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")