2. 本体系：决策树回归.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cdad9773",
   "metadata": {},
   "outputs": [],
   "source": [
    "#1. 输入库\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "#2. 打开d带中心数据\n",
    "with open('ML_DATA.csv', encoding = 'UTF-8') as f:\n",
    "    df = pd.read_csv(f, index_col = 0)  #index_col=0 告诉第一列是索引\n",
    "\n",
    "#剔除描述符\n",
    "excluded = ['D1','D2','D5','D6','D7','D8','D9','D10','D12','D13','D14','D18','D21','D25']\n",
    "df_new = df.drop(excluded, axis = 1)\n",
    "\n",
    "#如果考虑删除某些点，用下面的命令\n",
    "#excluded_row = [19, 21]\n",
    "#df_new = df_new.drop(excluded_row, axis=0)\n",
    "\n",
    "\n",
    "#设置自变量和因变量\n",
    "y = df_new['y'].values\n",
    "X_1 = df_new.drop('y', axis = 1)\n",
    "\n",
    "#归一化\n",
    "df = pd.DataFrame(X_1)\n",
    "X = df.apply(lambda x: (x - np.min(x)) / (np.max(x) - np.min(x)))\n",
    "\n",
    "#切割数据\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2,\n",
    "                                                   random_state = 1)    # 把样品分为测试和验证集，用split进行分割，测试集的比例为0.2，random_state意思是随机切割、抽取"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5bfbd55",
   "metadata": {},
   "source": [
    "1. 进行随机森林算法【不交叉验证】"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ad9eaa96",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import numpy as np\n",
    "\n",
    "clf = tree.DecisionTreeRegressor()  #进行决策树回归\n",
    "clf = clf.fit(X_train, y_train)   #进行拟合\n",
    "\n",
    "\n",
    "y_pred_train_clf = clf.predict(X_train)\n",
    "y_pred_test_clf = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "33e427a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training R2 = 1.000\n",
      "test R2 = -1.592\n",
      "RMSE(training)0.000\n",
      "RMSE(test)13.441\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#作图\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize = (5, 5))\n",
    "plt.scatter(y_train, y_pred_train_clf, alpha = 0.5, color = 'blue', label = 'training')\n",
    "plt.scatter(y_test, y_pred_test_clf, alpha = 0.5, color = 'red', label = 'test')\n",
    "plt.legend()\n",
    "plt.xlabel('$ΔH_P$', fontsize=15, fontname='Times New Roman')\n",
    "plt.ylabel('predict')\n",
    "\n",
    "plt.savefig(r'C:/Users/Administrator.DESKTOP-CDML10A/Desktop/决策树不交叉验证.tiff', dpi = 360)\n",
    "\n",
    "#10. 算均方误差\n",
    "from sklearn.metrics import mean_squared_error #引用均方差\n",
    "\n",
    "print('training R2 = %.3f' %clf.score(X_train, y_train))\n",
    "print('test R2 = %.3f' %clf.score(X_test, y_test))\n",
    "rmse_tr_clf = mean_squared_error(y_train, y_pred_train_clf, squared = False) #squared = False 是表示对均方差求根，及得到均方根误差\n",
    "rmse_te_clf = mean_squared_error(y_test, y_pred_test_clf, squared = False)\n",
    "print('RMSE(training)%.3f'%rmse_tr_clf)\n",
    "print('RMSE(test)%.3f'%rmse_te_clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd458485",
   "metadata": {},
   "source": [
    "2. 进行决策树回归算法【10折交叉验证】"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "18fdbe3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation results:\n",
      "Fold: 7, mean R2: -2.573\n",
      "Fold: 7, mean RMSE: 14.649\n"
     ]
    }
   ],
   "source": [
    "#进行决策树算法\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "clf = tree.DecisionTreeRegressor()  #进行决策树回归\n",
    "clf = clf.fit(X, y)   #进行拟合\n",
    "\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "\n",
    "crossvalidation = KFold(n_splits = 7, shuffle = True)  #shuffle是进行打乱的意思，在交叉验证之前进行打乱\n",
    "r2_scores = cross_val_score(clf, X, y, scoring = 'r2', cv = crossvalidation)  #cv是分类方式，这里是交叉验证\n",
    "rmse_scores = cross_val_score(clf, X, y, scoring = 'neg_root_mean_squared_error',\n",
    "                             cv = crossvalidation)\n",
    "\n",
    "print('Cross-validation results:')\n",
    "print('Fold: %i, mean R2: %.3f' %(len(r2_scores), np.mean(r2_scores)))\n",
    "print('Fold: %i, mean RMSE: %.3f' %(len(rmse_scores), -np.mean(rmse_scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9ab6577a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_predict\n",
    "y_cv = cross_val_predict(clf, X, y, cv = crossvalidation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7855c797",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (5, 5))\n",
    "plt.plot([-55, 10], [-55, 10], 'r--')\n",
    "plt.scatter(y, y_cv, s = 80, edgecolor = 'k', alpha = 0.7)\n",
    "\n",
    "plt.savefig(r'C:/Users/Administrator.DESKTOP-CDML10A/Desktop/决策树7折交叉验证.tiff', dpi = 360)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a541f9c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}