{ "id": 80069, "title": "Meta Data Guide", "url": "/competitions/quora-insincere-questions-classification/discussion/80069", "author_name": "", "post_date": "2019-02-10T07:23:47.772447300Z", "votes": 2, "comment_count": 1, "views": 0, "content": "
    \n

  1. cnn\n1.1 0,0 cnn0 cnn1 cnn2 cnn3 cnn5\n1.2 0,0--stack cnn0 cnn1 cnn2 cnn3 cnn5\n1.3 0,64 cnn0 cnn1 cnn2 cnn3 cnn5\n1.4 0,64--stack cnn0 cnn1 cnn2 cnn3 cnn5\n1.5.1 64-0 cnn1 cnn2 cnn3 cnn5 \n1.5.2 64-0-【dropout=0.2】 cnn1 cnn2 cnn3 cnn5\n1.5.3 64-0-【0.2】【spatial_dropout=0.2】 cnn1 cnn2 cnn3 cnn5\n1.5.4 64-0-【0.2】【0.2】【trainable】 cnn1 cnn2 cnn3 cnn5\n1.6 64-0--mix cnn0,1,2 cnn0,1,2,3,4 embed0,cnn0,1,2 embed0,cnn0,1,2,3,4\n1.7 320,64 cnn0 cnn1 cnn2 cnn3 cnn5\n1.8 320,64--stack cnn0 cnn1 cnn2 cnn3 cnn5

    2. \n

  2. combine\n2.1 lgb n-1-1 n-1-2 n-2-1 n-2-2 y-1-1 y-1-2 y-2-1 y-2-2\n2.2 tnn n-1-1 n-1-2 n-2-1 n-2-2 y-1-1 y-1-2 y-2-1 y-2-2

    3. \n

  3. classweight\n3.1 pure 1 1.5 1.25 1.75\n3.2 pure 2 2.5 3 3.5 4\n3.3 +nums 2 2.5 3 3.5 4

    4. \n

  4. data\n4.1.1 guess valid target(cw=1) normal&rnn0 n&rnn0,1 yes&rnn0 y&rnn0,1\n4.1.2 cw=2 y&rnn0 y&rnn0,1\n4.2 vocab_size=90000 rnn0 rnn0,1
    \n4.3.1 noise-submit normal delete replace
    \n4.3.2 noise-tune n-1-1 n-1-2 n-2-1 n-2-2 d-1-1 d-1-2 d-2-1 d-2-2 r-1-1 r-1-2 r-2-1 r-2-2
    \n4.4.1 vocab_size-submit all 95000 60000\n4.4.2 vocab_size-tune a-1-1 a-1-2 a-2-1 a-2-2 9-1-1 9-1-2 9-2-1 9-2-2 6-1-1 6-1-2 6-2-1 6-2-2

    5. \n

  5. embeds\n5.1 glove rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n5.2 glove+google rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n5.3 gl+para rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n5.4 gl+p+gg rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n5.5 gl+p+w rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n5.6 gl+p+w+gg rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n5.7 gl+w rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n5.8 gl+w+gg rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n5.9 google rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n5.10 para rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n5.11 p+gg rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n5.12 p+w rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n5.13 p+w+gg rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n5.14 wiki rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n5.15 w+gg rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01

    6. \n

  6. hidden\n6.1 64-0 false&cnn2,3 f&cnn1,2,3\n6.2 64-0-64 true&cnn2,3 t&cnn1,2,3 f&cnn2,3 f&cnn1,2,3\n6.3 64-64 t&cnn2,3 t&cnn1,2,3 f&cnn2,3 f&cnn1,2,3\n6.4 64-64-0 t&cnn2,3 t&cnn1,2,3 f&cnn2,3 f&cnn1,2,3\n6.5 64-64-64 t&cnn2,3 t&cnn1,2,3 f&cnn2,3 f&cnn1,2,3

    7. \n

  7. hidden unit\n7.1 0 rnn0 rnn0,1\n7.2 160 rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n7.3.1 64-submit n tn ty fn fy\n7.3.2 64-tune n-1-1 n-1-2 n-2-1 n-2-2 y-1-1 y-1-2 y-2-1 y-2-2 tn-1-1 tn-1-2 tn-2-1 tn-2-2 ty-1-1 ty-1-2 ty-2-1 ty-2-2 fn-1-1 fn-1-2 fn-2-1 fn-2-2 fy-1-1 fy-1-2 fy-2-1 fy-2-2\n7.4 plseu rnn0 rnn0,1 rnn0,1&tanh

    8. \n

  8. lr\n8.1 1.25e-4 rnn0 rnn0,1\n8.2 2.5 rnn0 rnn0,1\n8.3 20 rnn0 rnn0,1\n8.4 40 rnn0 rnn0,1\n8.5 5 rnn0 rnn0,1\n8.6 80 rnn0 rnn0,1

    9. \n

  9. lstm+lstm\n9.1.1 hidden_unit=0 rnn1 rnn1&sd rnn0,1 rnn0,1&sd\n9.1.2 0&attention&ll rnn1 rnn1&sd rnn0,1 rnn0,1&sd\n9.1.3 0&attention&l rnn1 rnn1&sd rnn0,1 rnn0,1&sd\n9.2.1 64&bn=True rnn1 rnn1&sd rnn0,1 rnn0,1&sd\n9.2.2 64&False rnn1 rnn1&sd rnn0,1 rnn0,1&sd\n9.3.1 256&bn=True rnn1 rnn1&sd rnn0,1 rnn0,1&sd\n9.3.2 256&False rnn1 rnn1&sd rnn0,1 rnn0,1&sd

    10. \n

  10. mix embeds\n10.1 glove gauss&tr&wvs&before gauss&tr&wvs&after mean&tr&wvs gauss&tr mean&tr the&tr gauss&rs&wvs&before gauss&rs&wvs&after mean&rs&wvs gauss&rs mean&rs the&rs\n10.2 gl+p gauss&tr&wvs&after mean&tr&wvs gauss&tr mean&tr the&tr gauss&rs&wvs&after mean&rs&wvs gauss&rs mean&rs the&rs\n10.3.1 gl+p+w+gg-submit gl gl+p all\n10.3.2 gl+p+w+gg-tune g11 g12 g21 g22 gp11 gp12 gp21 gp22 a11 a12 a21 a22\n10.4 random n11 n12 n21 n22 y11 y12 y21 y22

    11. \n

  11. num+seg+rnn+cnn\n11.1 64-0-0 True False\n11.2 160,64-0-0 tt tf ft ff\n11.3 320,64-0-0 tt tf ft ff\n11.4 nums(64) t f fm&t fm&f\n11.5 nums(320,64) tt tf ft ff fm&tt fm&tf fm&ft fm&ff

    12. \n

  12. pure cnn\n12.1 0 cnn3 cnn5\n12.2 16 t3 t5 f3 f5\n12.3 64 t3 t5 f3 f5\n12.4 128 t3 t5 f3 f5\n12.4 256 t3 t5 f3 f5

    13. \n

  13. pure gru\n13.1 0\n13.2 16 t f\n13.3 64 t f\n13.4 128 t f\n13.5 256 t f

    14. \n

  14. pure lstm\n14.1 0\n14.2 16 t f\n14.3 64 t f\n14.4 128 t f\n14.5 256 t f

    15. \n

  15. rnn cnn\n15.1 attention rnn00,rnn01 rnn00,rnn01,rnn10,rnn11\n15.2 cnn=1 rnn00,rnn01 rnn00,rnn01,rnn10,rnn11\n15.3.1 gru&lstm rnn00,rnn01 rnn00,rnn01,rnn10,rnn11\n15.3.2 lstm rnn01 rnn01,rnn11\n15.4.1 lstm&320,64 rnn0 rnn0,rnn1\n15.4.2 0,64 rnn01 rnn01,rnn11\n15.4.3 320,64-0 rnn0 rnn0,rnn1\n15.4.4 64-0 rnn01 rnn01,rnn11\n15.4.5 320,64-64 rnn01 rnn01,rnn11\n15.5.1 mixed_rnn_cnn&gru rnn00&cnn1,2,3&rnn_sd rnn00&cnn1,2,3,5&rnn_sd rnn00&cnn1,2,3&sd_rnn rnn00&cnn1,2,3,5&sd_rnn\n15.5.2 lstm rnn00&cnn1,2,3&rnn_sd rnn00&cnn1,2,3,5&rnn_sd rnn00&cnn1,2,3&sd_rnn rnn00&cnn1,2,3,5&sd_rnn\n15.6 seg_rnn_20 rnn0 rnn0,1 rnn0,1&tanh

    16. \n

  16. rnn cnn embed\n16.1 eid=0-1-2-3 rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n16.2 4-5-6-7 rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n16.3 8-10 rnn00,rnn10 rnn00,rnn01,rnn10,rnn11\n16.3 12-14 rnn00,rnn10 rnn00,rnn01,rnn10,rnn11\n16.1 16-17-18-19 rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n16.2 20-21-22-23 rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n16.3 24-26 rnn00,rnn10 rnn00,rnn01,rnn10,rnn11\n16.3 28-30 rnn00,rnn10 rnn00,rnn01,rnn10,rnn11\n16.1 32-33-34-35 rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n16.2 36-37-38-39 rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n16.3 40-42 rnn00,rnn10 rnn00,rnn01,rnn10,rnn11\n16.3 44-46 rnn00,rnn10 rnn00,rnn01,rnn10,rnn11

    17. \n

  17. rnn+cnn\n17.1 0 rnn00&cnn1,2,3&rnn_sd rnn00&cnn1,2,3,5&rnn_sd rnn00&cnn1,2,3&sd_rnn rnn00&cnn1,2,3,5&sd_rnn\n17.2.1 0-64&Ture rnn00&cnn1,2,3&rnn_sd rnn00&cnn1,2,3,5&rnn_sd rnn00&cnn1,2,3&sd_rnn rnn00&cnn1,2,3,5&sd_rnn\n17.2.2 0-64&False rnn00&cnn1,2,3&rnn_sd rnn00&cnn1,2,3,5&rnn_sd rnn00&cnn1,2,3&sd_rnn rnn00&cnn1,2,3,5&sd_rnn\n17.3.1 64&Ture rnn00&cnn1,2,3&rnn_sd rnn00&cnn1,2,3,5&rnn_sd rnn00&cnn1,2,3&sd_rnn rnn00&cnn1,2,3,5&sd_rnn\n17.3.2 64&False rnn00&cnn1,2,3&rnn_sd rnn00&cnn1,2,3,5&rnn_sd rnn00&cnn1,2,3&sd_rnn rnn00&cnn1,2,3,5&sd_rnn\n17.4.1 64-0&Ture rnn00&cnn1,2,3&rnn_sd rnn00&cnn1,2,3,5&rnn_sd rnn00&cnn1,2,3&sd_rnn rnn00&cnn1,2,3,5&sd_rnn\n17.4.2 64-0&False rnn00&cnn1,2,3&rnn_sd rnn00&cnn1,2,3,5&rnn_sd rnn00&cnn1,2,3&sd_rnn rnn00&cnn1,2,3,5&sd_rnn

    18. \n

  18. rtnn\n18.1 64-0;320,64 True False\n18.2 320,64;64-0 True False

    19. \n

  19. seg+rnn+cnn\n19.1 64 True False\n19.2.1 64-0-0&rnn1&false rnn00&cnn1,2,3&rnn_sd rnn00&cnn1,2,3,5&rnn_sd rnn00&cnn1,2,3&sd_rnn rnn00&cnn1,2,3,5&sd_rnn\n19.2.2 rnn0,1&false rnn00&cnn1,2,3&rnn_sd rnn00&cnn1,2,3,5&rnn_sd rnn00&cnn1,2,3&sd_rnn rnn00&cnn1,2,3,5&sd_rnn\n19.2.3 rnn1&true rnn00&cnn1,2,3&rnn_sd rnn00&cnn1,2,3,5&rnn_sd rnn00&cnn1,2,3&sd_rnn rnn00&cnn1,2,3,5&sd_rnn\n19.2.4 rnn0,1&true rnn00&cnn1,2,3&rnn_sd rnn00&cnn1,2,3,5&rnn_sd rnn00&cnn1,2,3&sd_rnn rnn00&cnn1,2,3,5&sd_rnn\n19.2.5 rnn0,1&cnn1,2,3&flase&sd_rnn cw=1:1 1:2&stratified 1:1&stratified\n19.3 64-0-64 tt tf ft ff\n19.4 64-64 tt tf ft ff\n19.5 64-64-0 tt tf ft ff\n19.6.1 64-64-64&true tt tf ft ff\n19.6.2 64-64-64&false tt tf ft ff\n19.7 backward forward&cnn2,3 forward&cnn1,2,3 backward&cnn2,3 backward&cnn1,2,3 bi&cnn2,3 bi&cnn1,2,3\n19.8.1 bi&forward fd&cnn2,3 fd&cnn1,2,3 bd&cnn2,3 bd&cnn1,2,3 bi&cnn2,3 bi&cnn1,2,3\n19.8.2 backward fd&cnn2,3 fd&cnn1,2,3 bd&cnn2,3 bd&cnn1,2,3 bi&cnn2,3 bi&cnn1,2,3\n19.8.3 bi fd&cnn2,3 fd&cnn1,2,3 bd&cnn2,3 bd&cnn1,2,3 bi&cnn2,3 bi&cnn1,2,3\n19.9 forward fd&cnn2,3 fd&cnn1,2,3 bd&cnn2,3 bd&cnn1,2,3 bi&cnn2,3 bi&cnn1,2,3\n19.10 best&steady best steady\n19.11 dropout tt tf ft ff

    20. \n

  20. tta\n20.1 oof_seed 11 12 21 22 31 32 41 42
    \n20.2.1 batch_size-submit 1024 512 768\n20.2.2 batch_size-tune 111 112 121 122 511 512 521 522 711 712 721 722
    \n20.3.1 digit-submit n y
    \n20.3.2 digit-tune n11 n12 n21 n22 y11 y12 y21 y22
    \n20.4.1 epoch-fixed 12-11 12-12 12-21 12-22 9-11 9-12 9-21 9-22 7-11 7-12 7-21 7-22
    \n20.4.2 epoch=20 011 012 021 022 211 212 221 222 311 312 321 322 411 412 421 422 511 512 521 522
    \n20.5.1 nums-submit n y
    \n20.5.2 nums-tune n11 n12 n21 n22 yf11 yf12 yf21 yf22 yt11 yt12 yt21 yt22
    \n20.6 pad pre&max pre&last pre&max&last post&max post&last post&max&last
    \n20.7.1 punct-submit n y56 y60
    \n20.7.2 punct-tune n11 n12 n21 n22 y11 y12 y21 y22 y&digits&11 yd12 yd21 yd22
    \n20.7.3 punct-tune-seed 1 21 22 3 4 5 6 7
    \n20.8 seg_max 14,56 7 7,56 7,14 7,14,56\n20.9.1 sug-submit n y\n20.9.2 sug-tune n11 n12 n21 n22 y11 y12 y21 y22

    21. \n

  21. rnn\n21.1.1 type&eid=0 ll11 ll12 ll21 ll22\n21.1.2 type&eid=2 ll11 ll12 ll21 ll22 lg11 lg12 lg21 lg22 gl11 gl12 gl21 gl22 gg11 gg12 gg21 gg22\n21.2.1 embed n11 n12 n21 n22 seed=1&11 112 121 122 211 212 221 222 mean&11 m12 m21 m22 the&11 t12 t21 t22 row_mean&11 rm12 rm21 rm22 it&11 i12 i21 i22 that&11 a12 a21 a22\n21.2.2 embed&torch the&111 t112 t121 t122 t211 t212 t221 t222

    22. \n
\n\n
\n\n

torch

\n\n
    \n

  1. md\n1.1.1 batch_size&eid=2 1024&11 112 121 512&11 512 521\n1.1.2 batch_size&eid=4 1024&11 112 121 122 512&11 512 521\n1.2 dropout 0.2&11 212 221 0.1&11 112 121 122\n1.3 cnn eid=3&11 312 321 322 011 012 021 022 111 112 121 122 411 412 421 422 511 512 521 522\n1.4.1 lr-fixed 1e-3&1 12 21 22 41 42 81 82 .51\n1.4.2 lr-clr n11 n12 n21 y11 y12 y21 y22\n1.5 classweight 111 112 121 122 211 212 221 222\n1.6 token n11 n12 n21 n22 with_lr_scheduler&11 w12 w21 w22 without_lr_scheduler&11 o12 o21

    2. \n

  2. rnn2\n2.1.1 type&normal gg11 gg12 gg21 ll11 ll12 ll21 ll22 gl11 gl12 gl21 lg11 lg12 lg21 lg22\n2.1.2 bi ll11 ll12 ll21 ll22 lg11 lg12 lg21 gl11 gl12 gl21 gl22 gg11 gg12 gg21

    3. \n
", "messages": [ { "id": "468976", "postDate": "02/10/2019 07:23:47", "content": "
    \n

  1. cnn\n1.1 0,0 cnn0 cnn1 cnn2 cnn3 cnn5\n1.2 0,0--stack cnn0 cnn1 cnn2 cnn3 cnn5\n1.3 0,64 cnn0 cnn1 cnn2 cnn3 cnn5\n1.4 0,64--stack cnn0 cnn1 cnn2 cnn3 cnn5\n1.5.1 64-0 cnn1 cnn2 cnn3 cnn5 \n1.5.2 64-0-【dropout=0.2】 cnn1 cnn2 cnn3 cnn5\n1.5.3 64-0-【0.2】【spatial_dropout=0.2】 cnn1 cnn2 cnn3 cnn5\n1.5.4 64-0-【0.2】【0.2】【trainable】 cnn1 cnn2 cnn3 cnn5\n1.6 64-0--mix cnn0,1,2 cnn0,1,2,3,4 embed0,cnn0,1,2 embed0,cnn0,1,2,3,4\n1.7 320,64 cnn0 cnn1 cnn2 cnn3 cnn5\n1.8 320,64--stack cnn0 cnn1 cnn2 cnn3 cnn5

    2. \n

  2. combine\n2.1 lgb n-1-1 n-1-2 n-2-1 n-2-2 y-1-1 y-1-2 y-2-1 y-2-2\n2.2 tnn n-1-1 n-1-2 n-2-1 n-2-2 y-1-1 y-1-2 y-2-1 y-2-2

    3. \n

  3. classweight\n3.1 pure 1 1.5 1.25 1.75\n3.2 pure 2 2.5 3 3.5 4\n3.3 +nums 2 2.5 3 3.5 4

    4. \n

  4. data\n4.1.1 guess valid target(cw=1) normal&rnn0 n&rnn0,1 yes&rnn0 y&rnn0,1\n4.1.2 cw=2 y&rnn0 y&rnn0,1\n4.2 vocab_size=90000 rnn0 rnn0,1
    \n4.3.1 noise-submit normal delete replace
    \n4.3.2 noise-tune n-1-1 n-1-2 n-2-1 n-2-2 d-1-1 d-1-2 d-2-1 d-2-2 r-1-1 r-1-2 r-2-1 r-2-2
    \n4.4.1 vocab_size-submit all 95000 60000\n4.4.2 vocab_size-tune a-1-1 a-1-2 a-2-1 a-2-2 9-1-1 9-1-2 9-2-1 9-2-2 6-1-1 6-1-2 6-2-1 6-2-2

    5. \n

  5. embeds\n5.1 glove rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n5.2 glove+google rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n5.3 gl+para rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n5.4 gl+p+gg rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n5.5 gl+p+w rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n5.6 gl+p+w+gg rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n5.7 gl+w rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n5.8 gl+w+gg rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n5.9 google rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n5.10 para rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n5.11 p+gg rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n5.12 p+w rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n5.13 p+w+gg rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n5.14 wiki rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n5.15 w+gg rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01

    6. \n

  6. hidden\n6.1 64-0 false&cnn2,3 f&cnn1,2,3\n6.2 64-0-64 true&cnn2,3 t&cnn1,2,3 f&cnn2,3 f&cnn1,2,3\n6.3 64-64 t&cnn2,3 t&cnn1,2,3 f&cnn2,3 f&cnn1,2,3\n6.4 64-64-0 t&cnn2,3 t&cnn1,2,3 f&cnn2,3 f&cnn1,2,3\n6.5 64-64-64 t&cnn2,3 t&cnn1,2,3 f&cnn2,3 f&cnn1,2,3

    7. \n

  7. hidden unit\n7.1 0 rnn0 rnn0,1\n7.2 160 rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n7.3.1 64-submit n tn ty fn fy\n7.3.2 64-tune n-1-1 n-1-2 n-2-1 n-2-2 y-1-1 y-1-2 y-2-1 y-2-2 tn-1-1 tn-1-2 tn-2-1 tn-2-2 ty-1-1 ty-1-2 ty-2-1 ty-2-2 fn-1-1 fn-1-2 fn-2-1 fn-2-2 fy-1-1 fy-1-2 fy-2-1 fy-2-2\n7.4 plseu rnn0 rnn0,1 rnn0,1&tanh

    8. \n

  8. lr\n8.1 1.25e-4 rnn0 rnn0,1\n8.2 2.5 rnn0 rnn0,1\n8.3 20 rnn0 rnn0,1\n8.4 40 rnn0 rnn0,1\n8.5 5 rnn0 rnn0,1\n8.6 80 rnn0 rnn0,1

    9. \n

  9. lstm+lstm\n9.1.1 hidden_unit=0 rnn1 rnn1&sd rnn0,1 rnn0,1&sd\n9.1.2 0&attention&ll rnn1 rnn1&sd rnn0,1 rnn0,1&sd\n9.1.3 0&attention&l rnn1 rnn1&sd rnn0,1 rnn0,1&sd\n9.2.1 64&bn=True rnn1 rnn1&sd rnn0,1 rnn0,1&sd\n9.2.2 64&False rnn1 rnn1&sd rnn0,1 rnn0,1&sd\n9.3.1 256&bn=True rnn1 rnn1&sd rnn0,1 rnn0,1&sd\n9.3.2 256&False rnn1 rnn1&sd rnn0,1 rnn0,1&sd

    10. \n

  10. mix embeds\n10.1 glove gauss&tr&wvs&before gauss&tr&wvs&after mean&tr&wvs gauss&tr mean&tr the&tr gauss&rs&wvs&before gauss&rs&wvs&after mean&rs&wvs gauss&rs mean&rs the&rs\n10.2 gl+p gauss&tr&wvs&after mean&tr&wvs gauss&tr mean&tr the&tr gauss&rs&wvs&after mean&rs&wvs gauss&rs mean&rs the&rs\n10.3.1 gl+p+w+gg-submit gl gl+p all\n10.3.2 gl+p+w+gg-tune g11 g12 g21 g22 gp11 gp12 gp21 gp22 a11 a12 a21 a22\n10.4 random n11 n12 n21 n22 y11 y12 y21 y22

    11. \n

  11. num+seg+rnn+cnn\n11.1 64-0-0 True False\n11.2 160,64-0-0 tt tf ft ff\n11.3 320,64-0-0 tt tf ft ff\n11.4 nums(64) t f fm&t fm&f\n11.5 nums(320,64) tt tf ft ff fm&tt fm&tf fm&ft fm&ff

    12. \n

  12. pure cnn\n12.1 0 cnn3 cnn5\n12.2 16 t3 t5 f3 f5\n12.3 64 t3 t5 f3 f5\n12.4 128 t3 t5 f3 f5\n12.4 256 t3 t5 f3 f5

    13. \n

  13. pure gru\n13.1 0\n13.2 16 t f\n13.3 64 t f\n13.4 128 t f\n13.5 256 t f

    14. \n

  14. pure lstm\n14.1 0\n14.2 16 t f\n14.3 64 t f\n14.4 128 t f\n14.5 256 t f

    15. \n

  15. rnn cnn\n15.1 attention rnn00,rnn01 rnn00,rnn01,rnn10,rnn11\n15.2 cnn=1 rnn00,rnn01 rnn00,rnn01,rnn10,rnn11\n15.3.1 gru&lstm rnn00,rnn01 rnn00,rnn01,rnn10,rnn11\n15.3.2 lstm rnn01 rnn01,rnn11\n15.4.1 lstm&320,64 rnn0 rnn0,rnn1\n15.4.2 0,64 rnn01 rnn01,rnn11\n15.4.3 320,64-0 rnn0 rnn0,rnn1\n15.4.4 64-0 rnn01 rnn01,rnn11\n15.4.5 320,64-64 rnn01 rnn01,rnn11\n15.5.1 mixed_rnn_cnn&gru rnn00&cnn1,2,3&rnn_sd rnn00&cnn1,2,3,5&rnn_sd rnn00&cnn1,2,3&sd_rnn rnn00&cnn1,2,3,5&sd_rnn\n15.5.2 lstm rnn00&cnn1,2,3&rnn_sd rnn00&cnn1,2,3,5&rnn_sd rnn00&cnn1,2,3&sd_rnn rnn00&cnn1,2,3,5&sd_rnn\n15.6 seg_rnn_20 rnn0 rnn0,1 rnn0,1&tanh

    16. \n

  16. rnn cnn embed\n16.1 eid=0-1-2-3 rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n16.2 4-5-6-7 rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n16.3 8-10 rnn00,rnn10 rnn00,rnn01,rnn10,rnn11\n16.3 12-14 rnn00,rnn10 rnn00,rnn01,rnn10,rnn11\n16.1 16-17-18-19 rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n16.2 20-21-22-23 rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n16.3 24-26 rnn00,rnn10 rnn00,rnn01,rnn10,rnn11\n16.3 28-30 rnn00,rnn10 rnn00,rnn01,rnn10,rnn11\n16.1 32-33-34-35 rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n16.2 36-37-38-39 rnn00,rnn10 rnn00 rnn00,rnn01,rnn10,rnn11 rnn00,rnn01\n16.3 40-42 rnn00,rnn10 rnn00,rnn01,rnn10,rnn11\n16.3 44-46 rnn00,rnn10 rnn00,rnn01,rnn10,rnn11

    17. \n

  17. rnn+cnn\n17.1 0 rnn00&cnn1,2,3&rnn_sd rnn00&cnn1,2,3,5&rnn_sd rnn00&cnn1,2,3&sd_rnn rnn00&cnn1,2,3,5&sd_rnn\n17.2.1 0-64&Ture rnn00&cnn1,2,3&rnn_sd rnn00&cnn1,2,3,5&rnn_sd rnn00&cnn1,2,3&sd_rnn rnn00&cnn1,2,3,5&sd_rnn\n17.2.2 0-64&False rnn00&cnn1,2,3&rnn_sd rnn00&cnn1,2,3,5&rnn_sd rnn00&cnn1,2,3&sd_rnn rnn00&cnn1,2,3,5&sd_rnn\n17.3.1 64&Ture rnn00&cnn1,2,3&rnn_sd rnn00&cnn1,2,3,5&rnn_sd rnn00&cnn1,2,3&sd_rnn rnn00&cnn1,2,3,5&sd_rnn\n17.3.2 64&False rnn00&cnn1,2,3&rnn_sd rnn00&cnn1,2,3,5&rnn_sd rnn00&cnn1,2,3&sd_rnn rnn00&cnn1,2,3,5&sd_rnn\n17.4.1 64-0&Ture rnn00&cnn1,2,3&rnn_sd rnn00&cnn1,2,3,5&rnn_sd rnn00&cnn1,2,3&sd_rnn rnn00&cnn1,2,3,5&sd_rnn\n17.4.2 64-0&False rnn00&cnn1,2,3&rnn_sd rnn00&cnn1,2,3,5&rnn_sd rnn00&cnn1,2,3&sd_rnn rnn00&cnn1,2,3,5&sd_rnn

    18. \n

  18. rtnn\n18.1 64-0;320,64 True False\n18.2 320,64;64-0 True False

    19. \n

  19. seg+rnn+cnn\n19.1 64 True False\n19.2.1 64-0-0&rnn1&false rnn00&cnn1,2,3&rnn_sd rnn00&cnn1,2,3,5&rnn_sd rnn00&cnn1,2,3&sd_rnn rnn00&cnn1,2,3,5&sd_rnn\n19.2.2 rnn0,1&false rnn00&cnn1,2,3&rnn_sd rnn00&cnn1,2,3,5&rnn_sd rnn00&cnn1,2,3&sd_rnn rnn00&cnn1,2,3,5&sd_rnn\n19.2.3 rnn1&true rnn00&cnn1,2,3&rnn_sd rnn00&cnn1,2,3,5&rnn_sd rnn00&cnn1,2,3&sd_rnn rnn00&cnn1,2,3,5&sd_rnn\n19.2.4 rnn0,1&true rnn00&cnn1,2,3&rnn_sd rnn00&cnn1,2,3,5&rnn_sd rnn00&cnn1,2,3&sd_rnn rnn00&cnn1,2,3,5&sd_rnn\n19.2.5 rnn0,1&cnn1,2,3&flase&sd_rnn cw=1:1 1:2&stratified 1:1&stratified\n19.3 64-0-64 tt tf ft ff\n19.4 64-64 tt tf ft ff\n19.5 64-64-0 tt tf ft ff\n19.6.1 64-64-64&true tt tf ft ff\n19.6.2 64-64-64&false tt tf ft ff\n19.7 backward forward&cnn2,3 forward&cnn1,2,3 backward&cnn2,3 backward&cnn1,2,3 bi&cnn2,3 bi&cnn1,2,3\n19.8.1 bi&forward fd&cnn2,3 fd&cnn1,2,3 bd&cnn2,3 bd&cnn1,2,3 bi&cnn2,3 bi&cnn1,2,3\n19.8.2 backward fd&cnn2,3 fd&cnn1,2,3 bd&cnn2,3 bd&cnn1,2,3 bi&cnn2,3 bi&cnn1,2,3\n19.8.3 bi fd&cnn2,3 fd&cnn1,2,3 bd&cnn2,3 bd&cnn1,2,3 bi&cnn2,3 bi&cnn1,2,3\n19.9 forward fd&cnn2,3 fd&cnn1,2,3 bd&cnn2,3 bd&cnn1,2,3 bi&cnn2,3 bi&cnn1,2,3\n19.10 best&steady best steady\n19.11 dropout tt tf ft ff

    20. \n

  20. tta\n20.1 oof_seed 11 12 21 22 31 32 41 42
    \n20.2.1 batch_size-submit 1024 512 768\n20.2.2 batch_size-tune 111 112 121 122 511 512 521 522 711 712 721 722
    \n20.3.1 digit-submit n y
    \n20.3.2 digit-tune n11 n12 n21 n22 y11 y12 y21 y22
    \n20.4.1 epoch-fixed 12-11 12-12 12-21 12-22 9-11 9-12 9-21 9-22 7-11 7-12 7-21 7-22
    \n20.4.2 epoch=20 011 012 021 022 211 212 221 222 311 312 321 322 411 412 421 422 511 512 521 522
    \n20.5.1 nums-submit n y
    \n20.5.2 nums-tune n11 n12 n21 n22 yf11 yf12 yf21 yf22 yt11 yt12 yt21 yt22
    \n20.6 pad pre&max pre&last pre&max&last post&max post&last post&max&last
    \n20.7.1 punct-submit n y56 y60
    \n20.7.2 punct-tune n11 n12 n21 n22 y11 y12 y21 y22 y&digits&11 yd12 yd21 yd22
    \n20.7.3 punct-tune-seed 1 21 22 3 4 5 6 7
    \n20.8 seg_max 14,56 7 7,56 7,14 7,14,56\n20.9.1 sug-submit n y\n20.9.2 sug-tune n11 n12 n21 n22 y11 y12 y21 y22

    21. \n

  21. rnn\n21.1.1 type&eid=0 ll11 ll12 ll21 ll22\n21.1.2 type&eid=2 ll11 ll12 ll21 ll22 lg11 lg12 lg21 lg22 gl11 gl12 gl21 gl22 gg11 gg12 gg21 gg22\n21.2.1 embed n11 n12 n21 n22 seed=1&11 112 121 122 211 212 221 222 mean&11 m12 m21 m22 the&11 t12 t21 t22 row_mean&11 rm12 rm21 rm22 it&11 i12 i21 i22 that&11 a12 a21 a22\n21.2.2 embed&torch the&111 t112 t121 t122 t211 t212 t221 t222

    22. \n
\n\n
\n\n

torch

\n\n
    \n

  1. md\n1.1.1 batch_size&eid=2 1024&11 112 121 512&11 512 521\n1.1.2 batch_size&eid=4 1024&11 112 121 122 512&11 512 521\n1.2 dropout 0.2&11 212 221 0.1&11 112 121 122\n1.3 cnn eid=3&11 312 321 322 011 012 021 022 111 112 121 122 411 412 421 422 511 512 521 522\n1.4.1 lr-fixed 1e-3&1 12 21 22 41 42 81 82 .51\n1.4.2 lr-clr n11 n12 n21 y11 y12 y21 y22\n1.5 classweight 111 112 121 122 211 212 221 222\n1.6 token n11 n12 n21 n22 with_lr_scheduler&11 w12 w21 w22 without_lr_scheduler&11 o12 o21

    2. \n

  2. rnn2\n2.1.1 type&normal gg11 gg12 gg21 ll11 ll12 ll21 ll22 gl11 gl12 gl21 lg11 lg12 lg21 lg22\n2.1.2 bi ll11 ll12 ll21 ll22 lg11 lg12 lg21 gl11 gl12 gl21 gl22 gg11 gg12 gg21

    3. \n
", "rawMarkdown": "1. cnn\n 1.1 0,0 [cnn0][1] [cnn1][2] [cnn2][3] [cnn3][4] [cnn5][5]\n 1.2 0,0--stack [cnn0][6] [cnn1][7] [cnn2][8] [cnn3][9] [cnn5][10]\n 1.3 0,64 [cnn0][11] [cnn1][12] [cnn2][13] [cnn3][14] [cnn5][15]\n 1.4 0,64--stack [cnn0 ][16] [cnn1][17] [cnn2][18] [cnn3][19] [cnn5][20]\n 1.5.1 64-0 [cnn1][21] [cnn2][22] [cnn3][23] [cnn5][24] \n 1.5.2 64-0-【dropout=0.2】 [cnn1][25] [cnn2][26] [cnn3][27] [cnn5][28]\n 1.5.3 64-0-【0.2】【spatial_dropout=0.2】 [cnn1][29] [cnn2][30] [cnn3][31] [cnn5][32]\n 1.5.4 64-0-【0.2】【0.2】【trainable】 [cnn1][33] [cnn2][34] [cnn3][35] [cnn5][36]\n 1.6 64-0--mix [cnn0,1,2][37] [cnn0,1,2,3,4][38] [embed0,cnn0,1,2][39] [embed0,cnn0,1,2,3,4][40]\n 1.7 320,64 [cnn0][41] [cnn1][42] [cnn2][43] [cnn3][44] [cnn5][45]\n 1.8 320,64--stack [cnn0][46] [cnn1][47] [cnn2][48] [cnn3][49] [cnn5][50]\n\n 2. combine\n 2.1 lgb [n-1-1][51] [n-1-2][52] [n-2-1][53] [n-2-2][54] [y-1-1][55] [y-1-2][56] [y-2-1][57] [y-2-2][58]\n 2.2 tnn [n-1-1][59] [n-1-2][60] [n-2-1][61] [n-2-2][62] [y-1-1][63] [y-1-2][64] [y-2-1][65] [y-2-2][66]\n\n 3. classweight\n 3.1 pure [1][67] [1.5][68] [1.25][69] [1.75][70]\n 3.2 pure [2][71] [2.5][72] [3][73] [3.5][74] [4][75]\n 3.3 +nums [2][76] [2.5][77] [3][78] [3.5][79] [4][80]\n\n 4. data\n 4.1.1 guess valid target(cw=1) [normal&rnn0][81] [n&rnn0,1][82] [yes&rnn0][83] [y&rnn0,1][84]\n 4.1.2 cw=2 [y&rnn0][85] [y&rnn0,1][86]\n 4.2 vocab_size=90000 [rnn0][87] [rnn0,1][88]
\n 4.3.1 noise-submit [normal][89] [delete][90] [replace][91]
\n 4.3.2 noise-tune [n-1-1][92] [n-1-2][93] [n-2-1][94] [n-2-2][95] [d-1-1][96] [d-1-2][97] [d-2-1][98] [d-2-2][99] [r-1-1][100] [r-1-2][101] [r-2-1][102] [r-2-2][103]
\n 4.4.1 vocab_size-submit [all][104] [95000][105] [60000][106]\n 4.4.2 vocab_size-tune [a-1-1][107] [a-1-2][108] [a-2-1][109] [a-2-2][110] [9-1-1][111] [9-1-2][112] [9-2-1][113] [9-2-2][114] [6-1-1][115] [6-1-2][116] [6-2-1][117] [6-2-2][118]\n\n 5. embeds\n 5.1 glove [rnn00,rnn10][119] [rnn00][120] [rnn00,rnn01,rnn10,rnn11][121] [rnn00,rnn01][122]\n 5.2 glove+google [rnn00,rnn10][123] [rnn00][124] [rnn00,rnn01,rnn10,rnn11][125] [rnn00,rnn01][126]\n 5.3 gl+para [rnn00,rnn10][127] [rnn00][128] [rnn00,rnn01,rnn10,rnn11][129] [rnn00,rnn01][130]\n 5.4 gl+p+gg [rnn00,rnn10][131] [rnn00][132] [rnn00,rnn01,rnn10,rnn11][133] [rnn00,rnn01][134]\n 5.5 gl+p+w [rnn00,rnn10][135] [rnn00][136] [rnn00,rnn01,rnn10,rnn11][137] [rnn00,rnn01][138]\n 5.6 gl+p+w+gg [rnn00,rnn10][139] [rnn00][140] [rnn00,rnn01,rnn10,rnn11][141] [rnn00,rnn01][142]\n 5.7 gl+w [rnn00,rnn10][143] [rnn00][144] [rnn00,rnn01,rnn10,rnn11][145] [rnn00,rnn01][146]\n 5.8 gl+w+gg [rnn00,rnn10][147] [rnn00][148] [rnn00,rnn01,rnn10,rnn11][149] [rnn00,rnn01][150]\n 5.9 google [rnn00,rnn10][151] [rnn00][152] [rnn00,rnn01,rnn10,rnn11][153] [rnn00,rnn01][154]\n 5.10 para [rnn00,rnn10][155] [rnn00][156] [rnn00,rnn01,rnn10,rnn11][157] [rnn00,rnn01][158]\n 5.11 p+gg [rnn00,rnn10][159] [rnn00][160] [rnn00,rnn01,rnn10,rnn11][161] [rnn00,rnn01][162]\n 5.12 p+w [rnn00,rnn10][163] [rnn00][164] [rnn00,rnn01,rnn10,rnn11][165] [rnn00,rnn01][166]\n 5.13 p+w+gg [rnn00,rnn10][167] [rnn00][168] [rnn00,rnn01,rnn10,rnn11][169] [rnn00,rnn01][170]\n 5.14 wiki [rnn00,rnn10][171] [rnn00][172] [rnn00,rnn01,rnn10,rnn11][173] [rnn00,rnn01][174]\n 5.15 w+gg [rnn00,rnn10][175] [rnn00][176] [rnn00,rnn01,rnn10,rnn11][177] [rnn00,rnn01][178]\n\n 6. hidden\n 6.1 64-0 [false&cnn2,3][179] [f&cnn1,2,3][180]\n 6.2 64-0-64 [true&cnn2,3][181] [t&cnn1,2,3][182] [f&cnn2,3][183] [f&cnn1,2,3][184]\n 6.3 64-64 [t&cnn2,3][185] [t&cnn1,2,3][186] [f&cnn2,3][187] [f&cnn1,2,3][188]\n 6.4 64-64-0 [t&cnn2,3][189] [t&cnn1,2,3][190] [f&cnn2,3][191] [f&cnn1,2,3][192]\n 6.5 64-64-64 [t&cnn2,3][193] [t&cnn1,2,3][194] [f&cnn2,3][195] [f&cnn1,2,3][196]\n\n 7. hidden unit\n 7.1 0 [rnn0][197] [rnn0,1][198]\n 7.2 160 [rnn00,rnn10][199] [rnn00][200] [rnn00,rnn01,rnn10,rnn11][201] [rnn00,rnn01][202]\n 7.3.1 64-submit [n][203] [tn][204] [ty][205] [fn][206] [fy][207]\n 7.3.2 64-tune [n-1-1][208] [n-1-2][209] [n-2-1][210] [n-2-2][211] [y-1-1][212] [y-1-2][213] [y-2-1][214] [y-2-2][215] [tn-1-1][216] [tn-1-2][217] [tn-2-1][218] [tn-2-2][219] [ty-1-1][220] [ty-1-2][221] [ty-2-1][222] [ty-2-2][223] [fn-1-1][224] [fn-1-2][225] [fn-2-1][226] [fn-2-2][227] [fy-1-1][228] [fy-1-2][229] [fy-2-1][230] [fy-2-2][231]\n 7.4 plseu [rnn0][232] [rnn0,1][233] [rnn0,1&tanh][234]\n \n 8. lr\n 8.1 1.25e-4 [rnn0][235] [rnn0,1][236]\n 8.2 2.5 [rnn0][237] [rnn0,1][238]\n 8.3 20 [rnn0][239] [rnn0,1][240]\n 8.4 40 [rnn0][241] [rnn0,1][242]\n 8.5 5 [rnn0][243] [rnn0,1][244]\n 8.6 80 [rnn0][245] [rnn0,1][246]\n \n 9. lstm+lstm\n 9.1.1 hidden_unit=0 [rnn1][247] [rnn1&sd][248] [rnn0,1][249] [rnn0,1&sd][250]\n 9.1.2 0&attention&ll [rnn1][251] [rnn1&sd][252] [rnn0,1][253] [rnn0,1&sd][254]\n 9.1.3 0&attention&l [rnn1][255] [rnn1&sd][256] [rnn0,1][257] [rnn0,1&sd][258]\n 9.2.1 64&bn=True [rnn1][259] [rnn1&sd][260] [rnn0,1][261] [rnn0,1&sd][262]\n 9.2.2 64&False [rnn1][263] [rnn1&sd][264] [rnn0,1][265] [rnn0,1&sd][266]\n 9.3.1 256&bn=True [rnn1][267] [rnn1&sd][268] [rnn0,1][269] [rnn0,1&sd][270]\n 9.3.2 256&False [rnn1][271] [rnn1&sd][272] [rnn0,1][273] [rnn0,1&sd][274]\n \n 10. mix embeds\n 10.1 glove [gauss&tr&wvs&before][275] [gauss&tr&wvs&after][276] [mean&tr&wvs][277] [gauss&tr][278] [mean&tr][279] [the&tr][280] [gauss&rs&wvs&before][281] [gauss&rs&wvs&after][282] [mean&rs&wvs][283] [gauss&rs][284] [mean&rs][285] [the&rs][286]\n 10.2 gl+p [gauss&tr&wvs&after][287] [mean&tr&wvs][288] [gauss&tr][289] [mean&tr][290] [the&tr][291] [gauss&rs&wvs&after][292] [mean&rs&wvs][293] [gauss&rs][294] [mean&rs][295] [the&rs][296]\n 10.3.1 gl+p+w+gg-submit [gl][297] [gl+p][298] [all][299]\n 10.3.2 gl+p+w+gg-tune [g11][300] [g12][301] [g21][302] [g22][303] [gp11][304] [gp12][305] [gp21][306] [gp22][307] [a11][308] [a12][309] [a21][310] [a22][311]\n 10.4 random [n11][312] [n12][313] [n21][314] [n22][315] [y11][316] [y12][317] [y21][318] [y22][319]\n\n 11. num+seg+rnn+cnn\n 11.1 64-0-0 [True][320] [False][321]\n 11.2 160,64-0-0 [tt][322] [tf][323] [ft][324] [ff][325]\n 11.3 320,64-0-0 [tt][326] [tf][327] [ft][328] [ff][329]\n 11.4 nums(64) [t][330] [f][331] [fm&t][332] [fm&f][333]\n 11.5 nums(320,64) [tt][334] [tf][335] [ft][336] [ff][337] [fm&tt][338] [fm&tf][339] [fm&ft][340] [fm&ff][341]\n \n 12. pure cnn\n 12.1 0 [cnn3][342] [cnn5][343]\n 12.2 16 [t3][344] [t5][345] [f3][346] [f5][347]\n 12.3 64 [t3][348] [t5][349] [f3][350] [f5][351]\n 12.4 128 [t3][352] [t5][353] [f3][354] [f5][355]\n 12.4 256 [t3][356] [t5][357] [f3][358] [f5][359]\n \n 13. pure gru\n 13.1 [0][360]\n 13.2 16 [t][361] [f][362]\n 13.3 64 [t][363] [f][364]\n 13.4 128 [t][365] [f][366]\n 13.5 256 [t][367] [f][368]\n \n 14. pure lstm\n 14.1 [0][369]\n 14.2 16 [t][370] [f][371]\n 14.3 64 [t][372] [f][373]\n 14.4 128 [t][374] [f][375]\n 14.5 256 [t][376] [f][377]\n \n 15. rnn cnn\n 15.1 attention [rnn00,rnn01][378] [rnn00,rnn01,rnn10,rnn11][379]\n 15.2 cnn=1 [rnn00,rnn01][380] [rnn00,rnn01,rnn10,rnn11][381]\n 15.3.1 gru&lstm [rnn00,rnn01][382] [rnn00,rnn01,rnn10,rnn11][383]\n 15.3.2 lstm [rnn01][384] [rnn01,rnn11][385]\n 15.4.1 lstm&320,64 [rnn0][386] [rnn0,rnn1][387]\n 15.4.2 0,64 [rnn01][388] [rnn01,rnn11][389]\n 15.4.3 320,64-0 [rnn0][390] [rnn0,rnn1][391]\n 15.4.4 64-0 [rnn01][392] [rnn01,rnn11][393]\n 15.4.5 320,64-64 [rnn01][394] [rnn01,rnn11][395]\n 15.5.1 mixed_rnn_cnn&gru [rnn00&cnn1,2,3&rnn_sd][396] [rnn00&cnn1,2,3,5&rnn_sd][397] [rnn00&cnn1,2,3&sd_rnn][398] [rnn00&cnn1,2,3,5&sd_rnn][399]\n 15.5.2 lstm [rnn00&cnn1,2,3&rnn_sd][400] [rnn00&cnn1,2,3,5&rnn_sd][401] [rnn00&cnn1,2,3&sd_rnn][402] [rnn00&cnn1,2,3,5&sd_rnn][403]\n 15.6 seg_rnn_20 [rnn0][404] [rnn0,1][405] [rnn0,1&tanh][406]\n \n 16. rnn cnn embed\n 16.1 eid=0-1-2-3 [rnn00,rnn10][407] [rnn00][408] [rnn00,rnn01,rnn10,rnn11][409] [rnn00,rnn01][410]\n 16.2 4-5-6-7 [rnn00,rnn10][411] [rnn00][412] [rnn00,rnn01,rnn10,rnn11][413] [rnn00,rnn01][414]\n 16.3 8-10 [rnn00,rnn10][415] [rnn00,rnn01,rnn10,rnn11][416]\n 16.3 12-14 [rnn00,rnn10][417] [rnn00,rnn01,rnn10,rnn11][418]\n 16.1 16-17-18-19 [rnn00,rnn10][419] [rnn00][420] [rnn00,rnn01,rnn10,rnn11][421] [rnn00,rnn01][422]\n 16.2 20-21-22-23 [rnn00,rnn10][423] [rnn00][424] [rnn00,rnn01,rnn10,rnn11][425] [rnn00,rnn01][426]\n 16.3 24-26 [rnn00,rnn10][427] [rnn00,rnn01,rnn10,rnn11][428]\n 16.3 28-30 [rnn00,rnn10][429] [rnn00,rnn01,rnn10,rnn11][430]\n 16.1 32-33-34-35 [rnn00,rnn10][431] [rnn00][432] [rnn00,rnn01,rnn10,rnn11][433] [rnn00,rnn01][434]\n 16.2 36-37-38-39 [rnn00,rnn10][435] [rnn00][436] [rnn00,rnn01,rnn10,rnn11][437] [rnn00,rnn01][438]\n 16.3 40-42 [rnn00,rnn10][439] [rnn00,rnn01,rnn10,rnn11][440]\n 16.3 44-46 [rnn00,rnn10][441] [rnn00,rnn01,rnn10,rnn11][442]\n \n 17. rnn+cnn\n 17.1 0 [rnn00&cnn1,2,3&rnn_sd][443] [rnn00&cnn1,2,3,5&rnn_sd][444] [rnn00&cnn1,2,3&sd_rnn][445] [rnn00&cnn1,2,3,5&sd_rnn][446]\n 17.2.1 0-64&Ture [rnn00&cnn1,2,3&rnn_sd][447] [rnn00&cnn1,2,3,5&rnn_sd][448] [rnn00&cnn1,2,3&sd_rnn][449] [rnn00&cnn1,2,3,5&sd_rnn][450]\n 17.2.2 0-64&False [rnn00&cnn1,2,3&rnn_sd][451] [rnn00&cnn1,2,3,5&rnn_sd][452] [rnn00&cnn1,2,3&sd_rnn][453] [rnn00&cnn1,2,3,5&sd_rnn][454]\n 17.3.1 64&Ture [rnn00&cnn1,2,3&rnn_sd][455] [rnn00&cnn1,2,3,5&rnn_sd][456] [rnn00&cnn1,2,3&sd_rnn][457] [rnn00&cnn1,2,3,5&sd_rnn][458]\n 17.3.2 64&False [rnn00&cnn1,2,3&rnn_sd][459] [rnn00&cnn1,2,3,5&rnn_sd][460] [rnn00&cnn1,2,3&sd_rnn][461] [rnn00&cnn1,2,3,5&sd_rnn][462]\n 17.4.1 64-0&Ture [rnn00&cnn1,2,3&rnn_sd][463] [rnn00&cnn1,2,3,5&rnn_sd][464] [rnn00&cnn1,2,3&sd_rnn][465] [rnn00&cnn1,2,3,5&sd_rnn][466]\n 17.4.2 64-0&False [rnn00&cnn1,2,3&rnn_sd][467] [rnn00&cnn1,2,3,5&rnn_sd][468] [rnn00&cnn1,2,3&sd_rnn][469] [rnn00&cnn1,2,3,5&sd_rnn][470]\n \n 18. rtnn\n 18.1 64-0;320,64 [True][471] [False][472]\n 18.2 320,64;64-0 [True][473] [False][474]\n \n 19. seg+rnn+cnn\n 19.1 64 [True][475] [False][476]\n 19.2.1 64-0-0&rnn1&false [rnn00&cnn1,2,3&rnn_sd][477] [rnn00&cnn1,2,3,5&rnn_sd][478] [rnn00&cnn1,2,3&sd_rnn][479] [rnn00&cnn1,2,3,5&sd_rnn][480]\n 19.2.2 rnn0,1&false [rnn00&cnn1,2,3&rnn_sd][481] [rnn00&cnn1,2,3,5&rnn_sd][482] [rnn00&cnn1,2,3&sd_rnn][483] [rnn00&cnn1,2,3,5&sd_rnn][484]\n 19.2.3 rnn1&true [rnn00&cnn1,2,3&rnn_sd][485] [rnn00&cnn1,2,3,5&rnn_sd][486] [rnn00&cnn1,2,3&sd_rnn][487] [rnn00&cnn1,2,3,5&sd_rnn][488]\n 19.2.4 rnn0,1&true [rnn00&cnn1,2,3&rnn_sd][489] [rnn00&cnn1,2,3,5&rnn_sd][490] [rnn00&cnn1,2,3&sd_rnn][491] [rnn00&cnn1,2,3,5&sd_rnn][492]\n 19.2.5 rnn0,1&cnn1,2,3&flase&sd_rnn [cw=1:1][493] [1:2&stratified][494] [1:1&stratified][495]\n 19.3 64-0-64 [tt][496] [tf][497] [ft][498] [ff][499]\n 19.4 64-64 [tt][500] [tf][501] [ft][502] [ff][503]\n 19.5 64-64-0 [tt][504] [tf][505] [ft][506] [ff][507]\n 19.6.1 64-64-64&true [tt][508] [tf][509] [ft][510] [ff][511]\n 19.6.2 64-64-64&false [tt][512] [tf][513] [ft][514] [ff][515]\n 19.7 backward [forward&cnn2,3][516] [forward&cnn1,2,3][517] [backward&cnn2,3][518] [backward&cnn1,2,3][519] [bi&cnn2,3][520] [bi&cnn1,2,3][521]\n 19.8.1 bi&forward [fd&cnn2,3][522] [fd&cnn1,2,3][523] [bd&cnn2,3][524] [bd&cnn1,2,3][525] [bi&cnn2,3][526] [bi&cnn1,2,3][527]\n 19.8.2 backward [fd&cnn2,3][528] [fd&cnn1,2,3][529] [bd&cnn2,3][530] [bd&cnn1,2,3][531] [bi&cnn2,3][532] [bi&cnn1,2,3][533]\n 19.8.3 bi [fd&cnn2,3][534] [fd&cnn1,2,3][535] [bd&cnn2,3][536] [bd&cnn1,2,3][537] [bi&cnn2,3][538] [bi&cnn1,2,3][539]\n 19.9 forward [fd&cnn2,3][540] [fd&cnn1,2,3][541] [bd&cnn2,3][542] [bd&cnn1,2,3][543] [bi&cnn2,3][544] [bi&cnn1,2,3][545]\n 19.10 best&steady [best][546] [steady][547]\n 19.11 dropout [tt][548] [tf][549] [ft][550] [ff][551]\n \n 20. tta\n 20.1 oof_seed [11][552] [12][553] [21][554] [22][555] [31][556] [32][557] [41][558] [42][559]
\n 20.2.1 batch_size-submit [1024][560] [512][561] [768][562]\n 20.2.2 batch_size-tune [111][563] [112][564] [121][565] [122][566] [511][567] [512][568] [521][569] [522][570] [711][571] [712][572] [721][573] [722][574]
\n 20.3.1 digit-submit [n][575] [y][576]
\n 20.3.2 digit-tune [n11][577] [n12][578] [n21][579] [n22][580] [y11][581] [y12][582] [y21][583] [y22][584]
\n 20.4.1 epoch-fixed [12-11][585] [12-12][586] [12-21][587] [12-22][588] [9-11][589] [9-12][590] [9-21][591] [9-22][592] [7-11][593] [7-12][594] [7-21][595] [7-22][596]
\n 20.4.2 epoch=20 [011][597] [012][598] [021][599] [022][600] [211][601] [212][602] [221][603] [222][604] [311][605] [312][606] [321][607] [322][608] [411][609] [412][610] [421][611] [422][612] [511][613] [512][614] [521][615] [522][616]
\n 20.5.1 nums-submit [n][617] [y][618]
\n 20.5.2 nums-tune [n11][619] [n12][620] [n21][621] [n22][622] [yf11][623] [yf12][624] [yf21][625] [yf22][626] [yt11][627] [yt12][628] [yt21][629] [yt22][630]
\n 20.6 pad [pre&max][631] [pre&last][632] [pre&max&last][633] [post&max][634] [post&last][635] [post&max&last][636]
\n 20.7.1 punct-submit [n][637] [y56][638] [y60][639]
\n 20.7.2 punct-tune [n11][640] [n12][641] [n21][642] [n22][643] [y11][644] [y12][645] [y21][646] [y22][647] [y&digits&11][648] [yd12][649] [yd21][650] [yd22][651]
\n 20.7.3 punct-tune-seed [1][652] [21][653] [22][654] [3][655] [4][656] [5][657] [6][658] [7][659]
\n 20.8 seg_max [14,56][660] [7][661] [7,56][662] [7,14][663] [7,14,56][664]\n 20.9.1 sug-submit [n][665] [y][666]\n 20.9.2 sug-tune [n11][667] [n12][668] [n21][669] [n22][670] [y11][671] [y12][672] [y21][673] [y22][674]\n \n 21. rnn\n 21.1.1 type&eid=0 [ll11][675] [ll12][676] [ll21][677] [ll22][678]\n 21.1.2 type&eid=2 [ll11][679] [ll12][680] [ll21][681] [ll22][682] [lg11][683] [lg12][684] [lg21][685] [lg22][686] [gl11][687] [gl12][688] [gl21][689] [gl22][690] [gg11][691] [gg12][692] [gg21][693] [gg22][694]\n 21.2.1 embed [n11][695] [n12][696] [n21][697] [n22][698] [seed=1&11][699] [112][700] [121][701] [122][702] [211][703] [212][704] [221][705] [222][706] [mean&11][707] [m12][708] [m21][709] [m22][710] [the&11][711] [t12][712] [t21][713] [t22][714] [row_mean&11][715] [rm12][716] [rm21][717] [rm22][718] [it&11][719] [i12][720] [i21][721] [i22][722] [that&11][723] [a12][724] [a21][725] [a22][726]\n 21.2.2 embed&torch [the&111][727] [t112][728] [t121][729] [t122][730] [t211][731] [t212][732] [t221][733] [t222][734]\n \n\n\n----------\n**torch**\n\n 1. md\n 1.1.1 batch_size&eid=2 [1024&11][735] [112][736] [121][737] [512&11][738] [512][739] [521][740]\n 1.1.2 batch_size&eid=4 [1024&11][741] [112][742] [121][743] [122][744] [512&11][745] [512][746] [521][747]\n 1.2 dropout [0.2&11][748] [212][749] [221][750] [0.1&11][751] [112][752] [121][753] [122][754]\n 1.3 cnn [eid=3&11][755] [312][756] [321][757] [322][758] [011][759] [012][760] [021][761] [022][762] [111][763] [112][764] [121][765] [122][766] [411][767] [412][768] [421][769] [422][770] [511][771] [512][772] [521][773] [522][774]\n 1.4.1 lr-fixed [1e-3&1][775] [12][776] [21][777] [22][778] [41][779] [42][780] [81][781] [82][782] [.51][783]\n 1.4.2 lr-clr [n11][784] [n12][785] [n21][786] [y11][787] [y12][788] [y21][789] [y22][790]\n 1.5 classweight [111][791] [112][792] [121][793] [122][794] [211][795] [212][796] [221][797] [222][798]\n 1.6 token [n11][799] [n12][800] [n21][801] [n22][802] [with_lr_scheduler&11][803] [w12][804] [w21][805] [w22][806] [without_lr_scheduler&11][807] [o12][808] [o21][809]\n \n 2. rnn2\n 2.1.1 type&normal [gg11][810] [gg12][811] [gg21][812] [ll11][813] [ll12][814] [ll21][815] [ll22][816] [gl11][817] [gl12][818] [gl21][819] [lg11][820] [lg12][821] [lg21][822] [lg22][823]\n 2.1.2 bi [ll11][824] [ll12][825] [ll21][826] [ll22][827] [lg11][828] [lg12][829] [lg21][830] [gl11][831] [gl12][832] [gl21][833] [gl22][834] [gg11][835] [gg12][836] [gg21][837]\n \n \n \n\n \n\n\n [1]: https://www.kaggle.com/yakolle/quora-tnn-2/output?scriptVersionId=8301643\n [2]: https://www.kaggle.com/yakolle/quora-tnn-3/output?scriptVersionId=8301685\n [3]: https://www.kaggle.com/yakolle/quora-tnn-4/output?scriptVersionId=8301577\n [4]: https://www.kaggle.com/yakolle/quora-tnn-5/output?scriptVersionId=8301579\n [5]: https://www.kaggle.com/yakolle/quora-tnn-6/output?scriptVersionId=8301581\n [6]: https://www.kaggle.com/yakolle/quora-tnn-2/output?scriptVersionId=8321885\n [7]: https://www.kaggle.com/yakolle/quora-tnn-3/output?scriptVersionId=8321887\n [8]: https://www.kaggle.com/yakolle/quora-tnn-4/output?scriptVersionId=8321889\n [9]: https://www.kaggle.com/yakolle/quora-tnn-5/output?scriptVersionId=8321891\n [10]: https://www.kaggle.com/yakolle/quora-tnn-6/output?scriptVersionId=8321892\n [11]: https://www.kaggle.com/yakolle/quora-tnn-7/output?scriptVersionId=8301875\n [12]: https://www.kaggle.com/yakolle/quora-tnn-8/output?scriptVersionId=8301878\n [13]: https://www.kaggle.com/yakolle/quora-tnn-9/output?scriptVersionId=8301880\n [14]: https://www.kaggle.com/yakolle/quora-tnn-10/output?scriptVersionId=8301882\n [15]: https://www.kaggle.com/yakolle/quora-tnn-11/output?scriptVersionId=8301885\n [16]: https://www.kaggle.com/yakolle/quora-tnn-7/output?scriptVersionId=8322073\n [17]: https://www.kaggle.com/yakolle/quora-tnn-8/output?scriptVersionId=8322074\n [18]: https://www.kaggle.com/yakolle/quora-tnn-9/output?scriptVersionId=8322077\n [19]: 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https://www.kaggle.com/yakolle/quora2-tnn-3?scriptVersionId=10200921\n [837]: https://www.kaggle.com/yakolle/quora2-tnn-5?scriptVersionId=10200923", "votes": null }, { "id": "469055", "postDate": "02/10/2019 11:57:10", "content": "

the code for extracting scores from log then plotting and sorting them

\n\n
import os    \nimport re\n\nimport numpy as np\nimport pandas as pd\nfrom matplotlib import pyplot as plt\n\n\ndef extract_scores(log_path):\n    def flush():\n        if 't_f1' in score_dic:\n            score_dic['t_pth'] = p_ths[0]\n            del p_ths[0]\n        if 'v_f1' in score_dic:\n            score_dic['v_pth'] = p_ths[0]\n            del p_ths[0]\n        cur_score_dic = score_table[tag] if tag in score_table else {_var_id: [] for _var_id in var_ids}\n        for _var_id in var_ids:\n            if _var_id in score_dic:\n                cur_score_dic[_var_id].append(score_dic[_var_id])\n        score_dic.clear()\n        score_table[tag] = cur_score_dic\n\n    with open(log_path, 'r', encoding='utf-8') as log_file:\n        lines = log_file.readlines()\n\n    p_ths = []\n    tag = None\n    var_ids1 = ['t_auc', 't_f1', 't_precision', 't_recall', 'v_auc', 'v_f1', 'v_precision', 'v_recall']\n    var_ids = var_ids1 + ['t_pth', 'v_pth']\n    score_dic = {}\n    score_table = {}\n    for line in lines:\n        matches = list(re.findall(r'last_best_p_th=([\\d.]+)', line))\n        p_ths += [float(match) for match in matches] if matches else []\n\n        for var_id in var_ids1:\n            pat = f'{var_id}\\(?([-\\w\\d.]*)\\)?: ([\\d.]+)'\n            matches = re.findall(pat, line)\n            cur_tag, score = matches[0] if matches else (None, None)\n            if cur_tag and cur_tag != tag:\n                if tag is not None:\n                    flush()\n                tag = cur_tag\n            if score:\n                score_dic[var_id] = float(score)\n    flush()\n\n    if 'best' in score_table:\n        bs_dic = score_table['best']\n        del score_table['best']\n        score_table['b'] = bs_dic\n\n        if 'steady' in score_table:\n            ss_dic = score_table['steady']\n\n            as_dic = {var_id: [] for var_id in var_ids}\n            b_aucs = bs_dic['v_auc']\n            s_aucs = ss_dic['v_auc']\n            if b_aucs and s_aucs:\n                for sid, b_scores in bs_dic.items():\n                    s_scores = ss_dic[sid]\n                    if b_scores and s_scores:\n                        a_scores = as_dic[sid]\n                        for i, b_auc in enumerate(b_aucs):\n                            a_scores.append(b_scores[i] if b_auc > s_aucs[i] else s_scores[i])\n                score_table['a'] = as_dic\n\n            rs_dic = {var_id: [] for var_id in var_ids}\n            b_recalls = bs_dic['v_recall']\n            s_recalls = ss_dic['v_recall']\n            if b_recalls and s_recalls:\n                for sid, b_scores in bs_dic.items():\n                    s_scores = ss_dic[sid]\n                    if b_scores and s_scores:\n                        r_scores = rs_dic[sid]\n                        for i, b_recall in enumerate(b_recalls):\n                            r_scores.append(b_scores[i] if b_recall > s_recalls[i] else s_scores[i])\n                score_table['r'] = rs_dic\n    if 'steady' in score_table:\n        score_table['s'] = score_table['steady']\n        del score_table['steady']\n    if 'merge' in score_table:\n        score_table['m'] = score_table['merge']\n        del score_table['merge']\n\n    return score_table\n\n\ndef get_score_table(log_dir):\n    rec_ids = []\n    scores_table = {}\n    recs = []\n    for file in os.listdir(log_dir):\n        fp = os.path.join(log_dir, file)\n        if os.path.isfile(fp):\n            rec_ids.append(file[:-4])\n            recs.append(extract_scores(fp))\n\n    rec_num = len(recs)\n    for i, rec in enumerate(recs):\n        for tag, score_dic in rec.items():\n            for score_id, scores in score_dic.items():\n                if scores:\n                    res_score_dic = scores_table[score_id] if score_id in scores_table else {}\n                    if len(scores) > 1:\n                        for j, score in enumerate(scores):\n                            entry_id = f'{tag}{j}'\n                            entry = res_score_dic[entry_id] if entry_id in res_score_dic else [np.nan] * rec_num\n                            entry[i] = score\n                            res_score_dic[entry_id] = entry\n                        scores_table[score_id] = res_score_dic\n                    else:\n                        score = scores[0]\n                        entry = res_score_dic[tag] if tag in res_score_dic else [np.nan] * rec_num\n                        entry[i] = score\n                        res_score_dic[tag] = entry\n                        scores_table[score_id] = res_score_dic\n    return rec_ids, scores_table\n\n\ndef plot_scores(ids, score_dic, idx_len_pair=None, score_id='auc', ignore_entries=None, y_steps=15, legend_max_cols=20):\n    def get_loc(_idx_len_pair):\n        _idx, _len = _idx_len_pair\n        _rows, _cols, _index = _len // 2, 2, _idx + 1\n        if _len % 2:\n            _rows += 1\n            if _index == _len:\n                _cols = 1\n                _index = _rows\n        return _rows, _cols, _index\n\n    colors = list('rybkcm')\n    markers = list(',x')\n    lines = ['-', '--', '-.', ':']\n    styles = [f'{color}{marker}{line}' for marker in markers for color in colors for line in lines]\n\n    if idx_len_pair is None:\n        plt.figure()\n    else:\n        if not idx_len_pair[0]:\n            plt.figure()\n        rows, cols, index = get_loc(idx_len_pair)\n        legend_max_cols //= cols\n        plt.subplot(rows, cols, index)\n\n    min_score, max_score = 1.0, 0.0\n    for i, (entry_id, scores) in enumerate(score_dic.items()):\n        if ignore_entries and entry_id in ignore_entries:\n            plt.plot(scores, visible=False)\n        else:\n            min_score = min(min_score, np.nanmin(scores))\n            max_score = max(max_score, np.nanmax(scores))\n            plt.plot(scores, styles[i])\n    plt.xticks(range(len(ids)), ids)\n    y_step = (max_score - min_score) / y_steps\n    plt.yticks(np.arange(min_score - y_step, max_score + 3 * y_step, y_step))\n    plt.legend(score_dic.keys(), bbox_to_anchor=(0, 0, 1, 1), loc='upper right', borderaxespad=0, fontsize=6,\n               ncol=legend_max_cols, mode='expand')\n    plt.title(score_id)\n    plt.grid()\n\n\ndef print_rank(ids, score_dic, score_id='auc', ignore_entries=None):\n    print(f'-------------------------------{score_id}-------------------------------')\n    for entry_id, scores in score_dic.items():\n        if not ignore_entries or entry_id not in ignore_entries:\n            s = pd.Series(scores, index=ids).rank(ascending=False)\n            print(f'{entry_id}: {s.to_dict()}')\n\n\ndef run():\n    log_root_dir = 'E:\\\\work\\\\kaggle\\\\quora\\\\logs'\n    dir_name = 'tc\\\\bs'\n\n    log_dir = os.path.join(log_root_dir, dir_name)\n    rec_ids, scores_table = get_score_table(log_dir)\n\n    entry_filter = ['a-gm', 'a-hm', 'r-gm', 'r-hm'] + [f'{tag}{i}' for tag in list('bs') for i in range(10)] + [\n        f'{ms}-{mt}{w}' for ms in list('ar') for mt in ['m', 'am'] for w in [0.4, 0.5]] + [\n                       f'{e}{mt}' for mt in ['m', 'am'] for e in\n                       [0, 1, 2, 3, 4, 11, 12, 13, 14, 15, 16, 17, 18, 19]]\n    for score_id, score_dic in scores_table.items():\n        for entry_id in list(score_dic.keys()):\n            if entry_id in entry_filter:\n                del score_dic[entry_id]\n\n    # score_ids = ['v_auc', 'v_f1', 'v_precision', 'v_recall']\n    score_ids = ['v_auc', 'v_recall', 'v_f1']\n    score_ids = [score_id for score_id in score_ids if score_id in scores_table]\n    for i, score_id in enumerate(score_ids):\n        plot_scores(rec_ids, scores_table[score_id], (i, len(score_ids)), score_id=score_id)\n    score_id = 'v_pth'\n    if score_id in scores_table:\n        plot_scores(rec_ids, scores_table[score_id], score_id=score_id)\n    plt.show()\n\n    for i, score_id in enumerate(score_ids):\n        print_rank(rec_ids, scores_table[score_id], score_id=score_id)\n\n\nif __name__ == '__main__':\n    run()\n
", "rawMarkdown": "the code for extracting scores from log then plotting and sorting them\n\n \n import os \n import re\n \n import numpy as np\n import pandas as pd\n from matplotlib import pyplot as plt\n \n \n def extract_scores(log_path):\n def flush():\n if 't_f1' in score_dic:\n score_dic['t_pth'] = p_ths[0]\n del p_ths[0]\n if 'v_f1' in score_dic:\n score_dic['v_pth'] = p_ths[0]\n del p_ths[0]\n cur_score_dic = score_table[tag] if tag in score_table else {_var_id: [] for _var_id in var_ids}\n for _var_id in var_ids:\n if _var_id in score_dic:\n cur_score_dic[_var_id].append(score_dic[_var_id])\n score_dic.clear()\n score_table[tag] = cur_score_dic\n \n with open(log_path, 'r', encoding='utf-8') as log_file:\n lines = log_file.readlines()\n \n p_ths = []\n tag = None\n var_ids1 = ['t_auc', 't_f1', 't_precision', 't_recall', 'v_auc', 'v_f1', 'v_precision', 'v_recall']\n var_ids = var_ids1 + ['t_pth', 'v_pth']\n score_dic = {}\n score_table = {}\n for line in lines:\n matches = list(re.findall(r'last_best_p_th=([\\d.]+)', line))\n p_ths += [float(match) for match in matches] if matches else []\n \n for var_id in var_ids1:\n pat = f'{var_id}\\(?([-\\w\\d.]*)\\)?: ([\\d.]+)'\n matches = re.findall(pat, line)\n cur_tag, score = matches[0] if matches else (None, None)\n if cur_tag and cur_tag != tag:\n if tag is not None:\n flush()\n tag = cur_tag\n if score:\n score_dic[var_id] = float(score)\n flush()\n \n if 'best' in score_table:\n bs_dic = score_table['best']\n del score_table['best']\n score_table['b'] = bs_dic\n \n if 'steady' in score_table:\n ss_dic = score_table['steady']\n \n as_dic = {var_id: [] for var_id in var_ids}\n b_aucs = bs_dic['v_auc']\n s_aucs = ss_dic['v_auc']\n if b_aucs and s_aucs:\n for sid, b_scores in bs_dic.items():\n s_scores = ss_dic[sid]\n if b_scores and s_scores:\n a_scores = as_dic[sid]\n for i, b_auc in enumerate(b_aucs):\n a_scores.append(b_scores[i] if b_auc > s_aucs[i] else s_scores[i])\n score_table['a'] = as_dic\n \n rs_dic = {var_id: [] for var_id in var_ids}\n b_recalls = bs_dic['v_recall']\n s_recalls = ss_dic['v_recall']\n if b_recalls and s_recalls:\n for sid, b_scores in bs_dic.items():\n s_scores = ss_dic[sid]\n if b_scores and s_scores:\n r_scores = rs_dic[sid]\n for i, b_recall in enumerate(b_recalls):\n r_scores.append(b_scores[i] if b_recall > s_recalls[i] else s_scores[i])\n score_table['r'] = rs_dic\n if 'steady' in score_table:\n score_table['s'] = score_table['steady']\n del score_table['steady']\n if 'merge' in score_table:\n score_table['m'] = score_table['merge']\n del score_table['merge']\n \n return score_table\n \n \n def get_score_table(log_dir):\n rec_ids = []\n scores_table = {}\n recs = []\n for file in os.listdir(log_dir):\n fp = os.path.join(log_dir, file)\n if os.path.isfile(fp):\n rec_ids.append(file[:-4])\n recs.append(extract_scores(fp))\n \n rec_num = len(recs)\n for i, rec in enumerate(recs):\n for tag, score_dic in rec.items():\n for score_id, scores in score_dic.items():\n if scores:\n res_score_dic = scores_table[score_id] if score_id in scores_table else {}\n if len(scores) > 1:\n for j, score in enumerate(scores):\n entry_id = f'{tag}{j}'\n entry = res_score_dic[entry_id] if entry_id in res_score_dic else [np.nan] * rec_num\n entry[i] = score\n res_score_dic[entry_id] = entry\n scores_table[score_id] = res_score_dic\n else:\n score = scores[0]\n entry = res_score_dic[tag] if tag in res_score_dic else [np.nan] * rec_num\n entry[i] = score\n res_score_dic[tag] = entry\n scores_table[score_id] = res_score_dic\n return rec_ids, scores_table\n \n \n def plot_scores(ids, score_dic, idx_len_pair=None, score_id='auc', ignore_entries=None, y_steps=15, legend_max_cols=20):\n def get_loc(_idx_len_pair):\n _idx, _len = _idx_len_pair\n _rows, _cols, _index = _len // 2, 2, _idx + 1\n if _len % 2:\n _rows += 1\n if _index == _len:\n _cols = 1\n _index = _rows\n return _rows, _cols, _index\n \n colors = list('rybkcm')\n markers = list(',x')\n lines = ['-', '--', '-.', ':']\n styles = [f'{color}{marker}{line}' for marker in markers for color in colors for line in lines]\n \n if idx_len_pair is None:\n plt.figure()\n else:\n if not idx_len_pair[0]:\n plt.figure()\n rows, cols, index = get_loc(idx_len_pair)\n legend_max_cols //= cols\n plt.subplot(rows, cols, index)\n \n min_score, max_score = 1.0, 0.0\n for i, (entry_id, scores) in enumerate(score_dic.items()):\n if ignore_entries and entry_id in ignore_entries:\n plt.plot(scores, visible=False)\n else:\n min_score = min(min_score, np.nanmin(scores))\n max_score = max(max_score, np.nanmax(scores))\n plt.plot(scores, styles[i])\n plt.xticks(range(len(ids)), ids)\n y_step = (max_score - min_score) / y_steps\n plt.yticks(np.arange(min_score - y_step, max_score + 3 * y_step, y_step))\n plt.legend(score_dic.keys(), bbox_to_anchor=(0, 0, 1, 1), loc='upper right', borderaxespad=0, fontsize=6,\n ncol=legend_max_cols, mode='expand')\n plt.title(score_id)\n plt.grid()\n \n \n def print_rank(ids, score_dic, score_id='auc', ignore_entries=None):\n print(f'-------------------------------{score_id}-------------------------------')\n for entry_id, scores in score_dic.items():\n if not ignore_entries or entry_id not in ignore_entries:\n s = pd.Series(scores, index=ids).rank(ascending=False)\n print(f'{entry_id}: {s.to_dict()}')\n \n \n def run():\n log_root_dir = 'E:\\\\work\\\\kaggle\\\\quora\\\\logs'\n dir_name = 'tc\\\\bs'\n \n log_dir = os.path.join(log_root_dir, dir_name)\n rec_ids, scores_table = get_score_table(log_dir)\n \n entry_filter = ['a-gm', 'a-hm', 'r-gm', 'r-hm'] + [f'{tag}{i}' for tag in list('bs') for i in range(10)] + [\n f'{ms}-{mt}{w}' for ms in list('ar') for mt in ['m', 'am'] for w in [0.4, 0.5]] + [\n f'{e}{mt}' for mt in ['m', 'am'] for e in\n [0, 1, 2, 3, 4, 11, 12, 13, 14, 15, 16, 17, 18, 19]]\n for score_id, score_dic in scores_table.items():\n for entry_id in list(score_dic.keys()):\n if entry_id in entry_filter:\n del score_dic[entry_id]\n \n # score_ids = ['v_auc', 'v_f1', 'v_precision', 'v_recall']\n score_ids = ['v_auc', 'v_recall', 'v_f1']\n score_ids = [score_id for score_id in score_ids if score_id in scores_table]\n for i, score_id in enumerate(score_ids):\n plot_scores(rec_ids, scores_table[score_id], (i, len(score_ids)), score_id=score_id)\n score_id = 'v_pth'\n if score_id in scores_table:\n plot_scores(rec_ids, scores_table[score_id], score_id=score_id)\n plt.show()\n \n for i, score_id in enumerate(score_ids):\n print_rank(rec_ids, scores_table[score_id], score_id=score_id)\n \n \n if __name__ == '__main__':\n run()", "votes": null } ], "comments": [ { "id": 469055, "author_name": "yakolle", "author_url": "", "post_date": "02/10/2019 11:57:10", "content": "

the code for extracting scores from log then plotting and sorting them

\n\n
import os    \nimport re\n\nimport numpy as np\nimport pandas as pd\nfrom matplotlib import pyplot as plt\n\n\ndef extract_scores(log_path):\n    def flush():\n        if 't_f1' in score_dic:\n            score_dic['t_pth'] = p_ths[0]\n            del p_ths[0]\n        if 'v_f1' in score_dic:\n            score_dic['v_pth'] = p_ths[0]\n            del p_ths[0]\n        cur_score_dic = score_table[tag] if tag in score_table else {_var_id: [] for _var_id in var_ids}\n        for _var_id in var_ids:\n            if _var_id in score_dic:\n                cur_score_dic[_var_id].append(score_dic[_var_id])\n        score_dic.clear()\n        score_table[tag] = cur_score_dic\n\n    with open(log_path, 'r', encoding='utf-8') as log_file:\n        lines = log_file.readlines()\n\n    p_ths = []\n    tag = None\n    var_ids1 = ['t_auc', 't_f1', 't_precision', 't_recall', 'v_auc', 'v_f1', 'v_precision', 'v_recall']\n    var_ids = var_ids1 + ['t_pth', 'v_pth']\n    score_dic = {}\n    score_table = {}\n    for line in lines:\n        matches = list(re.findall(r'last_best_p_th=([\\d.]+)', line))\n        p_ths += [float(match) for match in matches] if matches else []\n\n        for var_id in var_ids1:\n            pat = f'{var_id}\\(?([-\\w\\d.]*)\\)?: ([\\d.]+)'\n            matches = re.findall(pat, line)\n            cur_tag, score = matches[0] if matches else (None, None)\n            if cur_tag and cur_tag != tag:\n                if tag is not None:\n                    flush()\n                tag = cur_tag\n            if score:\n                score_dic[var_id] = float(score)\n    flush()\n\n    if 'best' in score_table:\n        bs_dic = score_table['best']\n        del score_table['best']\n        score_table['b'] = bs_dic\n\n        if 'steady' in score_table:\n            ss_dic = score_table['steady']\n\n            as_dic = {var_id: [] for var_id in var_ids}\n            b_aucs = bs_dic['v_auc']\n            s_aucs = ss_dic['v_auc']\n            if b_aucs and s_aucs:\n                for sid, b_scores in bs_dic.items():\n                    s_scores = ss_dic[sid]\n                    if b_scores and s_scores:\n                        a_scores = as_dic[sid]\n                        for i, b_auc in enumerate(b_aucs):\n                            a_scores.append(b_scores[i] if b_auc > s_aucs[i] else s_scores[i])\n                score_table['a'] = as_dic\n\n            rs_dic = {var_id: [] for var_id in var_ids}\n            b_recalls = bs_dic['v_recall']\n            s_recalls = ss_dic['v_recall']\n            if b_recalls and s_recalls:\n                for sid, b_scores in bs_dic.items():\n                    s_scores = ss_dic[sid]\n                    if b_scores and s_scores:\n                        r_scores = rs_dic[sid]\n                        for i, b_recall in enumerate(b_recalls):\n                            r_scores.append(b_scores[i] if b_recall > s_recalls[i] else s_scores[i])\n                score_table['r'] = rs_dic\n    if 'steady' in score_table:\n        score_table['s'] = score_table['steady']\n        del score_table['steady']\n    if 'merge' in score_table:\n        score_table['m'] = score_table['merge']\n        del score_table['merge']\n\n    return score_table\n\n\ndef get_score_table(log_dir):\n    rec_ids = []\n    scores_table = {}\n    recs = []\n    for file in os.listdir(log_dir):\n        fp = os.path.join(log_dir, file)\n        if os.path.isfile(fp):\n            rec_ids.append(file[:-4])\n            recs.append(extract_scores(fp))\n\n    rec_num = len(recs)\n    for i, rec in enumerate(recs):\n        for tag, score_dic in rec.items():\n            for score_id, scores in score_dic.items():\n                if scores:\n                    res_score_dic = scores_table[score_id] if score_id in scores_table else {}\n                    if len(scores) > 1:\n                        for j, score in enumerate(scores):\n                            entry_id = f'{tag}{j}'\n                            entry = res_score_dic[entry_id] if entry_id in res_score_dic else [np.nan] * rec_num\n                            entry[i] = score\n                            res_score_dic[entry_id] = entry\n                        scores_table[score_id] = res_score_dic\n                    else:\n                        score = scores[0]\n                        entry = res_score_dic[tag] if tag in res_score_dic else [np.nan] * rec_num\n                        entry[i] = score\n                        res_score_dic[tag] = entry\n                        scores_table[score_id] = res_score_dic\n    return rec_ids, scores_table\n\n\ndef plot_scores(ids, score_dic, idx_len_pair=None, score_id='auc', ignore_entries=None, y_steps=15, legend_max_cols=20):\n    def get_loc(_idx_len_pair):\n        _idx, _len = _idx_len_pair\n        _rows, _cols, _index = _len // 2, 2, _idx + 1\n        if _len % 2:\n            _rows += 1\n            if _index == _len:\n                _cols = 1\n                _index = _rows\n        return _rows, _cols, _index\n\n    colors = list('rybkcm')\n    markers = list(',x')\n    lines = ['-', '--', '-.', ':']\n    styles = [f'{color}{marker}{line}' for marker in markers for color in colors for line in lines]\n\n    if idx_len_pair is None:\n        plt.figure()\n    else:\n        if not idx_len_pair[0]:\n            plt.figure()\n        rows, cols, index = get_loc(idx_len_pair)\n        legend_max_cols //= cols\n        plt.subplot(rows, cols, index)\n\n    min_score, max_score = 1.0, 0.0\n    for i, (entry_id, scores) in enumerate(score_dic.items()):\n        if ignore_entries and entry_id in ignore_entries:\n            plt.plot(scores, visible=False)\n        else:\n            min_score = min(min_score, np.nanmin(scores))\n            max_score = max(max_score, np.nanmax(scores))\n            plt.plot(scores, styles[i])\n    plt.xticks(range(len(ids)), ids)\n    y_step = (max_score - min_score) / y_steps\n    plt.yticks(np.arange(min_score - y_step, max_score + 3 * y_step, y_step))\n    plt.legend(score_dic.keys(), bbox_to_anchor=(0, 0, 1, 1), loc='upper right', borderaxespad=0, fontsize=6,\n               ncol=legend_max_cols, mode='expand')\n    plt.title(score_id)\n    plt.grid()\n\n\ndef print_rank(ids, score_dic, score_id='auc', ignore_entries=None):\n    print(f'-------------------------------{score_id}-------------------------------')\n    for entry_id, scores in score_dic.items():\n        if not ignore_entries or entry_id not in ignore_entries:\n            s = pd.Series(scores, index=ids).rank(ascending=False)\n            print(f'{entry_id}: {s.to_dict()}')\n\n\ndef run():\n    log_root_dir = 'E:\\\\work\\\\kaggle\\\\quora\\\\logs'\n    dir_name = 'tc\\\\bs'\n\n    log_dir = os.path.join(log_root_dir, dir_name)\n    rec_ids, scores_table = get_score_table(log_dir)\n\n    entry_filter = ['a-gm', 'a-hm', 'r-gm', 'r-hm'] + [f'{tag}{i}' for tag in list('bs') for i in range(10)] + [\n        f'{ms}-{mt}{w}' for ms in list('ar') for mt in ['m', 'am'] for w in [0.4, 0.5]] + [\n                       f'{e}{mt}' for mt in ['m', 'am'] for e in\n                       [0, 1, 2, 3, 4, 11, 12, 13, 14, 15, 16, 17, 18, 19]]\n    for score_id, score_dic in scores_table.items():\n        for entry_id in list(score_dic.keys()):\n            if entry_id in entry_filter:\n                del score_dic[entry_id]\n\n    # score_ids = ['v_auc', 'v_f1', 'v_precision', 'v_recall']\n    score_ids = ['v_auc', 'v_recall', 'v_f1']\n    score_ids = [score_id for score_id in score_ids if score_id in scores_table]\n    for i, score_id in enumerate(score_ids):\n        plot_scores(rec_ids, scores_table[score_id], (i, len(score_ids)), score_id=score_id)\n    score_id = 'v_pth'\n    if score_id in scores_table:\n        plot_scores(rec_ids, scores_table[score_id], score_id=score_id)\n    plt.show()\n\n    for i, score_id in enumerate(score_ids):\n        print_rank(rec_ids, scores_table[score_id], score_id=score_id)\n\n\nif __name__ == '__main__':\n    run()\n
", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "468976": "1. cnn\n 1.1 0,0 [cnn0][1] [cnn1][2] [cnn2][3] [cnn3][4] [cnn5][5]\n 1.2 0,0--stack [cnn0][6] [cnn1][7] [cnn2][8] [cnn3][9] [cnn5][10]\n 1.3 0,64 [cnn0][11] [cnn1][12] [cnn2][13] [cnn3][14] [cnn5][15]\n 1.4 0,64--stack [cnn0 ][16] [cnn1][17] [cnn2][18] [cnn3][19] [cnn5][20]\n 1.5.1 64-0 [cnn1][21] [cnn2][22] [cnn3][23] [cnn5][24] \n 1.5.2 64-0-【dropout=0.2】 [cnn1][25] [cnn2][26] [cnn3][27] [cnn5][28]\n 1.5.3 64-0-【0.2】【spatial_dropout=0.2】 [cnn1][29] [cnn2][30] [cnn3][31] [cnn5][32]\n 1.5.4 64-0-【0.2】【0.2】【trainable】 [cnn1][33] [cnn2][34] [cnn3][35] [cnn5][36]\n 1.6 64-0--mix [cnn0,1,2][37] [cnn0,1,2,3,4][38] [embed0,cnn0,1,2][39] [embed0,cnn0,1,2,3,4][40]\n 1.7 320,64 [cnn0][41] [cnn1][42] [cnn2][43] [cnn3][44] [cnn5][45]\n 1.8 320,64--stack [cnn0][46] [cnn1][47] [cnn2][48] [cnn3][49] [cnn5][50]\n\n 2. combine\n 2.1 lgb [n-1-1][51] [n-1-2][52] [n-2-1][53] [n-2-2][54] [y-1-1][55] [y-1-2][56] [y-2-1][57] [y-2-2][58]\n 2.2 tnn [n-1-1][59] [n-1-2][60] [n-2-1][61] [n-2-2][62] [y-1-1][63] [y-1-2][64] [y-2-1][65] [y-2-2][66]\n\n 3. classweight\n 3.1 pure [1][67] [1.5][68] [1.25][69] [1.75][70]\n 3.2 pure [2][71] [2.5][72] [3][73] [3.5][74] [4][75]\n 3.3 +nums [2][76] [2.5][77] [3][78] [3.5][79] [4][80]\n\n 4. data\n 4.1.1 guess valid target(cw=1) [normal&rnn0][81] [n&rnn0,1][82] [yes&rnn0][83] [y&rnn0,1][84]\n 4.1.2 cw=2 [y&rnn0][85] [y&rnn0,1][86]\n 4.2 vocab_size=90000 [rnn0][87] [rnn0,1][88]
\n 4.3.1 noise-submit [normal][89] [delete][90] [replace][91]
\n 4.3.2 noise-tune [n-1-1][92] [n-1-2][93] [n-2-1][94] [n-2-2][95] [d-1-1][96] [d-1-2][97] [d-2-1][98] [d-2-2][99] [r-1-1][100] [r-1-2][101] [r-2-1][102] [r-2-2][103]
\n 4.4.1 vocab_size-submit [all][104] [95000][105] [60000][106]\n 4.4.2 vocab_size-tune [a-1-1][107] [a-1-2][108] [a-2-1][109] [a-2-2][110] [9-1-1][111] [9-1-2][112] [9-2-1][113] [9-2-2][114] [6-1-1][115] [6-1-2][116] [6-2-1][117] [6-2-2][118]\n\n 5. embeds\n 5.1 glove [rnn00,rnn10][119] [rnn00][120] [rnn00,rnn01,rnn10,rnn11][121] [rnn00,rnn01][122]\n 5.2 glove+google [rnn00,rnn10][123] [rnn00][124] [rnn00,rnn01,rnn10,rnn11][125] [rnn00,rnn01][126]\n 5.3 gl+para [rnn00,rnn10][127] [rnn00][128] [rnn00,rnn01,rnn10,rnn11][129] [rnn00,rnn01][130]\n 5.4 gl+p+gg [rnn00,rnn10][131] [rnn00][132] [rnn00,rnn01,rnn10,rnn11][133] [rnn00,rnn01][134]\n 5.5 gl+p+w [rnn00,rnn10][135] [rnn00][136] [rnn00,rnn01,rnn10,rnn11][137] [rnn00,rnn01][138]\n 5.6 gl+p+w+gg [rnn00,rnn10][139] [rnn00][140] [rnn00,rnn01,rnn10,rnn11][141] [rnn00,rnn01][142]\n 5.7 gl+w [rnn00,rnn10][143] [rnn00][144] [rnn00,rnn01,rnn10,rnn11][145] [rnn00,rnn01][146]\n 5.8 gl+w+gg [rnn00,rnn10][147] [rnn00][148] [rnn00,rnn01,rnn10,rnn11][149] [rnn00,rnn01][150]\n 5.9 google [rnn00,rnn10][151] [rnn00][152] [rnn00,rnn01,rnn10,rnn11][153] [rnn00,rnn01][154]\n 5.10 para [rnn00,rnn10][155] [rnn00][156] [rnn00,rnn01,rnn10,rnn11][157] [rnn00,rnn01][158]\n 5.11 p+gg [rnn00,rnn10][159] [rnn00][160] [rnn00,rnn01,rnn10,rnn11][161] [rnn00,rnn01][162]\n 5.12 p+w [rnn00,rnn10][163] [rnn00][164] [rnn00,rnn01,rnn10,rnn11][165] [rnn00,rnn01][166]\n 5.13 p+w+gg [rnn00,rnn10][167] [rnn00][168] [rnn00,rnn01,rnn10,rnn11][169] [rnn00,rnn01][170]\n 5.14 wiki [rnn00,rnn10][171] [rnn00][172] [rnn00,rnn01,rnn10,rnn11][173] [rnn00,rnn01][174]\n 5.15 w+gg [rnn00,rnn10][175] [rnn00][176] [rnn00,rnn01,rnn10,rnn11][177] [rnn00,rnn01][178]\n\n 6. hidden\n 6.1 64-0 [false&cnn2,3][179] [f&cnn1,2,3][180]\n 6.2 64-0-64 [true&cnn2,3][181] [t&cnn1,2,3][182] [f&cnn2,3][183] [f&cnn1,2,3][184]\n 6.3 64-64 [t&cnn2,3][185] [t&cnn1,2,3][186] [f&cnn2,3][187] [f&cnn1,2,3][188]\n 6.4 64-64-0 [t&cnn2,3][189] [t&cnn1,2,3][190] [f&cnn2,3][191] [f&cnn1,2,3][192]\n 6.5 64-64-64 [t&cnn2,3][193] [t&cnn1,2,3][194] [f&cnn2,3][195] [f&cnn1,2,3][196]\n\n 7. hidden unit\n 7.1 0 [rnn0][197] [rnn0,1][198]\n 7.2 160 [rnn00,rnn10][199] [rnn00][200] [rnn00,rnn01,rnn10,rnn11][201] [rnn00,rnn01][202]\n 7.3.1 64-submit [n][203] [tn][204] [ty][205] [fn][206] [fy][207]\n 7.3.2 64-tune [n-1-1][208] [n-1-2][209] [n-2-1][210] [n-2-2][211] [y-1-1][212] [y-1-2][213] [y-2-1][214] [y-2-2][215] [tn-1-1][216] [tn-1-2][217] [tn-2-1][218] [tn-2-2][219] [ty-1-1][220] [ty-1-2][221] [ty-2-1][222] [ty-2-2][223] [fn-1-1][224] [fn-1-2][225] [fn-2-1][226] [fn-2-2][227] [fy-1-1][228] [fy-1-2][229] [fy-2-1][230] [fy-2-2][231]\n 7.4 plseu [rnn0][232] [rnn0,1][233] [rnn0,1&tanh][234]\n \n 8. lr\n 8.1 1.25e-4 [rnn0][235] [rnn0,1][236]\n 8.2 2.5 [rnn0][237] [rnn0,1][238]\n 8.3 20 [rnn0][239] [rnn0,1][240]\n 8.4 40 [rnn0][241] [rnn0,1][242]\n 8.5 5 [rnn0][243] [rnn0,1][244]\n 8.6 80 [rnn0][245] [rnn0,1][246]\n \n 9. lstm+lstm\n 9.1.1 hidden_unit=0 [rnn1][247] [rnn1&sd][248] [rnn0,1][249] [rnn0,1&sd][250]\n 9.1.2 0&attention&ll [rnn1][251] [rnn1&sd][252] [rnn0,1][253] [rnn0,1&sd][254]\n 9.1.3 0&attention&l [rnn1][255] [rnn1&sd][256] [rnn0,1][257] [rnn0,1&sd][258]\n 9.2.1 64&bn=True [rnn1][259] [rnn1&sd][260] [rnn0,1][261] [rnn0,1&sd][262]\n 9.2.2 64&False [rnn1][263] [rnn1&sd][264] [rnn0,1][265] [rnn0,1&sd][266]\n 9.3.1 256&bn=True [rnn1][267] [rnn1&sd][268] [rnn0,1][269] [rnn0,1&sd][270]\n 9.3.2 256&False [rnn1][271] [rnn1&sd][272] [rnn0,1][273] [rnn0,1&sd][274]\n \n 10. mix embeds\n 10.1 glove [gauss&tr&wvs&before][275] [gauss&tr&wvs&after][276] [mean&tr&wvs][277] [gauss&tr][278] [mean&tr][279] [the&tr][280] [gauss&rs&wvs&before][281] [gauss&rs&wvs&after][282] [mean&rs&wvs][283] [gauss&rs][284] [mean&rs][285] [the&rs][286]\n 10.2 gl+p [gauss&tr&wvs&after][287] [mean&tr&wvs][288] [gauss&tr][289] [mean&tr][290] [the&tr][291] [gauss&rs&wvs&after][292] [mean&rs&wvs][293] [gauss&rs][294] [mean&rs][295] [the&rs][296]\n 10.3.1 gl+p+w+gg-submit [gl][297] [gl+p][298] [all][299]\n 10.3.2 gl+p+w+gg-tune [g11][300] [g12][301] [g21][302] [g22][303] [gp11][304] [gp12][305] [gp21][306] [gp22][307] [a11][308] [a12][309] [a21][310] [a22][311]\n 10.4 random [n11][312] [n12][313] [n21][314] [n22][315] [y11][316] [y12][317] [y21][318] [y22][319]\n\n 11. num+seg+rnn+cnn\n 11.1 64-0-0 [True][320] [False][321]\n 11.2 160,64-0-0 [tt][322] [tf][323] [ft][324] [ff][325]\n 11.3 320,64-0-0 [tt][326] [tf][327] [ft][328] [ff][329]\n 11.4 nums(64) [t][330] [f][331] [fm&t][332] [fm&f][333]\n 11.5 nums(320,64) [tt][334] [tf][335] [ft][336] [ff][337] [fm&tt][338] [fm&tf][339] [fm&ft][340] [fm&ff][341]\n \n 12. pure cnn\n 12.1 0 [cnn3][342] [cnn5][343]\n 12.2 16 [t3][344] [t5][345] [f3][346] [f5][347]\n 12.3 64 [t3][348] [t5][349] [f3][350] [f5][351]\n 12.4 128 [t3][352] [t5][353] [f3][354] [f5][355]\n 12.4 256 [t3][356] [t5][357] [f3][358] [f5][359]\n \n 13. pure gru\n 13.1 [0][360]\n 13.2 16 [t][361] [f][362]\n 13.3 64 [t][363] [f][364]\n 13.4 128 [t][365] [f][366]\n 13.5 256 [t][367] [f][368]\n \n 14. pure lstm\n 14.1 [0][369]\n 14.2 16 [t][370] [f][371]\n 14.3 64 [t][372] [f][373]\n 14.4 128 [t][374] [f][375]\n 14.5 256 [t][376] [f][377]\n \n 15. rnn cnn\n 15.1 attention [rnn00,rnn01][378] [rnn00,rnn01,rnn10,rnn11][379]\n 15.2 cnn=1 [rnn00,rnn01][380] [rnn00,rnn01,rnn10,rnn11][381]\n 15.3.1 gru&lstm [rnn00,rnn01][382] [rnn00,rnn01,rnn10,rnn11][383]\n 15.3.2 lstm [rnn01][384] [rnn01,rnn11][385]\n 15.4.1 lstm&320,64 [rnn0][386] [rnn0,rnn1][387]\n 15.4.2 0,64 [rnn01][388] [rnn01,rnn11][389]\n 15.4.3 320,64-0 [rnn0][390] [rnn0,rnn1][391]\n 15.4.4 64-0 [rnn01][392] [rnn01,rnn11][393]\n 15.4.5 320,64-64 [rnn01][394] [rnn01,rnn11][395]\n 15.5.1 mixed_rnn_cnn&gru [rnn00&cnn1,2,3&rnn_sd][396] [rnn00&cnn1,2,3,5&rnn_sd][397] [rnn00&cnn1,2,3&sd_rnn][398] [rnn00&cnn1,2,3,5&sd_rnn][399]\n 15.5.2 lstm [rnn00&cnn1,2,3&rnn_sd][400] [rnn00&cnn1,2,3,5&rnn_sd][401] [rnn00&cnn1,2,3&sd_rnn][402] [rnn00&cnn1,2,3,5&sd_rnn][403]\n 15.6 seg_rnn_20 [rnn0][404] [rnn0,1][405] [rnn0,1&tanh][406]\n \n 16. rnn cnn embed\n 16.1 eid=0-1-2-3 [rnn00,rnn10][407] [rnn00][408] [rnn00,rnn01,rnn10,rnn11][409] [rnn00,rnn01][410]\n 16.2 4-5-6-7 [rnn00,rnn10][411] [rnn00][412] [rnn00,rnn01,rnn10,rnn11][413] [rnn00,rnn01][414]\n 16.3 8-10 [rnn00,rnn10][415] [rnn00,rnn01,rnn10,rnn11][416]\n 16.3 12-14 [rnn00,rnn10][417] [rnn00,rnn01,rnn10,rnn11][418]\n 16.1 16-17-18-19 [rnn00,rnn10][419] [rnn00][420] [rnn00,rnn01,rnn10,rnn11][421] [rnn00,rnn01][422]\n 16.2 20-21-22-23 [rnn00,rnn10][423] [rnn00][424] [rnn00,rnn01,rnn10,rnn11][425] [rnn00,rnn01][426]\n 16.3 24-26 [rnn00,rnn10][427] [rnn00,rnn01,rnn10,rnn11][428]\n 16.3 28-30 [rnn00,rnn10][429] [rnn00,rnn01,rnn10,rnn11][430]\n 16.1 32-33-34-35 [rnn00,rnn10][431] [rnn00][432] [rnn00,rnn01,rnn10,rnn11][433] [rnn00,rnn01][434]\n 16.2 36-37-38-39 [rnn00,rnn10][435] [rnn00][436] [rnn00,rnn01,rnn10,rnn11][437] [rnn00,rnn01][438]\n 16.3 40-42 [rnn00,rnn10][439] [rnn00,rnn01,rnn10,rnn11][440]\n 16.3 44-46 [rnn00,rnn10][441] [rnn00,rnn01,rnn10,rnn11][442]\n \n 17. rnn+cnn\n 17.1 0 [rnn00&cnn1,2,3&rnn_sd][443] [rnn00&cnn1,2,3,5&rnn_sd][444] [rnn00&cnn1,2,3&sd_rnn][445] [rnn00&cnn1,2,3,5&sd_rnn][446]\n 17.2.1 0-64&Ture [rnn00&cnn1,2,3&rnn_sd][447] [rnn00&cnn1,2,3,5&rnn_sd][448] [rnn00&cnn1,2,3&sd_rnn][449] [rnn00&cnn1,2,3,5&sd_rnn][450]\n 17.2.2 0-64&False [rnn00&cnn1,2,3&rnn_sd][451] [rnn00&cnn1,2,3,5&rnn_sd][452] [rnn00&cnn1,2,3&sd_rnn][453] [rnn00&cnn1,2,3,5&sd_rnn][454]\n 17.3.1 64&Ture [rnn00&cnn1,2,3&rnn_sd][455] [rnn00&cnn1,2,3,5&rnn_sd][456] [rnn00&cnn1,2,3&sd_rnn][457] [rnn00&cnn1,2,3,5&sd_rnn][458]\n 17.3.2 64&False [rnn00&cnn1,2,3&rnn_sd][459] [rnn00&cnn1,2,3,5&rnn_sd][460] [rnn00&cnn1,2,3&sd_rnn][461] [rnn00&cnn1,2,3,5&sd_rnn][462]\n 17.4.1 64-0&Ture [rnn00&cnn1,2,3&rnn_sd][463] [rnn00&cnn1,2,3,5&rnn_sd][464] [rnn00&cnn1,2,3&sd_rnn][465] [rnn00&cnn1,2,3,5&sd_rnn][466]\n 17.4.2 64-0&False [rnn00&cnn1,2,3&rnn_sd][467] [rnn00&cnn1,2,3,5&rnn_sd][468] [rnn00&cnn1,2,3&sd_rnn][469] [rnn00&cnn1,2,3,5&sd_rnn][470]\n \n 18. rtnn\n 18.1 64-0;320,64 [True][471] [False][472]\n 18.2 320,64;64-0 [True][473] [False][474]\n \n 19. seg+rnn+cnn\n 19.1 64 [True][475] [False][476]\n 19.2.1 64-0-0&rnn1&false [rnn00&cnn1,2,3&rnn_sd][477] [rnn00&cnn1,2,3,5&rnn_sd][478] [rnn00&cnn1,2,3&sd_rnn][479] [rnn00&cnn1,2,3,5&sd_rnn][480]\n 19.2.2 rnn0,1&false [rnn00&cnn1,2,3&rnn_sd][481] [rnn00&cnn1,2,3,5&rnn_sd][482] [rnn00&cnn1,2,3&sd_rnn][483] [rnn00&cnn1,2,3,5&sd_rnn][484]\n 19.2.3 rnn1&true [rnn00&cnn1,2,3&rnn_sd][485] [rnn00&cnn1,2,3,5&rnn_sd][486] [rnn00&cnn1,2,3&sd_rnn][487] [rnn00&cnn1,2,3,5&sd_rnn][488]\n 19.2.4 rnn0,1&true [rnn00&cnn1,2,3&rnn_sd][489] [rnn00&cnn1,2,3,5&rnn_sd][490] [rnn00&cnn1,2,3&sd_rnn][491] [rnn00&cnn1,2,3,5&sd_rnn][492]\n 19.2.5 rnn0,1&cnn1,2,3&flase&sd_rnn [cw=1:1][493] [1:2&stratified][494] [1:1&stratified][495]\n 19.3 64-0-64 [tt][496] [tf][497] [ft][498] [ff][499]\n 19.4 64-64 [tt][500] [tf][501] [ft][502] [ff][503]\n 19.5 64-64-0 [tt][504] [tf][505] [ft][506] [ff][507]\n 19.6.1 64-64-64&true [tt][508] [tf][509] [ft][510] [ff][511]\n 19.6.2 64-64-64&false [tt][512] [tf][513] [ft][514] [ff][515]\n 19.7 backward [forward&cnn2,3][516] [forward&cnn1,2,3][517] [backward&cnn2,3][518] [backward&cnn1,2,3][519] [bi&cnn2,3][520] [bi&cnn1,2,3][521]\n 19.8.1 bi&forward [fd&cnn2,3][522] [fd&cnn1,2,3][523] [bd&cnn2,3][524] [bd&cnn1,2,3][525] [bi&cnn2,3][526] [bi&cnn1,2,3][527]\n 19.8.2 backward [fd&cnn2,3][528] [fd&cnn1,2,3][529] [bd&cnn2,3][530] [bd&cnn1,2,3][531] [bi&cnn2,3][532] [bi&cnn1,2,3][533]\n 19.8.3 bi [fd&cnn2,3][534] [fd&cnn1,2,3][535] [bd&cnn2,3][536] [bd&cnn1,2,3][537] [bi&cnn2,3][538] [bi&cnn1,2,3][539]\n 19.9 forward [fd&cnn2,3][540] [fd&cnn1,2,3][541] [bd&cnn2,3][542] [bd&cnn1,2,3][543] [bi&cnn2,3][544] [bi&cnn1,2,3][545]\n 19.10 best&steady [best][546] [steady][547]\n 19.11 dropout [tt][548] [tf][549] [ft][550] [ff][551]\n \n 20. tta\n 20.1 oof_seed [11][552] [12][553] [21][554] [22][555] [31][556] [32][557] [41][558] [42][559]
\n 20.2.1 batch_size-submit [1024][560] [512][561] [768][562]\n 20.2.2 batch_size-tune [111][563] [112][564] [121][565] [122][566] [511][567] [512][568] [521][569] [522][570] [711][571] [712][572] [721][573] [722][574]
\n 20.3.1 digit-submit [n][575] [y][576]
\n 20.3.2 digit-tune [n11][577] [n12][578] [n21][579] [n22][580] [y11][581] [y12][582] [y21][583] [y22][584]
\n 20.4.1 epoch-fixed [12-11][585] [12-12][586] [12-21][587] [12-22][588] [9-11][589] [9-12][590] [9-21][591] [9-22][592] [7-11][593] [7-12][594] [7-21][595] [7-22][596]
\n 20.4.2 epoch=20 [011][597] [012][598] [021][599] [022][600] [211][601] [212][602] [221][603] [222][604] [311][605] [312][606] [321][607] [322][608] [411][609] [412][610] [421][611] [422][612] [511][613] [512][614] [521][615] [522][616]
\n 20.5.1 nums-submit [n][617] [y][618]
\n 20.5.2 nums-tune [n11][619] [n12][620] [n21][621] [n22][622] [yf11][623] [yf12][624] [yf21][625] [yf22][626] [yt11][627] [yt12][628] [yt21][629] [yt22][630]
\n 20.6 pad [pre&max][631] [pre&last][632] [pre&max&last][633] [post&max][634] [post&last][635] [post&max&last][636]
\n 20.7.1 punct-submit [n][637] [y56][638] [y60][639]
\n 20.7.2 punct-tune [n11][640] [n12][641] [n21][642] [n22][643] [y11][644] [y12][645] [y21][646] [y22][647] [y&digits&11][648] [yd12][649] [yd21][650] [yd22][651]
\n 20.7.3 punct-tune-seed [1][652] [21][653] [22][654] [3][655] [4][656] [5][657] [6][658] [7][659]
\n 20.8 seg_max [14,56][660] [7][661] [7,56][662] [7,14][663] [7,14,56][664]\n 20.9.1 sug-submit [n][665] [y][666]\n 20.9.2 sug-tune [n11][667] [n12][668] [n21][669] [n22][670] [y11][671] [y12][672] [y21][673] [y22][674]\n \n 21. rnn\n 21.1.1 type&eid=0 [ll11][675] [ll12][676] [ll21][677] [ll22][678]\n 21.1.2 type&eid=2 [ll11][679] [ll12][680] [ll21][681] [ll22][682] [lg11][683] [lg12][684] [lg21][685] [lg22][686] [gl11][687] [gl12][688] [gl21][689] [gl22][690] [gg11][691] [gg12][692] [gg21][693] [gg22][694]\n 21.2.1 embed [n11][695] [n12][696] [n21][697] [n22][698] [seed=1&11][699] [112][700] [121][701] [122][702] [211][703] [212][704] [221][705] [222][706] [mean&11][707] [m12][708] [m21][709] [m22][710] [the&11][711] [t12][712] [t21][713] [t22][714] [row_mean&11][715] [rm12][716] [rm21][717] [rm22][718] [it&11][719] [i12][720] [i21][721] [i22][722] [that&11][723] [a12][724] [a21][725] [a22][726]\n 21.2.2 embed&torch [the&111][727] [t112][728] [t121][729] [t122][730] [t211][731] [t212][732] [t221][733] [t222][734]\n \n\n\n----------\n**torch**\n\n 1. md\n 1.1.1 batch_size&eid=2 [1024&11][735] [112][736] [121][737] [512&11][738] [512][739] [521][740]\n 1.1.2 batch_size&eid=4 [1024&11][741] [112][742] [121][743] [122][744] [512&11][745] [512][746] [521][747]\n 1.2 dropout [0.2&11][748] [212][749] [221][750] [0.1&11][751] [112][752] [121][753] [122][754]\n 1.3 cnn [eid=3&11][755] [312][756] [321][757] [322][758] [011][759] [012][760] [021][761] [022][762] [111][763] [112][764] [121][765] [122][766] [411][767] [412][768] [421][769] [422][770] [511][771] [512][772] [521][773] [522][774]\n 1.4.1 lr-fixed [1e-3&1][775] [12][776] [21][777] [22][778] [41][779] [42][780] [81][781] [82][782] [.51][783]\n 1.4.2 lr-clr [n11][784] [n12][785] [n21][786] [y11][787] [y12][788] [y21][789] [y22][790]\n 1.5 classweight [111][791] [112][792] [121][793] [122][794] [211][795] [212][796] [221][797] [222][798]\n 1.6 token [n11][799] [n12][800] [n21][801] [n22][802] [with_lr_scheduler&11][803] [w12][804] [w21][805] [w22][806] [without_lr_scheduler&11][807] [o12][808] [o21][809]\n \n 2. rnn2\n 2.1.1 type&normal [gg11][810] [gg12][811] [gg21][812] [ll11][813] [ll12][814] [ll21][815] [ll22][816] [gl11][817] [gl12][818] [gl21][819] [lg11][820] [lg12][821] [lg21][822] [lg22][823]\n 2.1.2 bi [ll11][824] [ll12][825] [ll21][826] [ll22][827] [lg11][828] [lg12][829] [lg21][830] [gl11][831] [gl12][832] [gl21][833] [gl22][834] [gg11][835] [gg12][836] [gg21][837]\n \n \n \n\n \n\n\n [1]: https://www.kaggle.com/yakolle/quora-tnn-2/output?scriptVersionId=8301643\n [2]: https://www.kaggle.com/yakolle/quora-tnn-3/output?scriptVersionId=8301685\n [3]: https://www.kaggle.com/yakolle/quora-tnn-4/output?scriptVersionId=8301577\n [4]: https://www.kaggle.com/yakolle/quora-tnn-5/output?scriptVersionId=8301579\n [5]: https://www.kaggle.com/yakolle/quora-tnn-6/output?scriptVersionId=8301581\n [6]: https://www.kaggle.com/yakolle/quora-tnn-2/output?scriptVersionId=8321885\n [7]: https://www.kaggle.com/yakolle/quora-tnn-3/output?scriptVersionId=8321887\n [8]: https://www.kaggle.com/yakolle/quora-tnn-4/output?scriptVersionId=8321889\n [9]: https://www.kaggle.com/yakolle/quora-tnn-5/output?scriptVersionId=8321891\n [10]: https://www.kaggle.com/yakolle/quora-tnn-6/output?scriptVersionId=8321892\n [11]: https://www.kaggle.com/yakolle/quora-tnn-7/output?scriptVersionId=8301875\n [12]: https://www.kaggle.com/yakolle/quora-tnn-8/output?scriptVersionId=8301878\n [13]: https://www.kaggle.com/yakolle/quora-tnn-9/output?scriptVersionId=8301880\n [14]: https://www.kaggle.com/yakolle/quora-tnn-10/output?scriptVersionId=8301882\n [15]: https://www.kaggle.com/yakolle/quora-tnn-11/output?scriptVersionId=8301885\n [16]: https://www.kaggle.com/yakolle/quora-tnn-7/output?scriptVersionId=8322073\n [17]: https://www.kaggle.com/yakolle/quora-tnn-8/output?scriptVersionId=8322074\n [18]: https://www.kaggle.com/yakolle/quora-tnn-9/output?scriptVersionId=8322077\n [19]: https://www.kaggle.com/yakolle/quora-tnn-10/output?scriptVersionId=8322081\n [20]: https://www.kaggle.com/yakolle/quora-tnn-11/output?scriptVersionId=8322082\n [21]: https://www.kaggle.com/yakolle/quora-tnn-2/output?scriptVersionId=8329968\n [22]: https://www.kaggle.com/yakolle/quora-tnn-3/output?scriptVersionId=8329970\n [23]: https://www.kaggle.com/yakolle/quora-tnn-4/output?scriptVersionId=8329971\n [24]: https://www.kaggle.com/yakolle/quora-tnn-5/output?scriptVersionId=8329972\n [25]: https://www.kaggle.com/yakolle/quora-tnn-2/output?scriptVersionId=8340306\n [26]: https://www.kaggle.com/yakolle/quora-tnn-3/output?scriptVersionId=8340308\n [27]: https://www.kaggle.com/yakolle/quora-tnn-4/output?scriptVersionId=8340310\n [28]: https://www.kaggle.com/yakolle/quora-tnn-5/output?scriptVersionId=8340312\n [29]: https://www.kaggle.com/yakolle/quora-tnn-2/output?scriptVersionId=8341619\n [30]: https://www.kaggle.com/yakolle/quora-tnn-3/output?scriptVersionId=8341622\n [31]: https://www.kaggle.com/yakolle/quora-tnn-4/output?scriptVersionId=8341623\n [32]: https://www.kaggle.com/yakolle/quora-tnn-5/output?scriptVersionId=8341626\n [33]: https://www.kaggle.com/yakolle/quora-tnn-2/output?scriptVersionId=8343909\n [34]: https://www.kaggle.com/yakolle/quora-tnn-3/output?scriptVersionId=8343911\n [35]: https://www.kaggle.com/yakolle/quora-tnn-4/output?scriptVersionId=8343912\n [36]: https://www.kaggle.com/yakolle/quora-tnn-5/output?scriptVersionId=8343914\n [37]: https://www.kaggle.com/yakolle/quora-tnn-2/output?scriptVersionId=8338339\n [38]: https://www.kaggle.com/yakolle/quora-tnn-3/output?scriptVersionId=8338340\n [39]: https://www.kaggle.com/yakolle/quora-tnn-4/output?scriptVersionId=8338349\n [40]: https://www.kaggle.com/yakolle/quora-tnn-5/output?scriptVersionId=8338356\n [41]: https://www.kaggle.com/yakolle/quora-tnn-2/output?scriptVersionId=8319923\n [42]: https://www.kaggle.com/yakolle/quora-tnn-3/output?scriptVersionId=8319924\n [43]: https://www.kaggle.com/yakolle/quora-tnn-4/output?scriptVersionId=8319926\n [44]: https://www.kaggle.com/yakolle/quora-tnn-5/output?scriptVersionId=8319927\n [45]: https://www.kaggle.com/yakolle/quora-tnn-11/output?scriptVersionId=8301885\n [46]: https://www.kaggle.com/yakolle/quora-tnn-2/output?scriptVersionId=8323956\n [47]: https://www.kaggle.com/yakolle/quora-tnn-3/output?scriptVersionId=8323962\n [48]: https://www.kaggle.com/yakolle/quora-tnn-4/output?scriptVersionId=8323963\n [49]: https://www.kaggle.com/yakolle/quora-tnn-5/output?scriptVersionId=8323964\n [50]: https://www.kaggle.com/yakolle/quora-tnn-6/output?scriptVersionId=8323967\n [51]: https://www.kaggle.com/yakolle/quora-tnn-4?scriptVersionId=9129907\n [52]: https://www.kaggle.com/yakolle/quora-tnn-5?scriptVersionId=9129910\n [53]: https://www.kaggle.com/yakolle/quora-tnn-6?scriptVersionId=9129912\n [54]: https://www.kaggle.com/yakolle/quora-tnn-7?scriptVersionId=9129914\n [55]: 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https://www.kaggle.com/yakolle/quora2-tnn-3?scriptVersionId=10200921\n [837]: https://www.kaggle.com/yakolle/quora2-tnn-5?scriptVersionId=10200923", "469055": "the code for extracting scores from log then plotting and sorting them\n\n \n import os \n import re\n \n import numpy as np\n import pandas as pd\n from matplotlib import pyplot as plt\n \n \n def extract_scores(log_path):\n def flush():\n if 't_f1' in score_dic:\n score_dic['t_pth'] = p_ths[0]\n del p_ths[0]\n if 'v_f1' in score_dic:\n score_dic['v_pth'] = p_ths[0]\n del p_ths[0]\n cur_score_dic = score_table[tag] if tag in score_table else {_var_id: [] for _var_id in var_ids}\n for _var_id in var_ids:\n if _var_id in score_dic:\n cur_score_dic[_var_id].append(score_dic[_var_id])\n score_dic.clear()\n score_table[tag] = cur_score_dic\n \n with open(log_path, 'r', encoding='utf-8') as log_file:\n lines = log_file.readlines()\n \n p_ths = []\n tag = None\n var_ids1 = ['t_auc', 't_f1', 't_precision', 't_recall', 'v_auc', 'v_f1', 'v_precision', 'v_recall']\n var_ids = var_ids1 + ['t_pth', 'v_pth']\n score_dic = {}\n score_table = {}\n for line in lines:\n matches = list(re.findall(r'last_best_p_th=([\\d.]+)', line))\n p_ths += [float(match) for match in matches] if matches else []\n \n for var_id in var_ids1:\n pat = f'{var_id}\\(?([-\\w\\d.]*)\\)?: ([\\d.]+)'\n matches = re.findall(pat, line)\n cur_tag, score = matches[0] if matches else (None, None)\n if cur_tag and cur_tag != tag:\n if tag is not None:\n flush()\n tag = cur_tag\n if score:\n score_dic[var_id] = float(score)\n flush()\n \n if 'best' in score_table:\n bs_dic = score_table['best']\n del score_table['best']\n score_table['b'] = bs_dic\n \n if 'steady' in score_table:\n ss_dic = score_table['steady']\n \n as_dic = {var_id: [] for var_id in var_ids}\n b_aucs = bs_dic['v_auc']\n s_aucs = ss_dic['v_auc']\n if b_aucs and s_aucs:\n for sid, b_scores in bs_dic.items():\n s_scores = ss_dic[sid]\n if b_scores and s_scores:\n a_scores = as_dic[sid]\n for i, b_auc in enumerate(b_aucs):\n a_scores.append(b_scores[i] if b_auc > s_aucs[i] else s_scores[i])\n score_table['a'] = as_dic\n \n rs_dic = {var_id: [] for var_id in var_ids}\n b_recalls = bs_dic['v_recall']\n s_recalls = ss_dic['v_recall']\n if b_recalls and s_recalls:\n for sid, b_scores in bs_dic.items():\n s_scores = ss_dic[sid]\n if b_scores and s_scores:\n r_scores = rs_dic[sid]\n for i, b_recall in enumerate(b_recalls):\n r_scores.append(b_scores[i] if b_recall > s_recalls[i] else s_scores[i])\n score_table['r'] = rs_dic\n if 'steady' in score_table:\n score_table['s'] = score_table['steady']\n del score_table['steady']\n if 'merge' in score_table:\n score_table['m'] = score_table['merge']\n del score_table['merge']\n \n return score_table\n \n \n def get_score_table(log_dir):\n rec_ids = []\n scores_table = {}\n recs = []\n for file in os.listdir(log_dir):\n fp = os.path.join(log_dir, file)\n if os.path.isfile(fp):\n rec_ids.append(file[:-4])\n recs.append(extract_scores(fp))\n \n rec_num = len(recs)\n for i, rec in enumerate(recs):\n for tag, score_dic in rec.items():\n for score_id, scores in score_dic.items():\n if scores:\n res_score_dic = scores_table[score_id] if score_id in scores_table else {}\n if len(scores) > 1:\n for j, score in enumerate(scores):\n entry_id = f'{tag}{j}'\n entry = res_score_dic[entry_id] if entry_id in res_score_dic else [np.nan] * rec_num\n entry[i] = score\n res_score_dic[entry_id] = entry\n scores_table[score_id] = res_score_dic\n else:\n score = scores[0]\n entry = res_score_dic[tag] if tag in res_score_dic else [np.nan] * rec_num\n entry[i] = score\n res_score_dic[tag] = entry\n scores_table[score_id] = res_score_dic\n return rec_ids, scores_table\n \n \n def plot_scores(ids, score_dic, idx_len_pair=None, score_id='auc', ignore_entries=None, y_steps=15, legend_max_cols=20):\n def get_loc(_idx_len_pair):\n _idx, _len = _idx_len_pair\n _rows, _cols, _index = _len // 2, 2, _idx + 1\n if _len % 2:\n _rows += 1\n if _index == _len:\n _cols = 1\n _index = _rows\n return _rows, _cols, _index\n \n colors = list('rybkcm')\n markers = list(',x')\n lines = ['-', '--', '-.', ':']\n styles = [f'{color}{marker}{line}' for marker in markers for color in colors for line in lines]\n \n if idx_len_pair is None:\n plt.figure()\n else:\n if not idx_len_pair[0]:\n plt.figure()\n rows, cols, index = get_loc(idx_len_pair)\n legend_max_cols //= cols\n plt.subplot(rows, cols, index)\n \n min_score, max_score = 1.0, 0.0\n for i, (entry_id, scores) in enumerate(score_dic.items()):\n if ignore_entries and entry_id in ignore_entries:\n plt.plot(scores, visible=False)\n else:\n min_score = min(min_score, np.nanmin(scores))\n max_score = max(max_score, np.nanmax(scores))\n plt.plot(scores, styles[i])\n plt.xticks(range(len(ids)), ids)\n y_step = (max_score - min_score) / y_steps\n plt.yticks(np.arange(min_score - y_step, max_score + 3 * y_step, y_step))\n plt.legend(score_dic.keys(), bbox_to_anchor=(0, 0, 1, 1), loc='upper right', borderaxespad=0, fontsize=6,\n ncol=legend_max_cols, mode='expand')\n plt.title(score_id)\n plt.grid()\n \n \n def print_rank(ids, score_dic, score_id='auc', ignore_entries=None):\n print(f'-------------------------------{score_id}-------------------------------')\n for entry_id, scores in score_dic.items():\n if not ignore_entries or entry_id not in ignore_entries:\n s = pd.Series(scores, index=ids).rank(ascending=False)\n print(f'{entry_id}: {s.to_dict()}')\n \n \n def run():\n log_root_dir = 'E:\\\\work\\\\kaggle\\\\quora\\\\logs'\n dir_name = 'tc\\\\bs'\n \n log_dir = os.path.join(log_root_dir, dir_name)\n rec_ids, scores_table = get_score_table(log_dir)\n \n entry_filter = ['a-gm', 'a-hm', 'r-gm', 'r-hm'] + [f'{tag}{i}' for tag in list('bs') for i in range(10)] + [\n f'{ms}-{mt}{w}' for ms in list('ar') for mt in ['m', 'am'] for w in [0.4, 0.5]] + [\n f'{e}{mt}' for mt in ['m', 'am'] for e in\n [0, 1, 2, 3, 4, 11, 12, 13, 14, 15, 16, 17, 18, 19]]\n for score_id, score_dic in scores_table.items():\n for entry_id in list(score_dic.keys()):\n if entry_id in entry_filter:\n del score_dic[entry_id]\n \n # score_ids = ['v_auc', 'v_f1', 'v_precision', 'v_recall']\n score_ids = ['v_auc', 'v_recall', 'v_f1']\n score_ids = [score_id for score_id in score_ids if score_id in scores_table]\n for i, score_id in enumerate(score_ids):\n plot_scores(rec_ids, scores_table[score_id], (i, len(score_ids)), score_id=score_id)\n score_id = 'v_pth'\n if score_id in scores_table:\n plot_scores(rec_ids, scores_table[score_id], score_id=score_id)\n plt.show()\n \n for i, score_id in enumerate(score_ids):\n print_rank(rec_ids, scores_table[score_id], score_id=score_id)\n \n \n if __name__ == '__main__':\n run()" }, "source": "meta" }