def get_commit_satisfy_condition(path_data_, nfile, nhunk, nline, nleng):
commits_structure = extract_commit_july(path_file=path_data_)
# nfile, nhunk, nline, nleng = 1, 8, 10, 120
filter_commits = filtering_commit_union(commits=commits_structure,
num_file=nfile,
num_hunk=nhunk,
num_loc=nline,
size_line=nleng)
print len(commits_structure), len(filter_commits)
commits = load_file(path_data_)
indexes = commits_index(commits=commits)
new_commits = list()
for i in xrange(0, len(indexes)):
if i == len(indexes) - 1:
id = commit_id(commit=commits[indexes[i]:])
if id in filter_commits:
new_commits += commits[indexes[i]:]
else:
id = commit_id(commit=commits[indexes[i]:indexes[i + 1]])
if id in filter_commits:
new_commits += commits[indexes[i]:indexes[i + 1]]
print i, id
# write_file("./satisfy_typediff_sorted.out", new_commits)
write_file(path_data_ + ".satisfy", new_commits)
def creating_sasha_data(path_data_, folds, random_state):
commits_structure = extract_commit_july(path_file=path_data_)
commits_id = [c["id"] for c in commits_structure]
commits_label = ["stable" if c["stable"] == "true" else "nonstable" for c in commits_structure]
commits_id_label = [id_ + "\t" + label_ for id_, label_ in zip(commits_id, commits_label)]
kf = KFold(n_splits=folds, random_state=random_state)
cnt_fold = 1
for train_index, test_index in kf.split(commits_structure):
train_id, train_label = get_elements(commits=commits_id, indexes=train_index), get_elements(
commits=commits_label, indexes=train_index)
test_id, test_label = get_elements(commits=commits_id, indexes=test_index), get_elements(
commits=commits_label, indexes=test_index)
train_file, test_file = get_elements(commits=commits_id_label, indexes=train_index), get_elements(
commits=commits_id_label, indexes=test_index)
print len(train_id), len(train_label)
print len(test_id), len(test_label)
print len(train_file), len(test_file)
write_file(path_file="./sasha_data/fold" + str(cnt_fold) + "/" + "train.txt", data=train_file)
write_file(path_file="./sasha_data/fold" + str(cnt_fold) + "/" + "test.txt", data=test_file)
cnt_fold += 1
def load_data_type(path, FLAGS):
commits_ = extract_commit_july(path_file=path)
msgs_, codes_ = extract_msg(commits=commits_), extract_code(
commits=commits_)
dict_msg_, dict_code_ = dictionary(data=msgs_), dictionary(data=codes_)
print len(commits_), len(dict_msg_), len(dict_code_)
pad_msg = mapping_commit_msg(msgs=msgs_,
max_length=FLAGS.msg_length,
dict_msg=dict_msg_)
pad_added_code = mapping_commit_code(type="added",
commits=commits_,
max_hunk=FLAGS.code_hunk,
max_code_line=FLAGS.code_line,
max_code_length=FLAGS.code_length,
dict_code=dict_code_)
pad_removed_code = mapping_commit_code(type="removed",
commits=commits_,
max_hunk=FLAGS.code_hunk,
max_code_line=FLAGS.code_line,
max_code_length=FLAGS.code_length,
dict_code=dict_code_)
labels = load_label_commits(commits=commits_)
return pad_msg, pad_added_code, pad_removed_code, labels, dict_msg_, dict_code_
def print_false_negative(id, y_pred, threshold, y_true):
y_pred = [1 if float(y) > threshold else 0 for y in y_pred]
false_negative = []
for i, p, t in zip(id, y_pred, y_true):
if p == 0 and t == 1:
false_negative.append(i)
print len(false_negative)
path_write = "./sasha_results/false_neg_%s.txt" % (str(threshold))
write_file(path_file=path_write, data=false_negative)
if __name__ == "__main__":
path_data = "./newres_funcalls_jul28.out.sorted.satisfy"
commits_structure = extract_commit_july(path_file=path_data)
commits_id = [c["id"] for c in commits_structure]
path_true = "./statistical_test_prob_ver3/true_label.txt"
y_true = load_file(path_file=path_true)
y_true = [int(y) for y in y_true]
path_pred, threshold = "./statistical_test_prob_ver3/sasha_results.txt", 50
y_pred = load_file(path_file=path_pred)
print_true_positive(id=commits_id,
y_pred=y_pred,
threshold=threshold,
y_true=y_true)
print_false_negative(id=commits_id,
y_pred=y_pred,
def checking_performance(id_label, true_label, model_label, model_name):
for i in range(5, 90, 1):
if model_name == "patchNet":
threshold = 1 - i / float(100)
elif model_name == "sasha":
threshold = 0
threshold_label = [1 if m >= threshold else 0 for m in model_label]
prc = precision_score(y_true=true_label, y_pred=threshold_label)
rc = recall_score(y_true=true_label, y_pred=threshold_label)
print threshold, prc, rc
exit()
if __name__ == "__main__":
path_data = "./satisfy_typediff_sorted.out"
commits_ = extract_commit_july(path_file=path_data)
print len(commits_), type(commits_)
commits_id = [c["id"] for c in commits_]
print len(commits_id)
path_file = "./statistical_test_prob_ver2/true_label.txt"
true_label = load_file(path_file=path_file)
true_label = [float(t) for t in true_label]
path_file = "./statistical_test_prob_ver2/PatchNet.txt"
patchNet = load_file(path_file=path_file)
patchNet = [float(t) for t in patchNet]
checking_performance(id_label=commits_id,
true_label=true_label,
model_label=patchNet,
model_name="patchNet")
if __name__ == "__main__":
# root_path_ = "./sasha_results/"
# files_path = ([root_path_ + x for x in os.listdir(root_path_)])
# print files_path
files_path = list()
files_path.append("./sasha_results/fold1.txt")
files_path.append("./sasha_results/fold2.txt")
files_path.append("./sasha_results/fold3.txt")
files_path.append("./sasha_results/fold4.txt")
files_path.append("./sasha_results/fold5.txt")
root_gt = "./satisfy_typediff_sorted.out"
commits_ = extract_commit_july(path_file=root_gt)
commits_id_ = [c["id"] for c in commits_]
commits_label_ = [c["stable"] for c in commits_]
dl_labels = load_file("./statistical_test/lstm_cnn_all.txt")
dl_labels = [float(l) for l in dl_labels]
fold_index = split_train_test(data=commits_, folds=5, random_state=None)
accuracy_, precision_, recall_, f1_, auc_ = list(), list(), list(), list(
), list()
probs, true_pos, false_neg = list(), list(), list()
threshold = 0.5
for f in files_path:
print f
test_fold = fold_index[check_fold(string=f) - 1]["test"]
def loading_baseline_july(tf, folds, random_state):
FLAGS = tf.flags.FLAGS
commits_ = extract_commit_july(path_file=FLAGS.path)
filter_commits = commits_
print len(commits_)
kf = KFold(n_splits=folds, random_state=random_state)
idx_folds = list()
for train_index, test_index in kf.split(filter_commits):
idx = dict()
idx["train"], idx["test"] = train_index, test_index
idx_folds.append(idx)
if "msg" in FLAGS.model:
msgs_, codes_ = extract_msg(commits=filter_commits), extract_code(
commits=filter_commits)
elif "all" in FLAGS.model:
msgs_, codes_ = extract_msg(commits=filter_commits), extract_code(
commits=filter_commits)
all_lines = add_two_list(list1=msgs_, list2=codes_)
msgs_ = all_lines
elif "code" in FLAGS.model:
msgs_, codes_ = extract_msg(commits=filter_commits), extract_code(
commits=filter_commits)
msgs_ = codes_
else:
print "You need to type correct model"
exit()
dict_msg_, dict_code_ = dictionary(data=msgs_), dictionary(data=codes_)
pad_msg = mapping_commit_msg(msgs=msgs_,
max_length=FLAGS.msg_length,
dict_msg=dict_msg_)
labels = load_label_commits(commits=filter_commits)
labels = convert_to_binary(labels)
# path_file = "./statistical_test_prob/true_label.txt"
# write_file(path_file=path_file, data=labels)
# exit()
print pad_msg.shape, labels.shape, len(dict_msg_)
cntfold = 0
pred_dict = dict()
pred_dict_list = list()
for i in xrange(cntfold, len(idx_folds)):
idx = idx_folds[i]
train_index, test_index = idx["train"], idx["test"]
X_train_msg, X_test_msg = np.array(get_items(items=pad_msg, indexes=train_index)), \
np.array(get_items(items=pad_msg, indexes=test_index))
Y_train, Y_test = np.array(get_items(items=labels, indexes=train_index)), \
np.array(get_items(items=labels, indexes=test_index))
if FLAGS.model == "lstm_cnn_all" or FLAGS.model == "lstm_cnn_msg" \
or FLAGS.model == "lstm_cnn_code" or FLAGS.model == "cnn_all" \
or FLAGS.model == "cnn_msg" or FLAGS.model == "cnn_code":
# path_model = "./keras_model/%s_%s.h5" % (FLAGS.model, str(cntfold))
path_model = "./keras_model/test_%s_%s.h5" % (FLAGS.model,
str(cntfold))
# path_model = "./keras_model/%s_%s_testing.h5" % (FLAGS.model, str(cntfold))
model = load_model(path_model)
else:
print "You need to give correct model name"
exit()
y_pred = model.predict(X_test_msg, batch_size=FLAGS.batch_size)
y_pred = np.ravel(y_pred)
pred_dict.update(make_dictionary(y_pred=y_pred, y_index=test_index))
y_pred = y_pred.tolist()
pred_dict_list += y_pred
# print len(pred_dict_list)
# exit()
# path_file = "./statistical_test_prob/" + FLAGS.model + ".txt"
# write_file(path_file=path_file, data=sorted_dict(dict=pred_dict))
path_file = "./statistical_test_prob/" + FLAGS.model + "_checking.txt"
write_file(path_file=path_file, data=pred_dict_list)
def running_baseline_july(tf, folds, random_state):
FLAGS = tf.flags.FLAGS
commits_ = extract_commit_july(path_file=FLAGS.path)
filter_commits = commits_
print len(commits_)
kf = KFold(n_splits=folds, random_state=random_state)
idx_folds = list()
for train_index, test_index in kf.split(filter_commits):
idx = dict()
idx["train"], idx["test"] = train_index, test_index
idx_folds.append(idx)
if "msg" in FLAGS.model:
msgs_, codes_ = extract_msg(commits=filter_commits), extract_code(commits=filter_commits)
elif "all" in FLAGS.model:
msgs_, codes_ = extract_msg(commits=filter_commits), extract_code(commits=filter_commits)
all_lines = add_two_list(list1=msgs_, list2=codes_)
msgs_ = all_lines
elif "code" in FLAGS.model:
msgs_, codes_ = extract_msg(commits=filter_commits), extract_code(commits=filter_commits)
msgs_ = codes_
else:
print "You need to type correct model"
exit()
dict_msg_, dict_code_ = dictionary(data=msgs_), dictionary(data=codes_)
pad_msg = mapping_commit_msg(msgs=msgs_, max_length=FLAGS.msg_length, dict_msg=dict_msg_)
labels = load_label_commits(commits=filter_commits)
labels = convert_to_binary(labels)
print pad_msg.shape, labels.shape, len(dict_msg_)
# exit()
timestamp = str(int(time.time()))
accuracy, precision, recall, f1, auc = list(), list(), list(), list(), list()
cntfold = 0
pred_dict, pred_dict_prob = dict(), dict()
for i in xrange(cntfold, len(idx_folds)):
idx = idx_folds[i]
train_index, test_index = idx["train"], idx["test"]
X_train_msg, X_test_msg = np.array(get_items(items=pad_msg, indexes=train_index)), \
np.array(get_items(items=pad_msg, indexes=test_index))
Y_train, Y_test = np.array(get_items(items=labels, indexes=train_index)), \
np.array(get_items(items=labels, indexes=test_index))
if FLAGS.model == "lstm_cnn_msg" or FLAGS.model == "lstm_cnn_code" or FLAGS.model == "lstm_cnn_all":
model = lstm_cnn(x_train=X_train_msg, y_train=Y_train, x_test=X_test_msg,
y_test=Y_test, dictionary_size=len(dict_msg_), FLAGS=FLAGS)
elif FLAGS.model == "cnn_msg" or FLAGS.model == "cnn_code" or FLAGS.model == "cnn_all":
model = cnn_model(x_train=X_train_msg, y_train=Y_train, x_test=X_test_msg,
y_test=Y_test, dictionary_size=len(dict_msg_), FLAGS=FLAGS)
else:
print "You need to give correct model name"
exit()
# model.save("./keras_model/" + FLAGS.model + "_" + str(cntfold) + ".h5")
# model.save("./keras_model/" + FLAGS.model + "_" + str(cntfold) + "_testing.h5")
# model.save("./keras_model/test_" + FLAGS.model + "_" + str(cntfold) + ".h5")
model.save("./keras_model/newres_funcalls_" + FLAGS.model + "_" + str(cntfold) + ".h5")
y_pred = model.predict(X_test_msg, batch_size=FLAGS.batch_size)
y_pred = np.ravel(y_pred)
y_pred_tolist = y_pred.tolist()
data_fold = [str(i) + "\t" + str(l) for i, l in zip(test_index, y_pred)]
path_file = "./statistical_test/newres_funcalls_%s_fold_%s.txt" % (FLAGS.model, str(cntfold))
write_file(path_file=path_file, data=data_fold)
y_pred[y_pred > 0.5] = 1
y_pred[y_pred <= 0.5] = 0
pred_dict.update(make_dictionary(y_pred=y_pred, y_index=test_index))
accuracy.append(accuracy_score(y_true=Y_test, y_pred=y_pred))
precision.append(precision_score(y_true=Y_test, y_pred=y_pred))
recall.append(recall_score(y_true=Y_test, y_pred=y_pred))
f1.append(f1_score(y_true=Y_test, y_pred=y_pred))
auc.append(auc_score(y_true=Y_test, y_pred=y_pred))
print "accuracy", accuracy_score(y_true=Y_test, y_pred=y_pred)
print "precision", precision_score(y_true=Y_test, y_pred=y_pred)
print "recall", recall_score(y_true=Y_test, y_pred=y_pred)
print "f1", f1_score(y_true=Y_test, y_pred=y_pred)
cntfold += 1
break
# sys.path.append(path_working)
from init_params import model_parameters
from keras_lstm import print_params
from ultis import extract_commit_july
from keras_lstm import lstm_cnn, cnn_model
from baselines import extract_msg, extract_code, add_two_list
from data_helpers import dictionary, mapping_commit_msg, load_label_commits
from data_helpers import convert_to_binary
tf_ = model_parameters()
FLAGS_ = tf_.flags.FLAGS
print_params(tf_)
FLAGS = FLAGS_
commits_ = extract_commit_july(path_file=FLAGS.path)
filter_commits = commits_
if "msg" in FLAGS.model:
msgs_, codes_ = extract_msg(commits=filter_commits), extract_code(
commits=filter_commits)
elif "all" in FLAGS.model:
msgs_, codes_ = extract_msg(commits=filter_commits), extract_code(
commits=filter_commits)
all_lines = add_two_list(list1=msgs_, list2=codes_)
msgs_ = all_lines
elif "code" in FLAGS.model:
msgs_, codes_ = extract_msg(commits=filter_commits), extract_code(
commits=filter_commits)
msgs_ = codes_
else:
