1 2 0 3 		best_performance: 0.6025	0.8309		0.6062	0.8321
1 3 0 2 		best_performance: 0.6185	0.8437		0.6186	0.8432
3 2 1 0 		best_performance: 0.6077	0.8360		0.6094	0.8376
0 2 1 3 		best_performance: 0.5944	0.8316		0.5967	0.8317
0 1 2 3 		best_performance: 0.3378	0.5128		0.3381	0.5150
0 2 3 1 		best_performance: 0.6055	0.8351		0.6115	0.8376
3 1 2 0 		best_performance: 0.6012	0.8333		0.5997	0.8309
2 0 3 1 		best_performance: 0.6155	0.8401		0.6168	0.8382
3 2 1 0 		best_performance: 0.6043	0.8343		0.6072	0.8356
0 1 2 3 		best_performance: 0.3374	0.5135		0.3410	0.5168
0 2 3 1 		best_performance: 0.6057	0.8351		0.6065	0.8371
0 2 1 3 		best_performance: 0.6022	0.8344		0.6024	0.8330
3 2 1 0 		best_performance: 0.6100	0.8386		0.6116	0.8372
2 3 1 0 		best_performance: 0.6155	0.8374		0.6176	0.8401
1 2 3 0 		best_performance: 0.6131	0.8393		0.6137	0.8374
2 1 0 3 		best_performance: 0.5981	0.8331		0.6028	0.8356
3 2 1 0 		best_performance: 0.6041	0.8373		0.6053	0.8385
1 3 2 0 		best_performance: 0.6103	0.8363		0.6117	0.8352
0 1 2 3 		best_performance: 0.3376	0.5144		0.3371	0.5134
1 2 3 0 0 2 1 1 0 1 0 -1 		best_performance: 0.6221	0.8392		0.6241	0.8384
3 2 1 0 0 1 3 1 0 3 1 -1 		best_performance: 0.6047	0.8350		0.6069	0.8368
1 2 3 0 2 3 2 -1 1 2 1 -1 		best_performance: 0.6125	0.8329		0.6183	0.8347
3 2 1 0 2 0 0 1 1 2 3 -1 		best_performance: 0.6085	0.8369		0.6100	0.8354
3 2 1 0 3 1 1 -1 0 2 0 1 		best_performance: 0.6107	0.8375		0.6135	0.8390
3 2 1 0 3 0 2 1 0 3 2 1 		best_performance: 0.6079	0.8341		0.6132	0.8361
3 2 1 0 3 3 3 1 3 0 1 1 		best_performance: 0.6051	0.8358		0.6076	0.8358
3 2 1 0 1 1 1 -1 2 3 2 -1 		best_performance: 0.6121	0.8399		0.6128	0.8433
1 2 3 0 3 0 3 -1 3 1 0 -1 		best_performance: 0.6126	0.8390		0.6150	0.8389
1 2 3 0 2 3 2 -1 2 1 0 1 		best_performance: 0.6178	0.8372		0.6217	0.8371
3 2 1 0 1 1 3 1 0 2 3 -1 		best_performance: 0.6116	0.8397		0.6159	0.8422
1 2 3 0 2 1 1 -1 3 1 0 -1 		best_performance: 0.6139	0.8385		0.6144	0.8402
1 3 2 0 1 0 2 -1 2 3 1 -1 		best_performance: 0.6035	0.8337		0.6077	0.8359
1 2 3 0 0 0 2 -1 3 1 0 -1 		best_performance: 0.6150	0.8388		0.6180	0.8405
1 2 3 0 0 2 1 -1 1 1 1 1 		best_performance: 0.6167	0.8376		0.6168	0.8381
1 2 3 0 1 0 2 1 2 3 3 1 		best_performance: 0.6179	0.8376		0.6192	0.8368
1 2 3 0 0 3 3 1 2 1 0 1 		best_performance: 0.6098	0.8363		0.6118	0.8371
1 2 3 0 0 1 0 -1 0 0 3 -1 		best_performance: 0.6180	0.8412		0.6196	0.8430
1 2 3 0 0 0 3 -1 1 1 0 1 		best_performance: 0.6148	0.8396		0.6164	0.8419
1 2 3 0 2 3 2 -1 2 1 1 1 		best_performance: 0.6207	0.8400		0.6227	0.8407
1 2 3 0 1 0 2 1 0 1 1 1 		best_performance: 0.6120	0.8410		0.6133	0.8413
3 2 1 0 3 1 3 1 2 2 0 1 		best_performance: 0.6115	0.8361		0.6138	0.8373
1 2 3 0 2 1 1 1 1 0 2 1 		best_performance: 0.6147	0.8410		0.6173	0.8418
1 2 3 0 1 2 2 -1 1 1 1 1 		best_performance: 0.6096	0.8343		0.6120	0.8331
1 2 3 0 1 3 2 -1 1 3 1 -1 		best_performance: 0.6219	0.8413		0.6240	0.8434
1 3 2 0 2 2 0 1 1 1 0 -1 		best_performance: 0.6087	0.8362		0.6101	0.8368
1 2 3 0 3 2 0 1 1 1 1 -1 		best_performance: 0.6125	0.8365		0.6159	0.8385
1 3 2 0 2 2 1 1 0 2 0 -1 		best_performance: 0.6110	0.8388		0.6170	0.8413
1 2 3 0 3 0 2 1 2 3 3 -1 		best_performance: 0.6076	0.8374		0.6079	0.8366
1 2 3 0 2 3 3 1 3 1 1 -1 		best_performance: 0.6107	0.8431		0.6143	0.8404
1 2 3 0 2 2 1 1 0 0 3 1 		best_performance: 0.6111	0.8377		0.6125	0.8393
1 3 2 0 2 3 2 -1 3 3 1 1 		best_performance: 0.6119	0.8366		0.6147	0.8414
1 2 3 0 3 1 1 -1 0 2 0 -1 		best_performance: 0.6093	0.8379		0.6140	0.8401
1 2 3 0 0 2 2 1 2 0 3 1 		best_performance: 0.6156	0.8416		0.6142	0.8421
1 2 3 0 2 3 3 1 2 0 0 1 		best_performance: 0.6060	0.8348		0.6097	0.8364
1 2 3 0 3 0 2 -1 2 3 1 -1 		best_performance: 0.6165	0.8391		0.6191	0.8410
1 2 3 0 3 1 1 -1 2 1 0 -1 		best_performance: 0.6097	0.8409		0.6128	0.8413
1 2 3 0 0 3 3 1 0 3 1 -1 		best_performance: 0.6138	0.8400		0.6176	0.8423
1 2 3 0 1 1 3 1 1 3 1 -1 		best_performance: 0.6195	0.8398		0.6229	0.8417
1 2 3 0 1 3 2 -1 2 3 3 1 		best_performance: 0.6106	0.8398		0.6114	0.8385
1 2 3 0 0 1 3 -1 0 2 2 -1 		best_performance: 0.6060	0.8364		0.6107	0.8381
1 2 3 0 2 0 3 1 0 0 0 1 		best_performance: 0.6137	0.8384		0.6159	0.8386
1 3 2 0 2 2 3 1 3 1 1 1 		best_performance: 0.6073	0.8370		0.6110	0.8405
3 2 1 0 3 3 1 1 2 3 2 -1 		best_performance: 0.6160	0.8406		0.6153	0.8409
1 2 3 0 0 1 3 -1 3 2 1 -1 		best_performance: 0.6167	0.8412		0.6206	0.8420
1 3 2 0 3 3 1 -1 2 2 3 -1 		best_performance: 0.6072	0.8379		0.6095	0.8378
3 2 1 0 2 0 1 -1 3 2 3 1 		best_performance: 0.6114	0.8388		0.6144	0.8401
3 2 1 0 0 3 1 1 2 2 0 -1 		best_performance: 0.6107	0.8374		0.6146	0.8391
1 2 3 0 1 3 3 1 1 2 2 -1 		best_performance: 0.6116	0.8366		0.6123	0.8348
1 2 3 0 0 3 3 -1 0 3 2 1 		best_performance: 0.6146	0.8395		0.6164	0.8410
1 2 3 0 0 1 3 -1 2 2 2 1 		best_performance: 0.6183	0.8378		0.6215	0.8379
1 2 3 0 3 2 2 -1 3 0 2 1 		best_performance: 0.6150	0.8376		0.6174	0.8383
3 2 1 0 1 3 1 -1 0 1 0 1 		best_performance: 0.6099	0.8373		0.6134	0.8372
1 3 2 0 2 0 2 -1 0 3 1 -1 		best_performance: 0.6150	0.8385		0.6174	0.8384
1 2 3 0 0 2 1 -1 0 1 3 1 		best_performance: 0.6156	0.8411		0.6173	0.8425
3 2 1 0 2 0 1 1 3 0 2 -1 		best_performance: 0.6150	0.8352		0.6165	0.8353
1 3 2 0 2 2 0 1 3 2 3 1 		best_performance: 0.6108	0.8384		0.6147	0.8395
1 3 2 0 1 2 3 1 2 2 0 1 		best_performance: 0.6167	0.8421		0.6197	0.8431
1 2 3 0 1 0 2 1 3 3 2 1 		best_performance: 0.6130	0.8377		0.6145	0.8412
1 2 3 0 3 2 1 -1 1 0 2 1 		best_performance: 0.6169	0.8416		0.6187	0.8401
0 3 2 1 		best_performance: 0.0771	0.1290		0.0767	0.1307
2 3 1 0 		best_performance: 0.1348	0.1899		0.1334	0.1899
3 0 2 1 		best_performance: 0.0918	0.1637		0.0939	0.1691
2 3 0 1 		best_performance: 0.1325	0.1850		0.1302	0.1849
0 3 1 2 		best_performance: 0.0892	0.1568		0.0887	0.1560
0 1 2 3 		best_performance: 0.0672	0.1245		0.0646	0.1200
0 3 2 1 		best_performance: 0.0233	0.0369		0.0232	0.0369
2 0 3 1 		best_performance: 0.0323	0.0466		0.0322	0.0468
0 1 3 2 		best_performance: 0.0418	0.0554		0.0424	0.0558
0 3 1 2 		best_performance: 0.0336	0.0468		0.0335	0.0461
2 3 0 1 		best_performance: 0.0051	0.0063		0.0050	0.0066
3 1 0 2 		best_performance: 0.0049	0.0064		0.0052	0.0067
0 2 1 3 		best_performance: 0.0050	0.0062		0.0053	0.0072
2 1 0 3 		best_performance: $valid mrr:0.3102, H@1:0.1929, H@10:0.5599		$test mrr:0.3933, H@1:0.2897, H@10:0.5995
3 2 0 1 		best_performance: $valid mrr:0.3258, H@1:0.2059, H@10:0.5872		$test mrr:0.4061, H@1:0.3022, H@10:0.6115
0 2 3 1 		best_performance: $valid mrr:0.3765, H@1:0.2422, H@10:0.6710		$test mrr:0.4946, H@1:0.3721, H@10:0.7444
2 3 1 0 		best_performance: $valid mrr:0.3761, H@1:0.2431, H@10:0.6698		$test mrr:0.4916, H@1:0.3706, H@10:0.7399
2 3 0 1 		best_performance: $valid mrr:0.3750, H@1:0.2404, H@10:0.6733		$test mrr:0.4936, H@1:0.3705, H@10:0.7474
0 1 3 2 		best_performance: $valid mrr:0.3758, H@1:0.2401, H@10:0.6716		$test mrr:0.4910, H@1:0.3675, H@10:0.7422
0 1 2 3 		best_performance: $valid mrr:0.2544, H@1:0.1554, H@10:0.4463		$test mrr:0.3656, H@1:0.2543, H@10:0.5802
1 3 2 0 		best_performance: $valid mrr:0.0071, H@1:0.0015, H@10:0.0108		$test mrr:0.0084, H@1:0.0021, H@10:0.0128
3 2 1 0 		best_performance: $valid mrr:0.0100, H@1:0.0022, H@10:0.0163		$test mrr:0.0107, H@1:0.0028, H@10:0.0182
0 2 1 3 		best_performance: $valid mrr:0.0067, H@1:0.0015, H@10:0.0100		$test mrr:0.0079, H@1:0.0019, H@10:0.0123
2 0 3 1 		best_performance: $valid mrr:0.0082, H@1:0.0017, H@10:0.0142		$test mrr:0.0075, H@1:0.0017, H@10:0.0111
0 1 2 3 		best_performance: $valid mrr:0.0067, H@1:0.0014, H@10:0.0099		$test mrr:0.0083, H@1:0.0020, H@10:0.0132
0.011500 0.003200 0.019600 0.013300 0.004100 0.023800
[np.int64(2), np.int64(3), np.int64(0), np.int64(1)]	$valid mrr:0.0068, H@1:0.0017, H@10:0.0105		$test mrr:0.0062, H@1:0.0014, H@10:0.0096
[np.int64(0), np.int64(1), np.int64(2), np.int64(3)]	$valid mrr:0.0056, H@1:0.0012, H@10:0.0074		$test mrr:0.0057, H@1:0.0013, H@10:0.0080
[np.int64(3), np.int64(0), np.int64(1), np.int64(2)]	$valid mrr:0.0061, H@1:0.0015, H@10:0.0077		$test mrr:0.0059, H@1:0.0013, H@10:0.0073
[np.int64(3), np.int64(1), np.int64(0), np.int64(2)]	$valid mrr:0.0050, H@1:0.0009, H@10:0.0054		$test mrr:0.0049, H@1:0.0011, H@10:0.0053
[np.int64(3), np.int64(1), np.int64(2), np.int64(0)]	$valid mrr:0.0055, H@1:0.0010, H@10:0.0070		$test mrr:0.0055, H@1:0.0010, H@10:0.0068
[np.int64(0), np.int64(3), np.int64(1), np.int64(2)]	0.005622 0.001346 0.006934 0.005444 0.001109 0.007107
[np.int64(3), np.int64(2), np.int64(1), np.int64(0)]	0.005630 0.001081 0.007709 0.005371 0.000849 0.007021
[np.int64(1), np.int64(2), np.int64(3), np.int64(0)]	0.005578 0.000938 0.008280 0.005157 0.000624 0.007784
[np.int64(0), np.int64(3), np.int64(2), np.int64(1)]	0.005395 0.000877 0.007118 0.005314 0.000953 0.006726
[np.int64(0), np.int64(1), np.int64(2), np.int64(3)]	0.005343 0.001040 0.006873 0.005070 0.000745 0.006223
[np.int64(3), np.int64(1), np.int64(2), np.int64(0)]	0.005500 0.001285 0.007342 0.005025 0.000901 0.007055
[np.int64(2), np.int64(0), np.int64(1), np.int64(3)]	0.005207 0.000979 0.006220 0.004981 0.000832 0.005773
[np.int64(1), np.int64(2), np.int64(3), np.int64(0)]	0.005213 0.001285 0.005935 0.005380 0.001352 0.006258
[np.int64(2), np.int64(3), np.int64(0), np.int64(1)]	0.006309 0.001652 0.008137 0.006214 0.001439 0.008442
[np.int64(0), np.int64(1), np.int64(2), np.int64(3)]	0.005446 0.001040 0.006873 0.005191 0.000867 0.006501
[np.int64(2), np.int64(1), np.int64(0), np.int64(3)]	0.005519 0.001509 0.006873 0.004981 0.000936 0.006327
[np.int64(1), np.int64(0), np.int64(3), np.int64(2)]	0.005759 0.000938 0.007811 0.005835 0.000971 0.008477
[np.int64(2), np.int64(0), np.int64(1), np.int64(3)]	0.004996 0.000612 0.007403 0.005392 0.000988 0.007749
[np.int64(1), np.int64(3), np.int64(0), np.int64(2)]	0.005718 0.001550 0.006241 0.005354 0.001196 0.006137
[np.int64(0), np.int64(1), np.int64(2), np.int64(3)]	0.005423 0.000857 0.006852 0.005251 0.000763 0.006622
[np.int64(1), np.int64(2), np.int64(0), np.int64(3)]	0.007089 0.001733 0.010992 0.021511 0.015948 0.025413
[np.int64(3), np.int64(1), np.int64(2), np.int64(0)]	0.007321 0.001632 0.011196 0.026314 0.019363 0.033024
[np.int64(2), np.int64(3), np.int64(0), np.int64(1)]	0.007076 0.001917 0.010890 0.022483 0.017370 0.026575
[np.int64(2), np.int64(0), np.int64(3), np.int64(1)]	0.006978 0.001999 0.009993 0.017809 0.012325 0.020993
[np.int64(0), np.int64(1), np.int64(2), np.int64(3)]	0.008919 0.002570 0.014398 0.030512 0.023992 0.036525
[np.int64(2), np.int64(1), np.int64(0), np.int64(3)]	0.007156 0.002141 0.009626 0.007133 0.002184 0.009222
[np.int64(1), np.int64(2), np.int64(3), np.int64(0)]	0.007017 0.002141 0.008443 0.007140 0.002098 0.008876
[np.int64(2), np.int64(1), np.int64(3), np.int64(0)]	0.006566 0.002162 0.007994 0.006501 0.001959 0.007680
[np.int64(2), np.int64(3), np.int64(0), np.int64(1)]	0.009517 0.003671 0.013460 0.009505 0.003623 0.012724
[np.int64(0), np.int64(1), np.int64(2), np.int64(3)]	0.007110 0.002325 0.008667 0.007017 0.002219 0.008702
[np.int64(3), np.int64(1), np.int64(0), np.int64(2)]	0.006760 0.002508 0.006730 0.006640 0.002340 0.006553
[np.int64(0), np.int64(2), np.int64(1), np.int64(3)]	0.007888 0.002753 0.011441 0.007567 0.002358 0.010661
[np.int64(1), np.int64(0), np.int64(3), np.int64(2)]	0.008311 0.002733 0.010319 0.008623 0.002878 0.011667
[np.int64(2), np.int64(3), np.int64(0), np.int64(1)]	0.006649 0.002305 0.007505 0.006654 0.002271 0.007454
[np.int64(2), np.int64(0), np.int64(1), np.int64(3)]	0.006721 0.002223 0.007056 0.006512 0.001959 0.006726
[np.int64(1), np.int64(0), np.int64(2), np.int64(3)]	0.006843 0.001774 0.010564 0.006920 0.001907 0.010314
[np.int64(2), np.int64(0), np.int64(3), np.int64(1)]	0.007621 0.002366 0.010646 0.007520 0.002340 0.010661
[np.int64(0), np.int64(1), np.int64(2), np.int64(3)]	0.007035 0.001958 0.008912 0.006965 0.001890 0.007992
[np.int64(2), np.int64(0), np.int64(1), np.int64(3)]	0.147818 0.098870 0.248216 0.147899 0.099001 0.248674
[np.int64(0), np.int64(2), np.int64(1), np.int64(3)]	0.155176 0.105131 0.259330 0.155688 0.106005 0.260046
[np.int64(3), np.int64(2), np.int64(0), np.int64(1)]	0.137793 0.089020 0.235082 0.140027 0.091859 0.238931
[np.int64(2), np.int64(3), np.int64(0), np.int64(1)]	0.153250 0.102561 0.257780 0.154782 0.104757 0.257636
[np.int64(0), np.int64(1), np.int64(2), np.int64(3)]	0.157794 0.108149 0.261839 0.159797 0.109957 0.262629
[np.int64(1), np.int64(2), np.int64(0), np.int64(3)]	0.170443 0.116572 0.283558 0.173278 0.120376 0.283067
[np.int64(1), np.int64(3), np.int64(0), np.int64(2)]	0.166789 0.112085 0.279276 0.170375 0.116215 0.282114
[np.int64(2), np.int64(3), np.int64(0), np.int64(1)]	0.170997 0.115063 0.288392 0.173407 0.117845 0.287886
[np.int64(3), np.int64(0), np.int64(1), np.int64(2)]	0.155808 0.100950 0.268161 0.155808 0.101584 0.268211
[np.int64(1), np.int64(3), np.int64(0), np.int64(2)]	0.145911 0.093833 0.254354 0.146694 0.094997 0.254741
[np.int64(1), np.int64(3), np.int64(0), np.int64(2)]	0.151457 0.099400 0.262206 0.153363 0.101168 0.263270
[np.int64(2), np.int64(1), np.int64(0), np.int64(3)]	0.163855 0.108313 0.280805 0.166077 0.111067 0.281749
[np.int64(0), np.int64(3), np.int64(1), np.int64(2)]	0.156085 0.101399 0.272505 0.157652 0.103717 0.271834
[np.int64(2), np.int64(3), np.int64(0), np.int64(1)]	0.168598 0.113187 0.284272 0.170401 0.115973 0.283622
[np.int64(1), np.int64(2), np.int64(3), np.int64(0)]	0.155146 0.101073 0.268100 0.155733 0.102052 0.266754
[np.int64(0), np.int64(1), np.int64(2), np.int64(3)]	0.163154 0.108292 0.278949 0.165475 0.111379 0.280484
[np.int64(0), np.int64(3), np.int64(2), np.int64(1)]	0.165723 0.110699 0.279520 0.167720 0.112939 0.281403
[np.int64(1), np.int64(2), np.int64(3), np.int64(0)]	0.156117 0.101888 0.265795 0.157178 0.103214 0.267569
[np.int64(2), np.int64(1), np.int64(3), np.int64(0)]	0.155446 0.098748 0.271587 0.157522 0.102573 0.271556
[np.int64(0), np.int64(1), np.int64(3), np.int64(2)]	0.166298 0.110801 0.281968 0.166388 0.111188 0.280363
[np.int64(3), np.int64(2), np.int64(1), np.int64(0)]	0.164748 0.108802 0.281050 0.167478 0.112731 0.282322
[np.int64(0), np.int64(1), np.int64(2), np.int64(3)]	0.163646 0.108292 0.276624 0.162972 0.108189 0.275942
[np.int64(2), np.int64(1), np.int64(3), np.int64(0)]	0.205416 0.132500 0.358669 0.207450 0.133984 0.359671
[np.int64(3), np.int64(1), np.int64(2), np.int64(0)]	0.216037 0.139454 0.376861 0.219850 0.145321 0.376885
[np.int64(2), np.int64(0), np.int64(3), np.int64(1)]	0.205351 0.133418 0.358160 0.208785 0.137676 0.359671
[np.int64(2), np.int64(3), np.int64(0), np.int64(1)]	0.214499 0.138700 0.374740 0.216828 0.141317 0.374146
[np.int64(0), np.int64(1), np.int64(2), np.int64(3)]	0.197996 0.124873 0.346066 0.200492 0.128090 0.347571
[np.int64(2), np.int64(3), np.int64(0), np.int64(1)]	0.202634 0.130134 0.352266 0.204038 0.131245 0.352044
[np.int64(0), np.int64(3), np.int64(2), np.int64(1)]	0.201145 0.127238 0.355794 0.202809 0.129494 0.354887
[np.int64(2), np.int64(1), np.int64(3), np.int64(0)]	0.008747 0.002447 0.013277 0.008862 0.002826 0.012620
[np.int64(1), np.int64(3), np.int64(0), np.int64(2)]	0.007679 0.002712 0.009585 0.007081 0.002115 0.008564
[np.int64(0), np.int64(2), np.int64(1), np.int64(3)]	0.152729 0.103010 0.256230 0.153020 0.102122 0.259162
[np.int64(1), np.int64(3), np.int64(2), np.int64(0)]	0.136481 0.086532 0.238875 0.136009 0.086139 0.238741
[np.int64(2), np.int64(3), np.int64(1), np.int64(0)]	0.132871 0.084411 0.231819 0.134565 0.087162 0.233852
[np.int64(2), np.int64(3), np.int64(0), np.int64(1)]	0.150965 0.101093 0.252376 0.151643 0.102486 0.253285
[np.int64(3), np.int64(1), np.int64(2), np.int64(0)]	0.151230 0.099869 0.257413 0.151330 0.100475 0.257463
[np.int64(0), np.int64(1), np.int64(2), np.int64(3)]	0.154997 0.104682 0.259555 0.155698 0.104913 0.261051
[np.int64(0), np.int64(1), np.int64(3), np.int64(2)]	0.203664 0.130297 0.354162 0.204128 0.131262 0.352841
[np.int64(2), np.int64(3), np.int64(1), np.int64(0)]	0.132073 0.083167 0.233287 0.132939 0.084787 0.232691
[np.int64(0), np.int64(1), np.int64(2), np.int64(3)]	0.084956 0.055920 0.138027 0.085608 0.056860 0.138387
[np.int64(1), np.int64(2), np.int64(0), np.int64(3)]	0.073338 0.045744 0.123241 0.073502 0.046233 0.122716
[np.int64(1), np.int64(3), np.int64(0), np.int64(2)]	0.062154 0.031448 0.119244 0.061719 0.031758 0.118590
[np.int64(2), np.int64(3), np.int64(0), np.int64(1)]	0.064302 0.035057 0.121283 0.064257 0.034618 0.122248
[np.int64(1), np.int64(0), np.int64(2), np.int64(3)]	0.083222 0.052820 0.141881 0.082589 0.052179 0.139462
[np.int64(2), np.int64(0), np.int64(3), np.int64(1)]	0.134809 0.086124 0.233715 0.135279 0.086693 0.236071
[np.int64(2), np.int64(1), np.int64(0), np.int64(3)]	$valid mrr:0.1708, H@1:0.1148, H@10:0.2884		$test mrr:0.1721, H@1:0.1162, H@10:0.2888
[np.int64(2), np.int64(1), np.int64(3), np.int64(0)]	$valid mrr:0.0770, H@1:0.0351, H@10:0.1608		$test mrr:0.0017, H@1:0.0000, H@10:0.0005
[np.int64(3), np.int64(2), np.int64(0), np.int64(1)]	$valid mrr:0.0750, H@1:0.0351, H@10:0.1541		$test mrr:0.0019, H@1:0.0002, H@10:0.0016
[np.int64(1), np.int64(0), np.int64(3), np.int64(2)]	$valid mrr:0.0789, H@1:0.0373, H@10:0.1635		$test mrr:0.0015, H@1:0.0000, H@10:0.0009
[np.int64(2), np.int64(1), np.int64(3), np.int64(0)]	$valid mrr:0.0736, H@1:0.0333, H@10:0.1552		$test mrr:0.0014, H@1:0.0000, H@10:0.0005
[np.int64(3), np.int64(0), np.int64(1), np.int64(2)]	$valid mrr:0.0749, H@1:0.0352, H@10:0.1551		$test mrr:0.0024, H@1:0.0004, H@10:0.0024
[np.int64(1), np.int64(0), np.int64(3), np.int64(2)]	$valid mrr:0.0773, H@1:0.0369, H@10:0.1588		$test mrr:0.0014, H@1:0.0000, H@10:0.0005
[np.int64(0), np.int64(1), np.int64(3), np.int64(2)]	$valid mrr:0.0808, H@1:0.0392, H@10:0.1663		$test mrr:0.0017, H@1:0.0000, H@10:0.0015
[np.int64(1), np.int64(3), np.int64(2), np.int64(0)]	$valid mrr:0.0756, H@1:0.0344, H@10:0.1587		$test mrr:0.0016, H@1:0.0000, H@10:0.0003
[np.int64(3), np.int64(2), np.int64(0), np.int64(1)]	$valid mrr:0.0769, H@1:0.0366, H@10:0.1602		$test mrr:0.0022, H@1:0.0000, H@10:0.0027
[np.int64(1), np.int64(0), np.int64(3), np.int64(2)]	$valid mrr:0.0817, H@1:0.0391, H@10:0.1713		$test mrr:0.0028, H@1:0.0007, H@10:0.0027
[np.int64(0), np.int64(3), np.int64(2), np.int64(1)]	$valid mrr:0.0824, H@1:0.0392, H@10:0.1701		$test mrr:0.0020, H@1:0.0004, H@10:0.0012
[np.int64(0), np.int64(1), np.int64(2), np.int64(3)]	$valid mrr:0.0823, H@1:0.0391, H@10:0.1697		$test mrr:0.0019, H@1:0.0000, H@10:0.0013
[np.int64(1), np.int64(2), np.int64(0), np.int64(3)]	$valid mrr:0.0744, H@1:0.0335, H@10:0.1584		$test mrr:0.0018, H@1:0.0000, H@10:0.0019
[np.int64(1), np.int64(0), np.int64(2), np.int64(3)]	$valid mrr:0.0054, H@1:0.0006, H@10:0.0074		$test mrr:0.0054, H@1:0.0005, H@10:0.0072
[np.int64(3), np.int64(1), np.int64(2), np.int64(0)]	$valid mrr:0.0896, H@1:0.0438, H@10:0.1861		$test mrr:0.1086, H@1:0.0597, H@10:0.2103
[np.int64(3), np.int64(1), np.int64(2), np.int64(0)]	$valid mrr:0.0029, H@1:0.0000, H@10:0.0000		$test mrr:0.0029, H@1:0.0000, H@10:0.0000
[np.int64(3), np.int64(2), np.int64(0), np.int64(1)]	$valid mrr:0.5000, H@1:0.0000, H@10:1.0000		$test mrr:0.5000, H@1:0.0000, H@10:1.0000
[np.int64(1), np.int64(2), np.int64(3), np.int64(0)]	$valid mrr:0.0599, H@1:0.0269, H@10:0.1229		$test mrr:0.0671, H@1:0.0325, H@10:0.1345
[np.int64(1), np.int64(0), np.int64(3), np.int64(2)]	$valid mrr:0.0338, H@1:0.0124, H@10:0.0700		$test mrr:0.0373, H@1:0.0150, H@10:0.0757
[np.int64(0), np.int64(1), np.int64(3), np.int64(2)]	$valid mrr:0.5000, H@1:0.0000, H@10:1.0000		$test mrr:0.5000, H@1:0.0000, H@10:1.0000
