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import pandas as pd
file_names = [#'./llm_for_code/datasets/codecontests/CN_py800_catmix_5rand_4fold_full_oripairs_matched.json'
# './dataset_lfs/CC_900_py_catmix_5rand_4fold_oripairs_temp.json',
# './dataset_lfs/CN_py800_catmix_5rand_4fold_200_oripairs_temp.json',
# './dataset_lfs/CN_py800_catmix_5rand_4fold_200_7m2_oripairs_temp.json',
# './dataset_lfs/CN_py800_catmix_5rand_4fold_200_7m3_oripairs_temp.json',
# './dataset_lfs/CN_py800_catmix_5rand_4fold_200_7m4_oripairs_temp.json',
# './dataset_lfs/CC_900_py_catmix_5rand_4fold_0_2_oripairs.json',
# './dataset_lfs/CC_900_py_catmix_5rand_4fold_101_3_oripairs_temp.json',
# './dataset_lfs/CC_900_py_catmix_5rand_4fold_101_3_rem_oripairs_temp.json',
# './dataset_lfs/CC_900_py_catmix_5rand_4fold_101_3_rem2_oripairs_temp.json',
# './dataset_lfs/CC_900_py_catmix_5rand_4fold_200_0_oripairs_temp.json',
# './dataset_lfs/CC_900_py_catmix_5rand_4fold_200_101_2_oripairs_temp.json',
# './dataset_lfs/CC_900_py_catmix_5rand_4fold_200_101_oripairs_temp.json',
# './dataset_lfs/CC_900_py_catmix_5rand_4fold_200_110_oripairs.json',
# './dataset_lfs/CC_900_py_catmix_5rand_4fold_1s_1k_oripairs_temp.json',
# './dataset_lfs/CC_900_py_catmix_5rand_4fold_1s_1k_2_oripairs_temp.json',
# './dataset_lfs/CC_900_py_catmix_5rand_4fold_1s_1k_3_oripairs_temp.json',
# './dataset_lfs/CC_900_py_catmix_5rand_4fold_100_4_oripairs_temp.json',
#"./dataset_lfs/allpairs_data_val_900_unperturbed.json",
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_2_oripairs_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_3_oripairs_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_4_oripairs_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_5_oripairs_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_oripairs_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_0_conc_1_oripairs_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_0_conc_oripairs_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_101_oripairs_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_2_oripairs.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_conc_2_oripairs.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_conc_3_oripairs.json',
#'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_conc_2_oripairs_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_3_oripairs.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_conc_500_1_oripairs_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_conc_500_2_oripairs_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_0s_conc_1_oripairs.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_0_conc_1_oripairs_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_0_conc_oripairs_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_conc_0s_500_0_oripairs.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_conc_pos_2_oripairs_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_conc_pos_oripairs_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_5_oripairs_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_conc_1_oripairs_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_conc_10s_500_0_oripairs_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_conc_10s_500_2_oripairs_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_conc_2_oripairs.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_conc_3_oripairs.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_conc_oripairs_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_conc_5_oripairs_temp.json',
]
file_names_per = [#'./dataset_lfs/CC_900_py_catmix_5rand_4fold_pairs_0_temp.json',
# './dataset_lfs/CN_py800_catmix_5rand_4fold_200_pairs_0_temp.json',
# './dataset_lfs/CN_py800_catmix_5rand_4fold_200_7m2_pairs_0_temp.json',
# './dataset_lfs/CN_py800_catmix_5rand_4fold_200_7m3_pairs_0_temp.json',
# './dataset_lfs/CN_py800_catmix_5rand_4fold_200_7m4_pairs_0_temp.json',
# './dataset_lfs/CC_900_py_catmix_5rand_4fold_0_2_pairs_0.json',
# './dataset_lfs/CC_900_py_catmix_5rand_4fold_101_3_pairs_0_temp.json',
# './dataset_lfs/CC_900_py_catmix_5rand_4fold_101_3_rem_pairs_0_temp.json',
# './dataset_lfs/CC_900_py_catmix_5rand_4fold_101_3_rem2_pairs_0_temp.json',
# './dataset_lfs/CC_900_py_catmix_5rand_4fold_200_0_pairs_0_temp.json',
# './dataset_lfs/CC_900_py_catmix_5rand_4fold_200_101_2_pairs_0_temp.json',
# './dataset_lfs/CC_900_py_catmix_5rand_4fold_200_101_pairs_0_temp.json',
# './dataset_lfs/CC_900_py_catmix_5rand_4fold_200_110_pairs_0.json',
# './dataset_lfs/CC_900_py_catmix_5rand_4fold_1s_1k_pairs_0_temp.json',
# './dataset_lfs/CC_900_py_catmix_5rand_4fold_1s_1k_2_pairs_0_temp.json',
# './dataset_lfs/CC_900_py_catmix_5rand_4fold_1s_1k_3_pairs_0_temp.json',
# './dataset_lfs/CC_900_py_catmix_5rand_4fold_100_4_pairs_0_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_2_pairs_0_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_3_pairs_0_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_4_pairs_0_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_5_pairs_0_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_pairs_0_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_0_conc_1_pairs_0_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_0_conc_pairs_0_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_101_pairs_0_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_2_pairs_0.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_conc_2_pairs_0.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_conc_3_pairs_0.json',
#'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_conc_2_pairs_0_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_3_pairs_0.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_conc_500_1_pairs_0_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_conc_500_2_pairs_0_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_0s_conc_1_pairs_0.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_0_conc_1_pairs_0_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_0_conc_pairs_0_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_conc_0s_500_0_pairs_0.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_conc_pos_2_pairs_0_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_conc_pos_pairs_0_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_5_pairs_0_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_conc_1_pairs_0_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_conc_10s_500_0_pairs_0_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_conc_10s_500_2_pairs_0_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_conc_2_pairs_0.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_conc_3_pairs_0.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_conc_pairs_0_temp.json',
'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_conc_5_pairs_0_temp.json',
]
#file_names = [ './dataset_lfs/CC_900_py_catmix_5rand_4fold_test_varc2_oripairs_temp.json',
# #'./dataset_lfs/CC_900_py_catmix_5rand_4fold_val_oripairs.json'
# './dataset_lfs/CC_900_py_catmix_5rand_4fold_test_varc_oripairs_temp.json',
# './llm_for_code/datasets/codecontests/CC_900_py_catmix_5rand_4fold_test_varc0s1_oripairs.json',
# './dataset_lfs/CC_900_py_catmix_5rand_4fold_test_varc10s_oripairs_temp.json',
#'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_2_pairs_0_temp_fulltrydup_train.json'
#]
# file_names = [ 'CC_1200_py_catmix_5rand_4fold_test_1s_v_oripairs.json',
# 'CC_1200_py_catmix_5rand_4fold_test_all_v_oripairs.json',
# 'CC_1200_py_catmix_5rand_4fold_test_all_v2_oripairs.json',
# 'CC_1200_py_catmix_5rand_4fold_test_pos1s_v_oripairs.json',
# 'CC_1200_py_catmix_5rand_4fold_test_v2_1s_oripairs_temp.json'
# ]
# file_names_per = [ 'CC_1200_py_catmix_5rand_4fold_test_1s_v_pairs_0.json',
# 'CC_1200_py_catmix_5rand_4fold_test_all_v_pairs_0.json',
# 'CC_1200_py_catmix_5rand_4fold_test_all_v2_pairs_0.json',
# 'CC_1200_py_catmix_5rand_4fold_test_pos1s_v_pairs_0.json',
# 'CC_1200_py_catmix_5rand_4fold_test_v2_1s_pairs_0_temp.json'
# ]
# file_names = [ 'CC_2200_py_catmix_5rand_1fold_test_1s_5_oripairs.json',
# 'CC_2200_py_catmix_5rand_1fold_test_all_2_oripairs.json',
# 'CC_2200_py_catmix_5rand_1fold_test_all_3_oripairs.json',
# 'CC_2200_py_catmix_5rand_1fold_test_all_4_oripairs.json',
# 'CC_2200_py_catmix_5rand_1fold_test_all_oripairs.json',
# 'CC_2200_py_catmix_5rand_1fold_test_ls_2_oripairs.json',
# 'CC_2200_py_catmix_5rand_1fold_test_ls_oripairs.json',
# 'CC_2200_py_catmix_5rand_1fold_test_0s_2_oripairs.json',
# 'CC_2200_py_catmix_5rand_1fold_test_0s_neg_oripairs.json',
# 'CC_2200_py_catmix_5rand_1fold_test_0s_neg2_oripairs.json',
# 'CC_2200_py_catmix_5rand_1fold_test_0s_neg22_oripairs.json',
# 'CC_2200_py_catmix_5rand_1fold_test_0s_oripairs.json',
# 'CC_2200_py_catmix_5rand_1fold_test_0s_pos_oripairs.json',
# ]
# file_names_per = [ 'CC_2200_py_catmix_5rand_1fold_test_1s_5_pairs_0.json',
# 'CC_2200_py_catmix_5rand_1fold_test_all_2_pairs_0.json',
# 'CC_2200_py_catmix_5rand_1fold_test_all_3_pairs_0.json',
# 'CC_2200_py_catmix_5rand_1fold_test_all_4_pairs_0.json',
# 'CC_2200_py_catmix_5rand_1fold_test_all_pairs_0.json',
# 'CC_2200_py_catmix_5rand_1fold_test_ls_2_pairs_0.json',
# 'CC_2200_py_catmix_5rand_1fold_test_ls_pairs_0.json',
# 'CC_2200_py_catmix_5rand_1fold_test_0s_2_pairs_0.json',
# 'CC_2200_py_catmix_5rand_1fold_test_0s_neg_pairs_0.json',
# 'CC_2200_py_catmix_5rand_1fold_test_0s_neg2_pairs_0.json',
# 'CC_2200_py_catmix_5rand_1fold_test_0s_neg22_pairs_0.json',
# 'CC_2200_py_catmix_5rand_1fold_test_0s_pairs_0.json',
# 'CC_2200_py_catmix_5rand_1fold_test_0s_pos_pairs_0.json',
# ]
#file_names_per = [ './dataset_lfs/CC_900_py_catmix_5rand_4fold_test_varc2_pairs_0_temp.json',
# # './dataset_lfs/CC_900_py_catmix_5rand_4fold_val_pairs_0.json'
# './dataset_lfs/CC_900_py_catmix_5rand_4fold_test_varc_pairs_0_temp.json',
# './llm_for_code/datasets/codecontests/CC_900_py_catmix_5rand_4fold_test_varc0s1_pairs_0.json',
# './dataset_lfs/CC_900_py_catmix_5rand_4fold_test_varc10s_pairs_0_temp.json',
#'./dataset_lfs/CC_900_py_catmix_5rand_4fold_train_all_2_pairs_0_temp_fulltrydup_train.json'
#]
# file_names = [
# 'CC_1600_py_catmix_5rand_4fold_test_all_v2_oripairs.json',
# 'CC_1600_py_catmix_5rand_4fold_test_all_oripairs_temp.json',
# 'CC_1600_py_catmix_5rand_4fold_test_1s_oripairs.json',
# 'CC_1600_py_catmix_5rand_4fold_test_pos1s_v_oripairs.json',
# 'CC_1600_py_catmix_5rand_4fold_test_pos1s_oripairs.json',
# 'CC_1600_py_catmix_5rand_4fold_test_neg1s_v_oripairs.json',
# 'CC_1600_py_catmix_5rand_4fold_test_neg1s_oripairs_temp.json',
# 'CC_1600_py_catmix_5rand_4fold_test_neg1s_2_oripairs_temp.json',
#]
# file_names_per = [
# 'CC_1600_py_catmix_5rand_4fold_test_all_v2_pairs_0.json',
# 'CC_1600_py_catmix_5rand_4fold_test_all_pairs_0_temp.json',
# 'CC_1600_py_catmix_5rand_4fold_test_1s_pairs_0.json',
# 'CC_1600_py_catmix_5rand_4fold_test_pos1s_v_pairs_0.json',
# 'CC_1600_py_catmix_5rand_4fold_test_pos1s_pairs_0.json',
# 'CC_1600_py_catmix_5rand_4fold_test_neg1s_v_pairs_0.json',
# 'CC_1600_py_catmix_5rand_4fold_test_neg1s_pairs_0_temp.json',
# 'CC_1600_py_catmix_5rand_4fold_test_neg1s_2_pairs_0_temp.json',
# ]
#file_names = ['CC_900_py_catmix_5rand_1fold_conctest_all_ind_oripairs.json', 'CC_900_py_catmix_5rand_1fold_conctest_1s_ind_oripairs.json',]
#file_names_per = ['CC_900_py_catmix_5rand_1fold_conctest_all_ind_pairs_0.json' ,'CC_900_py_catmix_5rand_1fold_conctest_1s_ind_pairs_0.json']
# file_names = ['CC_900_py_catmix_5rand_1fold_conctest_1s_loop_oripairs_temp.json',
# 'CC_900_py_catmix_5rand_1fold_conctest_all_loop_oripairs_temp.json']
# file_names_per = ['CC_900_py_catmix_5rand_1fold_conctest_1s_loop_pairs_0_temp.json',
# 'CC_900_py_catmix_5rand_1fold_conctest_all_loop_pairs_0_temp.json',]
# file_names = ['CC_900_py_catmix_5rand_1fold_conctest_boolinv_1s_oripairs.json',
# 'CC_900_py_catmix_5rand_1fold_conctest_boolinv_1s2_oripairs.json',
# 'CC_900_py_catmix_5rand_1fold_conctest_boolinv_1s3_oripairs.json',
# 'CC_900_py_catmix_5rand_1fold_conctest_boolinv_all_oripairs.json',
# 'CC_900_py_catmix_5rand_1fold_conctest_boolinv_all2_oripairs.json']
# file_names_per = ['CC_900_py_catmix_5rand_1fold_conctest_boolinv_1s_pairs_0.json',
# 'CC_900_py_catmix_5rand_1fold_conctest_boolinv_1s2_pairs_0.json',
# 'CC_900_py_catmix_5rand_1fold_conctest_boolinv_1s3_pairs_0.json',
# 'CC_900_py_catmix_5rand_1fold_conctest_boolinv_all_pairs_0.json',
# 'CC_900_py_catmix_5rand_1fold_conctest_boolinv_all2_pairs_0.json']
# file_names = ['CC_900_py_catmix_exact9rand_1fold_conctest_all_oripairs.json', # & 7
# 'CC_900_py_catmix_exact9rand_1fold_conctest_0s_oripairs.json',
# 'CC_900_py_catmix_exact9rand_1fold_conctest_1s_oripairs.json',]
# file_names_per = ['CC_900_py_catmix_exact9rand_1fold_conctest_all_pairs_0.json',
# 'CC_900_py_catmix_exact9rand_1fold_conctest_0s_pairs_0.json',
# 'CC_900_py_catmix_exact9rand_1fold_conctest_1s_pairs_0.json',]
# file_names = ['CC_900_py_catmix_exact1rand_1fold_conctest_all_oripairs.json', # & 3
# #'CC_900_py_catmix_exact3rand_1fold_conctest_0s_oripairs.json',
# 'CC_900_py_catmix_exact1rand_1fold_conctest_1s_oripairs.json',]
# file_names_per = ['CC_900_py_catmix_exact1rand_1fold_conctest_all_pairs_0.json',
# #'CC_900_py_catmix_exact3rand_1fold_conctest_0s_pairs_0.json',
# 'CC_900_py_catmix_exact1rand_1fold_conctest_1s_pairs_0.json',]
# file_names = ['CC_900_py_catmix_exact5rand_1fold_conctest_all_oripairs_temp.json', # & 7
# 'CC_900_py_catmix_exact5rand_1fold_conctest_0s_oripairs.json',
# 'CC_900_py_catmix_exact5rand_1fold_conctest_1s_oripairs_temp.json',
# 'CC_900_py_catmix_exact1rand_5fold_conctest_1s2_oripairs_temp.json',
# 'CC_900_py_catmix_exact1rand_5fold_conctest_all2_oripairs_temp.json',]
# file_names_per = ['CC_900_py_catmix_exact5rand_1fold_conctest_all_pairs_0_temp.json',
# 'CC_900_py_catmix_exact5rand_1fold_conctest_0s_pairs_0.json',
# 'CC_900_py_catmix_exact5rand_1fold_conctest_1s_pairs_0_temp.json',
# 'CC_900_py_catmix_exact1rand_5fold_conctest_1s2_pairs_0_temp.json',
# 'CC_900_py_catmix_exact1rand_5fold_conctest_all2_pairs_0_temp.json',]
# file_names = [ 'CC_900_py_catmix_5rand_1fold_conctest_expr_0s_4_oripairs.json',
# 'CC_900_py_catmix_5rand_1fold_conctest_expr_1s_4_oripairs.json',
# 'CC_900_py_catmix_5rand_1fold_conctest_expr_all_4_oripairs.json',
# ]
# file_names_per = ['CC_900_py_catmix_5rand_1fold_conctest_expr_0s_4_pairs_0.json',
# 'CC_900_py_catmix_5rand_1fold_conctest_expr_1s_4_pairs_0.json',
# 'CC_900_py_catmix_5rand_1fold_conctest_expr_all_4_pairs_0.json',
# ]
# file_names = ['CC_900_py_catmix_5rand_1fold_train_conctest_loopvarexpr_all_oripairs_temp.json',
# 'CC_900_py_catmix_5rand_1fold_train_conctest_loopvarexpr_0s_oripairs.json',
# 'CC_900_py_catmix_5rand_1fold_train_conctest_loopvarexpr_1s_oripairs_temp.json',
# 'CC_900_py_catmix_5rand_1fold_train_conctest_loopvarexpr_all_2_oripairs_temp.json',
# 'CC_900_py_catmix_5rand_1fold_train_conctest_loopvarexpr_all_3_oripairs_temp.json',
# 'CC_900_py_catmix_5rand_1fold_train_conctest_loopvarexpr_all_7_oripairs_temp.json']
# file_names_per = ['CC_900_py_catmix_5rand_1fold_train_conctest_loopvarexpr_all_pairs_0_temp.json',
# 'CC_900_py_catmix_5rand_1fold_train_conctest_loopvarexpr_0s_pairs_0.json',
# 'CC_900_py_catmix_5rand_1fold_train_conctest_loopvarexpr_1s_pairs_0_temp.json',
# 'CC_900_py_catmix_5rand_1fold_train_conctest_loopvarexpr_all_2_pairs_0_temp.json',
# 'CC_900_py_catmix_5rand_1fold_train_conctest_loopvarexpr_all_3_pairs_0_temp.json',
# 'CC_900_py_catmix_5rand_1fold_train_conctest_loopvarexpr_all_7_pairs_0_temp.json']
# file_names = ['CC_900_py_catmix_5rand_1fold_test_conctest_all_sptask_1s_13_oripairs_temp.json',
# 'CC_900_py_catmix_5rand_1fold_test_conctest_all_sptask_1s_14_oripairs_temp.json']
# file_names_per = ['CC_900_py_catmix_5rand_1fold_test_conctest_all_sptask_1s_13_pairs_0_temp.json',
# 'CC_900_py_catmix_5rand_1fold_test_conctest_all_sptask_1s_14_pairs_0_temp.json']
file_names = ['CC_900_py_catmix_1rand_1fold_train_conctest_all_11_oripairs_temp.json',
'CC_900_py_catmix_1rand_1fold_train_conctest_all_12_oripairs_temp.json',
'CC_900_py_catmix_1rand_1fold_train_conctest_0s_15_oripairs.json',
'CC_900_py_catmix_1rand_1fold_train_conctest_0s_16_oripairs.json',]
file_names_per = ['CC_900_py_catmix_1rand_1fold_train_conctest_all_11_pairs_0_temp.json',
'CC_900_py_catmix_1rand_1fold_train_conctest_all_12_pairs_0_temp.json',
'CC_900_py_catmix_1rand_1fold_train_conctest_0s_15_pairs_0.json',
'CC_900_py_catmix_1rand_1fold_train_conctest_0s_16_pairs_0.json',]
file_names = ['CC_900_py_catmix_5rand_1fold_train_conctest_iffp_all_2_oripairs.json']
file_names_per = ['CC_900_py_catmix_5rand_1fold_train_conctest_iffp_all_2_pairs_0.json']
file_names = ['AtCoder_D_catmix_5rand_oripairs.json',
'AtCoder_D_catmix_5rand_1s_oripairs.json' ]
file_names_per = ['AtCoder_D_catmix_5rand_pairs_0.json',
'AtCoder_D_catmix_5rand_1s_pairs_0.json']
file_names = ['CC_900_py_catmix_5rand_4fold_pyjava_bothper_oripairs.json',
'CC_900_py_catmix_5rand_4fold_pyjava_bothper_1s_3_oripairs.json',
'CC_900_py_catmix_5rand_4fold_pyjava_bothper_1s_oripairs.json',
'CC_900_py_catmix_5rand_4fold_pyjava_bothper_1s_2_oripairs.json',
#'CC_900_py_catmix_5rand_4fold_pyjava_bothper_2_oripairs.json',
]
file_names_per = ['CC_900_py_catmix_5rand_4fold_pyjava_bothper_pairs_0.json',
'CC_900_py_catmix_5rand_4fold_pyjava_bothper_1s_3_pairs_0.json',
'CC_900_py_catmix_5rand_4fold_pyjava_bothper_1s_pairs_0.json',
'CC_900_py_catmix_5rand_4fold_pyjava_bothper_1s_2_pairs_0.json',
#'CC_900_py_catmix_5rand_4fold_pyjava_bothper_2_pairs_0.json',
]
file_names = ['CC_900_py_catmix_5rand_4fold_pyjava_bothper_wneg_10s_3_oripairs.json',
'CC_900_py_catmix_5rand_4fold_pyjava_bothper_wneg_10s_2_oripairs_temp.json',
'CC_900_py_catmix_5rand_4fold_pyjava_bothper_wneg_10s_4_oripairs_temp.json',
'CC_900_py_catmix_5rand_4fold_pyjava_bothper_wneg_10s_oripairs_temp.json']
file_names_per = ['CC_900_py_catmix_5rand_4fold_pyjava_bothper_wneg_10s_3_pairs_0.json',
'CC_900_py_catmix_5rand_4fold_pyjava_bothper_wneg_10s_2_pairs_0_temp.json',
'CC_900_py_catmix_5rand_4fold_pyjava_bothper_wneg_10s_4_pairs_0_temp.json',
'CC_900_py_catmix_5rand_4fold_pyjava_bothper_wneg_10s_pairs_0_temp.json']
file_names = ["CC_900_py_catmix_5rand_1fold_pyjava_bothper_all_oripairs_temp.json",
"CC_900_py_catmix_5rand_1fold_pyjava_bothper_all_2_oripairs_temp.json",
"CC_900_py_catmix_5rand_1fold_pyjava_bothper_1s_oripairs_temp.json",
"CC_900_py_catmix_5rand_1fold_pyjava_bothper_1s_2_oripairs_temp.json",
"CC_900_py_catmix_5rand_1fold_pyjava_only_1s_50_oripairs_temp.json",
"CC_900_py_catmix_5rand_1fold_pyjava_only_11s_oripairs_temp.json"]
file_names_per = ["CC_900_py_catmix_5rand_1fold_pyjava_bothper_all_pairs_0_temp.json",
"CC_900_py_catmix_5rand_1fold_pyjava_bothper_all_2_pairs_0_temp.json",
"CC_900_py_catmix_5rand_1fold_pyjava_bothper_1s_pairs_0_temp.json",
"CC_900_py_catmix_5rand_1fold_pyjava_bothper_1s_2_pairs_0_temp.json",
"CC_900_py_catmix_5rand_1fold_pyjava_only_1s_50_pairs_0_temp.json",
"CC_900_py_catmix_5rand_1fold_pyjava_only_11s_pairs_0_temp.json"]
file_names = ["CC_900_py_catmix_5rand_1fold_pycpp_11s_oripairs_temp.json",
"CC_900_py_catmix_5rand_1fold_pycpp_1s_2_oripairs_temp.json",
"CC_900_py_catmix_5rand_1fold_pycpp_1s_oripairs_temp.json",
"CC_900_py_catmix_5rand_1fold_pycpp_all_oripairs_temp.json",
"CC_900_py_catmix_5rand_1fold_pycpp_all_2_oripairs_temp.json",
"CC_900_py_catmix_5rand_1fold_pycpp_all_3s_oripairs_temp.json",
]
file_names_per = ["CC_900_py_catmix_5rand_1fold_pycpp_11s_pairs_0_temp.json",
"CC_900_py_catmix_5rand_1fold_pycpp_1s_2_pairs_0_temp.json",
"CC_900_py_catmix_5rand_1fold_pycpp_1s_pairs_0_temp.json",
"CC_900_py_catmix_5rand_1fold_pycpp_all_pairs_0_temp.json",
"CC_900_py_catmix_5rand_1fold_pycpp_all_2_pairs_0_temp.json",
"CC_900_py_catmix_5rand_1fold_pycpp_all_3s_pairs_0_temp.json",
]
# file_names = ["CC_900_py_catmix_5rand_1fold_java_1s_2_oripairs_temp.json",
# "CC_900_py_catmix_5rand_1fold_java_all_oripairs.json",
# "CC_900_py_catmix_5rand_1fold_java_only_1s_oripairs_temp.json",
# "CC_900_py_catmix_5rand_1fold_java_only_all_oripairs_temp.json"]
# file_names_per = ["CC_900_py_catmix_5rand_1fold_java_1s_2_pairs_0_temp.json",
# "CC_900_py_catmix_5rand_1fold_java_all_pairs_0.json",
# "CC_900_py_catmix_5rand_1fold_java_only_1s_pairs_0_temp.json",
# "CC_900_py_catmix_5rand_1fold_java_only_all_pairs_0_temp.json"]
# file_names = ['CC_900_py_catmix_5rand_1fold_train_conctest_iffp_all_oripairs_temp.json',
# 'CC_900_py_catmix_5rand_1fold_train_conctest_iffp_all_3_oripairs_temp.json',
# 'CC_900_py_catmix_5rand_1fold_train_conctest_iffp_all_2_oripairs_temp.json',
# 'CC_900_py_catmix_1rand_1fold_train_conctest_0s_15_oripairs_temp.json',
# 'CC_900_py_catmix_1rand_1fold_train_conctest_0s_16_oripairs_temp.json']
# file_names_per = ['CC_900_py_catmix_5rand_1fold_train_conctest_iffp_all_pairs_0_temp.json',
# 'CC_900_py_catmix_5rand_1fold_train_conctest_iffp_all_3_pairs_0_temp.json',
# 'CC_900_py_catmix_5rand_1fold_train_conctest_iffp_all_2_pairs_0_temp.json',
# 'CC_900_py_catmix_1rand_1fold_train_conctest_0s_15_pairs_0_temp.json',
# 'CC_900_py_catmix_1rand_1fold_train_conctest_0s_16_pairs_0_temp.json']
file1 = './llm_for_code/datasets/codecontests/CC_900_py_catmix_5rand_1fold_pycpp_11s_oripairs_temp_fulltry.json' #CC_900_py_catmix_5rand_4fold_pyjava_bothper_oripairs_fulltry.json' #CC_900_py_catmix_5rand_4fold_oripairs_temp_fulltry.json'
file2 = './llm_for_code/datasets/codecontests/CC_900_py_catmix_5rand_1fold_pycpp_11s_pairs_0_temp_fulltry.json' #CC_900_py_catmix_5rand_4fold_pyjava_bothper_pairs_0_fulltry.json' #CC_900_py_catmix_5rand_4fold_pairs_0_temp_fulltry.json'
#file1 = './CC_2200_py_catmix_5rand_1fold_test_1s_5_oripairs_temp_fulltry.json' #'./llm_for_code/datasets/codecontests/CC_1200_py_catmix_5rand_4fold_test_1s_v_oripairs_fulltry.json'
#file2 = './CC_2200_py_catmix_5rand_1fold_test_1s_5_pairs_0_temp_fulltry.json' #'./llm_for_code/datasets/codecontests/CC_1200_py_catmix_5rand_4fold_test_1s_v_pairs_0_fulltry.json'
filt1 = []
with open(file1, 'r') as file:
for line in file:
filt1.append(json.loads(line))
filt2 = []
with open(file2, 'r') as file:
for line in file:
filt2.append(json.loads(line))
filtdf = pd.DataFrame(filt1[:])
filtdf_per = pd.DataFrame(filt2[:])
original_columns_df2 = filtdf_per.columns.tolist()
filtdf_per.columns = [f"{col}_df2" for col in filtdf_per.columns]
filtdf_combined = pd.concat([filtdf, filtdf_per], axis=1)
data = []
data_per = []
for fn in file_names:
with open("./dataset_lfs/"+fn, 'r') as file:
for line in file:
data.append(json.loads(line))
for fn in file_names_per:
with open("./dataset_lfs/"+fn, 'r') as file:
for line in file:
data_per.append(json.loads(line))
print(len(data), len(data_per))
def replace_third(val):
parts = val.split('_')
if len(parts) >= 3:
parts[2] = 'K'
return '_'.join(parts)
df = pd.DataFrame(data)
#df['category'] = df['category'].apply(replace_third)
df_per = pd.DataFrame(data_per)
original_columns_df2 = df_per.columns.tolist()
df_per.columns = [f"{col}_df2" for col in df_per.columns]
df_combined = pd.concat([df, df_per], axis=1)
print(df_combined.head(), len(df_combined))
df_combined = df_combined[~df_combined.apply(tuple, axis=1).isin(filtdf_combined.apply(tuple, axis=1))]
prohib_probid= [
x.get('probid') for x in filtdf['prog1']
if isinstance(x, dict) and 'probid' in x
]
prohib_probid += [
x.get('probid') for x in filtdf['prog2']
if isinstance(x, dict) and 'probid' in x
]
# # # Extract 'A1' from df and filter
# df_combined = df_combined[ ~df['prog1'].apply(lambda x: x.get('probid') if isinstance(x, dict) else None).isin(prohib_probid)]
# df_combined = df_combined[ ~df['prog2'].apply(lambda x: x.get('probid') if isinstance(x, dict) else None).isin(prohib_probid)]
# df_combined = df_combined[ ~df['prog1'].apply(lambda x: x.get('probid') if isinstance(x, dict) else None).str.startswith('p0').fillna(False)]
print("filtered, ", len(df_combined))
# Step 2: Update only the 'cat' column
#df_combined['category'] = df_combined['category'].str[:-1] + df_combined['category_df2'].str[-1]
# Define a custom function to compare dictionaries in prog1 and prog2
def compare_dicts(row):
#print(row)
return (str(row['prog1_df2']['scode']), str(row['prog2_df2']['scode'])) # Convert dictionaries to tuple of strings for comparison
# Apply the custom function and mark duplicates
df_combined = df_combined.dropna(subset=['prog1_df2'])
df_combined['unique_check'] = df_combined.apply(compare_dicts, axis=1)
# Drop duplicates based on the unique_check column
df_unique = df_combined.drop_duplicates(subset=['unique_check'])
# Drop the helper column used for comparison
df_unique = df_unique.drop(columns=['unique_check'])
print(len(data),len(df_unique) ," are unique")
#df = pd.DataFrame(data)
print(df_unique['category'].value_counts())
def get_sample_size(category):
catlen = {'2_0_0_1': (200-135-31), '2_0_0_0': (200-145-38),'1_1_0_1': (1000-845-155) , '1_1_0_0': (1000-818-182), '0_1_0_1': (200-200), '0_1_0_0': (200-200), '1_0_0_0' : (1000-742-144), '1_0_0_1':(1000-725-162) }
# if category[0] != '0':
# return 0
# catlen = {'0_1_0_0': (200-45), '0_1_0_1' : (200-36)}
print(catlen[category])
return catlen[category]
# For example: sample 10% of the group size, with a max of 50
# if category == '1_0_0_1' or category == '1_0_0_0':
# return 1000
""" pycpp
1_1_0_1 845
1_1_0_0 818
1_0_0_0 742
1_0_0_1 725
2_0_0_0 145
2_0_0_1 135
"""
if category[0] == '1':
return 300
# return 0
return 65
# Apply the custom sampling
sampled_df = (
df_unique.groupby('category', group_keys=False)
.apply(lambda x: x.sample(n=min(get_sample_size(x['category'].iloc[0]),len(x)), replace=False))
)
#sampled_df1 = df[~(df['category'].str[0] == '1')]
#sampled_df = sampled_df[sampled_df['category'].str.startswith('2')]
# Step 1: Identify and separate unique and duplicate rows
#df_unique = df_combined.drop_duplicates(subset=['unique_check'])
# df_duplicates = df_combined[~df_combined.index.isin(df_unique.index)]
# df_duplicates = df_duplicates.drop(columns=['unique_check'])
# # Step 2: Sample from df_unique first
# # Step 3: Identify categories that still need more samples
# remaining_counts = (
# sampled_df['category'].value_counts().reindex(df_combined['category'].unique(), fill_value=0)
# )
# print("remaining_counts: ", remaining_counts)
# # Step 4: Fill missing samples from df_duplicates
# additional_samples = None
# additional_samples = []
# for category, count in remaining_counts.items():
# required = get_sample_size(category) - count
# if required > 0:
# extra_samples = df_duplicates[df_duplicates['category'] == category].sample(
# n=min(required, len(df_duplicates[df_duplicates['category'] == category])),
# replace=False
# )
# additional_samples.append(extra_samples)
# # Concatenate the extra samples with the originally sampled DataFrame
# if additional_samples:
# additional_samples_df = pd.concat(additional_samples)
# sampled_df = pd.concat([sampled_df, additional_samples_df])
# # #Final sampled dataframe
sampled_df = sampled_df.reset_index(drop=True)
print(sampled_df['category'].value_counts(), len(sampled_df))
df1_final = sampled_df.iloc[:, :df.shape[1]]
df2_final = sampled_df.iloc[:, df.shape[1]:]
print(df1_final.head(), df2_final.head())
df2_final.columns = original_columns_df2
with open(file_names[0][:-5]+"_fulltry_rem.json" , 'w+') as outfile:
for _, sample in df1_final.iterrows():
outfile.write(f"{json.dumps(sample.to_dict())}\n")
with open(file_names_per[0][:-5]+"_fulltry_rem.json" , 'w+') as outfile:
for _, sample in df2_final.iterrows():
outfile.write(f"{json.dumps(sample.to_dict())}\n")
print("changed file using", file_names_per[0]) |