from __future__ import annotations import argparse import csv from io import StringIO from pathlib import Path import numpy as np from matplotlib.colors import to_rgba SCRIPT_DIR = Path(__file__).resolve().parent DEFAULT_OUTPUT_PNG = SCRIPT_DIR / "merged_prompt_evidence_row_neurips_v10.png" DEFAULT_OUTPUT_PDF = SCRIPT_DIR / "merged_prompt_evidence_row_neurips_v10.pdf" # ========================= # 一、内嵌原始数据 # 作用：把作图需要的矩阵和样本摘要直接写在脚本里，保证脚本可独立运行。 # ========================= EMBEDDED_ROUTING_ACCURACY_CSV = """ 1.000000, nan, nan, nan, nan, nan, nan, nan, nan, nan 0.188525, 0.807018, nan, nan, nan, nan, nan, nan, nan, nan 0.147541, 0.228070, 0.697674, nan, nan, nan, nan, nan, nan, nan 0.122951, 0.210526, 0.622093, 0.126050, nan, nan, nan, nan, nan, nan 0.122951, 0.210526, 0.610465, 0.126050, 0.047619, nan, nan, nan, nan, nan 0.114754, 0.201754, 0.581395, 0.075630, 0.047619, 0.107692, nan, nan, nan, nan 0.024590, 0.026316, 0.162791, 0.008403, 0.000000, 0.007692, 0.914130, nan, nan, nan 0.016393, 0.026316, 0.139535, 0.000000, 0.000000, 0.007692, 0.896739, 0.059140, nan, nan 0.016393, 0.017544, 0.116279, 0.000000, 0.000000, 0.007692, 0.891304, 0.059140, 0.098592, nan 0.016393, 0.017544, 0.116279, 0.000000, 0.000000, 0.007692, 0.885870, 0.059140, 0.098592, 0.031056 """ EMBEDDED_INFER_AUC_CSV = """ 0.508404, nan, nan, nan, nan, nan, nan, nan, nan, nan 0.484248, 0.530348, nan, nan, nan, nan, nan, nan, nan, nan 0.486145, 0.468685, 0.492018, nan, nan, nan, nan, nan, nan, nan 0.478149, 0.452437, 0.491144, 0.548022, nan, nan, nan, nan, nan, nan 0.478149, 0.452437, 0.499340, 0.548022, 0.434211, nan, nan, nan, nan, nan 0.465087, 0.451564, 0.493277, 0.518542, 0.407895, 0.585271, nan, nan, nan, nan 0.480627, 0.590434, 0.524748, 0.537515, 0.473684, 0.497348, 0.350500, nan, nan, nan 0.491326, 0.587130, 0.522272, 0.538602, 0.473684, 0.496091, 0.372225, 0.411558, nan, nan 0.491956, 0.587458, 0.519194, 0.554960, 0.473684, 0.496361, 0.369552, 0.416551, 0.429882, nan 0.491893, 0.581505, 0.517876, 0.554279, 0.473684, 0.497190, 0.368318, 0.416551, 0.431071, 0.388098 """ EMBEDDED_ORACLE_AUC_CSV = """ 0.508404, nan, nan, nan, nan, nan, nan, nan, nan, nan 0.508404, 0.603131, nan, nan, nan, nan, nan, nan, nan, nan 0.508404, 0.603131, 0.515332, nan, nan, nan, nan, nan, nan, nan 0.508404, 0.603131, 0.515332, 0.684092, nan, nan, nan, nan, nan, nan 0.508404, 0.603131, 0.515332, 0.684092, 0.684211, nan, nan, nan, nan, nan 0.508404, 0.603131, 0.515332, 0.684092, 0.684211, 0.605234, nan, nan, nan, nan 0.508404, 0.603131, 0.515332, 0.684092, 0.684211, 0.605234, 0.380243, nan, nan, nan 0.508404, 0.603131, 0.515332, 0.684092, 0.684211, 0.605234, 0.380243, 0.522766, nan, nan 0.508404, 0.603131, 0.515332, 0.684092, 0.684211, 0.605234, 0.380243, 0.522766, 0.838886, nan 0.508404, 0.603131, 0.515332, 0.684092, 0.684211, 0.605234, 0.380243, 0.522766, 0.838886, 0.614736 """ EMBEDDED_FINAL_CONFUSION_CSV = """ 0.016393, 0.008197, 0.065574, 0.000000, 0.000000, 0.008197, 0.827869, 0.049180, 0.016393, 0.008197 0.000000, 0.017544, 0.070175, 0.008772, 0.000000, 0.000000, 0.850877, 0.017544, 0.017544, 0.017544 0.000000, 0.011628, 0.116279, 0.005814, 0.000000, 0.011628, 0.726744, 0.052326, 0.058140, 0.017442 0.008403, 0.067227, 0.075630, 0.000000, 0.000000, 0.008403, 0.663866, 0.050420, 0.100840, 0.025210 0.000000, 0.000000, 0.047619, 0.000000, 0.000000, 0.047619, 0.857143, 0.047619, 0.000000, 0.000000 0.007692, 0.038462, 0.076923, 0.000000, 0.000000, 0.007692, 0.823077, 0.015385, 0.015385, 0.015385 0.001087, 0.011957, 0.051087, 0.001087, 0.000000, 0.007609, 0.885870, 0.026087, 0.009783, 0.005435 0.000000, 0.010753, 0.016129, 0.005376, 0.000000, 0.000000, 0.881720, 0.059140, 0.026882, 0.000000 0.000000, 0.037559, 0.070423, 0.014085, 0.000000, 0.009390, 0.718310, 0.032864, 0.098592, 0.018779 0.000000, 0.012422, 0.093168, 0.006211, 0.000000, 0.012422, 0.745342, 0.031056, 0.068323, 0.031056 """ PAIR_LABELS = ("ISC_", "LVH") PAIR_SUMMARY_TEXT = """ sample_id,ecg_id,oracle_task_id,true_label,condition,is_pair_non_easy,was_corrected_to_top1,margin_gain,top1_pred_noprompt,top1_pred_prompt,used_in_plot,proj_x,proj_y 162,162,9,ISC_,noprompt,1,0,-0.14715340733528137,LVH,LVH,1,0.2678664028644562,-0.054050788283348083 162,162,9,ISC_,prompt,1,0,-0.14715340733528137,LVH,LVH,1,0.08682997524738312,-0.19760681688785553 271,271,9,ISC_,noprompt,1,0,-0.055705249309539795,LVH,LVH,1,-0.09699732065200806,0.023979514837265015 271,271,9,ISC_,prompt,1,0,-0.055705249309539795,LVH,LVH,1,-0.14105947315692902,-0.06056339666247368 452,452,9,ISC_,noprompt,1,0,-0.21959400177001953,LVH,LVH,1,0.502670168876648,0.3090794086456299 452,452,9,ISC_,prompt,1,0,-0.21959400177001953,LVH,LVH,1,0.3502529263496399,0.1599009931087494 516,516,9,ISC_,noprompt,1,0,-0.04931129515171051,LVH,LVH,1,-0.30072590708732605,-0.1349230408668518 516,516,9,ISC_,prompt,1,0,-0.04931129515171051,LVH,LVH,1,-0.3231259286403656,-0.20594792068004608 534,534,9,ISC_,noprompt,1,0,-0.13794124126434326,LVH,LVH,1,0.32665926218032837,0.038868315517902374 534,534,9,ISC_,prompt,1,0,-0.13794124126434326,LVH,LVH,1,0.17360010743141174,-0.1171111911535263 1013,1013,9,ISC_,noprompt,1,0,-0.10743877291679382,LVH,LVH,1,0.14759129285812378,-0.00952231790870428 1013,1013,9,ISC_,prompt,1,0,-0.10743877291679382,LVH,LVH,1,0.005267970729619265,-0.12532474100589752 1219,1219,9,LVH,noprompt,0,,0.03263425827026367,LVH,LVH,1,-0.02294236794114113,-0.028781941160559654 1219,1219,9,LVH,prompt,0,,0.03263425827026367,LVH,LVH,1,-0.05959932506084442,-0.14073263108730316 1307,1307,9,LVH,noprompt,0,,0.12188002467155457,LVH,LVH,1,0.061607323586940765,0.14080078899860382 1307,1307,9,LVH,prompt,0,,0.12188002467155457,LVH,LVH,1,-0.05876383185386658,-0.038778871297836304 1320,1320,9,ISC_,noprompt,1,0,-0.0020210333168506622,LVH,LVH,1,-0.5044217705726624,-0.30719059705734253 1320,1320,9,ISC_,prompt,1,0,-0.0020210333168506622,LVH,LVH,1,-0.47736138105392456,-0.2914179861545563 1386,1386,9,LVH,noprompt,0,,0.017759844660758972,LVH,LVH,1,-0.37842410802841187,-0.19502893090248108 1386,1386,9,LVH,prompt,0,,0.017759844660758972,LVH,LVH,1,-0.37838980555534363,-0.23866939544677734 1456,1456,9,ISC_,noprompt,1,0,-0.003187030553817749,LVH,LVH,1,-0.3481767177581787,-0.24356280267238617 1456,1456,9,ISC_,prompt,1,0,-0.003187030553817749,LVH,LVH,1,-0.32026535272598267,-0.2348850965499878 1480,1480,9,LVH,noprompt,0,,0.01716706156730652,LVH,LVH,1,-0.10799790918827057,-0.16827167570590973 1480,1480,9,LVH,prompt,0,,0.01716706156730652,LVH,LVH,1,-0.13421446084976196,-0.2335631549358368 1522,1522,9,LVH,noprompt,0,,0.13331454992294312,LVH,LVH,1,0.15992721915245056,0.12055694311857224 1522,1522,9,LVH,prompt,0,,0.13331454992294312,LVH,LVH,1,0.014965499751269817,-0.04729701578617096 1534,1534,9,LVH,noprompt,0,,-0.006585277616977692,LVH,LVH,1,-0.44173845648765564,-0.3136237859725952 1534,1534,9,LVH,prompt,0,,-0.006585277616977692,LVH,LVH,1,-0.4078345000743866,-0.314796507358551 1961,1961,9,LVH,noprompt,0,,0.013015344738960266,LVH,LVH,1,-0.32449448108673096,-0.22188076376914978 1961,1961,9,LVH,prompt,0,,0.013015344738960266,LVH,LVH,1,-0.31900089979171753,-0.26538947224617004 2064,2064,9,ISC_,noprompt,1,0,0.004881270229816437,LVH,LVH,1,-0.4108034372329712,-0.3480914831161499 2064,2064,9,ISC_,prompt,1,0,0.004881270229816437,LVH,LVH,1,-0.37713462114334106,-0.3509710729122162 2103,2103,9,ISC_,noprompt,1,0,-0.017562061548233032,LVH,LVH,1,-0.25134897232055664,-0.04951866716146469 2103,2103,9,ISC_,prompt,1,0,-0.017562061548233032,LVH,LVH,1,-0.25913506746292114,-0.12306685745716095 2104,2104,9,ISC_,noprompt,1,0,-0.0621299147605896,LVH,LVH,1,-0.13119909167289734,-0.02677898295223713 2104,2104,9,ISC_,prompt,1,0,-0.0621299147605896,LVH,LVH,1,-0.18172454833984375,-0.12233129888772964 2119,2119,9,LVH,noprompt,0,,0.011401087045669556,LVH,LVH,1,-0.4554009735584259,-0.17714904248714447 2119,2119,9,LVH,prompt,0,,0.011401087045669556,LVH,LVH,1,-0.4331231415271759,-0.19064977765083313 2213,2213,9,LVH,noprompt,0,,0.01512540876865387,LVH,LVH,1,-0.40459758043289185,-0.05722963064908981 2213,2213,9,LVH,prompt,0,,0.01512540876865387,LVH,LVH,1,-0.3927733898162842,-0.11112960427999496 2317,2317,9,ISC_,noprompt,1,0,-0.09018546342849731,LVH,LVH,1,-0.09610415995121002,-0.03063231147825718 2317,2317,9,ISC_,prompt,1,0,-0.09018546342849731,LVH,LVH,1,-0.16221629083156586,-0.1391828954219818 2322,2322,9,ISC_,noprompt,1,0,-0.012593686580657959,LVH,LVH,1,-0.22617192566394806,-0.23322610557079315 2322,2322,9,ISC_,prompt,1,0,-0.012593686580657959,LVH,LVH,1,-0.23413872718811035,-0.27355507016181946 2473,2473,9,LVH,noprompt,0,,0.14263224601745605,LVH,LVH,1,0.10538504272699356,0.20962783694267273 2473,2473,9,LVH,prompt,0,,0.14263224601745605,LVH,LVH,1,-0.03295612335205078,0.01631954126060009 2565,2565,9,ISC_,noprompt,1,0,-0.10247069597244263,LVH,LVH,1,0.019135018810629845,-0.03846663981676102 2565,2565,9,ISC_,prompt,1,0,-0.10247069597244263,LVH,LVH,1,-0.07382635772228241,-0.18087376654148102 2693,2693,9,ISC_,noprompt,1,0,-0.009954385459423065,LVH,LVH,1,-0.4516024589538574,-0.2624627351760864 2693,2693,9,ISC_,prompt,1,0,-0.009954385459423065,LVH,LVH,1,-0.4226225018501282,-0.2710379958152771 2802,2802,9,ISC_,noprompt,1,0,-0.010875217616558075,LVH,LVH,1,-0.48645463585853577,-0.27954500913619995 2802,2802,9,ISC_,prompt,1,0,-0.010875217616558075,LVH,LVH,1,-0.4646258056163788,-0.2806525230407715 3092,3092,9,LVH,noprompt,0,,0.15096864104270935,LVH,LVH,1,0.06874994933605194,0.02473512291908264 3092,3092,9,LVH,prompt,0,,0.15096864104270935,LVH,LVH,1,-0.06716921180486679,-0.1179138571023941 3281,3281,9,ISC_,noprompt,1,0,-0.21728968620300293,LVH,LVH,1,0.32864972949028015,0.22999131679534912 3281,3281,9,ISC_,prompt,1,0,-0.21728968620300293,LVH,LVH,1,0.11724038422107697,0.02868923917412758 3371,3371,9,LVH,noprompt,0,,0.23609626293182373,LVH,LVH,1,0.38442790508270264,0.22372296452522278 3371,3371,9,LVH,prompt,0,,0.23609626293182373,LVH,LVH,1,0.18553155660629272,0.0016457820311188698 3454,3454,9,ISC_,noprompt,1,0,-0.13663321733474731,LVH,LVH,1,0.1825263500213623,0.15323704481124878 3454,3454,9,ISC_,prompt,1,0,-0.13663321733474731,LVH,LVH,1,0.06876455247402191,-0.0030840104445815086 3603,3603,9,LVH,noprompt,0,,0.09059330821037292,LVH,LVH,1,-0.14563536643981934,0.08401012420654297 3603,3603,9,LVH,prompt,0,,0.09059330821037292,LVH,LVH,1,-0.22031214833259583,-0.03596733510494232 3606,3606,9,LVH,noprompt,0,,0.052299484610557556,LVH,LVH,1,-0.20145131647586823,-0.2424408346414566 3606,3606,9,LVH,prompt,0,,0.052299484610557556,LVH,LVH,1,-0.24617543816566467,-0.2923429012298584 3660,3660,9,LVH,noprompt,0,,0.011157240718603134,LVH,LVH,1,-0.4817134737968445,-0.29791826009750366 3660,3660,9,LVH,prompt,0,,0.011157240718603134,LVH,LVH,1,-0.4681415557861328,-0.3093140423297882 3785,3785,9,LVH,noprompt,0,,0.050652116537094116,LVH,LVH,1,-0.2073412537574768,-0.050299808382987976 3785,3785,9,LVH,prompt,0,,0.050652116537094116,LVH,LVH,1,-0.25035375356674194,-0.14646375179290771 3827,3827,9,ISC_,noprompt,1,0,0.0057966262102127075,LVH,LVH,1,-0.4158770740032196,-0.07678204774856567 3827,3827,9,ISC_,prompt,1,0,0.0057966262102127075,LVH,LVH,1,-0.3845561444759369,-0.09023333340883255 3867,3867,9,ISC_,noprompt,1,0,-0.13266438245773315,LVH,LVH,1,0.21597102284431458,0.11853724718093872 3867,3867,9,ISC_,prompt,1,0,-0.13266438245773315,LVH,LVH,1,0.08129625767469406,-0.03391563892364502 3869,3869,9,ISC_,noprompt,1,0,-0.16712167859077454,LVH,LVH,1,0.27997887134552,0.13269135355949402 3869,3869,9,ISC_,prompt,1,0,-0.16712167859077454,LVH,LVH,1,0.1065467894077301,-0.07528726011514664 3895,3895,9,ISC_,noprompt,1,0,-0.18487882614135742,LVH,LVH,1,0.3374823331832886,-0.03318343311548233 3895,3895,9,ISC_,prompt,1,0,-0.18487882614135742,LVH,LVH,1,0.127924382686615,-0.1640450358390808 3910,3910,9,ISC_,noprompt,1,0,-0.03865019977092743,LVH,LVH,1,-0.3703320026397705,-0.1531478315591812 3910,3910,9,ISC_,prompt,1,0,-0.03865019977092743,LVH,LVH,1,-0.3820018172264099,-0.21911799907684326 4278,4278,9,LVH,noprompt,0,,0.025993555784225464,LVH,LVH,1,-0.35013097524642944,-0.10785801708698273 4278,4278,9,LVH,prompt,0,,0.025993555784225464,LVH,LVH,1,-0.3452373743057251,-0.15486910939216614 4335,4335,9,ISC_,noprompt,1,0,-0.1992059350013733,LVH,LVH,1,0.44551414251327515,0.19348439574241638 4335,4335,9,ISC_,prompt,1,0,-0.1992059350013733,LVH,LVH,1,0.2572978138923645,-0.010358328931033611 4410,4410,9,ISC_,noprompt,1,0,0.02501162886619568,LVH,LVH,1,-0.35555022954940796,-0.19726590812206268 4410,4410,9,ISC_,prompt,1,0,0.02501162886619568,LVH,LVH,1,-0.3089620769023895,-0.190809428691864 4458,4458,9,LVH,noprompt,0,,0.19924703240394592,LVH,LVH,1,0.2867167592048645,0.27134981751441956 4458,4458,9,LVH,prompt,0,,0.19924703240394592,LVH,LVH,1,0.11204464733600616,0.08481733500957489 4547,4547,9,LVH,noprompt,0,,0.02033981680870056,LVH,LVH,1,-0.31104135513305664,-0.2103646695613861 4547,4547,9,LVH,prompt,0,,0.02033981680870056,LVH,LVH,1,-0.32386428117752075,-0.24745133519172668 4614,4614,9,ISC_,noprompt,1,0,-0.06929504871368408,LVH,LVH,1,-0.11270034313201904,-0.2285209447145462 4614,4614,9,ISC_,prompt,1,0,-0.06929504871368408,LVH,LVH,1,-0.20353910326957703,-0.2781541347503662 4749,4749,9,ISC_,noprompt,1,0,-0.005395576357841492,LVH,LVH,1,-0.207876518368721,-0.23968328535556793 4749,4749,9,ISC_,prompt,1,0,-0.005395576357841492,LVH,LVH,1,-0.2141600251197815,-0.27594685554504395 5087,5087,9,ISC_,noprompt,1,0,-0.13783326745033264,LVH,LVH,1,0.09972654283046722,0.14634597301483154 5087,5087,9,ISC_,prompt,1,0,-0.13783326745033264,LVH,LVH,1,-0.03387784957885742,-0.016162768006324768 5810,5810,9,ISC_,noprompt,1,0,-0.10915321111679077,LVH,LVH,1,0.1882968246936798,-0.029047194868326187 5810,5810,9,ISC_,prompt,1,0,-0.10915321111679077,LVH,LVH,1,0.06645885109901428,-0.16904789209365845 5911,5911,9,LVH,noprompt,0,,0.2001560628414154,LVH,LVH,1,0.2425171285867691,0.36727195978164673 5911,5911,9,LVH,prompt,0,,0.2001560628414154,LVH,LVH,1,0.09248100221157074,0.18413636088371277 6098,6098,9,ISC_,noprompt,1,0,-0.05498109757900238,LVH,LVH,1,-0.21940438449382782,0.028213929384946823 6098,6098,9,ISC_,prompt,1,0,-0.05498109757900238,LVH,LVH,1,-0.2574828863143921,-0.08988262712955475 6141,6141,9,LVH,noprompt,0,,0.1864992082118988,LVH,LVH,1,0.16529637575149536,0.19444027543067932 6141,6141,9,LVH,prompt,0,,0.1864992082118988,LVH,LVH,1,-0.016613636165857315,0.023744400590658188 6180,6180,9,LVH,noprompt,0,,0.06576724350452423,LVH,LVH,1,-0.27627032995224,0.06969515979290009 6180,6180,9,LVH,prompt,0,,0.06576724350452423,LVH,LVH,1,-0.3127981424331665,-0.040972936898469925 6229,6229,9,ISC_,noprompt,1,0,-0.01575601100921631,LVH,LVH,1,-0.27271968126296997,-0.15946510434150696 6229,6229,9,ISC_,prompt,1,0,-0.01575601100921631,LVH,LVH,1,-0.2622452974319458,-0.19151587784290314 6337,6337,9,LVH,noprompt,0,,0.1277630627155304,LVH,LVH,1,0.10582906007766724,0.05873915180563927 6337,6337,9,LVH,prompt,0,,0.1277630627155304,LVH,LVH,1,-0.007125713862478733,-0.05423210188746452 6353,6353,9,ISC_,noprompt,1,0,-0.020837530493736267,LVH,LVH,1,-0.14946328103542328,-0.31198301911354065 6353,6353,9,ISC_,prompt,1,0,-0.020837530493736267,LVH,LVH,1,-0.1895619034767151,-0.3546215295791626 6427,6427,9,LVH,noprompt,0,,0.09991598129272461,LVH,LVH,1,0.10201632976531982,0.11307346820831299 6427,6427,9,LVH,prompt,0,,0.09991598129272461,LVH,LVH,1,0.006165702361613512,-0.039332740008831024 6625,6625,9,LVH,noprompt,0,,0.04368981719017029,LVH,LVH,1,-0.11837182939052582,-0.012672770768404007 6625,6625,9,LVH,prompt,0,,0.04368981719017029,LVH,LVH,1,-0.159315824508667,-0.16017434000968933 6666,6666,9,ISC_,noprompt,1,0,-0.05659842491149902,LVH,LVH,1,0.028030814602971077,-0.059671107679605484 6666,6666,9,ISC_,prompt,1,0,-0.05659842491149902,LVH,LVH,1,-0.03761547803878784,-0.17986515164375305 6788,6788,9,ISC_,noprompt,1,0,0.0027432218194007874,LVH,LVH,1,-0.4332585036754608,-0.2784726023674011 6788,6788,9,ISC_,prompt,1,0,0.0027432218194007874,LVH,LVH,1,-0.40313640236854553,-0.2770429849624634 6925,6925,9,LVH,noprompt,0,,0.15955084562301636,LVH,LVH,1,0.18746095895767212,0.19057299196720123 6925,6925,9,LVH,prompt,0,,0.15955084562301636,LVH,LVH,1,0.019199015572667122,0.007030412554740906 6992,6992,9,ISC_,noprompt,1,0,-0.22422701120376587,LVH,LVH,1,0.29720544815063477,0.17409223318099976 6992,6992,9,ISC_,prompt,1,0,-0.22422701120376587,LVH,LVH,1,0.07079324126243591,-0.04920671135187149 7354,7354,9,LVH,noprompt,0,,0.13641518354415894,LVH,LVH,1,0.17935945093631744,0.030905479565262794 7354,7354,9,LVH,prompt,0,,0.13641518354415894,LVH,LVH,1,0.003514255862683058,-0.1689949482679367 7529,7529,9,ISC_,noprompt,1,0,-0.1622878909111023,LVH,LVH,1,0.2677254378795624,0.23529884219169617 7529,7529,9,ISC_,prompt,1,0,-0.1622878909111023,LVH,LVH,1,0.12455528974533081,0.10624676942825317 7670,7670,9,LVH,noprompt,0,,0.21830469369888306,LVH,LVH,1,0.40634477138519287,0.2766052484512329 7670,7670,9,LVH,prompt,0,,0.21830469369888306,LVH,LVH,1,0.21518221497535706,0.055292755365371704 7684,7684,9,LVH,noprompt,0,,0.006277985870838165,LVH,LVH,1,-0.41882801055908203,-0.19795681536197662 7684,7684,9,LVH,prompt,0,,0.006277985870838165,LVH,LVH,1,-0.39670270681381226,-0.19716574251651764 7812,7812,9,LVH,noprompt,0,,0.11479097604751587,LVH,LVH,1,0.06541913002729416,0.15220072865486145 7812,7812,9,LVH,prompt,0,,0.11479097604751587,LVH,LVH,1,-0.06974669545888901,-0.009982293471693993 7822,7822,9,ISC_,noprompt,1,0,-0.11802911758422852,LVH,LVH,1,0.1738806515932083,-0.02958848513662815 7822,7822,9,ISC_,prompt,1,0,-0.11802911758422852,LVH,LVH,1,0.028989821672439575,-0.17147770524024963 7861,7861,9,ISC_,noprompt,1,0,-0.07514679431915283,LVH,LVH,1,-0.10689348727464676,-0.04203968495130539 7861,7861,9,ISC_,prompt,1,0,-0.07514679431915283,LVH,LVH,1,-0.18683256208896637,-0.16115710139274597 7873,7873,9,ISC_,noprompt,1,0,-0.18599575757980347,LVH,LVH,1,0.29721421003341675,0.24451206624507904 7873,7873,9,ISC_,prompt,1,0,-0.18599575757980347,LVH,LVH,1,0.12975750863552094,0.08535385131835938 7916,7916,9,LVH,noprompt,0,,0.1493251919746399,LVH,LVH,1,0.20031511783599854,0.18859553337097168 7916,7916,9,LVH,prompt,0,,0.1493251919746399,LVH,LVH,1,0.056836970150470734,0.0023787934333086014 7961,7961,9,ISC_,noprompt,1,0,-0.10785329341888428,LVH,LVH,1,-0.02163260243833065,0.05524248629808426 7961,7961,9,ISC_,prompt,1,0,-0.10785329341888428,LVH,LVH,1,-0.13835611939430237,-0.08546677231788635 8083,8083,9,LVH,noprompt,0,,0.048528045415878296,LVH,LVH,1,-0.2701365351676941,-0.07829054445028305 8083,8083,9,LVH,prompt,0,,0.048528045415878296,LVH,LVH,1,-0.31330808997154236,-0.18686528503894806 8121,8121,9,LVH,noprompt,0,,0.027330592274665833,LVH,LVH,1,-0.23221364617347717,-0.06289488822221756 8121,8121,9,LVH,prompt,0,,0.027330592274665833,LVH,LVH,1,-0.2545589804649353,-0.13904765248298645 8209,8209,9,ISC_,noprompt,1,0,-0.0879557728767395,LVH,LVH,1,0.028522226959466934,-0.011993185617029667 8209,8209,9,ISC_,prompt,1,0,-0.0879557728767395,LVH,LVH,1,-0.07205910980701447,-0.13675257563591003 8284,8284,9,LVH,noprompt,0,,0.03840731084346771,LVH,LVH,1,-0.16623355448246002,-0.21531665325164795 8284,8284,9,LVH,prompt,0,,0.03840731084346771,LVH,LVH,1,-0.207923024892807,-0.2753314971923828 8406,8406,9,ISC_,noprompt,1,0,-0.09755593538284302,LVH,LVH,1,0.14234662055969238,-0.11886858195066452 8406,8406,9,ISC_,prompt,1,0,-0.09755593538284302,LVH,LVH,1,-0.004153192974627018,-0.24082568287849426 8501,8501,9,ISC_,noprompt,1,0,-0.00623534619808197,LVH,LVH,1,-0.37307363748550415,-0.2593005895614624 8501,8501,9,ISC_,prompt,1,0,-0.00623534619808197,LVH,LVH,1,-0.35986536741256714,-0.2841683626174927 8524,8524,9,LVH,noprompt,0,,0.015359260141849518,LVH,LVH,1,-0.49769777059555054,-0.35986486077308655 8524,8524,9,LVH,prompt,0,,0.015359260141849518,LVH,LVH,1,-0.48809611797332764,-0.3632936477661133 8772,8772,9,LVH,noprompt,0,,0.07318189740180969,LVH,LVH,1,-0.1274436116218567,0.10759279876947403 8772,8772,9,LVH,prompt,0,,0.07318189740180969,LVH,LVH,1,-0.17774765193462372,-0.005969897843897343 8777,8777,9,LVH,noprompt,0,,0.0001827329397201538,LVH,LVH,1,-0.26576951146125793,-0.1443423330783844 8777,8777,9,LVH,prompt,0,,0.0001827329397201538,LVH,LVH,1,-0.2656165659427643,-0.18843185901641846 8873,8873,9,LVH,noprompt,0,,-0.0032391995191574097,LVH,LVH,1,-0.36625146865844727,-0.10818852484226227 8873,8873,9,LVH,prompt,0,,-0.0032391995191574097,LVH,LVH,1,-0.32255077362060547,-0.12690800428390503 9024,9024,9,LVH,noprompt,0,,0.03287401795387268,LVH,LVH,1,-0.2923722267150879,-0.06297290325164795 9024,9024,9,LVH,prompt,0,,0.03287401795387268,LVH,LVH,1,-0.3012656569480896,-0.14040511846542358 9095,9095,9,ISC_,noprompt,1,0,-0.17772114276885986,LVH,LVH,1,0.19152408838272095,0.17261585593223572 9095,9095,9,ISC_,prompt,1,0,-0.17772114276885986,LVH,LVH,1,0.016672445461153984,-0.013794691301882267 9180,9180,9,ISC_,noprompt,1,0,-0.19890132546424866,LVH,LVH,1,0.2612219750881195,0.18897107243537903 9180,9180,9,ISC_,prompt,1,0,-0.19890132546424866,LVH,LVH,1,0.08292190730571747,-0.013834704644978046 9299,9299,9,LVH,noprompt,0,,0.06531265377998352,LVH,LVH,1,-0.05812234431505203,-0.03193708136677742 9299,9299,9,LVH,prompt,0,,0.06531265377998352,LVH,LVH,1,-0.14657637476921082,-0.15157656371593475 9613,9613,9,ISC_,noprompt,1,0,-0.20639073848724365,LVH,LVH,1,0.43605244159698486,0.2215794324874878 9613,9613,9,ISC_,prompt,1,0,-0.20639073848724365,LVH,LVH,1,0.2449982762336731,0.04740137606859207 9756,9756,9,ISC_,noprompt,1,0,-0.1716676950454712,LVH,LVH,1,0.5549843311309814,0.24989722669124603 9756,9756,9,ISC_,prompt,1,0,-0.1716676950454712,LVH,LVH,1,0.47515732049942017,0.1407214105129242 9867,9867,9,ISC_,noprompt,1,0,-0.06370341777801514,LVH,LVH,1,-0.10065044462680817,0.09620144963264465 9867,9867,9,ISC_,prompt,1,0,-0.06370341777801514,LVH,LVH,1,-0.15294654667377472,-0.003924684599041939 10054,10054,9,ISC_,noprompt,1,0,-0.04348906874656677,LVH,LVH,1,-0.2505515515804291,-0.019232438877224922 10054,10054,9,ISC_,prompt,1,0,-0.04348906874656677,LVH,LVH,1,-0.2746933400630951,-0.08652221411466599 10061,10061,9,ISC_,noprompt,1,0,-0.1515590250492096,LVH,LVH,1,0.1849989891052246,0.1593683362007141 10061,10061,9,ISC_,prompt,1,0,-0.1515590250492096,LVH,LVH,1,0.017056990414857864,-0.012782351113855839 10115,10115,9,ISC_,noprompt,1,0,-0.10325071215629578,LVH,LVH,1,0.032687440514564514,0.06076251342892647 10115,10115,9,ISC_,prompt,1,0,-0.10325071215629578,LVH,LVH,1,-0.09090493619441986,-0.12560740113258362 10177,10177,9,ISC_,noprompt,1,0,-0.04924103617668152,LVH,LVH,1,-0.13112619519233704,-0.20809626579284668 10177,10177,9,ISC_,prompt,1,0,-0.04924103617668152,LVH,LVH,1,-0.16876928508281708,-0.2598637342453003 10180,10180,9,LVH,noprompt,0,,0.1145181655883789,LVH,LVH,1,0.03311043232679367,0.12016096711158752 10180,10180,9,LVH,prompt,0,,0.1145181655883789,LVH,LVH,1,-0.09970492124557495,-0.03080807253718376 10205,10205,9,LVH,noprompt,0,,0.15622037649154663,LVH,LVH,1,0.18597877025604248,0.00987326167523861 10205,10205,9,LVH,prompt,0,,0.15622037649154663,LVH,LVH,1,-0.002274765633046627,-0.19395729899406433 10388,10388,9,LVH,noprompt,0,,0.0022339709103107452,LVH,LVH,1,-0.4768618941307068,-0.267767995595932 10388,10388,9,LVH,prompt,0,,0.0022339709103107452,LVH,LVH,1,-0.4473022520542145,-0.2689933180809021 10563,10563,9,ISC_,noprompt,1,0,-0.10689389705657959,LVH,LVH,1,0.0334683433175087,-0.13676747679710388 10563,10563,9,ISC_,prompt,1,0,-0.10689389705657959,LVH,LVH,1,-0.11055544763803482,-0.22191524505615234 10594,10594,9,ISC_,noprompt,1,0,-0.24623507261276245,LVH,LVH,1,0.39144521951675415,0.3122342526912689 10594,10594,9,ISC_,prompt,1,0,-0.24623507261276245,LVH,LVH,1,0.1860230565071106,0.08469066023826599 10640,10640,9,ISC_,noprompt,1,0,0.003278285264968872,LVH,LVH,1,-0.492217481136322,-0.2982140779495239 10640,10640,9,ISC_,prompt,1,0,0.003278285264968872,LVH,LVH,1,-0.45949798822402954,-0.29193922877311707 11132,11132,9,ISC_,noprompt,1,0,-0.16319727897644043,LVH,LVH,1,0.21749019622802734,0.3814367651939392 11132,11132,9,ISC_,prompt,1,0,-0.16319727897644043,LVH,LVH,1,0.0955539122223854,0.21466729044914246 11139,11139,9,ISC_,noprompt,1,0,-0.015267938375473022,LVH,LVH,1,-0.22241179645061493,-0.05554230883717537 11139,11139,9,ISC_,prompt,1,0,-0.015267938375473022,LVH,LVH,1,-0.21876409649848938,-0.13217014074325562 11755,11755,9,ISC_,noprompt,1,0,-0.1910730004310608,LVH,LVH,1,0.4237329065799713,0.2243710607290268 11755,11755,9,ISC_,prompt,1,0,-0.1910730004310608,LVH,LVH,1,0.2535684406757355,0.03905365988612175 11901,11901,9,ISC_,noprompt,1,0,-0.14910820126533508,LVH,LVH,1,0.19979757070541382,0.05118348076939583 11901,11901,9,ISC_,prompt,1,0,-0.14910820126533508,LVH,LVH,1,0.032819509506225586,-0.13041172921657562 12031,12031,9,LVH,noprompt,0,,0.18838980793952942,LVH,LVH,1,0.26361462473869324,0.3029292821884155 12031,12031,9,LVH,prompt,0,,0.18838980793952942,LVH,LVH,1,0.08141690492630005,0.10457753390073776 12122,12122,9,LVH,noprompt,0,,0.04240898787975311,LVH,LVH,1,-0.152535080909729,-0.2513582408428192 12122,12122,9,LVH,prompt,0,,0.04240898787975311,LVH,LVH,1,-0.22336851060390472,-0.30962255597114563 12252,12252,9,ISC_,noprompt,1,0,-0.10768473148345947,LVH,LVH,1,0.2815779447555542,-0.12047834694385529 12252,12252,9,ISC_,prompt,1,0,-0.10768473148345947,LVH,LVH,1,0.13205191493034363,-0.22874274849891663 12301,12301,9,LVH,noprompt,0,,0.0063127875328063965,LVH,LVH,1,-0.2877381145954132,-0.26829564571380615 12301,12301,9,LVH,prompt,0,,0.0063127875328063965,LVH,LVH,1,-0.27695637941360474,-0.29094088077545166 12338,12338,9,LVH,noprompt,0,,0.13479843735694885,LVH,LVH,1,0.04780863597989082,0.18741482496261597 12338,12338,9,LVH,prompt,0,,0.13479843735694885,LVH,LVH,1,-0.08068552613258362,0.028220411390066147 12585,12585,9,LVH,noprompt,0,,0.07151314616203308,LVH,LVH,1,-0.0712493360042572,-0.06369186192750931 12585,12585,9,LVH,prompt,0,,0.07151314616203308,LVH,LVH,1,-0.11198274791240692,-0.17173853516578674 12646,12646,9,ISC_,noprompt,1,0,-0.06011590361595154,LVH,LVH,1,-0.04028400033712387,0.0860074833035469 12646,12646,9,ISC_,prompt,1,0,-0.06011590361595154,LVH,LVH,1,-0.09504222869873047,0.01843949966132641 12649,12649,9,ISC_,noprompt,1,0,-0.09189143776893616,LVH,LVH,1,-0.027055663987994194,0.04820439964532852 12649,12649,9,ISC_,prompt,1,0,-0.09189143776893616,LVH,LVH,1,-0.12691521644592285,-0.08075354993343353 12719,12719,9,LVH,noprompt,0,,0.02436034381389618,LVH,LVH,1,-0.24251843988895416,-0.17300599813461304 12719,12719,9,LVH,prompt,0,,0.02436034381389618,LVH,LVH,1,-0.25656190514564514,-0.23816819489002228 13007,13007,9,ISC_,noprompt,1,0,-0.05023849010467529,LVH,LVH,1,0.05076669156551361,-0.22327831387519836 13007,13007,9,ISC_,prompt,1,0,-0.05023849010467529,LVH,LVH,1,-0.010518079623579979,-0.3106585443019867 13233,13233,9,ISC_,noprompt,1,0,-0.20336699485778809,LVH,LVH,1,0.35437458753585815,0.1521698236465454 13233,13233,9,ISC_,prompt,1,0,-0.20336699485778809,LVH,LVH,1,0.11808712780475616,-0.04321736842393875 13556,13556,9,ISC_,noprompt,1,0,-0.1820330023765564,LVH,LVH,1,0.4263259768486023,0.09705165773630142 13556,13556,9,ISC_,prompt,1,0,-0.1820330023765564,LVH,LVH,1,0.2265286147594452,-0.07295188307762146 13666,13666,9,LVH,noprompt,0,,0.08259361982345581,LVH,LVH,1,0.11087165027856827,0.07204366475343704 13666,13666,9,LVH,prompt,0,,0.08259361982345581,LVH,LVH,1,0.01631329208612442,-0.07292266190052032 13835,13835,9,LVH,noprompt,0,,0.0697392076253891,LVH,LVH,1,-0.18805590271949768,-2.6284251362085342e-05 13835,13835,9,LVH,prompt,0,,0.0697392076253891,LVH,LVH,1,-0.23564302921295166,-0.09136773645877838 13899,13899,9,ISC_,noprompt,1,0,-0.03480665385723114,LVH,LVH,1,-0.27278977632522583,-0.2200794219970703 13899,13899,9,ISC_,prompt,1,0,-0.03480665385723114,LVH,LVH,1,-0.293035089969635,-0.26566916704177856 14004,14004,9,ISC_,noprompt,1,0,-0.13670441508293152,LVH,LVH,1,0.09870349615812302,0.022584296762943268 14004,14004,9,ISC_,prompt,1,0,-0.13670441508293152,LVH,LVH,1,-0.06409257650375366,-0.14328806102275848 14026,14026,9,ISC_,noprompt,1,0,-0.006177797913551331,LVH,LVH,1,-0.3667721748352051,-0.20504041016101837 14026,14026,9,ISC_,prompt,1,0,-0.006177797913551331,LVH,LVH,1,-0.34179171919822693,-0.25288695096969604 14167,14167,9,LVH,noprompt,0,,0.23240536451339722,LVH,LVH,1,0.5283969640731812,0.20867308974266052 14167,14167,9,LVH,prompt,0,,0.23240536451339722,LVH,LVH,1,0.34573277831077576,0.011063278652727604 14201,14201,9,ISC_,noprompt,1,0,-0.2129223346710205,LVH,LVH,1,0.5191229581832886,0.3697362542152405 14201,14201,9,ISC_,prompt,1,0,-0.2129223346710205,LVH,LVH,1,0.38257911801338196,0.21592578291893005 14350,14350,9,ISC_,noprompt,1,0,-0.006886020302772522,LVH,LVH,1,-0.33850908279418945,-0.24944549798965454 14350,14350,9,ISC_,prompt,1,0,-0.006886020302772522,LVH,LVH,1,-0.318747878074646,-0.27193114161491394 14670,14670,9,ISC_,noprompt,1,0,-0.011060178279876709,LVH,LVH,1,-0.3760223984718323,-0.2685486972332001 14670,14670,9,ISC_,prompt,1,0,-0.011060178279876709,LVH,LVH,1,-0.36259403824806213,-0.2867732048034668 14805,14805,9,LVH,noprompt,0,,0.044489145278930664,LVH,LVH,1,-0.22206489741802216,0.004264437593519688 14805,14805,9,LVH,prompt,0,,0.044489145278930664,LVH,LVH,1,-0.23528048396110535,-0.10279357433319092 14869,14869,9,ISC_,noprompt,1,0,-0.13264179229736328,LVH,LVH,1,0.1182151511311531,0.18808749318122864 14869,14869,9,ISC_,prompt,1,0,-0.13264179229736328,LVH,LVH,1,0.009376559406518936,0.06348036229610443 15500,15500,9,ISC_,noprompt,1,0,-0.18058806657791138,LVH,LVH,1,0.36144760251045227,0.11520034819841385 15500,15500,9,ISC_,prompt,1,0,-0.18058806657791138,LVH,LVH,1,0.18533751368522644,-0.05529604107141495 15787,15787,9,ISC_,noprompt,1,0,-0.211184561252594,LVH,LVH,1,0.45706766843795776,0.3214195966720581 15787,15787,9,ISC_,prompt,1,0,-0.211184561252594,LVH,LVH,1,0.33240675926208496,0.15710240602493286 15797,15797,9,ISC_,noprompt,1,0,-0.02091100811958313,LVH,LVH,1,-0.1745508909225464,-0.1194324940443039 15797,15797,9,ISC_,prompt,1,0,-0.02091100811958313,LVH,LVH,1,-0.17176473140716553,-0.16816309094429016 15863,15863,9,ISC_,noprompt,1,0,-0.06972172856330872,LVH,LVH,1,-0.03701060637831688,0.00023167487233877182 15863,15863,9,ISC_,prompt,1,0,-0.06972172856330872,LVH,LVH,1,-0.10744190216064453,-0.09912188351154327 15871,15871,9,ISC_,noprompt,1,0,-0.2226448655128479,LVH,LVH,1,0.48922792077064514,0.08017980307340622 15871,15871,9,ISC_,prompt,1,0,-0.2226448655128479,LVH,LVH,1,0.2522725462913513,-0.12474454939365387 16045,16045,9,LVH,noprompt,0,,0.1190500557422638,LVH,LVH,1,-0.025315633043646812,0.2871624231338501 16045,16045,9,LVH,prompt,0,,0.1190500557422638,LVH,LVH,1,-0.1335940957069397,0.14561568200588226 16182,16182,9,LVH,noprompt,0,,0.16607993841171265,LVH,LVH,1,0.4257323741912842,0.16821259260177612 16182,16182,9,LVH,prompt,0,,0.16607993841171265,LVH,LVH,1,0.26368385553359985,-0.010367773473262787 16214,16214,9,LVH,noprompt,0,,0.07777443528175354,LVH,LVH,1,-0.07070264965295792,-0.012477574869990349 16214,16214,9,LVH,prompt,0,,0.07777443528175354,LVH,LVH,1,-0.16284236311912537,-0.130262091755867 16591,16591,9,ISC_,noprompt,1,0,-0.14381572604179382,LVH,LVH,1,0.30436471104621887,0.04665880650281906 16591,16591,9,ISC_,prompt,1,0,-0.14381572604179382,LVH,LVH,1,0.1380508542060852,-0.0951637253165245 16609,16609,9,ISC_,noprompt,1,0,-0.032015085220336914,LVH,LVH,1,-0.226223886013031,-0.2052817940711975 16609,16609,9,ISC_,prompt,1,0,-0.032015085220336914,LVH,LVH,1,-0.2605729401111603,-0.2596454322338104 16876,16876,9,ISC_,noprompt,1,0,-0.197742760181427,LVH,LVH,1,0.4948529899120331,0.23193645477294922 16876,16876,9,ISC_,prompt,1,0,-0.197742760181427,LVH,LVH,1,0.36047977209091187,0.08700131624937057 16944,16944,9,LVH,noprompt,0,,0.03854018449783325,LVH,LVH,1,-0.3382492661476135,-0.08273180574178696 16944,16944,9,LVH,prompt,0,,0.03854018449783325,LVH,LVH,1,-0.34308519959 44977,-0.13646218180656433 17226,17226,9,LVH,noprompt,0,,0.06054055690765381,LVH,LVH,1,-0.16965404152870178,0.023798590525984764 17226,17226,9,LVH,prompt,0,,0.06054055690765381,LVH,LVH,1,-0.21242383122444153,-0.09139446914196014 17286,17286,9,ISC_,noprompt,1,0,-0.1686769723892212,LVH,LVH,1,0.3731457591056824,0.164602592587471 17286,17286,9,ISC_,prompt,1,0,-0.1686769723892212,LVH,LVH,1,0.20130114257335663,0.0014913864433765411 17298,17298,9,LVH,noprompt,0,,0.1707715392112732,LVH,LVH,1,0.3460303843021393,0.22389334440231323 17298,17298,9,LVH,prompt,0,,0.1707715392112732,LVH,LVH,1,0.1880010962486267,0.07346144318580627 17309,17309,9,ISC_,noprompt,1,0,-0.1896490454673767,LVH,LVH,1,0.21844711899757385,0.037247538566589355 17309,17309,9,ISC_,prompt,1,0,-0.1896490454673767,LVH,LVH,1,0.027368560433387756,-0.12267456203699112 17325,17325,9,LVH,noprompt,0,,0.1248779296875,LVH,LVH,1,0.16225267946720123,0.11271890997886658 17325,17325,9,LVH,prompt,0,,0.1248779296875,LVH,LVH,1,0.02310757339000702,-0.07129678875207901 17701,17701,9,LVH,noprompt,0,,0.07083705067634583,LVH,LVH,1,-0.06760884076356888,0.18296410143375397 17701,17701,9,LVH,prompt,0,,0.07083705067634583,LVH,LVH,1,-0.12252061814069748,0.08036750555038452 17866,17866,9,LVH,noprompt,0,,0.09767159819602966,LVH,LVH,1,-0.05677793547511101,-0.24166738986968994 17866,17866,9,LVH,prompt,0,,0.09767159819602966,LVH,LVH,1,-0.15504375100135803,-0.31882333755493164 17868,17868,9,LVH,noprompt,0,,0.11957484483718872,LVH,LVH,1,0.14737237989902496,-0.137262225151062 17868,17868,9,LVH,prompt,0,,0.11957484483718872,LVH,LVH,1,-0.011298829689621925,-0.26314276456832886 18017,18017,9,LVH,noprompt,0,,0.020465627312660217,LVH,LVH,1,-0.3118661642074585,-0.1710021197795868 18017,18017,9,LVH,prompt,0,,0.020465627312660217,LVH,LVH,1,-0.3341914117336273,-0.24008804559707642 18162,18162,9,ISC_,noprompt,1,0,-0.18471890687942505,LVH,LVH,1,0.3839637041091919,-0.04877297580242157 18162,18162,9,ISC_,prompt,1,0,-0.18471890687942505,LVH,LVH,1,0.17615914344787598,-0.20855596661567688 18234,18234,9,LVH,noprompt,0,,0.1070641279220581,LVH,LVH,1,0.05227284878492355,0.10779128968715668 18234,18234,9,LVH,prompt,0,,0.1070641279220581,LVH,LVH,1,-0.06945624947547913,-0.055118754506111145 18296,18296,9,ISC_,noprompt,1,0,-0.06676393747329712,LVH,LVH,1,-0.050246335566043854,0.0004467112012207508 18296,18296,9,ISC_,prompt,1,0,-0.06676393747329712,LVH,LVH,1,-0.12935049831867218,-0.10346107184886932 18644,18644,9,LVH,noprompt,0,,0.008317410945892334,LVH,LVH,1,-0.3843775987625122,-0.18586260080337524 18644,18644,9,LVH,prompt,0,,0.008317410945892334,LVH,LVH,1,-0.36413586139678955,-0.19549183547496796 18676,18676,9,ISC_,noprompt,1,0,-0.06470245122909546,LVH,LVH,1,-0.003054298460483551,-0.2227354347705841 18676,18676,9,ISC_,prompt,1,0,-0.06470245122909546,LVH,LVH,1,-0.09394746273756027,-0.31062281131744385 18712,18712,9,ISC_,noprompt,1,0,-0.2181439995765686,LVH,LVH,1,0.39044320583343506,0.1438772976398468 18712,18712,9,ISC_,prompt,1,0,-0.2181439995765686,LVH,LVH,1,0.1827344298362732,-0.08666647970676422 19526,19526,9,ISC_,noprompt,1,0,-0.1666048765182495,LVH,LVH,1,0.36510762572288513,0.17956723272800446 19526,19526,9,ISC_,prompt,1,0,-0.1666048765182495,LVH,LVH,1,0.21193251013755798,0.04298735409975052 20211,20211,9,LVH,noprompt,0,,0.19823449850082397,LVH,LVH,1,0.19898547232151031,0.15425756573677063 20211,20211,9,LVH,prompt,0,,0.19823449850082397,LVH,LVH,1,0.010558504611253738,-0.06695379316806793 20236,20236,9,ISC_,noprompt,1,0,-0.20385217666625977,LVH,LVH,1,0.4467070996761322,0.3760039806365967 20236,20236,9,ISC_,prompt,1,0,-0.20385217666625977,LVH,LVH,1,0.29138660430908203,0.19806508719921112 20242,20242,9,LVH,noprompt,0,,0.1895110309123993,LVH,LVH,1,0.23286807537078857,0.03456788882613182 20242,20242,9,LVH,prompt,0,,0.1895110309123993,LVH,LVH,1,0.0196638572961092,-0.14073511958122253 20369,20369,9,LVH,noprompt,0,,0.17506450414657593,LVH,LVH,1,0.2063712477684021,0.17521171271800995 20369,20369,9,LVH,prompt,0,,0.17506450414657593,LVH,LVH,1,0.05626995116472244,-0.016779545694589615 21021,21021,9,ISC_,noprompt,1,0,-0.15273258090019226,LVH,LVH,1,0.3056766092777252,-0.15424993634223938 21021,21021,9,ISC_,prompt,1,0,-0.15273258090019226,LVH,LVH,1,0.13791730999946594,-0.2598782777786255 21104,21104,9,ISC_,noprompt,1,0,-0.12258440256118774,LVH,LVH,1,0.32567453384399414,-0.08940017968416214 21104,21104,9,ISC_,prompt,1,0,-0.12258440256118774,LVH,LVH,1,0.17377525568008423,-0.2258286327123642 21173,21173,9,ISC_,noprompt,1,0,-0.2612963318824768,LVH,LVH,1,0.4909355640411377,0.11921227723360062 21173,21173,9,ISC_,prompt,1,0,-0.2612963318824768,LVH,LVH,1,0.2521728277206421,-0.0925387293100357 21555,21555,9,ISC_,noprompt,1,0,-0.212782621383667,LVH,LVH,1,0.45859548449516296,0.10895854979753494 21555,21555,9,ISC_,prompt,1,0,-0.212782621383667,LVH,LVH,1,0.2557072937488556,-0.042026277631521225 """ BAR_STATS = [ {"group": "already_correct", "n": 1706, "positive_rate": 0.9314185228604924}, {"group": "near_miss", "n": 146, "positive_rate": 0.03424657534246575}, {"group": "hard_error", "n": 306, "positive_rate": 0.3300653594771242}, ] # ========================= # 二、全局样式参数 # 作用：统一控制字体、颜色、字号、柱形纹理等视觉风格。 # 老师如果要改图的外观，优先看这一块。 # ========================= PANEL_LABEL_FONTSIZE = 18.0 AXIS_LABEL_FONTSIZE = 15.0 TICK_LABEL_FONTSIZE = 13.0 LEGEND_FONTSIZE = 12.2 VALUE_LABEL_FONTSIZE = 14.0 PANEL_C_VALUE_FONTSIZE = 16.0 CBAR_PAD_FRACTION = 0.012 CBAR_WIDTH_FRACTION = 0.022 CBAR_HEIGHT_FRACTION = 0.92 CBAR_Y_OFFSET_FRACTION = 0.04 CBAR_DX = 0.0 CBAR_DY = 0.0 CBAR_DW = -0.10 CBAR_DH = 0.0 INSET_TITLE_FONTSIZE = 10.5 INSET_SUBTITLE_FONTSIZE = 9.4 INSET_SUMMARY_FONTSIZE = 8.8 TEXT_COLOR = "#1A1A1A" GRID_COLOR = "#E6E6E6" FACE_COLOR = "#FAFAFA" BAR_BACKGROUND_COLOR = "#FFFFFF" BAR_EDGE_COLOR = "#333333" INFERRED_BAR_COLOR = to_rgba("#4DBBD5", alpha=0.32) # Nature palette ORACLE_BAR_COLOR = to_rgba("#3C5488", alpha=0.32) # Nature palette BAR_GAIN_COLOR = to_rgba("#E64B35", alpha=0.32) # Nature palette MARGIN_BG_COLOR = "#F8F8F8" MARGIN_BAR_COLORS = [ to_rgba("#3C5488", alpha=0.32), to_rgba("#4DBBD5", alpha=0.32), to_rgba("#E64B35", alpha=0.32), ] MARGIN_BAR_HATCHES = ["/", ".", "x"] MARGIN_HATCH_EDGE_COLOR = "#333333" MARGIN_GRID_COLOR = "#E5E5E5" MARGIN_TEXT_COLOR = "#1A1A1A" MARGIN_GUIDE_COLOR = "#B0B0B0" MARGIN_SPINE_COLOR = "#333333" BACKGROUND_COLOR = to_rgba("#D0D0D0", alpha=0.32) PAIR_COLORS = { "A": to_rgba("#3C5488", alpha=0.32), "B": to_rgba("#E64B35", alpha=0.32), } PAIR_CENTER_COLORS = {"A": "#3C5488", "B": "#E64B35"} INSET_CONDITION_COLORS = { "noprompt": { "A": to_rgba("#7EA3C9", alpha=0.45), "B": to_rgba("#C76D5E", alpha=0.45), "A_center": "#7EA3C9", "B_center": "#C76D5E", }, "prompt": { "A": to_rgba("#00A087", alpha=0.60), "B": to_rgba("#E64B35", alpha=0.60), "A_center": "#00A087", "B_center": "#E64B35", }, } # ========================= # 三、参数解析与基础配置 # 作用：读取命令行参数，并设置 matplotlib 的全局绘图风格。 # ========================= def parse_args() -> argparse.Namespace: # 定义脚本运行时可调整的输出路径、画布尺寸和分辨率。 parser = argparse.ArgumentParser( description="Redraw three prompt-analysis panels into one self-contained figure." ) parser.add_argument( "--output-png", default=str(DEFAULT_OUTPUT_PNG), help="Output PNG path." ) parser.add_argument( "--output-pdf", default=str(DEFAULT_OUTPUT_PDF), help="Output PDF path." ) parser.add_argument( "--fig-width", type=float, default=18, help="Figure width in inches." ) parser.add_argument( "--fig-height", type=float, default=4, help="Figure height in inches." ) parser.add_argument("--dpi", type=int, default=600, help="Output DPI.") return parser.parse_args() def configure_matplotlib() -> None: # 统一设置全局字体、线宽和导出格式，保证三联图风格一致。 import matplotlib.pyplot as plt plt.rcParams.update( { "font.family": "Arial", "font.sans-serif": ["Arial", "Helvetica", "DejaVu Sans"], "mathtext.fontset": "cm", # "font.weight": "bold", "font.size": 14, "axes.labelsize": 14, "axes.titlesize": 14, # "axes.labelweight": "bold", # "axes.titleweight": "bold", "xtick.labelsize": 14, "ytick.labelsize": 14, "xtick.direction": "out", "ytick.direction": "out", "xtick.major.size": 8, "ytick.major.size": 8, "xtick.major.width": 1, "ytick.major.width": 1, "legend.fontsize": 14, "axes.linewidth": 1.10, "hatch.linewidth": 0.70, "pdf.fonttype": 42, "ps.fonttype": 42, "figure.facecolor": "white", "savefig.facecolor": "white", } ) # ========================= # 四、数据读取与预处理 # 作用：把上面的文本数据转成矩阵/样本列表，供后面的绘图函数直接调用。 # ========================= def load_embedded_matrix(csv_text: str) -> np.ndarray: # 将内嵌 CSV 字符串解析为 numpy 矩阵。 return np.genfromtxt(StringIO(csv_text.strip()), delimiter=",", dtype=np.float32) def load_confusion_data() -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: # 一次性返回 panel (a)(b) 需要的四个核心矩阵。 return ( load_embedded_matrix(EMBEDDED_ROUTING_ACCURACY_CSV), load_embedded_matrix(EMBEDDED_INFER_AUC_CSV), load_embedded_matrix(EMBEDDED_ORACLE_AUC_CSV), load_embedded_matrix(EMBEDDED_FINAL_CONFUSION_CSV), ) def load_pair_rows() -> list[dict[str, object]]: # 读取用于 panel (c) 小嵌入图的样本点，只保留 used_in_plot=1 的记录。 rows: list[dict[str, object]] = [] reader = csv.DictReader(StringIO(PAIR_SUMMARY_TEXT.strip())) for raw in reader: if raw["used_in_plot"] != "1": continue proj_x_raw = raw.get("proj_x") proj_y_raw = raw.get("proj_y") if proj_x_raw is None or proj_y_raw is None: continue try: proj_x = float(proj_x_raw) proj_y = float(proj_y_raw) except (TypeError, ValueError): continue rows.append( { "true_label": raw["true_label"], "condition": raw["condition"].lower(), "is_pair_non_easy": raw["is_pair_non_easy"] == "1", "proj_x": proj_x, "proj_y": proj_y, } ) return rows def build_pair_plot(rows: list[dict[str, object]]) -> dict[str, object]: # 按 prompt / no-prompt 分组，并计算两类样本的中心点和显示范围。 rows_by_condition = { "noprompt": [row for row in rows if row["condition"] == "noprompt"], "prompt": [row for row in rows if row["condition"] == "prompt"], } x_all = [float(row["proj_x"]) for row in rows] y_all = [float(row["proj_y"]) for row in rows] x_min, x_max = min(x_all), max(x_all) y_min, y_max = min(y_all), max(y_all) x_pad = max((x_max - x_min) * 0.08, 0.035) y_pad = max((y_max - y_min) * 0.08, 0.035) centers: dict[str, dict[str, tuple[float, float]]] = {} for condition, subset in rows_by_condition.items(): centers[condition] = {} for label in PAIR_LABELS: class_rows = [row for row in subset if row["true_label"] == label] xs = np.array([row["proj_x"] for row in class_rows], dtype=np.float32) ys = np.array([row["proj_y"] for row in class_rows], dtype=np.float32) centers[condition][label] = (float(xs.mean()), float(ys.mean())) return { "pair_labels": list(PAIR_LABELS), "rows_by_condition": rows_by_condition, "centers": centers, "xlim": (x_min - x_pad, x_max + x_pad), "ylim": (y_min - y_pad, y_max + y_pad), } # ========================= # 五、通用绘图小工具 # 作用：封装面板编号、刻度加粗、热力图文字颜色等重复逻辑。 # ========================= def draw_panel_label(ax, label: str, x: float = -12.0, y: float = -12.0) -> None: # 在每个主子图区域左下角添加 (a)(b)(c)，避免受各自 axes 高宽比影响而错位。 from matplotlib.transforms import offset_copy fig = ax.figure top_spec = ax.get_subplotspec().get_topmost_subplotspec() bbox = top_spec.get_position(fig) text_transform = offset_copy(fig.transFigure, fig=fig, x=x, y=y, units="points") if label == '(a)': pad_x = 0.032 if label == '(b)': pad_x = 0.032 if label == '(c)': pad_x = 0.032 fig.text( bbox.x0 - pad_x, bbox.y0 - 0.13, label, transform=text_transform, ha="left", va="bottom", fontsize=14, fontweight="bold", color="black", ) def set_tick_bold(ax, fontsize: float | None = None) -> None: # 统一把坐标轴刻度文字设为粗体。 for label in ax.get_xticklabels() + ax.get_yticklabels(): label.set_fontweight("bold") if fontsize is not None: label.set_fontsize(fontsize) def heatmap_text_color(mappable, value: float) -> str: # 根据底色明暗自动选择黑字或白字，避免热力图数字看不清。 rgba = mappable.cmap(mappable.norm(value)) r, g, b = rgba[:3] luminance = 0.299 * r + 0.587 * g + 0.114 * b return "white" if luminance < 0.50 else "black" def format_heatmap_value(value: float) -> str: # 将 0~1 的比例转成两位整数百分数，例如 0.83 -> "83"。 return f"{int(np.rint(value * 100)):02d}" # ========================= # 六、Panel (a): 最终任务混淆矩阵 # 作用：展示最终预测任务与真实任务之间的混淆关系。 # ========================= def draw_confusion_panel(fig, heatmap_spec, cbar_spec) -> None: # 画左侧热力图，并重点标出第 7 列（T7）的数值。 from matplotlib.colors import LinearSegmentedColormap from matplotlib.patches import Rectangle _, _, _, final_confusion = load_confusion_data() num_tasks = final_confusion.shape[0] cmap = LinearSegmentedColormap.from_list( "water_blue_to_water_red", [ to_rgba("#4DBBD5", alpha=0.9), to_rgba("#8FD3E8", alpha=0.32), to_rgba("#E89AA0", alpha=0.32), to_rgba("#E64B35", alpha=0.9), ], ) ax = fig.add_subplot(heatmap_spec) ax.set_facecolor(FACE_COLOR) # Keep panel shape stable without over-constraining outer layout spacing. x_edges = np.arange(num_tasks + 1) y_edges = np.arange(num_tasks + 1) im = ax.pcolormesh( x_edges, y_edges, final_confusion, cmap=cmap, vmin=0.0, vmax=1.0, shading="flat", edgecolors="#FFFFFF", linewidth=0.6, ) ax.set_ylim(num_tasks, 0) ax.set_box_aspect(1) cax = fig.add_subplot(cbar_spec) apos = ax.get_position() cpos = cax.get_position() cax.set_position( [ cpos.x0 + cpos.width * CBAR_DX - 0.04, apos.y0, cpos.width * (1.3 + CBAR_DW), apos.height, ] ) cbar = fig.colorbar(im, cax=cax) cbar.set_ticks(np.linspace(0.0, 1.0, 6)) cbar.set_ticklabels([f"{v:.1f}" for v in np.linspace(0.0, 1.0, 6)]) cbar.set_label("Fraction", color=TEXT_COLOR, labelpad=5) # cbar.outline.set_linewidth(0.85) # cbar.outline.set_edgecolor("#6A6A6A") # cbar.ax.tick_params(width=0.85, length=3, pad=1.0, colors=TEXT_COLOR) # for label in cbar.ax.get_yticklabels(): # label.set_fontweight("bold") ax.set_xticks(np.arange(num_tasks) + 0.5) ax.set_xticklabels( [f"T{i + 1}" for i in range(num_tasks)], rotation=35, # ha="right", # rotation_mode="anchor", ) ax.set_yticks(np.arange(num_tasks) + 0.5) ax.set_yticklabels([f"T{i + 1}" for i in range(num_tasks)]) ax.set_xlabel("Predicted Task") ax.set_ylabel("Ground-Truth Task") # ax.tick_params(axis="x", width=0.95, length=3.2, colors=TEXT_COLOR) # ax.tick_params(axis="y", width=0.95, length=3.2, colors=TEXT_COLOR) # set_tick_bold(ax, fontsize=TICK_LABEL_FONTSIZE) highlight_col = 6 # Visual cue for routing collapse: many tasks routed into T7 branch. collapse_rect = Rectangle( (highlight_col, 0), 1, num_tasks, fill=False, # edgecolor="#2F5D9B", linewidth=1.2, linestyle="-", zorder=6, ) ax.add_patch(collapse_rect) for row_idx in range(num_tasks): value = final_confusion[row_idx, highlight_col] ax.text( highlight_col + 0.5, row_idx + 0.5, format_heatmap_value(value), ha="center", va="center", color=heatmap_text_color(im, float(value)), fontsize=VALUE_LABEL_FONTSIZE, fontweight="bold", zorder=5, ) # ax.text( # 0.25, # 0.5, # "Most tasks \n routed to T7", # transform=ax.transAxes, # ha="center", # va="center", # bbox=dict( # boxstyle="round,pad=0.3", facecolor="white", edgecolor="none", alpha=0.8 # ), # zorder=7, # ) ax.annotate( "Collapsed\nRouting", xy=(highlight_col, num_tasks * 0.50), transform=ax.transAxes, xycoords="data", ha="center", xytext=(0.25, 0.50), textcoords=ax.transAxes, bbox=dict( boxstyle="round,pad=0.3", facecolor="white", edgecolor="none", alpha=0.6 ), arrowprops=dict(arrowstyle="->", lw=1.2), zorder=7, ) for spine in ax.spines.values(): spine.set_visible(False) ax.set_frame_on(False) ax.patch.set_linewidth(0.0) ax.patch.set_edgecolor("none") draw_panel_label(ax, "(a)") # ========================= # 七、Panel (b): Inferred vs Oracle AUC 对比 # 作用：比较模型推断得到的 AUC 与 oracle 上界，并标出差值。 # ========================= def draw_oracle_panel(fig, outer_spec) -> None: # 画中间柱状图：左柱是 inferred，右柱是 oracle，中间叠加显示提升空间。 _, infer_auc_matrix, oracle_auc_matrix, _ = load_confusion_data() num_tasks = infer_auc_matrix.shape[0] task_ids = np.arange(1, num_tasks + 1) x_positions = np.arange(num_tasks, dtype=np.float32) * 1.50 + 1.0 infer_final = infer_auc_matrix[-1] * 100.0 oracle_final = oracle_auc_matrix[-1] * 100.0 gap_to_oracle = np.clip(oracle_final - infer_final, a_min=0.0, a_max=None) width = 0.60 ax = fig.add_subplot(outer_spec) # ax.set_anchor("S") # ax.set_facecolor(BAR_BACKGROUND_COLOR) ax.patch.set_facecolor("C1") ax.patch.set_alpha(0.1) ax.grid(axis="y", linestyle="--", linewidth=0.85, color=GRID_COLOR, zorder=0) ax.set_axisbelow(True) ax.bar( x_positions - width / 2, infer_final, width=width, label="Inferred", color=INFERRED_BAR_COLOR, edgecolor=BAR_EDGE_COLOR, linewidth=0.95, hatch="/", zorder=3, ) ax.bar( x_positions - width / 2, gap_to_oracle, width=width, bottom=infer_final, label="Uneven Oracle Gains", color=BAR_GAIN_COLOR, edgecolor=BAR_EDGE_COLOR, linewidth=0.95, zorder=4, ) ax.bar( x_positions + width / 2, oracle_final, width=width, label="Oracle", color=ORACLE_BAR_COLOR, edgecolor=BAR_EDGE_COLOR, linewidth=0.95, hatch=".", zorder=3, ) ax.set_xlabel("Ground-Truth Task") ax.set_ylabel("Macro-AUC (%)") ax.set_ylim(0.0, 100.0) ax.set_xlim(x_positions[0] - 0.86, x_positions[-1] + 0.86) ax.set_xticks(x_positions) ax.set_xticklabels([f"T{task_id}" for task_id in task_ids], rotation=35) ax.set_yticks(np.arange(0.0, 101.0, 20.0)) # ax.tick_params(axis="x", direction="out") # ax.tick_params(axis="y", which="both", direction="out", left=True, right=False) ax.yaxis.set_ticks_position("left") ax.spines["top"].set_visible(False) ax.spines["right"].set_visible(False) # ax.spines["left"].set_linewidth(1.05) # ax.spines["bottom"].set_linewidth(1.05) # set_tick_bold(ax, fontsize=TICK_LABEL_FONTSIZE) y_offsets = { 1: 0.0, 2: 0.0, 4: 0.0, 5: 5.6, 6: 0.8, 7: 2.8, 8: 0.8, 9: 0.0, 10: 0.0, } x_offsets = { 1: -0.05, 2: 0.04, 4: -0.05, 5: 0.06, 6: 0.25, 7: 0.05, 8: -0.04, 9: 0.05, 10: 0.25, } for task_id, x_pos, infer_val, gap_val in zip( task_ids, x_positions - width / 2, infer_final, gap_to_oracle ): if gap_val <= 0.05: continue y = min(infer_val + gap_val + 1 + y_offsets.get(int(task_id), 0.0), 99.0) x = x_pos + x_offsets.get(int(task_id), 0.0) x = x_pos y = infer_val + gap_val + 2 ax.text( x, y, f"+{gap_val:.1f}", ha="center", va="bottom", # color=BAR_GAIN_COLOR, fontsize=12, zorder=6, ) handles, labels = ax.get_legend_handles_labels() label_to_handle = {label: handle for handle, label in zip(handles, labels)} ordered_labels = ["Inferred", "Oracle", "Uneven Oracle Gains"] ordered_handles = [label_to_handle[label] for label in ordered_labels if label in label_to_handle] legend = ax.legend( ordered_handles, ordered_labels, frameon=False, loc="upper left", bbox_to_anchor=(0.015, 0.985), borderaxespad=0.0, handlelength=1.05, handletextpad=0.38, labelspacing=0.20, ncol=2, ) # ax.text( # 0.985, # 0.92, # "Uneven Oracle Gains", # transform=ax.transAxes, # ha="right", # va="top", # fontsize=11, # color="#4A4A4A", # style="italic", # zorder=7, # ) # for text in legend.get_texts(): # text.set_fontweight("bold") draw_panel_label(ax, "(b)") # ========================= # 八、Panel (c) 的嵌入散点小图 # 作用：分别画出 w/o prompt 与 w/ prompt 下 ISC 和 LVH 的分布变化。 # ========================= def draw_inset_panel(ax, pair_plot: dict[str, object], condition: str) -> None: # 先画背景样本，再高亮指定类别对，并连接两类中心点。 pair_a, pair_b = pair_plot["pair_labels"] rows = pair_plot["rows_by_condition"][condition] background_rows = [row for row in rows if not row["is_pair_non_easy"]] highlight_rows = [row for row in rows if row["is_pair_non_easy"]] cond_colors = INSET_CONDITION_COLORS.get(condition, INSET_CONDITION_COLORS["prompt"]) ax.scatter( [row["proj_x"] for row in background_rows], [row["proj_y"] for row in background_rows], s=7, marker="o", color=BACKGROUND_COLOR, alpha=0.25, edgecolors="none", zorder=1, ) subset_a = [row for row in highlight_rows if row["true_label"] == pair_a] ax.scatter( [row["proj_x"] for row in subset_a], [row["proj_y"] for row in subset_a], s=16, marker="o", color=cond_colors["A"], alpha=0.85, edgecolors="white", linewidths=0.22, zorder=2, ) subset_b = [row for row in highlight_rows if row["true_label"] == pair_b] ax.scatter( [row["proj_x"] for row in subset_b], [row["proj_y"] for row in subset_b], s=18, marker="x", color=cond_colors["B"], alpha=0.9, linewidths=1.0, zorder=2, ) c1 = pair_plot["centers"][condition][pair_a] c2 = pair_plot["centers"][condition][pair_b] ax.plot( [c1[0], c2[0]], [c1[1], c2[1]], linestyle=(0, (2.0, 2.0)), color="#8E8E8E", linewidth=0.78, zorder=2, ) ax.scatter( [c1[0]], [c1[1]], s=24, marker="o", color=cond_colors["A_center"], edgecolors="#2E2E2E", linewidths=0.45, zorder=3, ) ax.scatter( [c2[0]], [c2[1]], s=28, marker="X", color=cond_colors["B_center"], edgecolors="#2E2E2E", linewidths=0.40, zorder=3, ) ax.set_xlim(*pair_plot["xlim"]) ax.set_ylim(*pair_plot["ylim"]) ax.set_xticks([]) ax.set_yticks([]) for side in ["top", "right", "left", "bottom"]: ax.spines[side].set_visible(False) ax.set_facecolor("none") # ========================= # 九、Panel (c): 不同难度样本的 margin gain # 作用：主图是三组柱状图，嵌入图用来解释 near/hard 样本的类别分布变化。 # ========================= def draw_margin_panel(fig, outer_spec) -> None: # 画右侧主柱图，并在柱图中嵌入 ISC vs LVH 的二维投影示意。 from matplotlib.lines import Line2D from matplotlib.patches import FancyBboxPatch import matplotlib.patheffects as pe pair_plot = build_pair_plot(load_pair_rows()) ax = fig.add_subplot(outer_spec) # ax.set_anchor("S") # ax.set_facecolor("C2") ax.patch.set_facecolor('C2') ax.patch.set_alpha(0.1) xs = [0.10, 0.45, 0.78] heights = [row["positive_rate"] * 100.0 for row in BAR_STATS] bar_width = 0.125 bars = ax.bar( xs, heights, width=bar_width, color=MARGIN_BAR_COLORS, edgecolor=MARGIN_HATCH_EDGE_COLOR, linewidth=0.95, zorder=3, ) for bar, hatch in zip(bars, MARGIN_BAR_HATCHES): bar.set_hatch(hatch) ax.set_ylabel( "Positive Margin Gain (%)", color=MARGIN_TEXT_COLOR, labelpad=5, ) ax.set_ylim(0.0, 100.0) ax.set_xlim(-0.03, 0.92) ax.set_yticks([0, 20, 40, 60, 80, 100]) ax.set_xticks(xs) xtick_labels = [ "Correct\n($=1$)\n$n=1706$", "Near\n($=2-5$)\n$n=146$", "Hard\n($>5$)\n$n=306$", ] main_tick_texts = ax.set_xticklabels(xtick_labels, ha="right") for tick in main_tick_texts: tick.set_color(MARGIN_TEXT_COLOR) # tick.set_fontweight("bold") # tick.set_fontsize(11.2) tick.set_ha("center") tick.set_linespacing(0.95) ax.grid(axis="y", linestyle="-", color=MARGIN_GRID_COLOR, linewidth=0.80, zorder=0) ax.set_axisbelow(True) # ax.tick_params( # axis="y", # which="both", # direction="out", # length=3.5, # width=0.95, # colors=MARGIN_TEXT_COLOR, # pad=3.0, # ) # ax.tick_params( # axis="x", direction="out", length=0, pad=8.0, colors=MARGIN_TEXT_COLOR # ) ax.yaxis.set_ticks_position("left") for spine in ax.spines.values(): spine.set_linewidth(1.05) spine.set_color(MARGIN_SPINE_COLOR) # set_tick_bold(ax, fontsize=TICK_LABEL_FONTSIZE) for x, pct in zip(xs, heights): ax.text( x, pct + 2, f"{pct:.1f}", ha="center", va="bottom", fontsize=12, zorder=6, ) inset_x, inset_y, inset_w, inset_h = 0.1, 0.45, 0.8, 0.4 inset_frame = FancyBboxPatch( (inset_x, inset_y), inset_w, inset_h, boxstyle="round,pad=0.005,rounding_size=0.010", transform=ax.transAxes, facecolor="white", edgecolor="#D0D6DC", alpha=0.9, linewidth=0.90, zorder=4.0, ) inset_frame.set_path_effects( [ pe.SimplePatchShadow(offset=(0.9, -0.9), shadow_rgbFace=(0, 0, 0, 0.08)), pe.Normal(), ] ) ax.add_patch(inset_frame) ax.text( inset_x + inset_w / 2.0, inset_y + inset_h - 0.024, "ISC vs LVH", transform=ax.transAxes, ha="center", va="top", fontsize=INSET_TITLE_FONTSIZE + 0.5, color=MARGIN_TEXT_COLOR, zorder=7, ) ax.text( inset_x + 0.180, inset_y + inset_h - 0.080, "w/o prompt", transform=ax.transAxes, ha="center", va="top", fontsize=INSET_SUBTITLE_FONTSIZE, color=MARGIN_TEXT_COLOR, zorder=10, bbox=dict( boxstyle="round,pad=0.12", facecolor=(1, 1, 1, 0.72), edgecolor="none" ), ) ax.text( inset_x + inset_w - 0.180, inset_y + inset_h - 0.080, "w/ prompt", transform=ax.transAxes, ha="center", va="top", fontsize=INSET_SUBTITLE_FONTSIZE, color=MARGIN_TEXT_COLOR, zorder=10, bbox=dict( boxstyle="round,pad=0.12", facecolor=(1, 1, 1, 0.72), edgecolor="none" ), ) ax_left = ax.inset_axes([inset_x + 0.06, inset_y + 0.12, 0.285, 0.145], zorder=7) ax_right = ax.inset_axes([inset_x + 0.45, inset_y + 0.12, 0.285, 0.145], zorder=7) draw_inset_panel(ax_left, pair_plot, "noprompt") draw_inset_panel(ax_right, pair_plot, "prompt") for mini_ax in [ax_left, ax_right]: mini_ax.set_title("") mini_ax.set_xticks([]) mini_ax.set_yticks([]) mini_ax.tick_params(width=0.0, length=0.0) for text in mini_ax.texts: text.set_visible(False) for spine in mini_ax.spines.values(): spine.set_linewidth(0.65) ax.text( inset_x + inset_w / 2.0, inset_y + 0.01, "0/92 rescued \n gap 0.0094 $\\rightarrow$ 0.0074", transform=ax.transAxes, ha="center", va="bottom", fontsize=INSET_SUMMARY_FONTSIZE, color="#4F4F4F", zorder=7, ) x_left_corner = xs[1] - bar_width / 2.0 x_right_corner = xs[1] + bar_width / 2.0 y_top = heights[1] left_corner_axes = ax.transAxes.inverted().transform( ax.transData.transform((x_left_corner, y_top)) ) right_corner_axes = ax.transAxes.inverted().transform( ax.transData.transform((x_right_corner, y_top)) ) left_target = (inset_x + 0.12, inset_y) right_target = (inset_x + inset_w - 0.12, inset_y) guide1 = Line2D( [left_corner_axes[0], left_target[0]], [left_corner_axes[1], left_target[1]], transform=ax.transAxes, color=MARGIN_GUIDE_COLOR, linewidth=0.80, linestyle=(0, (2.0, 2.0)), zorder=2, ) guide2 = Line2D( [right_corner_axes[0], right_target[0]], [right_corner_axes[1], right_target[1]], transform=ax.transAxes, color=MARGIN_GUIDE_COLOR, linewidth=0.80, linestyle=(0, (2.0, 2.0)), zorder=2, ) ax.add_line(guide1) ax.add_line(guide2) ax.spines["top"].set_visible(False) ax.spines["right"].set_visible(False) draw_panel_label(ax, "(c)") # ========================= # 十、输出与主流程 # 作用：拼好三联图并导出为 PNG/PDF。 # ========================= def save_outputs(fig, output_png: Path, output_pdf: Path, dpi: int) -> None: # 确保输出目录存在，再保存两种格式。 output_png.parent.mkdir(parents=True, exist_ok=True) output_pdf.parent.mkdir(parents=True, exist_ok=True) fig.savefig(output_png, dpi=dpi, bbox_inches="tight", pad_inches=0.020) fig.savefig(output_pdf, bbox_inches="tight", pad_inches=0.020) def main() -> None: # 主入口：解析参数 -> 配置风格 -> 创建三联图 -> 保存结果。 args = parse_args() output_png = Path(args.output_png).expanduser().resolve() output_pdf = Path(args.output_pdf).expanduser().resolve() configure_matplotlib() import matplotlib.pyplot as plt fig = plt.figure(figsize=(args.fig_width, args.fig_height), facecolor="white") outer = fig.add_gridspec( 1, 4, width_ratios=[3, 0.10, 5.8, 2], wspace=0.32, ) draw_confusion_panel(fig, outer[0], outer[1]) draw_oracle_panel(fig, outer[2]) draw_margin_panel(fig, outer[3]) save_outputs(fig, output_png=output_png, output_pdf=output_pdf, dpi=args.dpi) plt.close(fig) print("Saved self-contained one-row figure:") print(f" PNG: {output_png}") print(f" PDF: {output_pdf}") import os os.system(f"pdfcrop {output_pdf} {output_pdf}") if __name__ == "__main__": main()