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train_lightgbm_CPU_parametric_class.py
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train_lightgbm_CPU_parametric_class.py
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from sklearn.metrics import average_precision_score, log_loss
from sklearn.model_selection import train_test_split
import dask.dataframe as dd
import os, sys
import time
import lightgbm as lgb
import RootPath
from Scripts.utilities import start_cluster
import gc
from sklearn.decomposition import PCA
import pandas as pd
def calculate_ctr(gt):
positive = len([x for x in gt if x == 1])
ctr = positive/float(len(gt))
return ctr
def rce(y_true, y_pred):
cross_entropy = log_loss(y_true, y_pred)
data_ctr = calculate_ctr(y_true)
strawman_cross_entropy = log_loss(y_true, [data_ctr for _ in range(len(y_true))])
return 'RCE', (1.0 - cross_entropy/strawman_cross_entropy)*100.0, False
def ap(y_true, y_pred):
return 'AP', average_precision_score(y_true, y_pred), False
if __name__ == '__main__':
print('Python %s on %s' % (sys.version, sys.platform))
#choose interaction(index in the array engCols (engagement Columns))
idx=0
engCols=['engagement_like_timestamp', 'engagement_retweet_timestamp','engagement_reply_timestamp', 'engagement_comment_timestamp']
if RootPath.is_aws():
print("Detected running on AWS!")
c = start_cluster(n_workers=16, threads_per_worker=1, memory_limit="24GB", processes=True)
else:
print("Running on local")
dataset_volume_path = '/home/ubuntu/new'
print(f"Dataset folder used: {RootPath.get_dataset_path()}")
parquet_dataset_path = os.path.join(dataset_volume_path,"train")
parquet_dataset_Test_path= os.path.join(dataset_volume_path,"test")
print('Start reading \n')
cols = [
'creator_follower_count',
'creator_following_count',
'creator_is_verified',
'creator_creation_timestamp',
'engager_follower_count',
'engager_following_count',
'engager_is_verified',
'engager_creation_timestamp',
'engagement_creator_follows_engager',
'engagement_reply_timestamp',
'engagement_retweet_timestamp',
'engagement_comment_timestamp',
'engagement_like_timestamp',
'is_from_official_val',
'number_of_photo',
'number_of_gif',
'number_of_video',
'tweet_links_count',
'tweet_domains_count',
'tweet_hashtags_count',
'tweet_hashtags_unique_count',
'mapped_language_id',
'mapped_tweet_type',
'tweet_timestamp_hour_sin',
'tweet_timestamp_hour_cos',
'tweet_timestamp_day',
'tweet_timestamp_weekday',
'tweet_timestamp_hour_bin',
'tweet_timestamp_creator_account_age_bin',
'text_is_reply',
'text_tokens_count',
'text_unknown_count',
'text_special_tokens_count',
'text_questions_count',
'text_semantic_separation',
'text_newline_count',
'text_separated_count',
'text_char_count',
'text_asking_like',
'text_asking_reply',
'text_comment_related_count',
'text_no_comment_related_count',
'text_asking_retweet',
'text_nsfw_count',
'text_kpop_count',
'text_covid_count',
'text_sports_count',
'text_japanesetrending_count',
'text_anime_count',
'text_vtuber_count',
'text_news_count',
'text_myanmar_count',
'text_genshin_count',
'text_crypto_count',
'text_trending_count',
'text_love_count',
'text_slang_count',
'text_mention_count',
'engager_follower_quantile',
'creator_follower_quantile',
'creator_follower_ratio',
'engager_follower_ratio',
'creator_vs_engager_follower_ratio',
'creator_vs_engager_following_ratio',
'CE_language__timestamp_hour_bin',
'CE_language__timestamp_hour_bin__timestamp_weekday',
'CE_language__type',
'CE_language__engager_follower_quantile',
'CE_type__timestamp_weekday',
'CE_type__timestamp_hour_bin',
'CE_timestamp_creator_account_age_bin__engager_follower_quantile__creator_follower_quantile',
'CE_language__presence_of_photo__presence_of_gif__presence_of_video',
'TE_mapped_engager_id_engagement_reply',
'TE_number_of_photo_engagement_reply',
'TE_number_of_gif_engagement_reply',
'TE_number_of_video_engagement_reply',
'TE_mapped_tweet_type_engagement_reply',
'TE_mapped_language_id_engagement_reply',
'TE_mapped_creator_id_engagement_reply',
'TE_mapped_tweet_links_id_1_engagement_reply',
'TE_mapped_tweet_links_id_2_engagement_reply',
'TE_mapped_tweet_hashtags_id_1_engagement_reply',
'TE_mapped_tweet_hashtags_id_2_engagement_reply',
'TE_mapped_domains_id_1_engagement_reply',
'TE_mapped_domains_id_2_engagement_reply',
"TE_('mapped_domains_id_1', 'mapped_language_id', 'engagement_creator_follows_engager', 'mapped_tweet_type', 'number_of_photo', 'creator_is_verified')_engagement_reply",
'TE_tweet_links_count_engagement_reply',
'TE_tweet_domains_count_engagement_reply',
'TE_tweet_hashtags_count_engagement_reply',
'TE_tweet_hashtags_unique_count_engagement_reply',
'TE_mapped_engager_id_engagement_retweet',
'TE_number_of_photo_engagement_retweet',
'TE_number_of_gif_engagement_retweet',
'TE_number_of_video_engagement_retweet',
'TE_mapped_tweet_type_engagement_retweet',
'TE_mapped_language_id_engagement_retweet',
'TE_mapped_creator_id_engagement_retweet',
'TE_mapped_tweet_links_id_1_engagement_retweet',
'TE_mapped_tweet_links_id_2_engagement_retweet',
'TE_mapped_tweet_hashtags_id_1_engagement_retweet',
'TE_mapped_tweet_hashtags_id_2_engagement_retweet',
'TE_mapped_domains_id_1_engagement_retweet',
'TE_mapped_domains_id_2_engagement_retweet',
"TE_('mapped_domains_id_1', 'mapped_language_id', 'engagement_creator_follows_engager', 'mapped_tweet_type', 'number_of_photo', 'creator_is_verified')_engagement_retweet",
'TE_tweet_links_count_engagement_retweet',
'TE_tweet_domains_count_engagement_retweet',
'TE_tweet_hashtags_count_engagement_retweet',
'TE_tweet_hashtags_unique_count_engagement_retweet',
'TE_mapped_engager_id_engagement_comment',
'TE_number_of_photo_engagement_comment',
'TE_number_of_gif_engagement_comment',
'TE_number_of_video_engagement_comment',
'TE_mapped_tweet_type_engagement_comment',
'TE_mapped_language_id_engagement_comment',
'TE_mapped_creator_id_engagement_comment',
'TE_mapped_tweet_links_id_1_engagement_comment',
'TE_mapped_tweet_links_id_2_engagement_comment',
'TE_mapped_tweet_hashtags_id_1_engagement_comment',
'TE_mapped_tweet_hashtags_id_2_engagement_comment',
'TE_mapped_domains_id_1_engagement_comment',
'TE_mapped_domains_id_2_engagement_comment',
"TE_('mapped_domains_id_1', 'mapped_language_id', 'engagement_creator_follows_engager', 'mapped_tweet_type', 'number_of_photo', 'creator_is_verified')_engagement_comment",
'TE_tweet_links_count_engagement_comment',
'TE_tweet_domains_count_engagement_comment',
'TE_tweet_hashtags_count_engagement_comment',
'TE_tweet_hashtags_unique_count_engagement_comment',
'TE_mapped_engager_id_engagement_like',
'TE_number_of_photo_engagement_like',
'TE_number_of_gif_engagement_like',
'TE_number_of_video_engagement_like',
'TE_mapped_tweet_type_engagement_like',
'TE_mapped_language_id_engagement_like',
'TE_mapped_creator_id_engagement_like',
'TE_mapped_tweet_links_id_1_engagement_like',
'TE_mapped_tweet_links_id_2_engagement_like',
'TE_mapped_tweet_hashtags_id_1_engagement_like',
'TE_mapped_tweet_hashtags_id_2_engagement_like',
'TE_mapped_domains_id_1_engagement_like',
'TE_mapped_domains_id_2_engagement_like',
"TE_('mapped_domains_id_1', 'mapped_language_id', 'engagement_creator_follows_engager', 'mapped_tweet_type', 'number_of_photo', 'creator_is_verified')_engagement_like",
'TE_tweet_links_count_engagement_like',
'TE_tweet_domains_count_engagement_like',
'TE_tweet_hashtags_count_engagement_like',
'TE_tweet_hashtags_unique_count_engagement_like',
'engager_hashtag_count_all_avg',
'engager_hashtag_count_all_max',
'engager_hashtag_count_reply_avg',
'engager_hashtag_count_reply_max',
'engager_hashtag_count_retweet_avg',
'engager_hashtag_count_retweet_max',
'engager_hashtag_count_comment_avg',
'engager_hashtag_count_comment_max',
'engager_hashtag_count_like_avg',
'engager_hashtag_count_like_max',
'engager_language_count_all',
'engager_language_count_reply',
'engager_language_count_retweet',
'engager_language_count_comment',
'engager_language_count_like',
'engager_photo_count_all',
'engager_photo_count_reply',
'engager_photo_count_retweet',
'engager_photo_count_comment',
'engager_photo_count_like',
'engager_gif_count_all',
'engager_gif_count_reply',
'engager_gif_count_retweet',
'engager_gif_count_comment',
'engager_gif_count_like',
'engager_video_count_all',
'engager_video_count_reply',
'engager_video_count_retweet',
'engager_video_count_comment',
'engager_video_count_like',
'engager_type_count_all',
'engager_type_count_reply',
'engager_type_count_retweet',
'engager_type_count_comment',
'engager_type_count_like',
'language_hourbin_count_all',
'language_hourbin_count_reply',
'language_hourbin_count_retweet',
'language_hourbin_count_comment',
'language_hourbin_count_like',
'engager_weekday_count_all',
'engager_weekday_count_reply',
'engager_weekday_count_retweet',
'engager_weekday_count_comment',
'engager_weekday_count_like'
]
df = dd.read_parquet(parquet_dataset_path, engine='fastparquet', columns=cols)
dfTest = dd.read_parquet(parquet_dataset_Test_path, engine='fastparquet', columns=cols)
df = df.sample(frac = 1)
df.columns=[c.replace("'",'').replace(",","") for c in df.columns]
dfTest.columns=[c.replace("'",'').replace(",","") for c in dfTest.columns]
# Drop other engagements
chosen=engCols[idx]
rest=[c for c in engCols if c!=chosen]
# Drop other engagements
df = df.drop(columns=rest)
df[chosen] = df[chosen].mask(df[chosen] < 0, 0)
df[chosen] = df[chosen].mask(df[chosen] > 0, 1)
dfTest = dfTest.drop(columns=rest)
dfTest[chosen] = dfTest[chosen].mask(dfTest[chosen] < 0, 0)
dfTest[chosen] = dfTest[chosen].mask(dfTest[chosen] > 0, 1)
print('Start y \n')
yTest = dfTest[chosen]
dfTest = dfTest.drop(columns=[chosen])
y = df[chosen]
df = df.drop(columns=[chosen])
print('Start compute \n')
# From Dask to Pandas
df = df.compute()
y = y.compute()
print('Start compute \n')
# From Dask to Pandas
dfTest = dfTest.compute()
yTest = yTest.compute()
print('Columns name:', df.columns)
#change the following dictionaries with the best parameters found in tuning process
params_like = {
'objective': 'binary',
'metric': 'binary_logloss',
'num_threads': -1,
'num_iterations': 500,
'num_leaves': 252,
'feature_pre_filter': False,
'lambda_l1': 8.921850367601058,
'lambda_l2': 1.8231279696115677e-07,
'feature_fraction': 0.9159999999999999,
'bagging_freq': 7,
'bagging_fraction': 0.7762930377458914,
'min_child_samples': 25,
'early_stopping_rounds': 10
}
params_retweet = {
'objective': 'binary',
'metric': 'binary_logloss',
'num_threads': -1,
'num_iterations': 300,
'num_leaves': 233,
'feature_pre_filter': False,
'lambda_l1': 8.225271342827561,
'lambda_l2': 8.532408683625533e-07,
'feature_fraction': 1.0,
'bagging_freq': 7,
'bagging_fraction': 0.988870004485298,
'min_child_samples': 100,
'early_stopping_rounds': 20,
}
params_reply = {
'objective': 'binary',
'metric': 'binary_logloss',
'num_threads': -1,
'num_iterations': 300,
'num_leaves': 212,
'feature_pre_filter': False,
'lambda_l1': 7.6697,
'lambda_l2': 5.0805,
'feature_fraction': 0.4,
'bagging_freq': 0,
'bagging_fraction': 1,
'min_child_samples': 100,
'early_stopping_rounds': 20
}
params_comment = {
'objective': 'binary',
'metric': 'binary_logloss',
'num_threads': -1,
'num_iterations': 300,
'num_leaves': 69,
'feature_pre_filter': False,
'lambda_l1': 0.004696535530052121,
'lambda_l2': 7.549146275647072,
'feature_fraction': 0.4,
'bagging_freq': 0,
'bagging_fraction': 1,
'min_child_samples': 20,
'early_stopping_rounds': 20,
}
params_array = [params_like, params_retweet, params_reply, params_comment]
X_train, X_val, y_train, y_val = train_test_split(df, y, stratify=y, test_size=0.3)
X_val, X_test, y_val, y_test = train_test_split(X_val, y_val, stratify=y_val, test_size=0.5)
gc.collect()
train = lgb.Dataset(X_train, y_train)
validation = lgb.Dataset(X_val, y_val)
gc.collect()
model = lgb.train(params_array[idx],
train_set=train,
valid_sets=validation,
valid_names='validation')
preds = model.predict(X_test)
with open(f"training_performances_{chosen.replace('engagement_','').replace('_timestamp','')}.txt", 'a+') as f:
f.write(f"train - {c} - ")
f.write(str(rce(y_test, preds)))
f.write(str(ap(y_test,preds)))
f.write("\n")
f.flush()
preds = model.predict(dfTest)
model.save_model(f"lightgbm_{chosen.replace('engagement_','').replace('_timestamp','')}_{time.time()}.txt", num_iteration=model.best_iteration)
with open(f"training_performances_{chosen.replace('engagement_','').replace('_timestamp','')}.txt", 'a+') as f:
f.write("val - ")
f.write(str(rce(yTest, preds)))
f.write(str(ap(yTest, preds)))
f.write("\n")
f.flush()
gc.collect()