def params_by_label(label):
if label in ["like"]:
lgbm_params = like_params.lgbm_get_params()
xgb_params = like_params.xgb_get_params()
elif label in ["reply"]:
lgbm_params = reply_params.lgbm_get_params()
xgb_params = reply_params.xgb_get_params()
elif label in ["retweet"]:
lgbm_params = retweet_params.lgbm_get_params()
xgb_params = retweet_params.xgb_get_params()
elif label in ["comment"]:
lgbm_params = comment_params.lgbm_get_params()
xgb_params = comment_params.xgb_get_params()
else:
assert False, "What?"
return lgbm_params, xgb_params
def main():
# Instantiate the parser
parser = argparse.ArgumentParser()
parser.add_argument('label', type=str,
help='required argument: label')
args = parser.parse_args()
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"
if LABEL in ["like"]:
lgbm_params = like_params.lgbm_get_params()
xgb_params = like_params.xgb_get_params()
elif LABEL in ["reply"]:
lgbm_params = reply_params.lgbm_get_params()
xgb_params = reply_params.xgb_get_params()
elif LABEL in ["retweet"]:
lgbm_params = retweet_params.lgbm_get_params()
xgb_params = retweet_params.xgb_get_params()
elif LABEL in ["comment"]:
lgbm_params = comment_params.lgbm_get_params()
xgb_params = comment_params.xgb_get_params()
else:
assert False, "What?"
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 = []
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 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=df_train_label,
df_to_predict=df_to_predict,
param_dict=lgbm_param_dict,
categorical_features_set=categorical_features_set))
print(f"time: {time.time()-start_time}")
del df_train, df_train_label
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=0.1)
for xgb_param_dict in xgb_params:
start_time = time.time()
feature_list.append(XGBEnsembling(dataset_id=train_dataset,
df_train=df_train,
df_train_label=df_train_label,
df_to_predict=df_to_predict,
param_dict=xgb_param_dict, ))
print(f"time: {time.time() - start_time}")
del df_train, df_train_label
df_feature_list = [x.load_or_create() for x in feature_list]
# 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_to_be_concatenated_list = [df_val] + df_feat_val_list + [df_val_label]
# creating the new validation set on which we will do meta optimization
df_val = pd.concat(df_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 = features + col_names_list
df_metatrain_label = df_metatrain[label]
df_metatrain = df_metatrain[extended_features]
df_metaval_label = df_metaval[label]
df_metaval = df_metaval[extended_features]
model_name = "lightgbm_classifier"
kind = LABEL
OP = Optimizer(model_name,
kind,
mode=0,
path=LABEL,
path_log=f"blending-lgbm-{LABEL}",
make_log=True,
make_save=False,
auto_save=False
)
OP.setParameters(n_calls=40, n_random_starts=20)
OP.loadTrainData(df_metatrain, df_metatrain_label)
OP.loadValData(df_metaval, df_metaval_label) # early stopping
OP.loadTestData(df_metaval, df_metaval_label) # evaluate objective
OP.setParamsLGB(objective='binary', early_stopping_rounds=10, eval_metric="binary", is_unbalance=False)
OP.setCategoricalFeatures(categorical_features_set)
# OP.loadModelHardCoded()
res = OP.optimize()
def main():
# Instantiate the parser
parser = argparse.ArgumentParser()
parser.add_argument('label', type=str, help='required argument: label')
args = parser.parse_args()
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"
if LABEL in ["like"]:
lgbm_params = like_params.lgbm_get_params()
xgb_params = like_params.xgb_get_params()
elif LABEL in ["reply"]:
lgbm_params = reply_params.lgbm_get_params()
xgb_params = reply_params.xgb_get_params()
elif LABEL in ["retweet"]:
lgbm_params = retweet_params.lgbm_get_params()
xgb_params = retweet_params.xgb_get_params()
elif LABEL in ["comment"]:
lgbm_params = comment_params.lgbm_get_params()
xgb_params = comment_params.xgb_get_params()
else:
assert False, "What?"
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 = []
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 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=df_train_label,
df_to_predict=df_to_predict,
param_dict=lgbm_param_dict,
categorical_features_set=categorical_features_set))
for xgb_param_dict in xgb_params:
start_time = time.time()
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=0.1)
feature_list.append(
XGBEnsembling(
dataset_id=train_dataset,
df_train=df_train,
df_train_label=df_train_label,
df_to_predict=df_to_predict,
param_dict=xgb_param_dict,
))
df_feature_list = [x.load_or_create() for x in feature_list]
# 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_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 = features + col_names_list
df_metatrain_label = df_metatrain[label]
df_metatrain = df_metatrain[extended_features]
df_metaval_label = df_metaval[label]
df_metaval = df_metaval[extended_features]
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")