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Alessandro Lucantonio
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Updated table.
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‎bench/results/process_results.ipynb

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{
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"ename": "ImportError",
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"evalue": "Missing optional dependency 'tabulate'. Use pip or conda to install tabulate.",
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"output_type": "error",
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"traceback": [
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"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
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"\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)",
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"\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/alpine/lib/python3.12/site-packages/pandas/compat/_optional.py:135\u001b[39m, in \u001b[36mimport_optional_dependency\u001b[39m\u001b[34m(name, extra, errors, min_version)\u001b[39m\n\u001b[32m 134\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m135\u001b[39m module = \u001b[43mimportlib\u001b[49m\u001b[43m.\u001b[49m\u001b[43mimport_module\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 136\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n",
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"\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/alpine/lib/python3.12/importlib/__init__.py:90\u001b[39m, in \u001b[36mimport_module\u001b[39m\u001b[34m(name, package)\u001b[39m\n\u001b[32m 89\u001b[39m level += \u001b[32m1\u001b[39m\n\u001b[32m---> \u001b[39m\u001b[32m90\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_bootstrap\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_gcd_import\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m[\u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpackage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m)\u001b[49m\n",
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"\u001b[36mFile \u001b[39m\u001b[32m<frozen importlib._bootstrap>:1381\u001b[39m, in \u001b[36m_gcd_import\u001b[39m\u001b[34m(name, package, level)\u001b[39m\n",
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"\u001b[36mFile \u001b[39m\u001b[32m<frozen importlib._bootstrap>:1354\u001b[39m, in \u001b[36m_find_and_load\u001b[39m\u001b[34m(name, import_)\u001b[39m\n",
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"\u001b[36mFile \u001b[39m\u001b[32m<frozen importlib._bootstrap>:1318\u001b[39m, in \u001b[36m_find_and_load_unlocked\u001b[39m\u001b[34m(name, import_)\u001b[39m\n",
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"\u001b[31mModuleNotFoundError\u001b[39m: No module named 'tabulate'",
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"\nDuring handling of the above exception, another exception occurred:\n",
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"\u001b[31mImportError\u001b[39m Traceback (most recent call last)",
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"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[85]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# Convert the DataFrame to Markdown\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m markdown_table = \u001b[43malgorithm_stats\u001b[49m\u001b[43m.\u001b[49m\u001b[43mto_markdown\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[32m 4\u001b[39m \u001b[38;5;66;03m# Print the Markdown table\u001b[39;00m\n\u001b[32m 5\u001b[39m \u001b[38;5;28mprint\u001b[39m(markdown_table)\n",
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"\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/alpine/lib/python3.12/site-packages/pandas/util/_decorators.py:333\u001b[39m, in \u001b[36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 327\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) > num_allow_args:\n\u001b[32m 328\u001b[39m warnings.warn(\n\u001b[32m 329\u001b[39m msg.format(arguments=_format_argument_list(allow_args)),\n\u001b[32m 330\u001b[39m \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[32m 331\u001b[39m stacklevel=find_stack_level(),\n\u001b[32m 332\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m333\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
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"\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/alpine/lib/python3.12/site-packages/pandas/core/frame.py:2983\u001b[39m, in \u001b[36mDataFrame.to_markdown\u001b[39m\u001b[34m(self, buf, mode, index, storage_options, **kwargs)\u001b[39m\n\u001b[32m 2981\u001b[39m kwargs.setdefault(\u001b[33m\"\u001b[39m\u001b[33mtablefmt\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mpipe\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 2982\u001b[39m kwargs.setdefault(\u001b[33m\"\u001b[39m\u001b[33mshowindex\u001b[39m\u001b[33m\"\u001b[39m, index)\n\u001b[32m-> \u001b[39m\u001b[32m2983\u001b[39m tabulate = \u001b[43mimport_optional_dependency\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtabulate\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 2984\u001b[39m result = tabulate.tabulate(\u001b[38;5;28mself\u001b[39m, **kwargs)\n\u001b[32m 2985\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m buf \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
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"\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/alpine/lib/python3.12/site-packages/pandas/compat/_optional.py:138\u001b[39m, in \u001b[36mimport_optional_dependency\u001b[39m\u001b[34m(name, extra, errors, min_version)\u001b[39m\n\u001b[32m 136\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n\u001b[32m 137\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m errors == \u001b[33m\"\u001b[39m\u001b[33mraise\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m138\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(msg)\n\u001b[32m 139\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 141\u001b[39m \u001b[38;5;66;03m# Handle submodules: if we have submodule, grab parent module from sys.modules\u001b[39;00m\n",
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"\u001b[31mImportError\u001b[39m: Missing optional dependency 'tabulate'. Use pip or conda to install tabulate."
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"| dataset | mean | median | std |\n",
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"|:------------------------------|------------:|---------:|-----------:|\n",
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"| 663_rabe_266 | 0.994973 | 0.994792 | 0.00138667 |\n",
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"| 527_analcatdata_election2000 | 0.992298 | 0.992383 | 0.00615176 |\n",
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"| 505_tecator | 0.985639 | 0.987823 | 0.00600829 |\n",
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"| 561_cpu | 0.976692 | 0.981073 | 0.0157433 |\n",
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"| 690_visualizing_galaxy | 0.96399 | 0.966614 | 0.00791278 |\n",
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"| 560_bodyfat | 0.595445 | 0.9646 | 0.678973 |\n",
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"| 227_cpu_small | 0.949931 | 0.95302 | 0.0102687 |\n",
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"| 197_cpu_act | 0.939741 | 0.948657 | 0.0297579 |\n",
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"| 523_analcatdata_neavote | 0.936577 | 0.943564 | 0.0278365 |\n",
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"| 556_analcatdata_apnea2 | 0.863564 | 0.867927 | 0.0218752 |\n",
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"| 229_pwLinear | 0.856554 | 0.864017 | 0.0295234 |\n",
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"| 1027_ESL | 0.859966 | 0.859878 | 0.0219077 |\n",
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"| 557_analcatdata_apnea1 | 0.860032 | 0.85602 | 0.0457254 |\n",
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"| 210_cloud | 0.785513 | 0.855809 | 0.174546 |\n",
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"| 695_chatfield_4 | 0.857361 | 0.854139 | 0.0323364 |\n",
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"| 1096_FacultySalaries | 0.60718 | 0.836315 | 0.51419 |\n",
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"| 529_pollen | 0.787219 | 0.782358 | 0.0118861 |\n",
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"| 230_machine_cpu | 0.561278 | 0.759371 | 0.550194 |\n",
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"| 503_wind | 0.7556 | 0.752685 | 0.0109313 |\n",
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"| 712_chscase_geyser1 | 0.749426 | 0.749685 | 0.0527733 |\n",
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"| 1089_USCrime | 0.708195 | 0.739885 | 0.140384 |\n",
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"| 228_elusage | 0.624231 | 0.721685 | 0.225679 |\n",
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"| 519_vinnie | 0.724621 | 0.713402 | 0.0364589 |\n",
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"| 225_puma8NH | 0.673587 | 0.67684 | 0.0196146 |\n",
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"| 666_rmftsa_ladata | 0.678517 | 0.663484 | 0.0452063 |\n",
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"| 659_sleuth_ex1714 | 0.577351 | 0.610788 | 0.252262 |\n",
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"| 706_sleuth_case1202 | 0.43508 | 0.572711 | 0.418311 |\n",
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"| 1029_LEV | 0.557463 | 0.553204 | 0.0361047 |\n",
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"| 547_no2 | 0.473821 | 0.44619 | 0.102342 |\n",
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"| 1030_ERA | 0.385125 | 0.381476 | 0.0469926 |\n",
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"| 1028_SWD | 0.339529 | 0.339576 | 0.0394794 |\n",
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"| 485_analcatdata_vehicle | 0.162436 | 0.304339 | 0.714552 |\n",
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"| 542_pollution | -0.510228 | 0.252399 | 2.12619 |\n",
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"| 522_pm10 | 0.242843 | 0.240674 | 0.0524153 |\n",
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"| 665_sleuth_case2002 | 0.16439 | 0.216995 | 0.215279 |\n",
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"| 192_vineyard | 0.204839 | 0.213078 | 0.382138 |\n",
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"| 687_sleuth_ex1605 | 0.00789428 | 0.15175 | 0.394736 |\n",
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"| 678_visualizing_environmental | 0.107825 | 0.111752 | 0.199401 |\n"
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]
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}
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],

‎bench/results/table.md

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1-
| dataset | mean | median | std |
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|:------------------------------|-----------:|-----------:|-----------:|
3-
| 527_analcatdata_election2000 | 0.997727 | 0.999273 | 0.00357541 |
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| 663_rabe_266 | 0.994945 | 0.995115 | 0.00134602 |
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| 560_bodyfat | 0.988467 | 0.992938 | 0.0121634 |
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| 505_tecator | 0.986861 | 0.986026 | 0.0039009 |
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| 561_cpu | 0.957349 | 0.967161 | 0.0330056 |
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| 690_visualizing_galaxy | 0.963404 | 0.964137 | 0.00867664 |
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| 197_cpu_act | 0.94309 | 0.945666 | 0.00966613 |
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| 227_cpu_small | 0.946096 | 0.945094 | 0.00812824 |
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| 523_analcatdata_neavote | 0.936577 | 0.943564 | 0.0278365 |
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| 1096_FacultySalaries | 0.662191 | 0.894004 | 0.525012 |
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| 557_analcatdata_apnea1 | 0.881416 | 0.889496 | 0.0397044 |
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| 230_machine_cpu | 0.778943 | 0.879675 | 0.273846 |
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| 556_analcatdata_apnea2 | 0.863157 | 0.867148 | 0.0347729 |
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| 1027_ESL | 0.858838 | 0.860647 | 0.0127587 |
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| 695_chatfield_4 | 0.827457 | 0.830825 | 0.0677194 |
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| 229_pwLinear | 0.810944 | 0.811717 | 0.0453826 |
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| 210_cloud | 0.761678 | 0.786611 | 0.159399 |
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| 529_pollen | 0.787219 | 0.782358 | 0.0118861 |
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| 1089_USCrime | 0.739218 | 0.756442 | 0.117112 |
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| 503_wind | 0.747271 | 0.745787 | 0.0088297 |
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| 712_chscase_geyser1 | 0.751443 | 0.745605 | 0.0549794 |
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| 519_vinnie | 0.728873 | 0.719948 | 0.0377254 |
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| 228_elusage | 0.621403 | 0.714127 | 0.216677 |
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| 659_sleuth_ex1714 | 0.562146 | 0.702428 | 0.309503 |
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| 666_rmftsa_ladata | 0.679718 | 0.672306 | 0.0620477 |
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| 225_puma8NH | 0.66854 | 0.667771 | 0.0127414 |
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| 706_sleuth_case1202 | 0.418764 | 0.568134 | 0.43742 |
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| 1029_LEV | 0.557169 | 0.560547 | 0.0330229 |
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| 547_no2 | 0.50562 | 0.502983 | 0.0920748 |
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| 485_analcatdata_vehicle | 0.244083 | 0.47083 | 0.702171 |
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| 192_vineyard | 0.381856 | 0.38018 | 0.200867 |
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| 1030_ERA | 0.373955 | 0.373216 | 0.0453621 |
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| 1028_SWD | 0.335559 | 0.343532 | 0.0556771 |
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| 542_pollution | 0.170091 | 0.279329 | 0.254557 |
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| 665_sleuth_case2002 | 0.242165 | 0.25769 | 0.146767 |
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| 522_pm10 | 0.235107 | 0.233109 | 0.0445476 |
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| 678_visualizing_environmental | 0.0604016 | 0.193514 | 0.358373 |
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| 687_sleuth_ex1605 | -0.0707247 | -0.0740387 | 0.372597 |
1+
| dataset | mean | median | std |
2+
|:------------------------------|------------:|---------:|-----------:|
3+
| 663_rabe_266 | 0.994973 | 0.994792 | 0.00138667 |
4+
| 527_analcatdata_election2000 | 0.992298 | 0.992383 | 0.00615176 |
5+
| 505_tecator | 0.985639 | 0.987823 | 0.00600829 |
6+
| 561_cpu | 0.976692 | 0.981073 | 0.0157433 |
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| 690_visualizing_galaxy | 0.96399 | 0.966614 | 0.00791278 |
8+
| 560_bodyfat | 0.595445 | 0.9646 | 0.678973 |
9+
| 227_cpu_small | 0.949931 | 0.95302 | 0.0102687 |
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| 197_cpu_act | 0.939741 | 0.948657 | 0.0297579 |
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| 523_analcatdata_neavote | 0.936577 | 0.943564 | 0.0278365 |
12+
| 556_analcatdata_apnea2 | 0.863564 | 0.867927 | 0.0218752 |
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| 229_pwLinear | 0.856554 | 0.864017 | 0.0295234 |
14+
| 1027_ESL | 0.859966 | 0.859878 | 0.0219077 |
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| 557_analcatdata_apnea1 | 0.860032 | 0.85602 | 0.0457254 |
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| 210_cloud | 0.785513 | 0.855809 | 0.174546 |
17+
| 695_chatfield_4 | 0.857361 | 0.854139 | 0.0323364 |
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| 1096_FacultySalaries | 0.60718 | 0.836315 | 0.51419 |
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| 529_pollen | 0.787219 | 0.782358 | 0.0118861 |
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| 230_machine_cpu | 0.561278 | 0.759371 | 0.550194 |
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| 503_wind | 0.7556 | 0.752685 | 0.0109313 |
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| 712_chscase_geyser1 | 0.749426 | 0.749685 | 0.0527733 |
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| 1089_USCrime | 0.708195 | 0.739885 | 0.140384 |
24+
| 228_elusage | 0.624231 | 0.721685 | 0.225679 |
25+
| 519_vinnie | 0.724621 | 0.713402 | 0.0364589 |
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| 225_puma8NH | 0.673587 | 0.67684 | 0.0196146 |
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| 666_rmftsa_ladata | 0.678517 | 0.663484 | 0.0452063 |
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| 659_sleuth_ex1714 | 0.577351 | 0.610788 | 0.252262 |
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| 706_sleuth_case1202 | 0.43508 | 0.572711 | 0.418311 |
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| 1029_LEV | 0.557463 | 0.553204 | 0.0361047 |
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| 547_no2 | 0.473821 | 0.44619 | 0.102342 |
32+
| 1030_ERA | 0.385125 | 0.381476 | 0.0469926 |
33+
| 1028_SWD | 0.339529 | 0.339576 | 0.0394794 |
34+
| 485_analcatdata_vehicle | 0.162436 | 0.304339 | 0.714552 |
35+
| 542_pollution | -0.510228 | 0.252399 | 2.12619 |
36+
| 522_pm10 | 0.242843 | 0.240674 | 0.0524153 |
37+
| 665_sleuth_case2002 | 0.16439 | 0.216995 | 0.215279 |
38+
| 192_vineyard | 0.204839 | 0.213078 | 0.382138 |
39+
| 687_sleuth_ex1605 | 0.00789428 | 0.15175 | 0.394736 |
40+
| 678_visualizing_environmental | 0.107825 | 0.111752 | 0.199401 |

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