{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "077f5f8a-ffe5-4405-8806-1b5559140a5d", "metadata": {}, "outputs": [], "source": [ "!pip install pandas hyperopt xgboost scikit-learn matplotlib numpy" ] }, { "cell_type": "code", "execution_count": 1, "id": "a3901bba-d66d-4358-89a7-50dc4b3dd91e", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "from hyperopt import hp, fmin, tpe, STATUS_OK, Trials\n", "from sklearn.model_selection import train_test_split\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "id": "a4713d33-c5a2-4f49-8aed-873069543bec", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", " | 比表面积 | \n", "总孔体积 | \n", "微孔体积 | \n", "平均孔径 | \n", "氮掺杂量at | \n", "氧掺杂量 | \n", "ID/IG | \n", "电流密度 | \n", "比电容 | \n", "
---|---|---|---|---|---|---|---|---|---|
0 | \n", "1141.8 | \n", "0.46 | \n", "0.42 | \n", "1.61 | \n", "1.74 | \n", "3.84 | \n", "1.1 | \n", "0.5 | \n", "206.5 | \n", "
1 | \n", "1141.8 | \n", "0.46 | \n", "0.42 | \n", "1.61 | \n", "1.74 | \n", "3.84 | \n", "1.1 | \n", "1.0 | \n", "179.1 | \n", "
2 | \n", "1141.8 | \n", "0.46 | \n", "0.42 | \n", "1.61 | \n", "1.74 | \n", "3.84 | \n", "1.1 | \n", "2.0 | \n", "163.3 | \n", "
3 | \n", "1141.8 | \n", "0.46 | \n", "0.42 | \n", "1.61 | \n", "1.74 | \n", "3.84 | \n", "1.1 | \n", "5.0 | \n", "146.0 | \n", "
4 | \n", "1141.8 | \n", "0.46 | \n", "0.42 | \n", "1.61 | \n", "1.74 | \n", "3.84 | \n", "1.1 | \n", "10.0 | \n", "137.8 | \n", "