Global definitions

In [1]:
# Python libs
import warnings
import joblib
from pathlib import Path
from itertools import product
from math import ceil
import random
import numpy as np
import pandas as pd

# Visualization
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns

import lightgbm as lgb

# Module settings
mpl.rc("figure", facecolor="white", dpi=196)
pd.set_option('display.max_columns', None)  # show all columns
In [2]:
DATA_INPUT_DIR = 'data_input/'
DATA_PROCESSED_DIR = 'data_processed/'
RESULTS_DIR = 'results/'

SALES_CSV = DATA_INPUT_DIR + 'sales_train_evaluation.csv'
PRICES_CSV = DATA_INPUT_DIR + 'sell_prices.csv'
CALENDAR_CSV = DATA_INPUT_DIR + 'calendar.csv'
SAMPLE_SUBMISSION_CSV = DATA_INPUT_DIR + 'sample_submission.csv'

SALES_DATA_FILE = DATA_PROCESSED_DIR + 'sales.pkl'
PRICES_DATA_FILE = DATA_PROCESSED_DIR + 'prices.pkl'
CALENDAR_DATA_FILE = DATA_PROCESSED_DIR + 'calendar.pkl'
SALES_PRICES_CALENDAR_DATA_FILE = DATA_PROCESSED_DIR + 'sales_prices_calendar.pkl'
SALES_LAG_ROLL_STATS_FILE = DATA_PROCESSED_DIR + 'sales_lag_roll_stats.pkl'
SALES_AGG_STATS_FILE = DATA_PROCESSED_DIR + 'sales_agg_stats.pkl'

SUBMISSION_FILE = RESULTS_DIR + 'submission.csv'

# Globals / Class attributes
FLAT_INDEX_COLS = ['id', 'd']
TARGET_COL = 'sales'
ID_D_TARGET_COLS = ['id', 'd', TARGET_COL]
END_TRAIN = 1941
SHIFT_DAY = 28
In [3]:
def reduce_mem_usage(df, verbose=True):
    numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']
    start_mem = df.memory_usage().sum() / 1024**2    
    for col in df.columns:
        col_type = df[col].dtypes
        if col_type in numerics:
            c_min = df[col].min()
            c_max = df[col].max()
            if str(col_type)[:3] == 'int':
                if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:
                    df[col] = df[col].astype(np.int8)
                elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:
                       df[col] = df[col].astype(np.int16)
                elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:
                    df[col] = df[col].astype(np.int32)
                elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:
                    df[col] = df[col].astype(np.int64)  
            else:
                if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:
                    df[col] =-inspection df[col].astype(np.float16)
                elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:
                    df[col] = df[col].astype(np.float32)
                else:
                    df[col] = df[col].astype(np.float64)    
    end_mem = df.memory_usage().sum() / 1024**2
    if verbose: print('Mem. usage decreased to {:5.2f} Mb ({:.1f}% reduction)'.format(end_mem, 100 * (start_mem - end_mem) / start_mem))

Data Preparation

Data Inspection and Visualization

Three data files given:

  • sales_train_evaluation.csv
  • sell_prices.csv
  • calendar.csv
In [4]:
sales = pd.read_csv(SALES_CSV)
prices = pd.read_csv(PRICES_CSV)
calendar = pd.read_csv(CALENDAR_CSV)

Inspect raw data tables

In [5]:
sales
Out[5]:
id item_id dept_id cat_id store_id state_id d_1 d_2 d_3 d_4 d_5 d_6 d_7 d_8 d_9 d_10 d_11 d_12 d_13 d_14 d_15 d_16 d_17 d_18 d_19 d_20 d_21 d_22 d_23 d_24 d_25 d_26 d_27 d_28 d_29 d_30 d_31 d_32 d_33 d_34 d_35 d_36 d_37 d_38 d_39 d_40 d_41 d_42 d_43 d_44 d_45 d_46 d_47 d_48 d_49 d_50 d_51 d_52 d_53 d_54 d_55 d_56 d_57 d_58 d_59 d_60 d_61 d_62 d_63 d_64 d_65 d_66 d_67 d_68 d_69 d_70 d_71 d_72 d_73 d_74 d_75 d_76 d_77 d_78 d_79 d_80 d_81 d_82 d_83 d_84 d_85 d_86 d_87 d_88 d_89 d_90 d_91 d_92 d_93 d_94 d_95 d_96 d_97 d_98 d_99 d_100 d_101 d_102 d_103 d_104 d_105 d_106 d_107 d_108 d_109 d_110 d_111 d_112 d_113 d_114 d_115 d_116 d_117 d_118 d_119 d_120 d_121 d_122 d_123 d_124 d_125 d_126 d_127 d_128 d_129 d_130 d_131 d_132 d_133 d_134 d_135 d_136 d_137 d_138 d_139 d_140 d_141 d_142 d_143 d_144 d_145 d_146 d_147 d_148 d_149 d_150 d_151 d_152 d_153 d_154 d_155 d_156 d_157 d_158 d_159 d_160 d_161 d_162 d_163 d_164 d_165 d_166 d_167 d_168 d_169 d_170 d_171 d_172 d_173 d_174 d_175 d_176 d_177 d_178 d_179 d_180 d_181 d_182 d_183 d_184 d_185 d_186 d_187 d_188 d_189 d_190 d_191 d_192 d_193 d_194 d_195 d_196 d_197 d_198 d_199 d_200 d_201 d_202 d_203 d_204 d_205 d_206 d_207 d_208 d_209 d_210 d_211 d_212 d_213 d_214 d_215 d_216 d_217 d_218 d_219 d_220 d_221 d_222 d_223 d_224 d_225 d_226 d_227 d_228 d_229 d_230 d_231 d_232 d_233 d_234 d_235 d_236 d_237 d_238 d_239 d_240 d_241 d_242 d_243 d_244 d_245 d_246 d_247 d_248 d_249 d_250 d_251 d_252 d_253 d_254 d_255 d_256 d_257 d_258 d_259 d_260 d_261 d_262 d_263 d_264 d_265 d_266 d_267 d_268 d_269 d_270 d_271 d_272 d_273 d_274 d_275 d_276 d_277 d_278 d_279 d_280 d_281 d_282 d_283 d_284 d_285 d_286 d_287 d_288 d_289 d_290 d_291 d_292 d_293 d_294 d_295 d_296 d_297 d_298 d_299 d_300 d_301 d_302 d_303 d_304 d_305 d_306 d_307 d_308 d_309 d_310 d_311 d_312 d_313 d_314 d_315 d_316 d_317 d_318 d_319 d_320 d_321 d_322 d_323 d_324 d_325 d_326 d_327 d_328 d_329 d_330 d_331 d_332 d_333 d_334 d_335 d_336 d_337 d_338 d_339 d_340 d_341 d_342 d_343 d_344 d_345 d_346 d_347 d_348 d_349 d_350 d_351 d_352 d_353 d_354 d_355 d_356 d_357 d_358 d_359 d_360 d_361 d_362 d_363 d_364 d_365 d_366 d_367 d_368 d_369 d_370 d_371 d_372 d_373 d_374 d_375 d_376 d_377 d_378 d_379 d_380 d_381 d_382 d_383 d_384 d_385 d_386 d_387 d_388 d_389 d_390 d_391 d_392 d_393 d_394 d_395 d_396 d_397 d_398 d_399 d_400 d_401 d_402 d_403 d_404 d_405 d_406 d_407 d_408 d_409 d_410 d_411 d_412 d_413 d_414 d_415 d_416 d_417 d_418 d_419 d_420 d_421 d_422 d_423 d_424 d_425 d_426 d_427 d_428 d_429 d_430 d_431 d_432 d_433 d_434 d_435 d_436 d_437 d_438 d_439 d_440 d_441 d_442 d_443 d_444 d_445 d_446 d_447 d_448 d_449 d_450 d_451 d_452 d_453 d_454 d_455 d_456 d_457 d_458 d_459 d_460 d_461 d_462 d_463 d_464 d_465 d_466 d_467 d_468 d_469 d_470 d_471 d_472 d_473 d_474 d_475 d_476 d_477 d_478 d_479 d_480 d_481 d_482 d_483 d_484 d_485 d_486 d_487 d_488 d_489 d_490 d_491 d_492 d_493 d_494 d_495 d_496 d_497 d_498 d_499 d_500 d_501 d_502 d_503 d_504 d_505 d_506 d_507 d_508 d_509 d_510 d_511 d_512 d_513 d_514 d_515 d_516 d_517 d_518 d_519 d_520 d_521 d_522 d_523 d_524 d_525 d_526 d_527 d_528 d_529 d_530 d_531 d_532 d_533 d_534 d_535 d_536 d_537 d_538 d_539 d_540 d_541 d_542 d_543 d_544 d_545 d_546 d_547 d_548 d_549 d_550 d_551 d_552 d_553 d_554 d_555 d_556 d_557 d_558 d_559 d_560 d_561 d_562 d_563 d_564 d_565 d_566 d_567 d_568 d_569 d_570 d_571 d_572 d_573 d_574 d_575 d_576 d_577 d_578 d_579 d_580 d_581 d_582 d_583 d_584 d_585 d_586 d_587 d_588 d_589 d_590 d_591 d_592 d_593 d_594 d_595 d_596 d_597 d_598 d_599 d_600 d_601 d_602 d_603 d_604 d_605 d_606 d_607 d_608 d_609 d_610 d_611 d_612 d_613 d_614 d_615 d_616 d_617 d_618 d_619 d_620 d_621 d_622 d_623 d_624 d_625 d_626 d_627 d_628 d_629 d_630 d_631 d_632 d_633 d_634 d_635 d_636 d_637 d_638 d_639 d_640 d_641 d_642 d_643 d_644 d_645 d_646 d_647 d_648 d_649 d_650 d_651 d_652 d_653 d_654 d_655 d_656 d_657 d_658 d_659 d_660 d_661 d_662 d_663 d_664 d_665 d_666 d_667 d_668 d_669 d_670 d_671 d_672 d_673 d_674 d_675 d_676 d_677 d_678 d_679 d_680 d_681 d_682 d_683 d_684 d_685 d_686 d_687 d_688 d_689 d_690 d_691 d_692 d_693 d_694 d_695 d_696 d_697 d_698 d_699 d_700 d_701 d_702 d_703 d_704 d_705 d_706 d_707 d_708 d_709 d_710 d_711 d_712 d_713 d_714 d_715 d_716 d_717 d_718 d_719 d_720 d_721 d_722 d_723 d_724 d_725 d_726 d_727 d_728 d_729 d_730 d_731 d_732 d_733 d_734 d_735 d_736 d_737 d_738 d_739 d_740 d_741 d_742 d_743 d_744 d_745 d_746 d_747 d_748 d_749 d_750 d_751 d_752 d_753 d_754 d_755 d_756 d_757 d_758 d_759 d_760 d_761 d_762 d_763 d_764 d_765 d_766 d_767 d_768 d_769 d_770 d_771 d_772 d_773 d_774 d_775 d_776 d_777 d_778 d_779 d_780 d_781 d_782 d_783 d_784 d_785 d_786 d_787 d_788 d_789 d_790 d_791 d_792 d_793 d_794 d_795 d_796 d_797 d_798 d_799 d_800 d_801 d_802 d_803 d_804 d_805 d_806 d_807 d_808 d_809 d_810 d_811 d_812 d_813 d_814 d_815 d_816 d_817 d_818 d_819 d_820 d_821 d_822 d_823 d_824 d_825 d_826 d_827 d_828 d_829 d_830 d_831 d_832 d_833 d_834 d_835 d_836 d_837 d_838 d_839 d_840 d_841 d_842 d_843 d_844 d_845 d_846 d_847 d_848 d_849 d_850 d_851 d_852 d_853 d_854 d_855 d_856 d_857 d_858 d_859 d_860 d_861 d_862 d_863 d_864 d_865 d_866 d_867 d_868 d_869 d_870 d_871 d_872 d_873 d_874 d_875 d_876 d_877 d_878 d_879 d_880 d_881 d_882 d_883 d_884 d_885 d_886 d_887 d_888 d_889 d_890 d_891 d_892 d_893 d_894 d_895 d_896 d_897 d_898 d_899 d_900 d_901 d_902 d_903 d_904 d_905 d_906 d_907 d_908 d_909 d_910 d_911 d_912 d_913 d_914 d_915 d_916 d_917 d_918 d_919 d_920 d_921 d_922 d_923 d_924 d_925 d_926 d_927 d_928 d_929 d_930 d_931 d_932 d_933 d_934 d_935 d_936 d_937 d_938 d_939 d_940 d_941 d_942 d_943 d_944 d_945 d_946 d_947 d_948 d_949 d_950 d_951 d_952 d_953 d_954 d_955 d_956 d_957 d_958 d_959 d_960 d_961 d_962 d_963 d_964 d_965 d_966 d_967 d_968 d_969 d_970 d_971 d_972 d_973 d_974 d_975 d_976 d_977 d_978 d_979 d_980 d_981 d_982 d_983 d_984 d_985 d_986 d_987 d_988 d_989 d_990 d_991 d_992 d_993 d_994 d_995 d_996 d_997 d_998 d_999 d_1000 d_1001 d_1002 d_1003 d_1004 d_1005 d_1006 d_1007 d_1008 d_1009 d_1010 d_1011 d_1012 d_1013 d_1014 d_1015 d_1016 d_1017 d_1018 d_1019 d_1020 d_1021 d_1022 d_1023 d_1024 d_1025 d_1026 d_1027 d_1028 d_1029 d_1030 d_1031 d_1032 d_1033 d_1034 d_1035 d_1036 d_1037 d_1038 d_1039 d_1040 d_1041 d_1042 d_1043 d_1044 d_1045 d_1046 d_1047 d_1048 d_1049 d_1050 d_1051 d_1052 d_1053 d_1054 d_1055 d_1056 d_1057 d_1058 d_1059 d_1060 d_1061 d_1062 d_1063 d_1064 d_1065 d_1066 d_1067 d_1068 d_1069 d_1070 d_1071 d_1072 d_1073 d_1074 d_1075 d_1076 d_1077 d_1078 d_1079 d_1080 d_1081 d_1082 d_1083 d_1084 d_1085 d_1086 d_1087 d_1088 d_1089 d_1090 d_1091 d_1092 d_1093 d_1094 d_1095 d_1096 d_1097 d_1098 d_1099 d_1100 d_1101 d_1102 d_1103 d_1104 d_1105 d_1106 d_1107 d_1108 d_1109 d_1110 d_1111 d_1112 d_1113 d_1114 d_1115 d_1116 d_1117 d_1118 d_1119 d_1120 d_1121 d_1122 d_1123 d_1124 d_1125 d_1126 d_1127 d_1128 d_1129 d_1130 d_1131 d_1132 d_1133 d_1134 d_1135 d_1136 d_1137 d_1138 d_1139 d_1140 d_1141 d_1142 d_1143 d_1144 d_1145 d_1146 d_1147 d_1148 d_1149 d_1150 d_1151 d_1152 d_1153 d_1154 d_1155 d_1156 d_1157 d_1158 d_1159 d_1160 d_1161 d_1162 d_1163 d_1164 d_1165 d_1166 d_1167 d_1168 d_1169 d_1170 d_1171 d_1172 d_1173 d_1174 d_1175 d_1176 d_1177 d_1178 d_1179 d_1180 d_1181 d_1182 d_1183 d_1184 d_1185 d_1186 d_1187 d_1188 d_1189 d_1190 d_1191 d_1192 d_1193 d_1194 d_1195 d_1196 d_1197 d_1198 d_1199 d_1200 d_1201 d_1202 d_1203 d_1204 d_1205 d_1206 d_1207 d_1208 d_1209 d_1210 d_1211 d_1212 d_1213 d_1214 d_1215 d_1216 d_1217 d_1218 d_1219 d_1220 d_1221 d_1222 d_1223 d_1224 d_1225 d_1226 d_1227 d_1228 d_1229 d_1230 d_1231 d_1232 d_1233 d_1234 d_1235 d_1236 d_1237 d_1238 d_1239 d_1240 d_1241 d_1242 d_1243 d_1244 d_1245 d_1246 d_1247 d_1248 d_1249 d_1250 d_1251 d_1252 d_1253 d_1254 d_1255 d_1256 d_1257 d_1258 d_1259 d_1260 d_1261 d_1262 d_1263 d_1264 d_1265 d_1266 d_1267 d_1268 d_1269 d_1270 d_1271 d_1272 d_1273 d_1274 d_1275 d_1276 d_1277 d_1278 d_1279 d_1280 d_1281 d_1282 d_1283 d_1284 d_1285 d_1286 d_1287 d_1288 d_1289 d_1290 d_1291 d_1292 d_1293 d_1294 d_1295 d_1296 d_1297 d_1298 d_1299 d_1300 d_1301 d_1302 d_1303 d_1304 d_1305 d_1306 d_1307 d_1308 d_1309 d_1310 d_1311 d_1312 d_1313 d_1314 d_1315 d_1316 d_1317 d_1318 d_1319 d_1320 d_1321 d_1322 d_1323 d_1324 d_1325 d_1326 d_1327 d_1328 d_1329 d_1330 d_1331 d_1332 d_1333 d_1334 d_1335 d_1336 d_1337 d_1338 d_1339 d_1340 d_1341 d_1342 d_1343 d_1344 d_1345 d_1346 d_1347 d_1348 d_1349 d_1350 d_1351 d_1352 d_1353 d_1354 d_1355 d_1356 d_1357 d_1358 d_1359 d_1360 d_1361 d_1362 d_1363 d_1364 d_1365 d_1366 d_1367 d_1368 d_1369 d_1370 d_1371 d_1372 d_1373 d_1374 d_1375 d_1376 d_1377 d_1378 d_1379 d_1380 d_1381 d_1382 d_1383 d_1384 d_1385 d_1386 d_1387 d_1388 d_1389 d_1390 d_1391 d_1392 d_1393 d_1394 d_1395 d_1396 d_1397 d_1398 d_1399 d_1400 d_1401 d_1402 d_1403 d_1404 d_1405 d_1406 d_1407 d_1408 d_1409 d_1410 d_1411 d_1412 d_1413 d_1414 d_1415 d_1416 d_1417 d_1418 d_1419 d_1420 d_1421 d_1422 d_1423 d_1424 d_1425 d_1426 d_1427 d_1428 d_1429 d_1430 d_1431 d_1432 d_1433 d_1434 d_1435 d_1436 d_1437 d_1438 d_1439 d_1440 d_1441 d_1442 d_1443 d_1444 d_1445 d_1446 d_1447 d_1448 d_1449 d_1450 d_1451 d_1452 d_1453 d_1454 d_1455 d_1456 d_1457 d_1458 d_1459 d_1460 d_1461 d_1462 d_1463 d_1464 d_1465 d_1466 d_1467 d_1468 d_1469 d_1470 d_1471 d_1472 d_1473 d_1474 d_1475 d_1476 d_1477 d_1478 d_1479 d_1480 d_1481 d_1482 d_1483 d_1484 d_1485 d_1486 d_1487 d_1488 d_1489 d_1490 d_1491 d_1492 d_1493 d_1494 d_1495 d_1496 d_1497 d_1498 d_1499 d_1500 d_1501 d_1502 d_1503 d_1504 d_1505 d_1506 d_1507 d_1508 d_1509 d_1510 d_1511 d_1512 d_1513 d_1514 d_1515 d_1516 d_1517 d_1518 d_1519 d_1520 d_1521 d_1522 d_1523 d_1524 d_1525 d_1526 d_1527 d_1528 d_1529 d_1530 d_1531 d_1532 d_1533 d_1534 d_1535 d_1536 d_1537 d_1538 d_1539 d_1540 d_1541 d_1542 d_1543 d_1544 d_1545 d_1546 d_1547 d_1548 d_1549 d_1550 d_1551 d_1552 d_1553 d_1554 d_1555 d_1556 d_1557 d_1558 d_1559 d_1560 d_1561 d_1562 d_1563 d_1564 d_1565 d_1566 d_1567 d_1568 d_1569 d_1570 d_1571 d_1572 d_1573 d_1574 d_1575 d_1576 d_1577 d_1578 d_1579 d_1580 d_1581 d_1582 d_1583 d_1584 d_1585 d_1586 d_1587 d_1588 d_1589 d_1590 d_1591 d_1592 d_1593 d_1594 d_1595 d_1596 d_1597 d_1598 d_1599 d_1600 d_1601 d_1602 d_1603 d_1604 d_1605 d_1606 d_1607 d_1608 d_1609 d_1610 d_1611 d_1612 d_1613 d_1614 d_1615 d_1616 d_1617 d_1618 d_1619 d_1620 d_1621 d_1622 d_1623 d_1624 d_1625 d_1626 d_1627 d_1628 d_1629 d_1630 d_1631 d_1632 d_1633 d_1634 d_1635 d_1636 d_1637 d_1638 d_1639 d_1640 d_1641 d_1642 d_1643 d_1644 d_1645 d_1646 d_1647 d_1648 d_1649 d_1650 d_1651 d_1652 d_1653 d_1654 d_1655 d_1656 d_1657 d_1658 d_1659 d_1660 d_1661 d_1662 d_1663 d_1664 d_1665 d_1666 d_1667 d_1668 d_1669 d_1670 d_1671 d_1672 d_1673 d_1674 d_1675 d_1676 d_1677 d_1678 d_1679 d_1680 d_1681 d_1682 d_1683 d_1684 d_1685 d_1686 d_1687 d_1688 d_1689 d_1690 d_1691 d_1692 d_1693 d_1694 d_1695 d_1696 d_1697 d_1698 d_1699 d_1700 d_1701 d_1702 d_1703 d_1704 d_1705 d_1706 d_1707 d_1708 d_1709 d_1710 d_1711 d_1712 d_1713 d_1714 d_1715 d_1716 d_1717 d_1718 d_1719 d_1720 d_1721 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0 HOBBIES_1_001_CA_1_evaluation HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 1 1 0 0 0 0 0 1 2 1 0 1 1 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 2 1 0 0 0 2 0 0 0 0 0 1 0 0 0 1 0 1 2 1 1 0 0 0 0 1 0 0 1 2 0 1 0 0 1 0 1 0 1 0 1 0 1 0 0 0 0 1 0 0 0 0 0 2 2 0 1 1 0 0 0 1 1 0 1 1 1 1 0 2 3 1 1 0 0 0 0 0 1 0 0 0 0 2 1 0 0 1 1 2 1 1 0 1 0 1 1 0 1 1 0 3 0 0 3 0 1 1 2 0 1 1 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 2 0 0 1 0 2 0 0 0 0 0 0 1 0 1 0 1 1 0 0 0 0 0 1 0 0 0 0 0 1 2 0 0 0 0 0 0 1 2 0 0 0 2 1 2 0 0 1 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 1 1 1 0 2 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 0 1 1 2 0 1 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 2 0 0 1 0 0 0 1 0 1 0 0 0 0 1 0 1 0 1 0 0 1 0 0 1 1 2 0 1 0 0 0 0 0 0 0 0 3 1 2 2 1 1 0 0 2 0 0 0 0 1 0 1 0 0 0 1 1 0 3 1 0 0 0 0 0 1 0 1 1 1 0 0 0 0 2 0 0 2 0 0 0 1 0 0 0 0 0 4 0 0 2 1 0 0 0 0 0 1 0 0 0 0 0 4 0 1 0 1 2 2 0 0 0 0 2 1 1 1 1 1 0 0 1 0 0 1 0 0 0 1 1 0 1 2 0 0 2 0 1 3 1 0 0 1 0 0 1 0 0 0 0 0 1 0 0 1 0 1 1 0 1 1 0 0 1 1 1 0 0 0 0 0 2 0 0 0 0 3 0 2 0 0 0 0 3 2 0 2 0 0 2 0 0 0 0 3 0 1 1 1 0 1 0 1 0 0 2 4 0 0 0 0 0 2 0 0 1 0 0 1 1 5 1 1 0 0 0 0 0 0 0 0 2 1 1 0 2 0 2 1 0 1 0 1 1 0 0 0 2 1 1 0 0 1 0 3 0 0 0 1 0 0 2 1 0 1 0 0 3 3 0 0 0 0 0 0 0 1 1 1 0 2 2 2 0 0 1 0 0 1 3 0 0 0 1 1 0 1 0 1 0 0 1 1 1 2 1 1 1 0 2 2 2 1 0 0 0 1 2 0 0 1 2 1 1 1 0 1 0 1 0 0 1 0 1 0 2 1 0 2 0 1 0 0 0 1 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 3 1 1 0 1 1 0 0 0 1 1 2 0 0 0 1 0 2 1 0 1 1 0 0 0 1 1 1 3 1 1 0 0 2 0 1 0 0 0 2 0 0 0 4 2 1 0 0 0 0 2 1 0 1 1 0 0 0 1 2 1 1 0 0 2 0 2 2 0 0 0 1 1 0 2 0 1 1 2 0 1 0 0 0 2 1 0 1 1 2 0 0 0 0 0 0 0 1 0 1 0 1 0 3 1 1 0 1 1 2 0 0 0 0 1 1 0 0 0 0 3 0 1 0 0 0 0 1 1 1 0 1 0 2 0 0 0 0 2 0 0 0 0 1 1 2 0 0 0 0 2 0 0 1 1 1 1 0 0 0 0 0 1 2 2 0 1 0 0 0 0 1 2 1 0 0 0 0 0 1 0 3 0 1 2 1 0 3 0 0 0 1 0 2 2 1 0 0 1 2 0 1 0 1 4 0 0 5 0 0 0 0 0 0 2 1 2 1 0 0 0 1 1 1 0 0 1 1 1 1 1 0 0 0 2 2 0 0 1 4 0 0 0 0 1 1 2 0 4 0 1 0 1 4 2 0 2 0 1 1 0 1 0 0 1 1 3 0 0 0 1 1 1 3 1 3 1 2 2 0 1 1 1 1 0 0 0 0 0 1 0 4 2 3 0 1 2 0 0 0 1 1 3 0 1 1 1 3 0 1 1 0 0 0 2 0 3 5 0 0 1 1 0 2 1 2 2 1 0 2 4 0 0 0 0 3 3 0 1
1 HOBBIES_1_002_CA_1_evaluation HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 1 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 1 1 1 0 1 0 0 0 0 0 1 0 0 1 0 0 0 1 0 1 1 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 1 0 1 0 1 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 2 0 0 0 2 0 0 3 1 1 0 1 4 2 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 3 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 2 0 0 0 1 0 0 0 0 0 0 0 0 2 2 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 2 0 0 0 0 2 1 1 0 0 0 2 1 3 0 3 1 3 2 1 3 3 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 2 0 1 0 1 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 2 0 0 0 0 0 1 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 2 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 1 1 0 0 2 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 1 0 0 1 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 2 0 0 1 0 0 0 0 2 0 1 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 1 0 0 0 1 1 0 0 0 0 1 0 1 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 1 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1 0 0 0 2 0 1 1 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 1 0 1 0 0 1 0 0 0 0 0 2 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 2 0 2 0 0 1 0 0 1 0 1 0 0 0 0 2 0 2 0 0 2 0 0 0 0 0 1 0 0 1 0 3 0 0 0 0 0 0 1 1 1 0 0 2 1 1 0 0 1 0 1 1 0 0 0 0 0 0 1 0 1 1 0 1 0 0 0 1 0 0 0 2 0 0 1 1 0 1 1 0 0 0 0 1 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 1 1 0 0 0 2 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1 1 0 1 1 0 0 0 0 0 0 0 2 0 0 0 1 0 0 0 0 2 0 0 1 0 1 0 0 0 1 0 1 0 1 1 1 1 0 2 0 1 1 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 1 0 1 0 1 0 0 1 4 0 2 3 3 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 1 0 0 0 1 0 3 0 1 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 0 0 0 2 1 0 0 0 0 0 1 0 0 1 1 0 2 0 2 1 0 1 0 0 0 2 1 1 1 0 0 0 1 0 1 0 0 0 1 0 0 1 1 0 0 0 3 4 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 1 1 0 0 0 0 0 0 0 2 1 0 0 1 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 1 0 2 1 0 0 0 1 1 0 0 0 0 1 1 0 1 0 1 1 0 0 0 0 0 1 0 1 1 0 3 0 0 0 0 0 0 0 1 0 0 0 0 0 2 1 0 0 1 1 0 2 0 1 0 2 1 1 5 0 1 0 3 5 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 2 1 1 0 0 0 0 0
2 HOBBIES_1_003_CA_1_evaluation HOBBIES_1_003 HOBBIES_1 HOBBIES CA_1 CA 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 2 0 0 1 0 1 1 1 0 0 0 1 1 0 0 0 0 0 0 0 2 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 2 0 1 0 0 0 1 0 3 0 1 0 1 1 0 1 2 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 2 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 2 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 1 1 0 0 1 0 1 1 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 3 1 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 0 1 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0 0 0 1 0 1 0 1 0 0 1 0 1 0 1 0 1 0 0 2 0 0 0 1 0 0 1 0 0 0 1 0 0 2 0 0 0 0 1 1 1 0 0 0 0 0 0 1 0 1 0 0 2 1 0 0 0 0 1 0 2 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 1 0 0 0 0 0 3 0 0 0 0 0 0 1 0 0 1 1 1 1 0 1 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 2 1 0 3 0 1 2 0 3 1 0 0 1 0 1 0 0 0 0 2 0 1 0 1 0 1 1 0 1 0 1 0 0 0 1 2 0 0 0 1 0 1 1 1 1 0 0 0 0 0 0 2 0 1 0 0 2 0 0 0 1 0 0 1 0 0 2 0 0 0 0 0 0 0 0 2 0 2 3 0 1 3 1 2 2 3 0 1 1 0 0 0 0 2 3 1 1 4 3 2 1 2 2 0 1 5 2 0 1 2 3 0 1 2 1 3 0 1 1 1 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 1 1 1 1 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 6 1 1 2 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 2 2 1 2 1 1 1 0 1 1 1 0 0 1 1 0 2 1 0 0 0 0 2 1 3 0 0 1 0 1 0 2 0 0 0 2 3 0 1
3 HOBBIES_1_004_CA_1_evaluation HOBBIES_1_004 HOBBIES_1 HOBBIES CA_1 CA 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 2 0 1 0 0 0 0 0 0 2 0 1 0 0 1 1 1 0 2 3 1 0 0 0 0 0 1 0 0 1 1 0 0 1 0 0 1 2 3 0 2 0 0 2 2 0 0 2 1 2 1 1 1 2 0 0 1 0 1 3 1 0 0 0 0 3 5 2 2 1 1 1 1 1 1 0 0 2 1 1 1 2 0 0 0 2 5 6 0 0 0 0 0 0 0 0 0 0 0 2 1 2 0 1 0 2 0 0 5 1 0 0 1 3 1 3 5 1 3 0 3 4 4 0 0 1 3 1 4 0 0 2 0 2 0 1 4 2 1 0 2 1 3 6 1 1 2 1 2 3 1 2 0 0 0 3 4 5 1 0 0 1 0 1 4 6 3 1 1 0 1 4 5 1 1 4 0 0 0 1 2 2 1 1 6 2 4 4 0 0 0 2 2 0 1 1 3 1 2 4 2 1 3 2 2 0 4 1 1 0 4 2 2 5 1 2 3 3 2 2 1 1 2 1 3 2 1 3 0 0 0 3 1 2 3 0 1 0 2 0 5 4 2 1 2 4 1 1 0 1 2 2 2 0 2 3 0 1 1 1 1 0 5 1 1 1 2 0 4 2 3 2 0 2 1 1 6 3 1 3 2 2 1 2 0 0 0 0 3 1 0 6 2 1 2 1 0 0 0 0 0 0 6 3 3 0 0 0 0 2 2 0 4 2 1 0 1 1 8 1 3 0 1 2 3 3 1 1 2 1 1 1 1 2 0 1 2 0 0 0 0 0 0 0 3 2 8 2 0 1 2 0 2 5 3 0 0 3 1 0 2 1 2 2 1 2 2 6 0 2 0 1 1 2 1 3 0 0 2 1 4 3 1 1 1 1 2 3 5 0 1 2 1 2 0 2 2 0 0 0 0 0 2 1 1 0 1 2 2 0 3 0 0 0 0 3 0 1 1 0 3 1 1 6 1 1 0 0 0 0 0 0 0 0 0 0 1 4 1 1 2 1 2 3 1 0 0 1 1 1 3 1 0 1 1 0 2 0 5 1 0 1 1 2 1 3 0 0 0 1 1 1 1 0 0 0 0 1 2 0 0 1 0 0 0 1 0 0 1 1 1 0 3 3 0 1 2 0 0 3 4 0 1 0 0 0 0 2 1 2 1 1 2 1 0 0 0 0 3 0 2 2 1 2 1 1 2 1 0 2 1 2 5 1 1 5 1 0 1 2 1 1 4 1 5 0 7 0 0 3 1 2 3 1 1 4 2 1 3 0 1 1 3 2 1 1 1 1 5 1 3 5 0 1 1 2 0 5 1 0 2 1 2 1 5 2 2 0 4 0 6 3 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 2 1 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 1 1 1 1 0 2 1 0 3 0 2 3 1 2 1 1 1 0 1 2 0 1 0 3 1 0 2 1 2 6 0 1 1 1 6 0 3 0 0 1 4 3 2 0 2 2 2 4 2 2 0 1 2 1 2 7 1 0 1 0 2 5 1 6 1 0 0 0 3 4 0 3 2 1 1 2 5 2 0 0 0 0 2 3 3 1 2 3 1 0 5 3 1 1 2 1 1 1 2 0 1 2 0 2 0 2 3 7 0 0 2 1 2 3 1 0 2 1 3 1 0 0 0 3 6 4 0 0 1 1 2 0 0 1 1 0 0 1 0 3 3 0 0 0 2 1 3 0 0 0 1 1 3 3 0 2 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 1 3 3 1 1 0 1 4 2 1 2 1 2 3 2 2 2 5 0 0 1 4 7 0 0 0 2 1 0 4 2 1 1 1 1 0 5 0 1 0 1 3 1 2 1 4 1 4 0 1 1 4 2 1 1 4 1 3 3 0 3 0 1 1 3 4 1 0 0 1 0 2 4 4 0 1 0 3 5 4 0 1 0 1 0 3 5 1 2 4 3 1 1 2 2 1 2 1 0 8 6 3 2 1 2 2 5 5 0 2 2 1 0 3 1 1 1 1 1 0 2 4 2 2 0 1 3 4 3 2 3 2 5 3 2 8 3 0 1 6 1 2 3 1 2 0 11 0 1 4 3 0 0 1 2 2 6 2 0 0 3 0 10 5 1 3 2 4 4 2 0 1 4 1 0 2 5 1 1 0 1 1 2 2 2 0 0 0 3 2 8 5 2 2 1 1 2 4 2 0 0 5 0 2 4 6 2 0 1 3 0 5 3 0 3 1 1 0 12 4 0 2 2 1 4 0 1 7 2 2 0 1 6 10 0 1 1 1 2 2 6 1 0 1 3 0 5 2 1 4 2 5 2 4 2 1 0 0 1 1 12 1 2 0 3 2 5 3 2 1 1 2 1 3 3 4 1 4 2 2 1 1 4 1 1 1 1 1 2 14 2 2 0 2 1 2 3 3 2 2 0 4 6 4 2 1 1 2 1 2 5 2 2 1 2 3 5 10 0 0 2 1 1 3 2 2 2 3 0 2 5 3 2 3 3 1 1 1 2 2 5 3 1 3 6 5 2 4 1 3 2 4 5 7 1 0 4 4 1 4 1 0 1 3 1 3 6 1 2 0 3 0 3 8 2 2 0 0 2 4 2 0 0 2 4 2 3 6 2 0 3 5 2 0 5 4 1 1 0 1 6 1 2 5 4 3 2 3 4 3 2 3 2 0 3 6 1 2 1 3 3 1 5 0 2 1 0 2 9 0 0 2 2 3 0 0 0 3 2 0 0 1 3 3 0 0 3 0 0 4 5 1 2 2 0 0 5 0 1 1 0 0 0 4 6 1 0 4 4 0 8 2 4 1 2 1 4 0 4 7 1 5 1 6 5 5 0 1 3 1 15 0 5 0 1 1 6 3 2 2 3 1 3 1 8 2 4 0 0 2 1 1 5 9 0 2 2 5 0 7 1 1 3 0 0 2 10 2 1 0 0 2 4 3 3 0 2 2 3 0 1 9 2 0 4 1 1 2 3 8 1 0 0 2 11 3 3 1 0 2 1 2 6 0 0 2 0 3 10 4 0 1 1 1 1 4 2 8 2 2 2 1 1 12 2 0 0 1 3 4 1 0 2 1 2 1 3 10 1 2 0 0 1 0 3 2 0 4 0 3 1 10 1 0 0 4 5 7 0 0 2 0 1 4 1 7 1 1 0 8 2 0 8 2 3 1 1 3 8 4 1 0 1 3 5 2 3 0 2 1 2 1 6 7 0 2 1 0 3 8 4 1 1 1 2 2 6 5 2 1 0 1 3 4 2 2 2 2 2 1 0 13 0 0 0 5 3 2 5 1 0 2 1 0 0 12 0 1 0 2 2 4 1 3 1 1 4 1 4 2 2 0 2 3 1 0 2 9 2 2 0 0 10 2 0 1 0 3 2 4 7 1 0 1 3 3 5 0 0 0 0 1 0 2 5 0 0 1 1 0 7 4 1 1 4 0 0 0 5 1 1 0 0 0 11 2 2 1 1 2 1 1 2 1 1 0 3 0 2 14 0 0 0 3 3 1 1 1 1 0 3 3 1 7 3 1 0 0 1 0 1 1 0 0 2 1 4 4 3 0 2 0 0 1 3 3 0 2 1 2 4 7 0 2 1 0 5 5 2 2 4 1 0 0 3 1 0 0 0 3 1 3 3 0 0 4 1 1 1 1 3 3 1 0 3 0 1 3 3 3 2 2 2 4 3 0 5 1 3 3 2 0 0 1 1 0 2 2 2 3 2 1 2 0 5 0 1 0 0 0 3 4 0 0 1 5 3 2 2 0 1 1 0 2 1 0 2 4 0 0 0 3 2 4 3 1 2 3 0 8 2 1 2 2 5 2 6 1 0 3 5 1 1 6 4 3 2 2 3 2 1 0 0 0 2 0 5 4 2 1 1 2 3 0 6 0 0 0 1 0 1 5 3 1 0 0 0 1 2 3 0 1 3 4 2 1 4 1 3 5 0 6 6 0 0 0 0 3 1 2 1 3 1 0 2 5 4 2 0 3 0 1 0 5 4 1 0 1 3 7 2 0 0 1 2 4 1 6 4 0 0 0 2 2 4 2 1 1 1 1 1 0 4 0 1 3 0 2 6
4 HOBBIES_1_005_CA_1_evaluation HOBBIES_1_005 HOBBIES_1 HOBBIES CA_1 CA 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 6 0 3 2 3 5 3 1 0 0 1 0 2 2 4 0 0 3 1 1 1 2 2 0 0 0 0 0 0 3 7 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3 2 0 0 0 0 0 0 0 2 0 6 2 4 2 0 0 3 2 4 1 0 0 0 0 1 1 1 2 0 0 0 0 0 0 0 0 2 6 7 9 4 7 6 3 4 2 0 1 5 2 2 0 0 0 0 0 0 1 3 3 0 0 2 0 0 1 1 3 0 0 0 0 0 1 1 4 0 1 0 0 3 1 2 1 1 1 1 0 1 1 0 0 1 0 0 0 3 4 0 0 3 0 1 2 0 2 3 2 1 4 0 1 0 1 0 1 1 0 0 1 2 0 0 0 2 2 0 0 0 0 1 1 3 3 6 0 0 2 2 1 4 3 2 8 0 0 0 0 0 0 0 0 1 0 2 1 0 2 5 0 3 0 1 1 0 0 0 2 0 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 3 3 1 2 0 0 3 0 0 0 4 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 1 3 0 2 0 0 0 0 2 5 3 2 2 3 1 3 1 1 2 0 1 2 2 2 5 2 1 1 2 2 0 1 4 1 0 0 1 0 1 0 1 1 2 1 2 0 1 1 0 1 2 0 1 0 1 1 0 1 2 0 0 0 2 0 0 1 0 0 1 0 2 0 1 1 0 1 4 2 1 0 0 0 0 0 2 3 0 1 1 0 0 3 4 0 0 2 0 0 0 1 3 2 0 1 0 2 2 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 4 0 0 2 3 1 0 0 2 1 5 1 3 0 1 1 0 0 1 2 1 2 2 1 1 1 0 2 2 0 0 2 0 0 2 1 2 0 0 3 1 1 3 2 2 0 0 4 1 1 7 2 3 0 1 0 0 0 0 1 3 0 2 1 3 1 1 2 0 2 0 7 0 0 0 2 0 1 1 1 1 1 0 1 0 1 2 0 2 1 3 5 2 4 0 0 1 3 5 2 0 1 1 2 0 4 1 1 0 1 3 5 2 2 0 0 1 0 0 1 3 2 0 0 4 1 0 6 0 0 0 2 2 4 0 1 1 2 1 2 0 2 0 0 0 0 0 2 3 1 0 2 0 0 2 1 4 0 0 2 1 2 2 1 0 0 0 0 0 2 0 2 1 0 2 2 3 0 2 2 0 1 2 0 2 1 1 0 1 4 1 1 1 2 0 0 0 0 0 0 0 1 0 0 1 1 2 3 1 1 1 3 0 2 1 1 1 1 2 1 0 0 0 0 1 0 0 0 0 0 0 1 1 0 1 0 1 1 5 4 1 0 2 3 1 3 1 0 0 0 0 0 0 0 0 0 1 0 2 1 2 1 1 1 2 2 0 1 0 1 0 0 0 1 0 0 0 0 4 1 3 0 0 0 1 0 3 2 0 2 1 3 5 1 2 0 1 0 1 1 0 2 4 0 0 0 0 0 1 6 1 0 0 1 1 1 0 0 2 3 2 0 0 0 0 0 2 0 2 2 0 1 1 5 0 0 1 2 2 3 0 1 0 2 3 3 0 1 0 1 3 4 1 1 1 0 2 1 2 2 0 3 1 0 2 2 0 1 3 3 1 1 1 0 0 3 0 0 1 0 0 2 3 1 0 3 0 1 1 1 1 1 4 1 0 0 0 1 1 1 0 0 0 1 2 0 3 1 1 0 0 0 0 1 1 0 0 0 0 1 0 0 1 0 3 2 1 2 0 1 1 0 0 4 0 1 1 0 1 1 0 1 2 0 1 0 0 0 3 0 0 1 4 1 0 2 2 5 0 0 1 2 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 3 1 0 0 0 0 2 0 1 1 2 0 0 4 2 2 1 2 0 1 3 1 0 0 0 0 2 0 0 2 0 2 1 0 1 1 1 0 2 2 2 1 0 2 2 0 0 0 0 1 0 1 2 0 0 2 1 0 0 0 0 3 2 3 0 1 0 0 1 1 1 0 0 1 1 1 0 0 0 1 2 0 3 1 1 0 1 0 0 0 0 0 2 0 1 1 0 2 3 0 0 1 1 1 0 1 2 0 0 0 0 3 1 0 0 0 1 0 2 1 1 2 2 0 1 1 4 0 0 0 1 2 1 0 0 0 0 0 2 1 0 0 1 2 0 1 3 1 1 3 0 5 0 0 0 0 1 0 2 1 1 0 2 0 0 0 0 0 1 0 2 0 2 2 0 1 0 1 0 0 0 1 0 1 0 1 0 4 0 0 0 2 0 1 0 2 2 1 2 0 0 0 1 0 0 0 0 2 3 2 0 0 0 1 0 1 1 3 0 0 0 2 2 0 2 0 0 1 1 0 3 0 0 0 0 0 2 1 5 0 0 0 2 0 0 0 0 0 0 0 0 2 3 1 1 0 1 1 1 0 0 0 0 1 2 2 0 0 1 0 0 0 3 3 1 1 1 0 1 9 6 0 0 0 1 2 6 1 0 0 1 1 0 1 2 0 1 0 0 2 2 4 1 1 0 3 2 7 2 0 0 1 0 1 1 0 0 3 1 0 0 2 3 1 0 1 0 0 5 4 2 0 2 1 3 1 3 2 1 0 0 2 4 2 3 2 2 0 0 1 0 3 0 0 3 0 1 1 1 0 0 0 1 1 0 0 0 1 2 1 1 1 1 1 1 1 0 2 0 0 2 1 0 4 1 2 1 0 0 1 1 5 1 0 1 2 0 1 1 1 0 1 3 3 1 1 4 1 0 0 0 2 1 2 0 2 1 1 1 2 2 1 0 1 1 2 0 1 4 2 0 2 2 1 3 1 1 2 0 2 0 1 0 0 1 0 1 4 0 0 1 0 2 0 1 3 2 1 1 2 2 3 3 0 0 1 1 0 2 0 2 1 2 0 2 3 4 2 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 3 1 0 0 0 0 2 1 1 0 0 0 0 0 5 1 2 1 0 1 1 1 0 0 2 1 0 1 0 0 1 1 0 0 3 3 1 1 0 3 2 2 3 3 2 1 0 0 1 2 1 1 0 2 1 1 0 0 2 1 0 0 0 0 0 1 1 0 1 1 1 2 1 2 1 0 2 2 1 0 3 3 3 4 1 2 0 2 0 5 2 0 0 1 0 3 1 0 1 0 2 1 0 0 1 3 1 1 0 4 0 2 1 2 4 0 1 0 0 3 1 2 2 0 1 1 0 4 0 0 4 0 2 2 2 1 2 1 1 1 4 0 2 1 2 0 0 1 0 1 1 2 2 3 1 0 2 3 0 1 1 4 0 3 2 1 2 1 2 2 1 2 0 1 1 2 0 2 0 0 0 4 2 1 2 0 0 0 0 0 2 1 0 0 1 2 0 1 2 1 2 1 2 3 3 0 3 1 5 3 2 1 2 3 4 0 0 1 0 0 1 0 0 1 0 0 0 0 2 0 0 3 0 0 1 2 2 0 1 0 0 0 1 0 0 3 0 0 1 1 0 3 1 0 4 1 2 0 0 0 1 1 2 0 0 5 2 2 2 1 0 0 0 3 0 0 0 3 1 1 1 1 2 1 0 0 1 0 2 1 1 0 3 1 1 2 1 1 0 3 2 2 2 3 1 0 0 0 0 1 0 4 4 0 1 4 0 1 0 1 0 1 1 2 0 1 1 2 1 1 0 1 1 2 2 2 4 1 0 2 3 1 0 3 2 3 1 1 3 2 3 2 2 2 2 0 0 0 2 1 0 0 2 1 0
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30485 FOODS_3_823_WI_3_evaluation FOODS_3_823 FOODS_3 FOODS WI_3 WI 0 0 2 2 0 3 1 4 1 0 0 3 4 4 0 0 1 0 1 1 7 7 3 6 3 3 7 12 4 2 7 5 12 5 3 3 7 6 4 6 3 6 2 3 4 1 1 3 2 1 2 3 4 7 2 4 4 4 7 7 2 4 7 4 4 7 3 4 2 3 10 6 8 5 1 0 2 1 0 3 0 2 3 1 1 1 7 1 4 2 2 0 0 0 5 1 1 3 1 6 6 3 4 3 2 2 1 2 2 1 1 0 0 0 2 3 1 0 1 1 1 1 1 3 0 0 1 2 1 0 1 3 1 2 2 0 3 2 1 0 0 1 2 0 0 0 0 0 2 0 2 0 1 2 1 1 0 0 2 1 1 0 1 1 2 1 3 1 0 2 0 0 0 1 0 1 0 1 1 0 2 0 0 2 1 0 1 0 1 1 1 0 3 0 1 0 1 2 3 0 1 0 1 1 0 0 1 0 0 0 0 0 0 0 1 1 0 5 1 0 1 0 0 1 0 1 0 1 1 1 2 1 2 0 1 0 0 1 2 2 2 0 0 1 0 1 1 2 1 0 3 0 0 0 0 2 0 0 1 1 2 0 1 1 0 0 3 2 1 1 2 1 0 1 0 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 0 2 0 0 2 2 0 0 1 2 0 1 0 2 2 0 1 0 1 0 0 0 0 0 1 1 2 0 1 1 2 0 2 0 2 1 1 1 1 2 0 0 0 3 0 0 0 0 2 0 0 1 2 0 0 0 2 0 1 0 1 0 2 0 1 1 0 0 0 1 0 3 1 0 0 1 1 1 3 1 1 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 3 3 1 2 1 3 2 0 1 0 0 3 2 1 0 0 0 1 1 2 0 2 1 0 2 0 0 3 3 0 1 1 1 3 3 1 1 2 2 1 0 2 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 1 2 2 0 0 0 0 0 0 3 0 0 1 0 2 2 2 2 2 0 0 0 0 2 1 1 0 0 0 0 0 2 1 2 0 0 5 2 0 2 0 0 5 0 1 1 0 1 1 0 0 1 1 0 0 0 0 2 0 2 4 0 0 0 0 0 0 2 0 0 1 0 0 1 0 0 0 0 0 0 1 2 0 0 0 0 1 0 0 0 0 1 0 1 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 2 0 4 0 0 0 2 0 0 1 1 0 2 1 2 1 0 3 0 0 1 0 1 1 0 0 0 0 0 0 0 3 2 2 4 1 0 1 1 0 0 0 0 1 0 2 0 0 0 0 0 3 0 0 0 1 0 0 0 0 1 0 1 0 0 1 1 1 1 0 0 0 0 1 0 0 0 0 1 2 1 0 0 1 0 1 4 0 0 0 1 1 4 0 0 0 0 1 0 2 1 3 2 0 1 0 2 0 0 0 0 0 0 0 0 2 1 0 0 0 0 0 2 0 0 2 1 0 1 1 2 1 0 0 0 2 4 1 0 1 0 0 0 1 0 1 0 1 1 0 1 0 0 0 1 2 0 0 0 1 0 0 0 2 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 1 1 0 1 0 0 0 2 0 0 1 0 2 0 0 1 1 2 0 0 1 0 0 0 0 0 0 0 2 2 0 0 0 2 1 0 0 1 2 2 2 0 0 0 0 0 0 0 0 0 2 1 0 1 4 0 2 1 0 1 0 0 0 0 1 0 2 0 2 1 1 0 2 0 0 0 1 0 1 0 0 1 0 2 1 0 1 0 2 0 0 1 2 0 2 0 0 0 1 1 1 0 0 1 1 0 2 1 0 1 0 1 0 1 0 1 0 0 1 1 0 1 0 2 1 0 0 1 0 0 1 0 0 4 1 5 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 1 2 1 0 0 0 0 0 3 1 1 1 0 0 1 0 1 2 1 0 0 2 0 2 1 0 1 1 0 1 2 0 1 0 1 0 0 2 0 1 1 0 3 0 0 1 1 3 3 1 0 0 0 1 2 3 0 0 0 1 0 2 1 1 0 0 4 0 6 1 0 1 0 0 0 1 1 0 0 0 0 0 1 0 2 0 1 0 0 3 1 1 0 0 0 2 3 1 0 1 3 0 3 0 0 2 0 0 0 1 0 0 3 0 0 0 0 0 0 1 0 1 0 0 2 0 0 2 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 2 0 0 0 0 0 1 0 0 1 0 0 0 2 2 0 0 0 2 0 0 1 1 1 0 0 1 0 1 0 3 0 1 1 0 0 1 1
30486 FOODS_3_824_WI_3_evaluation FOODS_3_824 FOODS_3 FOODS WI_3 WI 0 0 0 0 0 5 0 1 1 3 1 1 0 4 2 0 1 2 1 1 0 0 0 0 3 1 1 1 2 0 1 0 1 1 1 3 3 4 4 3 6 1 0 1 2 3 0 1 1 1 0 0 0 1 0 0 1 0 0 0 1 2 0 1 2 1 2 0 1 1 0 1 1 2 0 3 2 1 0 0 0 0 1 1 3 1 0 1 1 0 1 2 0 3 1 2 1 0 0 0 1 0 1 1 0 1 2 2 0 0 0 1 0 0 2 0 1 0 0 0 0 0 0 0 1 0 0 1 0 1 1 0 0 0 1 0 2 1 0 0 0 0 0 0 0 1 0 2 0 0 0 1 0 0 1 2 2 2 0 0 0 3 1 4 1 2 1 2 3 3 0 0 2 2 0 3 1 0 0 0 0 0 0 2 2 5 0 0 1 0 1 1 2 0 1 0 1 0 3 0 0 2 1 1 0 1 0 1 0 0 0 0 1 0 0 0 2 1 1 1 0 2 0 1 0 1 4 1 0 1 1 0 1 0 0 1 1 0 0 1 1 0 0 0 1 1 0 0 0 0 0 0 0 2 0 0 0 2 3 3 2 1 1 1 0 0 2 1 0 0 0 0 0 1 1 0 0 1 0 0 1 1 1 1 0 0 2 2 1 0 3 0 2 2 0 0 0 1 0 0 0 1 1 0 0 0 0 2 0 1 0 0 0 0 0 0 2 1 1 0 0 0 0 1 0 0 0 0 0 0 0 6 0 1 1 0 0 2 0 2 2 1 1 1 0 1 0 1 0 2 0 1 1 0 1 0 2 1 1 1 0 0 0 0 0 1 1 0 0 1 1 0 2 2 0 0 0 1 0 3 0 1 1 0 0 2 0 0 0 1 1 0 0 3 0 0 0 0 0 0 0 0 0 0 3 1 0 1 1 0 1 0 1 0 1 2 0 0 2 2 0 0 0 0 0 1 0 0 0 0 1 0 1 0 2 0 2 0 1 0 0 1 2 0 0 0 4 2 0 0 0 0 0 1 0 0 1 1 1 0 0 1 0 0 0 0 1 0 1 1 2 0 3 1 0 0 1 1 1 2 0 0 1 1 0 0 0 0 0 0 1 0 1 2 0 1 3 2 1 1 1 0 2 0 4 1 0 1 0 0 0 2 1 0 0 2 3 1 0 1 5 2 0 7 1 0 0 0 0 0 0 0 0 0 2 1 0 0 0 0 0 0 0 2 0 0 0 1 2 0 0 0 2 2 1 1 2 1 2 0 0 6 0 3 2 0 2 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 2 0 0 2 1 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 1 2 1 0 0 0 0 2 0 0 1 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 3 2 0 1 2 0 0 2 1 0 0 0 0 0 1 0 0 0 0 0 0 4 0 0 0 1 1 1 0 2 0 0 1 0 0 4 2 0 0 0 0 0 0 0 0 0 0 2 0 0 1 2 3 2 1 1 0 1 1 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 2 0 1 0 1 2 0 1 0 1 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 1 1 0 0 1 1 0 0 0 0 0 1 0 1 1 1 1 0 0 0 0 0 1 0 1 2 2 0 0 1 1 0 0 0 0 1 0 1 0 0 0 0 0 3 0 1 2 0 1 0 0 0 0 2 1 0 1 0 1 0 0 2 0 0 0 0 0 1 0 1 0 0 0 0 1 0 1 1 1 0 0 0 1 0 0 0 0 1 0 0 1 0 0 1 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 3 1 0 2 0 0 0 0 0 1 1 0 0 1 0 1 0 1 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 2 0 0 0 0 0 0 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 3 2 3 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 1 0 2 0 1 0 0 0 0 0 0 0 1 1 0 0 1 0 2 0 0 0 1 1 1 1 0 1 1 0 0 0 0 2 2 0 1 0 2 0 1 1 0 1 0 0 0 0 0 2 0 1 0 0 0 1 1 1 0 0 1 0 0 1 1 1 0 0 0 0 2 0 0 1 0 3 1 0 1 0 0 1 0 0 0 0 0 0 0 1 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 2 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 3 0 0 0 1 0 0 0 1 1 1 0 0 0 1 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 2 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 0 0 0 0 1 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0
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In [6]:
sales.info()
sales.describe()
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dtypes: int64(1941), object(6)
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1.110430 1.061299 1.137455 1.493473 1.727517 1.850607 1.325615 1.301509 1.283503 1.250115 1.417514 1.847819 1.766350 1.391505 1.281371 1.154247 1.230863 1.370581 1.586159 1.693670 1.248245 1.232207 1.159167 1.149000 1.328862 1.605838 1.633158 1.272319 1.163890 1.129715 1.139128 1.406888 1.653952 1.739324 1.416235 1.453395 1.298819 1.336930 1.436701 1.778911 1.495867 1.521810 1.291538 1.385635 1.328403 1.445490 1.781174 1.965267 1.389374 1.271794 1.216661 1.212299 1.395605 1.689669 1.782158
std 5.126689 5.365468 3.667454 4.415141 3.379344 4.785947 5.059495 6.617729 5.917204 4.206199 3.665969 3.697822 3.486595 3.484608 4.636395 4.662770 2.993414 3.296922 2.811648 2.611911 2.985123 3.923110 3.597620 3.084773 2.874189 2.727852 2.637849 2.842334 3.714944 3.688771 2.708641 2.922533 2.888537 3.085799 3.136922 3.944627 4.617819 3.076988 3.011551 3.023168 2.943395 3.219799 4.112437 3.858483 3.204146 3.178801 2.945178 2.910318 2.985018 3.799143 3.770863 2.940768 2.859035 2.601816 2.539745 3.011724 3.643936 3.753120 2.722738 2.428430 2.553065 2.930913 3.614927 4.081813 4.075848 3.440013 3.169972 3.080044 2.938010 3.181639 3.998989 4.064496 3.346293 3.069668 2.904153 2.895695 3.268764 3.646208 3.947118 3.113404 2.663721 2.799844 2.690062 3.411234 3.952394 3.550954 2.633130 2.371017 2.370184 2.336361 2.893274 3.469550 3.810263 2.994469 3.117015 2.743477 2.823539 4.787469 3.864910 3.466385 3.355397 3.064408 2.905838 2.996873 3.313320 3.486992 3.863191 2.906889 2.503189 2.333230 2.374141 2.843516 3.348643 3.422558 2.823771 2.601243 2.668984 2.835999 3.078649 3.291331 3.169069 3.018372 2.534923 3.053683 3.036350 3.620832 3.976574 4.099861 3.499081 3.420099 3.331409 3.316834 3.307328 3.737299 4.266954 3.426133 3.235580 3.424048 2.953379 3.202928 3.845742 3.804195 3.463005 3.297195 2.815128 2.949388 3.164065 3.486860 3.648117 2.950419 2.609082 2.633410 2.818619 4.193057 4.605405 4.212882 3.315874 3.591203 3.457706 3.476684 3.525981 4.199982 4.129806 3.536152 3.204502 2.959982 3.180130 3.532995 3.851802 3.895142 3.253198 2.780007 2.965236 2.946823 3.297406 3.801956 3.705734 2.919661 2.726874 2.665060 2.60254 3.080132 3.713137 3.776294 3.438473 3.464112 3.695421 3.138356 3.841424 4.423705 4.284293 3.654164 3.614528 3.30197 3.181374 3.831591 4.371173 4.685006 4.047184 3.538605 3.106242 3.205524 3.816692 4.596349 4.625838 3.503383 3.072394 2.931710 2.713012 3.117708 3.897194 4.229375 2.874061 2.635708 2.822938 3.492949 3.867191 4.403508 3.578657 3.920327 3.337459 3.152859 3.035086 3.638827 3.997853 4.287494 3.361611 3.160423 3.310507 3.418971 3.305297 3.963408 3.823957 2.986535 2.925692 2.861600 2.757401 3.533770 4.243583 5.072695 3.578870 3.408207 3.326264 3.271231 4.532747 5.816764 5.104353 4.187780 3.678991 3.728881 3.781757 4.375375 5.401974 4.733447 3.991809 3.896077 3.636550 3.787782 4.087513 5.233140 4.917479 3.804914 3.551107 3.111305 3.256827 4.269312 4.705038 4.577163 3.391484 3.398279 2.985014 3.413701 3.881694 4.764419 4.527858 3.303660 3.689733 3.416246 3.914081 3.862465 5.030195 4.928005 3.867986 3.472631 3.608273 3.613708 4.272797 5.394491 4.943759 3.816109 3.580187 3.113421 3.254758 4.135218 5.154228 4.371942 3.620042 4.045264 6.346170 4.405719 4.355537 3.543093 3.634479 2.806494 2.915555 2.866750 3.630248 4.127419 5.174451 4.819384 4.105536 3.852015 3.539930 3.654339 4.161905 4.952683 4.787429 3.901726 3.686772 3.683473 4.218042 4.516705 4.915300 4.313228 4.048551 3.738245 3.855985 4.290415 6.332411 6.598196 0.037552 4.080827 3.322562 3.691060 3.424374 5.538999 6.915889 3.138417 4.334097 3.679838 3.423041 3.799999 3.898835 4.890782 4.805100 3.610505 3.587361 3.763924 3.209078 3.852671 5.051660 4.997426 3.742963 3.001798 3.097722 3.168779 3.533499 5.218272 4.418677 3.301521 3.281110 3.011560 3.155860 3.912637 4.440223 4.157760 3.245294 3.316222 3.745791 3.676083 4.551669 5.773146 5.105935 3.961224 3.535021 3.780643 4.156838 4.247300 5.615267 5.342754 4.112584 3.499058 3.692474 3.046985 3.303217 4.693138 4.582874 3.812428 3.402179 2.920846 3.266624 3.906719 4.447724 4.592450 3.440187 2.982089 3.132091 3.790929 4.135408 5.126268 5.022461 4.161387 4.113889 3.564854 3.792822 4.176970 4.793699 4.941296 4.228114 3.699868 3.596096 3.838459 4.020055 4.823376 4.542105 3.496677 3.241962 3.138013 3.193725 3.636008 4.627949 4.624954 3.360490 2.977597 2.990275 2.932219 3.793006 4.570898 4.739806 3.888236 4.096658 3.535166 3.994218 4.791896 6.052181 5.186144 3.895638 3.620644 3.644145 3.460657 3.977895 4.751172 4.660336 3.280693 3.089763 3.030393 3.111246 3.687737 4.610515 4.644716 3.199621 3.017752 3.006376 3.029732 3.579664 4.333807 4.159277 3.202175 3.635138 3.57362 4.193760 3.958426 5.119492 4.948203 4.652003 3.844287 3.749579 3.626574 4.227139 5.724670 4.528098 4.060530 3.940018 3.551651 3.39989 4.309572 4.915368 4.942363 3.495019 3.503704 3.326931 3.65728 4.474293 4.694187 4.403990 4.275807 3.715105 3.349488 3.517257 4.673502 5.314214 5.214288 3.970099 4.169758 4.230744 3.968636 4.705715 5.563157 5.942916 5.056376 4.280870 3.820090 4.195877 5.306018 5.640554 5.117043 4.168357 3.942698 3.814049 3.601221 5.735970 5.106879 5.268596 3.836420 3.705410 3.647999 4.048349 4.499534 4.952282 4.975036 4.684861 5.526807 4.925674 4.287790 5.460536 5.387959 5.164806 4.273343 4.074058 4.066251 4.188088 4.478193 5.039092 5.534334 4.220753 3.886682 3.534199 3.761448 4.628367 5.121827 5.169011 3.864643 3.512260 3.605767 3.557906 4.114168 4.305327 4.739899 3.811025 3.624549 4.032717 4.467505 5.031874 5.125671 5.434123 4.574412 4.014598 4.142155 4.764363 4.805980 5.984397 5.985481 4.731373 4.604410 4.522344 4.281144 4.457053 5.525933 5.357862 4.041339 3.539636 3.592631 3.534079 4.002894 4.987939 4.975296 3.853261 3.513161 3.403931 3.507367 4.293018 5.772197 5.254929 5.694821 3.958664 3.797124 3.885975 4.227084 5.017666 5.842469 4.215169 3.906841 3.949536 3.595207 4.340788 5.410205 5.265273 3.847551 3.343046 3.086486 3.430604 4.203177 5.024553 4.726365 3.454959 3.372713 3.312527 3.454063 4.064851 5.179006 4.910268 4.319530 4.475106 4.110760 3.663085 4.036751 5.304560 4.877061 4.201955 3.899783 3.666955 3.647826 4.326227 5.020221 5.041008 3.891715 3.334470 3.341059 3.282728 3.931750 4.319206 4.340431 3.201888 2.939659 2.891194 2.900666 3.486268 4.519272 4.299637 3.110363 3.378267 3.102032 3.571495 4.042260 5.151260 4.828492 3.750041 3.729479 3.448908 3.462627 4.146637 4.857063 4.976125 3.708613 3.184004 3.224670 3.402345 3.685692 4.636037 4.416919 3.566887 3.900886 5.613890 4.878181 3.364411 3.661757 3.624415 2.805101 2.619644 2.715168 2.776580 3.655437 5.041806 4.710074 3.909906 3.308964 3.715744 3.593660 4.190227 4.812181 4.735286 3.698683 3.506004 3.557425 3.327926 3.859734 5.099513 4.622364 3.465122 3.487392 3.781424 3.690906 4.076194 5.273439 5.847256 6.259961 0.018991 3.670131 3.400977 3.855630 4.420550 4.759123 5.911918 2.830817 3.806247 4.290763 3.900511 4.999655 5.040123 3.780544 3.725425 3.574548 3.713152 3.959008 6.353421 4.713116 3.706187 3.872841 3.317394 3.081734 3.671527 4.884316 4.366622 3.534974 2.806866 2.636828 2.705775 2.973676 4.339781 4.123153 3.028330 2.954021 2.593137 2.909778 4.215598 5.555124 5.060175 3.296048 3.438233 3.535023 3.463905 4.035623 4.910856 4.494381 4.293642 4.212950 4.523878 3.500160 4.223044 4.836151 4.759981 3.541796 3.112712 3.225639 3.431770 3.668877 4.506840 4.596170 3.314697 2.914731 3.010108 3.193188 4.060771 5.327689 5.187499 3.952671 3.639742 3.549292 3.261861 3.846756 5.013580 4.809772 3.827351 3.679662 3.408912 3.793161 4.227286 4.853328 4.511973 3.468718 3.133460 3.085171 3.146089 3.620637 4.270666 4.178630 3.175767 3.142012 3.107193 3.340756 4.404191 5.447370 4.424780 3.800249 3.842212 3.919345 3.516411 4.534302 5.017657 4.901550 3.901275 3.715326 3.593762 3.931992 4.257929 4.618409 4.804051 3.769326 3.407546 3.055753 3.010802 3.541749 4.282116 4.284373 3.320689 3.188384 3.059558 3.042302 3.332726 3.923909 4.025515 2.913126 3.112988 3.602713 3.535716 4.011444 4.629568 4.468755 3.806116 3.521454 3.398076 3.576087 3.713114 5.084089 4.391731 3.757466 3.724009 3.696495 3.342136 3.746553 4.408275 4.440536 3.712986 3.227328 3.004243 3.298038 3.643830 4.246540 4.076877 4.072004 3.139300 2.829517 3.006250 3.842076 4.762379 5.062585 4.150775 3.632363 5.218092 4.515050 4.076095 4.971062 5.172846 3.928599 3.758730 3.519470 3.738726 4.397485 5.061457 4.633733 3.923513 3.380800 3.207686 3.406420 4.028210 4.598513 4.845151 3.601133 3.472095 3.427060 3.634270 4.485853 6.247522 5.732200 4.609735 4.286895 5.089670 4.993788 4.686764 4.692845 4.859649 4.191983 4.271183 3.797243 3.867125 4.444169 4.805382 5.025579 3.990423 3.603205 3.501352 3.844253 4.010134 4.652635 4.899416 3.542504 3.484071 4.924570 3.599148 4.100461 4.713070 4.663904 3.632075 3.340458 3.830264 4.272638 4.509132 5.153178 5.021145 4.326024 4.074528 4.129541 4.058569 4.739620 5.161838 5.638415 4.441949 4.666993 3.933302 4.664165 4.430094 4.953292 5.337675 4.370948 3.837378 4.098857 3.950302 6.089621 6.610942 6.604547 4.470011 4.693675 4.533783 4.798888 6.383960 6.980386 7.365633 6.384567 4.808603 4.740246 5.174131 5.122239 6.262760 6.567841 5.104232 5.046232 5.532741 5.695836 6.556826 7.256630 6.237616 5.210463 4.126588 4.101417 3.772732 4.369121 5.555844 5.640303 3.947113 4.006788 3.780107 4.091011 4.657772 5.420316 5.022184 3.788384 3.931015 3.854476 3.930702 4.161156 5.245162 5.263682 3.830744 3.704441 3.655756 3.664944 4.054287 4.937010 4.779758 3.883885 3.439844 4.794659 3.198762 3.555142 4.317393 4.551397 3.062597 2.931482 3.024572 2.914609 3.512546 4.244254 4.062951 3.543451 3.041411 3.338877 3.131114 4.087407 4.835969 4.945676 3.333546 3.503093 3.502455 3.564546 3.779396 4.71279 4.435516 4.479391 3.712929 3.077476 3.099420 3.999241 4.873698 4.325099 3.303798 3.042857 2.887811 3.063626 3.477154 4.080391 3.969272 3.216623 3.586090 4.869494 3.291038 3.290903 3.477103 4.073378 5.490765 4.863138 4.166900 4.54262 4.581455 5.912079 4.138178 3.530755 3.344428 3.398477 3.521800 3.956135 4.312865 4.484244 3.446793 3.261209 3.389748 3.524577 3.425548 4.257237 3.820709 3.921504 4.133365 0.040491 4.063103 4.098849 4.165534 4.005681 4.284123 5.997979 2.850483 3.493910 4.106438 4.799798 5.340848 3.739294 3.412672 3.927709 3.428033 3.404046 5.223173 5.046399 3.451757 3.515635 3.184438 3.470912 3.871446 4.333899 4.085432 3.142859 2.813103 2.949166 2.681569 3.227776 4.058389 4.439855 3.298194 2.633597 2.480878 2.867877 3.875437 4.873254 4.979560 4.308786 3.258369 3.802393 3.520698 3.462966 4.305790 4.607562 3.286686 3.471447 3.892424 4.141520 3.511910 4.192258 4.184924 4.922578 3.035007 2.970654 2.968046 3.173957 3.689376 4.025772 2.931790 2.808994 2.465739 2.679901 3.257847 4.030414 4.198879 3.553390 2.890334 3.012136 3.251207 3.304873 4.099457 5.378941 3.822583 3.555881 3.665102 3.321646 3.585181 4.191573 4.434953 3.214283 3.001087 2.934691 2.899467 3.380545 4.325296 4.266401 3.216641 2.774606 2.685450 2.969177 3.250457 4.007381 4.273346 2.950791 3.199534 3.342337 3.793991 3.563225 4.692509 4.881549 3.887163 3.973251 3.874929 3.495231 4.293836 4.637040 4.492225 4.208547 3.590058 3.345968 3.238957 3.781724 4.924661 4.675854 3.138706 2.982114 3.079755 3.057782 3.375949 4.132766 4.156101 3.214474 3.077312 2.978413 3.537844 4.040551 4.710585 4.430849 3.656242 3.338716 3.557957 3.263832 3.731896 4.473339 4.267809 4.038657 3.443318 3.551403 3.462769 3.537321 3.639098 4.003069 3.023631 2.830314 2.908389 2.887471 3.193460 3.696491 3.665048 3.273489 2.725990 2.580636 2.652021 3.276702 3.722853 4.215895 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In [7]:
prices
Out[7]:
store_id item_id wm_yr_wk sell_price
0 CA_1 HOBBIES_1_001 11325 9.58
1 CA_1 HOBBIES_1_001 11326 9.58
2 CA_1 HOBBIES_1_001 11327 8.26
3 CA_1 HOBBIES_1_001 11328 8.26
4 CA_1 HOBBIES_1_001 11329 8.26
... ... ... ... ...
6841116 WI_3 FOODS_3_827 11617 1.00
6841117 WI_3 FOODS_3_827 11618 1.00
6841118 WI_3 FOODS_3_827 11619 1.00
6841119 WI_3 FOODS_3_827 11620 1.00
6841120 WI_3 FOODS_3_827 11621 1.00

6841121 rows × 4 columns

In [8]:
prices.info()
prices.describe()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6841121 entries, 0 to 6841120
Data columns (total 4 columns):
 #   Column      Dtype  
---  ------      -----  
 0   store_id    object 
 1   item_id     object 
 2   wm_yr_wk    int64  
 3   sell_price  float64
dtypes: float64(1), int64(1), object(2)
memory usage: 208.8+ MB
Out[8]:
wm_yr_wk sell_price
count 6.841121e+06 6.841121e+06
mean 1.138294e+04 4.410952e+00
std 1.486100e+02 3.408814e+00
min 1.110100e+04 1.000000e-02
25% 1.124700e+04 2.180000e+00
50% 1.141100e+04 3.470000e+00
75% 1.151700e+04 5.840000e+00
max 1.162100e+04 1.073200e+02
In [9]:
calendar
Out[9]:
date wm_yr_wk weekday wday month year d event_name_1 event_type_1 event_name_2 event_type_2 snap_CA snap_TX snap_WI
0 2011-01-29 11101 Saturday 1 1 2011 d_1 NaN NaN NaN NaN 0 0 0
1 2011-01-30 11101 Sunday 2 1 2011 d_2 NaN NaN NaN NaN 0 0 0
2 2011-01-31 11101 Monday 3 1 2011 d_3 NaN NaN NaN NaN 0 0 0
3 2011-02-01 11101 Tuesday 4 2 2011 d_4 NaN NaN NaN NaN 1 1 0
4 2011-02-02 11101 Wednesday 5 2 2011 d_5 NaN NaN NaN NaN 1 0 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1964 2016-06-15 11620 Wednesday 5 6 2016 d_1965 NaN NaN NaN NaN 0 1 1
1965 2016-06-16 11620 Thursday 6 6 2016 d_1966 NaN NaN NaN NaN 0 0 0
1966 2016-06-17 11620 Friday 7 6 2016 d_1967 NaN NaN NaN NaN 0 0 0
1967 2016-06-18 11621 Saturday 1 6 2016 d_1968 NaN NaN NaN NaN 0 0 0
1968 2016-06-19 11621 Sunday 2 6 2016 d_1969 NBAFinalsEnd Sporting Father's day Cultural 0 0 0

1969 rows × 14 columns

In [10]:
calendar.info()
calendar.describe()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1969 entries, 0 to 1968
Data columns (total 14 columns):
 #   Column        Non-Null Count  Dtype 
---  ------        --------------  ----- 
 0   date          1969 non-null   object
 1   wm_yr_wk      1969 non-null   int64 
 2   weekday       1969 non-null   object
 3   wday          1969 non-null   int64 
 4   month         1969 non-null   int64 
 5   year          1969 non-null   int64 
 6   d             1969 non-null   object
 7   event_name_1  162 non-null    object
 8   event_type_1  162 non-null    object
 9   event_name_2  5 non-null      object
 10  event_type_2  5 non-null      object
 11  snap_CA       1969 non-null   int64 
 12  snap_TX       1969 non-null   int64 
 13  snap_WI       1969 non-null   int64 
dtypes: int64(7), object(7)
memory usage: 215.5+ KB
Out[10]:
wm_yr_wk wday month year snap_CA snap_TX snap_WI
count 1969.000000 1969.000000 1969.000000 1969.000000 1969.000000 1969.000000 1969.000000
mean 11347.086338 3.997461 6.325546 2013.288471 0.330117 0.330117 0.330117
std 155.277043 2.001141 3.416864 1.580198 0.470374 0.470374 0.470374
min 11101.000000 1.000000 1.000000 2011.000000 0.000000 0.000000 0.000000
25% 11219.000000 2.000000 3.000000 2012.000000 0.000000 0.000000 0.000000
50% 11337.000000 4.000000 6.000000 2013.000000 0.000000 0.000000 0.000000
75% 11502.000000 6.000000 9.000000 2015.000000 1.000000 1.000000 1.000000
max 11621.000000 7.000000 12.000000 2016.000000 1.000000 1.000000 1.000000

Plot Sales time series

At the beginning of the sales time series of each item, there could be some zeros, meaning the product was not available. So it's necessary to extract the 1st week when the item went in stock, and discard the 0 sales before that time point.

In [11]:
sales.iloc[:3][sales.columns[6:]].T.plot()
Out[11]:
<AxesSubplot:>
In [12]:
del sales, prices, calendar

Preprocess Sales Data

In [13]:
sales = pd.read_csv(SALES_CSV)
prices = pd.read_csv(PRICES_CSV)
calendar = pd.read_csv(CALENDAR_CSV)

Flatten Sales data

In [14]:
id_cols = ['id','item_id','dept_id','cat_id','store_id','state_id']
sales = pd.melt(sales, id_vars=id_cols, var_name='d', value_name='sales')
sales
Out[14]:
id item_id dept_id cat_id store_id state_id d sales
0 HOBBIES_1_001_CA_1_evaluation HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_1 0
1 HOBBIES_1_002_CA_1_evaluation HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_1 0
2 HOBBIES_1_003_CA_1_evaluation HOBBIES_1_003 HOBBIES_1 HOBBIES CA_1 CA d_1 0
3 HOBBIES_1_004_CA_1_evaluation HOBBIES_1_004 HOBBIES_1 HOBBIES CA_1 CA d_1 0
4 HOBBIES_1_005_CA_1_evaluation HOBBIES_1_005 HOBBIES_1 HOBBIES CA_1 CA d_1 0
... ... ... ... ... ... ... ... ...
59181085 FOODS_3_823_WI_3_evaluation FOODS_3_823 FOODS_3 FOODS WI_3 WI d_1941 1
59181086 FOODS_3_824_WI_3_evaluation FOODS_3_824 FOODS_3 FOODS WI_3 WI d_1941 0
59181087 FOODS_3_825_WI_3_evaluation FOODS_3_825 FOODS_3 FOODS WI_3 WI d_1941 2
59181088 FOODS_3_826_WI_3_evaluation FOODS_3_826 FOODS_3 FOODS WI_3 WI d_1941 0
59181089 FOODS_3_827_WI_3_evaluation FOODS_3_827 FOODS_3 FOODS WI_3 WI d_1941 1

59181090 rows × 8 columns

Add placeholding rows for test prediction

In [15]:
def add_test_rows(sales: pd.DataFrame, id_cols) -> pd.DataFrame:
    index_df = sales[id_cols].drop_duplicates()
    test_df = pd.DataFrame()
    for d in range(1, 29):
        df = index_df.copy()
        df['d'] = f'd_{END_TRAIN + d}'
        df[TARGET_COL] = np.nan
        test_df = pd.concat([test_df, df], axis=0)

    sales = pd.concat([sales, test_df]).reset_index(drop=True)
    return sales

sales = add_test_rows(sales, id_cols)
sales
Out[15]:
id item_id dept_id cat_id store_id state_id d sales
0 HOBBIES_1_001_CA_1_evaluation HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_1 0.0
1 HOBBIES_1_002_CA_1_evaluation HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_1 0.0
2 HOBBIES_1_003_CA_1_evaluation HOBBIES_1_003 HOBBIES_1 HOBBIES CA_1 CA d_1 0.0
3 HOBBIES_1_004_CA_1_evaluation HOBBIES_1_004 HOBBIES_1 HOBBIES CA_1 CA d_1 0.0
4 HOBBIES_1_005_CA_1_evaluation HOBBIES_1_005 HOBBIES_1 HOBBIES CA_1 CA d_1 0.0
... ... ... ... ... ... ... ... ...
60034805 FOODS_3_823_WI_3_evaluation FOODS_3_823 FOODS_3 FOODS WI_3 WI d_1969 NaN
60034806 FOODS_3_824_WI_3_evaluation FOODS_3_824 FOODS_3 FOODS WI_3 WI d_1969 NaN
60034807 FOODS_3_825_WI_3_evaluation FOODS_3_825 FOODS_3 FOODS WI_3 WI d_1969 NaN
60034808 FOODS_3_826_WI_3_evaluation FOODS_3_826 FOODS_3 FOODS WI_3 WI d_1969 NaN
60034809 FOODS_3_827_WI_3_evaluation FOODS_3_827 FOODS_3 FOODS WI_3 WI d_1969 NaN

60034810 rows × 8 columns

Remove the leading 0-Sales entries before products went in-stock

Extract in-stock week from Prices data

In [16]:
instock = prices.groupby(['store_id', 'item_id'])['wm_yr_wk'].agg(['min']).reset_index()
instock.columns = list(instock)[:2] + ['instock_week']
instock
Out[16]:
store_id item_id instock_week
0 CA_1 FOODS_1_001 11101
1 CA_1 FOODS_1_002 11101
2 CA_1 FOODS_1_003 11101
3 CA_1 FOODS_1_004 11206
4 CA_1 FOODS_1_005 11101
... ... ... ...
30485 WI_3 HOUSEHOLD_2_512 11101
30486 WI_3 HOUSEHOLD_2_513 11311
30487 WI_3 HOUSEHOLD_2_514 11101
30488 WI_3 HOUSEHOLD_2_515 11352
30489 WI_3 HOUSEHOLD_2_516 11101

30490 rows × 3 columns

In [17]:
sales = pd.merge(sales, instock, on=['store_id', 'item_id'], how='left')
sales
Out[17]:
id item_id dept_id cat_id store_id state_id d sales instock_week
0 HOBBIES_1_001_CA_1_evaluation HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_1 0.0 11325
1 HOBBIES_1_002_CA_1_evaluation HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_1 0.0 11121
2 HOBBIES_1_003_CA_1_evaluation HOBBIES_1_003 HOBBIES_1 HOBBIES CA_1 CA d_1 0.0 11401
3 HOBBIES_1_004_CA_1_evaluation HOBBIES_1_004 HOBBIES_1 HOBBIES CA_1 CA d_1 0.0 11106
4 HOBBIES_1_005_CA_1_evaluation HOBBIES_1_005 HOBBIES_1 HOBBIES CA_1 CA d_1 0.0 11117
... ... ... ... ... ... ... ... ... ...
60034805 FOODS_3_823_WI_3_evaluation FOODS_3_823 FOODS_3 FOODS WI_3 WI d_1969 NaN 11101
60034806 FOODS_3_824_WI_3_evaluation FOODS_3_824 FOODS_3 FOODS WI_3 WI d_1969 NaN 11101
60034807 FOODS_3_825_WI_3_evaluation FOODS_3_825 FOODS_3 FOODS WI_3 WI d_1969 NaN 11101
60034808 FOODS_3_826_WI_3_evaluation FOODS_3_826 FOODS_3 FOODS WI_3 WI d_1969 NaN 11331
60034809 FOODS_3_827_WI_3_evaluation FOODS_3_827 FOODS_3 FOODS WI_3 WI d_1969 NaN 11405

60034810 rows × 9 columns

In [18]:
del instock, prices

Extract current week from Calendar data

In [19]:
sales = pd.merge(sales, calendar[['wm_yr_wk', 'd']], on=['d'], how='left')
sales
Out[19]:
id item_id dept_id cat_id store_id state_id d sales instock_week wm_yr_wk
0 HOBBIES_1_001_CA_1_evaluation HOBBIES_1_001 HOBBIES_1 HOBBIES CA_1 CA d_1 0.0 11325 11101
1 HOBBIES_1_002_CA_1_evaluation HOBBIES_1_002 HOBBIES_1 HOBBIES CA_1 CA d_1 0.0 11121 11101
2 HOBBIES_1_003_CA_1_evaluation HOBBIES_1_003 HOBBIES_1 HOBBIES CA_1 CA d_1 0.0 11401 11101
3 HOBBIES_1_004_CA_1_evaluation HOBBIES_1_004 HOBBIES_1 HOBBIES CA_1 CA d_1 0.0 11106 11101
4 HOBBIES_1_005_CA_1_evaluation HOBBIES_1_005 HOBBIES_1 HOBBIES CA_1 CA d_1 0.0 11117 11101
... ... ... ... ... ... ... ... ... ... ...
60034805 FOODS_3_823_WI_3_evaluation FOODS_3_823 FOODS_3 FOODS WI_3 WI d_1969 NaN 11101 11621
60034806 FOODS_3_824_WI_3_evaluation FOODS_3_824 FOODS_3 FOODS WI_3 WI d_1969 NaN 11101 11621
60034807 FOODS_3_825_WI_3_evaluation FOODS_3_825 FOODS_3 FOODS WI_3 WI d_1969 NaN 11101 11621
60034808 FOODS_3_826_WI_3_evaluation FOODS_3_826 FOODS_3 FOODS WI_3 WI d_1969 NaN 11331 11621
60034809 FOODS_3_827_WI_3_evaluation FOODS_3_827 FOODS_3 FOODS WI_3 WI d_1969 NaN 11405 11621

60034810 rows × 10 columns

In [20]:
del calendar

Remove the leading 0-Sales entries

In [21]:
sales = sales.query("wm_yr_wk >= instock_week").reset_index(drop=True)
sales['instock_week'] = (sales['instock_week'] - sales['instock_week'].min()).astype(np.int16)  # minimize values to reduce memory

Convert IDs to Pandas Category for later use in lightgbm

Also reduce memory usage.

In [22]:
for col in id_cols:
    sales[col] = sales[col].astype('category')

Save flattened Sales data

In [23]:
reduce_mem_usage(sales)
sales.to_pickle(SALES_DATA_FILE)
del sales
Mem. usage decreased to 1002.86 Mb (35.3% reduction)

Preprocess Weekly Price Data

In [24]:
prices = pd.read_csv(PRICES_CSV)
calendar = pd.read_csv(CALENDAR_CSV)
sales = pd.read_pickle(SALES_DATA_FILE)

Add summary statistical features to price series per store / item

In [25]:
for feat in ['max', 'min', 'mean', 'std']:
    prices[f'price_{feat}'] = prices.groupby(['store_id', 'item_id'])['sell_price'].transform(feat)
prices['price_norm'] = prices['sell_price'] / prices['price_max']

prices['price_unique'] = prices.groupby(['store_id', 'item_id'])['sell_price'].transform('nunique')
prices['item_unique'] = prices.groupby(['store_id', 'sell_price'])['item_id'].transform('nunique')

Add momentum features

In [26]:
# Add month/year columns from Calendar data for temporary use
prices = pd.merge(prices, calendar[['wm_yr_wk', 'month', 'year']].drop_duplicates(subset=['wm_yr_wk']), on=['wm_yr_wk'], how='left')

# Add momentum columns
prices['price_growth'] = prices['sell_price'] / prices.groupby(['store_id', 'item_id'])['sell_price'].transform(lambda x: x.shift(1))
prices['price_deviation_month'] = prices['sell_price'] / prices.groupby(['store_id', 'item_id', 'month'])['sell_price'].transform('mean')
prices['price_deviation_year'] = prices['sell_price'] / prices.groupby(['store_id', 'item_id', 'year'])['sell_price'].transform('mean')

# Drop month/year columns
prices = prices.drop(['month', 'year'], axis=1)
prices
Out[26]:
store_id item_id wm_yr_wk sell_price price_max price_min price_mean price_std price_norm price_unique item_unique price_growth price_deviation_month price_deviation_year
0 CA_1 HOBBIES_1_001 11325 9.58 9.58 8.26 8.285714 0.152139 1.000000 3 3 NaN 1.127059 1.145166
1 CA_1 HOBBIES_1_001 11326 9.58 9.58 8.26 8.285714 0.152139 1.000000 3 3 1.000000 1.127059 1.145166
2 CA_1 HOBBIES_1_001 11327 8.26 9.58 8.26 8.285714 0.152139 0.862213 3 5 0.862213 0.971765 0.987377
3 CA_1 HOBBIES_1_001 11328 8.26 9.58 8.26 8.285714 0.152139 0.862213 3 5 1.000000 1.000000 0.987377
4 CA_1 HOBBIES_1_001 11329 8.26 9.58 8.26 8.285714 0.152139 0.862213 3 5 1.000000 1.000000 0.987377
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
6841116 WI_3 FOODS_3_827 11617 1.00 1.00 1.00 1.000000 0.000000 1.000000 1 142 1.000000 1.000000 1.000000
6841117 WI_3 FOODS_3_827 11618 1.00 1.00 1.00 1.000000 0.000000 1.000000 1 142 1.000000 1.000000 1.000000
6841118 WI_3 FOODS_3_827 11619 1.00 1.00 1.00 1.000000 0.000000 1.000000 1 142 1.000000 1.000000 1.000000
6841119 WI_3 FOODS_3_827 11620 1.00 1.00 1.00 1.000000 0.000000 1.000000 1 142 1.000000 1.000000 1.000000
6841120 WI_3 FOODS_3_827 11621 1.00 1.00 1.00 1.000000 0.000000 1.000000 1 142 1.000000 1.000000 1.000000

6841121 rows × 14 columns

Re-index weekly Prices to flattend Sales index (id and d)

In [27]:
on_cols = ['store_id', 'item_id', 'wm_yr_wk']
prices = pd.merge(sales[FLAT_INDEX_COLS + on_cols], prices, on=on_cols, how='left').drop(on_cols, axis=1)
prices
Out[27]:
id d sell_price price_max price_min price_mean price_std price_norm price_unique item_unique price_growth price_deviation_month price_deviation_year
0 HOBBIES_1_008_CA_1_evaluation d_1 0.46 0.50 0.42 0.476312 0.019764 0.920000 4 16 NaN 0.968864 0.949452
1 HOBBIES_1_009_CA_1_evaluation d_1 1.56 1.77 1.56 1.764787 0.032731 0.881356 2 9 NaN 0.885926 0.896552
2 HOBBIES_1_010_CA_1_evaluation d_1 3.17 3.17 2.97 2.981348 0.046350 1.000000 2 20 NaN 1.064224 1.044376
3 HOBBIES_1_012_CA_1_evaluation d_1 5.98 6.52 5.98 6.469504 0.115991 0.917178 3 71 NaN 0.922035 0.959180
4 HOBBIES_1_015_CA_1_evaluation d_1 0.70 0.72 0.68 0.706596 0.011339 0.972222 3 16 NaN 0.990160 1.001752
... ... ... ... ... ... ... ... ... ... ... ... ... ...
47735392 FOODS_3_823_WI_3_evaluation d_1969 2.98 2.98 2.48 2.801560 0.171600 1.000000 5 206 1.0 1.031856 1.022790
47735393 FOODS_3_824_WI_3_evaluation d_1969 2.48 2.68 2.00 2.507979 0.253165 0.925373 4 135 1.0 0.986005 1.111908
47735394 FOODS_3_825_WI_3_evaluation d_1969 3.98 4.38 3.98 4.115957 0.188591 0.908676 3 150 1.0 0.957652 1.000000
47735395 FOODS_3_826_WI_3_evaluation d_1969 1.28 1.28 1.28 1.280000 0.000000 1.000000 1 44 1.0 1.000000 1.000000
47735396 FOODS_3_827_WI_3_evaluation d_1969 1.00 1.00 1.00 1.000000 0.000000 1.000000 1 142 1.0 1.000000 1.000000

47735397 rows × 13 columns

Save preprocessed Prices data

In [28]:
reduce_mem_usage(prices)
prices.to_pickle(PRICES_DATA_FILE)
del prices, sales
Mem. usage decreased to 1776.68 Mb (63.2% reduction)

Preprocess Calendar data

Re-Index Calendar to the flattened Sales index (id and d)

In [29]:
prices = pd.read_csv(CALENDAR_CSV)
sales = pd.read_pickle(SALES_DATA_FILE)
In [30]:
cal_index_cols = ["date", "d"]
cal_event_cols = [
    "event_name_1",
    "event_type_1",
    "event_name_2",
    "event_type_2",
    "snap_CA",
    "snap_TX",
    "snap_WI",
]

calendar = pd.merge(sales[FLAT_INDEX_COLS], calendar[cal_index_cols+cal_event_cols], on=['d'], how='left')

Convert event column types to category to save memory

In [31]:
for col in cal_event_cols:
    calendar[col] = calendar[col].astype('category')

Convert date column and split to more features

In [32]:
calendar['date'] = pd.to_datetime(calendar['date'])
calendar['day'] = calendar['date'].dt.day.astype(np.int8)
calendar['week'] = calendar['date'].dt.isocalendar().week.astype(np.int8)
calendar['month'] = calendar['date'].dt.month.astype(np.int8)
calendar['year'] = (calendar['date'].dt.year - calendar['date'].dt.year.min()).astype(np.int8)
calendar['week_of_month'] = calendar['day'].apply(lambda day: ceil(day/7)).astype(np.int8)
calendar['weekday'] = calendar['date'].dt.dayofweek.astype(np.int8)
calendar['weekend'] = (calendar['weekday'] >= 5).astype(np.int8)

calendar.drop('date', axis=1, inplace=True)
calendar
Out[32]:
id d event_name_1 event_type_1 event_name_2 event_type_2 snap_CA snap_TX snap_WI day week month year week_of_month weekday weekend
0 HOBBIES_1_008_CA_1_evaluation d_1 NaN NaN NaN NaN 0 0 0 29 4 1 0 5 5 1
1 HOBBIES_1_009_CA_1_evaluation d_1 NaN NaN NaN NaN 0 0 0 29 4 1 0 5 5 1
2 HOBBIES_1_010_CA_1_evaluation d_1 NaN NaN NaN NaN 0 0 0 29 4 1 0 5 5 1
3 HOBBIES_1_012_CA_1_evaluation d_1 NaN NaN NaN NaN 0 0 0 29 4 1 0 5 5 1
4 HOBBIES_1_015_CA_1_evaluation d_1 NaN NaN NaN NaN 0 0 0 29 4 1 0 5 5 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
47735392 FOODS_3_823_WI_3_evaluation d_1969 NBAFinalsEnd Sporting Father's day Cultural 0 0 0 19 24 6 5 3 6 1
47735393 FOODS_3_824_WI_3_evaluation d_1969 NBAFinalsEnd Sporting Father's day Cultural 0 0 0 19 24 6 5 3 6 1
47735394 FOODS_3_825_WI_3_evaluation d_1969 NBAFinalsEnd Sporting Father's day Cultural 0 0 0 19 24 6 5 3 6 1
47735395 FOODS_3_826_WI_3_evaluation d_1969 NBAFinalsEnd Sporting Father's day Cultural 0 0 0 19 24 6 5 3 6 1
47735396 FOODS_3_827_WI_3_evaluation d_1969 NBAFinalsEnd Sporting Father's day Cultural 0 0 0 19 24 6 5 3 6 1

47735397 rows × 16 columns

Save preprocessed Calendar data

In [33]:
reduce_mem_usage(calendar)
calendar.to_pickle(CALENDAR_DATA_FILE)
del calendar, sales
Mem. usage decreased to 1458.01 Mb (0.0% reduction)

Additional Cleaning

In [34]:
sales = pd.read_pickle(SALES_DATA_FILE)

# Convert 'd' column to int
sales['d'] = sales['d'].apply(lambda x: x[2:]).astype(np.int16)

# Drop wm_yr_wk
sales.drop('wm_yr_wk', axis=1, inplace=True)


sales
sales.info()

sales.to_pickle(SALES_DATA_FILE)
Out[34]:
id item_id dept_id cat_id store_id state_id d sales instock_week
0 HOBBIES_1_008_CA_1_evaluation HOBBIES_1_008 HOBBIES_1 HOBBIES CA_1 CA 1 12.0 0
1 HOBBIES_1_009_CA_1_evaluation HOBBIES_1_009 HOBBIES_1 HOBBIES CA_1 CA 1 2.0 0
2 HOBBIES_1_010_CA_1_evaluation HOBBIES_1_010 HOBBIES_1 HOBBIES CA_1 CA 1 0.0 0
3 HOBBIES_1_012_CA_1_evaluation HOBBIES_1_012 HOBBIES_1 HOBBIES CA_1 CA 1 0.0 0
4 HOBBIES_1_015_CA_1_evaluation HOBBIES_1_015 HOBBIES_1 HOBBIES CA_1 CA 1 4.0 0
... ... ... ... ... ... ... ... ... ...
47735392 FOODS_3_823_WI_3_evaluation FOODS_3_823 FOODS_3 FOODS WI_3 WI 1969 NaN 0
47735393 FOODS_3_824_WI_3_evaluation FOODS_3_824 FOODS_3 FOODS WI_3 WI 1969 NaN 0
47735394 FOODS_3_825_WI_3_evaluation FOODS_3_825 FOODS_3 FOODS WI_3 WI 1969 NaN 0
47735395 FOODS_3_826_WI_3_evaluation FOODS_3_826 FOODS_3 FOODS WI_3 WI 1969 NaN 230
47735396 FOODS_3_827_WI_3_evaluation FOODS_3_827 FOODS_3 FOODS WI_3 WI 1969 NaN 304

47735397 rows × 9 columns

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 47735397 entries, 0 to 47735396
Data columns (total 9 columns):
 #   Column        Dtype   
---  ------        -----   
 0   id            category
 1   item_id       category
 2   dept_id       category
 3   cat_id        category
 4   store_id      category
 5   state_id      category
 6   d             int16   
 7   sales         float16 
 8   instock_week  int16   
dtypes: category(6), float16(1), int16(2)
memory usage: 638.7 MB

Create lags and rolling mean / standard deviation

In [35]:
sales = pd.read_pickle(SALES_DATA_FILE)
In [36]:
sales_lags_rolling = sales[FLAT_INDEX_COLS + [TARGET_COL]].copy()
del sales
In [37]:
# Lags from 28 days to 28 + 15 days
n_lag_days = 15
sales_lags_rolling = sales_lags_rolling.assign(**{
        f'{TARGET_COL}_lag_{lag_i}': sales_lags_rolling.groupby(['id'])[TARGET_COL].transform(lambda x: x.shift(lag_i)).astype(np.float16)
        for lag_i in range(SHIFT_DAY, SHIFT_DAY + n_lag_days)
    })
In [38]:
# Rollings on 28-day shift with different window lengths
win_lens = [7, 14, 30, 60, 180]
for window in win_lens:
    sales_lags_rolling['rolling_mean_'+str(window)] = sales_lags_rolling.groupby(['id'])[TARGET_COL].transform(lambda x: x.shift(SHIFT_DAY).rolling(window).mean()).astype(np.float16)
    sales_lags_rolling['rolling_std_'+str(window)]  = sales_lags_rolling.groupby(['id'])[TARGET_COL].transform(lambda x: x.shift(SHIFT_DAY).rolling(window).std()).astype(np.float16)
In [39]:
# Rollings on sliding shifts with different window lengths
shift_days = [1, 7, 14]
win_lens = [7, 14, 30, 60]
for shift_d in shift_days:
    for window in win_lens:
        col = f"rolling_mean_shift{shift_d}_win{window}"
        sales_lags_rolling[col] = sales_lags_rolling.groupby(['id'])[TARGET_COL].transform(lambda x: x.shift(shift_d).rolling(window).mean()).astype(np.float16)
In [40]:
reduce_mem_usage(sales_lags_rolling)
sales_lags_rolling.to_pickle(SALES_LAG_ROLL_STATS_FILE)
del sales_lags_rolling
Mem. usage decreased to 3643.16 Mb (0.0% reduction)

Create mean / std for aggregation level 2-12 (Guide Table 1)

In [41]:
sales = pd.read_pickle(SALES_DATA_FILE)
In [42]:
# Empty result dataframe
sales_agg_stats = sales[FLAT_INDEX_COLS].copy()

# remove validation labels
val_index = sales.query(f"d > {1941 - 28}").index
sales.loc[val_index, TARGET_COL] = np.nan

# Level columns according to Table 1 in the Guide document
level_cols = [
    ["state_id"],
    ["store_id"],
    ["cat_id"],
    ["dept_id"],
    ["state_id", "cat_id"],
    ["state_id", "dept_id"],
    ["store_id", "cat_id"],
    ["store_id", "dept_id"],
    ["item_id"],
    ["item_id", "state_id"],
    ["item_id", "store_id"],
]

# Create mean / std of sales for each level
for level_i, level_col in enumerate(level_cols, start=2):
    col = f"level{level_i}_" + "_".join(level_col)
    sales_agg_stats[col + '_mean'] = sales.groupby(level_col)["sales"].transform("mean").astype(np.float16)
    sales_agg_stats[col + '_std'] = sales.groupby(level_col)["sales"].transform("std").astype(np.float16)
In [43]:
reduce_mem_usage(sales_agg_stats)
sales_agg_stats.to_pickle(SALES_AGG_STATS_FILE)
del sales_agg_stats, sales
Mem. usage decreased to 2185.39 Mb (0.0% reduction)

Combine Sales, Prices and Calendar

In [44]:
sales = pd.read_pickle(SALES_DATA_FILE)
prices = pd.read_pickle(PRICES_DATA_FILE)
calendar = pd.read_pickle(CALENDAR_DATA_FILE)
In [45]:
sales.columns
prices.columns
calendar.columns
Out[45]:
Index(['id', 'item_id', 'dept_id', 'cat_id', 'store_id', 'state_id', 'd',
       'sales', 'instock_week'],
      dtype='object')
Out[45]:
Index(['id', 'd', 'sell_price', 'price_max', 'price_min', 'price_mean',
       'price_std', 'price_norm', 'price_unique', 'item_unique',
       'price_growth', 'price_deviation_month', 'price_deviation_year'],
      dtype='object')
Out[45]:
Index(['id', 'd', 'event_name_1', 'event_type_1', 'event_name_2',
       'event_type_2', 'snap_CA', 'snap_TX', 'snap_WI', 'day', 'week', 'month',
       'year', 'week_of_month', 'weekday', 'weekend'],
      dtype='object')