diff --git "a/task_yield/calculate_score.ipynb" "b/task_yield/calculate_score.ipynb" new file mode 100644--- /dev/null +++ "b/task_yield/calculate_score.ipynb" @@ -0,0 +1,696 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "ca46cb4c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 1 1\n", + "r2_score: 0.15467737625333045\n", + "rmse: 25.066010284143243\n", + "accuracy: 0.36414253897550114\n", + "1 1 5\n", + "r2_score: 0.7546653387922533\n", + "rmse: 13.50371994395307\n", + "accuracy: 0.6481069042316259\n", + "1 1 10\n", + "r2_score: 0.8000885689017581\n", + "rmse: 12.1896933776484\n", + "accuracy: 0.7594654788418709\n", + "1 1 100\n", + "r2_score: 0.8579965095082673\n", + "rmse: 10.273619506070625\n", + "accuracy: 0.77728285077951\n", + "1 2 1\n", + "r2_score: 0.18036927640272782\n", + "rmse: 24.526361089611076\n", + "accuracy: 0.3933333333333333\n", + "1 2 5\n", + "r2_score: 0.8042027056160173\n", + "rmse: 11.987464837634565\n", + "accuracy: 0.7011111111111111\n", + "1 2 10\n", + "r2_score: 0.832210597372465\n", + "rmse: 11.097017402260507\n", + "accuracy: 0.7288888888888889\n", + "1 2 100\n", + "r2_score: 0.8802085087988979\n", + "rmse: 9.376418622547654\n", + "accuracy: 0.7788888888888889\n", + "1 3 1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "r2_score: 0.17789007696361192\n", + "rmse: 25.47441551833311\n", + "accuracy: 0.3690078037904125\n", + "1 3 5\n", + "r2_score: 0.7415154873871591\n", + "rmse: 14.284220537620717\n", + "accuracy: 0.6376811594202898\n", + "1 3 10\n", + "r2_score: 0.7589464410075222\n", + "rmse: 13.794185350068053\n", + "accuracy: 0.6477146042363434\n", + "1 3 100\n", + "r2_score: 0.8125328778751065\n", + "rmse: 12.164712224906628\n", + "accuracy: 0.7112597547380156\n", + "1 4 1\n", + "r2_score: 0.3181854469808444\n", + "rmse: 21.8391337319274\n", + "accuracy: 0.4444444444444444\n", + "1 4 5\n", + "r2_score: 0.6407818163238432\n", + "rmse: 15.851903892195802\n", + "accuracy: 0.5544444444444444\n", + "1 4 10\n", + "r2_score: 0.6714193150941529\n", + "rmse: 15.160840877403869\n", + "accuracy: 0.5877777777777777\n", + "1 4 100\n", + "r2_score: 0.9257782178715369\n", + "rmse: 7.205563723245413\n", + "accuracy: 0.86\n", + "3 1 1\n", + "r2_score: 0.6031630613257328\n", + "rmse: 17.174338272728658\n", + "accuracy: 0.5590200445434298\n", + "3 1 5\n", + "r2_score: 0.7493031175159208\n", + "rmse: 13.650496062191454\n", + "accuracy: 0.7171492204899778\n", + "3 1 10\n", + "r2_score: 0.7922679912127688\n", + "rmse: 12.425837727469565\n", + "accuracy: 0.7483296213808464\n", + "3 1 100\n", + "r2_score: 0.8563877157981503\n", + "rmse: 10.33165182921212\n", + "accuracy: 0.767260579064588\n", + "3 2 1\n", + "r2_score: -1.0631888628930937\n", + "rmse: 38.91289082369856\n", + "accuracy: 0.2688888888888889\n", + "3 2 5\n", + "r2_score: 0.8235444863069042\n", + "rmse: 11.379983170763065\n", + "accuracy: 0.7044444444444444\n", + "3 2 10\n", + "r2_score: 0.8553734931364109\n", + "rmse: 10.302626403562249\n", + "accuracy: 0.7322222222222222\n", + "3 2 100\n", + "r2_score: 0.9089569615695245\n", + "rmse: 8.1742398138855\n", + "accuracy: 0.8177777777777778\n", + "3 3 1\n", + "r2_score: 0.6418854603855519\n", + "rmse: 16.813195397290137\n", + "accuracy: 0.5340022296544036\n", + "3 3 5\n", + "r2_score: 0.7511339830508885\n", + "rmse: 14.015935227189525\n", + "accuracy: 0.6488294314381271\n", + "3 3 10\n", + "r2_score: 0.7594202233855893\n", + "rmse: 13.780622687020031\n", + "accuracy: 0.6454849498327759\n", + "3 3 100\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "r2_score: 0.8115801332243869\n", + "rmse: 12.195584775192957\n", + "accuracy: 0.725752508361204\n", + "3 4 1\n", + "r2_score: -0.7591528036000603\n", + "rmse: 35.07955348128085\n", + "accuracy: 0.3244444444444444\n", + "3 4 5\n", + "r2_score: 0.6867344781605031\n", + "rmse: 14.803301131452203\n", + "accuracy: 0.5866666666666667\n", + "3 4 10\n", + "r2_score: 0.7554470043832927\n", + "rmse: 13.079428491611118\n", + "accuracy: 0.6511111111111111\n", + "3 4 100\n", + "r2_score: 0.7767194018049108\n", + "rmse: 12.497633017019943\n", + "accuracy: 0.6822222222222222\n", + "5 1 1\n", + "r2_score: 0.5953640692439692\n", + "rmse: 17.342279830258615\n", + "accuracy: 0.5612472160356348\n", + "5 1 5\n", + "r2_score: 0.759574698305172\n", + "rmse: 13.367926596884812\n", + "accuracy: 0.7104677060133631\n", + "5 1 10\n", + "r2_score: 0.7880081578948638\n", + "rmse: 12.552595717233906\n", + "accuracy: 0.7371937639198218\n", + "5 1 100\n", + "r2_score: 0.8643229527112111\n", + "rmse: 10.042160519985615\n", + "accuracy: 0.7939866369710468\n", + "5 2 1\n", + "r2_score: 0.39115742594242897\n", + "rmse: 21.13861406995961\n", + "accuracy: 0.46\n", + "5 2 5\n", + "r2_score: 0.8258966443592644\n", + "rmse: 11.303880925457774\n", + "accuracy: 0.6888888888888889\n", + "5 2 10\n", + "r2_score: 0.8576001314768951\n", + "rmse: 10.223010275668466\n", + "accuracy: 0.7322222222222222\n", + "5 2 100\n", + "r2_score: 0.9173084006913366\n", + "rmse: 7.790309265112717\n", + "accuracy: 0.8355555555555556\n", + "5 3 1\n", + "r2_score: 0.6017341960233324\n", + "rmse: 17.730696359029213\n", + "accuracy: 0.45484949832775917\n", + "5 3 5\n", + "r2_score: 0.7704161257698972\n", + "rmse: 13.462012002589882\n", + "accuracy: 0.6488294314381271\n", + "5 3 10\n", + "r2_score: 0.7626592547863994\n", + "rmse: 13.687541031785026\n", + "accuracy: 0.6477146042363434\n", + "5 3 100\n", + "r2_score: 0.8210443701532791\n", + "rmse: 11.885349745670583\n", + "accuracy: 0.7569676700111483\n", + "5 4 1\n", + "r2_score: 0.32945237765734725\n", + "rmse: 21.65793708398552\n", + "accuracy: 0.4311111111111111\n", + "5 4 5\n", + "r2_score: 0.7170522551572533\n", + "rmse: 14.068746125312007\n", + "accuracy: 0.6011111111111112\n", + "5 4 10\n", + "r2_score: 0.7487197775269139\n", + "rmse: 13.25810420881749\n", + "accuracy: 0.6422222222222222\n", + "5 4 100\n", + "r2_score: 0.7883744857034274\n", + "rmse: 12.167077870232053\n", + "accuracy: 0.6944444444444444\n", + "0.6416610360274971\n", + "0.376518629500678\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "/home/sagawa/miniconda3/envs/reactiont5/lib/python3.11/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.metrics import mean_squared_error, r2_score\n", + "r2s = []\n", + "for topk in [1, 3, 5]:\n", + " for test_num in [1,2,3,4]:\n", + " for frac in [1, 5, 10, 100]:\n", + " print(topk, test_num, frac)\n", + " df = pd.read_csv(f\"/data1/ReactionT5_neword/task_yield/finetune_v2/finetune_test{test_num}_frac{frac}_top{topk}similartotestall/yield_prediction_output.csv\")\n", + " df['YIELD'] = df['YIELD'] * 100\n", + " print('r2_score:',r2_score(df['YIELD'], df['prediction']))\n", + " print('rmse:',mean_squared_error(df['YIELD'], df['prediction'], squared=False))\n", + " print('accuracy:', sum(abs(df['YIELD'] - df['prediction']) <= 10)/len(df))\n", + " r2s.append(r2_score(df['YIELD'], df['prediction']))\n", + "print(np.mean(r2s))\n", + "print(np.std(r2s))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7c7ea367", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.15467737625333045, 0.7546653387922533, 0.8000885689017581, 0.8579965095082673]\n", + "[0.18036927640272782, 0.8042027056160173, 0.832210597372465, 0.8802085087988979]\n", + "[0.17789007696361192, 0.7415154873871591, 0.7589464410075222, 0.8125328778751065]\n", + "[0.3181854469808444, 0.6407818163238432, 0.6714193150941529, 0.9257782178715369]\n", + "[0.6031630613257328, 0.7493031175159208, 0.7922679912127688, 0.8563877157981503]\n", + "[-1.0631888628930937, 0.8235444863069042, 0.8553734931364109, 0.9089569615695245]\n", + "[0.6418854603855519, 0.7511339830508885, 0.7594202233855893, 0.8115801332243869]\n", + "[-0.7591528036000603, 0.6867344781605031, 0.7554470043832927, 0.7767194018049108]\n", + "[0.5953640692439692, 0.759574698305172, 0.7880081578948638, 0.8643229527112111]\n", + "[0.39115742594242897, 0.8258966443592644, 0.8576001314768951, 0.9173084006913366]\n", + "[0.6017341960233324, 0.7704161257698972, 0.7626592547863994, 0.8210443701532791]\n", + "[0.32945237765734725, 0.7170522551572533, 0.7487197775269139, 0.7883744857034274]\n" + ] + } + ], + "source": [ + "for i in range(0, len(r2s), 4):\n", + " print(r2s[i:i+4])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d6d378a7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1918130/4141901186.py:65: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " ax.set_yticklabels([f\"{i:.2}\" for i in ax.get_yticks()], fontsize=12)\n", + "/tmp/ipykernel_1918130/4141901186.py:65: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " ax.set_yticklabels([f\"{i:.2}\" for i in ax.get_yticks()], fontsize=12)\n", + "/tmp/ipykernel_1918130/4141901186.py:65: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " ax.set_yticklabels([f\"{i:.2}\" for i in ax.get_yticks()], fontsize=12)\n", + "/tmp/ipykernel_1918130/4141901186.py:65: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " ax.set_yticklabels([f\"{i:.2}\" for i in ax.get_yticks()], fontsize=12)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# top1_test1 = [0.6537058109391397,0.7523613870469584,0.8008242584219817,0.8672088471802164]\n", + "# top1_test2 = [0.4928635411631457,0.7004797685468043,0.7400380037835432,0.7956585692311889]\n", + "# top1_test3 = [0.513738657953877,0.7311688676995115,0.7446586025786215,0.7419271887379135]\n", + "# top1_test4 = [0.5998561040179731,0.5487119195824504,0.5102143199892137,0.5591198455295848]\n", + "# top3_test1 = [0.06388687848690755,0.7709183257240217,0.7973465056997633,0.8664257022229472]\n", + "# top3_test2 = [-0.25061282023646636,0.7900569994145266,0.8488202114982035,0.897981931301098]\n", + "# top3_test3 = [-0.17005256301346017,0.7396421953464685,0.7649490625724832,0.810152695741382]\n", + "# top3_test4 = [-0.0015984034787597245,0.608027756354537,0.6349920172555334,0.5779491003852721]\n", + "# top5_test1 = [0.6550375175825431,0.7566732829694809,0.8013112659597739,0.8624973821743596]\n", + "# top5_test2 = [-0.8077041967787084,0.8216216267055954,0.8553512803930077,0.9050444263233903]\n", + "# top5_test3 = [0.6091599293357663,0.7462360998287692,0.7614341599935998,0.8147697259549398]\n", + "# top5_test4 = [0.30188271888634843,0.6172890648478104,0.7185585382313793,0.784868370917551661]\n", + "\n", + "top1_test1 = [0.15467737625333045, 0.7546653387922533, 0.8000885689017581, 0.8579965095082673]\n", + "top1_test2 = [0.18036927640272782, 0.8042027056160173, 0.832210597372465, 0.8802085087988979]\n", + "top1_test3 = [0.17789007696361192, 0.7415154873871591, 0.7589464410075222, 0.8125328778751065]\n", + "top1_test4 = [0.3181854469808444, 0.6407818163238432, 0.6714193150941529, 0.9257782178715369]\n", + "top3_test1 = [0.6031630613257328, 0.7493031175159208, 0.7922679912127688, 0.8563877157981503]\n", + "top3_test2 = [-1.0631888628930937, 0.8235444863069042, 0.8553734931364109, 0.9089569615695245]\n", + "top3_test3 = [0.6418854603855519, 0.7511339830508885, 0.7594202233855893, 0.8115801332243869]\n", + "top3_test4 = [-0.7591528036000603, 0.6867344781605031, 0.7554470043832927, 0.7767194018049108]\n", + "top5_test1 = [0.5953640692439692, 0.759574698305172, 0.7880081578948638, 0.8643229527112111]\n", + "top5_test2 = [0.39115742594242897, 0.8258966443592644, 0.8576001314768951, 0.9173084006913366]\n", + "top5_test3 = [0.6017341960233324, 0.7704161257698972, 0.7626592547863994, 0.8210443701532791]\n", + "top5_test4 = [0.32945237765734725, 0.7170522551572533, 0.7487197775269139, 0.7883744857034274]\n", + "reactiont5_test1 = [0.837,0.84,0.843,0.866,0.877,0.882,0.877,0.875]\n", + "reactiont5_test2 = [0.847,0.897,0.899,0.901,0.91,0.911,0.919,0.916]\n", + "reactiont5_test3 = [0.784,0.784,0.788,0.806,0.807,0.809,0.812,0.812]\n", + "reactiont5_test4 = [0.833,0.838,0.837,0.84,0.832,0.83,0.821,0.819]\n", + "compoundt5_test1 = [0.345, 0.719, 0.775, 0.881, 0.895, 0.902, 0.869, 0.889]\n", + "compoundt5_test2 = [0.415, 0.774, 0.831, 0.876, 0.864, 0.879, 0.907, 0.901]\n", + "compoundt5_test3 = [0.335, 0.692, 0.667, 0.689, 0.675, 0.691, 0.68, 0.685]\n", + "compoundt5_test4 = [0.271, 0.472, 0.498, 0.548, 0.636, 0.629, 0.314, 0.538]\n", + "\n", + "# plot\n", + "import matplotlib.pyplot as plt\n", + "fig, axes = plt.subplots(2, 2, figsize=(16, 10))\n", + "ax1 = axes[0][0]\n", + "ax2 = axes[0][1]\n", + "ax3 = axes[1][0]\n", + "ax4 = axes[1][1]\n", + "ax1.plot([1,5,10,20,40,60,80,100], reactiont5_test1, \"s-\", label='no similar reaction (ReactionT5)', color='red', alpha=0.7)\n", + "ax2.plot([1,5,10,20,40,60,80,100], reactiont5_test2, \"s-\", label='no similar reaction (ReactionT5)', color='blue', alpha=0.7)\n", + "ax3.plot([1,5,10,20,40,60,80,100], reactiont5_test3, \"s-\", label='no similar reaction (ReactionT5)', color='green', alpha=0.7)\n", + "ax4.plot([1,5,10,20,40,60,80,100], reactiont5_test4, \"s-\", label='no similar reaction (ReactionT5)', color='orange', alpha=0.7)\n", + "ax1.plot([1,5,10,20,40,60,80,100], compoundt5_test1, \"o-\", label='no similar reaction (CompoundT5)', color='red', alpha=0.7)\n", + "ax2.plot([1,5,10,20,40,60,80,100], compoundt5_test2, \"o-\", label='no similar reaction (CompoundT5)', color='blue', alpha=0.7)\n", + "ax3.plot([1,5,10,20,40,60,80,100], compoundt5_test3, \"o-\", label='no similar reaction (CompoundT5)', color='green', alpha=0.7)\n", + "ax4.plot([1,5,10,20,40,60,80,100], compoundt5_test4, \"o-\", label='no similar reaction (CompoundT5)', color='orange', alpha=0.7)\n", + "ax1.plot([1,5,10,100], top1_test1, \"o--\", label='top1 similar reaction', color='red', alpha=0.7)\n", + "ax2.plot([1,5,10,100], top1_test2, \"o--\", label='top1 similar reaction', color='blue', alpha=0.7)\n", + "ax3.plot([1,5,10,100], top1_test3, \"o--\", label='top1 similar reaction', color='green', alpha=0.7)\n", + "ax4.plot([1,5,10,100], top1_test4, \"o--\", label='top1 similar reaction', color='orange', alpha=0.7)\n", + "ax1.plot([1,5,10,100], top3_test1, \"^:\", label='top3 similar reaction', color='red', alpha=0.7)\n", + "ax2.plot([1,5,10,100], top3_test2, \"^:\", label='top3 similar reaction', color='blue', alpha=0.7)\n", + "ax3.plot([1,5,10,100], top3_test3, \"^:\", label='top3 similar reaction', color='green', alpha=0.7)\n", + "ax4.plot([1,5,10,100], top3_test4, \"^:\", label='top3 similar reaction', color='orange', alpha=0.7)\n", + "ax1.plot([1,5,10,100], top5_test1, \"x-.\", label='top5 similar reaction', color='red', alpha=0.7)\n", + "ax2.plot([1,5,10,100], top5_test2, \"x-.\", label='top5 similar reaction', color='blue', alpha=0.7)\n", + "ax3.plot([1,5,10,100], top5_test3, \"x-.\", label='top5 similar reaction', color='green', alpha=0.7)\n", + "ax4.plot([1,5,10,100], top5_test4, \"x-.\", label='top5 similar reaction', color='orange', alpha=0.7)\n", + "for ax in [ax1, ax2, ax3, ax4]:\n", + " ax.set_xticks([1,5,10,20,40,60,80,100])\n", + " ax.set_xticklabels([1,5,10,20,40,60,80,100], fontsize=12)\n", + " ax.set_yticklabels([f\"{i:.2}\" for i in ax.get_yticks()], fontsize=12)\n", + "# plt.tight_layout()\n", + "ax1.legend(loc=\"best\", fontsize=12)\n", + "ax2.legend(loc=\"best\", fontsize=12)\n", + "ax3.legend(loc=\"best\", fontsize=12)\n", + "ax4.legend(loc=\"best\", fontsize=12)" + ] + }, + { + "cell_type": "markdown", + "id": "e2cb3811", + "metadata": {}, + "source": [ + "T5Chem" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fbaf3bd3", + "metadata": {}, + "outputs": [], + "source": [ + "test1\n", + "prediction: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 29/29 [00:01<00:00, 27.12it/s]\n", + "MAE: 31.17879061787939 RMSE: 40.960042920674375 r2: 0.4367573011808897 r:0.6608761617586836\n", + "prediction: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 29/29 [00:01<00:00, 27.60it/s]\n", + "MAE: 34.49945494126454 RMSE: 42.10167343831803 r2: 0.7298026014766159 r:0.8542848479732131\n", + "prediction: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 29/29 [00:01<00:00, 26.62it/s]\n", + "MAE: 34.92497861423926 RMSE: 43.26416443888785 r2: 0.7785436316761629 r:0.8823511952030002\n", + "prediction: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 29/29 [00:01<00:00, 25.41it/s]\n", + "MAE: 34.96683128545256 RMSE: 42.66184954682942 r2: 0.7942008620976463 r:0.8911794780500987\n", + "prediction: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 29/29 [00:01<00:00, 27.25it/s]\n", + "MAE: 35.48191659076591 RMSE: 43.52419738228914 r2: 0.8143530551819768 r:0.9024151235334971\n", + "prediction: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 29/29 [00:01<00:00, 25.46it/s]\n", + "MAE: 36.947844304646345 RMSE: 44.76009418388171 r2: 0.800590383878632 r:0.8947571647540086\n", + "prediction: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 29/29 [00:01<00:00, 27.14it/s]\n", + "MAE: 34.27637785894875 RMSE: 42.331490621736144 r2: 0.8218063837803935 r:0.9065353737060642\n", + "prediction: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 29/29 [00:01<00:00, 25.73it/s]\n", + "MAE: 35.11586118861203 RMSE: 43.832926527991766 r2: 0.8338770162805579 r:0.9131686680348586\n", + "\n", + "test3\n", + "prediction: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 29/29 [00:01<00:00, 27.03it/s]\n", + "MAE: 33.86989694935609 RMSE: 41.77767589798135 r2: 0.4171962889862155 r:0.6459073377708412\n", + "prediction: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 29/29 [00:01<00:00, 26.68it/s]\n", + "MAE: 29.090982421373983 RMSE: 38.76908549730092 r2: 0.5346920670647213 r:0.7312264129971792\n", + "prediction: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 29/29 [00:01<00:00, 26.32it/s]\n", + "MAE: 32.81822675897313 RMSE: 41.52909551970308 r2: 0.6105398139820889 r:0.7813704716599476\n", + "prediction: 100%|███████████████████████████████████████████████████████████████████████████████████████████████��██████████████████████| 29/29 [00:01<00:00, 26.97it/s]\n", + "MAE: 31.120493567895473 RMSE: 41.43322935258194 r2: 0.7223281750662077 r:0.8498989204994954\n", + "prediction: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 29/29 [00:01<00:00, 26.78it/s]\n", + "MAE: 30.583503214134694 RMSE: 40.176331220303595 r2: 0.712397145436735 r:0.8440362228226553\n", + "prediction: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 29/29 [00:01<00:00, 28.00it/s]\n", + "MAE: 30.011265108886164 RMSE: 39.762031024978 r2: 0.7585637960723094 r:0.8709556797405419\n", + "prediction: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 29/29 [00:01<00:00, 26.47it/s]\n", + "MAE: 32.0850998089683 RMSE: 41.200574521499185 r2: 0.7080006879853188 r:0.841427767538794\n", + "prediction: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 29/29 [00:01<00:00, 27.40it/s]\n", + "MAE: 31.709675070281488 RMSE: 41.84170554373656 r2: 0.7227222700809479 r:0.8501307370522183\n", + "\n", + "test2\n", + "prediction: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 29/29 [00:01<00:00, 26.58it/s]\n", + "MAE: 30.823935448991445 RMSE: 32.321039812893616 r2: 0.12245121109642941 r:0.34993029462512876\n", + "prediction: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 29/29 [00:01<00:00, 27.11it/s]\n", + "MAE: 34.48224925345897 RMSE: 42.640337972318484 r2: 0.772357127949913 r:0.8788385107344312\n", + "prediction: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 29/29 [00:01<00:00, 26.20it/s]\n", + "MAE: 31.864749330282727 RMSE: 40.999438961351466 r2: 0.8582785330323797 r:0.9264332318264386\n", + "prediction: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 29/29 [00:01<00:00, 26.81it/s]\n", + "MAE: 32.25012591720507 RMSE: 41.46026841036994 r2: 0.895244771366964 r:0.946173753264676\n", + "prediction: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 29/29 [00:01<00:00, 26.71it/s]\n", + "MAE: 34.872105552564996 RMSE: 44.490084757640005 r2: 0.9045412985105311 r:0.951073760814865\n", + "prediction: 100%|████████████████████████████████████████████��█████████████████████████████████████████████████████████████████████████| 29/29 [00:01<00:00, 26.05it/s]\n", + "MAE: 32.84549098445361 RMSE: 41.857480065108824 r2: 0.8766943744605268 r:0.9363195899160323\n", + "prediction: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 29/29 [00:01<00:00, 27.71it/s]\n", + "MAE: 36.70333702809332 RMSE: 46.69249920526493 r2: 0.9018420249993402 r:0.9496536342263637\n", + "prediction: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 29/29 [00:01<00:00, 26.23it/s]\n", + "MAE: 34.936724544375 RMSE: 44.803045702978366 r2: 0.9174620649685153 r:0.9578424009034656\n", + "\n", + "test4\n", + "prediction: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 29/29 [00:01<00:00, 27.31it/s]\n", + "MAE: 30.32751496193219 RMSE: 32.92293052900965 r2: 0.16500366867296562 r:0.4062064360309492\n", + "prediction: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 29/29 [00:01<00:00, 25.97it/s]\n", + "MAE: 33.56590426177524 RMSE: 40.65770129915131 r2: 0.5398595213786395 r:0.7347513330227033\n", + "prediction: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 29/29 [00:01<00:00, 26.09it/s]\n", + "MAE: 28.51332949298033 RMSE: 35.87177845259928 r2: 0.6294695377287944 r:0.7933911631274919\n", + "prediction: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 29/29 [00:01<00:00, 26.76it/s]\n", + "MAE: 32.37973786020354 RMSE: 40.19013829468609 r2: 0.65428987737365 r:0.8088818686147254\n", + "prediction: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 29/29 [00:01<00:00, 26.09it/s]\n", + "MAE: 27.9317079168963 RMSE: 35.10173674097486 r2: 0.6618065624114972 r:0.8135149429552583\n", + "prediction: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 29/29 [00:01<00:00, 26.86it/s]\n", + "MAE: 35.24395696730159 RMSE: 43.126381681021485 r2: 0.6454708948230878 r:0.8034120330335411\n", + "prediction: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 29/29 [00:01<00:00, 26.21it/s]\n", + "MAE: 35.543615257677025 RMSE: 43.55062114577698 r2: 0.7312357792207529 r:0.8551232538182744\n", + "prediction: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 29/29 [00:01<00:00, 27.57it/s]\n", + "MAE: 35.26501901986608 RMSE: 44.02281477027664 r2: 0.6823091872181337 r:0.8260200888708057\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "82df9040", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2079234/198661365.py:37: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " ax.set_yticklabels([f\"{i:.2}\" for i in ax.get_yticks()], fontsize=12)\n", + "/tmp/ipykernel_2079234/198661365.py:37: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " ax.set_yticklabels([f\"{i:.2}\" for i in ax.get_yticks()], fontsize=12)\n", + "/tmp/ipykernel_2079234/198661365.py:37: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " ax.set_yticklabels([f\"{i:.2}\" for i in ax.get_yticks()], fontsize=12)\n", + "/tmp/ipykernel_2079234/198661365.py:37: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " ax.set_yticklabels([f\"{i:.2}\" for i in ax.get_yticks()], fontsize=12)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "reactiont5_test1 = [0.837,0.84,0.843,0.866,0.877,0.882,0.877,0.875]\n", + "reactiont5_test2 = [0.847,0.897,0.899,0.901,0.91,0.911,0.919,0.916]\n", + "reactiont5_test3 = [0.784,0.784,0.788,0.806,0.807,0.809,0.812,0.812]\n", + "reactiont5_test4 = [0.833,0.838,0.837,0.84,0.832,0.83,0.821,0.819]\n", + "compoundt5_test1 = [0.345, 0.719, 0.775, 0.881, 0.895, 0.902, 0.869, 0.889]\n", + "compoundt5_test2 = [0.415, 0.774, 0.831, 0.876, 0.864, 0.879, 0.907, 0.901]\n", + "compoundt5_test3 = [0.335, 0.692, 0.667, 0.689, 0.675, 0.691, 0.68, 0.685]\n", + "compoundt5_test4 = [0.271, 0.472, 0.498, 0.548, 0.636, 0.629, 0.314, 0.538]\n", + "t5chem_test1 = [0.4367573011808897, 0.7298026014766159, 0.7785436316761629, 0.7942008620976463, 0.8143530551819768, 0.800590383878632, 0.8218063837803935, 0.8338770162805579]\n", + "t5chem_test2 = [0.12245121109642941, 0.772357127949913, 0.8582785330323797, 0.895244771366964, 0.9045412985105311, 0.8766943744605268, 0.9018420249993402, 0.9174620649685153]\n", + "t5chem_test3 = [0.4171962889862155, 0.5346920670647213, 0.6105398139820889, 0.7223281750662077, 0.712397145436735, 0.7585637960723094, 0.7080006879853188, 0.7227222700809479]\n", + "t5chem_test4 = [0.16500366867296562, 0.5398595213786395, 0.6294695377287944, 0.65428987737365, 0.6618065624114972, 0.6454708948230878, 0.7312357792207529, 0.6823091872181337]\n", + "\n", + "# plot\n", + "import matplotlib.pyplot as plt\n", + "fig, axes = plt.subplots(2, 2, figsize=(16, 10))\n", + "ax1 = axes[0][0]\n", + "ax2 = axes[0][1]\n", + "ax3 = axes[1][0]\n", + "ax4 = axes[1][1]\n", + "ax1.plot([1,5,10,20,40,60,80,100], reactiont5_test1, \"o-\", label='ReactionT5', color='red', alpha=0.7)\n", + "ax2.plot([1,5,10,20,40,60,80,100], reactiont5_test2, \"o-\", label='ReactionT5', color='red', alpha=0.7)\n", + "ax3.plot([1,5,10,20,40,60,80,100], reactiont5_test3, \"o-\", label='ReactionT5', color='red', alpha=0.7)\n", + "ax4.plot([1,5,10,20,40,60,80,100], reactiont5_test4, \"o-\", label='ReactionT5', color='red', alpha=0.7)\n", + "ax1.plot([1,5,10,20,40,60,80,100], compoundt5_test1, \"s--\", label='CompoundT5', color='blue', alpha=0.7)\n", + "ax2.plot([1,5,10,20,40,60,80,100], compoundt5_test2, \"s--\", label='CompoundT5', color='blue', alpha=0.7)\n", + "ax3.plot([1,5,10,20,40,60,80,100], compoundt5_test3, \"s--\", label='CompoundT5', color='blue', alpha=0.7)\n", + "ax4.plot([1,5,10,20,40,60,80,100], compoundt5_test4, \"s--\", label='CompoundT5', color='blue', alpha=0.7)\n", + "ax1.plot([1,5,10,20,40,60,80,100], t5chem_test1, \"v:\", label='T5Chem', color='green', alpha=0.7)\n", + "ax2.plot([1,5,10,20,40,60,80,100], t5chem_test2, \"v:\", label='T5Chem', color='green', alpha=0.7)\n", + "ax3.plot([1,5,10,20,40,60,80,100], t5chem_test3, \"v:\", label='T5Chem', color='green', alpha=0.7)\n", + "ax4.plot([1,5,10,20,40,60,80,100], t5chem_test4, \"v:\", label='T5Chem', color='green', alpha=0.7)\n", + "\n", + "for ax in [ax1, ax2, ax3, ax4]:\n", + " ax.set_xticks([1,5,10,20,40,60,80,100])\n", + " ax.set_xticklabels([1,5,10,20,40,60,80,100], fontsize=12)\n", + " ax.set_yticklabels([f\"{i:.2}\" for i in ax.get_yticks()], fontsize=12)\n", + "# plt.tight_layout()\n", + "ax1.legend(loc=\"best\", fontsize=12)\n", + "ax2.legend(loc=\"best\", fontsize=12)\n", + "ax3.legend(loc=\"best\", fontsize=12)\n", + "ax4.legend(loc=\"best\", fontsize=12)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "reactiont5", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}