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 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)