def start_predict(self):
# get predicting parameters
model_name = str(self.ui.combobox_model.currentText())
tweets_file = self.ui.textbox_tweets_file.text()
header_included = self.ui.checkbox_header.isChecked()
random_tweets = self.ui.spinbox_rand_tweets.value()
# check for file validity
try:
Utils.file_validation(tweets_file, 'Tweet')
except Exception as ex:
Utils.show_msg(text=ex.args[0], title="Input Error")
return
# reset values of widgets
self.reset_form()
# disable all widgets
self.disable_widgets(True)
self.model_callback = CallBackMultiPredictNNet(
self.update_batch_progress, self.update_tweet_progress,
random_tweets)
self.predictor = MultiPredictor(model_name, self.model_callback,
tweets_file, header_included,
random_tweets)
# create a thread for predictor
self.pred_thread = ModelPredictorThread(self.predictor)
self.pred_thread.finished.connect(self.on_predict_finished)
self.pred_thread.start()
def _perform_pre_processing(self, bot_tweet, doc_tweet, length_valid=3):
bot_tweet = Utils.preprocess_tweet(bot_tweet)
doc_tweet = Utils.preprocess_tweet(doc_tweet)
length_valid = True \
if (len(bot_tweet) >= length_valid and len(doc_tweet) >= length_valid) \
else False
return bot_tweet, doc_tweet, length_valid
def export_finished(self):
if self.export_thread.is_success():
Utils.show_msg(text="Exporting Complete!",
title="Successful",
msg_type=QMessageBox.Information)
os.startfile(self.export_thread.excel_path)
else:
Utils.show_msg(text=self.export_thread.error,
title="Error",
msg_type=QMessageBox.Critical)
def on_train_finished(self):
if self.model_thread.is_success():
if self.stop_requested:
self.log.enable_log()
self.log.write_log('Stopped Process Done Successfully!')
else:
self.ui.btn_save.setDisabled(False)
self.log.write_log('Training Process Completed Successfully!')
else:
Utils.show_msg(text=self.model_thread.error, title="Error")
self.change_widgets_disabled(False)
def on_predict_finished(self):
self.disable_widgets(False)
if not self.need_stop:
if self.pred_thread.is_success():
self.classify_tweets()
else:
Utils.show_msg(text=self.pred_thread.error, title="Error")
self.ui.btn_save.setDisabled(True)
self.ui.groupbox_threshold.setDisabled(True)
else:
self.reset_form()
self.ui.btn_save.setDisabled(True)
self.ui.groupbox_threshold.setDisabled(True)
def start_predict(self):
# get predicting parameters
model_name = str(self.ui.combobox_model.currentText())
bot_file = self.ui.textbox_bot_file.text()
human_file = self.ui.textbox_human_file.text()
bot_tweets = self.ui.spinbox_bot_tweets.value()
human_tweets = self.ui.spinbox_human_tweets.value()
total_tweets = bot_tweets + human_tweets
# check for file validity
try:
Utils.file_validation(bot_file, 'Tweet')
Utils.file_validation(human_file, 'Tweet')
except Exception as ex:
Utils.show_msg(text=ex.args[0], title="Input Error")
return
# reset values of widgets
self.reset_form()
# disable all widgets
self.disable_widgets(True)
self.model_callback = CallBackMultiPredictNNet(
self.update_batch_progress, self.update_tweet_progress,
total_tweets)
self.predictor = ModelTestPredictor(model_name, self.model_callback,
bot_file, human_file, bot_tweets,
human_tweets)
# create a thread for predictor
self.pred_thread = ModelPredictorThread(self.predictor)
self.pred_thread.finished.connect(self.on_predict_finished)
self.pred_thread.start()
def on_predict_finished(self):
if self.pred_thread.is_success():
bot_sim_score = self.predictor.get_similarity_score()
bot_percentage = int(round(bot_sim_score * 100))
human_percentage = 100 - bot_percentage
# calculate the max value for ui
scores_arr = [bot_percentage, human_percentage]
group_boxes_arr = [self.ui.groupbox_bot, self.ui.groupbox_human]
max_element = np.argmax(scores_arr)
min_element = 1-max_element
self.update_ui_scores(bot_percentage, human_percentage)
group_boxes_arr[max_element].setDisabled(False)
group_boxes_arr[min_element].setDisabled(True)
else:
Utils.show_msg(text=self.pred_thread.error, title="Error")
self.ui.btn_start.setDisabled(False)
self.ui.textbox_tweet.setReadOnly(False)
self.ui.combobox_model.setDisabled(False)
def predict(self):
if self.tweet_pred is None:
raise Exception(
'Can not Start Predicting without any Prediction Tweet!')
# perform pre-processing
clean_tweet_pred = Utils.preprocess_tweet(self.tweet_pred)
# build doc list by duplicate tweet prediction foreach line in bot list
tweet_pred_list = [clean_tweet_pred] * len(self.bot_list)
# convert tweet predicted to sequence
temp_pred_list = [clean_tweet_pred]
x_temp_pred_list = Utils.convert_text_to_sequences(
self.tokenizer, temp_pred_list, self.max_text_len)
# duplicate sequence to the length of bot size list
x_doc_list = [x_temp_pred_list[0]] * len(self.bot_list)
x_doc_list = np.array(x_doc_list)
# calculate word overlapping additional feature
if self.additional_feats_enabled:
additional_feat = Utils.compute_overlap_features(
self.bot_list, tweet_pred_list)
else:
additional_feat = np.zeros(len(self.bot_list))
# perform the prediction operation
predict_list = self.model.predict(
[self.x_bot_list, x_doc_list, additional_feat],
verbose=1,
callbacks=[self.callback_predict])
# calculate and save the how much current tweet similar to training bots list
self.bot_similarity_score = len(
list(filter(lambda x: x > 0.5, predict_list))) / len(predict_list)
def _generate_additional_feats(self, query_list, doc_list,
additional_feats_enabled):
if additional_feats_enabled:
self.logger.write_log(
f'Building additional features between queries and documents')
self.overlap_feats = Utils.compute_overlap_features(
query_list, doc_list)
else:
self.logger.write_log(
f'Additional features disabled - building not needed')
self.overlap_feats = np.zeros(len(query_list))
# determine max feat len
if self.overlap_feats.ndim > 1:
self.addit_feat_len = self.overlap_feats.shape[1]
def _build_users_dict(self, query_file):
self.logger.write_log(
f'Building dictionary of each user and its tweets')
curr_user = None
users_dict = {} # dictionary of lists
for line in query_file:
user = Utils.get_user_from_tweet(line)
if user is not None: # found tweet with username
curr_user = user
if curr_user is not None:
if curr_user not in users_dict: # it's the first tweet of this user, add it
users_dict[curr_user] = [line]
else:
users_dict[curr_user].append(line)
# elif line not in users_dict[curr_user]: # append to exists user only if not exists
# users_dict[curr_user].append(line)
return users_dict
def start_train(self):
# get training parameters
embedding_file = self.ui.textbox_embed.text()
bot_file = self.ui.textbox_bot.text()
human_file = self.ui.textbox_human.text()
train_split = self.ui.slider_train.value() / 100.0
test_split = 1 - train_split
val_split = self.ui.slider_val.value() / 100.0
epoches = self.ui.spinbox_epoches.value()
batch_size = self.ui.spinbox_batch.value()
addit_feat_enabled = self.ui.checkbox_additional_feats.isChecked()
early_stop = self.ui.spinbox_earlystop.value()
# get dataset config from combobox
gen_method = str(self.ui.combobox_gen_method.currentText())
if gen_method == "User Grouping":
dataset_config = DatasetConfig.USER_STATE
elif gen_method == "Random Pairing":
dataset_config = DatasetConfig.RANDOM_STATE
# Check for early stop validity
if early_stop > epoches:
Utils.show_msg(
text=
"Can not Insert Early Stop Epochs\nThat Bigger Than Training Epochs Number!",
title="Input Error")
return
# check for files validity
try:
Utils.file_validation(embedding_file, 'Embedding')
Utils.file_validation(bot_file, 'Bot')
Utils.file_validation(human_file, 'Human')
except Exception as ex:
Utils.show_msg(text=ex.args[0], title="Input Error")
return
# reset progressbars
self.ui.progressbar_epoches.setValue(0)
self.ui.progressbar_batch.setValue(0)
# reset graphs
self.reset_graphs()
# disable unnecessery widgets when starting training
self.change_widgets_disabled(True)
self.ui.btn_save.setDisabled(True)
self.log.write_log("Start pre-training phase...")
# create model instance with all parameters
self.custom_callback = CallBackTrainNNet(self.log, self.draw_graphs,
self.batch_graphs_clear,
self.update_progressbars,
self.get_status_stopped,
self.MAX_BATCH,
self.update_batch_range)
self.model = ModelTrainer(logger=self.log,
embedding_file=embedding_file,
bots_file=bot_file,
human_file=human_file,
validation_split=val_split,
test_split=test_split,
batch_size=batch_size,
epochs=epoches,
additional_feats_enabled=addit_feat_enabled,
early_stopping=early_stop,
dataset_config=dataset_config,
custom_callback=self.custom_callback)
# create a thread for training phase
self.model_thread = ModelTrainerThread(self.model)
self.model_thread.finished.connect(self.on_train_finished)
self.model_thread.start() # run the thread to start training
def train_model(self):
# load exists dataset or create a new one if not exists
#self._load_dataset()
# build dataset for training
self.dataset.perform_build(self.bots_file, self.human_file,
self.additional_feats_enabled)
self.logger.write_log('Splitting datasets into train and test sets')
data_train, data_test = self._split_train_test_sets()
q_train, d_train, addn_feat_train, y_train = data_train
q_test, d_test, addn_feat_test, y_test = data_test
self.logger.write_log(f'trains samples: {len(q_train)}')
self.logger.write_log(f'test samples: {len(q_test)}')
# extract some parameters that uses for our model
vocabulary = self.dataset.tokenizer.index_word
max_text_len = self.dataset.max_text_len
addit_feat_len = self.dataset.addit_feat_len
tokenizer = self.dataset.tokenizer
# convert texts to sequences
self.logger.write_log('convert texts to sequences')
x_q_train = Utils.convert_text_to_sequences(tokenizer, q_train,
max_text_len)
x_d_train = Utils.convert_text_to_sequences(tokenizer, d_train,
max_text_len)
x_q_test = Utils.convert_text_to_sequences(tokenizer, q_test,
max_text_len)
x_d_test = Utils.convert_text_to_sequences(tokenizer, d_test,
max_text_len)
# prepare data for predicting
self.bot_tweets = self._get_unique_matches(q_train, y_train)
self.x_bot_tweets = Utils.convert_text_to_sequences(
tokenizer, self.bot_tweets, max_text_len)
self.bot_test_tweets = q_test
self.doc_test_tweets = d_test
self.labels_test = y_test
# create our model with embedding matrix
self.model = self._create_model(vocabulary, max_text_len,
addit_feat_len)
self.logger.write_log(f'Start training process..')
# start fitting model
history = self.model.fit([
np.array(x_q_train),
np.array(x_d_train),
np.array(addn_feat_train)
],
np.array(y_train),
epochs=self.epochs,
batch_size=self.batch_size,
verbose=1,
validation_split=self.validation_split,
callbacks=self._get_callbacks())
def _get_unique_matches(self, query_train, label_train):
lst = list(zip(query_train, label_train))
lst_matches = list(filter(lambda x: x[1] == 1, lst))
bot_matches, _ = list(map(list, zip(*lst_matches)))
unique_matches = Utils.remove_duplicates(bot_matches)
return unique_matches