def calculate_hidden_size(self):
samples_in_training_data = 116100
scaling_factor = 5
input_neurons = self.input_size
output_neurons = self.output_size
size = int(samples_in_training_data / (scaling_factor * (input_neurons + output_neurons)))
LoggerHelper.info('Calculated hidden size is ' + str(size))
if size == 0:
LoggerHelper.error('Calculated hidden size is changed to 2')
return 2
else:
return size
def get_news_type(dnn_type):
dnn_type = dnn_type.strip()
if dnn_type == "CNN":
return NewsCnnMain()
elif dnn_type == "RNN":
return NewsDnnGeneralMain()
elif dnn_type == "TA":
return TaMain()
elif dnn_type == "PriceRNN":
return PriceRnnMain()
elif dnn_type == "CATE":
return NewsCateMain()
else: # Default RNN
LoggerHelper.error("DNN type (" + dnn_type + ") is not found. Default RNN (NewsDnnGeneralMain) is used.")
return NewsDnnGeneralMain()
async def __random_news_handler(self, request):
request = await request.json()
print(request)
default = self.get_news_data(self.db, self.defaultCollection,
request['object_id'])
if default is None:
res = {'isError': True, 'Message': "Object Is Not Found."}
res = JSONEncoder().encode(res)
return web.json_response(res)
else:
try:
self.toCollection.insert({
"_id":
default["_id"],
"title":
default["title"],
"summery":
default["summery"],
"article":
default['authors'],
"url":
default["url"],
"category":
request["categories"],
"price_after_minute":
default["price_after_minute"],
"price_after_hour":
default["price_after_hour"],
"price_after_day":
default["price_after_day"],
"price_before":
default["price_before"],
"wiki_relatedness":
default["wiki_relatedness"],
"tweet_count":
default["tweet_count"],
"tweet_percentage":
default["tweet_percentage"],
"wiki_relatedness_nor":
default["wiki_relatedness_nor"],
"tweet_count_nor":
default["tweet_count_nor"],
"date":
default["date"],
"authors":
default["authors"],
"comment":
request['comment'],
"price_effect":
request['effect']
})
default['is_controlled'] = True
default['is_incorrect'] = False
self.record_one_field(self.defaultCollection, default)
# price_effect effect
res = {'isError': False, 'Message': "Success"}
except Exception as exception:
res = {
'isError': True,
'Message': "Insert Error. Please inform the Admin"
}
LoggerHelper.error(type(exception).__name__)
LoggerHelper.error("Ex: " + str(exception))
LoggerHelper.error(traceback.format_exc())
res = JSONEncoder().encode(res)
return web.json_response(res)
LoggerHelper.info("News Stock Prediction is ended.")
# WordEmbedding(path=Config.word_embedding.path)
# news_dnn = NewsDnnMain(epochs=int(Config.training.epochs),
# batch_size=int(Config.training.batch_size),
# seq_length=int(Config.training.sequence_length),
# lr=float(Config.training.lr))3
if args.statistics:
LoggerHelper.info("Starting Statistic Collection Mode...")
Statistics().collect()
LoggerHelper.info("Statistic Collection is ended...")
if args.test:
LoggerHelper.info("Starting Test Mode...")
TransformersTest.sentiment_analysis_test()
LoggerHelper.info("Test Mode is ended...")
if args.webservice:
web_manager = WebManager()
web_manager.add_static_files()
web_manager.add_news_root()
web_manager.run()
if __name__ == "__main__":
try:
main()
except Exception as exception:
LoggerHelper.error("Ex: " + str(exception))
LoggerHelper.error(traceback.format_exc())
exit()