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Bugfix for argsort ordering. #301

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@xkszltl xkszltl commented Nov 7, 2024

Numpy uses quicksort by default, causing unit test failure when numpy 2.0 released possibly due to internal behavior change.
We should always use stable sorting for deterministic behavior.

This change also implies numpy>=1.15.0 to support kind.
Should be OK since that is from mid 2018 and the earliest supported python listed is 3.8 from late 2019.

Some intermediate results:

fitness = [
 0.33974567 0.81895563 0.94070405 0.52993818 0.88379917 0.77904015
 0.57553742 0.47380936 0.37427307 0.51682958 0.44839885 0.31961157
 0.61859446 0.81008902 0.92151043 0.09568099 0.94070405 0.27372766
 0.90532631 0.77971909 0.         0.35179379 0.67697571 0.75771999
 0.38116613 0.72893838 0.66027406 0.94070405 0.94070405 0.57791134
 0.81895563 0.6465814  0.61902008 0.78928374 0.77971909 0.33974567
 0.928166   0.50965352 0.72893838 0.96131336 0.63307761 0.81008902
 0.87809592 0.94070405 0.85015997 0.5519724  0.88508586 0.66670118
 0.53261073 0.88464403 0.92151043 0.94070405 0.84466016 0.92151043
 0.67346263 0.64351788 0.88046279 0.74943025 0.85015997 0.94070405
 0.92251378 0.79115731 0.322811   0.94070405 0.8047925  0.86139315
 0.88508586 0.94070405 0.69791616 0.93857883 0.89034748 0.23008709
 0.8074698  0.67242776 0.27152052 0.94070405 0.92151043 0.24725724
 0.88508586 0.8074698  0.46557846 0.70198849 0.51334045 0.53751801
 0.83399953 0.74622591 0.78625696 0.94070405 0.44174531 0.88508586
 0.57743352 0.94070405 0.28999104 0.37486687 0.84896842 0.92151043
 0.09305982 0.8074698  0.06718238 0.61554918 0.40841508 0.88382682
 0.92151043 0.90532631 0.84458929 0.07197116 0.03002106 0.89591207
 0.77971909 0.57791134 0.76741657 0.82206575 0.94070405 0.91762347
 0.86139315 0.77971909 0.37066566 0.82901212 0.94070405 0.51739491
 0.94070405 0.83795664 0.20879317 0.84058307 0.62029227 0.04250433
 0.88382682 0.64843241 0.         0.85015997 0.6218872  0.94070405
 0.53976123 0.91762347 0.08466398 0.42431066 0.44839885 0.89641524
 0.85706889 0.76292463 0.26448191 0.         0.60351109 0.7122769
 0.62225002 0.72038895 0.85015997 0.94070405 0.94070405 0.12910986
 0.88379917 0.94070405 0.5181633  0.88508586 0.94070405 0.92151043
 0.92158215 0.4051425  0.53836192 0.60054088 0.77971909 0.44839885
 0.88508586 0.56913785 0.44376123 0.94070405 0.38552886 0.88508586
 0.88508586 0.88508586 0.94070405 0.09222356 0.8096277  0.92151043
 0.81051991 0.02988189 0.90532631 0.28390354 0.77971909 0.29585511
 0.37720316 0.8074698  0.85015997 0.38552886 0.8074698  0.76741657
 0.77971909 0.27008694 0.62396678 0.94070405 0.2849127  0.68423676
 0.90532631 0.90459952 0.92151043 0.59974342 0.45411693 0.11342193
 0.70516271 0.88219066 0.72821262 0.94070405 0.09465967 0.94070405
 0.60690495 0.02250968 0.32600829 0.69264124 0.88219066 0.79500924
 0.52118044 0.77971909 0.94070405 0.86466465 0.88508586 0.08784893
 0.6424183  0.78384819 0.77971909 0.82418994 0.89034748 0.
 0.81854996 0.29415009 0.09222356 0.50276137 0.48749366 0.58708714
 0.84058307 0.77953462 0.7713532  0.92151043 0.88313334 0.49627758
 0.10918025 0.80488051 0.65845542 0.44254166 0.94070405 0.91762347
 0.92151043 0.38884114 0.94070405 0.92151043 0.88508586 0.94070405
 0.11971749 0.59217191 0.67585517 0.94070405 0.77971909 0.72775919
 0.83407586 0.94070405 0.23008709 0.83514714 0.77971909 0.93533783
 0.3714695  0.88464403 0.         0.49601688 0.51164985 0.6218872
 0.86139315 0.57225205 0.41146609 0.07472116 0.94070405 0.88382682
 0.18041259 0.79395903 0.43869894 0.38552886 0.57570353 0.41347527
 0.94070405 0.94070405 0.88508586 0.86113562 0.89034748 0.88046279
 0.94070405 0.52723207 0.89768744 0.58341587 0.77971909 0.51751904
 0.80843058 0.2851571  0.94070405 0.6459937  0.94070405 0.76741657
 0.06516033 0.95802584 0.92151043 0.84440653 0.77971909 0.82783373
 0.82163262 0.04811703 0.88508586 0.94070405 0.79516061 0.81895563
 0.34463159 0.92683427 0.94070405 0.88508586 0.87309917 0.26448191
 0.6424183  0.73195805 0.77971909 0.92367782 0.26448191 0.94070405
 0.81479518 0.4204927  0.88508586 0.92151043 0.88382682 0.26576284
 0.88508586 0.90532631 0.94070405 0.77971909 0.25035915 0.53850526
 0.61554918 0.90532631 0.55879117 0.75153108 0.757016   0.39199112
 0.77421967 0.8074698  0.29623808 0.23232798 0.77971909 0.38309104
 0.0122373  0.94070405 0.77971909 0.95428278 0.93541914 0.09465967
 0.97737236 0.83195164 0.77894074 0.88382682 0.90532631 0.81895563
 0.48658361 0.72276988 0.83514714 0.92151043 0.94070405 0.81008902
 0.94070405 0.76741657 0.16055894 0.67687135 0.77971909 0.73955796
 0.83514714 0.73377648 0.13088784 0.77971909 0.69120136 0.48487633
 0.92367782 0.63169887 0.7253329  0.75366789 0.87644307 0.87309917
 0.36724386 0.35503614 0.         0.83582908 0.94070405 0.92151043
 0.87498367 0.41575278 0.83514714 0.61554918 0.77971909 0.25573618
 0.04811703 0.77971909 0.34609843 0.92151043 0.89641524 0.65840902
 0.92151043 0.94070405 0.82411846 0.94070405 0.16146892 0.88508586
 0.45403708 0.94070405 0.8096277  0.63700282 0.88046279 0.78280947
 0.79525698 0.92151043 0.31825026 0.90789505 0.14922644 0.57109064
 0.85494232 0.8096277  0.64919865 0.77971909 0.08605848 0.84791636
 0.80513213 0.4156438  0.88508586 0.66074833 0.89034748 0.11146298
 0.88508586 0.76554458 0.92151043 0.94070405 0.94070405 0.90532631
 0.77971909 0.64380586 0.70972035 0.90532631 0.04811703 0.92151043
 0.33974567 0.58031222 0.92151043 0.92151043 0.88508586 0.94070405
 0.84039895 0.88508586 0.88508586 0.84058307 0.88379917 0.76070421
 0.88508586 0.26063012 0.88508586 0.92367782 0.09465967 0.74943025
 0.85092669 0.14726108 0.67306094 0.51975967 0.38116613 0.88508586
 0.79115731 0.04811703 0.82370975 0.91762347 0.64919865 0.94070405
 0.24079654 0.94070405 0.76047747 0.7971817  0.77971909 0.73373088
 0.35719023 0.37021036 0.77679554 0.50839417 0.90551043 0.89641524
 0.88508586 0.82227722 0.92151043 0.2379788  0.84198115 0.94070405
 0.94070405 0.04899257 0.25385147 0.40621502 0.10429103 0.74449425
 0.94070405 0.60250829 0.88508586 0.71577686 0.94070405 0.94070405
 0.31440268 0.83514714
]

With numpy 1:

hall_of_fame = [
 348  39 295 345 276 268 443 253  87  91 120 429 242 343 112 238 118 277
 326 430 249  67 131 189 165 201 382 203 360 154 358 151 148 397 212 399
 303 403  75 245 292 467 497 496   2 492  16  27  28 486 485 308  43 290
  51 469 170 317  63 282  59 147  69 346 257  36 307 372 453 315  60 156
 321 396  53 409 441 231 155 482  14 357 437  76 102 428 383 296  95 173
 240 194 393  50 243 440 113 133 239 465
]

est = [
 max(sub(abs(X0), X6), min(X0, X6)),
 sub(min(sub(min(sqrt(X9), X6), neg(X0)), X6), neg(X0)),
 neg(X0),
 div(sub(X0, X2), mul(add(mul(inv(X9), min(X2, 0.107)), inv(inv(X1))), div(max(max(X7, X8), sub(X6, X6)), sub(sqrt(X9), sub(X0, -0.548))))),
 sub(sqrt(sqrt(abs(add(X5, X1)))), neg(X0)),
 log(add(min(mul(add(max(0.405, X3), log(X5)), inv(min(X7, X9))), abs(min(neg(X6), inv(X8)))), neg(mul(mul(add(X5, X6), add(X8, X3)), log(sub(X1, -0.516)))))),
 sub(min(sqrt(X9), X6), mul(X2, log(X4))),
 log(add(min(X2, X6), mul(X8, X1))),
 min(sqrt(X9), X6),
 mul(max(mul(abs(0.308), mul(X6, X0)), add(neg(X9), min(X4, -0.368))), sub(abs(max(X8, X1)), abs(log(X0))))
]

With numpy 2:

hall_of_fame = [
 348  39 295 345   2  27 486 469 467  28  63  43  51  59 120  91 112 118
 131  67  75  87 212 238 242 165 170 189 201 203 249 253 443 245 268 154
 151 148 496 492 358 360 282 290 277 276 308 317 430 429 326 343 292 303
 397 497  16 485 403 382 399 147  69 346 257  36 307 453 315 372  60 156
  53  76  50 396 409  95 482 102 383 393 155 321 296  14 440 231 240 437
 243 441 357 194 173 428 465 239 113 133
]

est = [
 max(sub(abs(X0), X6), min(X0, X6)),
 sub(min(sub(min(sqrt(X9), X6), neg(X0)), X6), neg(X0)),
 X0,
 div(sub(X0, X2), mul(add(mul(inv(X9), min(X2, 0.107)), inv(inv(X1))), div(max(max(X7, X8), sub(X6, X6)), sub(sqrt(X9), sub(X0, -0.548))))),
 sub(sqrt(sqrt(abs(add(X5, X1)))), neg(X0)),
 log(add(min(mul(add(max(0.405, X3), log(X5)), inv(min(X7, X9))), abs(min(neg(X6), inv(X8)))), neg(mul(mul(add(X5, X6), add(X8, X3)), log(sub(X1, -0.516)))))),
 sub(min(sqrt(X9), X6), mul(X2, log(X4))),
 log(add(min(X2, X6), mul(X8, X1))),
 min(sqrt(X9), X6),
 mul(max(mul(abs(0.308), mul(X6, X0)), add(neg(X9), min(X4, -0.368))), sub(abs(max(X8, X1)), abs(log(X0))))
]

@xkszltl
Bugfix for argsort ordering.
03e9166 
Numpy uses quicksort by default, causing unit test failure when numpy 2.0 released possibly due to internal behavior change.
We should always use stable sorting for deterministic behavior.

This change also implies `numpy>=1.15.0` to support `kind`.
Should be OK since that is from mid 2018 and the earliest supported python listed is 3.8 from late 2019.

Some intermediate results:
```
fitness = [
 0.33974567 0.81895563 0.94070405 0.52993818 0.88379917 0.77904015
 0.57553742 0.47380936 0.37427307 0.51682958 0.44839885 0.31961157
 0.61859446 0.81008902 0.92151043 0.09568099 0.94070405 0.27372766
 0.90532631 0.77971909 0.         0.35179379 0.67697571 0.75771999
 0.38116613 0.72893838 0.66027406 0.94070405 0.94070405 0.57791134
 0.81895563 0.6465814  0.61902008 0.78928374 0.77971909 0.33974567
 0.928166   0.50965352 0.72893838 0.96131336 0.63307761 0.81008902
 0.87809592 0.94070405 0.85015997 0.5519724  0.88508586 0.66670118
 0.53261073 0.88464403 0.92151043 0.94070405 0.84466016 0.92151043
 0.67346263 0.64351788 0.88046279 0.74943025 0.85015997 0.94070405
 0.92251378 0.79115731 0.322811   0.94070405 0.8047925  0.86139315
 0.88508586 0.94070405 0.69791616 0.93857883 0.89034748 0.23008709
 0.8074698  0.67242776 0.27152052 0.94070405 0.92151043 0.24725724
 0.88508586 0.8074698  0.46557846 0.70198849 0.51334045 0.53751801
 0.83399953 0.74622591 0.78625696 0.94070405 0.44174531 0.88508586
 0.57743352 0.94070405 0.28999104 0.37486687 0.84896842 0.92151043
 0.09305982 0.8074698  0.06718238 0.61554918 0.40841508 0.88382682
 0.92151043 0.90532631 0.84458929 0.07197116 0.03002106 0.89591207
 0.77971909 0.57791134 0.76741657 0.82206575 0.94070405 0.91762347
 0.86139315 0.77971909 0.37066566 0.82901212 0.94070405 0.51739491
 0.94070405 0.83795664 0.20879317 0.84058307 0.62029227 0.04250433
 0.88382682 0.64843241 0.         0.85015997 0.6218872  0.94070405
 0.53976123 0.91762347 0.08466398 0.42431066 0.44839885 0.89641524
 0.85706889 0.76292463 0.26448191 0.         0.60351109 0.7122769
 0.62225002 0.72038895 0.85015997 0.94070405 0.94070405 0.12910986
 0.88379917 0.94070405 0.5181633  0.88508586 0.94070405 0.92151043
 0.92158215 0.4051425  0.53836192 0.60054088 0.77971909 0.44839885
 0.88508586 0.56913785 0.44376123 0.94070405 0.38552886 0.88508586
 0.88508586 0.88508586 0.94070405 0.09222356 0.8096277  0.92151043
 0.81051991 0.02988189 0.90532631 0.28390354 0.77971909 0.29585511
 0.37720316 0.8074698  0.85015997 0.38552886 0.8074698  0.76741657
 0.77971909 0.27008694 0.62396678 0.94070405 0.2849127  0.68423676
 0.90532631 0.90459952 0.92151043 0.59974342 0.45411693 0.11342193
 0.70516271 0.88219066 0.72821262 0.94070405 0.09465967 0.94070405
 0.60690495 0.02250968 0.32600829 0.69264124 0.88219066 0.79500924
 0.52118044 0.77971909 0.94070405 0.86466465 0.88508586 0.08784893
 0.6424183  0.78384819 0.77971909 0.82418994 0.89034748 0.
 0.81854996 0.29415009 0.09222356 0.50276137 0.48749366 0.58708714
 0.84058307 0.77953462 0.7713532  0.92151043 0.88313334 0.49627758
 0.10918025 0.80488051 0.65845542 0.44254166 0.94070405 0.91762347
 0.92151043 0.38884114 0.94070405 0.92151043 0.88508586 0.94070405
 0.11971749 0.59217191 0.67585517 0.94070405 0.77971909 0.72775919
 0.83407586 0.94070405 0.23008709 0.83514714 0.77971909 0.93533783
 0.3714695  0.88464403 0.         0.49601688 0.51164985 0.6218872
 0.86139315 0.57225205 0.41146609 0.07472116 0.94070405 0.88382682
 0.18041259 0.79395903 0.43869894 0.38552886 0.57570353 0.41347527
 0.94070405 0.94070405 0.88508586 0.86113562 0.89034748 0.88046279
 0.94070405 0.52723207 0.89768744 0.58341587 0.77971909 0.51751904
 0.80843058 0.2851571  0.94070405 0.6459937  0.94070405 0.76741657
 0.06516033 0.95802584 0.92151043 0.84440653 0.77971909 0.82783373
 0.82163262 0.04811703 0.88508586 0.94070405 0.79516061 0.81895563
 0.34463159 0.92683427 0.94070405 0.88508586 0.87309917 0.26448191
 0.6424183  0.73195805 0.77971909 0.92367782 0.26448191 0.94070405
 0.81479518 0.4204927  0.88508586 0.92151043 0.88382682 0.26576284
 0.88508586 0.90532631 0.94070405 0.77971909 0.25035915 0.53850526
 0.61554918 0.90532631 0.55879117 0.75153108 0.757016   0.39199112
 0.77421967 0.8074698  0.29623808 0.23232798 0.77971909 0.38309104
 0.0122373  0.94070405 0.77971909 0.95428278 0.93541914 0.09465967
 0.97737236 0.83195164 0.77894074 0.88382682 0.90532631 0.81895563
 0.48658361 0.72276988 0.83514714 0.92151043 0.94070405 0.81008902
 0.94070405 0.76741657 0.16055894 0.67687135 0.77971909 0.73955796
 0.83514714 0.73377648 0.13088784 0.77971909 0.69120136 0.48487633
 0.92367782 0.63169887 0.7253329  0.75366789 0.87644307 0.87309917
 0.36724386 0.35503614 0.         0.83582908 0.94070405 0.92151043
 0.87498367 0.41575278 0.83514714 0.61554918 0.77971909 0.25573618
 0.04811703 0.77971909 0.34609843 0.92151043 0.89641524 0.65840902
 0.92151043 0.94070405 0.82411846 0.94070405 0.16146892 0.88508586
 0.45403708 0.94070405 0.8096277  0.63700282 0.88046279 0.78280947
 0.79525698 0.92151043 0.31825026 0.90789505 0.14922644 0.57109064
 0.85494232 0.8096277  0.64919865 0.77971909 0.08605848 0.84791636
 0.80513213 0.4156438  0.88508586 0.66074833 0.89034748 0.11146298
 0.88508586 0.76554458 0.92151043 0.94070405 0.94070405 0.90532631
 0.77971909 0.64380586 0.70972035 0.90532631 0.04811703 0.92151043
 0.33974567 0.58031222 0.92151043 0.92151043 0.88508586 0.94070405
 0.84039895 0.88508586 0.88508586 0.84058307 0.88379917 0.76070421
 0.88508586 0.26063012 0.88508586 0.92367782 0.09465967 0.74943025
 0.85092669 0.14726108 0.67306094 0.51975967 0.38116613 0.88508586
 0.79115731 0.04811703 0.82370975 0.91762347 0.64919865 0.94070405
 0.24079654 0.94070405 0.76047747 0.7971817  0.77971909 0.73373088
 0.35719023 0.37021036 0.77679554 0.50839417 0.90551043 0.89641524
 0.88508586 0.82227722 0.92151043 0.2379788  0.84198115 0.94070405
 0.94070405 0.04899257 0.25385147 0.40621502 0.10429103 0.74449425
 0.94070405 0.60250829 0.88508586 0.71577686 0.94070405 0.94070405
 0.31440268 0.83514714
]
```

With numpy 1:
```
hall_of_fame = [
 348  39 295 345 276 268 443 253  87  91 120 429 242 343 112 238 118 277
 326 430 249  67 131 189 165 201 382 203 360 154 358 151 148 397 212 399
 303 403  75 245 292 467 497 496   2 492  16  27  28 486 485 308  43 290
  51 469 170 317  63 282  59 147  69 346 257  36 307 372 453 315  60 156
 321 396  53 409 441 231 155 482  14 357 437  76 102 428 383 296  95 173
 240 194 393  50 243 440 113 133 239 465
]

est = [
 max(sub(abs(X0), X6), min(X0, X6)),
 sub(min(sub(min(sqrt(X9), X6), neg(X0)), X6), neg(X0)),
 neg(X0),
 div(sub(X0, X2), mul(add(mul(inv(X9), min(X2, 0.107)), inv(inv(X1))), div(max(max(X7, X8), sub(X6, X6)), sub(sqrt(X9), sub(X0, -0.548))))),
 sub(sqrt(sqrt(abs(add(X5, X1)))), neg(X0)),
 log(add(min(mul(add(max(0.405, X3), log(X5)), inv(min(X7, X9))), abs(min(neg(X6), inv(X8)))), neg(mul(mul(add(X5, X6), add(X8, X3)), log(sub(X1, -0.516)))))),
 sub(min(sqrt(X9), X6), mul(X2, log(X4))),
 log(add(min(X2, X6), mul(X8, X1))),
 min(sqrt(X9), X6),
 mul(max(mul(abs(0.308), mul(X6, X0)), add(neg(X9), min(X4, -0.368))), sub(abs(max(X8, X1)), abs(log(X0))))
]
```

With numpy 2:
```
hall_of_fame = [
 348  39 295 345   2  27 486 469 467  28  63  43  51  59 120  91 112 118
 131  67  75  87 212 238 242 165 170 189 201 203 249 253 443 245 268 154
 151 148 496 492 358 360 282 290 277 276 308 317 430 429 326 343 292 303
 397 497  16 485 403 382 399 147  69 346 257  36 307 453 315 372  60 156
  53  76  50 396 409  95 482 102 383 393 155 321 296  14 440 231 240 437
 243 441 357 194 173 428 465 239 113 133
]

est = [
 max(sub(abs(X0), X6), min(X0, X6)),
 sub(min(sub(min(sqrt(X9), X6), neg(X0)), X6), neg(X0)),
 X0,
 div(sub(X0, X2), mul(add(mul(inv(X9), min(X2, 0.107)), inv(inv(X1))), div(max(max(X7, X8), sub(X6, X6)), sub(sqrt(X9), sub(X0, -0.548))))),
 sub(sqrt(sqrt(abs(add(X5, X1)))), neg(X0)),
 log(add(min(mul(add(max(0.405, X3), log(X5)), inv(min(X7, X9))), abs(min(neg(X6), inv(X8)))), neg(mul(mul(add(X5, X6), add(X8, X3)), log(sub(X1, -0.516)))))),
 sub(min(sqrt(X9), X6), mul(X2, log(X4))),
 log(add(min(X2, X6), mul(X8, X1))),
 min(sqrt(X9), X6),
 mul(max(mul(abs(0.308), mul(X6, X0)), add(neg(X9), min(X4, -0.368))), sub(abs(max(X8, X1)), abs(log(X0))))
]
```
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