def kfold_results(y_true, y_pred, nfolds):
kf = KFold(n_splits=nfolds, random_state=0)
accuracy, precision, recall, f1, auc = list(), list(), list(), list(
), list()
for train_index, test_index in kf.split(y_true):
y_true_test_index, y_pred_test_index = get_items(items=y_true, indexes=test_index), \
get_items(items=y_pred, indexes=test_index)
accuracy.append(
accuracy_score(y_true=y_true_test_index, y_pred=y_pred_test_index))
precision.append(
precision_score(y_true=y_true_test_index,
y_pred=y_pred_test_index))
recall.append(
recall_score(y_true=y_true_test_index, y_pred=y_pred_test_index))
f1.append(f1_score(y_true=y_true_test_index, y_pred=y_pred_test_index))
auc.append(
auc_score(y_true=y_true_test_index, y_pred=y_pred_test_index))
algorithm = ""
print "Accuracy and std of %s: %f %f" % (
algorithm, np.mean(np.array(accuracy)), np.std(np.array(accuracy)))
print "Precision of %s: %f %f" % (algorithm, np.mean(
np.array(precision)), np.std(np.array(precision)))
print "Recall of %s: %f %f" % (algorithm, np.mean(
np.array(recall)), np.std(np.array(recall)))
print "F1 of %s: %f %f" % (algorithm, np.mean(
np.array(f1)), np.std(np.array(f1)))
print "AUC of %s: %f %f" % (algorithm, np.mean(
np.array(auc)), np.std(np.array(auc)))
def baseline_testing(X_train, y_train, X_test, y_test, algorithm, type):
vectorizer = CountVectorizer()
X_train = vectorizer.fit_transform(X_train)
X_test = vectorizer.transform(X_test)
print X_train.shape, X_test.shape
if algorithm == "svm":
clf = LinearSVC(random_state=0)
elif algorithm == "lr":
clf = LogisticRegression()
elif algorithm == "dt":
clf = DecisionTreeClassifier()
elif algorithm == "nb":
clf = GaussianNB()
else:
print "Wrong algorithm name -- please retype again"
exit()
clf.fit(X=X_train.toarray(), y=y_train)
y_pred = clf.predict(X_test)
path_write = "./data_test_data_pred_results/cnn_" + type + ".txt"
write_file(path_file=path_write, data=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)
print "AUC: ", auc_score(y_true=y_test, y_pred=y_pred)
def checking_performance_v3(id_gt, label_gt, patches, threshold):
new_labels, pred_labels = list(), list()
new_patches_id, new_patches_label = list(), list()
for p in patches:
id_, label_ = p[0], p[1]
new_patches_id.append(id_)
new_patches_label.append(label_)
prob_patches = patches[0][1]
origin_prob = list()
true_positive, false_negative = list(), list()
for id_, true_label in zip(id_gt, label_gt):
if true_label == "true":
new_labels.append(1)
else:
new_labels.append(0)
if id_ in new_patches_id:
index_ = new_patches_id.index(id_)
origin_prob.append(id_ + "\t" +
str(float(new_patches_label[index_])))
if new_patches_label[index_] / prob_patches >= threshold:
pred_labels.append(1)
if true_label == "true":
true_positive.append(id_)
else:
pred_labels.append(0)
if true_label == "true":
false_negative.append(id_)
else:
# pred_labels.append(random.choice((1, 10)))
pred_labels.append(0)
origin_prob.append(id_ + "\t" + str(float(0)))
if true_label == "true":
false_negative.append(id_)
print len(new_labels), len(pred_labels)
true_list, false_list = [], []
for l in new_labels:
if l == 1:
true_list.append(l)
else:
false_list.append(l)
print len(true_list), len(false_list)
acc = accuracy_score(y_true=new_labels, y_pred=pred_labels)
prc = precision_score(y_true=new_labels, y_pred=pred_labels)
rc = recall_score(y_true=new_labels, y_pred=pred_labels)
f1 = f1_score(y_true=new_labels, y_pred=pred_labels)
auc = auc_score(y_true=new_labels, y_pred=pred_labels)
print acc, prc, rc, f1, auc
# dl_acc = accuracy_score(y_true=new_labels, y_pred=test_dl_labels)
# dl_prc = precision_score(y_true=new_labels, y_pred=test_dl_labels)
# dl_rc = recall_score(y_true=new_labels, y_pred=test_dl_labels)
# dl_f1 = f1_score(y_true=new_labels, y_pred=test_dl_labels)
# dl_auc = auc_score(y_true=new_labels, y_pred=test_dl_labels)
# print dl_acc, dl_prc, dl_rc, dl_f1, dl_auc
return acc, prc, rc, f1, auc, origin_prob, true_positive, false_negative
def checking_performance(id_gt, label_gt, patches):
prob_patches = patches[0][1]
new_labels, pred_labels = list(), list()
new_test_dl_labels = list()
for k, v in patches:
if v / prob_patches >= 0.5:
pred_labels.append(1)
else:
pred_labels.append(0)
index = id_gt.index(k)
true_label = label_gt[index]
new_test_dl_labels.append(test_dl_labels[index])
if true_label == "true":
new_labels.append(1)
else:
new_labels.append(0)
print len(new_labels), len(pred_labels), len(new_test_dl_labels)
true_list, false_list = [], []
for l in new_labels:
if l == 1:
true_list.append(l)
else:
false_list.append(l)
print len(true_list), len(false_list)
acc = accuracy_score(y_true=new_labels, y_pred=pred_labels)
prc = precision_score(y_true=new_labels, y_pred=pred_labels)
rc = recall_score(y_true=new_labels, y_pred=pred_labels)
f1 = f1_score(y_true=new_labels, y_pred=pred_labels)
auc = auc_score(y_true=new_labels, y_pred=pred_labels)
print acc, prc, rc, f1, auc
dl_acc = accuracy_score(y_true=new_labels, y_pred=new_test_dl_labels)
dl_prc = precision_score(y_true=new_labels, y_pred=new_test_dl_labels)
dl_rc = recall_score(y_true=new_labels, y_pred=new_test_dl_labels)
dl_f1 = f1_score(y_true=new_labels, y_pred=new_test_dl_labels)
dl_auc = auc_score(y_true=new_labels, y_pred=new_test_dl_labels)
print dl_acc, dl_prc, dl_rc, dl_f1, dl_auc
return acc, prc, rc, f1, auc
def get_predict_ICSE(name, X, y, algorithm, folds):
kf = KFold(n_splits=folds, random_state=None)
# kf.get_n_splits(X=X)
# kf = StratifiedKFold(n_splits=folds)
accuracy, precision, recall, f1, auc = list(), list(), list(), list(), list()
X = preprocessing.normalize(X)
pred_dict = dict()
for train_index, test_index in kf.split(X, y):
X_train, y_train = X[train_index], y[train_index]
X_test, y_test = X[test_index], y[test_index]
if algorithm == "svm":
clf = LinearSVC(random_state=0)
elif algorithm == "lr":
clf = LogisticRegression()
elif algorithm == "dt":
clf = DecisionTreeClassifier()
else:
print "Wrong algorithm name -- please retype again"
exit()
clf.fit(X=X_train, y=y_train)
y_pred = clf.predict(X_test)
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))
# y_pred = clf.predict(X)
# path_file = "./data/3_mar7/" + "new_features_ver1_pred.txt"
# write_file(path_file, y_pred)
# break
# print "Accuracy of %s: %f" % (algorithm, avg_list(accuracy))
# print "Precision of %s: %f" % (algorithm, avg_list(precision))
# print "Recall of %s: %f" % (algorithm, avg_list(recall))
# print "F1 of %s: %f" % (algorithm, avg_list(f1))
# path_file = "./data/3_mar7/" + "new_features_ver2_pred.txt"
# write_file(path_file=path_file, data=sorted_dict(dict=pred_dict))
print accuracy, "Accuracy and std of %s: %f %f" % (
algorithm, np.mean(np.array(accuracy)), np.std(np.array(accuracy)))
print precision, "Precision of %s: %f %f" % (algorithm, np.mean(np.array(precision)), np.std(np.array(precision)))
print recall, "Recall of %s: %f %f" % (algorithm, np.mean(np.array(recall)), np.std(np.array(recall)))
print f1, "F1 of %s: %f %f" % (algorithm, np.mean(np.array(f1)), np.std(np.array(f1)))
print auc, "AUC of %s: %f %f" % (algorithm, np.mean(np.array(auc)), np.std(np.array(auc)))
def get_predict_ICSE_new(X_train, y_train, X_test, y_test, algorithm):
if algorithm == "svm":
clf = LinearSVC()
elif algorithm == "lr":
clf = LogisticRegression()
elif algorithm == "dt":
clf = DecisionTreeClassifier()
else:
print "Wrong algorithm name -- please retype again"
exit()
clf.fit(X=X_train, y=y_train)
y_pred = clf.predict(X_test)
print "Accuracy of %s: %f" % (algorithm, accuracy_score(y_true=y_test, y_pred=y_pred))
print "Precision of %s: %f" % (algorithm, precision_score(y_true=y_test, y_pred=y_pred))
print "Recall of %s: %f" % (algorithm, recall_score(y_true=y_test, y_pred=y_pred))
print "F1 of %s: %f" % (algorithm, f1_score(y_true=y_test, y_pred=y_pred))
print "AUC of %s: %f" % (algorithm, auc_score(y_true=y_test, y_pred=y_pred))
def baseline_ver3(id, train, label, algorithm):
X_train, y_train = train, label
X_test, y_test = train, label
id_train, id_test = id, id
vectorizer = CountVectorizer()
X_train = vectorizer.fit_transform(X_train)
X_test = vectorizer.transform(X_test)
# X = vectorizer.transform(X)
# eval_train, eval_labels = loading_data("./data/3_mar7/typeaddres.out")
# eval_train = vectorizer.transform(eval_train)
if algorithm == "svm":
clf = LinearSVC(random_state=0)
elif algorithm == "lr":
clf = LogisticRegression()
elif algorithm == "dt":
clf = DecisionTreeClassifier()
else:
print "Wrong algorithm name -- please retype again"
exit()
clf.fit(X=X_train, y=y_train)
accuracy = accuracy_score(y_true=y_test, y_pred=clf.predict(X_test))
precision = precision_score(y_true=y_test, y_pred=clf.predict(X_test))
recall = recall_score(y_true=y_test, y_pred=clf.predict(X_test))
f1 = f1_score(y_true=y_test, y_pred=clf.predict(X_test))
auc = auc_score(y_true=y_test, y_pred=clf.predict(X_test))
print "Accuracy:", accuracy
print "Precision:", precision
print "Recall:", recall
print "F1:", f1
print "AUC:", auc
probs = clf.predict_proba(X_test)[:, 1]
path_write = "./statistical_test_ver2/%s.txt" % (algorithm)
write_file(path_file=path_write, data=probs)
def eval_patchNet_train_test(tf, checkpoint_dir, test):
FLAGS = tf.flags.FLAGS
allow_soft_placement = True # "Allow device soft device placement"
log_device_placement = False # "Log placement of ops on devices"
dirs = get_all_checkpoints(checkpoint_dir=checkpoint_dir)
graph = tf.Graph()
X_test_msg, X_test_added_code, X_test_removed_code, y_test = test[0], test[
1], test[2], test[3]
for checkpoint_file in dirs:
with graph.as_default():
session_conf = tf.ConfigProto(
allow_soft_placement=allow_soft_placement,
log_device_placement=log_device_placement)
sess = tf.Session(config=session_conf)
with sess.as_default():
# Load the saved meta graph and restore variables
saver = tf.train.import_meta_graph(
"{}.meta".format(checkpoint_file))
saver.restore(sess, checkpoint_file)
# Get the placeholders from the graph by name
input_msg = graph.get_operation_by_name("input_msg").outputs[0]
input_addedcode = graph.get_operation_by_name(
"input_addedcode").outputs[0]
input_removedcode = graph.get_operation_by_name(
"input_removedcode").outputs[0]
dropout_keep_prob = graph.get_operation_by_name(
"dropout_keep_prob").outputs[0]
# Tensors we want to evaluate
predictions = graph.get_operation_by_name(
"output/predictions").outputs[0]
scores = graph.get_operation_by_name(
"output/scores").outputs[0]
# Generate batches for one epoch
batches = mini_batches(X_msg=X_test_msg,
X_added_code=X_test_added_code,
X_removed_code=X_test_removed_code,
Y=y_test,
mini_batch_size=FLAGS.batch_size)
# Collect the predictions here
all_predictions, all_scores = [], []
for batch in batches:
batch_input_msg, batch_input_added_code, batch_input_removed_code, batch_input_labels = batch
batch_predictions = sess.run(
predictions, {
input_msg: batch_input_msg,
input_addedcode: batch_input_added_code,
input_removedcode: batch_input_removed_code,
dropout_keep_prob: 1.0
})
# print batch_predictions.shape
all_predictions = np.concatenate(
[all_predictions, batch_predictions])
batch_scores = sess.run(
scores, {
input_msg: batch_input_msg,
input_addedcode: batch_input_added_code,
input_removedcode: batch_input_removed_code,
dropout_keep_prob: 1.0
})
batch_scores = np.ravel(softmax(batch_scores)[:, [1]])
# print batch_scores.shape
all_scores = np.concatenate([all_scores, batch_scores])
split_checkpoint_file = checkpoint_file.split("/")
path_write = "./patchNet_results/%s_%s.txt" % (
split_checkpoint_file[-3], split_checkpoint_file[-1])
write_file(path_file=path_write, data=all_scores)
print checkpoint_file, "Accuracy:", accuracy_score(
y_true=convert_to_binary(y_test), y_pred=all_predictions)
print checkpoint_file, "Precision:", precision_score(
y_true=convert_to_binary(y_test), y_pred=all_predictions)
print checkpoint_file, "Recall:", recall_score(
y_true=convert_to_binary(y_test), y_pred=all_predictions)
print checkpoint_file, "F1:", f1_score(
y_true=convert_to_binary(y_test), y_pred=all_predictions)
print checkpoint_file, "AUC:", auc_score(
y_true=convert_to_binary(y_test), y_pred=all_predictions)
print "\n"
def evaluation_metrics(root, target):
print "Accuracy: %f" % (accuracy_score(y_true=root, y_pred=target))
print "Precision: %f" % (precision_score(y_true=root, y_pred=target))
print "Recall: %f" % (recall_score(y_true=root, y_pred=target))
print "F1: %f" % (f1_score(y_true=root, y_pred=target))
print "AUC: %f" % (auc_score(y_true=root, y_pred=target))
# path_pred = "./statistical_test_prob/lstm_cnn_all_fold_0.txt"
# path_pred = "./statistical_test_prob/lstm_cnn_all_check_fold_0.txt"
# path_pred = "./statistical_test_prob/lstm_cnn_all_checking.txt"
# path_pred = "./statistical_test/lstm_cnn_all_ver2.txt"
# path_pred = "./statistical_test_prob_ver3/PatchNet.txt"
# path_pred = "./statistical_test_prob_ver3/LPU-SVM.txt"
path_pred = "./statistical_test_prob_ver3/LS-CNN.txt"
# path_pred, threshold = "./statistical_test_prob_ver3/sasha_results.txt", 50
y_pred = load_file(path_file=path_pred)
if "sasha" in path_pred:
y_pred = np.array([1 if float(y) > threshold else 0 for y in y_pred])
else:
y_pred = np.array([1 if float(y) > 0.5 else 0 for y in y_pred])
# y_pred = np.array([float(y) for y in y_pred])
# y_pred[y_pred > 0.5] = 1
# y_pred[y_pred <= 0.5] = 0
split_data = split_train_test(data=y_true, folds=folds, random_state=None)
for i in xrange(len(split_data)):
train_index, test_index = split_data[i]["train"], split_data[i]["test"]
y_true_, y_pred_ = get_items(
items=y_true, indexes=test_index), get_items(items=y_pred,
indexes=test_index)
acc = accuracy_score(y_true=y_true_, y_pred=y_pred_)
prc = precision_score(y_true=y_true_, y_pred=y_pred_)
rc = recall_score(y_true=y_true_, y_pred=y_pred_)
f1 = f1_score(y_true=y_true_, y_pred=y_pred_)
auc = auc_score(y_true=y_true_, y_pred=y_pred_)
print acc, prc, rc, f1, auc
# Collect the predictions here
all_predictions = []
for batch in batches:
batch_input_msg, batch_input_added_code, batch_input_removed_code, batch_input_labels = batch
batch_predictions = sess.run(
predictions, {
input_msg: batch_input_msg,
input_addedcode: batch_input_added_code,
input_removedcode: batch_input_removed_code,
dropout_keep_prob: 1.0
})
all_predictions = np.concatenate(
[all_predictions, batch_predictions])
print checkpoint_file, "Accuracy:", accuracy_score(
y_true=convert_to_binary(y_test), y_pred=all_predictions)
print checkpoint_file, "Precision:", precision_score(
y_true=convert_to_binary(y_test), y_pred=all_predictions)
print checkpoint_file, "Recall:", recall_score(
y_true=convert_to_binary(y_test), y_pred=all_predictions)
print checkpoint_file, "F1:", f1_score(
y_true=convert_to_binary(y_test), y_pred=all_predictions)
print checkpoint_file, "AUC:", auc_score(
y_true=convert_to_binary(y_test), y_pred=all_predictions)
# y_pred = all_predictions
# split_checkpoint = checkpoint_file.split("/")
# path_file = "./statistical_test_ver2/3_mar7/" + split_checkpoint[2] \
# + "_" + split_checkpoint[-1] + ".txt"
# write_file(path_file, y_pred)
exit()
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, labels.shape, len(dict_msg_)
y_pred = model.predict(pad_msg, batch_size=FLAGS.batch_size)
y_pred = np.ravel(y_pred)
y_pred[y_pred > 0.5] = 1
y_pred[y_pred <= 0.5] = 0
accuracy = accuracy_score(y_true=labels, y_pred=y_pred)
precision = precision_score(y_true=labels, y_pred=y_pred)
recall = recall_score(y_true=labels, y_pred=y_pred)
f1 = f1_score(y_true=labels, y_pred=y_pred)
auc = auc_score(y_true=labels, y_pred=y_pred)
print "Accuracy and std of %s: %f" % (FLAGS.model, accuracy)
print "Precision of %s: %f" % (FLAGS.model, precision)
print "Recall of %s: %f" % (FLAGS.model, recall)
print "F1 of %s: %f" % (FLAGS.model, f1)
print "AUC of %s: %f" % (FLAGS.model, auc)
# f1.append(f1_score(y_true=Y_test, y_pred=y_pred))
# auc.append(auc_score(y_true=Y_test, y_pred=y_pred))
model.save("./lstm_model_ver2/rerun_" + FLAGS.model + "_" +
str(cntfold) + ".h5")
cntfold += 1
y_pred = model.predict(X_test_msg, batch_size=FLAGS.batch_size)
y_pred = np.ravel(y_pred)
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 of %s: %f" % (FLAGS.model, avg_list(accuracy))
# print "Precision of %s: %f" % (FLAGS.model, avg_list(precision))
# print "Recall of %s: %f" % (FLAGS.model, avg_list(recall))
# print "F1 of %s: %f" % (FLAGS.model, avg_list(f1))
# print "AUC of %s: %f" % (FLAGS.model, avg_list(auc))
# path_file = "./statistical_test/3_mar7/" + FLAGS.model + ".txt"
# write_file(path_file, y_pred)
# print "Accuracy of %s: %f" % (FLAGS.model, avg_list(accuracy))
# print "Precision of %s: %f" % (FLAGS.model, avg_list(precision))
# print "Recall of %s: %f" % (FLAGS.model, avg_list(recall))
# print "F1 of %s: %f" % (FLAGS.model, avg_list(f1))
# cntfold += 1
# exit()
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
