kf = KFold(n_splits=FLAGS.folds, random_state=FLAGS.seed)
for train_index, test_index in kf.split(filter_commits):
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))
X_train_added_code, X_test_added_code = np.array(get_items(items=pad_added_code, indexes=train_index)), \
np.array(get_items(items=pad_added_code, indexes=test_index))
X_train_removed_code, X_test_removed_code = np.array(get_items(items=pad_removed_code, indexes=train_index)), \
np.array(get_items(items=pad_removed_code, 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))
return X_test_msg, X_test_added_code, X_test_removed_code, y_test
if __name__ == "__main__":
tf = model_parameters()
FLAGS = tf.flags.FLAGS
print_params(tf)
if FLAGS.eval_test:
X_test_msg, X_test_added_code, X_test_removed_code, y_test = loading_data_lstm(FLAGS=FLAGS)
print X_test_msg.shape, X_test_added_code.shape, X_test_removed_code.shape, y_test.shape
# X_test_msg, X_test_added_code, X_test_removed_code, y_test = loading_data_all(FLAGS=FLAGS)
else:
print "You need to turn on the evaluating file."
exit()
# checkpoint_dir, model = "./runs/fold_0_1521641601/checkpoints", "keras_model"
# checkpoint_dir, model = "./runs/fold_0_1522031841/checkpoints", "lstm_all"
checkpoint_dir, model = "./runs/fold_0_1522045240/checkpoints", "lstm_code"
dirs = get_all_checkpoints(checkpoint_dir=checkpoint_dir)
# 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)
if __name__ == "__main__":
tf_ = model_parameters()
FLAGS_ = tf_.flags.FLAGS
print_params(tf_)
folds_, random_state_ = 5, None
loading_baseline_july(tf=tf_, folds=folds_, random_state=random_state_)