def prediction(LGBM, dataset_id, df, label):
tweets = Data.get_feature("raw_feature_tweet_id", dataset_id)["raw_feature_tweet_id"].array
users = Data.get_feature("raw_feature_engager_id", dataset_id)["raw_feature_engager_id"].array
# LGBM Prediction
prediction_start_time = time.time()
predictions = LGBM.get_prediction(df.to_numpy())
print(f"Prediction time: {time.time() - prediction_start_time} seconds")
# Uncomment to plot feature importance at the end of training
# LGBM.plot_fimportance()
create_submission_file(tweets, users, predictions, f"{dataset_id}_{label}_lgbm_blending_submission_2.csv")
def main():
# Instantiate the parser
parser = argparse.ArgumentParser()
parser.add_argument('label', type=str, help='required argument: label')
args = parser.parse_args()
nn_labels = ["like", "reply", "retweet", "comment"]
LABEL = args.label
assert LABEL in ["like", "reply", "retweet", "comment"], "LABEL not valid."
print(f"label is {LABEL}")
features = [
"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",
"creator_feature_number_of_like_engagements_received",
"creator_feature_number_of_retweet_engagements_received",
"creator_feature_number_of_reply_engagements_received",
"creator_feature_number_of_comment_engagements_received",
"creator_feature_number_of_negative_engagements_received",
"creator_feature_number_of_positive_engagements_received",
"creator_feature_number_of_like_engagements_given",
"creator_feature_number_of_retweet_engagements_given",
"creator_feature_number_of_reply_engagements_given",
"creator_feature_number_of_comment_engagements_given",
"creator_feature_number_of_negative_engagements_given",
"creator_feature_number_of_positive_engagements_given",
"engager_feature_number_of_like_engagements_received",
"engager_feature_number_of_retweet_engagements_received",
"engager_feature_number_of_reply_engagements_received",
"engager_feature_number_of_comment_engagements_received",
"engager_feature_number_of_negative_engagements_received",
"engager_feature_number_of_positive_engagements_received",
"number_of_engagements_like",
"number_of_engagements_retweet",
"number_of_engagements_reply",
"number_of_engagements_comment",
"number_of_engagements_negative",
"number_of_engagements_positive",
"engager_feature_number_of_previous_like_engagement",
"engager_feature_number_of_previous_reply_engagement",
"engager_feature_number_of_previous_retweet_engagement",
"engager_feature_number_of_previous_comment_engagement",
"engager_feature_number_of_previous_positive_engagement",
"engager_feature_number_of_previous_negative_engagement",
"engager_feature_number_of_previous_engagement",
"engager_feature_number_of_previous_like_engagement_ratio_1",
"engager_feature_number_of_previous_reply_engagement_ratio_1",
"engager_feature_number_of_previous_retweet_engagement_ratio_1",
"engager_feature_number_of_previous_comment_engagement_ratio_1",
"engager_feature_number_of_previous_positive_engagement_ratio_1",
"engager_feature_number_of_previous_negative_engagement_ratio_1",
"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",
"engager_feature_number_of_previous_like_engagement_between_creator_and_engager_by_creator",
"engager_feature_number_of_previous_reply_engagement_between_creator_and_engager_by_creator",
"engager_feature_number_of_previous_retweet_engagement_between_creator_and_engager_by_creator",
"engager_feature_number_of_previous_comment_engagement_between_creator_and_engager_by_creator",
"engager_feature_number_of_previous_negative_engagement_between_creator_and_engager_by_creator",
"engager_feature_number_of_previous_positive_engagement_between_creator_and_engager_by_creator",
"engager_feature_number_of_previous_like_engagement_between_creator_and_engager_by_engager",
"engager_feature_number_of_previous_reply_engagement_between_creator_and_engager_by_engager",
"engager_feature_number_of_previous_retweet_engagement_between_creator_and_engager_by_engager",
"engager_feature_number_of_previous_comment_engagement_between_creator_and_engager_by_engager",
"engager_feature_number_of_previous_negative_engagement_between_creator_and_engager_by_engager",
"engager_feature_number_of_previous_positive_engagement_between_creator_and_engager_by_engager",
# "tweet_feature_number_of_previous_like_engagements",
# "tweet_feature_number_of_previous_reply_engagements",
# "tweet_feature_number_of_previous_retweet_engagements",
# "tweet_feature_number_of_previous_comment_engagements",
# "tweet_feature_number_of_previous_positive_engagements",
# "tweet_feature_number_of_previous_negative_engagements",
"creator_feature_number_of_previous_like_engagements_given",
"creator_feature_number_of_previous_reply_engagements_given",
"creator_feature_number_of_previous_retweet_engagements_given",
"creator_feature_number_of_previous_comment_engagements_given",
"creator_feature_number_of_previous_positive_engagements_given",
"creator_feature_number_of_previous_negative_engagements_given",
"creator_feature_number_of_previous_like_engagements_received",
"creator_feature_number_of_previous_reply_engagements_received",
"creator_feature_number_of_previous_retweet_engagements_received",
"creator_feature_number_of_previous_comment_engagements_received",
"creator_feature_number_of_previous_positive_engagements_received",
"creator_feature_number_of_previous_negative_engagements_received",
"engager_feature_number_of_previous_like_engagement_with_language",
"engager_feature_number_of_previous_reply_engagement_with_language",
"engager_feature_number_of_previous_retweet_engagement_with_language",
"engager_feature_number_of_previous_comment_engagement_with_language",
"engager_feature_number_of_previous_positive_engagement_with_language",
"engager_feature_number_of_previous_negative_engagement_with_language",
"engager_feature_knows_hashtag_positive",
"engager_feature_knows_hashtag_negative",
"engager_feature_knows_hashtag_like",
"engager_feature_knows_hashtag_reply",
"engager_feature_knows_hashtag_rt",
"engager_feature_knows_hashtag_comment",
"creator_and_engager_have_same_main_language",
"is_tweet_in_creator_main_language",
"is_tweet_in_engager_main_language",
# "statistical_probability_main_language_of_engager_engage_tweet_language_1",
# "statistical_probability_main_language_of_engager_engage_tweet_language_2",
"creator_and_engager_have_same_main_grouped_language",
"is_tweet_in_creator_main_grouped_language",
"is_tweet_in_engager_main_grouped_language",
# # "hashtag_similarity_fold_ensembling_positive",
# # "link_similarity_fold_ensembling_positive",
# # "domain_similarity_fold_ensembling_positive"
"tweet_feature_creation_timestamp_hour_shifted",
"tweet_feature_creation_timestamp_day_phase",
"tweet_feature_creation_timestamp_day_phase_shifted"
]
label = [f"tweet_feature_engagement_is_{LABEL}"]
train_dataset = "cherry_train"
val_dataset = "cherry_val"
test_dataset = "new_test"
ensembling_list_dict = {
'like': ['reply', 'retweet', 'comment'],
'reply': ['reply', 'retweet', 'comment'],
'retweet': ['reply', 'retweet', 'comment'],
'comment': ['reply', 'retweet', 'comment'],
}
ensembling_list = ensembling_list_dict[LABEL]
ensembling_lgbm_params = {}
ensembling_xgb_params = {}
for ens_label in ensembling_list:
ensembling_lgbm_params[ens_label], ensembling_xgb_params[ens_label] \
= params_by_label(ens_label)
categorical_features_set = set([])
# Load train data
# loading_data_start_time = time.time()
# df_train, df_train_label = Data.get_dataset_xgb(train_dataset, features, label)
# print(f"Loading train data time: {loading_data_start_time - time.time()} seconds")
# Load val data
df_val, df_val_label = Data.get_dataset_xgb(val_dataset, features, label)
# Load test data
df_test = Data.get_dataset(features, test_dataset)
new_index = pd.Series(df_test.index).map(lambda x: x + len(df_val))
df_test.set_index(new_index, inplace=True)
# df to be predicted by the lgbm blending feature
df_to_predict = pd.concat([df_val, df_test])
# BLENDING FEATURE DECLARATION
feature_list = []
# NEW CODE ADDED
df_train = pd.DataFrame(columns=features)
df_train_label = pd.DataFrame(columns=label)
need_to_load_train_set = False
for ens_label in ensembling_list:
lgbm_params = ensembling_lgbm_params[ens_label]
for lgbm_param_dict in lgbm_params:
start_time = time.time()
if not LGBMEnsemblingFeature(
dataset_id=train_dataset,
df_train=df_train,
df_train_label=get_ensembling_label(
ens_label, train_dataset),
df_to_predict=df_to_predict,
param_dict=lgbm_param_dict,
categorical_features_set=categorical_features_set
).has_feature():
print(f"{ens_label} {lgbm_param_dict}")
need_to_load_train_set = True
if need_to_load_train_set:
df_train, df_train_label = get_dataset_xgb_batch(
total_n_split=1,
split_n=0,
dataset_id=train_dataset,
X_label=features,
Y_label=label,
sample=0.3)
for ens_label in ensembling_list:
lgbm_params = ensembling_lgbm_params[ens_label]
for lgbm_param_dict in lgbm_params:
start_time = time.time()
feature_list.append(
LGBMEnsemblingFeature(
dataset_id=train_dataset,
df_train=df_train,
df_train_label=get_ensembling_label(
ens_label, train_dataset),
df_to_predict=df_to_predict,
param_dict=lgbm_param_dict,
categorical_features_set=categorical_features_set))
# NEW PARTll
# ONLY THIS PART IS NEW
# LOAD THIS PART FIRST
del df_train, df_train_label
df_feature_list = [x.load_or_create() for x in feature_list]
for ens_label in ensembling_list:
start_time = time.time()
if ens_label == "like":
val_features_df = XGBFoldEnsemblingLike2(
val_dataset).load_or_create()
test_features_df = XGBFoldEnsemblingLike2(
test_dataset).load_or_create()
elif ens_label == "retweet":
val_features_df = XGBFoldEnsemblingRetweet2(
val_dataset).load_or_create()
test_features_df = XGBFoldEnsemblingRetweet2(
test_dataset).load_or_create()
elif ens_label == "reply":
val_features_df = XGBFoldEnsemblingReply2(
val_dataset).load_or_create()
test_features_df = XGBFoldEnsemblingReply2(
test_dataset).load_or_create()
elif ens_label == "comment":
val_features_df = XGBFoldEnsemblingComment2(
val_dataset).load_or_create()
test_features_df = XGBFoldEnsemblingComment2(
test_dataset).load_or_create()
else:
assert False, "oh oh something went wrong. label not found"
test_features_df.set_index(new_index, inplace=True)
xgb_feature_df = pd.concat([val_features_df, test_features_df])
df_feature_list.append(xgb_feature_df)
print(f"time: {time.time() - start_time}")
del val_features_df, test_features_df
# check dimensions
len_val = len(df_val)
for df_feat in df_feature_list:
assert len(df_feat) == (len_val + len(df_test)), \
f"Blending features are not of dimension expected, len val: {len_val} len test: {len(df_test)}\n " \
f"obtained len: {len(df_feat)} of {df_feat.columns[0]}\n"
# split feature dataframe in validation and testing
df_feat_val_list = [df_feat.iloc[:len_val] for df_feat in df_feature_list]
df_feat_test_list = [df_feat.iloc[len_val:] for df_feat in df_feature_list]
df_feat_nn_val_list = [
get_nn_prediction(l, val_dataset) for l in nn_labels
]
df_feat_nn_test_list = [
get_nn_prediction(l, test_dataset) for l in nn_labels
]
for df_feat_nn_test in df_feat_nn_test_list:
new_index = pd.Series(
df_feat_nn_test.index).map(lambda x: x + len(df_val))
df_feat_nn_test.set_index(new_index, inplace=True)
df_feat_val_list += df_feat_nn_val_list
df_feat_test_list += df_feat_nn_test_list
df_val_to_be_concatenated_list = [df_val
] + df_feat_val_list + [df_val_label]
df_test_to_be_concatenated_list = [df_test] + df_feat_test_list
# creating the new validation set on which we will do meta optimization
df_val = pd.concat(df_val_to_be_concatenated_list, axis=1)
df_test = pd.concat(df_test_to_be_concatenated_list, axis=1)
# now we are in full meta-model mode
# watchout! they are unsorted now, you got to re-sort the dfs
df_metatrain, df_metaval = train_test_split(df_val, test_size=0.3)
df_metatrain.sort_index(inplace=True)
df_metaval.sort_index(inplace=True)
# split dataframe columns in train and label
col_names_list = [df_feat.columns[0] for df_feat in df_feature_list]
extended_features = df_test.columns
df_metatrain_label = df_metatrain[label]
df_metatrain = df_metatrain[extended_features]
df_metaval_label = df_metaval[label]
df_metaval = df_metaval[extended_features]
for i in range(len(df_metatrain.columns)):
assert df_metatrain.columns[i] == df_test.columns[i], f'You f****d yourself. metatrain col {i}: {df_metatrain.columns[i]}' \
f' test col {i}: {df_test.columns[i]}'
model_name = "lightgbm_classifier"
kind = LABEL
params = {
'num_leaves': 544,
'max_depth': 7,
'lambda_l1': 50.0,
'lambda_l2': 2.841130937148593,
'colsample_bynode': 0.4,
'colsample_bytree': 1.0,
'bagging_fraction': 1.0,
'bagging_freq': 8,
'min_data_in_leaf': 611,
}
LGBM = LightGBM(
objective='binary',
num_threads=-1,
num_iterations=1000,
early_stopping_rounds=15,
**params,
)
# LGBM Training
training_start_time = time.time()
LGBM.fit(X=df_metatrain,
Y=df_metatrain_label,
X_val=df_metaval,
Y_val=df_metaval_label,
categorical_feature=set([]))
print(f"Training time: {time.time() - training_start_time} seconds")
# LGBM Evaluation
evaluation_start_time = time.time()
prauc, rce, conf, max_pred, min_pred, avg = LGBM.evaluate(
df_metaval.to_numpy(), df_metaval_label.to_numpy())
print(
"since I'm lazy I did the local test on the same test on which I did EarlyStopping"
)
print(f"PRAUC:\t{prauc}")
print(f"RCE:\t{rce}")
print(f"TN:\t{conf[0, 0]}")
print(f"FP:\t{conf[0, 1]}")
print(f"FN:\t{conf[1, 0]}")
print(f"TP:\t{conf[1, 1]}")
print(f"MAX_PRED:\t{max_pred}")
print(f"MIN_PRED:\t{min_pred}")
print(f"AVG:\t{avg}")
print(f"Evaluation time: {time.time() - evaluation_start_time} seconds")
tweets = Data.get_feature("raw_feature_tweet_id",
test_dataset)["raw_feature_tweet_id"].array
users = Data.get_feature("raw_feature_engager_id",
test_dataset)["raw_feature_engager_id"].array
# LGBM Prediction
prediction_start_time = time.time()
predictions = LGBM.get_prediction(df_test.to_numpy())
print(f"Prediction time: {time.time() - prediction_start_time} seconds")
# Uncomment to plot feature importance at the end of training
# LGBM.plot_fimportance()
create_submission_file(tweets, users, predictions,
f"{LABEL}_lgbm_blending_submission.csv")
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")
def main(label_1, label_2, test_dataset, model_id):
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_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"
]
print(f"Model : {model_id}")
print(f"Running on labels : {label_1} - {label_2}")
ip = '34.242.41.76'
submission_dir = f"Dataset/Features/{test_dataset}/ensembling"
submission_filename = f"{submission_dir}/nn_predictions"
training_chunksize = 192
training_batches_number = 20000
n_data_train = training_chunksize * training_batches_number
test_chunksize = 2048
train_dataset = "cherry_train"
print(f"Test dataset : {test_dataset}")
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': 2,
'weight_decay': 1e-5,
'lr': 2e-5,
'cap_length': 128,
'ffnn_params': ffnn_params
}
saved_model_path = f"./saved_models/saved_model_{label_1}_{label_2}_{model_id}"
rec = DualDistilBertRec(**rec_params)
train_df = get_dataset(features=feature_list, dataset_id=train_dataset)
if model_id == 1:
train_df = train_df.head(n_data_train)
elif model_id == 2:
train_df = train_df.iloc[n_data_train:2 * n_data_train]
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=test_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)
p_1 = predictions[:, 0]
p_2 = predictions[:, 1]
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
pathlib.Path(submission_dir).mkdir(parents=True, exist_ok=True)
create_submission_file(tweets, users, p_1,
submission_filename + f"_{label_1}_{model_id}.csv")
create_submission_file(tweets, users, p_2,
submission_filename + f"_{label_2}_{model_id}.csv")
