HR_data.classification.ipynb

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   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "196C7131C36E42FE9FD1773DC17ED008",
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   "outputs": [],
   "source": [
    "#读取数据\n",
    "# HR = pd.read_csv(\"../input/human_one/HR_comma_sep.csv\")\n",
    "HR = pd.read_csv(\"./HR_comma_sep.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AB4F6EC32218459A8AC0680D72E9F5C0",
    "mdEditEnable": false
   },
   "source": [
    "# 1.数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "0994353B543C43C49E9FE5CCA475FB4B",
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14999, 10)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看数据构成\n",
    "HR.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "92BA9A12FADA469F8DE132209E76E5C1",
    "scrolled": false
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>sales</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
       "0                0.38             0.53               2                   157   \n",
       "1                0.80             0.86               5                   262   \n",
       "2                0.11             0.88               7                   272   \n",
       "3                0.72             0.87               5                   223   \n",
       "4                0.37             0.52               2                   159   \n",
       "\n",
       "   time_spend_company  Work_accident  left  promotion_last_5years  sales  \\\n",
       "0                   3              0     1                      0  sales   \n",
       "1                   6              0     1                      0  sales   \n",
       "2                   4              0     1                      0  sales   \n",
       "3                   5              0     1                      0  sales   \n",
       "4                   3              0     1                      0  sales   \n",
       "\n",
       "   salary  \n",
       "0     low  \n",
       "1  medium  \n",
       "2  medium  \n",
       "3     low  \n",
       "4     low  "
      ]
     },
     "execution_count": 4,
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   ],
   "source": [
    "HR.head()"
   ]
  },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14999 entries, 0 to 14998\n",
      "Data columns (total 10 columns):\n",
      "satisfaction_level       14999 non-null float64\n",
      "last_evaluation          14999 non-null float64\n",
      "number_project           14999 non-null int64\n",
      "average_montly_hours     14999 non-null int64\n",
      "time_spend_company       14999 non-null int64\n",
      "Work_accident            14999 non-null int64\n",
      "left                     14999 non-null int64\n",
      "promotion_last_5years    14999 non-null int64\n",
      "sales                    14999 non-null object\n",
      "salary                   14999 non-null object\n",
      "dtypes: float64(2), int64(6), object(2)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# 查看空值\n",
    "HR.info()"
   ]
  },
  {
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   "metadata": {
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   "source": [
    "字段说明：\n",
    "\n",
    "| Column 1 | Column 2 | Column 3 |\n",
    "| -------- | -------- | -------- |\n",
    "|satisfaction_level     |  float64  | 满意度      \n",
    "|last_evaluation        |  float64\t| 上一次满意度\n",
    "|number_project         |    int64\t| 项目数\n",
    "|average_montly_hours   |    int64\t| 每月加班平均时间\n",
    "|time_spend_company     |    int64\t| 花在公司的时间\t\n",
    "|Work_accident          |    int64\t| 他们是否发生过工伤事故\n",
    "|left                   |    int64  | 员工是否已经离开\n",
    "|promotion_last_5years  |    int64  | 在过去的5年里他们是否升职了？\n",
    "|sales                  |   object  | 所属部门（专栏销售）\n",
    "|salary                 |   object  | 工资是否可以达到预期\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
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  {
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   "execution_count": 6,
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.612834</td>\n",
       "      <td>0.716102</td>\n",
       "      <td>3.803054</td>\n",
       "      <td>201.050337</td>\n",
       "      <td>3.498233</td>\n",
       "      <td>0.144610</td>\n",
       "      <td>0.238083</td>\n",
       "      <td>0.021268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.248631</td>\n",
       "      <td>0.171169</td>\n",
       "      <td>1.232592</td>\n",
       "      <td>49.943099</td>\n",
       "      <td>1.460136</td>\n",
       "      <td>0.351719</td>\n",
       "      <td>0.425924</td>\n",
       "      <td>0.144281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.440000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.820000</td>\n",
       "      <td>0.870000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>245.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>310.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "count        14999.000000     14999.000000    14999.000000   \n",
       "mean             0.612834         0.716102        3.803054   \n",
       "std              0.248631         0.171169        1.232592   \n",
       "min              0.090000         0.360000        2.000000   \n",
       "25%              0.440000         0.560000        3.000000   \n",
       "50%              0.640000         0.720000        4.000000   \n",
       "75%              0.820000         0.870000        5.000000   \n",
       "max              1.000000         1.000000        7.000000   \n",
       "\n",
       "       average_montly_hours  time_spend_company  Work_accident          left  \\\n",
       "count          14999.000000        14999.000000   14999.000000  14999.000000   \n",
       "mean             201.050337            3.498233       0.144610      0.238083   \n",
       "std               49.943099            1.460136       0.351719      0.425924   \n",
       "min               96.000000            2.000000       0.000000      0.000000   \n",
       "25%              156.000000            3.000000       0.000000      0.000000   \n",
       "50%              200.000000            3.000000       0.000000      0.000000   \n",
       "75%              245.000000            4.000000       0.000000      0.000000   \n",
       "max              310.000000           10.000000       1.000000      1.000000   \n",
       "\n",
       "       promotion_last_5years  \n",
       "count           14999.000000  \n",
       "mean                0.021268  \n",
       "std                 0.144281  \n",
       "min                 0.000000  \n",
       "25%                 0.000000  \n",
       "50%                 0.000000  \n",
       "75%                 0.000000  \n",
       "max                 1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看数值型数据的情况，是否有异常值\n",
    "HR.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "689CC6818FDF4FB18BADAFC61D5CE466",
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   "source": [
    "+ 从整体满意度上看，本次全体员工有不同程度满意度下降的情况\n",
    "+ 每月加班时间（average_montly_hours） 最大值到310，似乎是个异常值\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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    "id": "A4AA33E2D9F343978B0A17604841AF18",
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   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 做个箱线图看下加班时间是否是异常值\n",
    "plt.boxplot(HR['average_montly_hours'].values)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F5C60F13B1EF4D338568832877A15396",
    "mdEditEnable": false
   },
   "source": [
    "+ 300小时竟然不在异常值范围内！神奇！ 大神一样的存在！ --》 可能这是导致整体满意度下降的原因？ 加班严重"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "08C0C16A17DF435C8DC02C00F689FD64",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "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>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>satisfaction_level</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.105021</td>\n",
       "      <td>-0.142970</td>\n",
       "      <td>-0.020048</td>\n",
       "      <td>-0.100866</td>\n",
       "      <td>0.058697</td>\n",
       "      <td>-0.388375</td>\n",
       "      <td>0.025605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_evaluation</th>\n",
       "      <td>0.105021</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.349333</td>\n",
       "      <td>0.339742</td>\n",
       "      <td>0.131591</td>\n",
       "      <td>-0.007104</td>\n",
       "      <td>0.006567</td>\n",
       "      <td>-0.008684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_project</th>\n",
       "      <td>-0.142970</td>\n",
       "      <td>0.349333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.417211</td>\n",
       "      <td>0.196786</td>\n",
       "      <td>-0.004741</td>\n",
       "      <td>0.023787</td>\n",
       "      <td>-0.006064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average_montly_hours</th>\n",
       "      <td>-0.020048</td>\n",
       "      <td>0.339742</td>\n",
       "      <td>0.417211</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.127755</td>\n",
       "      <td>-0.010143</td>\n",
       "      <td>0.071287</td>\n",
       "      <td>-0.003544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_spend_company</th>\n",
       "      <td>-0.100866</td>\n",
       "      <td>0.131591</td>\n",
       "      <td>0.196786</td>\n",
       "      <td>0.127755</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002120</td>\n",
       "      <td>0.144822</td>\n",
       "      <td>0.067433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Work_accident</th>\n",
       "      <td>0.058697</td>\n",
       "      <td>-0.007104</td>\n",
       "      <td>-0.004741</td>\n",
       "      <td>-0.010143</td>\n",
       "      <td>0.002120</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.154622</td>\n",
       "      <td>0.039245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>left</th>\n",
       "      <td>-0.388375</td>\n",
       "      <td>0.006567</td>\n",
       "      <td>0.023787</td>\n",
       "      <td>0.071287</td>\n",
       "      <td>0.144822</td>\n",
       "      <td>-0.154622</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.061788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <td>0.025605</td>\n",
       "      <td>-0.008684</td>\n",
       "      <td>-0.006064</td>\n",
       "      <td>-0.003544</td>\n",
       "      <td>0.067433</td>\n",
       "      <td>0.039245</td>\n",
       "      <td>-0.061788</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       satisfaction_level  last_evaluation  number_project  \\\n",
       "satisfaction_level               1.000000         0.105021       -0.142970   \n",
       "last_evaluation                  0.105021         1.000000        0.349333   \n",
       "number_project                  -0.142970         0.349333        1.000000   \n",
       "average_montly_hours            -0.020048         0.339742        0.417211   \n",
       "time_spend_company              -0.100866         0.131591        0.196786   \n",
       "Work_accident                    0.058697        -0.007104       -0.004741   \n",
       "left                            -0.388375         0.006567        0.023787   \n",
       "promotion_last_5years            0.025605        -0.008684       -0.006064   \n",
       "\n",
       "                       average_montly_hours  time_spend_company  \\\n",
       "satisfaction_level                -0.020048           -0.100866   \n",
       "last_evaluation                    0.339742            0.131591   \n",
       "number_project                     0.417211            0.196786   \n",
       "average_montly_hours               1.000000            0.127755   \n",
       "time_spend_company                 0.127755            1.000000   \n",
       "Work_accident                     -0.010143            0.002120   \n",
       "left                               0.071287            0.144822   \n",
       "promotion_last_5years             -0.003544            0.067433   \n",
       "\n",
       "                       Work_accident      left  promotion_last_5years  \n",
       "satisfaction_level          0.058697 -0.388375               0.025605  \n",
       "last_evaluation            -0.007104  0.006567              -0.008684  \n",
       "number_project             -0.004741  0.023787              -0.006064  \n",
       "average_montly_hours       -0.010143  0.071287              -0.003544  \n",
       "time_spend_company          0.002120  0.144822               0.067433  \n",
       "Work_accident               1.000000 -0.154622               0.039245  \n",
       "left                       -0.154622  1.000000              -0.061788  \n",
       "promotion_last_5years       0.039245 -0.061788               1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看各指标的相关性（协方差）\n",
    "HR.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "604696CBCDFB4F9684288FBB95B4D77F",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = HR.corr()\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(corr,vmax=1,vmin=-1,square=True,annot=True,linewidths=.5,cmap=\"YlGnBu\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8B747CC373AD47BEBD048A87B8F7EEE0",
    "mdEditEnable": false
   },
   "source": [
    "+ 可能的因果关系：项目数增加 --》加班时间增加 --》满意度下降 --》 离职       相关性超过35%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "95D0841AC5624E3796C3465BC07E620B",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sales        salary  Current_Status\n",
       "IT           high    Leave                4\n",
       "                     Stay                79\n",
       "             low     Leave              172\n",
       "                     Stay               437\n",
       "             medium  Leave               97\n",
       "                     Stay               438\n",
       "RandD        high    Leave                4\n",
       "                     Stay                47\n",
       "             low     Leave               55\n",
       "                     Stay               309\n",
       "             medium  Leave               62\n",
       "                     Stay               310\n",
       "accounting   high    Leave                5\n",
       "                     Stay                69\n",
       "             low     Leave               99\n",
       "                     Stay               259\n",
       "             medium  Leave              100\n",
       "                     Stay               235\n",
       "hr           high    Leave                6\n",
       "                     Stay                39\n",
       "             low     Leave               92\n",
       "                     Stay               243\n",
       "             medium  Leave              117\n",
       "                     Stay               242\n",
       "management   high    Leave                1\n",
       "                     Stay               224\n",
       "             low     Leave               59\n",
       "                     Stay               121\n",
       "             medium  Leave               31\n",
       "                     Stay               194\n",
       "marketing    high    Leave                9\n",
       "                     Stay                71\n",
       "             low     Leave              126\n",
       "                     Stay               276\n",
       "             medium  Leave               68\n",
       "                     Stay               308\n",
       "product_mng  high    Leave                6\n",
       "                     Stay                62\n",
       "             low     Leave              105\n",
       "                     Stay               346\n",
       "             medium  Leave               87\n",
       "                     Stay               296\n",
       "sales        high    Leave               14\n",
       "                     Stay               255\n",
       "             low     Leave              697\n",
       "                     Stay              1402\n",
       "             medium  Leave              303\n",
       "                     Stay              1469\n",
       "support      high    Leave                8\n",
       "                     Stay               133\n",
       "             low     Leave              389\n",
       "                     Stay               757\n",
       "             medium  Leave              158\n",
       "                     Stay               784\n",
       "technical    high    Leave               25\n",
       "                     Stay               176\n",
       "             low     Leave              378\n",
       "                     Stay               994\n",
       "             medium  Leave              294\n",
       "                     Stay               853\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HR['Current_Status'] = HR['left'].apply(lambda x: 'Stay' if x == 0 else 'Leave')\n",
    "HR.groupby(['sales','salary','Current_Status']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "E95CC7F6E4EA401A88BB587F640543E0",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "leave = HR[(HR['left'] == 1)]\n",
    "stay = HR[(HR['left']==0)]\n",
    "dept_name = HR['sales'].unique()\n",
    "name=['Sales','Accounting','HR','Technical','Support','Management','IT','Product Management','Marketing','RandD']\n",
    "index = range(10)\n",
    "\n",
    "plt.figure(1,figsize=(12,8))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "leave['sales'].value_counts().plot(kind='bar')\n",
    "plt.title('Employess who LEAVE the company by department')\n",
    "plt.xticks(index,name)\n",
    "\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "stay['sales'].value_counts().plot(kind='bar',color='green')\n",
    "plt.title('Employess who STAY in the company by department')\n",
    "plt.xticks(index,name)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "1264416E77E04626AE3A9DCE0BF89890",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "L_salary_level_count = leave['salary'].value_counts()\n",
    "S_salary_level_count = stay['salary'].value_counts()\n",
    "\n",
    "plt.figure(1,figsize=(12,8))\n",
    "plt.subplot(1,2,1)\n",
    "L_salary_level_count.plot(kind='bar',rot=0)\n",
    "plt.title('The number of employees who \\n LEAVE the company by salary level ')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "S_salary_level_count.plot(kind='bar',rot=0,color='green')\n",
    "plt.title('The number of employees who \\n STAY the company by salary level ')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "E8A082521D2141E789C4D16B389EEEB2",
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def plot_dept_leave_salary(department):\n",
    "    dept_leave = leave[leave['sales'] ==department]\n",
    "    count = dept_leave['salary'].value_counts()\n",
    "    index = [1,2,3]\n",
    "#     color = ['red','blue','green']\n",
    "    plt.bar(index,count,width=0.5)\n",
    "    plt.xticks(index,['Low','Medium','High'])\n",
    "    \n",
    "def plot_dept_stay_salary(department):\n",
    "    dept_stay = stay[stay['sales'] ==department]\n",
    "    count = dept_stay['salary'].value_counts()\n",
    "    index = [1,2,3]\n",
    "    color = ['red','blue','green']\n",
    "    plt.bar(index,count,width=0.5,color='green')\n",
    "    plt.xticks(index,['Low','Medium','High'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "98C6C2D85A894E5D812FC2C9F3B6F328",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1,figsize=(12,8))\n",
    "for i in range(10):\n",
    "    plt.subplot(2,5,i+1)\n",
    "    plot_dept_leave_salary(dept_name[i])\n",
    "    plt.title(name[i])\n",
    "plt.suptitle('LEAVE')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "9FBBF96FC88146D88FCEA5836440B3CF",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1,figsize=(12,8))\n",
    "for i in range(10):\n",
    "    plt.subplot(2,5,i+1)\n",
    "    plot_dept_stay_salary(dept_name[i])\n",
    "    plt.title(name[i])\n",
    "plt.suptitle('STAY')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "3AD0DBE81BE24B2C80600CC175879B00",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "leave_time_spend_count = leave['time_spend_company'].value_counts().sort_index()\n",
    "stay_time_spend_count = stay['time_spend_company'].value_counts().sort_index()\n",
    "\n",
    "plt.figure(1,figsize=(12,8))\n",
    "plt.subplot(1,2,1)\n",
    "leave_time_spend_count.plot(kind='bar',rot=0)\n",
    "plt.title('How many years did they spend in the \\n company before LEAVING')\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel('Number of employees')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "stay_time_spend_count.plot(kind='bar',rot=0,color='green')\n",
    "plt.title('How many years did they spend in the \\n company continue STAYING')\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel('Number of employees')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "4B8C0CA35222418897E43B4C74945E0B",
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def plot_dept_leave_years(department):\n",
    "    year_leave = leave[leave['sales'] ==department]\n",
    "    count = year_leave['time_spend_company'].value_counts().sort_index()\n",
    "    index = range(0,len(count))\n",
    "    check = year_leave['time_spend_company'].unique()\n",
    "    plt.bar(index,count,width=0.5)\n",
    "    plt.xticks(index,sorted(check))\n",
    "\n",
    "def plot_dept_stay_years(department):\n",
    "    year_stay = stay[stay['sales'] ==department]\n",
    "    count = year_stay['time_spend_company'].value_counts().sort_index()\n",
    "    index = range(0,len(count))\n",
    "    check = year_stay['time_spend_company'].unique()\n",
    "    plt.bar(index,count,width=0.5,color='green')\n",
    "    plt.xticks(index,sorted(check))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "B9F37A4DA3604AD198D52AA2DBC44219",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1,figsize=(12,8))\n",
    "for i in range(10):\n",
    "    plt.subplot(2,5,i+1)\n",
    "    plot_dept_leave_years(dept_name[i])\n",
    "    plt.title(name[i])\n",
    "plt.suptitle('LEAVE')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "AF02CAB4CC3444919F9A59CF827A83AA",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1,figsize=(12,8))\n",
    "for i in range(10):\n",
    "    plt.subplot(2,5,i+1)\n",
    "    plot_dept_stay_years(dept_name[i])\n",
    "    plt.title(name[i])\n",
    "plt.suptitle('STAY')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D24157469775424080E80BB246E14972",
    "mdEditEnable": false
   },
   "source": [
    "# 2.Prediction Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "85444BB8F7DF4FF6A0C64466DFE6668C",
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "del HR['sales']\n",
    "del HR['salary']\n",
    "del HR['Current_Status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "FF1CC6B46D914906990F022B570CF7FC",
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "label = HR.pop('left')\n",
    "HR_train,HR_test,label_train,label_test = train_test_split(HR, label, test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "7156CD3B5F544E7FAEBD2A5F9774274B",
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "A9C101EE09DA497D8FCF83C20B3AB92E",
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "Training_score = []\n",
    "Testing_score = []\n",
    "Model = []\n",
    "Classifiers = [\n",
    "    LogisticRegression(),\n",
    "    KNeighborsClassifier(),\n",
    "    SVC(),\n",
    "    DecisionTreeClassifier(),\n",
    "    RandomForestClassifier(),\n",
    "    AdaBoostClassifier(),\n",
    "    GaussianNB()]\n",
    "def run_classifiers(HR_train,label_train,HR_test,label_test):\n",
    "    for classifier in Classifiers:\n",
    "        Model.append(classifier.__class__.__name__)\n",
    "        classifier.fit(HR_train,label_train)\n",
    "        Training_score.append(classifier.score(HR_train,label_train))\n",
    "        Testing_score.append(classifier.score(HR_test,label_test))\n",
    "        from sklearn.metrics import classification_report as cr\n",
    "        print(\"{} : trainning classification report\".format(classifier.__class__.__name__))\n",
    "        print(cr(label_train,classifier.predict(HR_train),digits=4))\n",
    "        print(\"{} : testing classification report\".format(classifier.__class__.__name__))\n",
    "        print(cr(label_test,classifier.predict(HR_test),digits=4))\n",
    "\n",
    "    for i in range(len(Model)):\n",
    "        print ( \"The {:<25} has a training score of {:^10.4f} and testing score of {:^10.4}\".format(Model[i],Training_score[i],Testing_score[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "30663C250EEC4D9F89426FBA2DF8E0A1",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression : trainning classification report\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0     0.7996    0.9281    0.8590      9134\n",
      "          1     0.5297    0.2583    0.3473      2865\n",
      "\n",
      "avg / total     0.7351    0.7681    0.7368     11999\n",
      "\n",
      "LogisticRegression : testing classification report\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0     0.7947    0.9198    0.8527      2294\n",
      "          1     0.4667    0.2280    0.3064       706\n",
      "\n",
      "avg / total     0.7175    0.7570    0.7241      3000\n",
      "\n",
      "KNeighborsClassifier : trainning classification report\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0     0.9849    0.9681    0.9764      9134\n",
      "          1     0.9036    0.9525    0.9274      2865\n",
      "\n",
      "avg / total     0.9655    0.9644    0.9647     11999\n",
      "\n",
      "KNeighborsClassifier : testing classification report\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0     0.9725    0.9542    0.9633      2294\n",
      "          1     0.8598    0.9122    0.8852       706\n",
      "\n",
      "avg / total     0.9459    0.9443    0.9449      3000\n",
      "\n",
      "SVC : trainning classification report\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0     0.9774    0.9719    0.9746      9134\n",
      "          1     0.9119    0.9284    0.9201      2865\n",
      "\n",
      "avg / total     0.9618    0.9615    0.9616     11999\n",
      "\n",
      "SVC : testing classification report\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0     0.9715    0.9664    0.9690      2294\n",
      "          1     0.8928    0.9079    0.9003       706\n",
      "\n",
      "avg / total     0.9530    0.9527    0.9528      3000\n",
      "\n",
      "DecisionTreeClassifier : trainning classification report\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0     1.0000    0.9998    0.9999      9134\n",
      "          1     0.9993    1.0000    0.9997      2865\n",
      "\n",
      "avg / total     0.9998    0.9998    0.9998     11999\n",
      "\n",
      "DecisionTreeClassifier : testing classification report\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0     0.9890    0.9791    0.9840      2294\n",
      "          1     0.9342    0.9646    0.9491       706\n",
      "\n",
      "avg / total     0.9761    0.9757    0.9758      3000\n",
      "\n",
      "RandomForestClassifier : trainning classification report\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0     0.9986    0.9998    0.9992      9134\n",
      "          1     0.9993    0.9955    0.9974      2865\n",
      "\n",
      "avg / total     0.9988    0.9987    0.9987     11999\n",
      "\n",
      "RandomForestClassifier : testing classification report\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0     0.9870    0.9961    0.9915      2294\n",
      "          1     0.9869    0.9575    0.9720       706\n",
      "\n",
      "avg / total     0.9870    0.9870    0.9869      3000\n",
      "\n",
      "AdaBoostClassifier : trainning classification report\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0     0.9703    0.9770    0.9737      9134\n",
      "          1     0.9251    0.9047    0.9148      2865\n",
      "\n",
      "avg / total     0.9595    0.9597    0.9596     11999\n",
      "\n",
      "AdaBoostClassifier : testing classification report\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0     0.9688    0.9756    0.9722      2294\n",
      "          1     0.9188    0.8980    0.9083       706\n",
      "\n",
      "avg / total     0.9571    0.9573    0.9572      3000\n",
      "\n",
      "GaussianNB : trainning classification report\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0     0.9010    0.8200    0.8586      9134\n",
      "          1     0.5540    0.7127    0.6234      2865\n",
      "\n",
      "avg / total     0.8181    0.7944    0.8024     11999\n",
      "\n",
      "GaussianNB : testing classification report\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0     0.9015    0.8221    0.8600      2294\n",
      "          1     0.5507    0.7082    0.6196       706\n",
      "\n",
      "avg / total     0.8190    0.7953    0.8034      3000\n",
      "\n",
      "The LogisticRegression        has a training score of   0.7681   and testing score of   0.757   \n",
      "The KNeighborsClassifier      has a training score of   0.9644   and testing score of   0.9443  \n",
      "The SVC                       has a training score of   0.9615   and testing score of   0.9527  \n",
      "The DecisionTreeClassifier    has a training score of   0.9998   and testing score of   0.9757  \n",
      "The RandomForestClassifier    has a training score of   0.9987   and testing score of   0.987   \n",
      "The AdaBoostClassifier        has a training score of   0.9597   and testing score of   0.9573  \n",
      "The GaussianNB                has a training score of   0.7944   and testing score of   0.7953  \n"
     ]
    }
   ],
   "source": [
    "run_classifiers(HR_train,label_train,HR_test,label_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用神经网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "D43D114E412A45C081966E265AF69D95",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense,Activation\n",
    "from keras.optimizers import SGD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5000\n",
      "11999/11999 [==============================] - 1s 105us/step - loss: 0.3471\n",
      "Epoch 2/5000\n",
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      "Epoch 3/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.3220\n",
      "Epoch 4/5000\n",
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      "Epoch 5/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.2985\n",
      "Epoch 6/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.2881\n",
      "Epoch 7/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.2784\n",
      "Epoch 8/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.2694\n",
      "Epoch 9/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.2612\n",
      "Epoch 10/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.2535\n",
      "Epoch 11/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.2464\n",
      "Epoch 12/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.2399\n",
      "Epoch 13/5000\n",
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      "Epoch 14/5000\n",
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      "Epoch 15/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.2231\n",
      "Epoch 16/5000\n",
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      "Epoch 17/5000\n",
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      "Epoch 18/5000\n",
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      "Epoch 20/5000\n",
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      "Epoch 21/5000\n",
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      "Epoch 24/5000\n",
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      "Epoch 35/5000\n",
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      "Epoch 36/5000\n",
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      "Epoch 37/5000\n",
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      "Epoch 38/5000\n",
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      "Epoch 39/5000\n",
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      "Epoch 40/5000\n",
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      "Epoch 41/5000\n",
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      "Epoch 42/5000\n",
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      "Epoch 43/5000\n",
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      "Epoch 44/5000\n",
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      "Epoch 45/5000\n",
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      "Epoch 46/5000\n",
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      "Epoch 47/5000\n",
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      "Epoch 48/5000\n",
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      "Epoch 49/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1815\n",
      "Epoch 50/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1814\n",
      "Epoch 51/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1813\n",
      "Epoch 52/5000\n",
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      "Epoch 53/5000\n",
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      "Epoch 54/5000\n",
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      "Epoch 55/5000\n",
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      "Epoch 56/5000\n",
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      "Epoch 57/5000\n",
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      "Epoch 58/5000\n",
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      "Epoch 59/5000\n",
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      "Epoch 60/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1809\n",
      "Epoch 61/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1809\n",
      "Epoch 62/5000\n",
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      "Epoch 63/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1809\n",
      "Epoch 64/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1809\n",
      "Epoch 65/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1809\n",
      "Epoch 66/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1809\n",
      "Epoch 67/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1809\n",
      "Epoch 68/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1808\n",
      "Epoch 69/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1808\n",
      "Epoch 70/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1808\n",
      "Epoch 71/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1808\n",
      "Epoch 72/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1808\n",
      "Epoch 73/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1808\n",
      "Epoch 74/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1808\n",
      "Epoch 75/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1808\n",
      "Epoch 76/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1807\n",
      "Epoch 77/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1807\n",
      "Epoch 78/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1807\n",
      "Epoch 79/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1807\n",
      "Epoch 80/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1806\n",
      "Epoch 81/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1806\n",
      "Epoch 82/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1806\n",
      "Epoch 83/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1805\n",
      "Epoch 84/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1805\n",
      "Epoch 85/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1805\n",
      "Epoch 86/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1805\n",
      "Epoch 87/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1805\n",
      "Epoch 88/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1805\n",
      "Epoch 89/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1804\n",
      "Epoch 90/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1804\n",
      "Epoch 91/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1804\n",
      "Epoch 92/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1804\n",
      "Epoch 93/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1804\n",
      "Epoch 94/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1804\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 95/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1803\n",
      "Epoch 96/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1803\n",
      "Epoch 97/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1803\n",
      "Epoch 98/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1803\n",
      "Epoch 99/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1803\n",
      "Epoch 100/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1802\n",
      "Epoch 101/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1802\n",
      "Epoch 102/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1802\n",
      "Epoch 103/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1802\n",
      "Epoch 104/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1802\n",
      "Epoch 105/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1802\n",
      "Epoch 106/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1801\n",
      "Epoch 107/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1801\n",
      "Epoch 108/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1801\n",
      "Epoch 109/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1801\n",
      "Epoch 110/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1801\n",
      "Epoch 111/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.1800\n",
      "Epoch 112/5000\n",
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     ]
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     ]
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     ]
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      "Epoch 4927/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0374\n",
      "Epoch 4928/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0374\n",
      "Epoch 4929/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0374\n",
      "Epoch 4930/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0374\n",
      "Epoch 4931/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0374\n",
      "Epoch 4932/5000\n",
      "11999/11999 [==============================] - 0s 2us/step - loss: 0.0374\n",
      "Epoch 4933/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0374\n",
      "Epoch 4934/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0374\n",
      "Epoch 4935/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0374\n",
      "Epoch 4936/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0374\n",
      "Epoch 4937/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0374\n",
      "Epoch 4938/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0374\n",
      "Epoch 4939/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0374\n",
      "Epoch 4940/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0374\n",
      "Epoch 4941/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0374\n",
      "Epoch 4942/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0374\n",
      "Epoch 4943/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0374\n",
      "Epoch 4944/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0374\n",
      "Epoch 4945/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0374\n",
      "Epoch 4946/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0374\n",
      "Epoch 4947/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0374\n",
      "Epoch 4948/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0373\n",
      "Epoch 4949/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0373\n",
      "Epoch 4950/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0373\n",
      "Epoch 4951/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0373\n",
      "Epoch 4952/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0373\n",
      "Epoch 4953/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0373\n",
      "Epoch 4954/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0373\n",
      "Epoch 4955/5000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0373\n",
      "Epoch 4956/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0373\n",
      "Epoch 4957/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0373\n",
      "Epoch 4958/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0373\n",
      "Epoch 4959/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0373\n",
      "Epoch 4960/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0373\n",
      "Epoch 4961/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0373\n",
      "Epoch 4962/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0373\n",
      "Epoch 4963/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0373\n",
      "Epoch 4964/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0373\n",
      "Epoch 4965/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0373\n",
      "Epoch 4966/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0373\n",
      "Epoch 4967/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0373\n",
      "Epoch 4968/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0373\n",
      "Epoch 4969/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0373\n",
      "Epoch 4970/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0373\n",
      "Epoch 4971/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0373\n",
      "Epoch 4972/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0373\n",
      "Epoch 4973/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0373\n",
      "Epoch 4974/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0373\n",
      "Epoch 4975/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0373\n",
      "Epoch 4976/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0373\n",
      "Epoch 4977/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0373\n",
      "Epoch 4978/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0372\n",
      "Epoch 4979/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0372\n",
      "Epoch 4980/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0372\n",
      "Epoch 4981/5000\n",
      "11999/11999 [==============================] - 0s 2us/step - loss: 0.0372\n",
      "Epoch 4982/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0372\n",
      "Epoch 4983/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0372\n",
      "Epoch 4984/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0372\n",
      "Epoch 4985/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0372\n",
      "Epoch 4986/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0372\n",
      "Epoch 4987/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0372\n",
      "Epoch 4988/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0372\n",
      "Epoch 4989/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0372\n",
      "Epoch 4990/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0372\n",
      "Epoch 4991/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0372\n",
      "Epoch 4992/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0372\n",
      "Epoch 4993/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0372\n",
      "Epoch 4994/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0372\n",
      "Epoch 4995/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0372\n",
      "Epoch 4996/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0372\n",
      "Epoch 4997/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0372\n",
      "Epoch 4998/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0372\n",
      "Epoch 4999/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0372\n",
      "Epoch 5000/5000\n",
      "11999/11999 [==============================] - 0s 1us/step - loss: 0.0372\n",
      "Sequential : trainning classification report\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0     0.9713    0.9749    0.9731      9134\n",
      "          1     0.9191    0.9082    0.9136      2865\n",
      "\n",
      "avg / total     0.9588    0.9590    0.9589     11999\n",
      "\n",
      "Sequential : testing classification report\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0     0.9651    0.9778    0.9714      2294\n",
      "          1     0.9246    0.8853    0.9045       706\n",
      "\n",
      "avg / total     0.9556    0.9560    0.9557      3000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#构建神经网络\n",
    "mdl = Sequential()\n",
    "#构建输入层\n",
    "mdl.add(Dense(50,input_dim=HR.shape[1]))\n",
    "mdl.add(Activation(\"sigmoid\"))\n",
    "#构建隐藏层\n",
    "mdl.add(Dense(2))\n",
    "mdl.add(Activation(\"softmax\"))\n",
    "#lr == 学习率\n",
    "sgd = SGD(lr=0.03)\n",
    "\n",
    "#编译过程\n",
    "mdl.compile(loss=\"mean_squared_error\",optimizer=\"adam\")\n",
    "# label标签需要是one-hot形式的\n",
    "mdl.fit(HR_train,np.array([[0,1] if i==1 else [1,0] for i in label_train]),epochs=5000,batch_size=11999)\n",
    "from sklearn.metrics import classification_report as cr\n",
    "print(\"{} : trainning classification report\".format(mdl.__class__.__name__))\n",
    "print(cr(label_train,mdl.predict_classes(HR_train),digits=4))\n",
    "print(\"{} : testing classification report\".format(mdl.__class__.__name__))\n",
    "print(cr(label_test,mdl.predict_classes(HR_test),digits=4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GridSearchCV 参数寻优"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最佳学习器: LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=1000, multi_class='ovr', n_jobs=1,\n",
      "          penalty='l1', random_state=None, solver='liblinear', tol=0.0001,\n",
      "          verbose=0, warm_start=False)\n",
      "最佳学习器得分: 0.7902325193766148\n",
      "best parameters set found:最佳参数组合 {'C': 0.01, 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "测试集得分： 0.771\n",
      "                                           parameters  mean_validation_score  \\\n",
      "0   {'C': 1e-05, 'penalty': 'l2', 'solver': 'libli...               0.761230   \n",
      "1   {'C': 1e-05, 'penalty': 'l1', 'solver': 'libli...               0.761230   \n",
      "2   {'C': 0.0001, 'penalty': 'l2', 'solver': 'libl...               0.761230   \n",
      "3   {'C': 0.0001, 'penalty': 'l1', 'solver': 'libl...               0.761230   \n",
      "4   {'C': 0.001, 'penalty': 'l2', 'solver': 'libli...               0.761230   \n",
      "5   {'C': 0.001, 'penalty': 'l1', 'solver': 'libli...               0.761230   \n",
      "6   {'C': 0.005, 'penalty': 'l2', 'solver': 'libli...               0.758813   \n",
      "7   {'C': 0.005, 'penalty': 'l1', 'solver': 'libli...               0.760730   \n",
      "8   {'C': 0.01, 'penalty': 'l2', 'solver': 'liblin...               0.749062   \n",
      "9   {'C': 0.01, 'penalty': 'l1', 'solver': 'liblin...               0.790233   \n",
      "10  {'C': 0.015, 'penalty': 'l2', 'solver': 'libli...               0.753563   \n",
      "11  {'C': 0.015, 'penalty': 'l1', 'solver': 'libli...               0.783315   \n",
      "12  {'C': 1e-05, 'max_iter': 10000, 'penalty': 'l2...               0.761230   \n",
      "13  {'C': 1e-05, 'max_iter': 10000, 'penalty': 'l2...               0.761230   \n",
      "14  {'C': 1e-05, 'max_iter': 10000, 'penalty': 'l2...               0.761230   \n",
      "15  {'C': 1e-05, 'max_iter': 100000, 'penalty': 'l...               0.761230   \n",
      "16  {'C': 1e-05, 'max_iter': 100000, 'penalty': 'l...               0.761230   \n",
      "17  {'C': 1e-05, 'max_iter': 100000, 'penalty': 'l...               0.761230   \n",
      "18  {'C': 0.0001, 'max_iter': 10000, 'penalty': 'l...               0.761230   \n",
      "19  {'C': 0.0001, 'max_iter': 10000, 'penalty': 'l...               0.761230   \n",
      "20  {'C': 0.0001, 'max_iter': 10000, 'penalty': 'l...               0.761230   \n",
      "21  {'C': 0.0001, 'max_iter': 100000, 'penalty': '...               0.761230   \n",
      "22  {'C': 0.0001, 'max_iter': 100000, 'penalty': '...               0.761230   \n",
      "23  {'C': 0.0001, 'max_iter': 100000, 'penalty': '...               0.761230   \n",
      "24  {'C': 0.001, 'max_iter': 10000, 'penalty': 'l2...               0.759480   \n",
      "25  {'C': 0.001, 'max_iter': 10000, 'penalty': 'l2...               0.759480   \n",
      "26  {'C': 0.001, 'max_iter': 10000, 'penalty': 'l2...               0.760647   \n",
      "27  {'C': 0.001, 'max_iter': 100000, 'penalty': 'l...               0.759480   \n",
      "28  {'C': 0.001, 'max_iter': 100000, 'penalty': 'l...               0.759480   \n",
      "29  {'C': 0.001, 'max_iter': 100000, 'penalty': 'l...               0.760647   \n",
      "30  {'C': 0.01, 'max_iter': 10000, 'penalty': 'l2'...               0.753229   \n",
      "31  {'C': 0.01, 'max_iter': 10000, 'penalty': 'l2'...               0.753229   \n",
      "32  {'C': 0.01, 'max_iter': 10000, 'penalty': 'l2'...               0.750063   \n",
      "33  {'C': 0.01, 'max_iter': 100000, 'penalty': 'l2...               0.753229   \n",
      "34  {'C': 0.01, 'max_iter': 100000, 'penalty': 'l2...               0.753229   \n",
      "35  {'C': 0.01, 'max_iter': 100000, 'penalty': 'l2...               0.750063   \n",
      "36  {'C': 0.05, 'max_iter': 10000, 'penalty': 'l2'...               0.778565   \n",
      "37  {'C': 0.05, 'max_iter': 10000, 'penalty': 'l2'...               0.778232   \n",
      "38  {'C': 0.05, 'max_iter': 10000, 'penalty': 'l2'...               0.778982   \n",
      "39  {'C': 0.05, 'max_iter': 100000, 'penalty': 'l2...               0.778565   \n",
      "40  {'C': 0.05, 'max_iter': 100000, 'penalty': 'l2...               0.778232   \n",
      "41  {'C': 0.05, 'max_iter': 100000, 'penalty': 'l2...               0.778982   \n",
      "\n",
      "                      cv_validation_scores  \n",
      "0   [0.76125, 0.76125, 0.7611902975743936]  \n",
      "1   [0.76125, 0.76125, 0.7611902975743936]  \n",
      "2   [0.76125, 0.76125, 0.7611902975743936]  \n",
      "3   [0.76125, 0.76125, 0.7611902975743936]  \n",
      "4   [0.76125, 0.76125, 0.7611902975743936]  \n",
      "5   [0.76125, 0.76125, 0.7611902975743936]  \n",
      "6    [0.75925, 0.7595, 0.7576894223555889]  \n",
      "7        [0.761, 0.761, 0.760190047511878]  \n",
      "8     [0.74875, 0.751, 0.7474368592148037]  \n",
      "9   [0.78775, 0.79425, 0.7886971742935734]  \n",
      "10   [0.75575, 0.74775, 0.757189297324331]  \n",
      "11  [0.78325, 0.78575, 0.7809452363090773]  \n",
      "12  [0.76125, 0.76125, 0.7611902975743936]  \n",
      "13  [0.76125, 0.76125, 0.7611902975743936]  \n",
      "14  [0.76125, 0.76125, 0.7611902975743936]  \n",
      "15  [0.76125, 0.76125, 0.7611902975743936]  \n",
      "16  [0.76125, 0.76125, 0.7611902975743936]  \n",
      "17  [0.76125, 0.76125, 0.7611902975743936]  \n",
      "18  [0.76125, 0.76125, 0.7611902975743936]  \n",
      "19  [0.76125, 0.76125, 0.7611902975743936]  \n",
      "20  [0.76125, 0.76125, 0.7611902975743936]  \n",
      "21  [0.76125, 0.76125, 0.7611902975743936]  \n",
      "22  [0.76125, 0.76125, 0.7611902975743936]  \n",
      "23  [0.76125, 0.76125, 0.7611902975743936]  \n",
      "24     [0.76025, 0.761, 0.757189297324331]  \n",
      "25     [0.76025, 0.761, 0.757189297324331]  \n",
      "26    [0.76125, 0.761, 0.7596899224806202]  \n",
      "27     [0.76025, 0.761, 0.757189297324331]  \n",
      "28     [0.76025, 0.761, 0.757189297324331]  \n",
      "29    [0.76125, 0.761, 0.7596899224806202]  \n",
      "30    [0.7515, 0.75375, 0.754438609652413]  \n",
      "31    [0.7515, 0.75375, 0.754438609652413]  \n",
      "32    [0.7505, 0.75325, 0.746436609152288]  \n",
      "33    [0.7515, 0.75375, 0.754438609652413]  \n",
      "34    [0.7515, 0.75375, 0.754438609652413]  \n",
      "35    [0.7505, 0.75325, 0.746436609152288]  \n",
      "36  [0.77825, 0.77875, 0.7786946736684172]  \n",
      "37    [0.77825, 0.779, 0.7774443610902726]  \n",
      "38    [0.77875, 0.781, 0.7771942985746436]  \n",
      "39  [0.77825, 0.77875, 0.7786946736684172]  \n",
      "40    [0.77825, 0.779, 0.7774443610902726]  \n",
      "41    [0.77875, 0.781, 0.7771942985746436]  \n",
      "    mean_fit_time  std_fit_time  mean_score_time  std_score_time param_C  \\\n",
      "0        0.007989  1.523186e-05         0.000987    1.511949e-05   1e-05   \n",
      "1        0.005651  4.714266e-04         0.000665    4.704150e-04   1e-05   \n",
      "2        0.008976  1.946680e-07         0.000998    3.893359e-07  0.0001   \n",
      "3        0.005319  4.695721e-04         0.000997    7.018853e-07  0.0001   \n",
      "4        0.011636  9.402681e-04         0.000997    3.371748e-07   0.001   \n",
      "5        0.006648  4.697408e-04         0.001330    4.700217e-04   0.001   \n",
      "6        0.014295  4.697972e-04         0.000665    4.704150e-04   0.005   \n",
      "7        0.016290  2.487992e-03         0.000665    4.701903e-04   0.005   \n",
      "8        0.014960  1.946680e-07         0.000332    4.701340e-04    0.01   \n",
      "9        0.023936  2.154351e-03         0.000333    4.705836e-04    0.01   \n",
      "10       0.014960  3.371748e-07         0.000000    0.000000e+00   0.015   \n",
      "11       0.023936  1.628495e-03         0.000998    2.973602e-07   0.015   \n",
      "12       0.067154  8.969863e-03         0.000665    4.702465e-04   1e-05   \n",
      "13       0.025265  9.394252e-04         0.000997    3.893359e-07   1e-05   \n",
      "14       2.896929  7.817691e-03         0.000991    9.216111e-06   1e-05   \n",
      "15       0.067487  1.084406e-02         0.000997    4.495664e-07   1e-05   \n",
      "16       0.025598  1.244049e-03         0.000997    2.973602e-07   1e-05   \n",
      "17       2.931833  2.295534e-02         0.000993    6.184602e-06   1e-05   \n",
      "18       0.064162  1.037475e-02         0.000998    4.052337e-07  0.0001   \n",
      "19       0.029587  2.860277e-03         0.000998    1.946680e-07  0.0001   \n",
      "20       2.550183  4.238321e-02         0.000674    4.766520e-04  0.0001   \n",
      "21       0.066156  9.714685e-03         0.000333    4.703588e-04  0.0001   \n",
      "22       0.028923  2.935608e-03         0.000665    4.701340e-04  0.0001   \n",
      "23       2.562815  6.126552e-02         0.000665    4.703027e-04  0.0001   \n",
      "24       0.075797  1.020321e-02         0.000665    4.701340e-04   0.001   \n",
      "25       0.046209  4.098546e-03         0.000998    3.371748e-07   0.001   \n",
      "26       2.055192  6.278181e-02         0.000978    1.451593e-05   0.001   \n",
      "27       0.075465  1.018230e-02         0.000998    3.371748e-07   0.001   \n",
      "28       0.045878  2.935851e-03         0.000997    0.000000e+00   0.001   \n",
      "29       2.086107  8.060916e-02         0.001312    4.638211e-04   0.001   \n",
      "30       0.073138  5.543271e-03         0.000665    4.701903e-04    0.01   \n",
      "31       0.068151  7.824904e-03         0.000998    3.893359e-07    0.01   \n",
      "32       1.525920  6.397447e-02         0.000998    4.899036e-07    0.01   \n",
      "33       0.072805  4.952850e-03         0.000665    4.701903e-04    0.01   \n",
      "34       0.066489  6.925408e-03         0.000998    4.899036e-07    0.01   \n",
      "35       1.511313  4.834764e-02         0.000978    1.399309e-05    0.01   \n",
      "36       0.086103  3.848357e-03         0.000665    4.699655e-04    0.05   \n",
      "37       0.043218  3.671951e-03         0.000997    1.946680e-07    0.05   \n",
      "38       2.534899  7.369606e-02         0.001326    4.730899e-04    0.05   \n",
      "39       0.087436  4.773921e-03         0.000662    4.681256e-04    0.05   \n",
      "40       0.045545  1.695217e-03         0.000997    2.973602e-07    0.05   \n",
      "41       2.527576  8.003682e-02         0.000674    4.769573e-04    0.05   \n",
      "\n",
      "   param_penalty param_solver param_max_iter  \\\n",
      "0             l2    liblinear            NaN   \n",
      "1             l1    liblinear            NaN   \n",
      "2             l2    liblinear            NaN   \n",
      "3             l1    liblinear            NaN   \n",
      "4             l2    liblinear            NaN   \n",
      "5             l1    liblinear            NaN   \n",
      "6             l2    liblinear            NaN   \n",
      "7             l1    liblinear            NaN   \n",
      "8             l2    liblinear            NaN   \n",
      "9             l1    liblinear            NaN   \n",
      "10            l2    liblinear            NaN   \n",
      "11            l1    liblinear            NaN   \n",
      "12            l2    newton-cg          10000   \n",
      "13            l2        lbfgs          10000   \n",
      "14            l2          sag          10000   \n",
      "15            l2    newton-cg         100000   \n",
      "16            l2        lbfgs         100000   \n",
      "17            l2          sag         100000   \n",
      "18            l2    newton-cg          10000   \n",
      "19            l2        lbfgs          10000   \n",
      "20            l2          sag          10000   \n",
      "21            l2    newton-cg         100000   \n",
      "22            l2        lbfgs         100000   \n",
      "23            l2          sag         100000   \n",
      "24            l2    newton-cg          10000   \n",
      "25            l2        lbfgs          10000   \n",
      "26            l2          sag          10000   \n",
      "27            l2    newton-cg         100000   \n",
      "28            l2        lbfgs         100000   \n",
      "29            l2          sag         100000   \n",
      "30            l2    newton-cg          10000   \n",
      "31            l2        lbfgs          10000   \n",
      "32            l2          sag          10000   \n",
      "33            l2    newton-cg         100000   \n",
      "34            l2        lbfgs         100000   \n",
      "35            l2          sag         100000   \n",
      "36            l2    newton-cg          10000   \n",
      "37            l2        lbfgs          10000   \n",
      "38            l2          sag          10000   \n",
      "39            l2    newton-cg         100000   \n",
      "40            l2        lbfgs         100000   \n",
      "41            l2          sag         100000   \n",
      "\n",
      "                                               params  split0_test_score  \\\n",
      "0   {'C': 1e-05, 'penalty': 'l2', 'solver': 'libli...            0.76125   \n",
      "1   {'C': 1e-05, 'penalty': 'l1', 'solver': 'libli...            0.76125   \n",
      "2   {'C': 0.0001, 'penalty': 'l2', 'solver': 'libl...            0.76125   \n",
      "3   {'C': 0.0001, 'penalty': 'l1', 'solver': 'libl...            0.76125   \n",
      "4   {'C': 0.001, 'penalty': 'l2', 'solver': 'libli...            0.76125   \n",
      "5   {'C': 0.001, 'penalty': 'l1', 'solver': 'libli...            0.76125   \n",
      "6   {'C': 0.005, 'penalty': 'l2', 'solver': 'libli...            0.75925   \n",
      "7   {'C': 0.005, 'penalty': 'l1', 'solver': 'libli...            0.76100   \n",
      "8   {'C': 0.01, 'penalty': 'l2', 'solver': 'liblin...            0.74875   \n",
      "9   {'C': 0.01, 'penalty': 'l1', 'solver': 'liblin...            0.78775   \n",
      "10  {'C': 0.015, 'penalty': 'l2', 'solver': 'libli...            0.75575   \n",
      "11  {'C': 0.015, 'penalty': 'l1', 'solver': 'libli...            0.78325   \n",
      "12  {'C': 1e-05, 'max_iter': 10000, 'penalty': 'l2...            0.76125   \n",
      "13  {'C': 1e-05, 'max_iter': 10000, 'penalty': 'l2...            0.76125   \n",
      "14  {'C': 1e-05, 'max_iter': 10000, 'penalty': 'l2...            0.76125   \n",
      "15  {'C': 1e-05, 'max_iter': 100000, 'penalty': 'l...            0.76125   \n",
      "16  {'C': 1e-05, 'max_iter': 100000, 'penalty': 'l...            0.76125   \n",
      "17  {'C': 1e-05, 'max_iter': 100000, 'penalty': 'l...            0.76125   \n",
      "18  {'C': 0.0001, 'max_iter': 10000, 'penalty': 'l...            0.76125   \n",
      "19  {'C': 0.0001, 'max_iter': 10000, 'penalty': 'l...            0.76125   \n",
      "20  {'C': 0.0001, 'max_iter': 10000, 'penalty': 'l...            0.76125   \n",
      "21  {'C': 0.0001, 'max_iter': 100000, 'penalty': '...            0.76125   \n",
      "22  {'C': 0.0001, 'max_iter': 100000, 'penalty': '...            0.76125   \n",
      "23  {'C': 0.0001, 'max_iter': 100000, 'penalty': '...            0.76125   \n",
      "24  {'C': 0.001, 'max_iter': 10000, 'penalty': 'l2...            0.76025   \n",
      "25  {'C': 0.001, 'max_iter': 10000, 'penalty': 'l2...            0.76025   \n",
      "26  {'C': 0.001, 'max_iter': 10000, 'penalty': 'l2...            0.76125   \n",
      "27  {'C': 0.001, 'max_iter': 100000, 'penalty': 'l...            0.76025   \n",
      "28  {'C': 0.001, 'max_iter': 100000, 'penalty': 'l...            0.76025   \n",
      "29  {'C': 0.001, 'max_iter': 100000, 'penalty': 'l...            0.76125   \n",
      "30  {'C': 0.01, 'max_iter': 10000, 'penalty': 'l2'...            0.75150   \n",
      "31  {'C': 0.01, 'max_iter': 10000, 'penalty': 'l2'...            0.75150   \n",
      "32  {'C': 0.01, 'max_iter': 10000, 'penalty': 'l2'...            0.75050   \n",
      "33  {'C': 0.01, 'max_iter': 100000, 'penalty': 'l2...            0.75150   \n",
      "34  {'C': 0.01, 'max_iter': 100000, 'penalty': 'l2...            0.75150   \n",
      "35  {'C': 0.01, 'max_iter': 100000, 'penalty': 'l2...            0.75050   \n",
      "36  {'C': 0.05, 'max_iter': 10000, 'penalty': 'l2'...            0.77825   \n",
      "37  {'C': 0.05, 'max_iter': 10000, 'penalty': 'l2'...            0.77825   \n",
      "38  {'C': 0.05, 'max_iter': 10000, 'penalty': 'l2'...            0.77875   \n",
      "39  {'C': 0.05, 'max_iter': 100000, 'penalty': 'l2...            0.77825   \n",
      "40  {'C': 0.05, 'max_iter': 100000, 'penalty': 'l2...            0.77825   \n",
      "41  {'C': 0.05, 'max_iter': 100000, 'penalty': 'l2...            0.77875   \n",
      "\n",
      "    split1_test_score  split2_test_score  mean_test_score  std_test_score  \\\n",
      "0             0.76125           0.761190         0.761230        0.000028   \n",
      "1             0.76125           0.761190         0.761230        0.000028   \n",
      "2             0.76125           0.761190         0.761230        0.000028   \n",
      "3             0.76125           0.761190         0.761230        0.000028   \n",
      "4             0.76125           0.761190         0.761230        0.000028   \n",
      "5             0.76125           0.761190         0.761230        0.000028   \n",
      "6             0.75950           0.757689         0.758813        0.000801   \n",
      "7             0.76100           0.760190         0.760730        0.000382   \n",
      "8             0.75100           0.747437         0.749062        0.001471   \n",
      "9             0.79425           0.788697         0.790233        0.002867   \n",
      "10            0.74775           0.757189         0.753563        0.004152   \n",
      "11            0.78575           0.780945         0.783315        0.001962   \n",
      "12            0.76125           0.761190         0.761230        0.000028   \n",
      "13            0.76125           0.761190         0.761230        0.000028   \n",
      "14            0.76125           0.761190         0.761230        0.000028   \n",
      "15            0.76125           0.761190         0.761230        0.000028   \n",
      "16            0.76125           0.761190         0.761230        0.000028   \n",
      "17            0.76125           0.761190         0.761230        0.000028   \n",
      "18            0.76125           0.761190         0.761230        0.000028   \n",
      "19            0.76125           0.761190         0.761230        0.000028   \n",
      "20            0.76125           0.761190         0.761230        0.000028   \n",
      "21            0.76125           0.761190         0.761230        0.000028   \n",
      "22            0.76125           0.761190         0.761230        0.000028   \n",
      "23            0.76125           0.761190         0.761230        0.000028   \n",
      "24            0.76100           0.757189         0.759480        0.001648   \n",
      "25            0.76100           0.757189         0.759480        0.001648   \n",
      "26            0.76100           0.759690         0.760647        0.000684   \n",
      "27            0.76100           0.757189         0.759480        0.001648   \n",
      "28            0.76100           0.757189         0.759480        0.001648   \n",
      "29            0.76100           0.759690         0.760647        0.000684   \n",
      "30            0.75375           0.754439         0.753229        0.001255   \n",
      "31            0.75375           0.754439         0.753229        0.001255   \n",
      "32            0.75325           0.746437         0.750063        0.002799   \n",
      "33            0.75375           0.754439         0.753229        0.001255   \n",
      "34            0.75375           0.754439         0.753229        0.001255   \n",
      "35            0.75325           0.746437         0.750063        0.002799   \n",
      "36            0.77875           0.778695         0.778565        0.000224   \n",
      "37            0.77900           0.777444         0.778232        0.000635   \n",
      "38            0.78100           0.777194         0.778982        0.001562   \n",
      "39            0.77875           0.778695         0.778565        0.000224   \n",
      "40            0.77900           0.777444         0.778232        0.000635   \n",
      "41            0.78100           0.777194         0.778982        0.001562   \n",
      "\n",
      "    rank_test_score  split0_train_score  split1_train_score  \\\n",
      "0                 9            0.761220            0.761220   \n",
      "1                 9            0.761220            0.761220   \n",
      "2                 9            0.761220            0.761220   \n",
      "3                 9            0.761220            0.761220   \n",
      "4                 9            0.761220            0.761220   \n",
      "5                 9            0.761220            0.761220   \n",
      "6                34            0.759220            0.757970   \n",
      "7                27            0.761095            0.760470   \n",
      "8                42            0.750594            0.746343   \n",
      "9                 1            0.792724            0.788599   \n",
      "10               35            0.756595            0.747718   \n",
      "11                2            0.784848            0.782223   \n",
      "12                9            0.761220            0.761220   \n",
      "13                9            0.761220            0.761220   \n",
      "14                9            0.761220            0.761220   \n",
      "15                9            0.761220            0.761220   \n",
      "16                9            0.761220            0.761220   \n",
      "17                9            0.761220            0.761220   \n",
      "18                9            0.761220            0.761220   \n",
      "19                9            0.761220            0.761220   \n",
      "20                9            0.761220            0.761220   \n",
      "21                9            0.761220            0.761220   \n",
      "22                9            0.761220            0.761220   \n",
      "23                9            0.761220            0.761220   \n",
      "24               30            0.760220            0.760595   \n",
      "25               30            0.760220            0.760595   \n",
      "26               28            0.760845            0.760970   \n",
      "27               30            0.760220            0.760595   \n",
      "28               30            0.760220            0.760595   \n",
      "29               28            0.760845            0.760970   \n",
      "30               36            0.752969            0.748219   \n",
      "31               36            0.752969            0.748219   \n",
      "32               40            0.752719            0.748094   \n",
      "33               36            0.752969            0.748219   \n",
      "34               36            0.752969            0.748219   \n",
      "35               40            0.752719            0.748094   \n",
      "36                5            0.779847            0.777972   \n",
      "37                7            0.780848            0.777847   \n",
      "38                3            0.781598            0.778472   \n",
      "39                5            0.779847            0.777972   \n",
      "40                7            0.780848            0.777847   \n",
      "41                3            0.781598            0.778472   \n",
      "\n",
      "    split2_train_score  mean_train_score  std_train_score  \n",
      "0             0.761250          0.761230         0.000014  \n",
      "1             0.761250          0.761230         0.000014  \n",
      "2             0.761250          0.761230         0.000014  \n",
      "3             0.761250          0.761230         0.000014  \n",
      "4             0.761250          0.761230         0.000014  \n",
      "5             0.761250          0.761230         0.000014  \n",
      "6             0.757750          0.758313         0.000647  \n",
      "7             0.760875          0.760813         0.000259  \n",
      "8             0.749000          0.748646         0.001753  \n",
      "9             0.787875          0.789733         0.002136  \n",
      "10            0.756125          0.753479         0.004078  \n",
      "11            0.781375          0.782815         0.001478  \n",
      "12            0.761250          0.761230         0.000014  \n",
      "13            0.761250          0.761230         0.000014  \n",
      "14            0.761250          0.761230         0.000014  \n",
      "15            0.761250          0.761230         0.000014  \n",
      "16            0.761250          0.761230         0.000014  \n",
      "17            0.761250          0.761230         0.000014  \n",
      "18            0.761250          0.761230         0.000014  \n",
      "19            0.761250          0.761230         0.000014  \n",
      "20            0.761250          0.761230         0.000014  \n",
      "21            0.761250          0.761230         0.000014  \n",
      "22            0.761250          0.761230         0.000014  \n",
      "23            0.761250          0.761230         0.000014  \n",
      "24            0.758125          0.759647         0.001087  \n",
      "25            0.758125          0.759647         0.001087  \n",
      "26            0.759750          0.760522         0.000548  \n",
      "27            0.758125          0.759647         0.001087  \n",
      "28            0.758125          0.759647         0.001087  \n",
      "29            0.759750          0.760522         0.000548  \n",
      "30            0.754500          0.751896         0.002674  \n",
      "31            0.754500          0.751896         0.002674  \n",
      "32            0.750750          0.750521         0.001895  \n",
      "33            0.754500          0.751896         0.002674  \n",
      "34            0.754500          0.751896         0.002674  \n",
      "35            0.750750          0.750521         0.001895  \n",
      "36            0.778500          0.778773         0.000790  \n",
      "37            0.778625          0.779107         0.001271  \n",
      "38            0.778875          0.779648         0.001388  \n",
      "39            0.778500          0.778773         0.000790  \n",
      "40            0.778625          0.779107         0.001271  \n",
      "41            0.778875          0.779648         0.001388  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "M:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# 对LogisticRegression参数寻优\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "params = [{\n",
    "    \"penalty\":[\"l2\",\"l1\"], \n",
    "    #\"C\":[0.00001,0.0001,0.001,0.01,0.05,0.1,0.5,1,5,10,50,100],\n",
    "    \"C\":[0.00001,0.0001,0.001,0.005,0.01,0.015],\n",
    "\n",
    "    \"solver\":[\"liblinear\"]\n",
    "}\n",
    ",{\n",
    "    \"penalty\":[\"l2\"], \n",
    "    \"C\":[0.00001,0.0001,0.001,0.01,0.05],\n",
    "    \"solver\":[\"newton-cg\",\"lbfgs\",\"sag\"],\n",
    "    \"max_iter\":[10000,100000]\n",
    "}\n",
    "]\n",
    "gsc = GridSearchCV(estimator=LogisticRegression(max_iter=1000) ,param_grid=params)\n",
    "gsc.fit(HR_train,label_train)\n",
    "print(\"最佳学习器:\" ,gsc.best_estimator_)\n",
    "print(\"最佳学习器得分:\",gsc.best_score_)\n",
    "print(\"best parameters set found:最佳参数组合\" ,gsc.best_params_)\n",
    "# print(\"取参数组合：\" ,gsc.get_params() )\n",
    "print(\"测试集得分：\",gsc.score(HR_test,label_test))\n",
    "print(pd.DataFrame(gsc.grid_scores_))\n",
    "print(pd.DataFrame(gsc.cv_results_))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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