 nowcasting-using-elastic-net.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-08T13:11:09.275636Z",
     "start_time": "2021-01-08T13:11:07.569340Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "from scipy import stats\n",
    "from scipy.stats import norm, skew\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import ElasticNetCV, ElasticNet\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "def ignore_warn(*args, **kwargs):\n",
    "    pass\n",
    "import warnings\n",
    "warnings.warn = ignore_warn\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 一、读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-08T13:11:09.744226Z",
     "start_time": "2021-01-08T13:11:09.737338Z"
    }
   },
   "outputs": [],
   "source": [
    "# 读取文件\n",
    "df=pd.read_csv('时间序列数据.txt')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['date'] = pd.to_datetime(df['date'])\n",
    "df = df.set_index('date')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 二、增加lag项"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "for i in range(1, 8):\n",
    "    df[\"lag_{}\".format(i)] = df.新增确诊.shift(i)\n",
    "print(\"done\")\n",
    "df=df.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 三、纠偏"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-08T13:11:26.487988Z",
     "start_time": "2021-01-08T13:11:26.056616Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " mu = 657.32 and sigma = 1070.09\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'confirmed distribution')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 26032 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 22686 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 30830 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 35786 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 26032 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 22686 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 30830 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 35786 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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BQ4cO1b7OycmpPaZAk8extTrelJk3b15tk+bPf/5zEhISmoyvvLyc8PDwNu1Tc+yaigkocXFxXH/99bz00kuAp/36/fffp6SkhOLiYt577z0mT558xusvLCykW7duREZGsmPHDlatWtVi+czMTN577z1KS0spKiriww8/bFQmLy+PyMhIbr31Vn7605+yfv16YmJimvzAakq3bt2oqqqq/TDdtGlT7fPdu3fzwQcfNGr+WrlyJRs3bmw0Nfdh2Lt3b/r37197DeKjjz5i5MiRgOeaUE2sxcXFLF26tPZM46233mqx6Ss/P5+TJ08Cnh5sy5cvZ8SIEagq8+bNIykpiR//+MfN1vfH+wHQvXt3Nm/eDMCuXbt49913ufHGG+uVaesxbMn48ePZvXs3+/bto6Kigvnz59c7i2rqOLZWx5syR48eBeDgwYO8++67Tb5XBQUFxMfHt2nQyJb4NamIyAwR2Ski2SJyfxPLRUSecpZvFpG01uqKyKMissMp/56IxDrzB4lIqYhsdKbn/blvpuP85Cc/4dixYwCkpaXx7W9/m4yMDCZMmMCdd97J2LFjz3jdM2bMwO12M3r0aH71q1/Vaz5rSlpaGjfccAOpqalcc801TSa0LVu2kJGRQWpqKg8//DC//OUv6d69O5MmTSIlJaX2Qv2sWbPIy8trcjvTp0/ns88+AzxnKtXV1YwZM4bf/OY3JCUl8corr5zxPtd4+umnueWWWxg9ejQbN27k5z//OQBHjhzhoosuYsyYMWRkZHDFFVcwY8YMSkpKWLZsWZNnGTX7cvjwYaZOncro0aMZP34806ZN45vf/Caff/45r732Gh9//HFtd9qFCxc2Wo8/3g+Am266idOnT5OSksJdd93FW2+91WwTZ1vddNNNXHjhhezcuZOEhAReeuklgoODeeaZZ7j88stJSkri+uuvJznZcy2xuePYUp2a49tSGYBrrrmGkSNHcuWVV/Lss8/SrVu3RvF+8sknzJo1yyf7DvivSzEQBOwBBgOhwCZgZIMys4BFgAATgdWt1QWmA8HO8/8C/st5PgjY2pYYrUtx6zpLl+Lz3fr16/XWW29VVdUhQ4boqVOnOjgic66YO3eu7tixo9nlnalLcQaQrap7VbUCmA/MblBmNvCqE+cqIFZE+rRUV1WXqmrNnTqrgKYbCY05h4wdO5apU6dSWFiIy+XyyQVVYyoqKpgzZw7Dh3t/vbQ1/kwq/YBDdV7nOPO8KeNNXYA78Jzp1EgUkQ0iskJEmjzvFZG7RGSdiKzLz8/3bk+M6QTuuOMOunbtyq5duzo6FHOOCA0NbVOnEW/4M6k01QWjYVed5sq0WldEfgG4gZq7dg4DA1R1LPBj4E0RadRHUVVfUNV0VU2Pj49vZReMMca0hT+7FOcA/eu8TgAaXoVsrkxoS3VF5FvAN4FLnfY9VLUcKHeeZ4nIHuACYJ0vdsYYY0zr/HmmshYYJiKJIhIK3AgsaFBmAXCb0wtsIlCoqodbqisiM4D7gKtUtaRmRSISLyJBzvPBwDBgrx/377yhLdwLYow5d53J/32/namoqltEvg8swdOb62VV3SYidzvLnwcW4ukBlg2UALe3VNdZ9TNAGLDMuclplareDWQCvxERN1AF3K2qx/21f+eL8PBwCgoKfNbd0hgTGNT5jfq23hQp5/O30PT0dF23zlrHWlJZWUlOTg5lZWUUl3s/PHZUmA3WYEygCw8PJyEhodGNkSKSpaqNBxLDhmkxrQgJCSExMRGAN1cf9LrezakD/BWSMaYTs2FajDHG+IwlFWOMMT5jScUYY4zPWFIxxhjjM5ZUjDHG+IwlFWOMMT5jScUYY4zPWFIxxhjjM5ZUjDHG+IwlFWOMMT5jScUYY4zPWFIxxhjjM5ZUjDHG+IwlFWOMMT5jScUYY4zPWFIxxhjjM5ZUjDHG+IwlFWOMMT5jScUYY4zPWFIxxhjjM5ZUjDHG+IwlFWOMMT5jScUYY4zPWFIxxhjjM5ZUjDHG+IwlFWOMMT5jScUYY4zPWFIxxhjjM5ZUjDHG+Ixfk4qIzBCRnSKSLSL3N7FcROQpZ/lmEUlrra6IPCoiO5zy74lIbJ1lDzjld4rI5f7cN2OMMY35LamISBDwLDATGAncJCIjGxSbCQxzpruAP3hRdxmQoqqjgV3AA06dkcCNQDIwA3jOWY8xxph24s8zlQwgW1X3qmoFMB+Y3aDMbOBV9VgFxIpIn5bqqupSVXU79VcBCXXWNV9Vy1V1H5DtrMcYY0w78WdS6QccqvM6x5nnTRlv6gLcASxqw/YQkbtEZJ2IrMvPz/diN4wxxnjLn0lFmpinXpZpta6I/AJwA2+0YXuo6guqmq6q6fHx8U1UMcYYc6aC/bjuHKB/ndcJQJ6XZUJbqisi3wK+CVyqqjWJw5vtGWOM8SN/nqmsBYaJSKKIhOK5iL6gQZkFwG1OL7CJQKGqHm6projMAO4DrlLVkgbrulFEwkQkEc/F/zV+3D9jjDEN+O1MRVXdIvJ9YAkQBLysqttE5G5n+fPAQmAWnovqJcDtLdV1Vv0MEAYsExGAVap6t7PuPwNf4mkWu0dVq/y1f8YYYxqTr1uPzj/p6em6bt26jg4jYLy5+qDXZW+eMMCPkRhjOpKIZKlqelPL7I56Y4wxPmNJxRhjjM9YUjHGGOMzllSMMcb4jCUVY4wxPmNJxRhjjM9YUjHGGOMzllSMMcb4jCUVY4wxPmNJxRhjjM9YUjHGGOMzllSMMcb4jCUVY4wxPmNJxRhjjM9YUjHGGOMzllSMMcb4jCUVY4wxPmNJxRhjjM9YUjHGGOMzXiUVEUnxdyDGGGMCn7dnKs+LyBoR+Z6IxPozIGOMMYHLq6SiqhcBtwD9gXUi8qaITPNrZMYYYwKO19dUVHU38EvgPmAK8JSI7BCRq/0VnDHGmMDi7TWV0SLye2A7cAlwpaomOc9/78f4jDHGBJBgL8s9A7wI/FxVS2tmqmqeiPzSL5EZY4wJON4mlVlAqapWAYiICwhX1RJVfc1v0RljjAko3l5TWQ5E1Hkd6cwzxhhjanmbVMJV9XTNC+d5pH9CMsYYE6i8TSrFIpJW80JExgGlLZQ3xhhzHvL2msqPgHdEJM953Qe4wS8RGWOMCVje3vy4FhgB/CvwPSBJVbNaqyciM0Rkp4hki8j9TSwXEXnKWb65wdlQk3VF5DoR2SYi1SKSXmf+IBEpFZGNzvS8N/tmjDHGd7w9UwEYDwxy6owVEVT11eYKi0gQ8CwwDcgB1orIAlX9sk6xmcAwZ5oA/AGY0ErdrcDVwB+b2OweVU1twz4ZY4zxIa+Sioi8BgwBNgJVzmwFmk0qQAaQrap7nXXMB2YDdZPKbOBVVVVglYjEikgfPMmrybqqut2Z503oxhhj2pG3ZyrpwEjnw99b/YBDdV7n4Dkbaa1MPy/rNiVRRDYAp4BfqurKhgVE5C7gLoABAwZ4sUpjjDHe8rb311agdxvX3dSpRMOk1FwZb+o2dBgYoKpjgR8Db4pIl0YrUX1BVdNVNT0+Pr6VVRpjjGkLb89UegBfisgaoLxmpqpe1UKdHDyjGtdIAPK8LBPqRd16VLW8JjZVzRKRPcAFwLqW6hljjPEdb5PKQ2ew7rXAMBFJBHKBG4GbG5RZAHzfuWYyAShU1cMiku9F3XpEJB44rqpVIjIYz8X/vWcQtzHGmDPkVVJR1RUiMhAYpqrLRSQSCGqljltEvg8sccq+rKrbRORuZ/nzwEI844plAyXA7S3VBRCRucDTQDzwdxHZqKqXA5nAb0TEjaczwd2qerwtB8MYY8zZEW+uvYvId/Bc3I5T1SEiMgx4XlUv9XeA/pSenq7r1lnrmLfeXH3Q67I3T7BOEMacq0QkS1XTm1rm7YX6e4BJeHpV1fxgV0/fhGeMMeZc4W1SKVfVipoXIhJM672xjDHGnGe8TSorROTnQITz2/TvAB/6LyxjjDGByNukcj+QD2wBvovnArv94qMxxph6vO39VY3n54Rf9G84xhhjApm3Y3/to4lrKKo62OcRGWOMCVhtGfurRjhwHRDn+3CMMcYEMm9/T6WgzpSrqk8Al/g3NGOMMYHG2+avtDovXXjOXGL8EpExxpiA5W3z1//Uee4G9gPX+zwaY4wxAc3b3l9T/R2IMcaYwOdt89ePW1quqo/7JhxjjDGBrC29v8bjGaoe4ErgH9T/dUZjjDHnubb8SFeaqhYBiMhDwDuqeqe/AjPGGBN4vB2mZQBQUed1BTDI59EYY4wJaN6eqbwGrBGR9/DcWT8XeNVvURljjAlI3vb+elhEFgGTnVm3q+oG/4VljDEmEHnb/AUQCZxS1SeBHOf3440xxphaXiUVEXkQuA94wJkVArzur6CMMcYEJm/PVOYCVwHFAKqahw3TYowxpgFvk0qFqirO8PciEuW/kIwxxgQqb5PKn0Xkj0CsiHwHWI79YJcxxpgGWu39JSICvA2MAE4Bw4H/p6rL/BybMcaYANNqUlFVFZH3VXUcYInEGGNMs7xt/lolIuP9GokxxpiA5+0d9VOBu0VkP54eYILnJGa0vwIzxhgTeFpMKiIyQFUPAjPbKR5jjDEBrLUzlffxjE58QET+qqrXtENMxhhjAlRr11SkzvPB/gzEGGNM4GstqWgzz40xxphGWksqY0TklIgUAaOd56dEpEhETrW2chGZISI7RSRbRO5vYrmIyFPO8s0iktZaXRG5TkS2iUi1iKQ3WN8DTvmdInJ567tvjDHGl1q8pqKqQWe6YhEJAp4FpgE5wFoRWaCqX9YpNhMY5kwTgD8AE1qpuxW4Gvhjg+2NBG4EkoG+wHIRuUBVq850H4wxxrRNW4a+b6sMIFtV96pqBTAfmN2gzGzgVfVYhWcYmD4t1VXV7aq6s4ntzQbmq2q5qu4Dsp31GGOMaSf+TCr9gEN1Xuc487wp403dM9keInKXiKwTkXX5+fmtrNIYY0xb+DOpSBPzGl7sb66MN3XPZHuo6guqmq6q6fHx8a2s0hhjTFt4e0f9mcgB+td5nQDkeVkm1Iu6Z7I9Y4wxfuTPM5W1wDARSRSRUDwX0Rc0KLMAuM3pBTYRKFTVw17WbWgBcKOIhDk/dTwMWOPLHTLGGNMyv52pqKpbRL4PLAGCgJdVdZuI3O0sfx5YCMzCc1G9BLi9pboAIjIXeBqIB/4uIhtV9XJn3X8GvgTcwD3W88sYY9qXeH7Q8fyUnp6u69at6+gwAsabqw96XfbmCQP8GIkxpiOJSJaqpje1zJ/NX8YYY84zllSMMcb4jCUVY4wxPmNJxRhjjM9YUjHGGOMzllSMMcb4jCUVY4wxPmNJxRhjjM9YUjHGGOMzllSMMcb4jCUVY4wxPmNJxRhjjM9YUjHGGOMzllSMMcb4jCUVY4wxPmNJxRhjjM9YUjHGGOMzllSMMcb4jN9+o96cmyqrqsk7WcqJkgpUITI0iH7dIokOsz8lY4wlFeOlrbmFzF97kB2Hi6ioqm60PKFbBBmD4hg7oBtBLumACI0xnYElFdOi48UV/PrDbXywMY/wEBepA2IZ3iuG7tGhBIlwqszNwYJiNhw6ybsbclmxK58rx/Tt6LCNMR3Ekopp1pp9x/neG1kUllbyg0uGEhsZSnhIUL0y3aPDSOwRReYF8ew8UsSiLV/xf//cj6ry8yuSCAsOambtxphzkV2oN016Z90hbn5xFV3CQ1jw/Yv48fThjRJKXSLCiN5d+P4lQ5k0pDuvfHGAf3lpDSeKK9oxamNMR7OkYhp5bdUB7v3LZiYO7s5790wiqU8Xr+uGBLm4YnRfnrwxlY2HTnLdH7/gaFGZH6M1xnQmllRMPX9ee4hfvb+Vy5J68tK30+kaEXJG65md2o9X78gg72QpN/5xFflF5T6O1BjTGVlSMbU+2n6E+9/dzORhPXjulnFnfT1k4uDuvHpHBocLy5j3ylpKKtw+itQY01lZUjEAZB89zQ/e2kBy3648f+s4QoN986eRPiiOZ24ey9bcQv7tzQ24m+iObIw5d1hSMZwud/Pd19YRHhLEH/9lHFE+vpHx0qRe/GZ2Ch/tOMqDC7ahqj5dvzGm87Auxec5VeXedzax71gxr985gb6xEX7Zzq0TB5JzopTnV+xhWM9ovj0p0S/bMcZ0LDtTOc+9uHIvi7Z+xf0zR/CNIT38uq2fXT6cy5J68vDC7aw/eMKv2zLGdAy/JhURmSEiO0UkW0Tub2K5iMhTzvLNIpLWWl0RiRORZSKy23ns5swfJCKlIrLRmZ73576dC7bmFvLfi3cyI7k335k82O/bc7mE/7kuld5dw7nnjfUUnLYeYcaca/yWVEQkCHgWmAmMBG4SkZENis0EhjnTXcAfvKh7P/CRqg4DPnJe19ijqqnOdLd/9uzcUFZZxb+/vZG4qFAeuWYUIu0zXlfXyBD+cMs4Coor+OH8jVRV2/UVY84l/jxTyQCyVXWvqlYA84HZDcrMBl5Vj1VArIj0aaXubOAV5/krwBw/7sM569ElO9l99DSPXjeG2MjQdt12Sr+u/MfsFD7LPsZzn2S367aNMf7lz6TSDzhU53WOM8+bMi3V7aWqhwGcx551yiWKyAYRWSEik89+F85N/8w+xkuf7eO2Cwcy5YL4Donh+vH9mZPalyc+2m3XV4w5h/gzqTTVntKwraO5Mt7UbegwMEBVxwI/Bt4UkUbji4jIXSKyTkTW5efnt7LKc8/pcjf3/mUzg3tE8cDMpA6N5TdzUujTNZwfzd9IUVllh8ZijPENfyaVHKB/ndcJQJ6XZVqqe8RpIsN5PAqgquWqWuA8zwL2ABc0DEpVX1DVdFVNj4/vmG/pHemxJTvJKyzl0etGExHasSMIdwkP4YkbUsk5UcKDC7Z1aCzGGN/wZ1JZCwwTkUQRCQVuBBY0KLMAuM3pBTYRKHSatFqquwD4lvP8W8AHACIS71zgR0QG47n4v9d/uxd4sg4c55Uv9vOtCwcxbmBcR4cDeO64//4lw3h3fS4LNjX8zmGMCTR+u/lRVd0i8n1gCRAEvKyq20Tkbmf588BCYBaQDZQAt7dU11n1I8CfRWQecBC4zpmfCfxGRNxAFXC3qh731/4FmrLKKn72l8307RrBvZcP7+hw6vnBJUP5bHc+v3hvC2kDYknoFtnRIRljzpCcz0NmpKen67p16zo6jHbxP0t38vTH2bxyR8YZX5x/c/VBr8vePGFAm9Z9sKCEWU+tJKlPDG99ZyLBQXZfrjGdlYhkqWp6U8vsf+554Mu8U/zh0z1cndavw3p7tWZA90h+MzuZtftP8IdP93R0OMaYM2RJ5Rznrqrmvr9uJjYyhF9d0fDe085l7th+XDXGuhkbE8gsqZzjXvpsH1tyC/n1VSl0i2rfmxzbSkT4j7kp9O4Szg/e2kBhqXUzNibQ2CjF57C9+ad5fNkupo/sxaxRvTs6HK+Eu5TfXTmU21/6Jz/63+U8cPkQAIKDg3G5XAQHBxMSEkJMTAwRERHtNryMMcY7llTOUdXVyn1/3UxYsIv/mJPSKT58i4uLOXToEAcPHuTgwYPk5ORw7Ngxjh07RkFBAcePH6ekpASAUGAVMPu55tdXk1y6dOlCjx496NOnT71p8ODB9OvXD5fLTsiNaS+WVM5Rr68+wNr9J3j02tH07BLertvW6mr27t3Ljh072LlzZ+3jkSNH6pWLjY2lZ8+exMXFkZqaSvfu3YmNjSU8PJzw8HBeWZ3H3hPl3D8ziZ7RIVRVVVFdXU15eTmnTp3i1KlTFBUVUVhYSH5+PuvXr+fIkSO43V//bHF4eDiJiYkMGTKECy64gOTkZFJSUujSpdFgC8YYH7Ckcg46dLyERxbtIPOCeK4dl+D37ZWXlpC7dwc52V+Sk72d3D07KCs5DYArKIgefQbQa3AKIyfPIq5nX7r17EO3nn0Jj4yqXUdTXZAvuaKMWU+u5K28UBZ8/yLCQ1ofAaCqqopjx46Rm5vL3r172bNnD3v27CErK4u//e1vteUGDhzI6NGjSUtLIyMjg8TExE5xNmdMoLOkco5RVX7+3hYE+N3V/hnSvsrtJnfvDvZt28DebevJ3bsDra4GEXr2G8TIjEz6DRlB7wFD6dG3P8EhZ9ZBoGdMOI9fn8ptL6/h1x9+ye+uHtVqnaCgIHr16kWvXr1IS0urt+zkyZNs27aNLVu2sHXrVlatWsWHH34IQI8ePcjIyKidBg0aZEnGmDNgSeUc805WDit3H+O3c1Lo58OfBs7JyWHNsvfZuzWLAzu3UFFWioiLPonD+Mas6xk4fBT9hiTVO/vwhcwL4rl7yhCeX7GH9IHduOYszrxiY2OZNGkSkyZNAjwJ+ODBg6xevZq1a9eyevVqFi5cCECfPn2YMmUKmZmZTJw4kYgI//zMsjHnGruj/hy6oz73ZCkznvgHSX26MP87E3G5zvybdnV1Ndu2beOjjz7ik08+YdeuXQDE9erH4OQ0EpPHMnDEaCKiYnwSe0t34LurqvmXl9aw/uAJ/nL3NxiV0NUn22xIVTlw4ACrV69m5cqVfPHFF5SUlBAWFkZGRgZTpkxh6tSp9O3b1y/bNyZQtHRHvSWVcySpVFUrN7+4iq25hSz84WQGdm/7GUNFRQWrVq3i448/5pNPPuHo0aO4XC7GjRvHJZdcwunuI4jr6Z8P1NaGdSk4Xc5Vz3yOqrLg3y6iR3SYX+Koq6KigrVr17JixQpWrFjBwYOeYWpGjx7NtGnTmD59OgMGtG04GmPOBZZUmnEuJZXnV+zhkUU7ePTa0VyX3r/1Co7KysraZp/ly5dTVFREZGQkF110EZdccgmZmZl069YNaNvYX23lzVhhW3MLueYP/yS1fyyvzZtAaHD7dhXet28fy5cvZ+nSpWzduhWApKSk2gQzZMiQdo3HmI5iSaUZ50pS2ZpbyNznPueypF48d0taqxeYq6qqyMrKYuHChSxdupQTJ04QHR3NtGnTuPzyy5k4cSJhYY3PBDo6qQC8vyGXH729kavT+vE/143psIvpubm5LFu2jKVLl7JhwwYAhgwZwowZM5gxYwZDhw7tkLiMaQ+WVJpxLiSV4nI3Vz3zGafL3Sz+YWazQ7GoKps2bWLhwoUsXryY/Px8IiIimDp1KrNmzeKiiy5qMpHU1RmSCsBTH+3m8WW7uGfqEO69fITfYvLW0aNHWbZsGUuWLGHdunWoKsOGDWPGjBnMnDmTxMTEjg7RGJ+ypNKMQE8qqsq/vbWBhVsO89q8CUwa2qPR8u3bt7Nw4UIWLVpEXl4eoaGhZGZmMmvWLKZMmUJkpPe/XdJZkkpNt+m31hziP+akcOvEgX6Lq63y8/NZunQpixYtYv369agqI0aMqD2DGTiw88RqzJmypNKMQE8qL3+2j9/87UvuvXw490z9urklOzubRYsWsXDhQvbv309wcDAXXnghs2bN4tJLLyUm5sx6bHWWpAKeHmF3vZbFpzuP8vj1qcwZ289PkZ25I0eOsGTJEhYvXlzbRDZy5EhmzpzJjBkzSEjw/42pxviDJZVmBHJSWbv/ODe9sIqpI3ryx1vHkZNzqDaR7Nq1CxEhIyODWbNmMW3atNqL7WejMyUVgJIKN/P+bx2r9xXw2HVjuDqt835I5+Xl1Z7BbN68GYBRo0Yxc+ZMLr/8cuumbAKKJZVmBGpSyTtZyuxnPye8opCb+h3n46VL2LbN82vLqamptd+Ee/bs6dPtdrakAlBaUcV3Xl3H53uO8d/XtK3nW0fJzc1l8eLFLF68uLYX2ZgxY2oTTO/egTGitDl/WVJpRiAmlT0H8rjl13+kcNdaKNgHQEpKSm0i8ec33s6YVADKKj2J5bPsY9w3YwTfzRwcMEOsHDx4kCVLlrBo0SK2b98OwLhx45gxYwbTp0/3+RcDY3zBkkozAiWpHD9+nKVLl/L3hQtZt3YdoCQMGsK1c65k5syZ7XYDXmdNKuBJLD99ZxN/23yYa8cl8PDcFMKCWx+AsjPZt29fbYLZtWsXiDDwglGMzMhkRPpFRHdtvgnzbI+fMW1hSaUZnTmp1NwHsXz5cjZs2EB1dTWR3ftS2COFB+66iXmzJrZ7TJ05qYCnV9gTy3fz5Ee7yRgUxzM3j233Yf99Zc+ePTz60ttsW72CY3kHEXExcMRoT4IZN4moLrH1yltSMe3JkkozOlNSUVV2797NsmXL+Oijj2qbQoYPH87USy5lvbs/K44E86srk5l3Ucfc99DZk0qNDzflce9fNhEREsTDc0cxa1Qfn627PdUc76O5+/ly9Qq2rfkHx7/KQVwuEpNSGZmRyfBxk4iM7mJJxbQrSyrN6OikUlZWxtq1a1m5cmXt2FIiwtixY7nsssu47LLLiO/dl++9sZ6Pdxzll1ckcefkwR0Wrz+TSlt48wGaffQ0P/nzRjblFDIntS//78pk4pq5MbS9nO3xU1WO5uxj2+oVfLlmBSeOHsYVFET/Yclce8U0MjMzGTZsmFfXk9oSiyUs05AllWZ0RFLZv38/K1euZOXKlaxZs4by8nLCw8PJyMjg0ksv5ZJLLqFHD89NjIWllfzr61l8sbeAh+eM6vD/3IGUVAAqq6p59pNsnv44m8iQIO6+eAjzLkr06se+/MGXx09V+ergHrav/Qe7N63h6CFPp40+ffowefJkJk+ezIUXXkhUVNMDi1pSMWfDkkoz2iOpHD9+vPa3Oj7//PPakW4HDRrE5MmTyczMJD09nfDw+m3/O746xd2vZZFzopRHrxvN3LEdfw9GoCWVGtlHi3hk0U6Wbz9Cn67h3Dl5MNeOS6BrRIifImyaP4/fJQNDa7+s/POf/6S4uJiQkBDS0tKYMGECEyZMICUlhdDQ0DbHYknFNGRJpRn+SConTpxg7dq1tYlk9+7dAERGRjJ+/PjaRNK/f/P3U3ywMZf7/7qFmPBgnrsljfRBcT6N8UwFalKpsWpvAY8t2cm6AyeICAlizth+zB3bj7QBsQQH+X/E46aOX1W1UlLhptxdTXllNWXuKirc1ZRVVlHurqZaFVXPmUm186iAS4TgICHIJQS7hMwL4gkJchEa5MKlVRzK/pIv169mS9Zq9u3x/A2Gh4czNi2NiRMmUBg9iN4DhxAU3HpibevxtoR17rOk0oyzTSput5vdu3ezadMmNm7cyKZNm9i/fz8AERERtb9/Pn78eFJSUggJafk/8NGiMn77t+18uCmvU/ZeCvSkUmNrbiGvfXGA9zfmUu6uJjYyhKnDezJxcBwp/bpyQa8YQs4iyagqJRVV5BeVk3+6nKOnyjlaVManO/MpKnNzurySojI3RWVuisvd+P1/YEUxrmN7CTqWjetYNq6iI575rhAkrj9B8YmE9hxMWK9EwmK6EewSQoJcBAd5Hkf26UJosMtJWp55IQ1fO/NCg4R/ZhcQ5JImp2CXq/Z5WLCL2y4cGDD3FJmvWVJpxpkmlV27dvHwww+zdetWSkpKAIiLiyM1NZUxY8aQnp5er6mhNVXVyttrD/HIou2UVVbzvalDuGfq0LP6YPOHcyWp1Cgqq2Tl7mMs336ET3Yc5URJJQChwS4GxkXSJzaCvl3DiY0MJTI0iIiQIESgoqqaCnc1lVWes4vjJRUcL66g4LTzWFxOWWV1o+25BGLCQ4gOCyYm3DNFh4UQHR5MeLCL8JAgwoJdhIUEER7sIjTYRZAIIoKI5+xEBASoUqWqSnE7jzNG9abSiav2rKeyijJ3FWU1z52znxPHC/hi9VqKcndzOm8PZUcPoNVuT4zR3Qnu3h/ploDGJlDdpS9BUV1xV2ntfvtSSJDUOyaexxC6hAcTHR5Mt8hQekSH0j06jO5RXz92jQg5q182NWenpaRiv1F/BqKioigpKWHu3Lm1iSQhIaHN37jcVdV8uDmPZz7OZk9+MRMHx/Hw3FEMiY/2U+TnhrYmt+aSUEx4CLNG9WHWqD5UVysHjpewJbeQrbmFHCwoIa+wlC/zTnGqtJKKqsYfpsEuITTYRbfIULpHe6YLesXQPTqUuKhQ4qPD6NkljPiYMHrGhLNwy2FcPvpWHlz7j0fb/maG8mbC192s3ZUVfHUgm5zs7eRkb+erg9mcOLCxdnlsjx4kJSUxbNgwBg8ezMBBiQwYmEhYVDSVVZ7kWun2JJ3Kqmo+3JRHVbXWm9zV6kmENa+rPMmvrLKacvfXCe/IqTIOHi9xkmA1pZVVTe5BkEuIiwqle1QoPbuE06dLOL27htOna81jBJ/tPkZ4iMvr/5fWFOcbllTOQL9+/XjnnXfOuP7RU2W8vzGXN1Yf5EBBCSN6x/DszWnMGtXbmgI6iMslJPaIIrFHFFeNaTzUjbvK8wGnQGjNtYs2flP2VULxteCQUBKGjiRh6MjaeWUlxRw5uIevDmQTUfIVO3bsYNWqVVRWVtaW6dGjB0OGDGHw4MEkJiaSkJBAv379iA+HsIi2/5x1U2quORWXV3G63M2Y/l0pOO05GzxeXEF+UQX5RWXsOHyK/NPlNGx4CQ1y0SUihC4RwXQND6FrRAhdIjyPNc+jQoPs/50PWVJpJ4eOl7By9zGWbPuKlbvzqVZIGxDLL2YlcVlSLzuV7+SCg1zEdLLmSH8Kj4xi4IjRDBwxuvYbfFVVFTk5Oezdu5c9e/bUPn744YecPn26Xv2I6C7E9uhFbI/exPboRUxcD6Jj44jpGkdU1zhiYuMIDY9oNY4gl6d5LCbccz1ydmrzP3FQWVXN0aJyvios5XBhGQs3H6awtJJTZW4KSyvZd6yYU2WVVDdIPMEuoUtECO9vzPWc6dQ764mgT9dwekSHEWT/R73i16QiIjOAJ4Eg4H9V9ZEGy8VZPgsoAb6tqutbqisiccDbwCBgP3C9qp5wlj0AzAOqgB+o6hJ/7l9TVJWTJZVk559ma24hW3ILWX/gBPsLPNde+sVG8K8XD+HqtARr5joHdJbrTP5Ufx8FIocQOWoIKaMgBc/ffPGpkxQeO8LJY19xMt/zWHjsCEdz9rFr4yqq3JWN1hsaHkF0125Ed40jIroLEVExRETFEB4dU/s8IjqG8KgYIiKjCYuI5LXP93jVYw3gomHxjeZVq3K6zM2pskoKSz3TKSfxCLDx0EkOF5Y1unYU5BJ6xYTVNq3Fx4TRLTKUuGhPE1xNE2hcVCixESHt0puws/JbUhGRIOBZYBqQA6wVkQWq+mWdYjOBYc40AfgDMKGVuvcDH6nqIyJyv/P6PhEZCdwIJAN9geUicoGqNt0oexaOF1ewcnc+x05XUHDacxp+7HQFX50q5UBBCUVl7tqyPaLDSO3flW99YxCTh8UzJD7KTrXNOUVEnOTQjX5DGv+8s1ZXU1pcRNHJ4xQXHqeo8DinTx6nuPCEM+8Ex4/mUXa6iNLiItyVFS1uzxUUTGh4OKFh4YSERRAaHkFoWDihYRGEhHnmB4eEEhQSQlBwCEHBwQQHe54H18wLCSE4OIS44FDig4MJCg/isuQ+uFxRuFwxlFQqJ0rdnChxU1DipqCkkoLico4VF7HlSCUnStwUVVSDuL6ePAcDEegSEUrX8GCiw0OIDPV0Poiq6ZgRFkyU00EjKiyY0GAXocHBhAW7CA0JIiw4iDCn00ZIkMtZ7iLEJbhcgksEl3iOu8vpwBHk+rozR93lHcGfZyoZQLaq7gUQkfnAbKBuUpkNvKqeLmirRCRWRPrgOQtpru5s4GKn/ivAp8B9zvz5qloO7BORbCeGL3y9YzknSvjh/I1A4wuGY/t3Y2D3SAbHR5HSt2un6hJsTEcQl4vImK5ExnSF/q2PW1dZUU5Z8WlKiz1JpvR0EaWnT1FRVkJFeRkV5WVUlpVSUV7qeV5eRkVZKUUnC2qXud2VVLkrcVdWUl3lbnWbAG+0cb9aaryrAPKd6WwodRJDbZJwugDSTNJoNpnUn5+QMoHl8184ywgb82dS6QccqvM6B8/ZSGtl+rVSt5eqHgZQ1cMiUvODE/2AVU2sqx4RuQu4y3l5WkR2ertDzdnrfdEewLGz3V4HCsj4b/E8BGTsjlZjv6WdAjlD5/Sx7+SajX/nts3I2y+e6XoHNrfAn0mlqXTZ8KaY5sp4U/dMtoeqvgD4Pj17QUTWNde3OxAEcvwWe8cJ5PgDOXbomPj9eTUpB6g7FkkCkOdlmZbqHnGayHAej7Zhe8YYY/zIn0llLTBMRBJFJBTPRfQFDcosAG4Tj4lAodO01VLdBcC3nOffAj6oM/9GEQkTkUQ8F//X+GvnjDHGNOa35i9VdYvI94EleLoFv6yq20Tkbmf588BCPN2Js/F0Kb69pbrOqh8B/iwi84CDwHVOnW0i8mc8F/PdwD3+6Pl1ljqk2c2HAjl+i73jBHL8gRw7dED85/XYX8YYY3zr/L1DxxhjjM9ZUjHGGOMzllR8RESuE5FtIlItIukNlj0gItkislNELq8zf5yIbHGWPeUMW4PT2eBtZ/5qERnUzrtTj4jMcGLPdkYx6HAi8rKIHBWRrXXmxYnIMhHZ7Tx2q7OsTe+Bn2PvLyKfiMh252/mhwEWf7iIrBGRTU78vw6k+J3tBonIBhH5WwDGvt/Z7kYRWdfp4ldVm3wwAUnAcDx3+KfXmT8S2ASEAYnAHiDIWbYGuBDPPTaLgJnO/O8BzzvPbwTe7sD9CnJiHgyEOvsyshMc70wgDdhaZ95/A/c7z+8H/utM3wM/x94HSHOexwC7nBgDJX4Bop3nIcBqYGKgxO9s98fAm8DfAulvx9nufqBHg3mdJn47U/ERVd2uqk3dnV87fIyq7sPT0y1DPPfYdFHVL9TzDr8KzKlT5xXn+V+AS9vrW1ATaofbUdUKoGbInA6lqv8AjjeYXfe4vUL949nW98CfsR9WZ+BUVS0CtuMZ/SFQ4ldVrRmWOMSZNFDiF5EE4Argf+vMDojYW9Bp4rek4n8tDUWT08T8enVU1Q0UAt39HmnTmou/M6o3hA9Qdwiftr4H7cJp2hyL59t+wMTvNB9txHPz8TJVDaT4nwB+BtQdijhQYgdPAl8qIlniGXYKOlH89nsqbSAiy4HeTSz6hap+0MR8OLOhaM5kmBp/6UyxnClfDgfkMyISDfwV+JGqnmrhZLTTxa+ee8BSRSQWeE9EUloo3mniF5FvAkdVNUtELvamShPzOvpvZ5Kq5oln3MNlIrKjhbLtHr8llTZQ1cvOoFpLQ9EkNDG/bp0cEQkGutK4qae9BNLwN0dEpI96Bhr1Zgiflt4DvxKREDwJ5Q1VfdeZHTDx11DVkyLyKTCDwIh/EnCViMwCwoEuIvJ6gMQOgKrmOY9HReQ9PE3UnSZ+a/7yvyaHj3FOUYtEZKJzveQ26g85UzMUzbXAx067Z0fwZridzqJNQ/i08h74jbOtl4Dtqvp4AMYf75yhICIRwGXAjkCIX1UfUNUEVR2E52/5Y1W9NRBiBxCRKBGJqXkOTAe2dqr4/dVD4XybgLl4sn85cARYUmfZL/D0uthJnR4WQLrzB7EHeIavRzgIB97Bc1FtDTC4g/dtFp4eSnvwNPV1huP9FnAYqHSO+zw8150+AnY7j3Fn+h74OfaL8DQ1bAY2OtOsAIp/NLDBiX8r8P+c+QERf51tX8zXvb8CInY8vTA3OdO2mv+PnSl+G6bFGGOMz1jzlzHGGJ+xpGKMMcZnLKkYY4zxGUsqxhhjfMaSijHGGJ+xpGKMMcZn7I56Y9qJiDyEZzRftzMrGFjVzDzaMl9VH/JX3Ma0hSUVY9rXjap6EsC5K/1HzcxrrmxL843pcNb8ZYwxxmcsqRhjjPEZSyrGGGN8xpKKMcYYn7GkYowxxmcsqRhjjPEZ61JsTPs5CrwqIjW/je4CFjczjzOYb0yHs99TMcYY4zPW/GWMMcZnLKkYY4zxGUsqxhhjfMaSijHGGJ+xpGKMMcZn/j90gmjNERt3awAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['新增确诊'] , fit=norm);\n",
    " \n",
    "#得到均值和标准差\n",
    "(mu, sigma) = norm.fit(df['新增确诊'])\n",
    "print( '\\n mu = {:.2f} and sigma = {:.2f}\\n'.format(mu, sigma))\n",
    " \n",
    "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)],\n",
    "            loc='best')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('confirmed distribution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-08T13:11:27.242440Z",
     "start_time": "2021-01-08T13:11:27.112298Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    " #画QQ图\n",
    "fig = plt.figure()\n",
    "res = stats.probplot(df['新增确诊'], plot=plt)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-08T13:11:28.353728Z",
     "start_time": "2021-01-08T13:11:28.031915Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " mu = 4.49 and sigma = 2.48\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 26032 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 22686 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 30830 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 35786 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 26032 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 22686 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 30830 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 35786 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(np.log1p(df['新增确诊']) , fit=norm);\n",
    " \n",
    "# 纠偏,对数化处理。\n",
    "(mu, sigma) = norm.fit(np.log1p(df['新增确诊']))\n",
    "print( '\\n mu = {:.2f} and sigma = {:.2f}\\n'.format(mu, sigma))\n",
    " \n",
    "#纠偏后再化分布图\n",
    "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)],\n",
    "            loc='best')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('log(confirmed+1) distribution')\n",
    " \n",
    "#画QQ图\n",
    "fig = plt.figure()\n",
    "res = stats.probplot(np.log1p(df['新增确诊']), plot=plt)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-08T13:11:36.670380Z",
     "start_time": "2021-01-08T13:11:36.642337Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据清洗前的特征变量的数量为: 16\n",
      "数据清洗后的特征变量的数量为: 16\n"
     ]
    }
   ],
   "source": [
    "#df.drop(['日期'], axis=1, inplace=True)\n",
    "print(\"数据清洗前的特征变量的数量为:\",len(df.columns)-1)\n",
    "#对目标变量进行对数化纠偏\n",
    "df[\"新增确诊\"] = np.log1p(df[\"新增确诊\"])\n",
    "#对数值型特征变量进行对数化处理:1.过滤出数值型变量,2.计算偏度,3.对偏度大于0.75的数据进行对数化纠偏\n",
    "#1.过滤出数值型变量\n",
    "numeric_feats = df.dtypes[df.dtypes != \"object\"].index\n",
    "#2.计算偏度\n",
    "skewed_feats = df[numeric_feats].apply(lambda x: skew(x.dropna())) #compute skewness\n",
    "skewed_feats = skewed_feats[skewed_feats > 0.75]\n",
    "skewed_feats = skewed_feats.index\n",
    "#3.对偏度大于0.75的数据进行对数化纠偏\n",
    "df[skewed_feats] = np.log1p(df[skewed_feats])\n",
    "#4.对分类型变量进行one-hot编码,分类型变量会被横向展开为多个变量\n",
    "df = pd.get_dummies(df)\n",
    "#用均值填充空值\n",
    "df = df.fillna(df.mean())\n",
    " \n",
    "print(\"数据清洗后的特征变量的数量为:\",len(df.columns)-1)\n",
    "\n",
    "y = df['新增确诊']\n",
    "X = df.drop(['新增确诊'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-08T13:15:11.824006Z",
     "start_time": "2021-01-08T13:15:11.819487Z"
    }
   },
   "source": [
    "#创建训练集和测试集\n",
    "\n",
    "X_train, X_test, y_train, y_test  = train_test_split(X, y, test_size = 0.5, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 三、训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop([\"新增确诊\"], axis=1)\n",
    "y = df.新增确诊"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最佳 alpha: 0.04734089\n",
      "最佳 l1_ratio: 1.020\n",
      "迭代次数 48\n"
     ]
    }
   ],
   "source": [
    "N = 50\n",
    "X_train = X.iloc[:N,]\n",
    "X_test = X.iloc[N:,]\n",
    "y_train = y[:N]\n",
    "y_test = y[N:]\n",
    "\n",
    "# 利用弹性网络\n",
    "from sklearn.model_selection import cross_val_score\n",
    "cv_model = ElasticNetCV(l1_ratio=1.02, eps=1e-3, n_alphas=200, fit_intercept=True, \n",
    "                        normalize=True, precompute='auto', max_iter=200, tol=0.006, cv=10, \n",
    "                        copy_X=True, verbose=0, n_jobs=-1, positive=False, random_state=0)\n",
    "\n",
    "# 训练模型              \n",
    "cv_model.fit(X_train, y_train)\n",
    "\n",
    "# 计算最佳迭代次数、alpha和ratio\n",
    "print('最佳 alpha: %.8f'%cv_model.alpha_)\n",
    "print('最佳 l1_ratio: %.3f'%cv_model.l1_ratio_)\n",
    "print('迭代次数 %d'%cv_model.n_iter_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-08T13:15:13.612744Z",
     "start_time": "2021-01-08T13:15:13.604356Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train r2 score:  0.9098747539184582\n",
      "Test r2 score:  0.8258381350139532\n",
      "Train RMSE: 0.9197\n",
      "Test RMSE: 0.4641\n"
     ]
    }
   ],
   "source": [
    "# 输出结果\n",
    "y_train_pred = cv_model.predict(X_train)\n",
    "y_pred = cv_model.predict(X_test)\n",
    "print('Train r2 score: ', r2_score(y_train_pred, y_train))\n",
    "print('Test r2 score: ', r2_score(y_test, y_pred))\n",
    "train_mse = mean_squared_error(y_train_pred, y_train)\n",
    "test_mse = mean_squared_error(y_pred, y_test)\n",
    "train_rmse = np.sqrt(train_mse)\n",
    "test_rmse = np.sqrt(test_mse)\n",
    "print('Train RMSE: %.4f' % train_rmse)\n",
    "print('Test RMSE: %.4f' % test_rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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