def predict_model(args, logdir, infile, embedfile):
"""Test a classifier to label ASTs"""
with open(infile, 'rb') as fh:
_, trees, cv, labels = pickle.load(fh)
with open(embedfile, 'rb') as fh:
embeddings, embed_lookup = pickle.load(fh)
num_feats = len(embeddings[0])
# build the inputs and outputs of the network
nodes_node, children_node, hidden_node = network.init_net(
num_feats, len(labels))
out_node = network.out_layer(hidden_node)
### init the graph
sess = tf.Session() #config=tf.ConfigProto(device_count={'GPU':0}))
sess.run(tf.global_variables_initializer())
with tf.name_scope('saver'):
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(logdir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
else:
raise 'Checkpoint not found.'
correct_labels = []
# make predicitons from the input
predictions = []
step = 0
for batch in sampling.batch_samples(
sampling.gen_samples(trees, labels, embeddings, embed_lookup), 1):
nodes, children, batch_labels = batch
output = sess.run([out_node],
feed_dict={
nodes_node: nodes,
children_node: children,
})
correct_labels.append(np.argmax(batch_labels))
predictions.append(np.argmax(output))
# print(step, '/', len(trees))
orig_num = np.argmax(batch_labels)
orig_label = labels[orig_num] + ' \t'
orig_label = ""
pred_num = np.argmax(output)
pred_label = labels[pred_num]
pred_score = output[0][0][pred_num]
pred_item = str(step + 1) + '/' + str(len(trees))
if 'meta' in trees[step].keys() and 'name' in trees[step]['meta'].keys(
):
pred_item += " " + trees[step]['meta']['name']
print(pred_label + ' \t' + str(pred_score) + ' \t' + orig_label +
pred_item)
step += 1
def test_model(logdir, infile, embedfile):
"""Test a classifier to label ASTs"""
with open(infile, 'rb') as fh:
_, trees, labels = pickle.load(fh)
with open(embedfile, 'rb') as fh:
embeddings, embed_lookup = pickle.load(fh)
num_feats = len(embeddings[0])
# build the inputs and outputs of the network
nodes_node, children_node, hidden_node = network.init_net(
num_feats, len(labels))
out_node = network.out_layer(hidden_node)
### init the graph
sess = tf.Session() #config=tf.ConfigProto(device_count={'GPU':0}))
sess.run(tf.global_variables_initializer())
with tf.name_scope('saver'):
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(logdir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
else:
raise 'Checkpoint not found.'
correct_labels = []
# make predicitons from the input
predictions = []
step = 0
for batch in sampling.batch_samples(
sampling.gen_samples(trees, labels, embeddings, embed_lookup), 1):
nodes, children, batch_labels = batch
# print(nodes)
# print(children)
# print(batch_labels)
# return
output = sess.run([out_node],
feed_dict={
nodes_node: nodes,
children_node: children,
})
correct_labels.append(np.argmax(batch_labels))
predictions.append(np.argmax(output))
step += 1
print(step, '/', len(trees))
target_names = list(labels)
print('Accuracy:', accuracy_score(correct_labels, predictions))
print(
classification_report(correct_labels,
predictions,
target_names=target_names))
print(confusion_matrix(correct_labels, predictions))
def classify_item(logdir, infile, embedfile, label_file):
with open(label_file, 'rb') as fh:
labels = pickle.load(fh)
with open(infile, 'rb') as fh:
trees, labels = pickle.load(fh)
with open(embedfile, 'rb') as fh:
embeddings, embed_lookup = pickle.load(fh)
num_feats = len(embeddings[0])
# build the inputs and outputs of the network
nodes_node, children_node, hidden_node = network.init_net(
num_feats, len(labels))
out_node = network.out_layer(hidden_node)
### init the graph
sess = tf.Session() #config=tf.ConfigProto(device_count={'GPU':0}))
sess.run(tf.global_variables_initializer())
with tf.name_scope('saver'):
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(logdir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
else:
raise 'Checkpoint not found.'
for batch in sampling.batch_samples(
sampling.gen_samples(trees, labels, embeddings, embed_lookup), 1):
nodes, children, batch_labels = batch
# print(nodes)
# print(children)
# print(batch_labels)
# return
output = sess.run([out_node],
feed_dict={
nodes_node: nodes,
children_node: children,
})
print(labels[np.argmax(output)])
import classifier.tbcnn.sampling as sampling
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
def classify_item(logdir, infile, embedfile):
with open(embedfile, 'rb') as fh:
embeddings, embed_lookup = pickle.load(fh)
num_feats = len(embeddings[0])
# build the inputs and outputs of the network
nodes_node, children_node, hidden_node = network.init_net(
num_feats,
len(labels)
)
out_node = network.out_layer(hidden_node)
### init the graph
sess = tf.Session()#config=tf.ConfigProto(device_count={'GPU':0}))
sess.run(tf.global_variables_initializer())
with tf.name_scope('saver'):
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(logdir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
else:
raise 'Checkpoint not found.'
correct_labels = []
# make predicitons from the input
def train_model(logdir, infile, embedfile, epochs=EPOCHS):
"""Train a classifier to label ASTs"""
with open(infile, 'rb') as fh:
trees, _, labels = pickle.load(fh)
with open(embedfile, 'rb') as fh:
embeddings, embed_lookup = pickle.load(fh)
num_feats = len(embeddings[0])
# build the inputs and outputs of the network
nodes_node, children_node, hidden_node = network.init_net(
num_feats, len(labels))
out_node = network.out_layer(hidden_node)
labels_node, loss_node = network.loss_layer(hidden_node, len(labels))
optimizer = tf.train.AdamOptimizer(LEARN_RATE)
train_step = optimizer.minimize(loss_node)
tf.summary.scalar('loss', loss_node)
### init the graph
sess = tf.Session() #config=tf.ConfigProto(device_count={'GPU':0}))
sess.run(tf.global_variables_initializer())
with tf.name_scope('saver'):
saver = tf.train.Saver()
summaries = tf.summary.merge_all()
writer = tf.summary.FileWriter(logdir, sess.graph)
checkfile = os.path.join(logdir, 'cnn_tree.ckpt')
num_batches = len(trees) // BATCH_SIZE + (1 if len(trees) % BATCH_SIZE != 0
else 0)
for epoch in range(1, epochs + 1):
for i, batch in enumerate(
sampling.batch_samples(
sampling.gen_samples(trees, labels, embeddings,
embed_lookup), BATCH_SIZE)):
nodes, children, batch_labels = batch
step = (epoch - 1) * num_batches + i * BATCH_SIZE
if not nodes:
continue # don't try to train on an empty batch
_, summary, err, out = sess.run(
[train_step, summaries, loss_node, out_node],
feed_dict={
nodes_node: nodes,
children_node: children,
labels_node: batch_labels
})
print('Epoch:', epoch, 'Step:', step, 'Loss:', err, 'Max nodes:',
len(nodes[0]))
writer.add_summary(summary, step)
if step % CHECKPOINT_EVERY == 0:
# save state so we can resume later
saver.save(sess, os.path.join(checkfile), step)
print('Checkpoint saved.')
saver.save(sess, os.path.join(checkfile), step)
# compute the training accuracy
correct_labels = []
predictions = []
print('Computing training accuracy...')
for batch in sampling.batch_samples(
sampling.gen_samples(trees, labels, embeddings, embed_lookup), 1):
nodes, children, batch_labels = batch
output = sess.run([out_node],
feed_dict={
nodes_node: nodes,
children_node: children,
})
correct_labels.append(np.argmax(batch_labels))
predictions.append(np.argmax(output))
target_names = list(labels)
print('Accuracy:', accuracy_score(correct_labels, predictions))
print(
classification_report(correct_labels,
predictions,
target_names=target_names))
print(confusion_matrix(correct_labels, predictions))
def train_model(logdir, infile, embedfile, conv_feature, learn_rate, batch_size, epochs=50, checkpoint_every=10000):
"""Train a classifier to label ASTs"""
with open(infile, 'rb') as fh:
trees, _, labels = pickle.load(fh)
with open(embedfile, 'rb') as fh:
embeddings, embed_lookup = pickle.load(fh)
num_feats = len(embeddings[0])
# build the inputs and outputs of the network
nodes_node, children_node, hidden_node = network.init_net(
num_feats,
conv_feature,
len(labels)
)
out_node = network.out_layer(hidden_node)
labels_node, loss_node = network.loss_layer(hidden_node, len(labels))
optimizer = tf.train.GradientDescentOptimizer(learn_rate)
train_step = optimizer.minimize(loss_node)
tf.summary.scalar('loss', loss_node)
### init the graph
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
with tf.name_scope('saver'):
saver = tf.train.Saver()
summaries = tf.summary.merge_all()
writer = tf.summary.FileWriter(logdir, sess.graph)
checkfile = os.path.join(logdir, 'cnn_tree.ckpt')
num_batches = len(trees) // batch_size + (1 if len(trees) % batch_size != 0 else 0)
for epoch in range(1, epochs + 1):
max_step = 0
for i, batch in enumerate(sampling.batch_samples(
sampling.gen_samples(trees, labels, embeddings, embed_lookup), batch_size
)):
nodes, children, batch_labels = batch
step = ((epoch - 1) * num_batches + i)
max_step = max(step, max_step)
if not nodes:
continue # don't try to train on an empty batch
_, summary, err, out = sess.run(
[train_step, summaries, loss_node, out_node],
feed_dict={
nodes_node: nodes,
children_node: children,
labels_node: batch_labels
}
)
if i % 200 == 0:
print('Epoch:', epoch,
'Step:', step,
'Loss:', err,
'Max nodes:', len(nodes[0])
)
sys.stdout.flush()
if step % checkpoint_every == 0:
# save state so we can resume later
writer.add_summary(summary, step)
saver.save(sess, os.path.join(checkfile), step)
saver.save(sess, os.path.join(checkfile), step)
# compute the training accuracy
correct_labels = []
predictions = []
print('Computing training accuracy...')
for batch in sampling.batch_samples(
sampling.gen_samples(trees, labels, embeddings, embed_lookup), 1
):
nodes, children, batch_labels = batch
output = sess.run([out_node],
feed_dict={
nodes_node: nodes,
children_node: children,
}
)
correct_labels.append(np.argmax(batch_labels))
predictions.append(np.argmax(output))
target_names = list(labels)
print('Accuracy:', accuracy_score(correct_labels, predictions))
print(classification_report(correct_labels, predictions, target_names=target_names))
print(confusion_matrix(correct_labels, predictions))
