs, mean_kl_loss / processed_graphs,
mean_qed_loss / processed_graphs, node_loss_error,
node_pred_error / processed_graphs),
end='\r')
mean_edge_loss /= processed_graphs
mean_node_loss /= processed_graphs
mean_kl_loss /= processed_graphs
mean_qed_loss /= processed_graphs
loss = loss / processed_graphs
instance_per_sec = processed_graphs / (time.time() - start_time)
return loss, mean_edge_loss, mean_node_loss, mean_kl_loss, mean_qed_loss, instance_per_sec
def main(opt):
from pathlib import Path
mis_prediction_path = os.path.join("mis_prediction", opt.transformation)
Path(mis_prediction_path).mkdir(parents=True, exist_ok=True)
opt.model_path = os.path.join(opt.model_path, form_model_path(opt))
checkfile = os.path.join(opt.model_path, 'cnn_tree.ckpt')
ckpt = tf.train.get_checkpoint_state(opt.model_path)
if ckpt and ckpt.model_checkpoint_path:
print("Continue training with old model : " + str(checkfile))
train_label_lookup, node_type_lookup, node_token_lookup, val_label_lookup = load_vocabs(
opt)
opt.label_lookup = train_label_lookup
opt.num_labels = len(train_label_lookup.keys())
opt.node_type_lookup = node_type_lookup
opt.node_token_lookup = node_token_lookup
if opt.task == 1:
train_dataset = MethodNamePredictionData(opt, opt.train_path, True,
False, False)
val_opt = copy.deepcopy(opt)
val_opt.label_lookup = val_label_lookup
val_opt.num_labels = len(val_label_lookup.keys())
val_opt.node_token_lookup = node_token_lookup
validation_dataset = MethodNamePredictionData(val_opt, opt.val_path, False,
False, True)
ggnn = DenseGGNNModel(opt)
# For debugging purpose
nodes_representation = ggnn.nodes_representation
graph_representation = ggnn.graph_representation
logits = ggnn.logits
label_embeddings = ggnn.label_embeddings
softmax_values = ggnn.softmax_values
attention_scores = ggnn.attention_scores
loss_node = ggnn.loss
optimizer = tf.compat.v1.train.AdamOptimizer(opt.lr)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
training_point = optimizer.minimize(loss_node)
saver = tf.train.Saver(save_relative_paths=True, max_to_keep=5)
init = tf.global_variables_initializer()
best_f1_score = get_best_f1_score(opt)
print("Best f1 score : " + str(best_f1_score))
with tf.Session() as sess:
sess.run(init)
if ckpt and ckpt.model_checkpoint_path:
print("Continue training with old model")
print("Checkpoint path : " + str(ckpt.model_checkpoint_path))
saver.restore(sess, ckpt.model_checkpoint_path)
for i, var in enumerate(saver._var_list):
print('Var {}: {}'.format(i, var))
print("Testing model.............")
average_f1 = 0.0
validation_batch_iterator = ThreadedIterator(
validation_dataset.make_minibatch_iterator(), max_queue_size=5)
all_predicted_labels = []
all_ground_truth_labels = []
all_paths = []
for val_step, val_batch_data in enumerate(
validation_batch_iterator):
print("----------------------------------------")
label_embeddings_matrix, scores = sess.run(
[label_embeddings, logits],
feed_dict={
ggnn.placeholders["num_vertices"]:
val_batch_data["num_vertices"],
ggnn.placeholders["adjacency_matrix"]:
val_batch_data['adjacency_matrix'],
ggnn.placeholders["node_type_indices"]:
val_batch_data["node_type_indices"],
ggnn.placeholders["node_token_indices"]:
val_batch_data["node_token_indices"],
ggnn.placeholders["is_training"]:
False
})
predictions = np.argmax(scores, axis=1)
ground_truths = np.argmax(val_batch_data['labels'], axis=1)
predicted_labels = []
for prediction in predictions:
predicted_labels.append(
train_label_lookup.inverse[prediction])
ground_truth_labels = []
for ground_truth in ground_truths:
ground_truth_labels.append(
val_label_lookup.inverse[ground_truth])
# all_predicted_labels.extend(predicted_labels)
# all_ground_truth_labels.extend(ground_truth_labels)
for i, file_path in enumerate(val_batch_data["paths"]):
ground_truth = ground_truth_labels[i]
predicted = predicted_labels[i]
with open(mis_prediction_path, "a") as f10:
line = file_path + "," + ground_truth + "," + predicted
f10.write(line)
f10.write("\n")
def main(opt):
train_label_lookup = {}
train_label_lookup_by_index = {}
train_node_type_lookup = {}
train_node_token_lookup = {}
val_label_lookup = {}
val_label_lookup_by_index = {}
val_node_type_lookup = {}
val_node_token_lookup = {}
node_type_vocabulary_path = "preprocessed_data/node_type_vocab.txt"
train_label_vocabulary_path = "preprocessed_data/train_label_vocab.txt"
train_token_vocabulary_path = "preprocessed_data/train_token_vocab.txt"
val_label_vocabulary_path = "preprocessed_data/val_label_vocab.txt"
val_token_vocabulary_path = "preprocessed_data/val_token_vocab.txt"
with open(train_label_vocabulary_path, "r") as f1:
data = f1.readlines()
for line in data:
splits = line.replace("\n","").split(",")
train_label_lookup[splits[1]] = int(splits[0])
train_label_lookup_by_index[int(splits[0])] = splits[1]
with open(node_type_vocabulary_path, "r") as f2:
data = f2.readlines()
for line in data:
splits = line.replace("\n","").split(",")
train_node_type_lookup[splits[1]] = int(splits[0])
with open(train_token_vocabulary_path, "r") as f3:
data = f3.readlines()
for line in data:
splits = line.replace("\n","").split(",")
train_node_token_lookup[splits[1]] = int(splits[0])
with open(val_label_vocabulary_path, "r") as f4:
data = f4.readlines()
for line in data:
splits = line.replace("\n","").split(",")
val_label_lookup[splits[1]] = int(splits[0])
val_label_lookup_by_index[int(splits[0])] = splits[1]
with open(val_token_vocabulary_path, "r") as f5:
data = f5.readlines()
for line in data:
splits = line.replace("\n","").split(",")
val_node_token_lookup[splits[1]] = int(splits[0])
train_node_token_lookup["captain_america"] = len(train_node_token_lookup.keys())
val_node_token_lookup["captain_america"] = len(val_node_token_lookup.keys())
checkfile = os.path.join(opt.model_path, 'cnn_tree.ckpt')
ckpt = tf.train.get_checkpoint_state(opt.model_path)
# print(train_label_lookup)
opt.label_lookup = train_label_lookup
opt.num_labels = len(train_label_lookup.keys())
opt.node_type_lookup = train_node_type_lookup
opt.node_token_lookup = train_node_token_lookup
opt.path = "sample_data/java-small-graph/training"
train_dataset = MethodNamePredictionData(opt, True, False, False)
opt.n_edge_types = train_dataset.n_edge_types
val_opt = copy.deepcopy(opt)
val_opt.path = "sample_data/java-small-graph/validation"
val_opt.label_lookup = val_label_lookup
val_opt.num_labels = len(val_label_lookup.keys())
val_opt.node_token_lookup = val_node_token_lookup
validation_dataset = MethodNamePredictionData(val_opt, False, False, True)
ggnn = DenseGGNNModel(opt)
# For debugging purpose
nodes_representation = ggnn.nodes_representation
graph_representation = ggnn.graph_representation
logits = ggnn.logits
softmax_values = ggnn.softmax_values
attention_scores = ggnn.attention_scores
loss_node = ggnn.loss
optimizer = tf.train.AdamOptimizer(opt.lr)
training_point = optimizer.minimize(loss_node)
saver = tf.train.Saver(save_relative_paths=True, max_to_keep=5)
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
if ckpt and ckpt.model_checkpoint_path:
print("Continue training with old model")
print("Checkpoint path : " + str(ckpt.model_checkpoint_path))
saver.restore(sess, ckpt.model_checkpoint_path)
for i, var in enumerate(saver._var_list):
print('Var {}: {}'.format(i, var))
for epoch in range(1, opt.epochs + 1):
train_batch_iterator = ThreadedIterator(train_dataset.make_minibatch_iterator(), max_queue_size=1)
for train_step, train_batch_data in enumerate(train_batch_iterator):
_ , err, softmax_values_data, attention_scores_data = sess.run(
[training_point, loss_node, softmax_values, attention_scores],
feed_dict={
ggnn.placeholders["num_vertices"]: train_batch_data["num_vertices"],
ggnn.placeholders["adjacency_matrix"]: train_batch_data['adjacency_matrix'],
ggnn.placeholders["labels"]: train_batch_data['labels'],
ggnn.placeholders["node_type_indices"]: train_batch_data["node_type_indices"],
ggnn.placeholders["node_token_indices"]: train_batch_data["node_token_indices"],
ggnn.placeholders["graph_state_keep_prob"]: 0.5,
ggnn.placeholders["edge_weight_dropout_keep_prob"]: 0.5
}
)
print("Epoch:", epoch, "Step:",train_step,"Loss:", err)
if train_step % opt.checkpoint_every == 0:
#--------------------------------------
print("Validating.......")
# predictions = []
validation_batch_iterator = ThreadedIterator(validation_dataset.make_minibatch_iterator(), max_queue_size=5)
for _, val_batch_data in enumerate(validation_batch_iterator):
# Note: putting ggnn.placeholders["labels"]: train_batch_data['labels'] seems stupid but it is a work-around, num labels in train data vs validation data is different
softmax_values_data = sess.run(
[softmax_values],
feed_dict={
ggnn.placeholders["num_vertices"]: val_batch_data["num_vertices"],
ggnn.placeholders["adjacency_matrix"]: val_batch_data['adjacency_matrix'],
ggnn.placeholders["labels"]: train_batch_data['labels'],
ggnn.placeholders["node_type_indices"]: val_batch_data["node_type_indices"],
ggnn.placeholders["node_token_indices"]: val_batch_data["node_token_indices"],
ggnn.placeholders["graph_state_keep_prob"]: 1.0,
ggnn.placeholders["edge_weight_dropout_keep_prob"]: 1.0
}
)
predictions = np.argmax(softmax_values_data[0],axis=1)
ground_truths = np.argmax(val_batch_data['labels'],axis=1)
# print(ground_truths)
predicted_labels = []
for prediction in predictions:
predicted_labels.append(train_label_lookup_by_index[prediction])
ground_truth_labels = []
for ground_truth in ground_truths:
ground_truth_labels.append(val_label_lookup_by_index[ground_truth])
predicted_labels = transform_data(predicted_labels)
ground_truth_labels = transform_data(ground_truth_labels)
print("----------")
print("Predicted: " + str(predicted_labels))
print("Ground truth: " + str(ground_truth_labels))
