-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathgiskard_scan_report.html
1088 lines (812 loc) · 112 KB
/
giskard_scan_report.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
<!doctype html>
<html lang="en">
<head>
<title>Giskard Scan Results</title>
<base target="_blank">
<meta charset="utf-8">
<style>pre code.hljs{display:block;overflow-x:auto;padding:1em}code.hljs{padding:3px 5px}
/*!
Theme: GitHub Dark
Description: Dark theme as seen on github.com
Author: github.com
Maintainer: @Hirse
Updated: 2021-05-15
Outdated base version: https://github.com/primer/github-syntax-dark
Current colors taken from GitHub's CSS
*/.hljs{background:#0d1117;color:#c9d1d9}.hljs-doctag,.hljs-keyword,.hljs-meta .hljs-keyword,.hljs-template-tag,.hljs-template-variable,.hljs-type,.hljs-variable.language_{color:#ff7b72}.hljs-title,.hljs-title.class_,.hljs-title.class_.inherited__,.hljs-title.function_{color:#d2a8ff}.hljs-attr,.hljs-attribute,.hljs-literal,.hljs-meta,.hljs-number,.hljs-operator,.hljs-selector-attr,.hljs-selector-class,.hljs-selector-id,.hljs-variable{color:#79c0ff}.hljs-meta .hljs-string,.hljs-regexp,.hljs-string{color:#a5d6ff}.hljs-built_in,.hljs-symbol{color:#ffa657}.hljs-code,.hljs-comment,.hljs-formula{color:#8b949e}.hljs-name,.hljs-quote,.hljs-selector-pseudo,.hljs-selector-tag{color:#7ee787}.hljs-subst{color:#c9d1d9}.hljs-section{color:#1f6feb;font-weight:700}.hljs-bullet{color:#f2cc60}.hljs-emphasis{color:#c9d1d9;font-style:italic}.hljs-strong{color:#c9d1d9;font-weight:700}.hljs-addition{background-color:#033a16;color:#aff5b4}.hljs-deletion{background-color:#67060c;color:#ffdcd7}.hljs-copy-wrapper{overflow:hidden;position:relative}.hljs-copy-button:focus,.hljs-copy-wrapper:hover .hljs-copy-button{transform:translateX(0)}.hljs-copy-button{background-color:#2d2b57;background-color:var(--hljs-theme-background);background-image:url('data:image/svg+xml;charset=utf-8,<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="none" viewBox="0 0 24 24"><path fill="%23fff" fill-rule="evenodd" d="M6 5a1 1 0 0 0-1 1v14a1 1 0 0 0 1 1h12a1 1 0 0 0 1-1V6a1 1 0 0 0-1-1h-2a1 1 0 1 1 0-2h2a3 3 0 0 1 3 3v14a3 3 0 0 1-3 3H6a3 3 0 0 1-3-3V6a3 3 0 0 1 3-3h2a1 1 0 0 1 0 2H6Z" clip-rule="evenodd"/><path fill="%23fff" fill-rule="evenodd" d="M7 3a2 2 0 0 1 2-2h6a2 2 0 0 1 2 2v2a2 2 0 0 1-2 2H9a2 2 0 0 1-2-2V3Zm8 0H9v2h6V3Z" clip-rule="evenodd"/></svg>');background-position:50%;background-repeat:no-repeat;border:1px solid #ffffff22;border-radius:.25rem;color:#fff;height:2rem;position:absolute;right:1em;text-indent:-9999px;top:1em;transition:background-color .2s ease,transform .2s ease-out;width:2rem}.hljs-copy-button:hover{border-color:#ffffff44}.hljs-copy-button:active{border-color:#ffffff66}.hljs-copy-button[data-copied=true]{background-image:none;text-indent:0;width:auto}@media (prefers-reduced-motion){.hljs-copy-button{transition:none}}.hljs-copy-alert{clip:rect(0 0 0 0);-webkit-clip-path:inset(50%);clip-path:inset(50%);height:1px;overflow:hidden;position:absolute;white-space:nowrap;width:1px}
/*! tailwindcss v3.3.2 | MIT License | https://tailwindcss.com*/*,:after,:before{border:0 solid #e5e7eb;box-sizing:border-box}:after,:before{--tw-content:""}html{-webkit-text-size-adjust:100%;font-feature-settings:normal;font-family:ui-sans-serif,system-ui,-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,Noto Sans,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol,Noto Color Emoji;font-variation-settings:normal;line-height:1.5;-moz-tab-size:4;-o-tab-size:4;tab-size:4}body{line-height:inherit;margin:0}hr{border-top-width:1px;color:inherit;height:0}abbr:where([title]){-webkit-text-decoration:underline dotted;text-decoration:underline dotted}h1,h2,h3,h4,h5,h6{font-size:inherit;font-weight:inherit}a{color:inherit;text-decoration:inherit}b,strong{font-weight:bolder}code,kbd,pre,samp{font-family:ui-monospace,SFMono-Regular,Menlo,Monaco,Consolas,Liberation Mono,Courier New,monospace;font-size:1em}small{font-size:80%}sub,sup{font-size:75%;line-height:0;position:relative;vertical-align:baseline}sub{bottom:-.25em}sup{top:-.5em}table{border-collapse:collapse;border-color:inherit;text-indent:0}button,input,optgroup,select,textarea{color:inherit;font-family:inherit;font-size:100%;font-weight:inherit;line-height:inherit;margin:0;padding:0}button,select{text-transform:none}[type=button],[type=reset],[type=submit],button{-webkit-appearance:button;background-color:transparent;background-image:none}:-moz-focusring{outline:auto}:-moz-ui-invalid{box-shadow:none}progress{vertical-align:baseline}::-webkit-inner-spin-button,::-webkit-outer-spin-button{height:auto}[type=search]{-webkit-appearance:textfield;outline-offset:-2px}::-webkit-search-decoration{-webkit-appearance:none}::-webkit-file-upload-button{-webkit-appearance:button;font:inherit}summary{display:list-item}blockquote,dd,dl,figure,h1,h2,h3,h4,h5,h6,hr,p,pre{margin:0}fieldset{margin:0}fieldset,legend{padding:0}menu,ol,ul{list-style:none;margin:0;padding:0}textarea{resize:vertical}input::-moz-placeholder,textarea::-moz-placeholder{color:#9ca3af;opacity:1}input::placeholder,textarea::placeholder{color:#9ca3af;opacity:1}[role=button],button{cursor:pointer}:disabled{cursor:default}audio,canvas,embed,iframe,img,object,svg,video{display:block;vertical-align:middle}img,video{height:auto;max-width:100%}[hidden]{display:none}*,:after,:before{--tw-border-spacing-x:0;--tw-border-spacing-y:0;--tw-translate-x:0;--tw-translate-y:0;--tw-rotate:0;--tw-skew-x:0;--tw-skew-y:0;--tw-scale-x:1;--tw-scale-y:1;--tw-pan-x: ;--tw-pan-y: ;--tw-pinch-zoom: ;--tw-scroll-snap-strictness:proximity;--tw-gradient-from-position: ;--tw-gradient-via-position: ;--tw-gradient-to-position: ;--tw-ordinal: ;--tw-slashed-zero: ;--tw-numeric-figure: ;--tw-numeric-spacing: ;--tw-numeric-fraction: ;--tw-ring-inset: ;--tw-ring-offset-width:0px;--tw-ring-offset-color:#fff;--tw-ring-color:rgba(59,130,246,.5);--tw-ring-offset-shadow:0 0 #0000;--tw-ring-shadow:0 0 #0000;--tw-shadow:0 0 #0000;--tw-shadow-colored:0 0 #0000;--tw-blur: ;--tw-brightness: ;--tw-contrast: ;--tw-grayscale: ;--tw-hue-rotate: ;--tw-invert: ;--tw-saturate: ;--tw-sepia: ;--tw-drop-shadow: ;--tw-backdrop-blur: ;--tw-backdrop-brightness: ;--tw-backdrop-contrast: ;--tw-backdrop-grayscale: ;--tw-backdrop-hue-rotate: ;--tw-backdrop-invert: ;--tw-backdrop-opacity: ;--tw-backdrop-saturate: ;--tw-backdrop-sepia: }::backdrop{--tw-border-spacing-x:0;--tw-border-spacing-y:0;--tw-translate-x:0;--tw-translate-y:0;--tw-rotate:0;--tw-skew-x:0;--tw-skew-y:0;--tw-scale-x:1;--tw-scale-y:1;--tw-pan-x: ;--tw-pan-y: ;--tw-pinch-zoom: ;--tw-scroll-snap-strictness:proximity;--tw-gradient-from-position: ;--tw-gradient-via-position: ;--tw-gradient-to-position: ;--tw-ordinal: ;--tw-slashed-zero: ;--tw-numeric-figure: ;--tw-numeric-spacing: ;--tw-numeric-fraction: ;--tw-ring-inset: ;--tw-ring-offset-width:0px;--tw-ring-offset-color:#fff;--tw-ring-color:rgba(59,130,246,.5);--tw-ring-offset-shadow:0 0 #0000;--tw-ring-shadow:0 0 #0000;--tw-shadow:0 0 #0000;--tw-shadow-colored:0 0 #0000;--tw-blur: ;--tw-brightness: ;--tw-contrast: ;--tw-grayscale: ;--tw-hue-rotate: ;--tw-invert: ;--tw-saturate: ;--tw-sepia: ;--tw-drop-shadow: ;--tw-backdrop-blur: ;--tw-backdrop-brightness: ;--tw-backdrop-contrast: ;--tw-backdrop-grayscale: ;--tw-backdrop-hue-rotate: ;--tw-backdrop-invert: ;--tw-backdrop-opacity: ;--tw-backdrop-saturate: ;--tw-backdrop-sepia: }.m-4{margin:1rem}.my-1{margin-bottom:.25rem;margin-top:.25rem}.my-2{margin-bottom:.5rem;margin-top:.5rem}.my-4{margin-top:1rem}.mb-4,.my-4{margin-bottom:1rem}.ml-1{margin-left:.25rem}.ml-2{margin-left:.5rem}.ml-4{margin-left:1rem}.mr-1{margin-right:.25rem}.mr-2{margin-right:.5rem}.mt-1{margin-top:.25rem}.mt-1\.5{margin-top:.375rem}.mt-4{margin-top:1rem}.block{display:block}.inline-block{display:inline-block}.flex{display:flex}.table{display:table}.hidden{display:none}.h-11{height:2.75rem}.h-6{height:1.5rem}.h-max{height:-moz-max-content;height:max-content}.w-6{width:1.5rem}.w-full{width:100%}.w-min{width:-moz-min-content;width:min-content}.flex-grow{flex-grow:1}.table-auto{table-layout:auto}.cursor-pointer{cursor:pointer}.resize{resize:both}.list-disc{list-style-type:disc}.flex-wrap{flex-wrap:wrap}.items-end{align-items:flex-end}.items-center{align-items:center}.space-x-1>:not([hidden])~:not([hidden]){--tw-space-x-reverse:0;margin-left:calc(.25rem*(1 - var(--tw-space-x-reverse)));margin-right:calc(.25rem*var(--tw-space-x-reverse))}.overflow-hidden{overflow:hidden}.overflow-scroll{overflow:scroll}.overflow-x-auto{overflow-x:auto}.overflow-y-clip{overflow-y:clip}.text-ellipsis{text-overflow:ellipsis}.whitespace-nowrap{white-space:nowrap}.rounded{border-radius:.25rem}.rounded-full{border-radius:9999px}.rounded-sm{border-radius:.125rem}.rounded-t{border-top-left-radius:.25rem;border-top-right-radius:.25rem}.border{border-width:1px}.border-b{border-bottom-width:1px}.border-l{border-left-width:1px}.border-r{border-right-width:1px}.border-t{border-top-width:1px}.border-amber-200{--tw-border-opacity:1;border-color:rgb(253 230 138/var(--tw-border-opacity))}.border-blue-200{--tw-border-opacity:1;border-color:rgb(191 219 254/var(--tw-border-opacity))}.border-gray-500{--tw-border-opacity:1;border-color:rgb(107 114 128/var(--tw-border-opacity))}.border-gray-600{--tw-border-opacity:1;border-color:rgb(75 85 99/var(--tw-border-opacity))}.border-red-400{--tw-border-opacity:1;border-color:rgb(248 113 113/var(--tw-border-opacity))}.border-zinc-100\/50{border-color:hsla(240,5%,96%,.5)}.border-zinc-500{--tw-border-opacity:1;border-color:rgb(113 113 122/var(--tw-border-opacity))}.border-b-gray-500{--tw-border-opacity:1;border-bottom-color:rgb(107 114 128/var(--tw-border-opacity))}.bg-amber-100\/40{background-color:hsla(48,96%,89%,.4)}.bg-amber-200{--tw-bg-opacity:1;background-color:rgb(253 230 138/var(--tw-bg-opacity))}.bg-blue-300{--tw-bg-opacity:1;background-color:rgb(147 197 253/var(--tw-bg-opacity))}.bg-blue-300\/25{background-color:rgba(147,197,253,.25)}.bg-green-100\/40{background-color:rgba(220,252,231,.4)}.bg-red-400{--tw-bg-opacity:1;background-color:rgb(248 113 113/var(--tw-bg-opacity))}.bg-zinc-500{--tw-bg-opacity:1;background-color:rgb(113 113 122/var(--tw-bg-opacity))}.bg-zinc-700{--tw-bg-opacity:1;background-color:rgb(63 63 70/var(--tw-bg-opacity))}.p-3{padding:.75rem}.p-4{padding:1rem}.px-1{padding-left:.25rem;padding-right:.25rem}.px-2{padding-left:.5rem;padding-right:.5rem}.px-3{padding-left:.75rem;padding-right:.75rem}.px-4{padding-left:1rem;padding-right:1rem}.py-0{padding-bottom:0;padding-top:0}.py-0\.5{padding-bottom:.125rem;padding-top:.125rem}.py-2{padding-bottom:.5rem;padding-top:.5rem}.pt-1{padding-top:.25rem}.text-left{text-align:left}.text-center{text-align:center}.text-right{text-align:right}.align-middle{vertical-align:middle}.text-sm{font-size:.875rem;line-height:1.25rem}.text-xs{font-size:.75rem;line-height:1rem}.font-bold{font-weight:700}.font-medium{font-weight:500}.uppercase{text-transform:uppercase}.leading-4{line-height:1rem}.text-amber-100{--tw-text-opacity:1;color:rgb(254 243 199/var(--tw-text-opacity))}.text-amber-200{--tw-text-opacity:1;color:rgb(253 230 138/var(--tw-text-opacity))}.text-amber-900{--tw-text-opacity:1;color:rgb(120 53 15/var(--tw-text-opacity))}.text-blue-200{--tw-text-opacity:1;color:rgb(191 219 254/var(--tw-text-opacity))}.text-blue-300{--tw-text-opacity:1;color:rgb(147 197 253/var(--tw-text-opacity))}.text-blue-900{--tw-text-opacity:1;color:rgb(30 58 138/var(--tw-text-opacity))}.text-gray-400{--tw-text-opacity:1;color:rgb(156 163 175/var(--tw-text-opacity))}.text-green-50{--tw-text-opacity:1;color:rgb(240 253 244/var(--tw-text-opacity))}.text-red-400{--tw-text-opacity:1;color:rgb(248 113 113/var(--tw-text-opacity))}.text-white{--tw-text-opacity:1;color:rgb(255 255 255/var(--tw-text-opacity))}.text-zinc-100{--tw-text-opacity:1;color:rgb(244 244 245/var(--tw-text-opacity))}.text-zinc-100\/90{color:hsla(240,5%,96%,.9)}.underline{text-decoration-line:underline}.filter{filter:var(--tw-blur) var(--tw-brightness) var(--tw-contrast) var(--tw-grayscale) var(--tw-hue-rotate) var(--tw-invert) var(--tw-saturate) var(--tw-sepia) var(--tw-drop-shadow)}p a{text-decoration:underline}.tab-header{min-width:3rem}.active.tab-header{min-width:-moz-fit-content;min-width:fit-content}table.dataframe{max-width:100%;overflow:auto;width:100%}.dataframe tr{border-bottom:1px solid #555;vertical-align:top}.dataframe td,.dataframe th{padding:.5rem}.dataframe th{text-align:left!important}.prose p,.prose ul{margin-bottom:.25rem;margin-top:.25rem}.prose ul{list-style-type:disc;margin-left:1rem}.first\:border-t:first-child{border-top-width:1px}.hover\:border-zinc-500:hover{--tw-border-opacity:1;border-color:rgb(113 113 122/var(--tw-border-opacity))}.hover\:bg-zinc-400:hover{--tw-bg-opacity:1;background-color:rgb(161 161 170/var(--tw-bg-opacity))}.hover\:bg-zinc-500:hover{--tw-bg-opacity:1;background-color:rgb(113 113 122/var(--tw-bg-opacity))}.hover\:bg-zinc-700:hover{--tw-bg-opacity:1;background-color:rgb(63 63 70/var(--tw-bg-opacity))}.hover\:text-white:hover{--tw-text-opacity:1;color:rgb(255 255 255/var(--tw-text-opacity))}.group.open .group-\[\.open\]\:inline-block{display:inline-block}.group.open .group-\[\.open\]\:hidden{display:none}.group.active .group-\[\.active\]\:border-gray-500{--tw-border-opacity:1;border-color:rgb(107 114 128/var(--tw-border-opacity))}.group.active .group-\[\.active\]\:border-b-zinc-800{--tw-border-opacity:1;border-bottom-color:rgb(39 39 42/var(--tw-border-opacity))}.group.active .group-\[\.active\]\:bg-zinc-800{--tw-bg-opacity:1;background-color:rgb(39 39 42/var(--tw-bg-opacity))}.peer.open~.peer-\[\.open\]\:table-row{display:table-row}:is(.dark .dark\:bg-zinc-800){--tw-bg-opacity:1;background-color:rgb(39 39 42/var(--tw-bg-opacity))}:is(.dark .dark\:bg-zinc-900){--tw-bg-opacity:1;background-color:rgb(24 24 27/var(--tw-bg-opacity))}:is(.dark .dark\:fill-white){fill:#fff}:is(.dark .dark\:text-white){--tw-text-opacity:1;color:rgb(255 255 255/var(--tw-text-opacity))}</style>
</head>
<body>
<div class="dark">
<div id="gsk-scan" class="dark:text-white dark:bg-zinc-800 rounded border border-gray-500">
<!-- TAB HEADER -->
<div class="flex items-end pt-1 dark:bg-zinc-900 rounded-t">
<div class="flex items-center px-4 dark:fill-white border-b border-gray-500 h-11">
<div>
<svg xmlns="http://www.w3.org/2000/svg" width="30" height="15" fill="none">
<path fill="#fff" fill-rule="evenodd"
d="M22.504 1.549a4.196 4.196 0 0 1 2.573-.887v.002a3.783 3.783 0 0 1 2.706 1.086 3.783 3.783 0 0 1 1.126 2.69 3.771 3.771 0 0 1-1.126 2.69 3.77 3.77 0 0 1-2.706 1.085l-4.794.011-2.533 3.467L8.203 15l2.881-3.335a9.829 9.829 0 0 1-4.663-1.68H3.185L0 7.163h3.934C4.263 3.165 8.187 0 12.96 0c2.24 0 4.489.696 6.175 1.909a7.423 7.423 0 0 1 1.882 1.919 4.194 4.194 0 0 1 1.487-2.28ZM7.05 3.249l3.91 3.915h1.505L7.89 2.584a7.773 7.773 0 0 0-.84.665Zm4.079-2.008 5.923 5.923h1.503l-6.086-6.087c-.45.023-.898.078-1.34.164ZM4.574 8.226h-1.77l.784.693h1.584a8.454 8.454 0 0 1-.598-.693Zm9.479 0H5.984c1.469 1.477 3.656 2.377 5.977 2.422l2.092-2.422Zm-2.458 4.472 5.492-1.902 1.878-2.569h-3.508l-3.862 4.47Zm10.361-5.552h3.265a2.714 2.714 0 0 0 1.747-4.648 2.711 2.711 0 0 0-1.888-.773 3.127 3.127 0 0 0-3.123 3.124v2.297Zm3.659-3.73a.677.677 0 1 1-.134 1.348.677.677 0 0 1 .134-1.348Z"
clip-rule="evenodd" />
</svg>
</div>
<span class="uppercase text-sm ml-2 py-2 leading-4 w-min">7 issues detected</span>
</div>
<div data-tab-target="Data_Leakage"
class="tab-header cursor-pointer group active">
<div
class="overflow-hidden text-ellipsis group-[.active]:bg-zinc-800 group-[.active]:border-b-zinc-800 group-[.active]:border-gray-500 border-l px-3 py-2 border-r border-t border-b border-gray-600 border-b-gray-500 h-11 whitespace-nowrap">
Data Leakage
<span
class="ml-1 rounded-full text-xs min-w-4 min-h-4 px-1 py-0.5 inline-block text-center bg-red-400">
1
</span>
</div>
</div>
<div data-tab-target="Robustness"
class="tab-header cursor-pointer group">
<div
class="overflow-hidden text-ellipsis group-[.active]:bg-zinc-800 group-[.active]:border-b-zinc-800 group-[.active]:border-gray-500 border-l px-3 py-2 border-r border-t border-b border-gray-600 border-b-gray-500 h-11 whitespace-nowrap">
Robustness
<span
class="ml-1 rounded-full text-xs min-w-4 min-h-4 px-1 py-0.5 inline-block text-center bg-red-400">
3
</span>
</div>
</div>
<div data-tab-target="Performance"
class="tab-header cursor-pointer group">
<div
class="overflow-hidden text-ellipsis group-[.active]:bg-zinc-800 group-[.active]:border-b-zinc-800 group-[.active]:border-gray-500 border-l px-3 py-2 border-r border-t border-b border-gray-600 border-b-gray-500 h-11 whitespace-nowrap">
Performance
<span
class="ml-1 rounded-full text-xs min-w-4 min-h-4 px-1 py-0.5 inline-block text-center bg-amber-200 text-amber-900">
3
</span>
</div>
</div>
<div class="flex-grow border-b border-gray-500 h-11"></div>
</div>
<!-- TAB HEADER END -->
<div id="Data_Leakage" role="tabpanel" class="m-4 mb-4">
<div class="p-3 bg-amber-100/40 rounded-sm w-full flex align-middle">
<div class="text-amber-100 mt-1.5">
<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5"
stroke="currentColor" class="w-6 h-6">
<path stroke-linecap="round" stroke-linejoin="round"
d="M12 9v3.75m-9.303 3.376c-.866 1.5.217 3.374 1.948 3.374h14.71c1.73 0 2.813-1.874 1.948-3.374L13.949 3.378c-.866-1.5-3.032-1.5-3.898 0L2.697 16.126zM12 15.75h.007v.008H12v-.008z" />
</svg>
</div>
<div class="ml-2 text-amber-100 text-sm">
<div class="prose">
<p>Your model seems to present some data leakage. The model provides different results depending on whether it is computing on a single data point or the entire dataset. This happens when:</p>
<ul>
<li>Preprocessing steps, such as scaling, missing value imputation, or outlier handling, are fitted inside the prediction pipeline</li>
<li>Train-test splitting is done after preprocessing or feature selection</li>
</ul>
<p>To learn more about causes and solutions, check our <a href="https://docs.giskard.ai/en/latest/knowledge/key_vulnerabilities/data_leakage/index.html">guide on data leakage.</a></p>
</div>
</div>
</div>
<div class="flex items-center space-x-1">
<h2 class="uppercase my-4 mr-2 font-medium">Issues</h2>
<span class="text-xs border rounded px-1 uppercase text-red-400 border-red-400">1
major</span>
</div>
<div class="overflow-x-auto overflow-y-clip h-max">
<table class="table-auto w-full text-white">
<tbody class="first:border-t border-b border-zinc-500">
<tr class="gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700">
<td class="p-3">
<span class="mono text-blue-300">
Whole dataset
</span>
</td>
<td class="p-3 text-xs text-right space-x-1">
<a href="#"
class="gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm">Show details</a>
<a href="#"
class="hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm">Hide details</a>
</td>
</tr>
<tr class="gsk-issue-detail text-left hidden peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700">
<td colspan="1000" class="p-3">
<h4 class="font-bold text-sm">Description</h4>
We found 1 examples for which the model provides a different output depending on whether it is computing on a single data point or on a batch.
<h4 class="font-bold text-sm mt-4">Examples</h4>
<div class="text-white text-sm overflow-scroll">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Whole-dataset prediction</th>
<th>Single-sample prediction</th>
</tr>
</thead>
<tbody>
<tr>
<th>111</th>
<td>[0.0051106215, 0.9948894]</td>
<td>[0.0051105022, 0.9948895]</td>
</tr>
</tbody>
</table>
</div>
</div>
<h4 class="font-bold text-sm mt-4">Taxonomy</h4>
<span class="inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2">
avid-effect:performance:P0103
</span>
</td>
</tr>
</tbody>
</table>
</div>
</div>
<div id="Robustness" role="tabpanel" class="m-4 mb-4 hidden">
<div class="p-3 bg-amber-100/40 rounded-sm w-full flex align-middle">
<div class="text-amber-100 mt-1.5">
<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5"
stroke="currentColor" class="w-6 h-6">
<path stroke-linecap="round" stroke-linejoin="round"
d="M12 9v3.75m-9.303 3.376c-.866 1.5.217 3.374 1.948 3.374h14.71c1.73 0 2.813-1.874 1.948-3.374L13.949 3.378c-.866-1.5-3.032-1.5-3.898 0L2.697 16.126zM12 15.75h.007v.008H12v-.008z" />
</svg>
</div>
<div class="ml-2 text-amber-100 text-sm">
<div class="prose">
<p>Your model seems to be sensitive to small perturbations in the input data. These perturbations can include adding typos,
changing word order, or turning text into uppercase or lowercase. This happens when:</p>
<ul>
<li>There is not enough diversity in the training data</li>
<li>Overreliance on spurious correlations like the presence of specific word</li>
<li>Use of complex models with large number of parameters that tend to overfit the training data</li>
</ul>
<p>To learn more about causes and solutions, check our <a href="https://docs.giskard.ai/en/latest/knowledge/key_vulnerabilities/robustness/index.html">guide on robustness issues</a>.</p>
</div>
</div>
</div>
<div class="flex items-center space-x-1">
<h2 class="uppercase my-4 mr-2 font-medium">Issues</h2>
<span class="text-xs border rounded px-1 uppercase text-red-400 border-red-400">1
major</span>
<span class="text-xs border rounded px-1 uppercase text-amber-200 border-amber-200">2
medium</span>
</div>
<div class="overflow-x-auto overflow-y-clip h-max">
<table class="table-auto w-full text-white">
<tbody class="first:border-t border-b border-zinc-500">
<tr class="gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700">
<td class="p-3">
<span class="mono text-blue-300">
Feature `text`
</span>
</td>
<td class="p-3">
<span>Transform to uppercase</span>
</td>
<td class="p-3">
<span>Fail rate = 0.182</span>
</td>
<td class="p-3">
<span class="text-red-400">
182/1000 tested samples (18.2%) changed prediction after perturbation
</span>
</td>
<td class="p-3">
<span class="text-gray-400">
1000 samples affected<br>
(92.8% of dataset)
</span>
<td class="p-3 text-xs text-right space-x-1">
<a href="#"
class="gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm">Show details</a>
<a href="#"
class="hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm">Hide details</a>
</td>
</tr>
<tr class="gsk-issue-detail text-left hidden peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700">
<td colspan="1000" class="p-3">
<h4 class="font-bold text-sm">Description</h4>
When feature “text” is perturbed with the transformation “Transform to uppercase”, the model changes its prediction in 18.2% of the cases. We expected the predictions not to be affected by this transformation.
<h4 class="font-bold text-sm mt-4">Examples</h4>
<div class="text-white text-sm overflow-scroll">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>text</th>
<th>Transform to uppercase(text)</th>
<th>Original prediction</th>
<th>Prediction after perturbation</th>
</tr>
</thead>
<tbody>
<tr>
<th>638</th>
<td>Virer tous les laxistes incompétents.</td>
<td>VIRER TOUS LES LAXISTES INCOMPÉTENTS.</td>
<td>1 (p = 0.99)</td>
<td>0 (p = 1.00)</td>
</tr>
<tr>
<th>486</th>
<td>On découvrirait plus de bienfaits dans une synthèse numérique spécifique, analysant de nombreuses données recoupées (logiciel conçu par des informaticiens et magistrats scrupuleux et intègres) que dans les conclusions opposées d'avocats inégaux en compétence et probité, et pour certains, corrompus.</td>
<td>ON DÉCOUVRIRAIT PLUS DE BIENFAITS DANS UNE SYNTHÈSE NUMÉRIQUE SPÉCIFIQUE, ANALYSANT DE NOMBREUSES DONNÉES RECOUPÉES (LOGICIEL CONÇU PAR DES INFORMATICIENS ET MAGISTRATS SCRUPULEUX ET INTÈGRES) QUE DANS LES CONCLUSIONS OPPOSÉES D'AVOCATS INÉGAUX EN COMPÉTENCE ET PROBITÉ, ET POUR CERTAINS, CORROMPUS.</td>
<td>0 (p = 1.00)</td>
<td>1 (p = 0.64)</td>
</tr>
<tr>
<th>397</th>
<td>Supprimer la fabrique à incompétents (l'ENA) et interdire l'accès à une fonction publique à ceux-ci.</td>
<td>SUPPRIMER LA FABRIQUE À INCOMPÉTENTS (L'ENA) ET INTERDIRE L'ACCÈS À UNE FONCTION PUBLIQUE À CEUX-CI.</td>
<td>1 (p = 0.99)</td>
<td>0 (p = 1.00)</td>
</tr>
</tbody>
</table>
</div>
</div>
<h4 class="font-bold text-sm mt-4">Taxonomy</h4>
<span class="inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2">
avid-effect:performance:P0201
</span>
</td>
</tr>
</tbody>
<tbody class="first:border-t border-b border-zinc-500">
<tr class="gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700">
<td class="p-3">
<span class="mono text-blue-300">
Feature `text`
</span>
</td>
<td class="p-3">
<span>Transform to title case</span>
</td>
<td class="p-3">
<span>Fail rate = 0.060</span>
</td>
<td class="p-3">
<span class="text-amber-200">
60/1000 tested samples (6.0%) changed prediction after perturbation
</span>
</td>
<td class="p-3">
<span class="text-gray-400">
1000 samples affected<br>
(92.8% of dataset)
</span>
<td class="p-3 text-xs text-right space-x-1">
<a href="#"
class="gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm">Show details</a>
<a href="#"
class="hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm">Hide details</a>
</td>
</tr>
<tr class="gsk-issue-detail text-left hidden peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700">
<td colspan="1000" class="p-3">
<h4 class="font-bold text-sm">Description</h4>
When feature “text” is perturbed with the transformation “Transform to title case”, the model changes its prediction in 6.0% of the cases. We expected the predictions not to be affected by this transformation.
<h4 class="font-bold text-sm mt-4">Examples</h4>
<div class="text-white text-sm overflow-scroll">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>text</th>
<th>Transform to title case(text)</th>
<th>Original prediction</th>
<th>Prediction after perturbation</th>
</tr>
</thead>
<tbody>
<tr>
<th>9</th>
<td>Trop de fonctionnaires payés à ne rien faire dans l'administration française!!!</td>
<td>Trop De Fonctionnaires Payés À Ne Rien Faire Dans L'Administration Française!!!</td>
<td>1 (p = 0.79)</td>
<td>0 (p = 0.99)</td>
</tr>
<tr>
<th>24</th>
<td>Le Sénat est totalement déconnecté de la population de part le suffrage universel, ne sert guère qu'à ralentir l'appareil législatif en instaurant des délais sur des votes sur lesquels il n'aura pas le dernier mot.</td>
<td>Le Sénat Est Totalement Déconnecté De La Population De Part Le Suffrage Universel, Ne Sert Guère Qu'À Ralentir L'Appareil Législatif En Instaurant Des Délais Sur Des Votes Sur Lesquels Il N'Aura Pas Le Dernier Mot.</td>
<td>1 (p = 0.99)</td>
<td>0 (p = 1.00)</td>
</tr>
<tr>
<th>706</th>
<td>Tant que ce copinage durera, la fracture entre fonctionnaires et citoyens durera.</td>
<td>Tant Que Ce Copinage Durera, La Fracture Entre Fonctionnaires Et Citoyens Durera.</td>
<td>1 (p = 0.99)</td>
<td>0 (p = 0.99)</td>
</tr>
</tbody>
</table>
</div>
</div>
<h4 class="font-bold text-sm mt-4">Taxonomy</h4>
<span class="inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2">
avid-effect:performance:P0201
</span>
</td>
</tr>
</tbody>
<tbody class="first:border-t border-b border-zinc-500">
<tr class="gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700">
<td class="p-3">
<span class="mono text-blue-300">
Feature `text`
</span>
</td>
<td class="p-3">
<span>Add typos</span>
</td>
<td class="p-3">
<span>Fail rate = 0.051</span>
</td>
<td class="p-3">
<span class="text-amber-200">
51/1000 tested samples (5.1%) changed prediction after perturbation
</span>
</td>
<td class="p-3">
<span class="text-gray-400">
1000 samples affected<br>
(92.8% of dataset)
</span>
<td class="p-3 text-xs text-right space-x-1">
<a href="#"
class="gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm">Show details</a>
<a href="#"
class="hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm">Hide details</a>
</td>
</tr>
<tr class="gsk-issue-detail text-left hidden peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700">
<td colspan="1000" class="p-3">
<h4 class="font-bold text-sm">Description</h4>
When feature “text” is perturbed with the transformation “Add typos”, the model changes its prediction in 5.1% of the cases. We expected the predictions not to be affected by this transformation.
<h4 class="font-bold text-sm mt-4">Examples</h4>
<div class="text-white text-sm overflow-scroll">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>text</th>
<th>Add typos(text)</th>
<th>Original prediction</th>
<th>Prediction after perturbation</th>
</tr>
</thead>
<tbody>
<tr>
<th>524</th>
<td>Il y a trop d'incompétents et d’irresponsables, il faut tous les auditer</td>
<td>Il y a trop d'incompéttes et d’irresponsables, il faut tous oes auditer</td>
<td>1 (p = 0.99)</td>
<td>0 (p = 0.99)</td>
</tr>
<tr>
<th>427</th>
<td>La France est un pays qui selon les études internationales est tres corrompu , il faut lutter contre cela</td>
<td>La France est un pays qui selon les études internationales est tres orrompu , li faut lutter contre cela</td>
<td>1 (p = 0.99)</td>
<td>0 (p = 1.00)</td>
</tr>
<tr>
<th>453</th>
<td>l'agence régionale de santé qui dépends de l'Etat est inefficace l'agence de l’Ardèche incompétente</td>
<td>l'agence régionale de santé quo dépendd de l'Etat est ibnefitcace l'aence de l’Ardèche incomlétente</td>
<td>1 (p = 0.99)</td>
<td>0 (p = 1.00)</td>
</tr>
</tbody>
</table>
</div>
</div>
<h4 class="font-bold text-sm mt-4">Taxonomy</h4>
<span class="inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2">
avid-effect:performance:P0201
</span>
</td>
</tr>
</tbody>
</table>
</div>
</div>
<div id="Performance" role="tabpanel" class="m-4 mb-4 hidden">
<div class="p-3 bg-amber-100/40 rounded-sm w-full flex align-middle">
<div class="text-amber-100 mt-1.5">
<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5"
stroke="currentColor" class="w-6 h-6">
<path stroke-linecap="round" stroke-linejoin="round"
d="M12 9v3.75m-9.303 3.376c-.866 1.5.217 3.374 1.948 3.374h14.71c1.73 0 2.813-1.874 1.948-3.374L13.949 3.378c-.866-1.5-3.032-1.5-3.898 0L2.697 16.126zM12 15.75h.007v.008H12v-.008z" />
</svg>
</div>
<div class="ml-2 text-amber-100 text-sm">
<div class="prose">
<p>We found some data slices in your dataset on which your model performance is lower than average. Performance bias may
happen for different reasons:</p>
<ul>
<li>Not enough examples in the low-performing data slice in the training set</li>
<li>Wrong labels in the training set in the low-performing data slice</li>
<li>Drift between your training set and test set</li>
</ul>
<p>To learn more about causes and solutions, check our <a href="https://docs.giskard.ai/en/latest/knowledge/key_vulnerabilities/performance_bias/index.html">guide on performance bias.</a></p>
</div>
</div>
</div>
<div class="flex items-center space-x-1">
<h2 class="uppercase my-4 mr-2 font-medium">Issues</h2>
<span class="text-xs border rounded px-1 uppercase text-amber-200 border-amber-200">3
medium</span>
</div>
<div class="overflow-x-auto overflow-y-clip h-max">
<table class="table-auto w-full text-white">
<tbody class="first:border-t border-b border-zinc-500">
<tr class="gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700">
<td class="p-3">
<code class="mono text-blue-300">
`text` contains "services"
</code>
</td>
<td class="p-3">
<span>Balanced Accuracy = 0.880</span>
<span class="text-gray-400">
(Global = 0.961)
</span>
</td>
<td class="p-3">
<span class="text-amber-200">
-8.46% than global
</span>
</td>
<td class="p-3">
<span class="text-gray-400">
66 samples affected<br>
(6.1% of dataset)
</span>
<td class="p-3 text-xs text-right space-x-1">
<a href="#"
class="gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm">Show details</a>
<a href="#"
class="hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm">Hide details</a>
</td>
</tr>
<tr class="gsk-issue-detail text-left hidden peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700">
<td colspan="1000" class="p-3">
<h4 class="font-bold text-sm">Description</h4>
For records in the dataset where `text` contains "services", the Balanced Accuracy is 8.46% lower than the global Balanced Accuracy.
<h4 class="font-bold text-sm mt-4">Examples</h4>
<div class="text-white text-sm overflow-scroll">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>text</th>
<th>label</th>
<th>Predicted `label`</th>
</tr>
</thead>
<tbody>
<tr>
<th>22</th>
<td>Mauvaise répartition des services, lourdeurs, incompétences.</td>
<td>True</td>
<td>0 (p = 1.00)</td>
</tr>
<tr>
<th>313</th>
<td>beaucoup moins de personnel dans certains services qui ne servent à rien et qui sont incompétents (exemple : pole emploi) donc moins de personnes plus de compétences et moins de privilèges (retraite, jours de carence)</td>
<td>True</td>
<td>0 (p = 0.99)</td>
</tr>
<tr>
<th>716</th>
<td>La lutte pour faire valoir nos bons droits et parfois notre bonne foi, est devenue systématique devant des services publics incompétents, incohérents entre eux et antipathiques.</td>
<td>False</td>
<td>1 (p = 0.99)</td>
</tr>
</tbody>
</table>
</div>
</div>
<h4 class="font-bold text-sm mt-4">Significance</h4>
<p class="text-sm my-2">
The hypothesis that the <em>Balanced Accuracy</em> on the data slice was different with respect
to the rest of the data was asserted with p-value = None.
</p>
<h4 class="font-bold text-sm mt-4">Taxonomy</h4>
<span class="inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2">
avid-effect:performance:P0204
</span>
</td>
</tr>
</tbody>
<tbody class="first:border-t border-b border-zinc-500">
<tr class="gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700">
<td class="p-3">
<code class="mono text-blue-300">
`avg_word_length(text)` < 4.643 AND `avg_word_length(text)` >= 4.359
</code>
</td>
<td class="p-3">
<span>Balanced Accuracy = 0.901</span>
<span class="text-gray-400">
(Global = 0.961)
</span>
</td>
<td class="p-3">
<span class="text-amber-200">
-6.24% than global
</span>
</td>
<td class="p-3">
<span class="text-gray-400">
62 samples affected<br>
(5.8% of dataset)
</span>
<td class="p-3 text-xs text-right space-x-1">
<a href="#"
class="gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm">Show details</a>
<a href="#"
class="hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm">Hide details</a>
</td>
</tr>
<tr class="gsk-issue-detail text-left hidden peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700">
<td colspan="1000" class="p-3">
<h4 class="font-bold text-sm">Description</h4>
For records in the dataset where `avg_word_length(text)` < 4.643 AND `avg_word_length(text)` >= 4.359, the Balanced Accuracy is 6.24% lower than the global Balanced Accuracy.
<h4 class="font-bold text-sm mt-4">Examples</h4>
<div class="text-white text-sm overflow-scroll">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>text</th>
<th>avg_word_length(text)</th>
<th>label</th>
<th>Predicted `label`</th>
</tr>
</thead>
<tbody>
<tr>
<th>144</th>
<td>Réduire le nombre de députés et sénateurs qui sont sur-payés et on a le sentiment qu'ils ne servent à rien.</td>
<td>4.400000</td>
<td>True</td>
<td>0 (p = 1.00)</td>
</tr>
<tr>
<th>297</th>
<td>Un ministre des affaires étrangères qui aura exercé 14 mois dans sa fonction aura les mêmes privilèges que le ministre des affaires étrangères qui aura œuvré pendant tout le mandat du président, là encore ce n'est pas normal, pour moi il n'a le droit à rien, surtout qu'il a eu ce poste par copinage.</td>
<td>4.574074</td>
<td>False</td>
<td>1 (p = 0.81)</td>
</tr>
<tr>
<th>427</th>
<td>La France est un pays qui selon les études internationales est tres corrompu , il faut lutter contre cela</td>
<td>4.578947</td>
<td>False</td>
<td>1 (p = 0.99)</td>
</tr>
</tbody>
</table>
</div>
</div>
<h4 class="font-bold text-sm mt-4">Significance</h4>
<p class="text-sm my-2">
The hypothesis that the <em>Balanced Accuracy</em> on the data slice was different with respect
to the rest of the data was asserted with p-value = None.
</p>
<h4 class="font-bold text-sm mt-4">Taxonomy</h4>
<span class="inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2">
avid-effect:performance:P0204
</span>
</td>
</tr>
</tbody>
<tbody class="first:border-t border-b border-zinc-500">
<tr class="gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700">
<td class="p-3">
<code class="mono text-blue-300">
`avg_whitespace(text)` >= 0.154 AND `avg_whitespace(text)` < 0.157
</code>
</td>
<td class="p-3">
<span>Balanced Accuracy = 0.909</span>
<span class="text-gray-400">
(Global = 0.961)
</span>
</td>
<td class="p-3">
<span class="text-amber-200">
-5.41% than global
</span>
</td>
<td class="p-3">
<span class="text-gray-400">
62 samples affected<br>
(5.8% of dataset)
</span>
<td class="p-3 text-xs text-right space-x-1">
<a href="#"
class="gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm">Show details</a>
<a href="#"
class="hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm">Hide details</a>
</td>
</tr>
<tr class="gsk-issue-detail text-left hidden peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700">
<td colspan="1000" class="p-3">
<h4 class="font-bold text-sm">Description</h4>
For records in the dataset where `avg_whitespace(text)` >= 0.154 AND `avg_whitespace(text)` < 0.157, the Balanced Accuracy is 5.41% lower than the global Balanced Accuracy.
<h4 class="font-bold text-sm mt-4">Examples</h4>
<div class="text-white text-sm overflow-scroll">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>text</th>
<th>avg_whitespace(text)</th>
<th>label</th>
<th>Predicted `label`</th>
</tr>
</thead>
<tbody>
<tr>
<th>155</th>
<td>Beaucoup de fonctionnaires très grassement payés et n'assurent de surcroit pas le travail quand d'autre corps de métier ne sont pas rémunérés à leur juste valeur.</td>
<td>0.154321</td>
<td>True</td>
<td>0 (p = 1.00)</td>
</tr>
<tr>