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nn_test_multi_output.py
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nn_test_multi_output.py
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from Models.NN.MultiNNRec import MultiDistilBertRec
from Utils.Data.Data import get_dataset, get_feature, get_feature_reader
from Utils.Submission.Submission import create_submission_file
import numpy as np
import time
from Utils.TelegramBot import telegram_bot_send_update
def main():
'''
feature_list = [
"raw_feature_creator_follower_count", # 0
"raw_feature_creator_following_count", # 1
"raw_feature_engager_follower_count", # 2
"raw_feature_engager_following_count", # 3
"tweet_feature_number_of_photo", # 4
"tweet_feature_number_of_video", # 5
"tweet_feature_number_of_gif", # 6
"tweet_feature_number_of_hashtags", # 7
"tweet_feature_creation_timestamp_hour", # 8
"tweet_feature_creation_timestamp_week_day", # 9
"tweet_feature_number_of_mentions", # 10
"number_of_engagements_like", # 11
"number_of_engagements_retweet", # 12
"number_of_engagements_reply", # 13
"number_of_engagements_comment", # 14
"number_of_engagements_positive", # 15
"number_of_engagements_negative", # 16
"engager_feature_number_of_previous_like_engagement_ratio", # 17
"engager_feature_number_of_previous_reply_engagement_ratio", # 18
"engager_feature_number_of_previous_retweet_engagement_ratio", # 19
"engager_feature_number_of_previous_comment_engagement_ratio", # 20
"engager_feature_number_of_previous_positive_engagement_ratio", # 21
"engager_feature_number_of_previous_negative_engagement_ratio" # 22
]
'''
'''
feature_list = [
"raw_feature_creator_follower_count",
"raw_feature_creator_following_count",
"raw_feature_engager_follower_count",
"raw_feature_engager_following_count",
"raw_feature_creator_is_verified",
"raw_feature_engager_is_verified",
"raw_feature_engagement_creator_follows_engager",
"tweet_feature_number_of_photo",
"tweet_feature_number_of_video",
"tweet_feature_number_of_gif",
"tweet_feature_number_of_media",
"tweet_feature_is_retweet",
"tweet_feature_is_quote",
"tweet_feature_is_top_level",
"tweet_feature_number_of_hashtags",
"tweet_feature_creation_timestamp_hour",
"tweet_feature_creation_timestamp_week_day",
#"tweet_feature_number_of_mentions",
"tweet_feature_token_length",
"tweet_feature_token_length_unique",
"tweet_feature_text_topic_word_count_adult_content",
"tweet_feature_text_topic_word_count_kpop",
"tweet_feature_text_topic_word_count_covid",
"tweet_feature_text_topic_word_count_sport",
"number_of_engagements_with_language_like",
"number_of_engagements_with_language_retweet",
"number_of_engagements_with_language_reply",
"number_of_engagements_with_language_comment",
"number_of_engagements_with_language_negative",
"number_of_engagements_with_language_positive",
"number_of_engagements_ratio_like",
"number_of_engagements_ratio_retweet",
"number_of_engagements_ratio_reply",
"number_of_engagements_ratio_comment",
"number_of_engagements_ratio_negative",
"number_of_engagements_ratio_positive",
"number_of_engagements_between_creator_and_engager_like",
"number_of_engagements_between_creator_and_engager_retweet",
"number_of_engagements_between_creator_and_engager_reply",
"number_of_engagements_between_creator_and_engager_comment",
"number_of_engagements_between_creator_and_engager_negative",
"number_of_engagements_between_creator_and_engager_positive",
"number_of_engagements_like",
"number_of_engagements_retweet",
"number_of_engagements_reply",
"number_of_engagements_comment",
"number_of_engagements_negative",
"number_of_engagements_positive",
"tweet_feature_creation_timestamp_hour_shifted",
"tweet_feature_creation_timestamp_day_phase",
"tweet_feature_creation_timestamp_day_phase_shifted",
"engager_feature_number_of_previous_like_engagement_ratio",
"engager_feature_number_of_previous_reply_engagement_ratio",
"engager_feature_number_of_previous_retweet_engagement_ratio",
"engager_feature_number_of_previous_comment_engagement_ratio",
"engager_feature_number_of_previous_positive_engagement_ratio",
"engager_feature_number_of_previous_negative_engagement_ratio",
"adjacency_between_creator_and_engager_retweet",
"adjacency_between_creator_and_engager_reply",
"adjacency_between_creator_and_engager_comment",
"adjacency_between_creator_and_engager_like",
"adjacency_between_creator_and_engager_positive",
"adjacency_between_creator_and_engager_negative",
"graph_two_steps_adjacency_positive",
"graph_two_steps_adjacency_negative",
"graph_two_steps_adjacency_like",
"graph_two_steps_adjacency_reply",
"graph_two_steps_adjacency_retweet",
"graph_two_steps_adjacency_comment",
"graph_two_steps_positive",
"graph_two_steps_negative",
"graph_two_steps_like",
"graph_two_steps_reply",
"graph_two_steps_retweet",
"graph_two_steps_comment"
]
'''
feature_list = [
"raw_feature_creator_follower_count", # 0
"raw_feature_creator_following_count", # 1
]
print("Running on labels : like - retweet - reply - comment")
ip = '34.242.41.76'
submission_filename = "Dataset/Features/cherry_val/ensembling/nn_predictions"
chunksize = 2048
train_dataset = "cherry_train"
test_dataset = "new_test"
ffnn_params = {'hidden_size_1': 128, 'hidden_size_2': 64, 'hidden_dropout_prob_1': 0.5, 'hidden_dropout_prob_2': 0.5}
rec_params = {'epochs': 5, 'weight_decay': 1e-5, 'lr': 2e-5, 'cap_length': 128, 'ffnn_params': ffnn_params}
saved_model_path = "./saved_models/saved_model_multi_label"
rec = MultiDistilBertRec(**rec_params)
train_df = get_dataset(features=feature_list, dataset_id=train_dataset)
train_df = train_df.head(3840000)
train_df = rec._normalize_features(train_df, is_train=True)
### PREDICTION
test_df = get_dataset(features=feature_list, dataset_id=test_dataset)
#test_df = test_df.head(2500)
prediction_start_time = time.time()
text_test_reader_df = get_feature_reader(feature_name="raw_feature_tweet_text_token",
dataset_id=test_dataset,
chunksize=chunksize)
predictions = rec.get_prediction(df_test_features=test_df,
df_test_tokens_reader=text_test_reader_df,
pretrained_model_dict_path=saved_model_path)
print(f"Prediction time: {time.time() - prediction_start_time} seconds")
print(predictions)
print(predictions.shape)
predictions_like = predictions[:,0]
predictions_retweet = predictions[:,1]
predictions_reply = predictions[:,2]
predictions_comment = predictions[:,3]
#print(predictions_like)
#print(predictions_like.shape)
tweets = get_feature("raw_feature_tweet_id", test_dataset)["raw_feature_tweet_id"].array
users = get_feature("raw_feature_engager_id", test_dataset)["raw_feature_engager_id"].array
#tweets = tweets.head(2500).array
#users = users.head(2500).array
create_submission_file(tweets, users, predictions_like, submission_filename+"_like.csv")
create_submission_file(tweets, users, predictions_like, submission_filename+"_retweet.csv")
create_submission_file(tweets, users, predictions_like, submission_filename+"_reply.csv")
create_submission_file(tweets, users, predictions_like, submission_filename+"_comment.csv")
#bot_string = f"DistilBertDoubleInput NN - like_retweet \n ---------------- \n"
#bot_string = bot_string + f"@lucaconterio la submission pronta! \nIP: {ip} \nFile: {submission_filename}"
#telegram_bot_send_update(bot_string)
if __name__ == '__main__':
main()