2024-04-25 20:48:49 +00:00
{
"cells": [
{
"cell_type": "code",
"id": "initial_id",
"metadata": {
"collapsed": true,
"ExecuteTime": {
2024-04-26 23:26:52 +00:00
"end_time": "2024-04-26T22:58:24.716842Z",
"start_time": "2024-04-26T22:58:21.950179Z"
2024-04-25 20:48:49 +00:00
}
},
"source": [
2024-05-03 00:30:08 +00:00
"from experiments.metrics_analysis import *\n",
2025-01-27 18:31:12 +00:00
"DATABASE_URI = \"postgresql://cf_admin:<key>@codeflash-pgsql-db-prod.postgres.database.azure.com:5432/postgres\"\n",
2024-04-25 20:48:49 +00:00
"df = load_data(DATABASE_URI)\n",
"\n",
"df = process_column_pairs(df, \"metadata\")\n",
"df = process_column_pairs(df, \"test_framework\")\n",
"df = process_column_pairs(df, \"generated_test\")\n"
],
"outputs": [],
"execution_count": 1
},
{
"metadata": {
"ExecuteTime": {
2024-04-26 23:26:52 +00:00
"end_time": "2024-04-26T22:24:01.903846Z",
"start_time": "2024-04-26T22:24:01.618546Z"
2024-04-25 20:48:49 +00:00
}
},
"cell_type": "code",
"source": [
"all_candidates_time_saved = df.apply(\n",
" lambda row: row[\"original_runtime\"]\n",
" - min(\n",
" runtime for opt_id, runtime in row[\"optimized_runtime\"].items() if row[\"is_correct\"].get(opt_id)\n",
" )\n",
" if row[\"optimized_runtime\"] and row[\"is_correct\"]\n",
" else None,\n",
" axis=1,\n",
" ).mean()"
],
"id": "fddbc4005df15df3",
"outputs": [
{
"ename": "ValueError",
"evalue": "min() iterable argument is empty",
"output_type": "error",
"traceback": [
"\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
"\u001B[0;31mValueError\u001B[0m Traceback (most recent call last)",
"Cell \u001B[0;32mIn[2], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m all_candidates_time_saved \u001B[38;5;241m=\u001B[39m \u001B[43mdf\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mapply\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 2\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;28;43;01mlambda\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mrow\u001B[49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mrow\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43moriginal_runtime\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\n\u001B[1;32m 3\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;241;43m-\u001B[39;49m\u001B[43m \u001B[49m\u001B[38;5;28;43mmin\u001B[39;49m\u001B[43m(\u001B[49m\n\u001B[1;32m 4\u001B[0m \u001B[43m \u001B[49m\u001B[43mruntime\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mfor\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mopt_id\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mruntime\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;129;43;01min\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mrow\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43moptimized_runtime\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mitems\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mif\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mrow\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mis_correct\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[43mopt_id\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 5\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 6\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;28;43;01mif\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mrow\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43moptimized_runtime\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;129;43;01mand\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mrow\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mis_correct\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\n\u001B[1;32m 7\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;28;43;01melse\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[1;32m 8\u001B[0m \u001B[43m \u001B[49m\u001B[43maxis\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m1\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m 9\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241m.\u001B[39mmean()\n",
"File \u001B[0;32m~/miniforge3/envs/codeflash311/lib/python3.12/site-packages/pandas/core/frame.py:10374\u001B[0m, in \u001B[0;36mDataFrame.apply\u001B[0;34m(self, func, axis, raw, result_type, args, by_row, engine, engine_kwargs, **kwargs)\u001B[0m\n\u001B[1;32m 10360\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mpandas\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mcore\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mapply\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m frame_apply\n\u001B[1;32m 10362\u001B[0m op \u001B[38;5;241m=\u001B[39m frame_apply(\n\u001B[1;32m 10363\u001B[0m \u001B[38;5;28mself\u001B[39m,\n\u001B[1;32m 10364\u001B[0m func\u001B[38;5;241m=\u001B[39mfunc,\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 10372\u001B[0m kwargs\u001B[38;5;241m=\u001B[39mkwargs,\n\u001B[1;32m 10373\u001B[0m )\n\u001B[0;32m> 10374\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mop\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mapply\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241m.\u001B[39m__finalize__(\u001B[38;5;28mself\u001B[39m, method\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mapply\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n",
"File \u001B[0;32m~/miniforge3/envs/codeflash311/lib/python3.12/site-packages/pandas/core/apply.py:916\u001B[0m, in \u001B[0;36mFrameApply.apply\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m 913\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mraw:\n\u001B[1;32m 914\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mapply_raw(engine\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mengine, engine_kwargs\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mengine_kwargs)\n\u001B[0;32m--> 916\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mapply_standard\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n",
"File \u001B[0;32m~/miniforge3/envs/codeflash311/lib/python3.12/site-packages/pandas/core/apply.py:1063\u001B[0m, in \u001B[0;36mFrameApply.apply_standard\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m 1061\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mapply_standard\u001B[39m(\u001B[38;5;28mself\u001B[39m):\n\u001B[1;32m 1062\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mengine \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mpython\u001B[39m\u001B[38;5;124m\"\u001B[39m:\n\u001B[0;32m-> 1063\u001B[0m results, res_index \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mapply_series_generator\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 1064\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m 1065\u001B[0m results, res_index \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mapply_series_numba()\n",
"File \u001B[0;32m~/miniforge3/envs/codeflash311/lib/python3.12/site-packages/pandas/core/apply.py:1081\u001B[0m, in \u001B[0;36mFrameApply.apply_series_generator\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m 1078\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m option_context(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmode.chained_assignment\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m):\n\u001B[1;32m 1079\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m i, v \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28menumerate\u001B[39m(series_gen):\n\u001B[1;32m 1080\u001B[0m \u001B[38;5;66;03m# ignore SettingWithCopy here in case the user mutates\u001B[39;00m\n\u001B[0;32m-> 1081\u001B[0m results[i] \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mv\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 1082\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(results[i], ABCSeries):\n\u001B[1;32m 1083\u001B[0m \u001B[38;5;66;03m# If we have a view on v, we need to make a copy because\u001B[39;00m\n\u001B[1;32m 1084\u001B[0m \u001B[38;5;66;03m# series_generator will swap out the underlying data\u001B[39;00m\n\u001B[1;32m 1085\u001B[0m results[i] \u001B[38;5;241m=\u001B[39m results[i]\u001B[38;5;241m.\u001B[39mcopy(deep\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m)\n",
"Cell \u001B[0;32mIn[2], line 3\u001B[0m, in \u001B[0;36m<lambda>\u001B[0;34m(row)\u001B[0m\n\u001B[1;32m 1\u001B[0m all_candidates_time_saved \u001B[38;5;241m=\u001B[39m df\u001B[38;5;241m.\u001B[39mapply(\n\u001B[1;32m 2\u001B[0m \u001B[38;5;28;01mlambda\u001B[39;00m row: row[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124moriginal_runtime\u001B[39m\u001B[38;5;124m\"\u001B[39m]\n\u001B[0;32m----> 3\u001B[0m \u001B[38;5;241m-\u001B[39m \u001B[38;5;28;43mmin\u001B[39;49m\u001B[43m(\u001B[49m\n\u001B[1;32m 4\u001B[0m \u001B[43m \u001B[49m\u001B[43mruntime\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mfor\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mopt_id\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mruntime\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;129;43;01min\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mrow\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43moptimized_runtime\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mitems\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mif\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mrow\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mis_correct\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[43mopt_id\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 5\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 6\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m row[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124moptimized_runtime\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;129;01mand\u001B[39;00m row[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mis_correct\u001B[39m\u001B[38;5;124m\"\u001B[39m]\n\u001B[1;32m 7\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[1;32m 8\u001B[0m axis\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1\u001B[39m,\n\u001B[1;32m 9\u001B[0m )\u001B[38;5;241m.\u001B[39mmean()\n",
"\u001B[0;31mValueError\u001B[0m: min() iterable argument is empty"
]
}
],
"execution_count": 2
},
{
"metadata": {
"ExecuteTime": {
2024-04-26 23:26:52 +00:00
"end_time": "2024-04-26T22:24:05.316905Z",
"start_time": "2024-04-26T22:24:05.267608Z"
2024-04-25 20:48:49 +00:00
}
},
"cell_type": "code",
"source": "df",
"id": "7a0a082adc1d0e83",
"outputs": [
{
"data": {
"text/plain": [
" trace_id \\\n",
"0 0737f6d0-2837-4e89-9329-b874dd85EXP1 \n",
"1 c8d3c1f5-dc36-4662-93f0-89b1a728EXP1 \n",
"2 004b83d1-d244-4574-a686-75528771EXP0 \n",
"3 f12774fd-abde-4a96-a956-bb497851EXP0 \n",
"4 f9f80c75-23a2-4da7-b7cb-46b55e98EXP0 \n",
"5 e8b0b6c8-be2d-4187-bbab-a452ea2cEXP0 \n",
"6 675984d2-40c5-44e7-89f6-0ef88958EXP1 \n",
"7 87fae3ca-9e3b-4741-9cc2-7a8928d7EXP0 \n",
"8 87fae3ca-9e3b-4741-9cc2-7a8928d7EXP1 \n",
"9 d0fe1471-f997-41e6-a7d5-e2bf7335EXP0 \n",
"10 d0fe1471-f997-41e6-a7d5-e2bf7335EXP1 \n",
"11 07aaa994-f3c1-45bd-8291-8f5d0449EXP0 \n",
"12 07aaa994-f3c1-45bd-8291-8f5d0449EXP1 \n",
"13 52ecc4df-ebc3-4eb9-9ca5-6ab4649dEXP0 \n",
"14 52ecc4df-ebc3-4eb9-9ca5-6ab4649dEXP1 \n",
"15 19d849fc-eed9-4475-b9bc-1a290181EXP0 \n",
"16 19d849fc-eed9-4475-b9bc-1a290181EXP1 \n",
"17 5754ebe4-f66f-424c-95a9-10d9f464EXP0 \n",
"18 5754ebe4-f66f-424c-95a9-10d9f464EXP1 \n",
"19 004b83d1-d244-4574-a686-75528771EXP1 \n",
"20 f9f80c75-23a2-4da7-b7cb-46b55e98EXP1 \n",
"21 169e3ad1-83b3-4767-b5bc-0d28042dEXP0 \n",
"22 169e3ad1-83b3-4767-b5bc-0d28042dEXP1 \n",
"23 f12774fd-abde-4a96-a956-bb497851EXP1 \n",
"24 e8b0b6c8-be2d-4187-bbab-a452ea2cEXP1 \n",
"25 dcf32584-d3c7-4596-8586-f851c86aEXP1 \n",
"26 dcf32584-d3c7-4596-8586-f851c86aEXP0 \n",
"27 0737f6d0-2837-4e89-9329-b874dd85EXP0 \n",
"28 e6f50399-d2f6-4091-90e7-ccaa973cEXP0 \n",
"29 e6f50399-d2f6-4091-90e7-ccaa973cEXP1 \n",
"30 7a02881b-1a2b-4bf9-8ed3-46a44c11EXP0 \n",
"31 7a02881b-1a2b-4bf9-8ed3-46a44c11EXP1 \n",
"32 73242db2-ba0f-48f4-8609-9006feb3EXP0 \n",
"33 73242db2-ba0f-48f4-8609-9006feb3EXP1 \n",
"34 543270b0-9c95-49d2-99d2-c3325728EXP1 \n",
"35 543270b0-9c95-49d2-99d2-c3325728EXP0 \n",
"36 cdd5326e-ade3-44a8-b060-c679e2c5EXP0 \n",
"37 cdd5326e-ade3-44a8-b060-c679e2c5EXP1 \n",
"38 a636440e-20eb-446e-829e-3276ef43EXP0 \n",
"39 a636440e-20eb-446e-829e-3276ef43EXP1 \n",
"40 c8d3c1f5-dc36-4662-93f0-89b1a728EXP0 \n",
"\n",
" original_code \\\n",
"0 \\ndef problem_p02847(input_data):\\n def mai... \n",
"1 \\ndef problem_p03196(input_data):\\n n, p = ... \n",
"2 \\ndef problem_p03861(input_data):\\n import ... \n",
"3 \\ndef problem_p02910(input_data):\\n s = eva... \n",
"4 \\ndef problem_p03965(input_data):\\n # len(s... \n",
"5 \\ndef problem_p02553(input_data):\\n import ... \n",
"6 \\ndef problem_p02818(input_data):\\n a, b, k... \n",
"7 \\ndef problem_p02765(input_data):\\n N, R = ... \n",
"8 \\ndef problem_p02765(input_data):\\n N, R = ... \n",
"9 \\ndef problem_p03778(input_data):\\n W, a, b... \n",
"10 \\ndef problem_p03778(input_data):\\n W, a, b... \n",
"11 \\ndef problem_p03729(input_data):\\n A, B, C... \n",
"12 \\ndef problem_p03729(input_data):\\n A, B, C... \n",
"13 \\ndef problem_p03544(input_data):\\n N = int... \n",
"14 \\ndef problem_p03544(input_data):\\n N = int... \n",
"15 \\ndef problem_p02960(input_data):\\n S = eva... \n",
"16 \\ndef problem_p02960(input_data):\\n S = eva... \n",
"17 \\ndef problem_p02690(input_data):\\n X = int... \n",
"18 \\ndef problem_p02690(input_data):\\n X = int... \n",
"19 \\ndef problem_p03861(input_data):\\n import ... \n",
"20 \\ndef problem_p03965(input_data):\\n # len(s... \n",
"21 \\ndef problem_p03149(input_data):\\n s = eva... \n",
"22 \\ndef problem_p03149(input_data):\\n s = eva... \n",
"23 \\ndef problem_p02910(input_data):\\n s = eva... \n",
"24 \\ndef problem_p02553(input_data):\\n import ... \n",
"25 \\ndef problem_p03024(input_data):\\n S = eva... \n",
"26 \\ndef problem_p03024(input_data):\\n S = eva... \n",
"27 \\ndef problem_p02847(input_data):\\n def mai... \n",
"28 \\ndef problem_p02667(input_data):\\n t = eva... \n",
"29 \\ndef problem_p02667(input_data):\\n t = eva... \n",
"30 \\ndef problem_p03643(input_data):\\n N = eva... \n",
"31 \\ndef problem_p03643(input_data):\\n N = eva... \n",
"32 \\ndef problem_p02970(input_data):\\n import ... \n",
"33 \\ndef problem_p02970(input_data):\\n import ... \n",
"34 \\ndef problem_p02957(input_data):\\n import ... \n",
"35 \\ndef problem_p02957(input_data):\\n import ... \n",
"36 \\ndef problem_p02927(input_data):\\n M, D = ... \n",
"37 \\ndef problem_p02927(input_data):\\n M, D = ... \n",
"38 \\ndef problem_p03547(input_data):\\n #!/usr/... \n",
"39 \\ndef problem_p03547(input_data):\\n #!/usr/... \n",
"40 \\ndef problem_p03196(input_data):\\n n, p = ... \n",
"\n",
" optimizations_raw \\\n",
"0 {'00db208c-b4df-4ac0-bf2c-4448f0c18897': 'def ... \n",
"1 {'426916b1-b122-44a7-aae3-c806b81887bd': 'def ... \n",
"2 {'0a631960-6c33-4de4-8d38-b31e888918c7': 'def ... \n",
"3 {'00f11305-5a9b-43cf-9d97-720b0a465d52': '\n",
"def... \n",
"4 {'000711ac-1663-496b-837b-946040155a9c': '\n",
"def... \n",
"5 {'04e41d16-15f6-4341-a976-f420ed3180a6': 'def ... \n",
"6 {'0ac2d5fc-7315-4ca1-af84-1a7d65cd93a6': 'def ... \n",
"7 {'21645d50-d094-47d5-9a16-9163d90ee040': '\n",
"def... \n",
"8 {'3b74021f-df2a-43c1-a98c-c53c62c152b4': 'def ... \n",
"9 {'093d324b-76aa-4ad7-ab24-a4d10341d635': 'def ... \n",
"10 {'0ac7066f-a5cd-417c-8121-0614754e6b0a': 'def ... \n",
"11 {'4bf3dbec-6785-4cde-854b-f6331cba2a76': 'def ... \n",
"12 {'0e07a41c-8928-4d69-a0ba-33c8c2a29368': 'def ... \n",
"13 {'0717e2a6-a0ff-4e2b-950e-3e89cdf1a943': 'def ... \n",
"14 {'2bdd685e-9076-4c06-af56-ed592ec2add9': 'def ... \n",
"15 {'0e834ccc-68e2-4ba2-929e-0362e7288322': 'def ... \n",
"16 {'0ca93779-ea4a-48a9-9437-600334e2b5bb': 'def ... \n",
"17 {'0c30fb73-0bac-475d-b78b-c6c099bc12ad': 'def ... \n",
"18 {'10eb6770-047a-4f60-b9ad-0d2688bab93b': 'def ... \n",
"19 {'0aafef05-c468-48e0-a795-986763cf6b7c': 'def ... \n",
"20 {'04068a66-840b-4ddb-a8c7-289be75266e4': 'def ... \n",
"21 {'226ed33b-0ca7-4e7c-8384-b8dc96b1b1f4': 'def ... \n",
"22 {'09301913-2eb2-40b2-8b51-49566e08ced7': 'from... \n",
"23 {'0f86c075-6684-4c13-92cf-3166941c5c4c': 'def ... \n",
"24 {'0485cc8e-6ebd-4c81-9aed-61687af1c8ed': 'def ... \n",
"25 {'1692b2dd-d865-466f-97d0-cd4859ea585d': 'def ... \n",
"26 {'1b3fcdb9-bdf2-419b-bf10-b4482dc75d8b': 'def ... \n",
"27 {'0257e7cf-b4a8-4771-bb02-6e4963fa9811': '\n",
"def... \n",
"28 {'7ce6c3ed-4d5c-4a93-8f13-607d4fc92e03': 'def ... \n",
"29 {'349055c3-aec3-4872-8ad0-67965b7fcca8': 'def ... \n",
"30 {'3af102b2-e9a4-45d1-b6a4-da7d4cf77bf0': 'def ... \n",
"31 {'2442d889-079f-412a-b19c-e6b7c8cc77b2': 'def ... \n",
"32 {'06c81727-979e-4749-a61e-eeff019418b2': 'def ... \n",
"33 {'164e2619-d401-448d-8ef2-f17d6afac3b9': 'impo... \n",
"34 {'62e3b5fb-cf2b-4f76-8744-ccfd527ffd3f': 'def ... \n",
"35 {'45485c8f-f983-460a-9f33-2bb65c902530': 'def ... \n",
"36 {'06b34dc8-32bf-4e73-b344-4bc0962ba53e': 'def ... \n",
"37 {'114cd4d1-089f-482f-9cd1-994e06230713': 'def ... \n",
"38 {'248e3790-6c3e-4aa8-b33f-83f774e3aa1e': 'def ... \n",
"39 {'2db980c5-9316-418d-abcb-99587afe301c': 'def ... \n",
"40 {'083209b7-561e-4272-80c5-ecce1708dc67': '\n",
"def... \n",
"\n",
" optimizations_post \\\n",
"0 {'00db208c-b4df-4ac0-bf2c-4448f0c18897': 'def ... \n",
"1 {'426916b1-b122-44a7-aae3-c806b81887bd': 'def ... \n",
"2 {'0a631960-6c33-4de4-8d38-b31e888918c7': 'def ... \n",
"3 {'00f11305-5a9b-43cf-9d97-720b0a465d52': '\n",
"def... \n",
"4 {'000711ac-1663-496b-837b-946040155a9c': '\n",
"def... \n",
"5 {'04e41d16-15f6-4341-a976-f420ed3180a6': 'def ... \n",
"6 {'0ac2d5fc-7315-4ca1-af84-1a7d65cd93a6': 'def ... \n",
"7 {'21645d50-d094-47d5-9a16-9163d90ee040': '\n",
"def... \n",
"8 {'3b74021f-df2a-43c1-a98c-c53c62c152b4': 'def ... \n",
"9 {'093d324b-76aa-4ad7-ab24-a4d10341d635': 'def ... \n",
"10 {'0ac7066f-a5cd-417c-8121-0614754e6b0a': 'def ... \n",
"11 {'4bf3dbec-6785-4cde-854b-f6331cba2a76': 'def ... \n",
"12 {'0e07a41c-8928-4d69-a0ba-33c8c2a29368': 'def ... \n",
"13 {'0717e2a6-a0ff-4e2b-950e-3e89cdf1a943': 'def ... \n",
"14 {'2bdd685e-9076-4c06-af56-ed592ec2add9': 'def ... \n",
"15 {'0e834ccc-68e2-4ba2-929e-0362e7288322': 'def ... \n",
"16 {'0ca93779-ea4a-48a9-9437-600334e2b5bb': 'def ... \n",
"17 {'0c30fb73-0bac-475d-b78b-c6c099bc12ad': 'def ... \n",
"18 {'10eb6770-047a-4f60-b9ad-0d2688bab93b': 'def ... \n",
"19 {'3207699e-1363-4157-b61b-76c3522e7723': 'def ... \n",
"20 {'04068a66-840b-4ddb-a8c7-289be75266e4': 'def ... \n",
"21 {'226ed33b-0ca7-4e7c-8384-b8dc96b1b1f4': 'def ... \n",
"22 {'09301913-2eb2-40b2-8b51-49566e08ced7': 'from... \n",
"23 {'0f86c075-6684-4c13-92cf-3166941c5c4c': 'def ... \n",
"24 {'0485cc8e-6ebd-4c81-9aed-61687af1c8ed': 'def ... \n",
"25 {'1692b2dd-d865-466f-97d0-cd4859ea585d': 'def ... \n",
"26 {'1b3fcdb9-bdf2-419b-bf10-b4482dc75d8b': 'def ... \n",
"27 {'0257e7cf-b4a8-4771-bb02-6e4963fa9811': '\n",
"def... \n",
"28 {'7ce6c3ed-4d5c-4a93-8f13-607d4fc92e03': 'def ... \n",
"29 {'349055c3-aec3-4872-8ad0-67965b7fcca8': 'def ... \n",
"30 {'3af102b2-e9a4-45d1-b6a4-da7d4cf77bf0': 'def ... \n",
"31 {'44cb2a7c-e9f1-49e0-b644-2591ff3f91f1': 'def ... \n",
"32 {'06c81727-979e-4749-a61e-eeff019418b2': 'def ... \n",
"33 {'164e2619-d401-448d-8ef2-f17d6afac3b9': 'impo... \n",
"34 {'62e3b5fb-cf2b-4f76-8744-ccfd527ffd3f': 'def ... \n",
"35 {'45485c8f-f983-460a-9f33-2bb65c902530': 'def ... \n",
"36 {'06b34dc8-32bf-4e73-b344-4bc0962ba53e': 'def ... \n",
"37 {'114cd4d1-089f-482f-9cd1-994e06230713': 'def ... \n",
"38 {'7fb2383c-34db-44bf-890e-16c3f4e5c4bd': 'def ... \n",
"39 {'2db980c5-9316-418d-abcb-99587afe301c': 'def ... \n",
"40 {'083209b7-561e-4272-80c5-ecce1708dc67': '\n",
"def... \n",
"\n",
" explanations_post \\\n",
"0 {'00db208c-b4df-4ac0-bf2c-4448f0c18897': 'The ... \n",
"1 {'426916b1-b122-44a7-aae3-c806b81887bd': 'The ... \n",
"2 {'0a631960-6c33-4de4-8d38-b31e888918c7': 'Here... \n",
"3 {'00f11305-5a9b-43cf-9d97-720b0a465d52': 'Your... \n",
"4 {'000711ac-1663-496b-837b-946040155a9c': 'Here... \n",
"5 {'04e41d16-15f6-4341-a976-f420ed3180a6': 'Here... \n",
"6 {'0ac2d5fc-7315-4ca1-af84-1a7d65cd93a6': 'In t... \n",
"7 {'21645d50-d094-47d5-9a16-9163d90ee040': 'The ... \n",
"8 {'3b74021f-df2a-43c1-a98c-c53c62c152b4': 'As t... \n",
"9 {'093d324b-76aa-4ad7-ab24-a4d10341d635': 'The ... \n",
"10 {'0ac7066f-a5cd-417c-8121-0614754e6b0a': 'The ... \n",
"11 {'4bf3dbec-6785-4cde-854b-f6331cba2a76': 'The ... \n",
"12 {'0e07a41c-8928-4d69-a0ba-33c8c2a29368': 'In t... \n",
"13 {'0717e2a6-a0ff-4e2b-950e-3e89cdf1a943': 'This... \n",
"14 {'2bdd685e-9076-4c06-af56-ed592ec2add9': 'The ... \n",
"15 {'0e834ccc-68e2-4ba2-929e-0362e7288322': 'The ... \n",
"16 {'0ca93779-ea4a-48a9-9437-600334e2b5bb': 'The ... \n",
"17 {'0c30fb73-0bac-475d-b78b-c6c099bc12ad': 'The ... \n",
"18 {'10eb6770-047a-4f60-b9ad-0d2688bab93b': 'I've... \n",
"19 {'3207699e-1363-4157-b61b-76c3522e7723': 'I've... \n",
"20 {'04068a66-840b-4ddb-a8c7-289be75266e4': 'The ... \n",
"21 {'226ed33b-0ca7-4e7c-8384-b8dc96b1b1f4': 'To o... \n",
"22 {'09301913-2eb2-40b2-8b51-49566e08ced7': 'The ... \n",
"23 {'0f86c075-6684-4c13-92cf-3166941c5c4c': 'The ... \n",
"24 {'0485cc8e-6ebd-4c81-9aed-61687af1c8ed': 'Revi... \n",
"25 {'1692b2dd-d865-466f-97d0-cd4859ea585d': 'In t... \n",
"26 {'1b3fcdb9-bdf2-419b-bf10-b4482dc75d8b': 'Your... \n",
"27 {'0257e7cf-b4a8-4771-bb02-6e4963fa9811': 'The ... \n",
"28 {'7ce6c3ed-4d5c-4a93-8f13-607d4fc92e03': 'The ... \n",
"29 {'349055c3-aec3-4872-8ad0-67965b7fcca8': 'In t... \n",
"30 {'3af102b2-e9a4-45d1-b6a4-da7d4cf77bf0': 'The ... \n",
"31 {'44cb2a7c-e9f1-49e0-b644-2591ff3f91f1': 'I've... \n",
"32 {'06c81727-979e-4749-a61e-eeff019418b2': 'Your... \n",
"33 {'164e2619-d401-448d-8ef2-f17d6afac3b9': 'The ... \n",
"34 {'62e3b5fb-cf2b-4f76-8744-ccfd527ffd3f': 'I've... \n",
"35 {'45485c8f-f983-460a-9f33-2bb65c902530': 'The ... \n",
"36 {'06b34dc8-32bf-4e73-b344-4bc0962ba53e': 'The ... \n",
"37 {'114cd4d1-089f-482f-9cd1-994e06230713': 'I've... \n",
"38 {'7fb2383c-34db-44bf-890e-16c3f4e5c4bd': 'The ... \n",
"39 {'2db980c5-9316-418d-abcb-99587afe301c': 'I've... \n",
"40 {'083209b7-561e-4272-80c5-ecce1708dc67': 'Here... \n",
"\n",
" speedup_ratio original_runtime \\\n",
"0 {'00db208c-b4df-4ac0-bf2c-4448f0c18897': None,... 14625.0 \n",
"1 None NaN \n",
"2 {'0a631960-6c33-4de4-8d38-b31e888918c7': 0.014... 60039.0 \n",
"3 {'00f11305-5a9b-43cf-9d97-720b0a465d52': None,... 1029209.0 \n",
"4 {'000711ac-1663-496b-837b-946040155a9c': -0.11... 244251.0 \n",
"5 {'04e41d16-15f6-4341-a976-f420ed3180a6': 1774.... 81020500.0 \n",
"6 {'0ac2d5fc-7315-4ca1-af84-1a7d65cd93a6': 0.061... 34711.0 \n",
"7 {'21645d50-d094-47d5-9a16-9163d90ee040': 0.257... 8333.0 \n",
"8 {'3b74021f-df2a-43c1-a98c-c53c62c152b4': -0.03... 8333.0 \n",
"9 {'093d324b-76aa-4ad7-ab24-a4d10341d635': 2284.... 84961335.0 \n",
"10 {'0ac7066f-a5cd-417c-8121-0614754e6b0a': 2321.... 84961335.0 \n",
"11 {'4bf3dbec-6785-4cde-854b-f6331cba2a76': None,... 19625.0 \n",
"12 {'0e07a41c-8928-4d69-a0ba-33c8c2a29368': None,... 19625.0 \n",
"13 {'0717e2a6-a0ff-4e2b-950e-3e89cdf1a943': None,... 193978374.0 \n",
"14 {'2bdd685e-9076-4c06-af56-ed592ec2add9': None,... 193978374.0 \n",
"15 {'0e834ccc-68e2-4ba2-929e-0362e7288322': -0.22... 1018957.0 \n",
"16 {'0ca93779-ea4a-48a9-9437-600334e2b5bb': -0.01... 1018957.0 \n",
"17 {'0c30fb73-0bac-475d-b78b-c6c099bc12ad': 1.474... 164707456.0 \n",
"18 {'10eb6770-047a-4f60-b9ad-0d2688bab93b': 163.9... 164707456.0 \n",
"19 {'3207699e-1363-4157-b61b-76c3522e7723': 0.222... 60039.0 \n",
"20 {'0d174514-651a-4407-b89f-119639914ac0': -0.02... 244251.0 \n",
"21 {'226ed33b-0ca7-4e7c-8384-b8dc96b1b1f4': None,... 241291.0 \n",
"22 {'09301913-2eb2-40b2-8b51-49566e08ced7': None,... 241291.0 \n",
"23 {'0f86c075-6684-4c13-92cf-3166941c5c4c': None,... 1029209.0 \n",
"24 {'0485cc8e-6ebd-4c81-9aed-61687af1c8ed': None,... 81020500.0 \n",
"25 {'1692b2dd-d865-466f-97d0-cd4859ea585d': None,... 202419.0 \n",
"26 {'1b3fcdb9-bdf2-419b-bf10-b4482dc75d8b': None,... 202419.0 \n",
"27 {'0257e7cf-b4a8-4771-bb02-6e4963fa9811': None,... 14625.0 \n",
"28 {'7ce6c3ed-4d5c-4a93-8f13-607d4fc92e03': None,... 13320210.0 \n",
"29 {'349055c3-aec3-4872-8ad0-67965b7fcca8': None,... 13320210.0 \n",
"30 {'3af102b2-e9a4-45d1-b6a4-da7d4cf77bf0': None,... 46792.0 \n",
"31 {'44cb2a7c-e9f1-49e0-b644-2591ff3f91f1': None,... 46792.0 \n",
"32 {'06c81727-979e-4749-a61e-eeff019418b2': None,... 35739709.0 \n",
"33 {'164e2619-d401-448d-8ef2-f17d6afac3b9': 1156.... 35739709.0 \n",
"34 {'62e3b5fb-cf2b-4f76-8744-ccfd527ffd3f': None,... 768040.0 \n",
"35 {'45485c8f-f983-460a-9f33-2bb65c902530': None,... 768040.0 \n",
"36 {'06b34dc8-32bf-4e73-b344-4bc0962ba53e': None,... 250921544.0 \n",
"37 {'114cd4d1-089f-482f-9cd1-994e06230713': 0.107... 250921544.0 \n",
"38 {'7fb2383c-34db-44bf-890e-16c3f4e5c4bd': None,... 14665.0 \n",
"39 {'2db980c5-9316-418d-abcb-99587afe301c': -0.02... 14665.0 \n",
"40 None NaN \n",
"\n",
" optimized_runtime \\\n",
"0 {'00db208c-b4df-4ac0-bf2c-4448f0c18897': None,... \n",
"1 None \n",
"2 {'0a631960-6c33-4de4-8d38-b31e888918c7': 59209... \n",
"3 {'00f11305-5a9b-43cf-9d97-720b0a465d52': None,... \n",
"4 {'000711ac-1663-496b-837b-946040155a9c': 27683... \n",
"5 {'04e41d16-15f6-4341-a976-f420ed3180a6': 45627... \n",
"6 {'0ac2d5fc-7315-4ca1-af84-1a7d65cd93a6': 32706... \n",
"7 {'21645d50-d094-47d5-9a16-9163d90ee040': 6625,... \n",
"8 {'3b74021f-df2a-43c1-a98c-c53c62c152b4': 8624,... \n",
"9 {'093d324b-76aa-4ad7-ab24-a4d10341d635': 37170... \n",
"10 {'0ac7066f-a5cd-417c-8121-0614754e6b0a': 36581... \n",
"11 {'4bf3dbec-6785-4cde-854b-f6331cba2a76': None,... \n",
"12 {'0e07a41c-8928-4d69-a0ba-33c8c2a29368': None,... \n",
"13 {'0717e2a6-a0ff-4e2b-950e-3e89cdf1a943': None,... \n",
"14 {'2bdd685e-9076-4c06-af56-ed592ec2add9': None,... \n",
"15 {'0e834ccc-68e2-4ba2-929e-0362e7288322': 13076... \n",
"16 {'0ca93779-ea4a-48a9-9437-600334e2b5bb': 10304... \n",
"17 {'0c30fb73-0bac-475d-b78b-c6c099bc12ad': 66554... \n",
"18 {'10eb6770-047a-4f60-b9ad-0d2688bab93b': 99866... \n",
"19 {'3207699e-1363-4157-b61b-76c3522e7723': 49127... \n",
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"\n",
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"\n",
" generated_test test_framework \\\n",
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"34 2024-04-18 07:21:48.578802+00:00 None \n",
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"\n",
" metadata \\\n",
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"\n",
" explanations_raw user_id \n",
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" <th>1</th>\n",
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" <td>{'426916b1-b122-44a7-aae3-c806b81887bd': 'def ...</td>\n",
" <td>{'426916b1-b122-44a7-aae3-c806b81887bd': 'def ...</td>\n",
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" <td>None</td>\n",
" <td>None</td>\n",
" <td>[# imports\\nimport pytest # used for our unit...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 07:36:01.262393+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'426916b1-b122-44a7-aae3-c806b81887bd': 'The ...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>004b83d1-d244-4574-a686-75528771EXP0</td>\n",
" <td>\\ndef problem_p03861(input_data):\\n import ...</td>\n",
" <td>{'0a631960-6c33-4de4-8d38-b31e888918c7': 'def ...</td>\n",
" <td>{'0a631960-6c33-4de4-8d38-b31e888918c7': 'def ...</td>\n",
" <td>{'0a631960-6c33-4de4-8d38-b31e888918c7': 'Here...</td>\n",
" <td>{'0a631960-6c33-4de4-8d38-b31e888918c7': 0.014...</td>\n",
" <td>60039.0</td>\n",
" <td>{'0a631960-6c33-4de4-8d38-b31e888918c7': 59209...</td>\n",
" <td>{'0a631960-6c33-4de4-8d38-b31e888918c7': True,...</td>\n",
" <td>[# imports\\nimport pytest # used for our unit...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 06:35:45.693971+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'0a631960-6c33-4de4-8d38-b31e888918c7': 'Here...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>f12774fd-abde-4a96-a956-bb497851EXP0</td>\n",
" <td>\\ndef problem_p02910(input_data):\\n s = eva...</td>\n",
" <td>{'00f11305-5a9b-43cf-9d97-720b0a465d52': '\n",
"def...</td>\n",
" <td>{'00f11305-5a9b-43cf-9d97-720b0a465d52': '\n",
"def...</td>\n",
" <td>{'00f11305-5a9b-43cf-9d97-720b0a465d52': 'Your...</td>\n",
" <td>{'00f11305-5a9b-43cf-9d97-720b0a465d52': None,...</td>\n",
" <td>1029209.0</td>\n",
" <td>{'00f11305-5a9b-43cf-9d97-720b0a465d52': None,...</td>\n",
" <td>{'00f11305-5a9b-43cf-9d97-720b0a465d52': False...</td>\n",
" <td>[# imports\\nimport pytest # used for our unit...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 06:48:48.109749+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'00f11305-5a9b-43cf-9d97-720b0a465d52': 'Your...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>f9f80c75-23a2-4da7-b7cb-46b55e98EXP0</td>\n",
" <td>\\ndef problem_p03965(input_data):\\n # len(s...</td>\n",
" <td>{'000711ac-1663-496b-837b-946040155a9c': '\n",
"def...</td>\n",
" <td>{'000711ac-1663-496b-837b-946040155a9c': '\n",
"def...</td>\n",
" <td>{'000711ac-1663-496b-837b-946040155a9c': 'Here...</td>\n",
" <td>{'000711ac-1663-496b-837b-946040155a9c': -0.11...</td>\n",
" <td>244251.0</td>\n",
" <td>{'000711ac-1663-496b-837b-946040155a9c': 27683...</td>\n",
" <td>{'000711ac-1663-496b-837b-946040155a9c': True,...</td>\n",
" <td>[# imports\\nimport pytest # used for our unit...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 06:41:27.396525+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'000711ac-1663-496b-837b-946040155a9c': 'Here...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>e8b0b6c8-be2d-4187-bbab-a452ea2cEXP0</td>\n",
" <td>\\ndef problem_p02553(input_data):\\n import ...</td>\n",
" <td>{'04e41d16-15f6-4341-a976-f420ed3180a6': 'def ...</td>\n",
" <td>{'04e41d16-15f6-4341-a976-f420ed3180a6': 'def ...</td>\n",
" <td>{'04e41d16-15f6-4341-a976-f420ed3180a6': 'Here...</td>\n",
" <td>{'04e41d16-15f6-4341-a976-f420ed3180a6': 1774....</td>\n",
" <td>81020500.0</td>\n",
" <td>{'04e41d16-15f6-4341-a976-f420ed3180a6': 45627...</td>\n",
" <td>{'04e41d16-15f6-4341-a976-f420ed3180a6': True,...</td>\n",
" <td>[# imports\\nimport pytest # used for our unit...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 06:53:42.593377+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'04e41d16-15f6-4341-a976-f420ed3180a6': 'Here...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>675984d2-40c5-44e7-89f6-0ef88958EXP1</td>\n",
" <td>\\ndef problem_p02818(input_data):\\n a, b, k...</td>\n",
" <td>{'0ac2d5fc-7315-4ca1-af84-1a7d65cd93a6': 'def ...</td>\n",
" <td>{'0ac2d5fc-7315-4ca1-af84-1a7d65cd93a6': 'def ...</td>\n",
" <td>{'0ac2d5fc-7315-4ca1-af84-1a7d65cd93a6': 'In t...</td>\n",
" <td>{'0ac2d5fc-7315-4ca1-af84-1a7d65cd93a6': 0.061...</td>\n",
" <td>34711.0</td>\n",
" <td>{'0ac2d5fc-7315-4ca1-af84-1a7d65cd93a6': 32706...</td>\n",
" <td>{'0ac2d5fc-7315-4ca1-af84-1a7d65cd93a6': True,...</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2024-04-18 05:51:17.050279+00:00</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>{'0ac2d5fc-7315-4ca1-af84-1a7d65cd93a6': 'In t...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>87fae3ca-9e3b-4741-9cc2-7a8928d7EXP0</td>\n",
" <td>\\ndef problem_p02765(input_data):\\n N, R = ...</td>\n",
" <td>{'21645d50-d094-47d5-9a16-9163d90ee040': '\n",
"def...</td>\n",
" <td>{'21645d50-d094-47d5-9a16-9163d90ee040': '\n",
"def...</td>\n",
" <td>{'21645d50-d094-47d5-9a16-9163d90ee040': 'The ...</td>\n",
" <td>{'21645d50-d094-47d5-9a16-9163d90ee040': 0.257...</td>\n",
" <td>8333.0</td>\n",
" <td>{'21645d50-d094-47d5-9a16-9163d90ee040': 6625,...</td>\n",
" <td>{'21645d50-d094-47d5-9a16-9163d90ee040': True,...</td>\n",
" <td>[# imports\\nimport pytest # used for our unit...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 05:56:30.403736+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'21645d50-d094-47d5-9a16-9163d90ee040': 'The ...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>87fae3ca-9e3b-4741-9cc2-7a8928d7EXP1</td>\n",
" <td>\\ndef problem_p02765(input_data):\\n N, R = ...</td>\n",
" <td>{'3b74021f-df2a-43c1-a98c-c53c62c152b4': 'def ...</td>\n",
" <td>{'3b74021f-df2a-43c1-a98c-c53c62c152b4': 'def ...</td>\n",
" <td>{'3b74021f-df2a-43c1-a98c-c53c62c152b4': 'As t...</td>\n",
" <td>{'3b74021f-df2a-43c1-a98c-c53c62c152b4': -0.03...</td>\n",
" <td>8333.0</td>\n",
" <td>{'3b74021f-df2a-43c1-a98c-c53c62c152b4': 8624,...</td>\n",
" <td>{'3b74021f-df2a-43c1-a98c-c53c62c152b4': True,...</td>\n",
" <td>[# imports\\nimport pytest # used for our unit...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 05:56:46.264470+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'3b74021f-df2a-43c1-a98c-c53c62c152b4': 'As t...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>d0fe1471-f997-41e6-a7d5-e2bf7335EXP0</td>\n",
" <td>\\ndef problem_p03778(input_data):\\n W, a, b...</td>\n",
" <td>{'093d324b-76aa-4ad7-ab24-a4d10341d635': 'def ...</td>\n",
" <td>{'093d324b-76aa-4ad7-ab24-a4d10341d635': 'def ...</td>\n",
" <td>{'093d324b-76aa-4ad7-ab24-a4d10341d635': 'The ...</td>\n",
" <td>{'093d324b-76aa-4ad7-ab24-a4d10341d635': 2284....</td>\n",
" <td>84961335.0</td>\n",
" <td>{'093d324b-76aa-4ad7-ab24-a4d10341d635': 37170...</td>\n",
" <td>{'093d324b-76aa-4ad7-ab24-a4d10341d635': True,...</td>\n",
" <td>[# imports\\nimport pytest # used for our unit...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 06:01:46.507944+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'093d324b-76aa-4ad7-ab24-a4d10341d635': 'The ...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>d0fe1471-f997-41e6-a7d5-e2bf7335EXP1</td>\n",
" <td>\\ndef problem_p03778(input_data):\\n W, a, b...</td>\n",
" <td>{'0ac7066f-a5cd-417c-8121-0614754e6b0a': 'def ...</td>\n",
" <td>{'0ac7066f-a5cd-417c-8121-0614754e6b0a': 'def ...</td>\n",
" <td>{'0ac7066f-a5cd-417c-8121-0614754e6b0a': 'The ...</td>\n",
" <td>{'0ac7066f-a5cd-417c-8121-0614754e6b0a': 2321....</td>\n",
" <td>84961335.0</td>\n",
" <td>{'0ac7066f-a5cd-417c-8121-0614754e6b0a': 36581...</td>\n",
" <td>{'0ac7066f-a5cd-417c-8121-0614754e6b0a': True,...</td>\n",
" <td>[# imports\\nimport pytest # used for our unit...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 06:02:35.106678+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'0ac7066f-a5cd-417c-8121-0614754e6b0a': 'The ...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>07aaa994-f3c1-45bd-8291-8f5d0449EXP0</td>\n",
" <td>\\ndef problem_p03729(input_data):\\n A, B, C...</td>\n",
" <td>{'4bf3dbec-6785-4cde-854b-f6331cba2a76': 'def ...</td>\n",
" <td>{'4bf3dbec-6785-4cde-854b-f6331cba2a76': 'def ...</td>\n",
" <td>{'4bf3dbec-6785-4cde-854b-f6331cba2a76': 'The ...</td>\n",
" <td>{'4bf3dbec-6785-4cde-854b-f6331cba2a76': None,...</td>\n",
" <td>19625.0</td>\n",
" <td>{'4bf3dbec-6785-4cde-854b-f6331cba2a76': None,...</td>\n",
" <td>{'4bf3dbec-6785-4cde-854b-f6331cba2a76': False...</td>\n",
" <td>[# imports\\nimport pytest # used for our unit...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 06:08:15.901816+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'4bf3dbec-6785-4cde-854b-f6331cba2a76': 'The ...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>07aaa994-f3c1-45bd-8291-8f5d0449EXP1</td>\n",
" <td>\\ndef problem_p03729(input_data):\\n A, B, C...</td>\n",
" <td>{'0e07a41c-8928-4d69-a0ba-33c8c2a29368': 'def ...</td>\n",
" <td>{'0e07a41c-8928-4d69-a0ba-33c8c2a29368': 'def ...</td>\n",
" <td>{'0e07a41c-8928-4d69-a0ba-33c8c2a29368': 'In t...</td>\n",
" <td>{'0e07a41c-8928-4d69-a0ba-33c8c2a29368': None,...</td>\n",
" <td>19625.0</td>\n",
" <td>{'0e07a41c-8928-4d69-a0ba-33c8c2a29368': None,...</td>\n",
" <td>{'0e07a41c-8928-4d69-a0ba-33c8c2a29368': False...</td>\n",
" <td>[# imports\\nimport pytest # used for our unit...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 06:08:59.623131+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'0e07a41c-8928-4d69-a0ba-33c8c2a29368': 'In t...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>52ecc4df-ebc3-4eb9-9ca5-6ab4649dEXP0</td>\n",
" <td>\\ndef problem_p03544(input_data):\\n N = int...</td>\n",
" <td>{'0717e2a6-a0ff-4e2b-950e-3e89cdf1a943': 'def ...</td>\n",
" <td>{'0717e2a6-a0ff-4e2b-950e-3e89cdf1a943': 'def ...</td>\n",
" <td>{'0717e2a6-a0ff-4e2b-950e-3e89cdf1a943': 'This...</td>\n",
" <td>{'0717e2a6-a0ff-4e2b-950e-3e89cdf1a943': None,...</td>\n",
" <td>193978374.0</td>\n",
" <td>{'0717e2a6-a0ff-4e2b-950e-3e89cdf1a943': None,...</td>\n",
" <td>{'0717e2a6-a0ff-4e2b-950e-3e89cdf1a943': False...</td>\n",
" <td>[# imports\\nimport pytest # used for our unit...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 06:12:32.238456+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'0717e2a6-a0ff-4e2b-950e-3e89cdf1a943': 'This...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>52ecc4df-ebc3-4eb9-9ca5-6ab4649dEXP1</td>\n",
" <td>\\ndef problem_p03544(input_data):\\n N = int...</td>\n",
" <td>{'2bdd685e-9076-4c06-af56-ed592ec2add9': 'def ...</td>\n",
" <td>{'2bdd685e-9076-4c06-af56-ed592ec2add9': 'def ...</td>\n",
" <td>{'2bdd685e-9076-4c06-af56-ed592ec2add9': 'The ...</td>\n",
" <td>{'2bdd685e-9076-4c06-af56-ed592ec2add9': None,...</td>\n",
" <td>193978374.0</td>\n",
" <td>{'2bdd685e-9076-4c06-af56-ed592ec2add9': None,...</td>\n",
" <td>{'2bdd685e-9076-4c06-af56-ed592ec2add9': False...</td>\n",
" <td>[# imports\\nimport pytest # used for our unit...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 06:13:22.694382+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'2bdd685e-9076-4c06-af56-ed592ec2add9': 'The ...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>19d849fc-eed9-4475-b9bc-1a290181EXP0</td>\n",
" <td>\\ndef problem_p02960(input_data):\\n S = eva...</td>\n",
" <td>{'0e834ccc-68e2-4ba2-929e-0362e7288322': 'def ...</td>\n",
" <td>{'0e834ccc-68e2-4ba2-929e-0362e7288322': 'def ...</td>\n",
" <td>{'0e834ccc-68e2-4ba2-929e-0362e7288322': 'The ...</td>\n",
" <td>{'0e834ccc-68e2-4ba2-929e-0362e7288322': -0.22...</td>\n",
" <td>1018957.0</td>\n",
" <td>{'0e834ccc-68e2-4ba2-929e-0362e7288322': 13076...</td>\n",
" <td>{'0e834ccc-68e2-4ba2-929e-0362e7288322': True,...</td>\n",
" <td>[# imports\\nimport pytest # used for our unit...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 06:25:10.191869+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'0e834ccc-68e2-4ba2-929e-0362e7288322': 'The ...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>19d849fc-eed9-4475-b9bc-1a290181EXP1</td>\n",
" <td>\\ndef problem_p02960(input_data):\\n S = eva...</td>\n",
" <td>{'0ca93779-ea4a-48a9-9437-600334e2b5bb': 'def ...</td>\n",
" <td>{'0ca93779-ea4a-48a9-9437-600334e2b5bb': 'def ...</td>\n",
" <td>{'0ca93779-ea4a-48a9-9437-600334e2b5bb': 'The ...</td>\n",
" <td>{'0ca93779-ea4a-48a9-9437-600334e2b5bb': -0.01...</td>\n",
" <td>1018957.0</td>\n",
" <td>{'0ca93779-ea4a-48a9-9437-600334e2b5bb': 10304...</td>\n",
" <td>{'0ca93779-ea4a-48a9-9437-600334e2b5bb': True,...</td>\n",
" <td>[# imports\\nimport pytest # used for our unit...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 06:26:25.453153+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'0ca93779-ea4a-48a9-9437-600334e2b5bb': 'The ...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>5754ebe4-f66f-424c-95a9-10d9f464EXP0</td>\n",
" <td>\\ndef problem_p02690(input_data):\\n X = int...</td>\n",
" <td>{'0c30fb73-0bac-475d-b78b-c6c099bc12ad': 'def ...</td>\n",
" <td>{'0c30fb73-0bac-475d-b78b-c6c099bc12ad': 'def ...</td>\n",
" <td>{'0c30fb73-0bac-475d-b78b-c6c099bc12ad': 'The ...</td>\n",
" <td>{'0c30fb73-0bac-475d-b78b-c6c099bc12ad': 1.474...</td>\n",
" <td>164707456.0</td>\n",
" <td>{'0c30fb73-0bac-475d-b78b-c6c099bc12ad': 66554...</td>\n",
" <td>{'0c30fb73-0bac-475d-b78b-c6c099bc12ad': True,...</td>\n",
" <td>[# imports\\nimport pytest # used for our unit...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 06:31:18.776319+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'0c30fb73-0bac-475d-b78b-c6c099bc12ad': 'The ...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>5754ebe4-f66f-424c-95a9-10d9f464EXP1</td>\n",
" <td>\\ndef problem_p02690(input_data):\\n X = int...</td>\n",
" <td>{'10eb6770-047a-4f60-b9ad-0d2688bab93b': 'def ...</td>\n",
" <td>{'10eb6770-047a-4f60-b9ad-0d2688bab93b': 'def ...</td>\n",
" <td>{'10eb6770-047a-4f60-b9ad-0d2688bab93b': 'I've...</td>\n",
" <td>{'10eb6770-047a-4f60-b9ad-0d2688bab93b': 163.9...</td>\n",
" <td>164707456.0</td>\n",
" <td>{'10eb6770-047a-4f60-b9ad-0d2688bab93b': 99866...</td>\n",
" <td>{'10eb6770-047a-4f60-b9ad-0d2688bab93b': True,...</td>\n",
" <td>[# imports\\nimport pytest # used for our unit...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 06:32:36.168592+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'10eb6770-047a-4f60-b9ad-0d2688bab93b': 'I've...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>004b83d1-d244-4574-a686-75528771EXP1</td>\n",
" <td>\\ndef problem_p03861(input_data):\\n import ...</td>\n",
" <td>{'0aafef05-c468-48e0-a795-986763cf6b7c': 'def ...</td>\n",
" <td>{'3207699e-1363-4157-b61b-76c3522e7723': 'def ...</td>\n",
" <td>{'3207699e-1363-4157-b61b-76c3522e7723': 'I've...</td>\n",
" <td>{'3207699e-1363-4157-b61b-76c3522e7723': 0.222...</td>\n",
" <td>60039.0</td>\n",
" <td>{'3207699e-1363-4157-b61b-76c3522e7723': 49127...</td>\n",
" <td>{'3207699e-1363-4157-b61b-76c3522e7723': True,...</td>\n",
" <td>[# imports\\nimport pytest # used for our unit...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 06:36:30.203958+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'0aafef05-c468-48e0-a795-986763cf6b7c': 'The ...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>f9f80c75-23a2-4da7-b7cb-46b55e98EXP1</td>\n",
" <td>\\ndef problem_p03965(input_data):\\n # len(s...</td>\n",
" <td>{'04068a66-840b-4ddb-a8c7-289be75266e4': 'def ...</td>\n",
" <td>{'04068a66-840b-4ddb-a8c7-289be75266e4': 'def ...</td>\n",
" <td>{'04068a66-840b-4ddb-a8c7-289be75266e4': 'The ...</td>\n",
" <td>{'0d174514-651a-4407-b89f-119639914ac0': -0.02...</td>\n",
" <td>244251.0</td>\n",
" <td>{'0d174514-651a-4407-b89f-119639914ac0': 25029...</td>\n",
" <td>{'0d174514-651a-4407-b89f-119639914ac0': True,...</td>\n",
" <td>[# imports\\nimport pytest # used for our unit...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 06:42:13.555734+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'04068a66-840b-4ddb-a8c7-289be75266e4': 'The ...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>169e3ad1-83b3-4767-b5bc-0d28042dEXP0</td>\n",
" <td>\\ndef problem_p03149(input_data):\\n s = eva...</td>\n",
" <td>{'226ed33b-0ca7-4e7c-8384-b8dc96b1b1f4': 'def ...</td>\n",
" <td>{'226ed33b-0ca7-4e7c-8384-b8dc96b1b1f4': 'def ...</td>\n",
" <td>{'226ed33b-0ca7-4e7c-8384-b8dc96b1b1f4': 'To o...</td>\n",
" <td>{'226ed33b-0ca7-4e7c-8384-b8dc96b1b1f4': None,...</td>\n",
" <td>241291.0</td>\n",
" <td>{'226ed33b-0ca7-4e7c-8384-b8dc96b1b1f4': None,...</td>\n",
" <td>{'226ed33b-0ca7-4e7c-8384-b8dc96b1b1f4': False...</td>\n",
" <td>[# imports\\nimport pytest # used for our unit...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 06:45:42.034069+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'226ed33b-0ca7-4e7c-8384-b8dc96b1b1f4': 'To o...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>169e3ad1-83b3-4767-b5bc-0d28042dEXP1</td>\n",
" <td>\\ndef problem_p03149(input_data):\\n s = eva...</td>\n",
" <td>{'09301913-2eb2-40b2-8b51-49566e08ced7': 'from...</td>\n",
" <td>{'09301913-2eb2-40b2-8b51-49566e08ced7': 'from...</td>\n",
" <td>{'09301913-2eb2-40b2-8b51-49566e08ced7': 'The ...</td>\n",
" <td>{'09301913-2eb2-40b2-8b51-49566e08ced7': None,...</td>\n",
" <td>241291.0</td>\n",
" <td>{'09301913-2eb2-40b2-8b51-49566e08ced7': None,...</td>\n",
" <td>{'09301913-2eb2-40b2-8b51-49566e08ced7': False...</td>\n",
" <td>[# imports\\nimport pytest # used for our unit...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 06:46:18.874782+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'09301913-2eb2-40b2-8b51-49566e08ced7': 'The ...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>f12774fd-abde-4a96-a956-bb497851EXP1</td>\n",
" <td>\\ndef problem_p02910(input_data):\\n s = eva...</td>\n",
" <td>{'0f86c075-6684-4c13-92cf-3166941c5c4c': 'def ...</td>\n",
" <td>{'0f86c075-6684-4c13-92cf-3166941c5c4c': 'def ...</td>\n",
" <td>{'0f86c075-6684-4c13-92cf-3166941c5c4c': 'The ...</td>\n",
" <td>{'0f86c075-6684-4c13-92cf-3166941c5c4c': None,...</td>\n",
" <td>1029209.0</td>\n",
" <td>{'0f86c075-6684-4c13-92cf-3166941c5c4c': None,...</td>\n",
" <td>{'0f86c075-6684-4c13-92cf-3166941c5c4c': False...</td>\n",
" <td>[# imports\\nimport pytest # used for our unit...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 06:49:45.442555+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'0f86c075-6684-4c13-92cf-3166941c5c4c': 'The ...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>e8b0b6c8-be2d-4187-bbab-a452ea2cEXP1</td>\n",
" <td>\\ndef problem_p02553(input_data):\\n import ...</td>\n",
" <td>{'0485cc8e-6ebd-4c81-9aed-61687af1c8ed': 'def ...</td>\n",
" <td>{'0485cc8e-6ebd-4c81-9aed-61687af1c8ed': 'def ...</td>\n",
" <td>{'0485cc8e-6ebd-4c81-9aed-61687af1c8ed': 'Revi...</td>\n",
" <td>{'0485cc8e-6ebd-4c81-9aed-61687af1c8ed': None,...</td>\n",
" <td>81020500.0</td>\n",
" <td>{'0485cc8e-6ebd-4c81-9aed-61687af1c8ed': None,...</td>\n",
" <td>{'0485cc8e-6ebd-4c81-9aed-61687af1c8ed': False...</td>\n",
" <td>[# imports\\nimport pytest # used for our unit...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 06:54:46.367119+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'0485cc8e-6ebd-4c81-9aed-61687af1c8ed': 'Revi...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>dcf32584-d3c7-4596-8586-f851c86aEXP1</td>\n",
" <td>\\ndef problem_p03024(input_data):\\n S = eva...</td>\n",
" <td>{'1692b2dd-d865-466f-97d0-cd4859ea585d': 'def ...</td>\n",
" <td>{'1692b2dd-d865-466f-97d0-cd4859ea585d': 'def ...</td>\n",
" <td>{'1692b2dd-d865-466f-97d0-cd4859ea585d': 'In t...</td>\n",
" <td>{'1692b2dd-d865-466f-97d0-cd4859ea585d': None,...</td>\n",
" <td>202419.0</td>\n",
" <td>{'1692b2dd-d865-466f-97d0-cd4859ea585d': None,...</td>\n",
" <td>{'1692b2dd-d865-466f-97d0-cd4859ea585d': False...</td>\n",
" <td>[# imports\\nimport pytest # used for our unit...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 07:01:06.938556+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'1692b2dd-d865-466f-97d0-cd4859ea585d': 'In t...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>dcf32584-d3c7-4596-8586-f851c86aEXP0</td>\n",
" <td>\\ndef problem_p03024(input_data):\\n S = eva...</td>\n",
" <td>{'1b3fcdb9-bdf2-419b-bf10-b4482dc75d8b': 'def ...</td>\n",
" <td>{'1b3fcdb9-bdf2-419b-bf10-b4482dc75d8b': 'def ...</td>\n",
" <td>{'1b3fcdb9-bdf2-419b-bf10-b4482dc75d8b': 'Your...</td>\n",
" <td>{'1b3fcdb9-bdf2-419b-bf10-b4482dc75d8b': None,...</td>\n",
" <td>202419.0</td>\n",
" <td>{'1b3fcdb9-bdf2-419b-bf10-b4482dc75d8b': None,...</td>\n",
" <td>{'1b3fcdb9-bdf2-419b-bf10-b4482dc75d8b': False...</td>\n",
" <td>[# imports\\nimport pytest # used for our unit...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 07:00:48.192315+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'1b3fcdb9-bdf2-419b-bf10-b4482dc75d8b': 'Your...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>0737f6d0-2837-4e89-9329-b874dd85EXP0</td>\n",
" <td>\\ndef problem_p02847(input_data):\\n def mai...</td>\n",
" <td>{'0257e7cf-b4a8-4771-bb02-6e4963fa9811': '\n",
"def...</td>\n",
" <td>{'0257e7cf-b4a8-4771-bb02-6e4963fa9811': '\n",
"def...</td>\n",
" <td>{'0257e7cf-b4a8-4771-bb02-6e4963fa9811': 'The ...</td>\n",
" <td>{'0257e7cf-b4a8-4771-bb02-6e4963fa9811': None,...</td>\n",
" <td>14625.0</td>\n",
" <td>{'0257e7cf-b4a8-4771-bb02-6e4963fa9811': None,...</td>\n",
" <td>{'0257e7cf-b4a8-4771-bb02-6e4963fa9811': False...</td>\n",
" <td>[# imports\\nimport pytest # used for our unit...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 07:03:54.257947+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'0257e7cf-b4a8-4771-bb02-6e4963fa9811': 'The ...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>e6f50399-d2f6-4091-90e7-ccaa973cEXP0</td>\n",
" <td>\\ndef problem_p02667(input_data):\\n t = eva...</td>\n",
" <td>{'7ce6c3ed-4d5c-4a93-8f13-607d4fc92e03': 'def ...</td>\n",
" <td>{'7ce6c3ed-4d5c-4a93-8f13-607d4fc92e03': 'def ...</td>\n",
" <td>{'7ce6c3ed-4d5c-4a93-8f13-607d4fc92e03': 'The ...</td>\n",
" <td>{'7ce6c3ed-4d5c-4a93-8f13-607d4fc92e03': None,...</td>\n",
" <td>13320210.0</td>\n",
" <td>{'7ce6c3ed-4d5c-4a93-8f13-607d4fc92e03': None,...</td>\n",
" <td>{'7ce6c3ed-4d5c-4a93-8f13-607d4fc92e03': False...</td>\n",
" <td>[# imports\\nimport pytest # used for our unit...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 07:08:28.178195+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'7ce6c3ed-4d5c-4a93-8f13-607d4fc92e03': 'The ...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>e6f50399-d2f6-4091-90e7-ccaa973cEXP1</td>\n",
" <td>\\ndef problem_p02667(input_data):\\n t = eva...</td>\n",
" <td>{'349055c3-aec3-4872-8ad0-67965b7fcca8': 'def ...</td>\n",
" <td>{'349055c3-aec3-4872-8ad0-67965b7fcca8': 'def ...</td>\n",
" <td>{'349055c3-aec3-4872-8ad0-67965b7fcca8': 'In t...</td>\n",
" <td>{'349055c3-aec3-4872-8ad0-67965b7fcca8': None,...</td>\n",
" <td>13320210.0</td>\n",
" <td>{'349055c3-aec3-4872-8ad0-67965b7fcca8': None,...</td>\n",
" <td>{'349055c3-aec3-4872-8ad0-67965b7fcca8': False...</td>\n",
" <td>[# imports\\nimport pytest # used for our unit...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 07:09:26.559622+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'349055c3-aec3-4872-8ad0-67965b7fcca8': 'In t...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>7a02881b-1a2b-4bf9-8ed3-46a44c11EXP0</td>\n",
" <td>\\ndef problem_p03643(input_data):\\n N = eva...</td>\n",
" <td>{'3af102b2-e9a4-45d1-b6a4-da7d4cf77bf0': 'def ...</td>\n",
" <td>{'3af102b2-e9a4-45d1-b6a4-da7d4cf77bf0': 'def ...</td>\n",
" <td>{'3af102b2-e9a4-45d1-b6a4-da7d4cf77bf0': 'The ...</td>\n",
" <td>{'3af102b2-e9a4-45d1-b6a4-da7d4cf77bf0': None,...</td>\n",
" <td>46792.0</td>\n",
" <td>{'3af102b2-e9a4-45d1-b6a4-da7d4cf77bf0': None,...</td>\n",
" <td>{'3af102b2-e9a4-45d1-b6a4-da7d4cf77bf0': False...</td>\n",
" <td>[# imports\\nimport pytest # used for our unit...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 07:12:12.843008+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'3af102b2-e9a4-45d1-b6a4-da7d4cf77bf0': 'The ...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>7a02881b-1a2b-4bf9-8ed3-46a44c11EXP1</td>\n",
" <td>\\ndef problem_p03643(input_data):\\n N = eva...</td>\n",
" <td>{'2442d889-079f-412a-b19c-e6b7c8cc77b2': 'def ...</td>\n",
" <td>{'44cb2a7c-e9f1-49e0-b644-2591ff3f91f1': 'def ...</td>\n",
" <td>{'44cb2a7c-e9f1-49e0-b644-2591ff3f91f1': 'I've...</td>\n",
" <td>{'44cb2a7c-e9f1-49e0-b644-2591ff3f91f1': None,...</td>\n",
" <td>46792.0</td>\n",
" <td>{'44cb2a7c-e9f1-49e0-b644-2591ff3f91f1': None,...</td>\n",
" <td>{'44cb2a7c-e9f1-49e0-b644-2591ff3f91f1': False...</td>\n",
" <td>[# imports\\nimport pytest # used for our unit...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 07:12:39.893205+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'2442d889-079f-412a-b19c-e6b7c8cc77b2': 'The ...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>73242db2-ba0f-48f4-8609-9006feb3EXP0</td>\n",
" <td>\\ndef problem_p02970(input_data):\\n import ...</td>\n",
" <td>{'06c81727-979e-4749-a61e-eeff019418b2': 'def ...</td>\n",
" <td>{'06c81727-979e-4749-a61e-eeff019418b2': 'def ...</td>\n",
" <td>{'06c81727-979e-4749-a61e-eeff019418b2': 'Your...</td>\n",
" <td>{'06c81727-979e-4749-a61e-eeff019418b2': None,...</td>\n",
" <td>35739709.0</td>\n",
" <td>{'06c81727-979e-4749-a61e-eeff019418b2': None,...</td>\n",
" <td>{'06c81727-979e-4749-a61e-eeff019418b2': False...</td>\n",
" <td>[# imports\\nimport numpy as np\\nimport pytest ...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 07:14:45.772244+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'06c81727-979e-4749-a61e-eeff019418b2': 'Your...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>73242db2-ba0f-48f4-8609-9006feb3EXP1</td>\n",
" <td>\\ndef problem_p02970(input_data):\\n import ...</td>\n",
" <td>{'164e2619-d401-448d-8ef2-f17d6afac3b9': 'impo...</td>\n",
" <td>{'164e2619-d401-448d-8ef2-f17d6afac3b9': 'impo...</td>\n",
" <td>{'164e2619-d401-448d-8ef2-f17d6afac3b9': 'The ...</td>\n",
" <td>{'164e2619-d401-448d-8ef2-f17d6afac3b9': 1156....</td>\n",
" <td>35739709.0</td>\n",
" <td>{'164e2619-d401-448d-8ef2-f17d6afac3b9': 30876...</td>\n",
" <td>{'164e2619-d401-448d-8ef2-f17d6afac3b9': True,...</td>\n",
" <td>[# imports\\nimport numpy as np\\nimport pytest ...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 07:15:18.481698+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'164e2619-d401-448d-8ef2-f17d6afac3b9': 'The ...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>543270b0-9c95-49d2-99d2-c3325728EXP1</td>\n",
" <td>\\ndef problem_p02957(input_data):\\n import ...</td>\n",
" <td>{'62e3b5fb-cf2b-4f76-8744-ccfd527ffd3f': 'def ...</td>\n",
" <td>{'62e3b5fb-cf2b-4f76-8744-ccfd527ffd3f': 'def ...</td>\n",
" <td>{'62e3b5fb-cf2b-4f76-8744-ccfd527ffd3f': 'I've...</td>\n",
" <td>{'62e3b5fb-cf2b-4f76-8744-ccfd527ffd3f': None,...</td>\n",
" <td>768040.0</td>\n",
" <td>{'62e3b5fb-cf2b-4f76-8744-ccfd527ffd3f': None,...</td>\n",
" <td>{'62e3b5fb-cf2b-4f76-8744-ccfd527ffd3f': False...</td>\n",
" <td>[# imports\\nimport sys\\nfrom io import StringI...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 07:21:48.578802+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'62e3b5fb-cf2b-4f76-8744-ccfd527ffd3f': 'I've...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>543270b0-9c95-49d2-99d2-c3325728EXP0</td>\n",
" <td>\\ndef problem_p02957(input_data):\\n import ...</td>\n",
" <td>{'45485c8f-f983-460a-9f33-2bb65c902530': 'def ...</td>\n",
" <td>{'45485c8f-f983-460a-9f33-2bb65c902530': 'def ...</td>\n",
" <td>{'45485c8f-f983-460a-9f33-2bb65c902530': 'The ...</td>\n",
" <td>{'45485c8f-f983-460a-9f33-2bb65c902530': None,...</td>\n",
" <td>768040.0</td>\n",
" <td>{'45485c8f-f983-460a-9f33-2bb65c902530': None,...</td>\n",
" <td>{'45485c8f-f983-460a-9f33-2bb65c902530': False...</td>\n",
" <td>[# imports\\nimport sys\\nfrom io import StringI...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 07:20:45.579750+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'45485c8f-f983-460a-9f33-2bb65c902530': 'The ...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>cdd5326e-ade3-44a8-b060-c679e2c5EXP0</td>\n",
" <td>\\ndef problem_p02927(input_data):\\n M, D = ...</td>\n",
" <td>{'06b34dc8-32bf-4e73-b344-4bc0962ba53e': 'def ...</td>\n",
" <td>{'06b34dc8-32bf-4e73-b344-4bc0962ba53e': 'def ...</td>\n",
" <td>{'06b34dc8-32bf-4e73-b344-4bc0962ba53e': 'The ...</td>\n",
" <td>{'06b34dc8-32bf-4e73-b344-4bc0962ba53e': None,...</td>\n",
" <td>250921544.0</td>\n",
" <td>{'06b34dc8-32bf-4e73-b344-4bc0962ba53e': None,...</td>\n",
" <td>{'06b34dc8-32bf-4e73-b344-4bc0962ba53e': False...</td>\n",
" <td>[# imports\\nimport pytest # used for our unit...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 07:24:21.714743+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'06b34dc8-32bf-4e73-b344-4bc0962ba53e': 'The ...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>cdd5326e-ade3-44a8-b060-c679e2c5EXP1</td>\n",
" <td>\\ndef problem_p02927(input_data):\\n M, D = ...</td>\n",
" <td>{'114cd4d1-089f-482f-9cd1-994e06230713': 'def ...</td>\n",
" <td>{'114cd4d1-089f-482f-9cd1-994e06230713': 'def ...</td>\n",
" <td>{'114cd4d1-089f-482f-9cd1-994e06230713': 'I've...</td>\n",
" <td>{'114cd4d1-089f-482f-9cd1-994e06230713': 0.107...</td>\n",
" <td>250921544.0</td>\n",
" <td>{'114cd4d1-089f-482f-9cd1-994e06230713': 22653...</td>\n",
" <td>{'114cd4d1-089f-482f-9cd1-994e06230713': True,...</td>\n",
" <td>[# imports\\nimport pytest # used for our unit...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 07:25:03.355030+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'114cd4d1-089f-482f-9cd1-994e06230713': 'I've...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>a636440e-20eb-446e-829e-3276ef43EXP0</td>\n",
" <td>\\ndef problem_p03547(input_data):\\n #!/usr/...</td>\n",
" <td>{'248e3790-6c3e-4aa8-b33f-83f774e3aa1e': 'def ...</td>\n",
" <td>{'7fb2383c-34db-44bf-890e-16c3f4e5c4bd': 'def ...</td>\n",
" <td>{'7fb2383c-34db-44bf-890e-16c3f4e5c4bd': 'The ...</td>\n",
" <td>{'7fb2383c-34db-44bf-890e-16c3f4e5c4bd': None,...</td>\n",
" <td>14665.0</td>\n",
" <td>{'7fb2383c-34db-44bf-890e-16c3f4e5c4bd': None,...</td>\n",
" <td>{'7fb2383c-34db-44bf-890e-16c3f4e5c4bd': False...</td>\n",
" <td>[# imports\\nimport pytest # used for our unit...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 07:27:58.768033+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'248e3790-6c3e-4aa8-b33f-83f774e3aa1e': 'Your...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>a636440e-20eb-446e-829e-3276ef43EXP1</td>\n",
" <td>\\ndef problem_p03547(input_data):\\n #!/usr/...</td>\n",
" <td>{'2db980c5-9316-418d-abcb-99587afe301c': 'def ...</td>\n",
" <td>{'2db980c5-9316-418d-abcb-99587afe301c': 'def ...</td>\n",
" <td>{'2db980c5-9316-418d-abcb-99587afe301c': 'I've...</td>\n",
" <td>{'2db980c5-9316-418d-abcb-99587afe301c': -0.02...</td>\n",
" <td>14665.0</td>\n",
" <td>{'2db980c5-9316-418d-abcb-99587afe301c': 14997...</td>\n",
" <td>{'2db980c5-9316-418d-abcb-99587afe301c': True,...</td>\n",
" <td>[# imports\\nimport pytest # used for our unit...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 07:28:28.739280+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'2db980c5-9316-418d-abcb-99587afe301c': 'I've...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>c8d3c1f5-dc36-4662-93f0-89b1a728EXP0</td>\n",
" <td>\\ndef problem_p03196(input_data):\\n n, p = ...</td>\n",
" <td>{'083209b7-561e-4272-80c5-ecce1708dc67': '\n",
"def...</td>\n",
" <td>{'083209b7-561e-4272-80c5-ecce1708dc67': '\n",
"def...</td>\n",
" <td>{'083209b7-561e-4272-80c5-ecce1708dc67': 'Here...</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[# imports\\nimport pytest # used for our unit...</td>\n",
" <td>pytest</td>\n",
" <td>2024-04-18 07:35:07.978001+00:00</td>\n",
" <td>None</td>\n",
" <td>{'test_timeout': 15, 'function_to_optimize': '...</td>\n",
" <td>{'083209b7-561e-4272-80c5-ecce1708dc67': 'Here...</td>\n",
" <td>github|2283165</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
2024-04-26 23:26:52 +00:00
"execution_count": 3,
2024-04-25 20:48:49 +00:00
"metadata": {},
"output_type": "execute_result"
}
],
2024-04-26 23:26:52 +00:00
"execution_count": 3
2024-04-25 20:48:49 +00:00
},
{
"metadata": {
"ExecuteTime": {
2024-04-27 00:31:54 +00:00
"end_time": "2024-04-26T23:33:51.208334Z",
2024-04-26 23:26:52 +00:00
"start_time": "2024-04-26T22:58:27.961164Z"
2024-04-25 20:48:49 +00:00
}
},
"cell_type": "code",
"source": "calculate_performance(df)\n",
"id": "ea375d959696374b",
2024-04-27 00:31:54 +00:00
"outputs": [
{
"data": {
"text/plain": [
"{'average_percentage_gain_pr': 522.0994123998028,\n",
" 'geometric_mean_gain_pr': 15.735330689387313,\n",
" 'mean_average_percentage_gain_all': 378.0666648314715,\n",
" 'geometric_mean_gain_all': 7.314994705062598,\n",
" 'average_time_saved_pr': 36230549.047619045,\n",
" 'mean_average_time_saved_all': 25169222.310344826}"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 2
2024-04-25 20:48:49 +00:00
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-04-23T23:28:10.480757Z",
"start_time": "2024-04-23T23:28:10.477906Z"
}
},
"cell_type": "code",
"source": [
"def get_best_correct_speedup_ratio(speedup_ratios, is_correct):\n",
" correct_speedup_ratios = {\n",
" uuid: ratio for uuid, ratio in speedup_ratios.items() if is_correct.get(uuid)\n",
" } if speedup_ratios is not None else {}\n",
"\n",
" if correct_speedup_ratios:\n",
" return max(correct_speedup_ratios.values())\n",
" return None\n",
"\n",
"df[\"best_speedup_ratio\"] = df.apply(\n",
" lambda row: get_best_correct_speedup_ratio(\n",
" row[\"speedup_ratio\"], row[\"is_correct\"]\n",
" ) if row[\"speedup_ratio\"] is not None else None,\n",
" axis=1\n",
")"
],
"id": "951fb220c4512681",
"outputs": [],
"execution_count": 14
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-04-23T23:28:29.229045Z",
"start_time": "2024-04-23T23:28:29.226155Z"
}
},
"cell_type": "code",
"source": "df[\"best_speedup_ratio\"]",
"id": "ff815a02b1ffc97d",
"outputs": [
{
"data": {
"text/plain": [
"0 NaN\n",
"1 NaN\n",
"2 0.221124\n",
"3 1.810426\n",
"4 0.027525\n",
"5 1819.685393\n",
"6 0.072121\n",
"7 0.257811\n",
"8 0.136215\n",
"9 2321.426674\n",
"10 2334.771018\n",
"11 -0.020953\n",
"12 -0.014760\n",
"13 NaN\n",
"14 NaN\n",
"15 0.128617\n",
"16 0.128518\n",
"17 169.122981\n",
"18 163.927139\n",
"19 0.222118\n",
"20 -0.015617\n",
"21 -0.078898\n",
"22 NaN\n",
"23 NaN\n",
"24 1821.364426\n",
"25 NaN\n",
"26 0.049494\n",
"27 0.008551\n",
"28 0.312158\n",
"29 0.145024\n",
"30 NaN\n",
"31 NaN\n",
"32 1141.135658\n",
"33 1188.697713\n",
"34 NaN\n",
"35 NaN\n",
"36 -0.109723\n",
"37 0.107670\n",
"38 0.213890\n",
"39 0.200966\n",
"40 NaN\n",
"Name: best_speedup_ratio, dtype: float64"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 15
},
{
"metadata": {},
"cell_type": "markdown",
"source": "df[\"best_speedup_ratio\"]",
"id": "52fa6272d7d760be"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-04-23T23:21:27.361628Z",
"start_time": "2024-04-23T23:21:27.354893Z"
}
},
"cell_type": "code",
"source": [
"perf_threshold=1.05\n",
"\n",
"valid_candidates = df[\n",
" df[\"is_correct\"].dropna().apply(lambda x: x.get(\"overall\"))\n",
" & (df[\"best_speedup_ratio\"].apply(lambda x: x is not None and x >= perf_threshold))\n",
"].dropna(subset=[\"best_speedup_ratio\"])\n",
"valid_candidates"
],
"id": "7bcfd35946af5ac0",
"outputs": [
{
"data": {
"text/plain": [
"Empty DataFrame\n",
"Columns: [trace_id, original_code, optimizations_raw, optimizations_post, explanations_post, speedup_ratio, original_runtime, optimized_runtime, is_correct, generated_test, test_framework, created_at, aiservice_commit_id, metadata, explanations_raw, user_id, best_speedup_ratio]\n",
"Index: []"
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>trace_id</th>\n",
" <th>original_code</th>\n",
" <th>optimizations_raw</th>\n",
" <th>optimizations_post</th>\n",
" <th>explanations_post</th>\n",
" <th>speedup_ratio</th>\n",
" <th>original_runtime</th>\n",
" <th>optimized_runtime</th>\n",
" <th>is_correct</th>\n",
" <th>generated_test</th>\n",
" <th>test_framework</th>\n",
" <th>created_at</th>\n",
" <th>aiservice_commit_id</th>\n",
" <th>metadata</th>\n",
" <th>explanations_raw</th>\n",
" <th>user_id</th>\n",
" <th>best_speedup_ratio</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 10
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-04-23T02:09:05.092447Z",
"start_time": "2024-04-23T02:09:04.813022Z"
}
},
"cell_type": "code",
"source": "calculate_performance(df)",
"id": "67411eb2488c840a",
"outputs": [
{
"ename": "TypeError",
"evalue": "'>' not supported between instances of 'NoneType' and 'NoneType'",
"output_type": "error",
"traceback": [
"\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
"\u001B[0;31mTypeError\u001B[0m Traceback (most recent call last)",
"Cell \u001B[0;32mIn[3], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m \u001B[43mcalculate_performance\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdf\u001B[49m\u001B[43m)\u001B[49m\n",
"File \u001B[0;32m~/repos/tacit/codeflash/cli/codeflash/optimization/metrics_analysis.py:23\u001B[0m, in \u001B[0;36mcalculate_performance\u001B[0;34m(df, perf_threshold)\u001B[0m\n\u001B[1;32m 21\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mcalculate_performance\u001B[39m(df, perf_threshold\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1.0\u001B[39m):\n\u001B[1;32m 22\u001B[0m \u001B[38;5;66;03m# Extract the best speedup ratio from the speedup_ratio dictionary, accounting for empty dictionaries\u001B[39;00m\n\u001B[0;32m---> 23\u001B[0m df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mbest_speedup_ratio\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m=\u001B[39m \u001B[43mdf\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mspeedup_ratio\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m]\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdropna\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mapply\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43;01mlambda\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mx\u001B[49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mmax\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mvalues\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mif\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mx\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01melse\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[1;32m 25\u001B[0m \u001B[38;5;66;03m# Filter out the rows where a valid candidate above the perf threshold was found\u001B[39;00m\n\u001B[1;32m 26\u001B[0m valid_candidates \u001B[38;5;241m=\u001B[39m df[df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mis_correct\u001B[39m\u001B[38;5;124m'\u001B[39m]\u001B[38;5;241m.\u001B[39mapply(\u001B[38;5;28;01mlambda\u001B[39;00m x: x\u001B[38;5;241m.\u001B[39mget(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124moverall\u001B[39m\u001B[38;5;124m'\u001B[39m)) \u001B[38;5;241m&\u001B[39m (df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mbest_speedup_ratio\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m>\u001B[39m\u001B[38;5;241m=\u001B[39m perf_threshold)]\u001B[38;5;241m.\u001B[39mdropna(subset\u001B[38;5;241m=\u001B[39m[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mbest_speedup_ratio\u001B[39m\u001B[38;5;124m'\u001B[39m])\n",
"File \u001B[0;32m~/miniforge3/envs/codeflash311/lib/python3.12/site-packages/pandas/core/series.py:4924\u001B[0m, in \u001B[0;36mSeries.apply\u001B[0;34m(self, func, convert_dtype, args, by_row, **kwargs)\u001B[0m\n\u001B[1;32m 4789\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mapply\u001B[39m(\n\u001B[1;32m 4790\u001B[0m \u001B[38;5;28mself\u001B[39m,\n\u001B[1;32m 4791\u001B[0m func: AggFuncType,\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 4796\u001B[0m \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs,\n\u001B[1;32m 4797\u001B[0m ) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m DataFrame \u001B[38;5;241m|\u001B[39m Series:\n\u001B[1;32m 4798\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m 4799\u001B[0m \u001B[38;5;124;03m Invoke function on values of Series.\u001B[39;00m\n\u001B[1;32m 4800\u001B[0m \n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 4915\u001B[0m \u001B[38;5;124;03m dtype: float64\u001B[39;00m\n\u001B[1;32m 4916\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m 4917\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mSeriesApply\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 4918\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m 4919\u001B[0m \u001B[43m \u001B[49m\u001B[43mfunc\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 4920\u001B[0m \u001B[43m \u001B[49m\u001B[43mconvert_dtype\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mconvert_dtype\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 4921\u001B[0m \u001B[43m \u001B[49m\u001B[43mby_row\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mby_row\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 4922\u001B[0m \u001B[43m \u001B[49m\u001B[43margs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 4923\u001B[0m \u001B[43m \u001B[49m\u001B[43mkwargs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m-> 4924\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mapply\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n",
"File \u001B[0;32m~/miniforge3/envs/codeflash311/lib/python3.12/site-packages/pandas/core/apply.py:1427\u001B[0m, in \u001B[0;36mSeriesApply.apply\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m 1424\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mapply_compat()\n\u001B[1;32m 1426\u001B[0m \u001B[38;5;66;03m# self.func is Callable\u001B[39;00m\n\u001B[0;32m-> 1427\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mapply_standard\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n",
"File \u001B[0;32m~/miniforge3/envs/codeflash311/lib/python3.12/site-packages/pandas/core/apply.py:1507\u001B[0m, in \u001B[0;36mSeriesApply.apply_standard\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m 1501\u001B[0m \u001B[38;5;66;03m# row-wise access\u001B[39;00m\n\u001B[1;32m 1502\u001B[0m \u001B[38;5;66;03m# apply doesn't have a `na_action` keyword and for backward compat reasons\u001B[39;00m\n\u001B[1;32m 1503\u001B[0m \u001B[38;5;66;03m# we need to give `na_action=\"ignore\"` for categorical data.\u001B[39;00m\n\u001B[1;32m 1504\u001B[0m \u001B[38;5;66;03m# TODO: remove the `na_action=\"ignore\"` when that default has been changed in\u001B[39;00m\n\u001B[1;32m 1505\u001B[0m \u001B[38;5;66;03m# Categorical (GH51645).\u001B[39;00m\n\u001B[1;32m 1506\u001B[0m action \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mignore\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(obj\u001B[38;5;241m.\u001B[39mdtype, CategoricalDtype) \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[0;32m-> 1507\u001B[0m mapped \u001B[38;5;241m=\u001B[39m \u001B[43mobj\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_map_values\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 1508\u001B[0m \u001B[43m \u001B[49m\u001B[43mmapper\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcurried\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mna_action\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43maction\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mconvert\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mconvert_dtype\u001B[49m\n\u001B[1;32m 1509\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 1511\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(mapped) \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(mapped[\u001B[38;5;241m0\u001B[39m], ABCSeries):\n\u001B[1;32m 1512\u001B[0m \u001B[38;5;66;03m# GH#43986 Need to do list(mapped) in order to get treated as nested\u001B[39;00m\n\u001B[1;32m 1513\u001B[0m \u001B[38;5;66;03m# See also GH#25959 regarding EA support\u001B[39;00m\n\u001B[1;32m 1514\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m obj\u001B[38;5;241m.\u001B[39m_constructor_expanddim(\u001B[38;5;28mlist\u001B[39m(mapped), index\u001B[38;5;241m=\u001B[39mobj\u001B[38;5;241m.\u001B[39mindex)\n",
"File \u001B[0;32m~/miniforge3/envs/codeflash311/lib/python3.12/site-packages/pandas/core/base.py:921\u001B[0m, in \u001B[0;36mIndexOpsMixin._map_values\u001B[0;34m(self, mapper, na_action, convert)\u001B[0m\n\u001B[1;32m 918\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(arr, ExtensionArray):\n\u001B[1;32m 919\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m arr\u001B[38;5;241m.\u001B[39mmap(mapper, na_action\u001B[38;5;241m=\u001B[39mna_action)\n\u001B[0;32m--> 921\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43malgorithms\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmap_array\u001B[49m\u001B[43m(\u001B[49m\u001B[43marr\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmapper\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mna_action\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mna_action\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mconvert\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mconvert\u001B[49m\u001B[43m)\u001B[49m\n",
"File \u001B[0;32m~/miniforge3/envs/codeflash311/lib/python3.12/site-packages/pandas/core/algorithms.py:1743\u001B[0m, in \u001B[0;36mmap_array\u001B[0;34m(arr, mapper, na_action, convert)\u001B[0m\n\u001B[1;32m 1741\u001B[0m values \u001B[38;5;241m=\u001B[39m arr\u001B[38;5;241m.\u001B[39mastype(\u001B[38;5;28mobject\u001B[39m, copy\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m)\n\u001B[1;32m 1742\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m na_action \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m-> 1743\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mlib\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmap_infer\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvalues\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmapper\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mconvert\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mconvert\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 1744\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m 1745\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m lib\u001B[38;5;241m.\u001B[39mmap_infer_mask(\n\u001B[1;32m 1746\u001B[0m values, mapper, mask\u001B[38;5;241m=\u001B[39misna(values)\u001B[38;5;241m.\u001B[39mview(np\u001B[38;5;241m.\u001B[39muint8), convert\u001B[38;5;241m=\u001B[39mconvert\n\u001B[1;32m 1747\u001B[0m )\n",
"File \u001B[0;32mlib.pyx:2972\u001B[0m, in \u001B[0;36mpandas._libs.lib.map_infer\u001B[0;34m()\u001B[0m\n",
"File \u001B[0;32m~/repos/tacit/codeflash/cli/codeflash/optimization/metrics_analysis.py:23\u001B[0m, in \u001B[0;36mcalculate_performance.<locals>.<lambda>\u001B[0;34m(x)\u001B[0m\n\u001B[1;32m 21\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mcalculate_performance\u001B[39m(df, perf_threshold\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1.0\u001B[39m):\n\u001B[1;32m 22\u001B[0m \u001B[38;5;66;03m# Extract the best speedup ratio from the speedup_ratio dictionary, accounting for empty dictionaries\u001B[39;00m\n\u001B[0;32m---> 23\u001B[0m df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mbest_speedup_ratio\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m=\u001B[39m df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mspeedup_ratio\u001B[39m\u001B[38;5;124m'\u001B[39m]\u001B[38;5;241m.\u001B[39mdropna()\u001B[38;5;241m.\u001B[39mapply(\u001B[38;5;28;01mlambda\u001B[39;00m x: \u001B[38;5;28;43mmax\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mvalues\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m \u001B[38;5;28;01mif\u001B[39;00m x \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m)\n\u001B[1;32m 25\u001B[0m \u001B[38;5;66;03m# Filter out the rows where a valid candidate above the perf threshold was found\u001B[39;00m\n\u001B[1;32m 26\u001B[0m valid_candidates \u001B[38;5;241m=\u001B[39m df[df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mis_correct\u001B[39m\u001B[38;5;124m'\u001B[39m]\u001B[38;5;241m.\u001B[39mapply(\u001B[38;5;28;01mlambda\u001B[39;00m x: x\u001B[38;5;241m.\u001B[39mget(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124moverall\u001B[39m\u001B[38;5;124m'\u001B[39m)) \u001B[38;5;241m&\u001B[39m (df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mbest_speedup_ratio\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m>\u001B[39m\u001B[38;5;241m=\u001B[39m perf_threshold)]\u001B[38;5;241m.\u001B[39mdropna(subset\u001B[38;5;241m=\u001B[39m[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mbest_speedup_ratio\u001B[39m\u001B[38;5;124m'\u001B[39m])\n",
"\u001B[0;31mTypeError\u001B[0m: '>' not supported between instances of 'NoneType' and 'NoneType'"
]
}
],
"execution_count": 3
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-04-23T21:04:50.982514Z",
"start_time": "2024-04-23T21:04:50.966209Z"
}
},
"cell_type": "code",
"source": "df['speedup_ratio']\n",
"id": "c619a060bfe71c23",
"outputs": [
{
"data": {
"text/plain": [
"0 None\n",
"1 None\n",
"2 None\n",
"3 {'4ae3816e-41f5-418a-b944-e864bb301d96': 0.028...\n",
"4 None\n",
" ... \n",
"3927 None\n",
"3928 None\n",
"3929 None\n",
"3930 None\n",
"3931 {}\n",
"Name: speedup_ratio, Length: 3932, dtype: object"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 7
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-04-23T02:13:23.115764Z",
"start_time": "2024-04-23T02:13:03.932230Z"
}
},
"cell_type": "code",
"source": [
2024-05-03 00:30:08 +00:00
"\n",
"from experiments import metrics_analysis\n",
2024-04-25 20:48:49 +00:00
"import importlib\n",
"importlib.reload(metrics_analysis)\n",
"\n",
"calculate_performance(df)"
],
"id": "1c14f5cf71b7758",
"outputs": [
{
"ename": "AttributeError",
"evalue": "'NoneType' object has no attribute 'get'",
"output_type": "error",
"traceback": [
"\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
"\u001B[0;31mAttributeError\u001B[0m Traceback (most recent call last)",
"Cell \u001B[0;32mIn[4], line 5\u001B[0m\n\u001B[1;32m 2\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mimportlib\u001B[39;00m\n\u001B[1;32m 3\u001B[0m importlib\u001B[38;5;241m.\u001B[39mreload(metrics_analysis)\n\u001B[0;32m----> 5\u001B[0m \u001B[43mcalculate_performance\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdf\u001B[49m\u001B[43m)\u001B[49m\n",
"File \u001B[0;32m~/repos/tacit/codeflash/cli/codeflash/optimization/metrics_analysis.py:26\u001B[0m, in \u001B[0;36mcalculate_performance\u001B[0;34m(df, perf_threshold)\u001B[0m\n\u001B[1;32m 23\u001B[0m df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mbest_speedup_ratio\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m=\u001B[39m df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mspeedup_ratio\u001B[39m\u001B[38;5;124m'\u001B[39m]\u001B[38;5;241m.\u001B[39mdropna()\u001B[38;5;241m.\u001B[39mapply(\u001B[38;5;28;01mlambda\u001B[39;00m x: \u001B[38;5;28mmax\u001B[39m(\u001B[38;5;28mfilter\u001B[39m(\u001B[38;5;28;01mNone\u001B[39;00m, x\u001B[38;5;241m.\u001B[39mvalues())) \u001B[38;5;28;01mif\u001B[39;00m x \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28many\u001B[39m(x\u001B[38;5;241m.\u001B[39mvalues()) \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m)\n\u001B[1;32m 25\u001B[0m \u001B[38;5;66;03m# Filter out the rows where a valid candidate above the perf threshold was found\u001B[39;00m\n\u001B[0;32m---> 26\u001B[0m valid_candidates \u001B[38;5;241m=\u001B[39m df[\u001B[43mdf\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mis_correct\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m]\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mapply\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43;01mlambda\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mx\u001B[49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mx\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43moverall\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m \u001B[38;5;241m&\u001B[39m (df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mbest_speedup_ratio\u001B[39m\u001B[38;5;124m'\u001B[39m]\u001B[38;5;241m.\u001B[39mapply(\u001B[38;5;28;01mlambda\u001B[39;00m x: x \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m x \u001B[38;5;241m>\u001B[39m\u001B[38;5;241m=\u001B[39m perf_threshold))]\u001B[38;5;241m.\u001B[39mdropna(subset\u001B[38;5;241m=\u001B[39m[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mbest_speedup_ratio\u001B[39m\u001B[38;5;124m'\u001B[39m])\n\u001B[1;32m 28\u001B[0m \u001B[38;5;66;03m# Calculate the average speedup ratio of the PR\u001B[39;00m\n\u001B[1;32m 29\u001B[0m pr_gain \u001B[38;5;241m=\u001B[39m valid_candidates[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mbest_speedup_ratio\u001B[39m\u001B[38;5;124m'\u001B[39m]\u001B[38;5;241m.\u001B[39mmean()\n",
"File \u001B[0;32m~/miniforge3/envs/codeflash311/lib/python3.12/site-packages/pandas/core/series.py:4924\u001B[0m, in \u001B[0;36mSeries.apply\u001B[0;34m(self, func, convert_dtype, args, by_row, **kwargs)\u001B[0m\n\u001B[1;32m 4789\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mapply\u001B[39m(\n\u001B[1;32m 4790\u001B[0m \u001B[38;5;28mself\u001B[39m,\n\u001B[1;32m 4791\u001B[0m func: AggFuncType,\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 4796\u001B[0m \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs,\n\u001B[1;32m 4797\u001B[0m ) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m DataFrame \u001B[38;5;241m|\u001B[39m Series:\n\u001B[1;32m 4798\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m 4799\u001B[0m \u001B[38;5;124;03m Invoke function on values of Series.\u001B[39;00m\n\u001B[1;32m 4800\u001B[0m \n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 4915\u001B[0m \u001B[38;5;124;03m dtype: float64\u001B[39;00m\n\u001B[1;32m 4916\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m 4917\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mSeriesApply\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 4918\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m 4919\u001B[0m \u001B[43m \u001B[49m\u001B[43mfunc\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 4920\u001B[0m \u001B[43m \u001B[49m\u001B[43mconvert_dtype\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mconvert_dtype\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 4921\u001B[0m \u001B[43m \u001B[49m\u001B[43mby_row\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mby_row\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 4922\u001B[0m \u001B[43m \u001B[49m\u001B[43margs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 4923\u001B[0m \u001B[43m \u001B[49m\u001B[43mkwargs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m-> 4924\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mapply\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n",
"File \u001B[0;32m~/miniforge3/envs/codeflash311/lib/python3.12/site-packages/pandas/core/apply.py:1427\u001B[0m, in \u001B[0;36mSeriesApply.apply\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m 1424\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mapply_compat()\n\u001B[1;32m 1426\u001B[0m \u001B[38;5;66;03m# self.func is Callable\u001B[39;00m\n\u001B[0;32m-> 1427\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mapply_standard\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n",
"File \u001B[0;32m~/miniforge3/envs/codeflash311/lib/python3.12/site-packages/pandas/core/apply.py:1507\u001B[0m, in \u001B[0;36mSeriesApply.apply_standard\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m 1501\u001B[0m \u001B[38;5;66;03m# row-wise access\u001B[39;00m\n\u001B[1;32m 1502\u001B[0m \u001B[38;5;66;03m# apply doesn't have a `na_action` keyword and for backward compat reasons\u001B[39;00m\n\u001B[1;32m 1503\u001B[0m \u001B[38;5;66;03m# we need to give `na_action=\"ignore\"` for categorical data.\u001B[39;00m\n\u001B[1;32m 1504\u001B[0m \u001B[38;5;66;03m# TODO: remove the `na_action=\"ignore\"` when that default has been changed in\u001B[39;00m\n\u001B[1;32m 1505\u001B[0m \u001B[38;5;66;03m# Categorical (GH51645).\u001B[39;00m\n\u001B[1;32m 1506\u001B[0m action \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mignore\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(obj\u001B[38;5;241m.\u001B[39mdtype, CategoricalDtype) \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[0;32m-> 1507\u001B[0m mapped \u001B[38;5;241m=\u001B[39m \u001B[43mobj\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_map_values\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 1508\u001B[0m \u001B[43m \u001B[49m\u001B[43mmapper\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcurried\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mna_action\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43maction\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mconvert\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mconvert_dtype\u001B[49m\n\u001B[1;32m 1509\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 1511\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(mapped) \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(mapped[\u001B[38;5;241m0\u001B[39m], ABCSeries):\n\u001B[1;32m 1512\u001B[0m \u001B[38;5;66;03m# GH#43986 Need to do list(mapped) in order to get treated as nested\u001B[39;00m\n\u001B[1;32m 1513\u001B[0m \u001B[38;5;66;03m# See also GH#25959 regarding EA support\u001B[39;00m\n\u001B[1;32m 1514\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m obj\u001B[38;5;241m.\u001B[39m_constructor_expanddim(\u001B[38;5;28mlist\u001B[39m(mapped), index\u001B[38;5;241m=\u001B[39mobj\u001B[38;5;241m.\u001B[39mindex)\n",
"File \u001B[0;32m~/miniforge3/envs/codeflash311/lib/python3.12/site-packages/pandas/core/base.py:921\u001B[0m, in \u001B[0;36mIndexOpsMixin._map_values\u001B[0;34m(self, mapper, na_action, convert)\u001B[0m\n\u001B[1;32m 918\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(arr, ExtensionArray):\n\u001B[1;32m 919\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m arr\u001B[38;5;241m.\u001B[39mmap(mapper, na_action\u001B[38;5;241m=\u001B[39mna_action)\n\u001B[0;32m--> 921\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43malgorithms\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmap_array\u001B[49m\u001B[43m(\u001B[49m\u001B[43marr\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmapper\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mna_action\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mna_action\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mconvert\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mconvert\u001B[49m\u001B[43m)\u001B[49m\n",
"File \u001B[0;32m~/miniforge3/envs/codeflash311/lib/python3.12/site-packages/pandas/core/algorithms.py:1743\u001B[0m, in \u001B[0;36mmap_array\u001B[0;34m(arr, mapper, na_action, convert)\u001B[0m\n\u001B[1;32m 1741\u001B[0m values \u001B[38;5;241m=\u001B[39m arr\u001B[38;5;241m.\u001B[39mastype(\u001B[38;5;28mobject\u001B[39m, copy\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m)\n\u001B[1;32m 1742\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m na_action \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m-> 1743\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mlib\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmap_infer\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvalues\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmapper\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mconvert\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mconvert\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 1744\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m 1745\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m lib\u001B[38;5;241m.\u001B[39mmap_infer_mask(\n\u001B[1;32m 1746\u001B[0m values, mapper, mask\u001B[38;5;241m=\u001B[39misna(values)\u001B[38;5;241m.\u001B[39mview(np\u001B[38;5;241m.\u001B[39muint8), convert\u001B[38;5;241m=\u001B[39mconvert\n\u001B[1;32m 1747\u001B[0m )\n",
"File \u001B[0;32mlib.pyx:2972\u001B[0m, in \u001B[0;36mpandas._libs.lib.map_infer\u001B[0;34m()\u001B[0m\n",
"File \u001B[0;32m~/repos/tacit/codeflash/cli/codeflash/optimization/metrics_analysis.py:26\u001B[0m, in \u001B[0;36mcalculate_performance.<locals>.<lambda>\u001B[0;34m(x)\u001B[0m\n\u001B[1;32m 23\u001B[0m df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mbest_speedup_ratio\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m=\u001B[39m df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mspeedup_ratio\u001B[39m\u001B[38;5;124m'\u001B[39m]\u001B[38;5;241m.\u001B[39mdropna()\u001B[38;5;241m.\u001B[39mapply(\u001B[38;5;28;01mlambda\u001B[39;00m x: \u001B[38;5;28mmax\u001B[39m(\u001B[38;5;28mfilter\u001B[39m(\u001B[38;5;28;01mNone\u001B[39;00m, x\u001B[38;5;241m.\u001B[39mvalues())) \u001B[38;5;28;01mif\u001B[39;00m x \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28many\u001B[39m(x\u001B[38;5;241m.\u001B[39mvalues()) \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m)\n\u001B[1;32m 25\u001B[0m \u001B[38;5;66;03m# Filter out the rows where a valid candidate above the perf threshold was found\u001B[39;00m\n\u001B[0;32m---> 26\u001B[0m valid_candidates \u001B[38;5;241m=\u001B[39m df[df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mis_correct\u001B[39m\u001B[38;5;124m'\u001B[39m]\u001B[38;5;241m.\u001B[39mapply(\u001B[38;5;28;01mlambda\u001B[39;00m x: \u001B[43mx\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget\u001B[49m(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124moverall\u001B[39m\u001B[38;5;124m'\u001B[39m)) \u001B[38;5;241m&\u001B[39m (df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mbest_speedup_ratio\u001B[39m\u001B[38;5;124m'\u001B[39m]\u001B[38;5;241m.\u001B[39mapply(\u001B[38;5;28;01mlambda\u001B[39;00m x: x \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m x \u001B[38;5;241m>\u001B[39m\u001B[38;5;241m=\u001B[39m perf_threshold))]\u001B[38;5;241m.\u001B[39mdropna(subset\u001B[38;5;241m=\u001B[39m[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mbest_speedup_ratio\u001B[39m\u001B[38;5;124m'\u001B[39m])\n\u001B[1;32m 28\u001B[0m \u001B[38;5;66;03m# Calculate the average speedup ratio of the PR\u001B[39;00m\n\u001B[1;32m 29\u001B[0m pr_gain \u001B[38;5;241m=\u001B[39m valid_candidates[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mbest_speedup_ratio\u001B[39m\u001B[38;5;124m'\u001B[39m]\u001B[38;5;241m.\u001B[39mmean()\n",
"\u001B[0;31mAttributeError\u001B[0m: 'NoneType' object has no attribute 'get'"
]
}
],
"execution_count": 4
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-04-23T02:13:26.086705Z",
"start_time": "2024-04-23T02:13:26.082397Z"
}
},
"cell_type": "code",
"source": " df['best_speedup_ratio'] = df['speedup_ratio'].dropna().apply(lambda x: max(filter(None, x.values())) if x and any(x.values()) else None)\n",
"id": "be9583f02c713663",
"outputs": [],
"execution_count": 5
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-04-23T02:13:30.618811Z",
"start_time": "2024-04-23T02:13:30.614047Z"
}
},
"cell_type": "code",
"source": "df['best_speedup_ratio']",
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{
"data": {
"text/plain": [
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"3 0.028571\n",
"4 NaN\n",
" ... \n",
"3927 NaN\n",
"3928 NaN\n",
"3929 NaN\n",
"3930 NaN\n",
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"Name: best_speedup_ratio, Length: 3932, dtype: float64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 6
},
{
"metadata": {},
"cell_type": "code",
"outputs": [],
"execution_count": null,
"source": "",
"id": "20f5fdd9cd70f363"
}
],
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