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 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 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
