建模算法/降维/terrorism.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "33b02c16-447c-497d-b0fc-39cab084af0a",
   "metadata": {},
   "source": [
    "# 1、导入包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d9a05e4e-4f8d-4814-b5ab-e2120e5ef6d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58b2d584-c70d-4621-8f4a-30e4ee5f3790",
   "metadata": {},
   "source": [
    "# 2、读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "05d009cc-a927-49e5-afc3-73459653c3da",
   "metadata": {},
   "outputs": [],
   "source": [
    "terrorism_data = pd.read_excel(\"附件1.xlsx\",sheet_name='Data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bf99b07d-11cb-4c48-90a2-782b6eb2e534",
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   "outputs": [
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       "      <th>imonth</th>\n",
       "      <th>iday</th>\n",
       "      <th>approxdate</th>\n",
       "      <th>extended</th>\n",
       "      <th>resolution</th>\n",
       "      <th>country</th>\n",
       "      <th>country_txt</th>\n",
       "      <th>region</th>\n",
       "      <th>...</th>\n",
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       "      <th>related</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>199801010001</td>\n",
       "      <td>1998</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>34</td>\n",
       "      <td>Burundi</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>“Burundi Rebels, Ex-Rwandan Army Soldiers Blam...</td>\n",
       "      <td>“Burundi--Attack Reported on Bujumbura Airport...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CETIS</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>199801010002</td>\n",
       "      <td>1998</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>167</td>\n",
       "      <td>Russia</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>“Bomb injures 3 in Moscow subway system,” The ...</td>\n",
       "      <td>“Bomb injures 3 in Moscow subway,” Charleston ...</td>\n",
       "      <td>“Bomb Injures 3 Workers in Moscow Metro,” Los ...</td>\n",
       "      <td>CETIS</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>0</td>\n",
       "      <td>-9</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>199801010003</td>\n",
       "      <td>1998</td>\n",
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       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>603</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>“Protestant gunmen kill Catholic in New Year's...</td>\n",
       "      <td>“Ulster Peace Shattered by Shooting: Catholic ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CETIS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>199801020001</td>\n",
       "      <td>1998</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>95</td>\n",
       "      <td>Iraq</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>“Iraq Condemns Attack on UNSCOM Baghdad Office...</td>\n",
       "      <td>Farouk Choukri , “Iraq, UN Officials Continue ...</td>\n",
       "      <td>“Iraqi Interior Minister on UNSCOM Attack, Kuw...</td>\n",
       "      <td>CETIS</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>199801020002</td>\n",
       "      <td>1998</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>155</td>\n",
       "      <td>West Bank and Gaza Strip</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>“Woman Shot,” The Philadelphia Inquirer, Janua...</td>\n",
       "      <td>“Israeli Woman Critically Hurt by Gunfire in W...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CETIS</td>\n",
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       "      <td>-9</td>\n",
       "      <td>0</td>\n",
       "      <td>-9</td>\n",
       "      <td>NaN</td>\n",
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       "<p>5 rows × 135 columns</p>\n",
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       "        eventid  iyear  imonth  iday approxdate  extended resolution  country  \\\n",
       "0  199801010001   1998       1     1        NaN         0        NaT       34   \n",
       "1  199801010002   1998       1     1        NaN         0        NaT      167   \n",
       "2  199801010003   1998       1     1        NaN         0        NaT      603   \n",
       "3  199801020001   1998       1     2        NaN         0        NaT       95   \n",
       "4  199801020002   1998       1     2        NaN         0        NaT      155   \n",
       "\n",
       "                country_txt  region  ... addnotes  \\\n",
       "0                   Burundi      11  ...      NaN   \n",
       "1                    Russia       9  ...      NaN   \n",
       "2            United Kingdom       8  ...      NaN   \n",
       "3                      Iraq      10  ...      NaN   \n",
       "4  West Bank and Gaza Strip      10  ...      NaN   \n",
       "\n",
       "                                              scite1  \\\n",
       "0  “Burundi Rebels, Ex-Rwandan Army Soldiers Blam...   \n",
       "1  “Bomb injures 3 in Moscow subway system,” The ...   \n",
       "2  “Protestant gunmen kill Catholic in New Year's...   \n",
       "3  “Iraq Condemns Attack on UNSCOM Baghdad Office...   \n",
       "4  “Woman Shot,” The Philadelphia Inquirer, Janua...   \n",
       "\n",
       "                                              scite2  \\\n",
       "0  “Burundi--Attack Reported on Bujumbura Airport...   \n",
       "1  “Bomb injures 3 in Moscow subway,” Charleston ...   \n",
       "2  “Ulster Peace Shattered by Shooting: Catholic ...   \n",
       "3  Farouk Choukri , “Iraq, UN Officials Continue ...   \n",
       "4  “Israeli Woman Critically Hurt by Gunfire in W...   \n",
       "\n",
       "                                              scite3  dbsource  INT_LOG  \\\n",
       "0                                                NaN     CETIS        0   \n",
       "1  “Bomb Injures 3 Workers in Moscow Metro,” Los ...     CETIS       -9   \n",
       "2                                                NaN     CETIS        0   \n",
       "3  “Iraqi Interior Minister on UNSCOM Attack, Kuw...     CETIS       -9   \n",
       "4                                                NaN     CETIS       -9   \n",
       "\n",
       "   INT_IDEO INT_MISC INT_ANY  related  \n",
       "0         1        0       1      NaN  \n",
       "1        -9        0      -9      NaN  \n",
       "2         0        1       1      NaN  \n",
       "3        -9        1       1      NaN  \n",
       "4        -9        0      -9      NaN  \n",
       "\n",
       "[5 rows x 135 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "terrorism_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "229b64e3-de33-4347-b4ca-5f24c3cba4af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "eventid        int64\n",
       "iyear          int64\n",
       "imonth         int64\n",
       "iday           int64\n",
       "approxdate    object\n",
       "               ...  \n",
       "INT_LOG        int64\n",
       "INT_IDEO       int64\n",
       "INT_MISC       int64\n",
       "INT_ANY        int64\n",
       "related       object\n",
       "Length: 135, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "terrorism_data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81a9f532-b065-48eb-acb2-20400cb616c6",
   "metadata": {},
   "source": [
    "# 3、特征选取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8436f162-9059-48b2-ad27-dac1d2b81e80",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_columns = [\n",
    "                ## 二元变量: \n",
    "                'extended' , 'crit1', 'crit2', 'crit3', 'doubtterr',\n",
    "                'multiple', 'success', 'suicide', 'guncertain1',\n",
    "                'claimed', 'property', 'ishostkid',\n",
    "                 #财产损失程度\n",
    "                'propextent',      \n",
    "                ## 数值变量:\n",
    "                'nkill', 'nwound','nperps','nkillter','nwoundte','propvalue','nhostkid','nreleased','ransom',       \n",
    "                ## 分类变量: \n",
    "                'region', 'attacktype1', 'targtype1',\n",
    "                'natlty1', 'weaptype1', 'hostkidoutcome'\n",
    "              ]\n",
    "# data_columns = [\n",
    "#                 'weaptype1','region','success','attacktype1','targtype1','nkill','nwound','suicide','nkillter','nwoundte', 'propextent','nperps'\n",
    "#               ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cc24b49a-3d3c-42df-9b41-a7ce7a6ee9e1",
   "metadata": {},
   "outputs": [
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       "      <th>propvalue</th>\n",
       "      <th>nhostkid</th>\n",
       "      <th>nreleased</th>\n",
       "      <th>ransom</th>\n",
       "      <th>region</th>\n",
       "      <th>attacktype1</th>\n",
       "      <th>targtype1</th>\n",
       "      <th>natlty1</th>\n",
       "      <th>weaptype1</th>\n",
       "      <th>hostkidoutcome</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>9</td>\n",
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       "      <td>19</td>\n",
       "      <td>167.0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>233.0</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>999.0</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>97.0</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
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       "<p>5 rows × 28 columns</p>\n",
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      ],
      "text/plain": [
       "   extended  crit1  crit2  crit3  doubtterr  multiple  success  suicide  \\\n",
       "0         0      1      1      1        1.0       0.0        1        0   \n",
       "1         0      1      1      1        0.0       0.0        1        0   \n",
       "2         0      1      1      1        0.0       0.0        1        0   \n",
       "3         0      1      1      1        0.0       0.0        1        0   \n",
       "4         0      1      1      1        0.0       0.0        0        0   \n",
       "\n",
       "   guncertain1  claimed  ...  propvalue  nhostkid  nreleased  ransom  region  \\\n",
       "0          0.0        0  ...        NaN       NaN        NaN     NaN      11   \n",
       "1          0.0        0  ...        NaN       NaN        NaN     NaN       9   \n",
       "2          0.0        1  ...        NaN       NaN        NaN     NaN       8   \n",
       "3          0.0        0  ...        NaN       NaN        NaN     NaN      10   \n",
       "4          0.0        0  ...        NaN       NaN        NaN     NaN      10   \n",
       "\n",
       "   attacktype1  targtype1  natlty1  weaptype1  hostkidoutcome  \n",
       "0            2          4     34.0          5             NaN  \n",
       "1            3         19    167.0          6             NaN  \n",
       "2            2         14    233.0          5             NaN  \n",
       "3            3          7    999.0          6             NaN  \n",
       "4            2         14     97.0          5             NaN  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = terrorism_data.copy()\n",
    "df = data.loc[:,data_columns]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d4ff48f-e362-437a-8d7c-6a128994e3bc",
   "metadata": {},
   "source": [
    "# 4、数据预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75b97a4e-4767-4cb1-8927-d51256a3cdeb",
   "metadata": {},
   "source": [
    "## 4.1缺失数据统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6a652e92-7eb8-4985-9ddf-552cfd41a968",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda3\\envs\\pytorch\\lib\\site-packages\\missingno\\missingno.py:258: RuntimeWarning: overflow encountered in long_scalars\n",
      "  ax2.set_yticklabels([int(n * len(df)) for n in ax1.get_yticks()], fontsize=fontsize)\n",
      "D:\\anaconda3\\envs\\pytorch\\lib\\site-packages\\missingno\\missingno.py:265: RuntimeWarning: overflow encountered in long_scalars\n",
      "  ax2.set_yticklabels([int(n * len(df)) for n in ax1.get_yticks()], fontsize=fontsize)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import missingno as msno\n",
    "msno.matrix(df, labels=True)\n",
    "df.isnull().sum()/df.shape[0]\n",
    "msno.bar(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6e5055a-734f-4750-8799-63d64eeedc49",
   "metadata": {},
   "source": [
    "## 4.2 缺失值处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "77c405a8-3f90-4862-8e8d-00a6c38efa62",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"nhostkidisnull\"] = df[\"nhostkid\"].isnull()\n",
    "df[\"nhostkidisnull\"] = df[\"nhostkidisnull\"].astype('int')\n",
    "df[\"ransomisnull\"] = df[\"ransom\"].isnull()\n",
    "df[\"ransomisnull\"] = df[\"ransomisnull\"].astype(int)\n",
    "df[\"hostkidoutcomeisnull\"] = df[\"hostkidoutcome\"].isnull()\n",
    "df[\"hostkidoutcomeisnull\"] = df[\"hostkidoutcomeisnull\"].astype(int)\n",
    "df[\"nreleasedisnull\"] = df[\"nreleased\"].isnull()\n",
    "df[\"nreleasedisnull\"] = df[\"nreleasedisnull\"].astype(int)\n",
    "df = df.drop(['nhostkid','ransom','hostkidoutcome','nreleased'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "110682e7-bed9-4240-8cac-55441f9760a6",
   "metadata": {},
   "source": [
    "## 4.3 采用0替换未知值-9,-99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1ada9e7f-0951-42ea-90b4-d1b0ae783cfc",
   "metadata": {},
   "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>extended</th>\n",
       "      <th>crit1</th>\n",
       "      <th>crit2</th>\n",
       "      <th>crit3</th>\n",
       "      <th>doubtterr</th>\n",
       "      <th>multiple</th>\n",
       "      <th>success</th>\n",
       "      <th>suicide</th>\n",
       "      <th>guncertain1</th>\n",
       "      <th>claimed</th>\n",
       "      <th>...</th>\n",
       "      <th>propvalue</th>\n",
       "      <th>region</th>\n",
       "      <th>attacktype1</th>\n",
       "      <th>targtype1</th>\n",
       "      <th>natlty1</th>\n",
       "      <th>weaptype1</th>\n",
       "      <th>nhostkidisnull</th>\n",
       "      <th>ransomisnull</th>\n",
       "      <th>hostkidoutcomeisnull</th>\n",
       "      <th>nreleasedisnull</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>37661.190113</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>34.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>37661.190113</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>167.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>37661.190113</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>233.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>37661.190113</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>999.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>37661.190113</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>97.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>37661.190113</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>118.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>37661.190113</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>118.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>37661.190113</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>37661.190113</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>86.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>37661.190113</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>168.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   extended  crit1  crit2  crit3  doubtterr  multiple  success  suicide  \\\n",
       "0         0      1      1      1        1.0       0.0        1        0   \n",
       "1         0      1      1      1        0.0       0.0        1        0   \n",
       "2         0      1      1      1        0.0       0.0        1        0   \n",
       "3         0      1      1      1        0.0       0.0        1        0   \n",
       "4         0      1      1      1        0.0       0.0        0        0   \n",
       "5         0      1      1      1        0.0       1.0        1        0   \n",
       "6         0      1      1      1        0.0       1.0        1        0   \n",
       "7         0      1      1      1        0.0       0.0        0        0   \n",
       "8         0      1      1      1        0.0       1.0        1        0   \n",
       "9         0      1      1      1        0.0       1.0        1        0   \n",
       "\n",
       "   guncertain1  claimed  ...     propvalue  region  attacktype1  targtype1  \\\n",
       "0          0.0        0  ...  37661.190113      11            2          4   \n",
       "1          0.0        0  ...  37661.190113       9            3         19   \n",
       "2          0.0        1  ...  37661.190113       8            2         14   \n",
       "3          0.0        0  ...  37661.190113      10            3          7   \n",
       "4          0.0        0  ...  37661.190113      10            2         14   \n",
       "5          1.0        1  ...  37661.190113       9            3          3   \n",
       "6          1.0        1  ...  37661.190113       9            3          3   \n",
       "7          0.0        0  ...  37661.190113       8            3          3   \n",
       "8          0.0        0  ...  37661.190113       3            3         10   \n",
       "9          0.0        0  ...  37661.190113      11            2         14   \n",
       "\n",
       "   natlty1  weaptype1  nhostkidisnull  ransomisnull  hostkidoutcomeisnull  \\\n",
       "0     34.0          5               1             1                     1   \n",
       "1    167.0          6               1             1                     1   \n",
       "2    233.0          5               1             1                     1   \n",
       "3    999.0          6               1             1                     1   \n",
       "4     97.0          5               1             1                     1   \n",
       "5    118.0          6               1             1                     1   \n",
       "6    118.0          6               1             1                     1   \n",
       "7     15.0          6               1             1                     1   \n",
       "8     86.0          6               1             1                     1   \n",
       "9    168.0          5               1             1                     1   \n",
       "\n",
       "   nreleasedisnull  \n",
       "0                1  \n",
       "1                1  \n",
       "2                1  \n",
       "3                1  \n",
       "4                1  \n",
       "5                1  \n",
       "6                1  \n",
       "7                1  \n",
       "8                1  \n",
       "9                1  \n",
       "\n",
       "[10 rows x 28 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#把未知的-9,-99替换成0\n",
    "df[['claimed','property','ishostkid','propvalue','nperps']] = df.loc[:,['claimed','property','ishostkid','propvalue','nperps']].replace(-9,0)\n",
    "df[['nperps','propvalue']] = df.loc[:,['nperps','propvalue']].replace(-99,0)\n",
    "df = df.fillna(df.mean())\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8866372-4ee3-4db8-a266-4bb89451c1ec",
   "metadata": {},
   "source": [
    "## 4.4 缺失值插补"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "97546b12-6a7e-46dc-ad9c-9c562a06dc62",
   "metadata": {},
   "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>extended</th>\n",
       "      <th>crit1</th>\n",
       "      <th>crit2</th>\n",
       "      <th>crit3</th>\n",
       "      <th>doubtterr</th>\n",
       "      <th>multiple</th>\n",
       "      <th>success</th>\n",
       "      <th>suicide</th>\n",
       "      <th>guncertain1</th>\n",
       "      <th>claimed</th>\n",
       "      <th>...</th>\n",
       "      <th>propvalue</th>\n",
       "      <th>region</th>\n",
       "      <th>attacktype1</th>\n",
       "      <th>targtype1</th>\n",
       "      <th>natlty1</th>\n",
       "      <th>weaptype1</th>\n",
       "      <th>nhostkidisnull</th>\n",
       "      <th>ransomisnull</th>\n",
       "      <th>hostkidoutcomeisnull</th>\n",
       "      <th>nreleasedisnull</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>114183.000000</td>\n",
       "      <td>114183.000000</td>\n",
       "      <td>114183.000000</td>\n",
       "      <td>114183.000000</td>\n",
       "      <td>114183.000000</td>\n",
       "      <td>114183.000000</td>\n",
       "      <td>114183.000000</td>\n",
       "      <td>114183.000000</td>\n",
       "      <td>114183.000000</td>\n",
       "      <td>114183.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.141830e+05</td>\n",
       "      <td>114183.000000</td>\n",
       "      <td>114183.000000</td>\n",
       "      <td>114183.000000</td>\n",
       "      <td>114183.000000</td>\n",
       "      <td>114183.000000</td>\n",
       "      <td>114183.000000</td>\n",
       "      <td>114183.000000</td>\n",
       "      <td>114183.000000</td>\n",
       "      <td>114183.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.060736</td>\n",
       "      <td>0.989324</td>\n",
       "      <td>0.995586</td>\n",
       "      <td>0.878791</td>\n",
       "      <td>0.161777</td>\n",
       "      <td>0.157958</td>\n",
       "      <td>0.873869</td>\n",
       "      <td>0.057198</td>\n",
       "      <td>0.104057</td>\n",
       "      <td>0.163378</td>\n",
       "      <td>...</td>\n",
       "      <td>3.766119e+04</td>\n",
       "      <td>8.024548</td>\n",
       "      <td>3.343168</td>\n",
       "      <td>8.752310</td>\n",
       "      <td>127.688838</td>\n",
       "      <td>6.391179</td>\n",
       "      <td>0.918105</td>\n",
       "      <td>0.910144</td>\n",
       "      <td>0.918219</td>\n",
       "      <td>0.920251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.238846</td>\n",
       "      <td>0.102771</td>\n",
       "      <td>0.066291</td>\n",
       "      <td>0.326371</td>\n",
       "      <td>0.368246</td>\n",
       "      <td>0.364702</td>\n",
       "      <td>0.331998</td>\n",
       "      <td>0.232221</td>\n",
       "      <td>0.304827</td>\n",
       "      <td>0.369712</td>\n",
       "      <td>...</td>\n",
       "      <td>1.173638e+06</td>\n",
       "      <td>2.370121</td>\n",
       "      <td>1.860464</td>\n",
       "      <td>6.527255</td>\n",
       "      <td>95.804500</td>\n",
       "      <td>2.070908</td>\n",
       "      <td>0.274206</td>\n",
       "      <td>0.285976</td>\n",
       "      <td>0.274032</td>\n",
       "      <td>0.270906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.154510e+03</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.766119e+04</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.766119e+04</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.500000e+08</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1004.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            extended          crit1          crit2          crit3  \\\n",
       "count  114183.000000  114183.000000  114183.000000  114183.000000   \n",
       "mean        0.060736       0.989324       0.995586       0.878791   \n",
       "std         0.238846       0.102771       0.066291       0.326371   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       1.000000       1.000000       1.000000   \n",
       "50%         0.000000       1.000000       1.000000       1.000000   \n",
       "75%         0.000000       1.000000       1.000000       1.000000   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "           doubtterr       multiple        success        suicide  \\\n",
       "count  114183.000000  114183.000000  114183.000000  114183.000000   \n",
       "mean        0.161777       0.157958       0.873869       0.057198   \n",
       "std         0.368246       0.364702       0.331998       0.232221   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       1.000000       0.000000   \n",
       "50%         0.000000       0.000000       1.000000       0.000000   \n",
       "75%         0.000000       0.000000       1.000000       0.000000   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "         guncertain1        claimed  ...     propvalue         region  \\\n",
       "count  114183.000000  114183.000000  ...  1.141830e+05  114183.000000   \n",
       "mean        0.104057       0.163378  ...  3.766119e+04       8.024548   \n",
       "std         0.304827       0.369712  ...  1.173638e+06       2.370121   \n",
       "min         0.000000       0.000000  ...  0.000000e+00       1.000000   \n",
       "25%         0.000000       0.000000  ...  3.154510e+03       6.000000   \n",
       "50%         0.000000       0.000000  ...  3.766119e+04       9.000000   \n",
       "75%         0.000000       0.000000  ...  3.766119e+04      10.000000   \n",
       "max         1.000000       1.000000  ...  3.500000e+08      12.000000   \n",
       "\n",
       "         attacktype1      targtype1        natlty1      weaptype1  \\\n",
       "count  114183.000000  114183.000000  114183.000000  114183.000000   \n",
       "mean        3.343168       8.752310     127.688838       6.391179   \n",
       "std         1.860464       6.527255      95.804500       2.070908   \n",
       "min         1.000000       1.000000       4.000000       1.000000   \n",
       "25%         2.000000       3.000000      92.000000       5.000000   \n",
       "50%         3.000000       4.000000      97.000000       6.000000   \n",
       "75%         3.000000      14.000000     160.000000       6.000000   \n",
       "max         9.000000      22.000000    1004.000000      13.000000   \n",
       "\n",
       "       nhostkidisnull   ransomisnull  hostkidoutcomeisnull  nreleasedisnull  \n",
       "count   114183.000000  114183.000000         114183.000000    114183.000000  \n",
       "mean         0.918105       0.910144              0.918219         0.920251  \n",
       "std          0.274206       0.285976              0.274032         0.270906  \n",
       "min          0.000000       0.000000              0.000000         0.000000  \n",
       "25%          1.000000       1.000000              1.000000         1.000000  \n",
       "50%          1.000000       1.000000              1.000000         1.000000  \n",
       "75%          1.000000       1.000000              1.000000         1.000000  \n",
       "max          1.000000       1.000000              1.000000         1.000000  \n",
       "\n",
       "[8 rows x 28 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n",
    "df.shape\n",
    "df.isnull().sum()\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76770f1e-e458-4ea8-9c3e-09c38f2baf74",
   "metadata": {},
   "source": [
    "## 4.5 标准化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ee81acdc-25d7-48b1-abaa-67657413af2b",
   "metadata": {},
   "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>extended</th>\n",
       "      <th>crit1</th>\n",
       "      <th>crit2</th>\n",
       "      <th>crit3</th>\n",
       "      <th>doubtterr</th>\n",
       "      <th>multiple</th>\n",
       "      <th>success</th>\n",
       "      <th>suicide</th>\n",
       "      <th>guncertain1</th>\n",
       "      <th>claimed</th>\n",
       "      <th>...</th>\n",
       "      <th>propvalue</th>\n",
       "      <th>region</th>\n",
       "      <th>attacktype1</th>\n",
       "      <th>targtype1</th>\n",
       "      <th>natlty1</th>\n",
       "      <th>weaptype1</th>\n",
       "      <th>nhostkidisnull</th>\n",
       "      <th>ransomisnull</th>\n",
       "      <th>hostkidoutcomeisnull</th>\n",
       "      <th>nreleasedisnull</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.254289</td>\n",
       "      <td>0.10388</td>\n",
       "      <td>0.066585</td>\n",
       "      <td>0.371383</td>\n",
       "      <td>2.276259</td>\n",
       "      <td>-0.433116</td>\n",
       "      <td>0.379914</td>\n",
       "      <td>-0.246307</td>\n",
       "      <td>-0.341364</td>\n",
       "      <td>-0.441906</td>\n",
       "      <td>...</td>\n",
       "      <td>6.199490e-18</td>\n",
       "      <td>1.255401</td>\n",
       "      <td>-0.721953</td>\n",
       "      <td>-0.728072</td>\n",
       "      <td>-0.977917</td>\n",
       "      <td>-0.671773</td>\n",
       "      <td>0.298662</td>\n",
       "      <td>0.314207</td>\n",
       "      <td>0.298436</td>\n",
       "      <td>0.29438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.254289</td>\n",
       "      <td>0.10388</td>\n",
       "      <td>0.066585</td>\n",
       "      <td>0.371383</td>\n",
       "      <td>-0.439317</td>\n",
       "      <td>-0.433116</td>\n",
       "      <td>0.379914</td>\n",
       "      <td>-0.246307</td>\n",
       "      <td>-0.341364</td>\n",
       "      <td>-0.441906</td>\n",
       "      <td>...</td>\n",
       "      <td>6.199490e-18</td>\n",
       "      <td>0.411562</td>\n",
       "      <td>-0.184453</td>\n",
       "      <td>1.569985</td>\n",
       "      <td>0.410327</td>\n",
       "      <td>-0.188893</td>\n",
       "      <td>0.298662</td>\n",
       "      <td>0.314207</td>\n",
       "      <td>0.298436</td>\n",
       "      <td>0.29438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.254289</td>\n",
       "      <td>0.10388</td>\n",
       "      <td>0.066585</td>\n",
       "      <td>0.371383</td>\n",
       "      <td>-0.439317</td>\n",
       "      <td>-0.433116</td>\n",
       "      <td>0.379914</td>\n",
       "      <td>-0.246307</td>\n",
       "      <td>-0.341364</td>\n",
       "      <td>2.262902</td>\n",
       "      <td>...</td>\n",
       "      <td>6.199490e-18</td>\n",
       "      <td>-0.010357</td>\n",
       "      <td>-0.721953</td>\n",
       "      <td>0.803966</td>\n",
       "      <td>1.099230</td>\n",
       "      <td>-0.671773</td>\n",
       "      <td>0.298662</td>\n",
       "      <td>0.314207</td>\n",
       "      <td>0.298436</td>\n",
       "      <td>0.29438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.254289</td>\n",
       "      <td>0.10388</td>\n",
       "      <td>0.066585</td>\n",
       "      <td>0.371383</td>\n",
       "      <td>-0.439317</td>\n",
       "      <td>-0.433116</td>\n",
       "      <td>0.379914</td>\n",
       "      <td>-0.246307</td>\n",
       "      <td>-0.341364</td>\n",
       "      <td>-0.441906</td>\n",
       "      <td>...</td>\n",
       "      <td>6.199490e-18</td>\n",
       "      <td>0.833481</td>\n",
       "      <td>-0.184453</td>\n",
       "      <td>-0.268460</td>\n",
       "      <td>9.094679</td>\n",
       "      <td>-0.188893</td>\n",
       "      <td>0.298662</td>\n",
       "      <td>0.314207</td>\n",
       "      <td>0.298436</td>\n",
       "      <td>0.29438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.254289</td>\n",
       "      <td>0.10388</td>\n",
       "      <td>0.066585</td>\n",
       "      <td>0.371383</td>\n",
       "      <td>-0.439317</td>\n",
       "      <td>-0.433116</td>\n",
       "      <td>-2.632150</td>\n",
       "      <td>-0.246307</td>\n",
       "      <td>-0.341364</td>\n",
       "      <td>-0.441906</td>\n",
       "      <td>...</td>\n",
       "      <td>6.199490e-18</td>\n",
       "      <td>0.833481</td>\n",
       "      <td>-0.721953</td>\n",
       "      <td>0.803966</td>\n",
       "      <td>-0.320328</td>\n",
       "      <td>-0.671773</td>\n",
       "      <td>0.298662</td>\n",
       "      <td>0.314207</td>\n",
       "      <td>0.298436</td>\n",
       "      <td>0.29438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114178</th>\n",
       "      <td>-0.254289</td>\n",
       "      <td>0.10388</td>\n",
       "      <td>0.066585</td>\n",
       "      <td>-2.692611</td>\n",
       "      <td>2.276259</td>\n",
       "      <td>-0.433116</td>\n",
       "      <td>0.379914</td>\n",
       "      <td>-0.246307</td>\n",
       "      <td>-0.341364</td>\n",
       "      <td>2.262902</td>\n",
       "      <td>...</td>\n",
       "      <td>6.199490e-18</td>\n",
       "      <td>1.255401</td>\n",
       "      <td>-0.721953</td>\n",
       "      <td>-0.728072</td>\n",
       "      <td>0.566896</td>\n",
       "      <td>-0.671773</td>\n",
       "      <td>0.298662</td>\n",
       "      <td>0.314207</td>\n",
       "      <td>0.298436</td>\n",
       "      <td>0.29438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114179</th>\n",
       "      <td>-0.254289</td>\n",
       "      <td>0.10388</td>\n",
       "      <td>0.066585</td>\n",
       "      <td>-2.692611</td>\n",
       "      <td>2.276259</td>\n",
       "      <td>-0.433116</td>\n",
       "      <td>0.379914</td>\n",
       "      <td>-0.246307</td>\n",
       "      <td>-0.341364</td>\n",
       "      <td>-0.441906</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.208927e-02</td>\n",
       "      <td>0.833481</td>\n",
       "      <td>-0.184453</td>\n",
       "      <td>-0.728072</td>\n",
       "      <td>0.410327</td>\n",
       "      <td>-0.188893</td>\n",
       "      <td>0.298662</td>\n",
       "      <td>0.314207</td>\n",
       "      <td>0.298436</td>\n",
       "      <td>0.29438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114180</th>\n",
       "      <td>-0.254289</td>\n",
       "      <td>0.10388</td>\n",
       "      <td>0.066585</td>\n",
       "      <td>0.371383</td>\n",
       "      <td>-0.439317</td>\n",
       "      <td>-0.433116</td>\n",
       "      <td>0.379914</td>\n",
       "      <td>-0.246307</td>\n",
       "      <td>-0.341364</td>\n",
       "      <td>-0.441906</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.208927e-02</td>\n",
       "      <td>-1.276116</td>\n",
       "      <td>1.965548</td>\n",
       "      <td>0.803966</td>\n",
       "      <td>0.337261</td>\n",
       "      <td>0.776868</td>\n",
       "      <td>0.298662</td>\n",
       "      <td>0.314207</td>\n",
       "      <td>0.298436</td>\n",
       "      <td>0.29438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114181</th>\n",
       "      <td>-0.254289</td>\n",
       "      <td>0.10388</td>\n",
       "      <td>0.066585</td>\n",
       "      <td>0.371383</td>\n",
       "      <td>-0.439317</td>\n",
       "      <td>-0.433116</td>\n",
       "      <td>-2.632150</td>\n",
       "      <td>-0.246307</td>\n",
       "      <td>-0.341364</td>\n",
       "      <td>-0.441906</td>\n",
       "      <td>...</td>\n",
       "      <td>6.199490e-18</td>\n",
       "      <td>-0.854196</td>\n",
       "      <td>-0.184453</td>\n",
       "      <td>-1.034479</td>\n",
       "      <td>-0.372517</td>\n",
       "      <td>-0.188893</td>\n",
       "      <td>0.298662</td>\n",
       "      <td>0.314207</td>\n",
       "      <td>0.298436</td>\n",
       "      <td>0.29438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114182</th>\n",
       "      <td>-0.254289</td>\n",
       "      <td>0.10388</td>\n",
       "      <td>0.066585</td>\n",
       "      <td>0.371383</td>\n",
       "      <td>-0.439317</td>\n",
       "      <td>-0.433116</td>\n",
       "      <td>-2.632150</td>\n",
       "      <td>-0.246307</td>\n",
       "      <td>-0.341364</td>\n",
       "      <td>-0.441906</td>\n",
       "      <td>...</td>\n",
       "      <td>6.199490e-18</td>\n",
       "      <td>-1.276116</td>\n",
       "      <td>-0.184453</td>\n",
       "      <td>1.723188</td>\n",
       "      <td>0.337261</td>\n",
       "      <td>-0.188893</td>\n",
       "      <td>0.298662</td>\n",
       "      <td>0.314207</td>\n",
       "      <td>0.298436</td>\n",
       "      <td>0.29438</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>114183 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        extended    crit1     crit2     crit3  doubtterr  multiple   success  \\\n",
       "0      -0.254289  0.10388  0.066585  0.371383   2.276259 -0.433116  0.379914   \n",
       "1      -0.254289  0.10388  0.066585  0.371383  -0.439317 -0.433116  0.379914   \n",
       "2      -0.254289  0.10388  0.066585  0.371383  -0.439317 -0.433116  0.379914   \n",
       "3      -0.254289  0.10388  0.066585  0.371383  -0.439317 -0.433116  0.379914   \n",
       "4      -0.254289  0.10388  0.066585  0.371383  -0.439317 -0.433116 -2.632150   \n",
       "...          ...      ...       ...       ...        ...       ...       ...   \n",
       "114178 -0.254289  0.10388  0.066585 -2.692611   2.276259 -0.433116  0.379914   \n",
       "114179 -0.254289  0.10388  0.066585 -2.692611   2.276259 -0.433116  0.379914   \n",
       "114180 -0.254289  0.10388  0.066585  0.371383  -0.439317 -0.433116  0.379914   \n",
       "114181 -0.254289  0.10388  0.066585  0.371383  -0.439317 -0.433116 -2.632150   \n",
       "114182 -0.254289  0.10388  0.066585  0.371383  -0.439317 -0.433116 -2.632150   \n",
       "\n",
       "         suicide  guncertain1   claimed  ...     propvalue    region  \\\n",
       "0      -0.246307    -0.341364 -0.441906  ...  6.199490e-18  1.255401   \n",
       "1      -0.246307    -0.341364 -0.441906  ...  6.199490e-18  0.411562   \n",
       "2      -0.246307    -0.341364  2.262902  ...  6.199490e-18 -0.010357   \n",
       "3      -0.246307    -0.341364 -0.441906  ...  6.199490e-18  0.833481   \n",
       "4      -0.246307    -0.341364 -0.441906  ...  6.199490e-18  0.833481   \n",
       "...          ...          ...       ...  ...           ...       ...   \n",
       "114178 -0.246307    -0.341364  2.262902  ...  6.199490e-18  1.255401   \n",
       "114179 -0.246307    -0.341364 -0.441906  ... -3.208927e-02  0.833481   \n",
       "114180 -0.246307    -0.341364 -0.441906  ... -3.208927e-02 -1.276116   \n",
       "114181 -0.246307    -0.341364 -0.441906  ...  6.199490e-18 -0.854196   \n",
       "114182 -0.246307    -0.341364 -0.441906  ...  6.199490e-18 -1.276116   \n",
       "\n",
       "        attacktype1  targtype1   natlty1  weaptype1  nhostkidisnull  \\\n",
       "0         -0.721953  -0.728072 -0.977917  -0.671773        0.298662   \n",
       "1         -0.184453   1.569985  0.410327  -0.188893        0.298662   \n",
       "2         -0.721953   0.803966  1.099230  -0.671773        0.298662   \n",
       "3         -0.184453  -0.268460  9.094679  -0.188893        0.298662   \n",
       "4         -0.721953   0.803966 -0.320328  -0.671773        0.298662   \n",
       "...             ...        ...       ...        ...             ...   \n",
       "114178    -0.721953  -0.728072  0.566896  -0.671773        0.298662   \n",
       "114179    -0.184453  -0.728072  0.410327  -0.188893        0.298662   \n",
       "114180     1.965548   0.803966  0.337261   0.776868        0.298662   \n",
       "114181    -0.184453  -1.034479 -0.372517  -0.188893        0.298662   \n",
       "114182    -0.184453   1.723188  0.337261  -0.188893        0.298662   \n",
       "\n",
       "        ransomisnull  hostkidoutcomeisnull  nreleasedisnull  \n",
       "0           0.314207              0.298436          0.29438  \n",
       "1           0.314207              0.298436          0.29438  \n",
       "2           0.314207              0.298436          0.29438  \n",
       "3           0.314207              0.298436          0.29438  \n",
       "4           0.314207              0.298436          0.29438  \n",
       "...              ...                   ...              ...  \n",
       "114178      0.314207              0.298436          0.29438  \n",
       "114179      0.314207              0.298436          0.29438  \n",
       "114180      0.314207              0.298436          0.29438  \n",
       "114181      0.314207              0.298436          0.29438  \n",
       "114182      0.314207              0.298436          0.29438  \n",
       "\n",
       "[114183 rows x 28 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_norm2 = (df-df.mean())/df.std()     \n",
    "df_norm2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8d6fefb-948a-41f0-801c-dbd8ccf2777b",
   "metadata": {},
   "source": [
    "# 5、因子分析降维"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "44af97a9-2534-47a6-89ee-824c57e72220",
   "metadata": {},
   "outputs": [],
   "source": [
    "from factor_analyzer import FactorAnalyzer\n",
    "from factor_analyzer.factor_analyzer import calculate_bartlett_sphericity\n",
    "from factor_analyzer.factor_analyzer import calculate_kmo\n",
    "from factor_analyze import Factor_analyzer\n",
    "import numpy as np\n",
    "from sklearn import preprocessing\n",
    "from matplotlib import cm\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "sns.set(font_scale=1.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7d826dc-0347-412d-aabc-b2b4c5ad4309",
   "metadata": {},
   "source": [
    "- 熵权法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f706c377-597f-4379-ab82-1f04391c56c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "def get_entropy_weight(data):\n",
    "    \"\"\"\n",
    "    :param data: 评价指标数据框\n",
    "    :return: 各指标权重列表\n",
    "    \"\"\" \n",
    "    data = pd.DataFrame(data)\n",
    "    data = data.apply(lambda data: ((data - np.min(data)) / (np.max(data) - np.min(data))))\n",
    "    #计算k\n",
    "    m,n=data.shape   #m是行，n是列\n",
    "    yij=np.array(data.sum(axis=0))  \n",
    "    data = np.array(data)\n",
    "    #计算pij\n",
    "    pij=np.array(data/yij)\n",
    "    a=pij*1.0\n",
    "    a[np.where(pij==0)]=0.0001\n",
    "#    #计算每个指标的熵\n",
    "    e=(-1.0/np.log(n))*np.sum(pij*np.log(a),axis=0)\n",
    "    w=(1-e)/np.sum(1-e)\n",
    "    recodes=np.sum(data*w,axis=1)\n",
    "    return recodes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0126082-fe4f-4708-ad16-0c3380741bac",
   "metadata": {},
   "source": [
    "- 因子分析法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7bbcac66-b5f3-41c5-ba12-4d49345b3ded",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "原始数据:\n",
      "         extended  crit1  crit2  crit3  doubtterr  multiple  success  suicide  \\\n",
      "0              0      1      1      1        1.0       0.0        1        0   \n",
      "1              0      1      1      1        0.0       0.0        1        0   \n",
      "2              0      1      1      1        0.0       0.0        1        0   \n",
      "3              0      1      1      1        0.0       0.0        1        0   \n",
      "4              0      1      1      1        0.0       0.0        0        0   \n",
      "...          ...    ...    ...    ...        ...       ...      ...      ...   \n",
      "114178         0      1      1      0        1.0       0.0        1        0   \n",
      "114179         0      1      1      0        1.0       0.0        1        0   \n",
      "114180         0      1      1      1        0.0       0.0        1        0   \n",
      "114181         0      1      1      1        0.0       0.0        0        0   \n",
      "114182         0      1      1      1        0.0       0.0        0        0   \n",
      "\n",
      "        guncertain1  claimed  ...     propvalue  region  attacktype1  \\\n",
      "0               0.0        0  ...  37661.190113      11            2   \n",
      "1               0.0        0  ...  37661.190113       9            3   \n",
      "2               0.0        1  ...  37661.190113       8            2   \n",
      "3               0.0        0  ...  37661.190113      10            3   \n",
      "4               0.0        0  ...  37661.190113      10            2   \n",
      "...             ...      ...  ...           ...     ...          ...   \n",
      "114178          0.0        1  ...  37661.190113      11            2   \n",
      "114179          0.0        0  ...      0.000000      10            3   \n",
      "114180          0.0        0  ...      0.000000       5            7   \n",
      "114181          0.0        0  ...  37661.190113       6            3   \n",
      "114182          0.0        0  ...  37661.190113       5            3   \n",
      "\n",
      "        targtype1  natlty1  weaptype1  nhostkidisnull  ransomisnull  \\\n",
      "0               4     34.0          5               1             1   \n",
      "1              19    167.0          6               1             1   \n",
      "2              14    233.0          5               1             1   \n",
      "3               7    999.0          6               1             1   \n",
      "4              14     97.0          5               1             1   \n",
      "...           ...      ...        ...             ...           ...   \n",
      "114178          4    182.0          5               1             1   \n",
      "114179          4    167.0          6               1             1   \n",
      "114180         14    160.0          8               1             1   \n",
      "114181          2     92.0          6               1             1   \n",
      "114182         20    160.0          6               1             1   \n",
      "\n",
      "        hostkidoutcomeisnull  nreleasedisnull  \n",
      "0                          1                1  \n",
      "1                          1                1  \n",
      "2                          1                1  \n",
      "3                          1                1  \n",
      "4                          1                1  \n",
      "...                      ...              ...  \n",
      "114178                     1                1  \n",
      "114179                     1                1  \n",
      "114180                     1                1  \n",
      "114181                     1                1  \n",
      "114182                     1                1  \n",
      "\n",
      "[114183 rows x 28 columns]\n",
      "\n",
      "归一化之后的数据:\n",
      "         extended    crit1     crit2     crit3  doubtterr  multiple   success  \\\n",
      "0      -0.254289  0.10388  0.066585  0.371383   2.276259 -0.433116  0.379914   \n",
      "1      -0.254289  0.10388  0.066585  0.371383  -0.439317 -0.433116  0.379914   \n",
      "2      -0.254289  0.10388  0.066585  0.371383  -0.439317 -0.433116  0.379914   \n",
      "3      -0.254289  0.10388  0.066585  0.371383  -0.439317 -0.433116  0.379914   \n",
      "4      -0.254289  0.10388  0.066585  0.371383  -0.439317 -0.433116 -2.632150   \n",
      "...          ...      ...       ...       ...        ...       ...       ...   \n",
      "114178 -0.254289  0.10388  0.066585 -2.692611   2.276259 -0.433116  0.379914   \n",
      "114179 -0.254289  0.10388  0.066585 -2.692611   2.276259 -0.433116  0.379914   \n",
      "114180 -0.254289  0.10388  0.066585  0.371383  -0.439317 -0.433116  0.379914   \n",
      "114181 -0.254289  0.10388  0.066585  0.371383  -0.439317 -0.433116 -2.632150   \n",
      "114182 -0.254289  0.10388  0.066585  0.371383  -0.439317 -0.433116 -2.632150   \n",
      "\n",
      "         suicide  guncertain1   claimed  ...     propvalue    region  \\\n",
      "0      -0.246307    -0.341364 -0.441906  ...  6.199490e-18  1.255401   \n",
      "1      -0.246307    -0.341364 -0.441906  ...  6.199490e-18  0.411562   \n",
      "2      -0.246307    -0.341364  2.262902  ...  6.199490e-18 -0.010357   \n",
      "3      -0.246307    -0.341364 -0.441906  ...  6.199490e-18  0.833481   \n",
      "4      -0.246307    -0.341364 -0.441906  ...  6.199490e-18  0.833481   \n",
      "...          ...          ...       ...  ...           ...       ...   \n",
      "114178 -0.246307    -0.341364  2.262902  ...  6.199490e-18  1.255401   \n",
      "114179 -0.246307    -0.341364 -0.441906  ... -3.208927e-02  0.833481   \n",
      "114180 -0.246307    -0.341364 -0.441906  ... -3.208927e-02 -1.276116   \n",
      "114181 -0.246307    -0.341364 -0.441906  ...  6.199490e-18 -0.854196   \n",
      "114182 -0.246307    -0.341364 -0.441906  ...  6.199490e-18 -1.276116   \n",
      "\n",
      "        attacktype1  targtype1   natlty1  weaptype1  nhostkidisnull  \\\n",
      "0         -0.721953  -0.728072 -0.977917  -0.671773        0.298662   \n",
      "1         -0.184453   1.569985  0.410327  -0.188893        0.298662   \n",
      "2         -0.721953   0.803966  1.099230  -0.671773        0.298662   \n",
      "3         -0.184453  -0.268460  9.094679  -0.188893        0.298662   \n",
      "4         -0.721953   0.803966 -0.320328  -0.671773        0.298662   \n",
      "...             ...        ...       ...        ...             ...   \n",
      "114178    -0.721953  -0.728072  0.566896  -0.671773        0.298662   \n",
      "114179    -0.184453  -0.728072  0.410327  -0.188893        0.298662   \n",
      "114180     1.965548   0.803966  0.337261   0.776868        0.298662   \n",
      "114181    -0.184453  -1.034479 -0.372517  -0.188893        0.298662   \n",
      "114182    -0.184453   1.723188  0.337261  -0.188893        0.298662   \n",
      "\n",
      "        ransomisnull  hostkidoutcomeisnull  nreleasedisnull  \n",
      "0           0.314207              0.298436          0.29438  \n",
      "1           0.314207              0.298436          0.29438  \n",
      "2           0.314207              0.298436          0.29438  \n",
      "3           0.314207              0.298436          0.29438  \n",
      "4           0.314207              0.298436          0.29438  \n",
      "...              ...                   ...              ...  \n",
      "114178      0.314207              0.298436          0.29438  \n",
      "114179      0.314207              0.298436          0.29438  \n",
      "114180      0.314207              0.298436          0.29438  \n",
      "114181      0.314207              0.298436          0.29438  \n",
      "114182      0.314207              0.298436          0.29438  \n",
      "\n",
      "[114183 rows x 28 columns]\n",
      "\n",
      "相关系数:\n",
      "                       extended     crit1     crit2     crit3  doubtterr  \\\n",
      "extended              1.000000 -0.014972 -0.009618  0.052758  -0.036536   \n",
      "crit1                -0.014972  1.000000 -0.006917 -0.038579  -0.235302   \n",
      "crit2                -0.009618 -0.006917  1.000000 -0.024729  -0.151206   \n",
      "crit3                 0.052758 -0.038579 -0.024729  1.000000  -0.844498   \n",
      "doubtterr            -0.036536 -0.235302 -0.151206 -0.844498   1.000000   \n",
      "multiple             -0.016131  0.027000  0.003482  0.015683  -0.031812   \n",
      "success               0.092854 -0.014311 -0.014154  0.003908   0.014321   \n",
      "suicide              -0.053160  0.023018 -0.005218 -0.033787   0.019401   \n",
      "guncertain1           0.031274  0.008344 -0.010254  0.062718  -0.044397   \n",
      "claimed               0.030247  0.032767  0.000122 -0.071627   0.044390   \n",
      "property             -0.108715  0.008466  0.020164  0.101031  -0.096573   \n",
      "ishostkid             0.794057 -0.023671 -0.009021  0.071084  -0.048194   \n",
      "propextent            0.019826  0.010987 -0.002987 -0.091049   0.083900   \n",
      "nkill                 0.032664  0.009525 -0.011748 -0.028703   0.026832   \n",
      "nwound               -0.006002  0.006100  0.000891  0.009848  -0.010672   \n",
      "nperps                0.026715  0.003809 -0.003776  0.001260   0.002925   \n",
      "nkillter              0.005082  0.009731 -0.004859 -0.090445   0.076108   \n",
      "nwoundte              0.001526  0.005969 -0.001756 -0.054119   0.043968   \n",
      "propvalue            -0.000267  0.000641  0.000390  0.002106  -0.002244   \n",
      "region                0.007113  0.047530 -0.008285 -0.080026   0.051437   \n",
      "attacktype1           0.314123  0.006473 -0.015412 -0.019898   0.017755   \n",
      "targtype1             0.015739 -0.002702 -0.079419  0.270228  -0.228574   \n",
      "natlty1               0.016653 -0.017030 -0.014874 -0.125947   0.129171   \n",
      "weaptype1             0.241603  0.011433 -0.021617 -0.048709   0.042734   \n",
      "nhostkidisnull       -0.794058  0.023672  0.009022 -0.071089   0.048204   \n",
      "ransomisnull         -0.732499  0.022786  0.006335 -0.066210   0.040487   \n",
      "hostkidoutcomeisnull -0.794265  0.023730  0.009055 -0.070980   0.048052   \n",
      "nreleasedisnull      -0.791939  0.023525  0.008684 -0.069608   0.047946   \n",
      "\n",
      "                      multiple   success   suicide  guncertain1   claimed  \\\n",
      "extended             -0.016131  0.092854 -0.053160     0.031274  0.030247   \n",
      "crit1                 0.027000 -0.014311  0.023018     0.008344  0.032767   \n",
      "crit2                 0.003482 -0.014154 -0.005218    -0.010254  0.000122   \n",
      "crit3                 0.015683  0.003908 -0.033787     0.062718 -0.071627   \n",
      "doubtterr            -0.031812  0.014321  0.019401    -0.044397  0.044390   \n",
      "multiple              1.000000  0.022923  0.024961     0.013076  0.111089   \n",
      "success               0.022923  1.000000 -0.026382     0.047282  0.051228   \n",
      "suicide               0.024961 -0.026382  1.000000    -0.019161  0.154337   \n",
      "guncertain1           0.013076  0.047282 -0.019161     1.000000 -0.090610   \n",
      "claimed               0.111089  0.051228  0.154337    -0.090610  1.000000   \n",
      "property              0.089340  0.211924  0.023142     0.035700  0.039933   \n",
      "ishostkid            -0.035916  0.110584 -0.059399     0.039689  0.035956   \n",
      "propextent           -0.004014  0.014253  0.011858     0.037142  0.036868   \n",
      "nkill                 0.019589  0.051684  0.152122     0.005968  0.074783   \n",
      "nwound                0.019233  0.030004  0.100456     0.006625  0.036954   \n",
      "nperps                0.022884  0.015298 -0.001157     0.013286  0.019171   \n",
      "nkillter              0.014609 -0.019938  0.097713    -0.022535  0.065944   \n",
      "nwoundte              0.006252 -0.012837  0.000374    -0.015802  0.050844   \n",
      "propvalue            -0.003886  0.000908  0.011417     0.001438  0.002252   \n",
      "region                0.038533  0.029712  0.087687    -0.099022 -0.036520   \n",
      "attacktype1           0.080548  0.053459 -0.051798     0.012520  0.036354   \n",
      "targtype1             0.032281 -0.101772 -0.041765     0.008346 -0.088949   \n",
      "natlty1               0.010963  0.010230 -0.011237    -0.003428 -0.013173   \n",
      "weaptype1             0.046145 -0.015187 -0.046526     0.015182  0.023410   \n",
      "nhostkidisnull        0.035912 -0.110581  0.059397    -0.040102 -0.035873   \n",
      "ransomisnull          0.039270 -0.101201  0.061962    -0.046541 -0.029385   \n",
      "hostkidoutcomeisnull  0.035755 -0.110493  0.059332    -0.039640 -0.035734   \n",
      "nreleasedisnull       0.033629 -0.108919  0.058169    -0.037629 -0.034302   \n",
      "\n",
      "                      ...  propvalue    region  attacktype1  targtype1  \\\n",
      "extended              ...  -0.000267  0.007113     0.314123   0.015739   \n",
      "crit1                 ...   0.000641  0.047530     0.006473  -0.002702   \n",
      "crit2                 ...   0.000390 -0.008285    -0.015412  -0.079419   \n",
      "crit3                 ...   0.002106 -0.080026    -0.019898   0.270228   \n",
      "doubtterr             ...  -0.002244  0.051437     0.017755  -0.228574   \n",
      "multiple              ...  -0.003886  0.038533     0.080548   0.032281   \n",
      "success               ...   0.000908  0.029712     0.053459  -0.101772   \n",
      "suicide               ...   0.011417  0.087687    -0.051798  -0.041765   \n",
      "guncertain1           ...   0.001438 -0.099022     0.012520   0.008346   \n",
      "claimed               ...   0.002252 -0.036520     0.036354  -0.088949   \n",
      "property              ...   0.000192 -0.042404     0.027602  -0.017748   \n",
      "ishostkid             ...  -0.000428 -0.001247     0.364595   0.028164   \n",
      "propextent            ...  -0.031205  0.004287    -0.009295   0.032580   \n",
      "nkill                 ...   0.004091  0.060639     0.015012  -0.001018   \n",
      "nwound                ...   0.002430  0.009134    -0.004707   0.006162   \n",
      "nperps                ...   0.000985 -0.027276     0.015918  -0.012587   \n",
      "nkillter              ...   0.009072  0.027389     0.036094  -0.038395   \n",
      "nwoundte              ...  -0.000324 -0.028293     0.029474  -0.032603   \n",
      "propvalue             ...   1.000000 -0.007977     0.003125   0.000259   \n",
      "region                ...  -0.007977  1.000000    -0.017458   0.038384   \n",
      "attacktype1           ...   0.003125 -0.017458     1.000000   0.031392   \n",
      "targtype1             ...   0.000259  0.038384     0.031392   1.000000   \n",
      "natlty1               ...   0.003177  0.121712     0.016522  -0.014322   \n",
      "weaptype1             ...   0.001401  0.022221     0.712524   0.023185   \n",
      "nhostkidisnull        ...   0.000428  0.001409    -0.364602  -0.028147   \n",
      "ransomisnull          ...  -0.000894 -0.001514    -0.345119  -0.024216   \n",
      "hostkidoutcomeisnull  ...   0.000427  0.001176    -0.364446  -0.027958   \n",
      "nreleasedisnull       ...   0.000707 -0.000470    -0.361069  -0.027401   \n",
      "\n",
      "                       natlty1  weaptype1  nhostkidisnull  ransomisnull  \\\n",
      "extended              0.016653   0.241603       -0.794058     -0.732499   \n",
      "crit1                -0.017030   0.011433        0.023672      0.022786   \n",
      "crit2                -0.014874  -0.021617        0.009022      0.006335   \n",
      "crit3                -0.125947  -0.048709       -0.071089     -0.066210   \n",
      "doubtterr             0.129171   0.042734        0.048204      0.040487   \n",
      "multiple              0.010963   0.046145        0.035912      0.039270   \n",
      "success               0.010230  -0.015187       -0.110581     -0.101201   \n",
      "suicide              -0.011237  -0.046526        0.059397      0.061962   \n",
      "guncertain1          -0.003428   0.015182       -0.040102     -0.046541   \n",
      "claimed              -0.013173   0.023410       -0.035873     -0.029385   \n",
      "property              0.025732  -0.046584        0.129064      0.118679   \n",
      "ishostkid             0.014307   0.254670       -0.999300     -0.919365   \n",
      "propextent            0.017380   0.006927       -0.016525     -0.031015   \n",
      "nkill                 0.009132   0.017361       -0.036818     -0.032622   \n",
      "nwound                0.002730  -0.004113       -0.001304     -0.000073   \n",
      "nperps                0.009681   0.009078       -0.025563     -0.021221   \n",
      "nkillter              0.002139   0.040126        0.013219      0.015868   \n",
      "nwoundte             -0.043074   0.032200        0.011378      0.013051   \n",
      "propvalue             0.003177   0.001401        0.000428     -0.000894   \n",
      "region                0.121712   0.022221        0.001409     -0.001514   \n",
      "attacktype1           0.016522   0.712524       -0.364602     -0.345119   \n",
      "targtype1            -0.014322   0.023185       -0.028147     -0.024216   \n",
      "natlty1               1.000000  -0.002310       -0.014270      0.005838   \n",
      "weaptype1            -0.002310   1.000000       -0.254926     -0.235493   \n",
      "nhostkidisnull       -0.014270  -0.254926        1.000000      0.919478   \n",
      "ransomisnull          0.005838  -0.235493        0.919478      1.000000   \n",
      "hostkidoutcomeisnull -0.014316  -0.254518        0.999010      0.919186   \n",
      "nreleasedisnull      -0.014750  -0.252050        0.985072      0.912819   \n",
      "\n",
      "                      hostkidoutcomeisnull  nreleasedisnull  \n",
      "extended                         -0.794265        -0.791939  \n",
      "crit1                             0.023730         0.023525  \n",
      "crit2                             0.009055         0.008684  \n",
      "crit3                            -0.070980        -0.069608  \n",
      "doubtterr                         0.048052         0.047946  \n",
      "multiple                          0.035755         0.033629  \n",
      "success                          -0.110493        -0.108919  \n",
      "suicide                           0.059332         0.058169  \n",
      "guncertain1                      -0.039640        -0.037629  \n",
      "claimed                          -0.035734        -0.034302  \n",
      "property                          0.128949         0.126186  \n",
      "ishostkid                        -0.998775        -0.984717  \n",
      "propextent                       -0.016514        -0.014883  \n",
      "nkill                            -0.036892        -0.036938  \n",
      "nwound                           -0.001362        -0.001536  \n",
      "nperps                           -0.025471        -0.022302  \n",
      "nkillter                          0.013192         0.012572  \n",
      "nwoundte                          0.011355         0.011074  \n",
      "propvalue                         0.000427         0.000707  \n",
      "region                            0.001176        -0.000470  \n",
      "attacktype1                      -0.364446        -0.361069  \n",
      "targtype1                        -0.027958        -0.027401  \n",
      "natlty1                          -0.014316        -0.014750  \n",
      "weaptype1                        -0.254518        -0.252050  \n",
      "nhostkidisnull                    0.999010         0.985072  \n",
      "ransomisnull                      0.919186         0.912819  \n",
      "hostkidoutcomeisnull              1.000000         0.984993  \n",
      "nreleasedisnull                   0.984993         1.000000  \n",
      "\n",
      "[28 rows x 28 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda3\\envs\\pytorch\\lib\\site-packages\\factor_analyzer\\utils.py:248: UserWarning: The inverse of the variance-covariance matrix was calculated using the Moore-Penrose generalized matrix inversion, due to its determinant being at or very close to zero.\n",
      "  warnings.warn('The inverse of the variance-covariance matrix '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "KMO测度: 0.8057508874161082\n",
      "\n",
      "KMO测度大于0.6，适合做因子分析\n",
      "\n",
      "巴特利特球形检验: 0.0\n",
      "\n",
      "巴特利特球形检验的伴随概率小于0.05，适合做因子分析\n",
      "\n",
      "特征值: [7.38478752e+00 2.95009433e+00 2.20453761e+00 1.59718415e+00\n",
      " 1.45852666e+00 1.35062522e+00 1.19848015e+00 1.12727757e+00\n",
      " 1.06936844e+00 1.02289472e+00 9.78191113e-01 9.36977146e-01\n",
      " 8.32253241e-01 8.25491683e-01 7.65210668e-01 5.52536987e-01\n",
      " 4.70239522e-01 4.27500008e-01 3.93484756e-01 3.10626558e-01\n",
      " 8.32210439e-02 3.65862563e-02 1.85332799e-02 3.18821647e-03\n",
      " 2.10605319e-03 7.69024539e-05 1.90819666e-07 3.08907604e-16]\n",
      "\n",
      "特征向量 [ 7.38206327e+00  2.94559949e+00  2.19977764e+00  1.59219961e+00\n",
      "  1.45354900e+00  1.34565214e+00  1.19354907e+00  1.12231732e+00\n",
      "  1.06439316e+00  1.01795752e+00  9.73229164e-01  9.32037999e-01\n",
      "  8.27314197e-01  8.20519773e-01  7.60228146e-01  5.47570598e-01\n",
      "  4.65650520e-01  4.22569822e-01  3.88538753e-01  3.05720434e-01\n",
      "  7.82246495e-02  3.15917335e-02  1.41976740e-02  2.28660906e-04\n",
      " -1.06835684e-03 -1.15200161e-03 -1.15350494e-03 -1.67784892e-03]\n",
      "\n",
      "=================绘制碎石图==================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "由碎石图可知有10个因子的特征值大于1，所以选择10个因子重新进行因子分析\n",
      "\n",
      "旋转后的因子荷载矩阵 [[-9.77828985e-01 -6.68280424e-03  3.76412943e-03  1.34563745e-02\n",
      "   8.39617612e-02 -7.37743635e-02 -2.52904209e-02  1.89594568e-02\n",
      "   1.79236145e-03 -2.41691150e-02]\n",
      " [ 4.38754281e-02 -2.66284336e-01 -2.87053494e-02 -2.60151982e-02\n",
      "   1.33991434e-02 -3.56401784e-02  3.78170695e-02 -7.42068651e-02\n",
      "  -2.35176386e-02  7.64123585e-02]\n",
      " [ 1.97843484e-02 -1.78111971e-01 -4.73403131e-02 -3.58030916e-02\n",
      "  -3.82971467e-02 -1.51085610e-02 -3.34510808e-02 -2.08440896e-01\n",
      "  -8.73892747e-03 -3.00635491e-02]\n",
      " [-1.44180914e-01 -8.13516498e-01  1.00086908e-03 -1.15007040e-01\n",
      "  -7.45573606e-02  2.01193093e-01 -1.11045150e-01  2.82074792e-01\n",
      "   1.02201430e-02 -1.46121213e-01]\n",
      " [ 1.02295361e-01  9.82087065e-01  5.88275418e-03  1.02053295e-01\n",
      "   4.99183764e-02 -1.64905351e-01  7.88792482e-02 -1.37662788e-01\n",
      "  -1.48699282e-03  7.84407826e-02]\n",
      " [ 4.23959236e-02 -5.03612093e-02 -5.50221704e-02 -6.32239647e-02\n",
      "   8.16242389e-02  1.19790130e-01  1.67982250e-01  3.95081173e-02\n",
      "  -5.91460218e-02  1.51580800e-02]\n",
      " [-3.49533611e-01  1.08819307e-01  1.08742364e-03 -9.68630248e-02\n",
      "  -1.23889317e-01  3.42632988e-01 -4.19275750e-03 -1.53888282e-01\n",
      "  -6.07872410e-02 -6.61687026e-02]\n",
      " [ 1.45000256e-01 -1.57967490e-03  2.32325391e-01  5.24353276e-02\n",
      "  -1.17435795e-01 -4.42600760e-02  2.56940885e-01 -7.11875934e-02\n",
      "   2.73107085e-03  1.46523627e-01]\n",
      " [-1.29519151e-01 -5.20969732e-02 -3.54894048e-02 -1.04234952e-01\n",
      "  -4.99225959e-02  2.74311059e-03 -2.42487053e-01 -2.12836901e-02\n",
      "  -4.57224030e-02 -2.89642432e-01]\n",
      " [-1.43807271e-01  4.65342440e-02  4.27758423e-02  5.08992826e-02\n",
      "  -4.36278771e-02 -1.58869024e-02  9.76534021e-01 -9.08300044e-02\n",
      "  -7.92498576e-03 -6.50048763e-02]\n",
      " [ 2.30219661e-01 -2.68189452e-02 -4.17195076e-02 -1.22905893e-01\n",
      "  -9.26983342e-02  9.02155915e-01  3.91163631e-02 -5.62674573e-03\n",
      "  -6.91417961e-02 -1.14332175e-01]\n",
      " [-9.93239583e-01 -9.36572443e-03  2.33838166e-02  1.32840697e-02\n",
      "   7.96252334e-02 -6.13819011e-02 -1.61574612e-02  2.93962078e-02\n",
      "   8.21995442e-03 -2.25735807e-02]\n",
      " [-6.69313731e-02  1.49512721e-01 -7.85961671e-02 -1.87262512e-02\n",
      "  -7.91157301e-02 -3.62331483e-01  1.62395931e-03  1.98747083e-04\n",
      "  -9.56688146e-02 -5.86794159e-02]\n",
      " [-1.17661697e-01  5.25483285e-02  9.18379611e-01  3.60023244e-01\n",
      "  -2.73135199e-02  3.08750860e-02  2.69948100e-02  3.11151867e-02\n",
      "  -2.16793698e-02  4.99481111e-02]\n",
      " [-2.15749589e-02 -1.03763653e-03  8.48040894e-01 -2.20657057e-02\n",
      "  -1.19004207e-02  3.73061493e-02  3.11427387e-03  1.37796174e-02\n",
      "  -1.57636691e-02 -2.65085644e-02]\n",
      " [-9.51078141e-02  1.08853625e-02 -3.13266211e-02  1.01541493e-01\n",
      "  -4.04467196e-02 -1.64072881e-02 -2.64816289e-02 -3.17446849e-02\n",
      "  -2.85288194e-02 -1.04028378e-01]\n",
      " [-5.22456633e-03  8.44979784e-02  2.20278276e-01  9.43002442e-01\n",
      "  -3.77810157e-03 -3.62646404e-02  4.19224853e-02  3.13491576e-03\n",
      "  -3.97985136e-03  7.04408936e-02]\n",
      " [ 8.15367812e-03  3.97737709e-02  4.41691125e-02  6.30281858e-01\n",
      "   2.55503078e-02 -6.75429345e-02  2.20101560e-02 -2.83044491e-02\n",
      "  -1.24206330e-02 -7.33051020e-02]\n",
      " [-1.65320596e-02  1.53144888e-02 -3.88901657e-02 -4.19210657e-02\n",
      "  -2.26742760e-02  2.24915533e-02 -3.92582510e-02 -3.53752184e-03\n",
      "   9.95652360e-01 -1.40851416e-02]\n",
      " [-4.80172530e-02  7.06622824e-02  7.99161850e-03 -7.18504466e-02\n",
      "  -5.65661500e-02 -3.82263360e-02 -4.55407838e-02  1.19283928e-02\n",
      "  -5.19994643e-02  8.82883956e-01]\n",
      " [-6.95738130e-01  4.77813420e-02 -6.43288695e-02  1.01739046e-02\n",
      "   6.88856019e-01  4.32715087e-02  6.27416996e-03  4.24295103e-02\n",
      "  -2.32896383e-02 -4.12306884e-02]\n",
      " [-6.00777828e-02 -3.13917892e-01 -5.84336998e-02 -1.31963387e-01\n",
      "  -7.95219026e-03 -8.83051929e-02 -1.34696795e-01  9.23648258e-01\n",
      "  -2.38089677e-02  2.22845362e-03]\n",
      " [-5.83716927e-02  2.95605379e-01 -5.95408010e-02 -1.12496392e-01\n",
      "  -5.17093466e-02 -1.90139793e-02 -8.22149895e-02 -2.44131851e-02\n",
      "  -3.54148628e-02  2.07519246e-01]\n",
      " [-5.62118633e-01  5.75528178e-02 -6.46879846e-02  1.76800164e-02\n",
      "   7.96747924e-01 -4.30753595e-02 -5.30351798e-03  3.38551479e-02\n",
      "  -2.19214516e-02 -5.78484401e-03]\n",
      " [ 9.93240103e-01  9.36022917e-03 -2.34296830e-02 -1.33131345e-02\n",
      "  -7.96986820e-02  6.13238869e-02  1.62035495e-02 -2.93942548e-02\n",
      "  -8.22186171e-03  2.26807054e-02]\n",
      " [ 9.91984585e-01  8.78033187e-03 -2.49073305e-02 -1.38371891e-02\n",
      "  -7.88754021e-02  6.24683919e-02  1.75521043e-02 -2.86151554e-02\n",
      "  -8.93950550e-03  2.53738226e-02]\n",
      " [ 9.93252045e-01  9.28420286e-03 -2.34917747e-02 -1.33609222e-02\n",
      "  -7.96066042e-02  6.12658636e-02  1.61600751e-02 -2.93681757e-02\n",
      "  -8.24184079e-03  2.24789128e-02]\n",
      " [ 9.93289078e-01  9.23581329e-03 -2.40434652e-02 -1.37971464e-02\n",
      "  -7.97332933e-02  5.99831443e-02  1.58081976e-02 -2.95097220e-02\n",
      "  -8.34877677e-03  2.07611654e-02]]\n",
      "        特征值     方差贡献率   方差累计贡献率\n",
      "0  6.992982  0.249749  0.249749\n",
      "1  1.980090  0.070717  0.320467\n",
      "2  1.704880  0.060889  0.381355\n",
      "3  1.536066  0.054859  0.436215\n",
      "4  1.222376  0.043656  0.479871\n",
      "5  1.194877  0.042674  0.522545\n",
      "6  1.163557  0.041556  0.564101\n",
      "7  1.050956  0.037534  0.601635\n",
      "8  1.022953  0.036534  0.638169\n",
      "9  1.016986  0.036321  0.674490\n",
      "\n",
      "==========以下采用回归方法计算因子得分============\n",
      "\n",
      "因子得分      factor1   factor2   factor3   factor4   factor5   factor6   factor7  \\\n",
      "0  -0.460669 -0.010026 -0.021971 -0.032033 -0.028310 -0.053373 -0.034823   \n",
      "1   0.030264 -0.038236 -0.048471 -0.044282 -0.017222 -0.034074  0.002148   \n",
      "2  -0.006865 -0.038913 -0.057127 -0.050938 -0.042146 -0.037425 -0.042975   \n",
      "3  -0.108590  0.038598 -0.033157 -0.060327 -0.100251  0.103634 -0.038906   \n",
      "4   0.024957  0.950022 -0.057176 -0.053454 -0.080357 -0.006792 -0.001901   \n",
      "5   0.057442 -0.024582 -0.056800 -0.053826  0.044339  0.052750  0.070188   \n",
      "6  -0.274306  0.064341 -0.033228 -0.067635 -0.090104  0.157739 -0.043318   \n",
      "7   0.085699 -0.022462  0.091497 -0.020011 -0.071339 -0.055012  0.111822   \n",
      "8  -0.089679 -0.020071 -0.041269 -0.083150 -0.056656 -0.038519 -0.153055   \n",
      "9  -0.135000 -0.006405 -0.028554 -0.024199 -0.057489 -0.050096  0.939399   \n",
      "10  0.204269  0.060398 -0.074161 -0.066364 -0.088320  0.828362  0.005971   \n",
      "11  0.016028  0.012270 -0.033257 -0.015184 -0.040541  0.033128 -0.087189   \n",
      "12 -0.016185  0.082909 -0.074622 -0.045636 -0.082494 -0.231776 -0.028163   \n",
      "13 -0.103889  0.022909  0.688741  0.055912  0.003291  0.018871 -0.035048   \n",
      "14  0.028191 -0.002434  0.470378 -0.081404  0.005360 -0.004139 -0.021204   \n",
      "15 -0.064294  0.001887 -0.054721  0.032719 -0.041628 -0.031495 -0.039493   \n",
      "16  0.033750  0.004853 -0.055795  0.804348 -0.037402  0.023415 -0.012349   \n",
      "17  0.014008  0.000969 -0.041705  0.324673  0.001479 -0.045035 -0.023954   \n",
      "18 -0.015629  0.019540 -0.045742 -0.050521 -0.025889  0.015684 -0.042549   \n",
      "19 -0.051579  0.009764 -0.039724 -0.077951 -0.065019  0.004785 -0.029429   \n",
      "20 -0.291524  0.014283 -0.022566 -0.016011  0.332464  0.007083 -0.004612   \n",
      "21 -0.027195 -0.105084 -0.070458 -0.095326 -0.039067 -0.093546 -0.044951   \n",
      "22 -0.061699  0.168985 -0.057322 -0.091380 -0.045473 -0.031524 -0.070774   \n",
      "23 -0.134293  0.008484 -0.051814 -0.022415  0.602990 -0.006217 -0.013263   \n",
      "24 -0.274305 -0.054802 -0.044999 -0.021225  0.152382 -0.055960 -0.006908   \n",
      "25  0.469082  0.015860 -0.036524 -0.033904 -0.017086  0.019347 -0.015724   \n",
      "26  0.020844  0.039374  0.016618 -0.012658 -0.014460 -0.009237 -0.047732   \n",
      "27  0.238935 -0.018033 -0.010005 -0.017032  0.003888  0.011173 -0.029092   \n",
      "\n",
      "     factor8   factor9  factor10  \n",
      "0   0.003641 -0.007045 -0.033534  \n",
      "1   0.015538 -0.018817  0.002413  \n",
      "2  -0.082299 -0.012405 -0.050597  \n",
      "3   0.306781  0.009498 -0.165484  \n",
      "4   0.323593  0.001700 -0.138407  \n",
      "5   0.016574 -0.041212 -0.001065  \n",
      "6  -0.074983 -0.046613 -0.061640  \n",
      "7  -0.038995 -0.002262  0.072575  \n",
      "8  -0.033688 -0.044402 -0.197643  \n",
      "9  -0.003586 -0.002579 -0.059835  \n",
      "10  0.026422 -0.063445 -0.064457  \n",
      "11 -0.040600 -0.010353 -0.052961  \n",
      "12 -0.023029 -0.068713 -0.067358  \n",
      "13  0.031211 -0.008008  0.004266  \n",
      "14 -0.003947 -0.009480 -0.037217  \n",
      "15 -0.025008 -0.026121 -0.077416  \n",
      "16  0.038116 -0.002284  0.063556  \n",
      "17 -0.015066 -0.012304 -0.068629  \n",
      "18 -0.004315  0.993432 -0.010579  \n",
      "19 -0.015590 -0.041741  0.830972  \n",
      "20  0.009264 -0.010394 -0.001062  \n",
      "21  0.900290 -0.028879 -0.025316  \n",
      "22 -0.014838 -0.030054  0.102923  \n",
      "23  0.001131 -0.017740 -0.021145  \n",
      "24  0.039347 -0.003606 -0.122846  \n",
      "25 -0.011492 -0.004410  0.005108  \n",
      "26 -0.046513 -0.005029  0.018787  \n",
      "27 -0.026042 -0.019706  0.015983  \n",
      "\n",
      "==============采用熵权法计算各个因子的权重===============\n",
      "\n",
      "熵权法求得的权重为：\n",
      " [0.51003924 0.37196525 0.42383248 0.41006175 0.39194759 0.42354063\n",
      " 0.37187584 0.43419561 0.43432332 0.44575935]\n",
      "\n",
      "各个样本得分如下：\n",
      "                 0\n",
      "0       11.321376\n",
      "1       -0.184785\n",
      "2        0.004869\n",
      "3       -0.035602\n",
      "4        0.451091\n",
      "...           ...\n",
      "114178   1.163633\n",
      "114179   0.768988\n",
      "114180  -0.559018\n",
      "114181  -0.221425\n",
      "114182   0.112322\n",
      "\n",
      "[114183 rows x 1 columns]\n",
      "\n",
      "前十大事件综合得分：\n",
      "             ID        综合得分\n",
      "4968      4968  111.608350\n",
      "5619      5619   65.399514\n",
      "5620      5620   65.380402\n",
      "8840      8840   62.262324\n",
      "69238    69238   40.922916\n",
      "66010    66010   35.105054\n",
      "102690  102690   34.610041\n",
      "82          82   33.724561\n",
      "91580    91580   32.712984\n",
      "73834    73834   32.152509\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>eventid</th>\n",
       "      <th>iyear</th>\n",
       "      <th>imonth</th>\n",
       "      <th>iday</th>\n",
       "      <th>approxdate</th>\n",
       "      <th>extended</th>\n",
       "      <th>resolution</th>\n",
       "      <th>country</th>\n",
       "      <th>country_txt</th>\n",
       "      <th>region</th>\n",
       "      <th>...</th>\n",
       "      <th>addnotes</th>\n",
       "      <th>scite1</th>\n",
       "      <th>scite2</th>\n",
       "      <th>scite3</th>\n",
       "      <th>dbsource</th>\n",
       "      <th>INT_LOG</th>\n",
       "      <th>INT_IDEO</th>\n",
       "      <th>INT_MISC</th>\n",
       "      <th>INT_ANY</th>\n",
       "      <th>related</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4968</th>\n",
       "      <td>200107240001</td>\n",
       "      <td>2001</td>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>186</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Peter Foster, Richard Allyne, Vilma Wimaladasa...</td>\n",
       "      <td>\"Tamil Tigers hit Sri Lanka airport; Suicide b...</td>\n",
       "      <td>\"Attack on airbase, airport leaves 14 dead, 13...</td>\n",
       "      <td>CETIS</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5619</th>\n",
       "      <td>200109110004</td>\n",
       "      <td>2001</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>217</td>\n",
       "      <td>United States</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>This attack was one of four related incidents ...</td>\n",
       "      <td>United States Government, The 9/11 Commission ...</td>\n",
       "      <td>Lindsay Kines, “United States on high alert af...</td>\n",
       "      <td>Joe Frolick, “Hijackers Ram Two Airliners Into...</td>\n",
       "      <td>CETIS</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>200109110004, 200109110005, 200109110006, 2001...</td>\n",
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       "    <tr>\n",
       "      <th>5620</th>\n",
       "      <td>200109110005</td>\n",
       "      <td>2001</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>217</td>\n",
       "      <td>United States</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>This attack was one of four related incidents ...</td>\n",
       "      <td>United States Government, The 9/11 Commission ...</td>\n",
       "      <td>Lindsay Kines, “United States on high alert af...</td>\n",
       "      <td>Joe Frolick, “Hijackers Ram Two Airliners Into...</td>\n",
       "      <td>CETIS</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>200109110005, 200109110004, 200109110006, 2001...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8840</th>\n",
       "      <td>200403210001</td>\n",
       "      <td>2004</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>141</td>\n",
       "      <td>Nepal</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>The Agence France Presse reported that there w...</td>\n",
       "      <td>Kedar Man Sing, “2 Deadly clashes in Nepal as ...</td>\n",
       "      <td>“AFP: Clashes in Nepal Reportedly Leave 500 Ma...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CETIS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69238</th>\n",
       "      <td>201408200027</td>\n",
       "      <td>2014</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaT</td>\n",
       "      <td>200</td>\n",
       "      <td>Syria</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"IS 'kills dozens of Syrian soldiers',\" BBC, A...</td>\n",
       "      <td>\"Islamic State group seizes Syrian air base,\" ...</td>\n",
       "      <td>\"IS steps up   attack   on last Syria army bas...</td>\n",
       "      <td>START Primary Collection</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66010</th>\n",
       "      <td>201406150063</td>\n",
       "      <td>2014</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaT</td>\n",
       "      <td>95</td>\n",
       "      <td>Iraq</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>Casualty numbers for this incident conflict ac...</td>\n",
       "      <td>\"Iraq exhumes 470 bodies of 'ISIL massacre vic...</td>\n",
       "      <td>\"Scene of Iraqi Massacre Becomes Shiite Pilgri...</td>\n",
       "      <td>\"Iraq Empties Mass Graves in Search for Cadets...</td>\n",
       "      <td>START Primary Collection</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102690</th>\n",
       "      <td>201612100011</td>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaT</td>\n",
       "      <td>200</td>\n",
       "      <td>Syria</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"IS Kills 12 People in Historic Syrian City of...</td>\n",
       "      <td>\"Russian military explain how Palmyra was reta...</td>\n",
       "      <td>\"Islamic State militants return to Syria's anc...</td>\n",
       "      <td>START Primary Collection</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>199802010001</td>\n",
       "      <td>1998</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>186</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>“300 rebels reported killed in Sri Lanka: 'Rin...</td>\n",
       "      <td>“Asia: Heavy Fighting Grips Sri Lanka, Toll hi...</td>\n",
       "      <td>“Asia: Sri Lankan Army Claims 220 Rebels Dead,...</td>\n",
       "      <td>CETIS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91580</th>\n",
       "      <td>201602180049</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>209</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"PKK attack on oil pipeline cost KRG $100mn: S...</td>\n",
       "      <td>\"BRIEF: Oil pipeline blown up in Turkey [Trend...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>START Primary Collection</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73834</th>\n",
       "      <td>201412070129</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>217</td>\n",
       "      <td>United States</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"Man Convicted of Starting Massive Da Vinci Fi...</td>\n",
       "      <td>\"Man Pleads Guilty, Gets 15 Years In Prison Fo...</td>\n",
       "      <td>\"Man accused of Da Vinci apartment arson was a...</td>\n",
       "      <td>START Primary Collection</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>0</td>\n",
       "      <td>-9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 135 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             eventid  iyear  imonth  iday approxdate  extended resolution  \\\n",
       "4968    200107240001   2001       7    24        NaN         0        NaT   \n",
       "5619    200109110004   2001       9    11        NaN         0        NaT   \n",
       "5620    200109110005   2001       9    11        NaN         0        NaT   \n",
       "8840    200403210001   2004       3    21        NaN         0        NaT   \n",
       "69238   201408200027   2014       8    19        NaN         1        NaT   \n",
       "66010   201406150063   2014       6    12        NaN         1        NaT   \n",
       "102690  201612100011   2016      12    10        NaN         1        NaT   \n",
       "82      199802010001   1998       2     1        NaN         0        NaT   \n",
       "91580   201602180049   2016       2    17        NaN         0        NaT   \n",
       "73834   201412070129   2014      12     7        NaN         0        NaT   \n",
       "\n",
       "        country    country_txt  region  ...  \\\n",
       "4968        186      Sri Lanka       6  ...   \n",
       "5619        217  United States       1  ...   \n",
       "5620        217  United States       1  ...   \n",
       "8840        141          Nepal       6  ...   \n",
       "69238       200          Syria      10  ...   \n",
       "66010        95           Iraq      10  ...   \n",
       "102690      200          Syria      10  ...   \n",
       "82          186      Sri Lanka       6  ...   \n",
       "91580       209         Turkey      10  ...   \n",
       "73834       217  United States       1  ...   \n",
       "\n",
       "                                                 addnotes  \\\n",
       "4968                                                  NaN   \n",
       "5619    This attack was one of four related incidents ...   \n",
       "5620    This attack was one of four related incidents ...   \n",
       "8840    The Agence France Presse reported that there w...   \n",
       "69238                                                 NaN   \n",
       "66010   Casualty numbers for this incident conflict ac...   \n",
       "102690                                                NaN   \n",
       "82                                                    NaN   \n",
       "91580                                                 NaN   \n",
       "73834                                                 NaN   \n",
       "\n",
       "                                                   scite1  \\\n",
       "4968    Peter Foster, Richard Allyne, Vilma Wimaladasa...   \n",
       "5619    United States Government, The 9/11 Commission ...   \n",
       "5620    United States Government, The 9/11 Commission ...   \n",
       "8840    Kedar Man Sing, “2 Deadly clashes in Nepal as ...   \n",
       "69238   \"IS 'kills dozens of Syrian soldiers',\" BBC, A...   \n",
       "66010   \"Iraq exhumes 470 bodies of 'ISIL massacre vic...   \n",
       "102690  \"IS Kills 12 People in Historic Syrian City of...   \n",
       "82      “300 rebels reported killed in Sri Lanka: 'Rin...   \n",
       "91580   \"PKK attack on oil pipeline cost KRG $100mn: S...   \n",
       "73834   \"Man Convicted of Starting Massive Da Vinci Fi...   \n",
       "\n",
       "                                                   scite2  \\\n",
       "4968    \"Tamil Tigers hit Sri Lanka airport; Suicide b...   \n",
       "5619    Lindsay Kines, “United States on high alert af...   \n",
       "5620    Lindsay Kines, “United States on high alert af...   \n",
       "8840    “AFP: Clashes in Nepal Reportedly Leave 500 Ma...   \n",
       "69238   \"Islamic State group seizes Syrian air base,\" ...   \n",
       "66010   \"Scene of Iraqi Massacre Becomes Shiite Pilgri...   \n",
       "102690  \"Russian military explain how Palmyra was reta...   \n",
       "82      “Asia: Heavy Fighting Grips Sri Lanka, Toll hi...   \n",
       "91580   \"BRIEF: Oil pipeline blown up in Turkey [Trend...   \n",
       "73834   \"Man Pleads Guilty, Gets 15 Years In Prison Fo...   \n",
       "\n",
       "                                                   scite3  \\\n",
       "4968    \"Attack on airbase, airport leaves 14 dead, 13...   \n",
       "5619    Joe Frolick, “Hijackers Ram Two Airliners Into...   \n",
       "5620    Joe Frolick, “Hijackers Ram Two Airliners Into...   \n",
       "8840                                                  NaN   \n",
       "69238   \"IS steps up   attack   on last Syria army bas...   \n",
       "66010   \"Iraq Empties Mass Graves in Search for Cadets...   \n",
       "102690  \"Islamic State militants return to Syria's anc...   \n",
       "82      “Asia: Sri Lankan Army Claims 220 Rebels Dead,...   \n",
       "91580                                                 NaN   \n",
       "73834   \"Man accused of Da Vinci apartment arson was a...   \n",
       "\n",
       "                        dbsource  INT_LOG  INT_IDEO INT_MISC INT_ANY  \\\n",
       "4968                       CETIS        0         1        1       1   \n",
       "5619                       CETIS        0         1        0       1   \n",
       "5620                       CETIS        0         1        0       1   \n",
       "8840                       CETIS        0         0        0       0   \n",
       "69238   START Primary Collection        0         1        0       1   \n",
       "66010   START Primary Collection        0         1        0       1   \n",
       "102690  START Primary Collection        0         1        0       1   \n",
       "82                         CETIS        0         0        0       0   \n",
       "91580   START Primary Collection        0         0        0       0   \n",
       "73834   START Primary Collection       -9        -9        0      -9   \n",
       "\n",
       "                                                  related  \n",
       "4968                                                  NaN  \n",
       "5619    200109110004, 200109110005, 200109110006, 2001...  \n",
       "5620    200109110005, 200109110004, 200109110006, 2001...  \n",
       "8840                                                  NaN  \n",
       "69238                                                 NaN  \n",
       "66010                                                 NaN  \n",
       "102690                                                NaN  \n",
       "82                                                    NaN  \n",
       "91580                                                 NaN  \n",
       "73834                                                 NaN  \n",
       "\n",
       "[10 rows x 135 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def Factor_analyzer(data):\n",
    "    \"\"\"\n",
    "    :param data: 原始数据\n",
    "    :return: 所有样本的综合得分，前十大恐怖事件ID\n",
    "    \"\"\" \n",
    "    print(\"\\n原始数据:\\n\",data)\n",
    "    #归一化\n",
    "    data = (data-data.mean())/data.std()  \n",
    "    print(\"\\n归一化之后的数据:\\n\",pd.DataFrame(data))\n",
    "    # 皮尔森相关系数\n",
    "    data_corr=data.corr()\n",
    "    print(\"\\n相关系数:\\n\",pd.DataFrame(data_corr))\n",
    "    #热力图\n",
    "    cmap = cm.Blues\n",
    "    sns.set()\n",
    "    plt.figure(figsize=(10, 10))\n",
    "    ax = sns.heatmap(data=data_corr,square=True,cmap=cmap, vmin=0, vmax=1) \n",
    "    plt.title('correlation coefficient--headmap')\n",
    "    ax.set_yticks(range(len(data_corr.columns)))\n",
    "    ax.set_yticklabels(data_corr.columns)\n",
    "    ax.set_xticks(range(len(data_corr)))\n",
    "    ax.set_xticklabels(data_corr.columns)\n",
    "    plt.show()\n",
    "    # KMO测度,0.9以上非常好；0.8以上好；0.7一般；0.6差；0.5很差；0.5以下不能接受；\n",
    "    kmo_all,kmo_model=calculate_kmo(data)\n",
    "    print(\"\\nKMO测度:\", kmo_model)\n",
    "    if kmo_model < 0.6:\n",
    "        print(\"\\nKMO测度不足0.6，不适合做因子分析\")\n",
    "    else:\n",
    "        print(\"\\nKMO测度大于0.6，适合做因子分析\")\n",
    "    #Bartlett's球状检验,用来判断变量是否适合用于做因子分析\n",
    "    #Bartlett球度统计量越大越好，其伴随概率<0.05，说明数据适合做因子分析\n",
    "    chi_square_value,p_value=calculate_bartlett_sphericity(data)\n",
    "    print(\"\\n巴特利特球形检验:\",p_value)\n",
    "    if p_value > 0.05:\n",
    "        print(\"\\n巴特利特球形检验的伴随概率大于0.05，不适合做因子分析\")\n",
    "    else:\n",
    "        print(\"\\n巴特利特球形检验的伴随概率小于0.05，适合做因子分析\")\n",
    "     # 求特征值和特征向量\n",
    "    fa = FactorAnalyzer(n_factors = 25,rotation=None)\n",
    "    fa.fit(data_corr)\n",
    "    ev, v = fa.get_eigenvalues()\n",
    "    print(\"\\n特征值:\",ev)\n",
    "    print(\"\\n特征向量\",v)\n",
    "    #绘制碎石图，选取特征值大于1的因子\n",
    "    print(\"\\n=================绘制碎石图==================\")\n",
    "    plt.figure(figsize=(6, 6))\n",
    "    plt.scatter(range(1,data.shape[1]+1),ev)\n",
    "    plt.plot(range(1,data.shape[1]+1),ev)\n",
    "    plt.title('Scree Plot')\n",
    "    plt.xlabel('Factors')\n",
    "    plt.ylabel('Eigenvalue')\n",
    "    plt.grid()\n",
    "    plt.show()\n",
    "    m = 0\n",
    "    for i in range(len(ev)):\n",
    "        if ev[i] > 1:\n",
    "            m = m+1;\n",
    "        else:\n",
    "            break;\n",
    "    print(\"由碎石图可知有\" + str(m) +\"个因子的特征值大于1，所以选择\" + str(m) +\"个因子重新进行因子分析\")\n",
    "    #根据选择的因子数重新执行因子分析\n",
    "    #使用最大方差法旋转因子载荷矩阵\n",
    "    fa = FactorAnalyzer(n_factors = m,rotation='varimax')\n",
    "    fa.fit(data_corr)\n",
    "    load = fa.loadings_\n",
    "    #旋转后的因子的载荷矩阵\n",
    "    print(\"\\n旋转后的因子荷载矩阵\",load)\n",
    "    fa_var = fa.get_factor_variance()\n",
    "    fa_df = pd.DataFrame(\n",
    "        {'特征值': fa_var[0], '方差贡献率': fa_var[1], '方差累计贡献率': fa_var[2]})\n",
    "    print(fa_df)\n",
    "     # 因子得分（回归方法）（系数矩阵的逆乘以因子载荷矩阵）\n",
    "    print(\"\\n==========以下采用回归方法计算因子得分============\")\n",
    "    X1 = np.mat(data_corr)\n",
    "    X1 = np.linalg.inv(X1)\n",
    "    factor_score = np.dot(X1,load)\n",
    "    factor_score = pd.DataFrame(factor_score)\n",
    "    tmp_columns = []\n",
    "    for index in range(m):\n",
    "       tmp = \"factor\" + str(index + 1)\n",
    "       tmp_columns.append(tmp)\n",
    "    factor_score.columns = tmp_columns\n",
    "    print(\"\\n因子得分\",factor_score)\n",
    "    print(\"\\n==============采用熵权法计算各个因子的权重===============\")\n",
    "    fa_t_score = np.dot(np.mat(data), np.mat(factor_score))\n",
    "    w = get_entropy_weight(fa_t_score.T)\n",
    "    print(\"\\n熵权法求得的权重为：\\n\",w)\n",
    "    fa_t_score = np.dot(fa_t_score,w)\n",
    "    fa_t_score = pd.DataFrame(fa_t_score.T)\n",
    "    print(\"\\n各个样本得分如下：\\n\",fa_t_score)\n",
    "    fa_t_score.columns = ['综合得分']\n",
    "    fa_t_score.insert(0, 'ID', range(0, data.shape[0]))\n",
    "    top10 = fa_t_score.sort_values(by='综合得分', ascending=False).head(10)\n",
    "    index = top10.ID\n",
    "    print(\"\\n前十大事件综合得分：\\n\", fa_t_score.sort_values(by='综合得分', ascending=False).head(10))\n",
    "    return fa_t_score,index\n",
    "fa_t_score,index = Factor_analyzer(df)\n",
    "terrorism_data.iloc[index,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f17252ce-cca2-401e-89ef-f003e3db8a46",
   "metadata": {},
   "source": [
    "# 6、PCA降维"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4d1274d3-6f81-43f4-98a0-e84c457363c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "原始数据:\n",
      "         extended  crit1  crit2  crit3  doubtterr  multiple  success  suicide  \\\n",
      "0              0      1      1      1        1.0       0.0        1        0   \n",
      "1              0      1      1      1        0.0       0.0        1        0   \n",
      "2              0      1      1      1        0.0       0.0        1        0   \n",
      "3              0      1      1      1        0.0       0.0        1        0   \n",
      "4              0      1      1      1        0.0       0.0        0        0   \n",
      "...          ...    ...    ...    ...        ...       ...      ...      ...   \n",
      "114178         0      1      1      0        1.0       0.0        1        0   \n",
      "114179         0      1      1      0        1.0       0.0        1        0   \n",
      "114180         0      1      1      1        0.0       0.0        1        0   \n",
      "114181         0      1      1      1        0.0       0.0        0        0   \n",
      "114182         0      1      1      1        0.0       0.0        0        0   \n",
      "\n",
      "        guncertain1  claimed  ...     propvalue  region  attacktype1  \\\n",
      "0               0.0        0  ...  37661.190113      11            2   \n",
      "1               0.0        0  ...  37661.190113       9            3   \n",
      "2               0.0        1  ...  37661.190113       8            2   \n",
      "3               0.0        0  ...  37661.190113      10            3   \n",
      "4               0.0        0  ...  37661.190113      10            2   \n",
      "...             ...      ...  ...           ...     ...          ...   \n",
      "114178          0.0        1  ...  37661.190113      11            2   \n",
      "114179          0.0        0  ...      0.000000      10            3   \n",
      "114180          0.0        0  ...      0.000000       5            7   \n",
      "114181          0.0        0  ...  37661.190113       6            3   \n",
      "114182          0.0        0  ...  37661.190113       5            3   \n",
      "\n",
      "        targtype1  natlty1  weaptype1  nhostkidisnull  ransomisnull  \\\n",
      "0               4     34.0          5               1             1   \n",
      "1              19    167.0          6               1             1   \n",
      "2              14    233.0          5               1             1   \n",
      "3               7    999.0          6               1             1   \n",
      "4              14     97.0          5               1             1   \n",
      "...           ...      ...        ...             ...           ...   \n",
      "114178          4    182.0          5               1             1   \n",
      "114179          4    167.0          6               1             1   \n",
      "114180         14    160.0          8               1             1   \n",
      "114181          2     92.0          6               1             1   \n",
      "114182         20    160.0          6               1             1   \n",
      "\n",
      "        hostkidoutcomeisnull  nreleasedisnull  \n",
      "0                          1                1  \n",
      "1                          1                1  \n",
      "2                          1                1  \n",
      "3                          1                1  \n",
      "4                          1                1  \n",
      "...                      ...              ...  \n",
      "114178                     1                1  \n",
      "114179                     1                1  \n",
      "114180                     1                1  \n",
      "114181                     1                1  \n",
      "114182                     1                1  \n",
      "\n",
      "[114183 rows x 28 columns]\n",
      "\n",
      "归一化之后的数据:\n",
      "         extended    crit1     crit2     crit3  doubtterr  multiple   success  \\\n",
      "0      -0.254289  0.10388  0.066585  0.371383   2.276259 -0.433116  0.379914   \n",
      "1      -0.254289  0.10388  0.066585  0.371383  -0.439317 -0.433116  0.379914   \n",
      "2      -0.254289  0.10388  0.066585  0.371383  -0.439317 -0.433116  0.379914   \n",
      "3      -0.254289  0.10388  0.066585  0.371383  -0.439317 -0.433116  0.379914   \n",
      "4      -0.254289  0.10388  0.066585  0.371383  -0.439317 -0.433116 -2.632150   \n",
      "...          ...      ...       ...       ...        ...       ...       ...   \n",
      "114178 -0.254289  0.10388  0.066585 -2.692611   2.276259 -0.433116  0.379914   \n",
      "114179 -0.254289  0.10388  0.066585 -2.692611   2.276259 -0.433116  0.379914   \n",
      "114180 -0.254289  0.10388  0.066585  0.371383  -0.439317 -0.433116  0.379914   \n",
      "114181 -0.254289  0.10388  0.066585  0.371383  -0.439317 -0.433116 -2.632150   \n",
      "114182 -0.254289  0.10388  0.066585  0.371383  -0.439317 -0.433116 -2.632150   \n",
      "\n",
      "         suicide  guncertain1   claimed  ...     propvalue    region  \\\n",
      "0      -0.246307    -0.341364 -0.441906  ...  6.199490e-18  1.255401   \n",
      "1      -0.246307    -0.341364 -0.441906  ...  6.199490e-18  0.411562   \n",
      "2      -0.246307    -0.341364  2.262902  ...  6.199490e-18 -0.010357   \n",
      "3      -0.246307    -0.341364 -0.441906  ...  6.199490e-18  0.833481   \n",
      "4      -0.246307    -0.341364 -0.441906  ...  6.199490e-18  0.833481   \n",
      "...          ...          ...       ...  ...           ...       ...   \n",
      "114178 -0.246307    -0.341364  2.262902  ...  6.199490e-18  1.255401   \n",
      "114179 -0.246307    -0.341364 -0.441906  ... -3.208927e-02  0.833481   \n",
      "114180 -0.246307    -0.341364 -0.441906  ... -3.208927e-02 -1.276116   \n",
      "114181 -0.246307    -0.341364 -0.441906  ...  6.199490e-18 -0.854196   \n",
      "114182 -0.246307    -0.341364 -0.441906  ...  6.199490e-18 -1.276116   \n",
      "\n",
      "        attacktype1  targtype1   natlty1  weaptype1  nhostkidisnull  \\\n",
      "0         -0.721953  -0.728072 -0.977917  -0.671773        0.298662   \n",
      "1         -0.184453   1.569985  0.410327  -0.188893        0.298662   \n",
      "2         -0.721953   0.803966  1.099230  -0.671773        0.298662   \n",
      "3         -0.184453  -0.268460  9.094679  -0.188893        0.298662   \n",
      "4         -0.721953   0.803966 -0.320328  -0.671773        0.298662   \n",
      "...             ...        ...       ...        ...             ...   \n",
      "114178    -0.721953  -0.728072  0.566896  -0.671773        0.298662   \n",
      "114179    -0.184453  -0.728072  0.410327  -0.188893        0.298662   \n",
      "114180     1.965548   0.803966  0.337261   0.776868        0.298662   \n",
      "114181    -0.184453  -1.034479 -0.372517  -0.188893        0.298662   \n",
      "114182    -0.184453   1.723188  0.337261  -0.188893        0.298662   \n",
      "\n",
      "        ransomisnull  hostkidoutcomeisnull  nreleasedisnull  \n",
      "0           0.314207              0.298436          0.29438  \n",
      "1           0.314207              0.298436          0.29438  \n",
      "2           0.314207              0.298436          0.29438  \n",
      "3           0.314207              0.298436          0.29438  \n",
      "4           0.314207              0.298436          0.29438  \n",
      "...              ...                   ...              ...  \n",
      "114178      0.314207              0.298436          0.29438  \n",
      "114179      0.314207              0.298436          0.29438  \n",
      "114180      0.314207              0.298436          0.29438  \n",
      "114181      0.314207              0.298436          0.29438  \n",
      "114182      0.314207              0.298436          0.29438  \n",
      "\n",
      "[114183 rows x 28 columns]\n",
      "\n",
      "相关系数:\n",
      "                       extended     crit1     crit2     crit3  doubtterr  \\\n",
      "extended              1.000000 -0.014972 -0.009618  0.052758  -0.036536   \n",
      "crit1                -0.014972  1.000000 -0.006917 -0.038579  -0.235302   \n",
      "crit2                -0.009618 -0.006917  1.000000 -0.024729  -0.151206   \n",
      "crit3                 0.052758 -0.038579 -0.024729  1.000000  -0.844498   \n",
      "doubtterr            -0.036536 -0.235302 -0.151206 -0.844498   1.000000   \n",
      "multiple             -0.016131  0.027000  0.003482  0.015683  -0.031812   \n",
      "success               0.092854 -0.014311 -0.014154  0.003908   0.014321   \n",
      "suicide              -0.053160  0.023018 -0.005218 -0.033787   0.019401   \n",
      "guncertain1           0.031274  0.008344 -0.010254  0.062718  -0.044397   \n",
      "claimed               0.030247  0.032767  0.000122 -0.071627   0.044390   \n",
      "property             -0.108715  0.008466  0.020164  0.101031  -0.096573   \n",
      "ishostkid             0.794057 -0.023671 -0.009021  0.071084  -0.048194   \n",
      "propextent            0.019826  0.010987 -0.002987 -0.091049   0.083900   \n",
      "nkill                 0.032664  0.009525 -0.011748 -0.028703   0.026832   \n",
      "nwound               -0.006002  0.006100  0.000891  0.009848  -0.010672   \n",
      "nperps                0.026715  0.003809 -0.003776  0.001260   0.002925   \n",
      "nkillter              0.005082  0.009731 -0.004859 -0.090445   0.076108   \n",
      "nwoundte              0.001526  0.005969 -0.001756 -0.054119   0.043968   \n",
      "propvalue            -0.000267  0.000641  0.000390  0.002106  -0.002244   \n",
      "region                0.007113  0.047530 -0.008285 -0.080026   0.051437   \n",
      "attacktype1           0.314123  0.006473 -0.015412 -0.019898   0.017755   \n",
      "targtype1             0.015739 -0.002702 -0.079419  0.270228  -0.228574   \n",
      "natlty1               0.016653 -0.017030 -0.014874 -0.125947   0.129171   \n",
      "weaptype1             0.241603  0.011433 -0.021617 -0.048709   0.042734   \n",
      "nhostkidisnull       -0.794058  0.023672  0.009022 -0.071089   0.048204   \n",
      "ransomisnull         -0.732499  0.022786  0.006335 -0.066210   0.040487   \n",
      "hostkidoutcomeisnull -0.794265  0.023730  0.009055 -0.070980   0.048052   \n",
      "nreleasedisnull      -0.791939  0.023525  0.008684 -0.069608   0.047946   \n",
      "\n",
      "                      multiple   success   suicide  guncertain1   claimed  \\\n",
      "extended             -0.016131  0.092854 -0.053160     0.031274  0.030247   \n",
      "crit1                 0.027000 -0.014311  0.023018     0.008344  0.032767   \n",
      "crit2                 0.003482 -0.014154 -0.005218    -0.010254  0.000122   \n",
      "crit3                 0.015683  0.003908 -0.033787     0.062718 -0.071627   \n",
      "doubtterr            -0.031812  0.014321  0.019401    -0.044397  0.044390   \n",
      "multiple              1.000000  0.022923  0.024961     0.013076  0.111089   \n",
      "success               0.022923  1.000000 -0.026382     0.047282  0.051228   \n",
      "suicide               0.024961 -0.026382  1.000000    -0.019161  0.154337   \n",
      "guncertain1           0.013076  0.047282 -0.019161     1.000000 -0.090610   \n",
      "claimed               0.111089  0.051228  0.154337    -0.090610  1.000000   \n",
      "property              0.089340  0.211924  0.023142     0.035700  0.039933   \n",
      "ishostkid            -0.035916  0.110584 -0.059399     0.039689  0.035956   \n",
      "propextent           -0.004014  0.014253  0.011858     0.037142  0.036868   \n",
      "nkill                 0.019589  0.051684  0.152122     0.005968  0.074783   \n",
      "nwound                0.019233  0.030004  0.100456     0.006625  0.036954   \n",
      "nperps                0.022884  0.015298 -0.001157     0.013286  0.019171   \n",
      "nkillter              0.014609 -0.019938  0.097713    -0.022535  0.065944   \n",
      "nwoundte              0.006252 -0.012837  0.000374    -0.015802  0.050844   \n",
      "propvalue            -0.003886  0.000908  0.011417     0.001438  0.002252   \n",
      "region                0.038533  0.029712  0.087687    -0.099022 -0.036520   \n",
      "attacktype1           0.080548  0.053459 -0.051798     0.012520  0.036354   \n",
      "targtype1             0.032281 -0.101772 -0.041765     0.008346 -0.088949   \n",
      "natlty1               0.010963  0.010230 -0.011237    -0.003428 -0.013173   \n",
      "weaptype1             0.046145 -0.015187 -0.046526     0.015182  0.023410   \n",
      "nhostkidisnull        0.035912 -0.110581  0.059397    -0.040102 -0.035873   \n",
      "ransomisnull          0.039270 -0.101201  0.061962    -0.046541 -0.029385   \n",
      "hostkidoutcomeisnull  0.035755 -0.110493  0.059332    -0.039640 -0.035734   \n",
      "nreleasedisnull       0.033629 -0.108919  0.058169    -0.037629 -0.034302   \n",
      "\n",
      "                      ...  propvalue    region  attacktype1  targtype1  \\\n",
      "extended              ...  -0.000267  0.007113     0.314123   0.015739   \n",
      "crit1                 ...   0.000641  0.047530     0.006473  -0.002702   \n",
      "crit2                 ...   0.000390 -0.008285    -0.015412  -0.079419   \n",
      "crit3                 ...   0.002106 -0.080026    -0.019898   0.270228   \n",
      "doubtterr             ...  -0.002244  0.051437     0.017755  -0.228574   \n",
      "multiple              ...  -0.003886  0.038533     0.080548   0.032281   \n",
      "success               ...   0.000908  0.029712     0.053459  -0.101772   \n",
      "suicide               ...   0.011417  0.087687    -0.051798  -0.041765   \n",
      "guncertain1           ...   0.001438 -0.099022     0.012520   0.008346   \n",
      "claimed               ...   0.002252 -0.036520     0.036354  -0.088949   \n",
      "property              ...   0.000192 -0.042404     0.027602  -0.017748   \n",
      "ishostkid             ...  -0.000428 -0.001247     0.364595   0.028164   \n",
      "propextent            ...  -0.031205  0.004287    -0.009295   0.032580   \n",
      "nkill                 ...   0.004091  0.060639     0.015012  -0.001018   \n",
      "nwound                ...   0.002430  0.009134    -0.004707   0.006162   \n",
      "nperps                ...   0.000985 -0.027276     0.015918  -0.012587   \n",
      "nkillter              ...   0.009072  0.027389     0.036094  -0.038395   \n",
      "nwoundte              ...  -0.000324 -0.028293     0.029474  -0.032603   \n",
      "propvalue             ...   1.000000 -0.007977     0.003125   0.000259   \n",
      "region                ...  -0.007977  1.000000    -0.017458   0.038384   \n",
      "attacktype1           ...   0.003125 -0.017458     1.000000   0.031392   \n",
      "targtype1             ...   0.000259  0.038384     0.031392   1.000000   \n",
      "natlty1               ...   0.003177  0.121712     0.016522  -0.014322   \n",
      "weaptype1             ...   0.001401  0.022221     0.712524   0.023185   \n",
      "nhostkidisnull        ...   0.000428  0.001409    -0.364602  -0.028147   \n",
      "ransomisnull          ...  -0.000894 -0.001514    -0.345119  -0.024216   \n",
      "hostkidoutcomeisnull  ...   0.000427  0.001176    -0.364446  -0.027958   \n",
      "nreleasedisnull       ...   0.000707 -0.000470    -0.361069  -0.027401   \n",
      "\n",
      "                       natlty1  weaptype1  nhostkidisnull  ransomisnull  \\\n",
      "extended              0.016653   0.241603       -0.794058     -0.732499   \n",
      "crit1                -0.017030   0.011433        0.023672      0.022786   \n",
      "crit2                -0.014874  -0.021617        0.009022      0.006335   \n",
      "crit3                -0.125947  -0.048709       -0.071089     -0.066210   \n",
      "doubtterr             0.129171   0.042734        0.048204      0.040487   \n",
      "multiple              0.010963   0.046145        0.035912      0.039270   \n",
      "success               0.010230  -0.015187       -0.110581     -0.101201   \n",
      "suicide              -0.011237  -0.046526        0.059397      0.061962   \n",
      "guncertain1          -0.003428   0.015182       -0.040102     -0.046541   \n",
      "claimed              -0.013173   0.023410       -0.035873     -0.029385   \n",
      "property              0.025732  -0.046584        0.129064      0.118679   \n",
      "ishostkid             0.014307   0.254670       -0.999300     -0.919365   \n",
      "propextent            0.017380   0.006927       -0.016525     -0.031015   \n",
      "nkill                 0.009132   0.017361       -0.036818     -0.032622   \n",
      "nwound                0.002730  -0.004113       -0.001304     -0.000073   \n",
      "nperps                0.009681   0.009078       -0.025563     -0.021221   \n",
      "nkillter              0.002139   0.040126        0.013219      0.015868   \n",
      "nwoundte             -0.043074   0.032200        0.011378      0.013051   \n",
      "propvalue             0.003177   0.001401        0.000428     -0.000894   \n",
      "region                0.121712   0.022221        0.001409     -0.001514   \n",
      "attacktype1           0.016522   0.712524       -0.364602     -0.345119   \n",
      "targtype1            -0.014322   0.023185       -0.028147     -0.024216   \n",
      "natlty1               1.000000  -0.002310       -0.014270      0.005838   \n",
      "weaptype1            -0.002310   1.000000       -0.254926     -0.235493   \n",
      "nhostkidisnull       -0.014270  -0.254926        1.000000      0.919478   \n",
      "ransomisnull          0.005838  -0.235493        0.919478      1.000000   \n",
      "hostkidoutcomeisnull -0.014316  -0.254518        0.999010      0.919186   \n",
      "nreleasedisnull      -0.014750  -0.252050        0.985072      0.912819   \n",
      "\n",
      "                      hostkidoutcomeisnull  nreleasedisnull  \n",
      "extended                         -0.794265        -0.791939  \n",
      "crit1                             0.023730         0.023525  \n",
      "crit2                             0.009055         0.008684  \n",
      "crit3                            -0.070980        -0.069608  \n",
      "doubtterr                         0.048052         0.047946  \n",
      "multiple                          0.035755         0.033629  \n",
      "success                          -0.110493        -0.108919  \n",
      "suicide                           0.059332         0.058169  \n",
      "guncertain1                      -0.039640        -0.037629  \n",
      "claimed                          -0.035734        -0.034302  \n",
      "property                          0.128949         0.126186  \n",
      "ishostkid                        -0.998775        -0.984717  \n",
      "propextent                       -0.016514        -0.014883  \n",
      "nkill                            -0.036892        -0.036938  \n",
      "nwound                           -0.001362        -0.001536  \n",
      "nperps                           -0.025471        -0.022302  \n",
      "nkillter                          0.013192         0.012572  \n",
      "nwoundte                          0.011355         0.011074  \n",
      "propvalue                         0.000427         0.000707  \n",
      "region                            0.001176        -0.000470  \n",
      "attacktype1                      -0.364446        -0.361069  \n",
      "targtype1                        -0.027958        -0.027401  \n",
      "natlty1                          -0.014316        -0.014750  \n",
      "weaptype1                        -0.254518        -0.252050  \n",
      "nhostkidisnull                    0.999010         0.985072  \n",
      "ransomisnull                      0.919186         0.912819  \n",
      "hostkidoutcomeisnull              1.000000         0.984993  \n",
      "nreleasedisnull                   0.984993         1.000000  \n",
      "\n",
      "[28 rows x 28 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "选择累计贡献率大于等于0.95的因子\n",
      "         特征值     方差贡献率   方差累计贡献率\n",
      "0   5.849536  0.208912  0.208912\n",
      "1   2.157024  0.077037  0.285949\n",
      "2   1.829865  0.065352  0.351301\n",
      "3   1.494630  0.053380  0.404681\n",
      "4   1.342591  0.047950  0.452630\n",
      "5   1.236764  0.044170  0.496800\n",
      "6   1.190395  0.042514  0.539314\n",
      "7   1.092575  0.039021  0.578335\n",
      "8   1.078086  0.038503  0.616838\n",
      "9   1.053093  0.037610  0.654449\n",
      "10  1.005514  0.035911  0.690360\n",
      "11  0.990792  0.035385  0.725745\n",
      "12  0.970863  0.034674  0.760419\n",
      "13  0.929197  0.033186  0.793604\n",
      "14  0.920482  0.032874  0.826479\n",
      "15  0.880549  0.031448  0.857927\n",
      "16  0.761612  0.027200  0.885127\n",
      "17  0.726176  0.025935  0.911062\n",
      "18  0.672632  0.024023  0.935085\n",
      "19  0.657019  0.023465  0.958550\n",
      "\n",
      "成分得分系数矩阵: \n",
      "           0         1         2         3         4         5         6   \\\n",
      "0   0.143831  0.004822 -0.003061  0.050269 -0.003894  0.009369 -0.023761   \n",
      "1  -0.003954 -0.055118  0.075587 -0.063373  0.013016 -0.070408 -0.336508   \n",
      "2  -0.002323 -0.037687  0.018743  0.021974 -0.054305  0.139823 -0.166610   \n",
      "3   0.014254 -0.387165  0.187803 -0.022377  0.041968  0.012189  0.011702   \n",
      "4  -0.010147  0.388499 -0.207833  0.041919 -0.037639 -0.013964  0.141735   \n",
      "5  -0.004777  0.000530  0.074412 -0.190281 -0.178226 -0.060778 -0.223818   \n",
      "6   0.021167  0.016955  0.038715  0.021429 -0.453025  0.141573  0.097812   \n",
      "7  -0.013322  0.076844  0.159882  0.080418 -0.085131 -0.161992 -0.310394   \n",
      "8   0.008599 -0.053388  0.010397 -0.010732 -0.052901  0.149917  0.339989   \n",
      "9   0.006693  0.094337  0.098024 -0.027257 -0.186427  0.096681 -0.462726   \n",
      "10 -0.023727 -0.069402  0.093542 -0.124902 -0.471890  0.122401  0.086492   \n",
      "11  0.168186 -0.002837 -0.005848  0.076870 -0.010201  0.006820 -0.016652   \n",
      "12  0.004206  0.069191 -0.044484  0.055224  0.173858 -0.045470 -0.086472   \n",
      "13  0.007251  0.153676  0.415807  0.086808 -0.003739 -0.130699  0.190251   \n",
      "14  0.000461  0.079875  0.331386  0.094609 -0.106509 -0.248655  0.277959   \n",
      "15  0.005350  0.024576  0.059923 -0.015867  0.001406  0.182188  0.001534   \n",
      "16 -0.001103  0.177804  0.279654 -0.020119  0.231082  0.227500 -0.051498   \n",
      "17 -0.001081  0.119510  0.184293 -0.050247  0.252144  0.372519 -0.057851   \n",
      "18 -0.000006 -0.000389  0.010763 -0.005094 -0.014309  0.016238  0.010780   \n",
      "19 -0.000240  0.066472  0.012896  0.026195 -0.011084 -0.459857 -0.186360   \n",
      "20  0.080343  0.060060  0.013146 -0.509121 -0.005259 -0.046907  0.061583   \n",
      "21  0.006898 -0.178633  0.074374 -0.072329  0.238886 -0.276150  0.096882   \n",
      "22  0.001859  0.092145 -0.065052  0.018643 -0.111071 -0.290551  0.069133   \n",
      "23  0.062018  0.076021  0.002502 -0.531528  0.072411 -0.085015  0.063864   \n",
      "24 -0.168209  0.002851  0.005842 -0.076803  0.010217 -0.006930  0.016471   \n",
      "25 -0.159342  0.003370  0.008609 -0.076308  0.006544 -0.011545  0.011539   \n",
      "26 -0.168180  0.002782  0.005843 -0.076970  0.010275 -0.006799  0.016604   \n",
      "27 -0.167034  0.002635  0.005605 -0.076784  0.010942 -0.005503  0.017341   \n",
      "\n",
      "          7         8         9         10        11        12        13  \\\n",
      "0  -0.027227  0.002483  0.002997  0.003024  0.004005 -0.000928 -0.028661   \n",
      "1  -0.046640 -0.092390 -0.515613  0.338775 -0.371373 -0.367033 -0.039739   \n",
      "2   0.168963  0.352209 -0.515552 -0.232619  0.478948  0.307945  0.038366   \n",
      "3  -0.050446 -0.009095  0.124699 -0.059331  0.025121  0.068320  0.054707   \n",
      "4   0.026789 -0.040561  0.131810  0.004399 -0.004991 -0.016962 -0.047208   \n",
      "5  -0.103063 -0.277497  0.131008 -0.058574  0.297494  0.180250 -0.353163   \n",
      "6  -0.219289 -0.130525 -0.074560 -0.059941 -0.256615  0.154981  0.421450   \n",
      "7   0.198918 -0.071861  0.139373  0.121195  0.004181  0.038660 -0.241356   \n",
      "8   0.050552 -0.416930 -0.305363  0.253909  0.031783  0.150658 -0.501654   \n",
      "9   0.212426 -0.169668  0.262378 -0.018034  0.041421  0.016286  0.046268   \n",
      "10 -0.190140  0.045609  0.061765 -0.045782 -0.078192  0.029306 -0.015423   \n",
      "11 -0.011159  0.006355  0.011522  0.002739  0.001612 -0.003902 -0.029039   \n",
      "12  0.084689 -0.588645 -0.223138 -0.076439  0.065335  0.324817  0.489227   \n",
      "13  0.082584  0.015052 -0.056934 -0.021068  0.016194 -0.021482  0.053917   \n",
      "14  0.302040  0.032721 -0.067081 -0.028038  0.057506 -0.114121  0.088181   \n",
      "15 -0.235098 -0.168072  0.016129  0.125966  0.580836 -0.632671  0.258694   \n",
      "16 -0.281872  0.047022 -0.001380 -0.010531 -0.057229  0.127742 -0.083810   \n",
      "17 -0.288513  0.059568  0.024848 -0.059339 -0.143294  0.154994 -0.067935   \n",
      "18  0.009079  0.219542  0.143091  0.837087  0.170316  0.354999  0.246978   \n",
      "19 -0.392910  0.133862 -0.127457 -0.052754 -0.070575  0.098211  0.074527   \n",
      "20  0.084482  0.041675 -0.031722 -0.015641 -0.008267  0.001473  0.075520   \n",
      "21 -0.162351 -0.191839  0.237212 -0.040901  0.062593  0.113892  0.074493   \n",
      "22 -0.413188  0.002209 -0.143002  0.037971  0.266139  0.071530 -0.186762   \n",
      "23  0.126673  0.051745 -0.059491 -0.004404 -0.015855 -0.016199  0.085477   \n",
      "24  0.011072 -0.006225 -0.011403 -0.002850 -0.001607  0.003863  0.029291   \n",
      "25  0.000664  0.002316 -0.004285 -0.003195  0.004584 -0.003015  0.018526   \n",
      "26  0.011224 -0.006445 -0.011531 -0.002715 -0.001588  0.003847  0.029242   \n",
      "27  0.012151 -0.008548 -0.011794 -0.001642 -0.000272  0.002156  0.031511   \n",
      "\n",
      "          14        15        16        17        18        19  \n",
      "0   0.019170  0.009177 -0.016746  0.032359 -0.002095  0.030210  \n",
      "1   0.254107 -0.082569 -0.234433  0.025656 -0.008979 -0.110926  \n",
      "2  -0.033971  0.051902 -0.263871 -0.180985 -0.012762 -0.154029  \n",
      "3  -0.128009 -0.085776  0.346531  0.110477 -0.001619  0.081066  \n",
      "4   0.050428  0.095560 -0.230396 -0.082092  0.006033 -0.028082  \n",
      "5   0.534371  0.378704  0.084089  0.312678  0.000240 -0.125179  \n",
      "6  -0.075605  0.149675  0.149602 -0.083804 -0.034948 -0.672808  \n",
      "7  -0.668936  0.074171 -0.197687  0.345857  0.237553 -0.306758  \n",
      "8  -0.240584  0.116312  0.118490 -0.424676 -0.013324  0.095740  \n",
      "9   0.057358 -0.364101  0.183218 -0.670021 -0.100706  0.141366  \n",
      "10 -0.078466 -0.119797 -0.566753  0.135651  0.053788  0.654927  \n",
      "11  0.016237  0.002020 -0.036733  0.014126  0.007715  0.020118  \n",
      "12 -0.045838 -0.101839 -0.051756  0.304655  0.075093  0.339454  \n",
      "13  0.077961 -0.000807  0.014744  0.016170 -0.296965  0.045242  \n",
      "14  0.238318 -0.024854  0.060246 -0.045588  0.403906  0.025996  \n",
      "15 -0.177280  0.163869 -0.002503 -0.054264  0.073201  0.003184  \n",
      "16 -0.098253 -0.023559 -0.035490  0.116526 -0.672288  0.007073  \n",
      "17  0.071763 -0.043855  0.013434 -0.091086  0.829004 -0.024493  \n",
      "18  0.102023  0.038525  0.002553  0.015943  0.020911  0.050226  \n",
      "19 -0.188438  0.452988  0.227133 -0.357748  0.082905  0.399915  \n",
      "20 -0.109035 -0.047642  0.024938  0.040183  0.011235 -0.038145  \n",
      "21  0.058650 -0.034539 -0.679746 -0.427622 -0.022965 -0.369527  \n",
      "22 -0.016281 -0.725756  0.185072  0.131986  0.075331 -0.182564  \n",
      "23 -0.145860 -0.013764  0.116228 -0.021989  0.008586 -0.019393  \n",
      "24 -0.016123 -0.002049  0.036767 -0.014062 -0.007687 -0.020155  \n",
      "25 -0.014032 -0.020024  0.045775 -0.009147 -0.006937 -0.052042  \n",
      "26 -0.016336 -0.002197  0.036810 -0.014568 -0.007772 -0.020424  \n",
      "27 -0.017758 -0.003106  0.038275 -0.017340 -0.007815 -0.022105  \n",
      "\n",
      "主成分得分      factor1   factor2   factor3   factor4   factor5   factor6   factor7  \\\n",
      "0   0.143831  0.004822 -0.003061  0.050269 -0.003894  0.009369 -0.023761   \n",
      "1  -0.003954 -0.055118  0.075587 -0.063373  0.013016 -0.070408 -0.336508   \n",
      "2  -0.002323 -0.037687  0.018743  0.021974 -0.054305  0.139823 -0.166610   \n",
      "3   0.014254 -0.387165  0.187803 -0.022377  0.041968  0.012189  0.011702   \n",
      "4  -0.010147  0.388499 -0.207833  0.041919 -0.037639 -0.013964  0.141735   \n",
      "5  -0.004777  0.000530  0.074412 -0.190281 -0.178226 -0.060778 -0.223818   \n",
      "6   0.021167  0.016955  0.038715  0.021429 -0.453025  0.141573  0.097812   \n",
      "7  -0.013322  0.076844  0.159882  0.080418 -0.085131 -0.161992 -0.310394   \n",
      "8   0.008599 -0.053388  0.010397 -0.010732 -0.052901  0.149917  0.339989   \n",
      "9   0.006693  0.094337  0.098024 -0.027257 -0.186427  0.096681 -0.462726   \n",
      "10 -0.023727 -0.069402  0.093542 -0.124902 -0.471890  0.122401  0.086492   \n",
      "11  0.168186 -0.002837 -0.005848  0.076870 -0.010201  0.006820 -0.016652   \n",
      "12  0.004206  0.069191 -0.044484  0.055224  0.173858 -0.045470 -0.086472   \n",
      "13  0.007251  0.153676  0.415807  0.086808 -0.003739 -0.130699  0.190251   \n",
      "14  0.000461  0.079875  0.331386  0.094609 -0.106509 -0.248655  0.277959   \n",
      "15  0.005350  0.024576  0.059923 -0.015867  0.001406  0.182188  0.001534   \n",
      "16 -0.001103  0.177804  0.279654 -0.020119  0.231082  0.227500 -0.051498   \n",
      "17 -0.001081  0.119510  0.184293 -0.050247  0.252144  0.372519 -0.057851   \n",
      "18 -0.000006 -0.000389  0.010763 -0.005094 -0.014309  0.016238  0.010780   \n",
      "19 -0.000240  0.066472  0.012896  0.026195 -0.011084 -0.459857 -0.186360   \n",
      "20  0.080343  0.060060  0.013146 -0.509121 -0.005259 -0.046907  0.061583   \n",
      "21  0.006898 -0.178633  0.074374 -0.072329  0.238886 -0.276150  0.096882   \n",
      "22  0.001859  0.092145 -0.065052  0.018643 -0.111071 -0.290551  0.069133   \n",
      "23  0.062018  0.076021  0.002502 -0.531528  0.072411 -0.085015  0.063864   \n",
      "24 -0.168209  0.002851  0.005842 -0.076803  0.010217 -0.006930  0.016471   \n",
      "25 -0.159342  0.003370  0.008609 -0.076308  0.006544 -0.011545  0.011539   \n",
      "26 -0.168180  0.002782  0.005843 -0.076970  0.010275 -0.006799  0.016604   \n",
      "27 -0.167034  0.002635  0.005605 -0.076784  0.010942 -0.005503  0.017341   \n",
      "\n",
      "     factor8   factor9  factor10  factor11  factor12  factor13  factor14  \\\n",
      "0  -0.027227  0.002483  0.002997  0.003024  0.004005 -0.000928 -0.028661   \n",
      "1  -0.046640 -0.092390 -0.515613  0.338775 -0.371373 -0.367033 -0.039739   \n",
      "2   0.168963  0.352209 -0.515552 -0.232619  0.478948  0.307945  0.038366   \n",
      "3  -0.050446 -0.009095  0.124699 -0.059331  0.025121  0.068320  0.054707   \n",
      "4   0.026789 -0.040561  0.131810  0.004399 -0.004991 -0.016962 -0.047208   \n",
      "5  -0.103063 -0.277497  0.131008 -0.058574  0.297494  0.180250 -0.353163   \n",
      "6  -0.219289 -0.130525 -0.074560 -0.059941 -0.256615  0.154981  0.421450   \n",
      "7   0.198918 -0.071861  0.139373  0.121195  0.004181  0.038660 -0.241356   \n",
      "8   0.050552 -0.416930 -0.305363  0.253909  0.031783  0.150658 -0.501654   \n",
      "9   0.212426 -0.169668  0.262378 -0.018034  0.041421  0.016286  0.046268   \n",
      "10 -0.190140  0.045609  0.061765 -0.045782 -0.078192  0.029306 -0.015423   \n",
      "11 -0.011159  0.006355  0.011522  0.002739  0.001612 -0.003902 -0.029039   \n",
      "12  0.084689 -0.588645 -0.223138 -0.076439  0.065335  0.324817  0.489227   \n",
      "13  0.082584  0.015052 -0.056934 -0.021068  0.016194 -0.021482  0.053917   \n",
      "14  0.302040  0.032721 -0.067081 -0.028038  0.057506 -0.114121  0.088181   \n",
      "15 -0.235098 -0.168072  0.016129  0.125966  0.580836 -0.632671  0.258694   \n",
      "16 -0.281872  0.047022 -0.001380 -0.010531 -0.057229  0.127742 -0.083810   \n",
      "17 -0.288513  0.059568  0.024848 -0.059339 -0.143294  0.154994 -0.067935   \n",
      "18  0.009079  0.219542  0.143091  0.837087  0.170316  0.354999  0.246978   \n",
      "19 -0.392910  0.133862 -0.127457 -0.052754 -0.070575  0.098211  0.074527   \n",
      "20  0.084482  0.041675 -0.031722 -0.015641 -0.008267  0.001473  0.075520   \n",
      "21 -0.162351 -0.191839  0.237212 -0.040901  0.062593  0.113892  0.074493   \n",
      "22 -0.413188  0.002209 -0.143002  0.037971  0.266139  0.071530 -0.186762   \n",
      "23  0.126673  0.051745 -0.059491 -0.004404 -0.015855 -0.016199  0.085477   \n",
      "24  0.011072 -0.006225 -0.011403 -0.002850 -0.001607  0.003863  0.029291   \n",
      "25  0.000664  0.002316 -0.004285 -0.003195  0.004584 -0.003015  0.018526   \n",
      "26  0.011224 -0.006445 -0.011531 -0.002715 -0.001588  0.003847  0.029242   \n",
      "27  0.012151 -0.008548 -0.011794 -0.001642 -0.000272  0.002156  0.031511   \n",
      "\n",
      "    factor15  factor16  factor17  factor18  factor19  factor20  \n",
      "0   0.019170  0.009177 -0.016746  0.032359 -0.002095  0.030210  \n",
      "1   0.254107 -0.082569 -0.234433  0.025656 -0.008979 -0.110926  \n",
      "2  -0.033971  0.051902 -0.263871 -0.180985 -0.012762 -0.154029  \n",
      "3  -0.128009 -0.085776  0.346531  0.110477 -0.001619  0.081066  \n",
      "4   0.050428  0.095560 -0.230396 -0.082092  0.006033 -0.028082  \n",
      "5   0.534371  0.378704  0.084089  0.312678  0.000240 -0.125179  \n",
      "6  -0.075605  0.149675  0.149602 -0.083804 -0.034948 -0.672808  \n",
      "7  -0.668936  0.074171 -0.197687  0.345857  0.237553 -0.306758  \n",
      "8  -0.240584  0.116312  0.118490 -0.424676 -0.013324  0.095740  \n",
      "9   0.057358 -0.364101  0.183218 -0.670021 -0.100706  0.141366  \n",
      "10 -0.078466 -0.119797 -0.566753  0.135651  0.053788  0.654927  \n",
      "11  0.016237  0.002020 -0.036733  0.014126  0.007715  0.020118  \n",
      "12 -0.045838 -0.101839 -0.051756  0.304655  0.075093  0.339454  \n",
      "13  0.077961 -0.000807  0.014744  0.016170 -0.296965  0.045242  \n",
      "14  0.238318 -0.024854  0.060246 -0.045588  0.403906  0.025996  \n",
      "15 -0.177280  0.163869 -0.002503 -0.054264  0.073201  0.003184  \n",
      "16 -0.098253 -0.023559 -0.035490  0.116526 -0.672288  0.007073  \n",
      "17  0.071763 -0.043855  0.013434 -0.091086  0.829004 -0.024493  \n",
      "18  0.102023  0.038525  0.002553  0.015943  0.020911  0.050226  \n",
      "19 -0.188438  0.452988  0.227133 -0.357748  0.082905  0.399915  \n",
      "20 -0.109035 -0.047642  0.024938  0.040183  0.011235 -0.038145  \n",
      "21  0.058650 -0.034539 -0.679746 -0.427622 -0.022965 -0.369527  \n",
      "22 -0.016281 -0.725756  0.185072  0.131986  0.075331 -0.182564  \n",
      "23 -0.145860 -0.013764  0.116228 -0.021989  0.008586 -0.019393  \n",
      "24 -0.016123 -0.002049  0.036767 -0.014062 -0.007687 -0.020155  \n",
      "25 -0.014032 -0.020024  0.045775 -0.009147 -0.006937 -0.052042  \n",
      "26 -0.016336 -0.002197  0.036810 -0.014568 -0.007772 -0.020424  \n",
      "27 -0.017758 -0.003106  0.038275 -0.017340 -0.007815 -0.022105  \n",
      "\n",
      "==============采用熵权法计算各个因子的权重===============\n",
      "\n",
      "熵权法求得的权重为：\n",
      " [0.43389939 0.41997934 0.46816428 0.51093964 0.48138861 0.47841789\n",
      " 0.48395431 0.46222017 0.52382801 0.45297558 0.46685408 0.44676709\n",
      " 0.46412857 0.51808389 0.49463159 0.49747974 0.49028221 0.47481122\n",
      " 0.47155332 0.45173136]\n",
      "\n",
      "各个样本得分如下：\n",
      "                0\n",
      "0       2.675332\n",
      "1      -1.313566\n",
      "2      -2.035353\n",
      "3      -6.000208\n",
      "4       0.831532\n",
      "...          ...\n",
      "114178 -1.138537\n",
      "114179  0.205626\n",
      "114180 -0.882241\n",
      "114181  2.188628\n",
      "114182 -0.147388\n",
      "\n",
      "[114183 rows x 1 columns]\n",
      "\n",
      "前十大事件综合得分：\n",
      "           ID        综合得分\n",
      "4968    4968  314.360669\n",
      "5619    5619  166.730540\n",
      "5620    5620  166.722183\n",
      "8840    8840   90.384379\n",
      "91580  91580   86.755881\n",
      "73834  73834   86.365049\n",
      "564      564   67.904377\n",
      "6285    6285   65.841256\n",
      "69379  69379   61.435337\n",
      "96079  96079   52.149924\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>eventid</th>\n",
       "      <th>iyear</th>\n",
       "      <th>imonth</th>\n",
       "      <th>iday</th>\n",
       "      <th>approxdate</th>\n",
       "      <th>extended</th>\n",
       "      <th>resolution</th>\n",
       "      <th>country</th>\n",
       "      <th>country_txt</th>\n",
       "      <th>region</th>\n",
       "      <th>...</th>\n",
       "      <th>addnotes</th>\n",
       "      <th>scite1</th>\n",
       "      <th>scite2</th>\n",
       "      <th>scite3</th>\n",
       "      <th>dbsource</th>\n",
       "      <th>INT_LOG</th>\n",
       "      <th>INT_IDEO</th>\n",
       "      <th>INT_MISC</th>\n",
       "      <th>INT_ANY</th>\n",
       "      <th>related</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4968</th>\n",
       "      <td>200107240001</td>\n",
       "      <td>2001</td>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>186</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Peter Foster, Richard Allyne, Vilma Wimaladasa...</td>\n",
       "      <td>\"Tamil Tigers hit Sri Lanka airport; Suicide b...</td>\n",
       "      <td>\"Attack on airbase, airport leaves 14 dead, 13...</td>\n",
       "      <td>CETIS</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5619</th>\n",
       "      <td>200109110004</td>\n",
       "      <td>2001</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>217</td>\n",
       "      <td>United States</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>This attack was one of four related incidents ...</td>\n",
       "      <td>United States Government, The 9/11 Commission ...</td>\n",
       "      <td>Lindsay Kines, “United States on high alert af...</td>\n",
       "      <td>Joe Frolick, “Hijackers Ram Two Airliners Into...</td>\n",
       "      <td>CETIS</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>200109110004, 200109110005, 200109110006, 2001...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5620</th>\n",
       "      <td>200109110005</td>\n",
       "      <td>2001</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>217</td>\n",
       "      <td>United States</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>This attack was one of four related incidents ...</td>\n",
       "      <td>United States Government, The 9/11 Commission ...</td>\n",
       "      <td>Lindsay Kines, “United States on high alert af...</td>\n",
       "      <td>Joe Frolick, “Hijackers Ram Two Airliners Into...</td>\n",
       "      <td>CETIS</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>200109110005, 200109110004, 200109110006, 2001...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8840</th>\n",
       "      <td>200403210001</td>\n",
       "      <td>2004</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>141</td>\n",
       "      <td>Nepal</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>The Agence France Presse reported that there w...</td>\n",
       "      <td>Kedar Man Sing, “2 Deadly clashes in Nepal as ...</td>\n",
       "      <td>“AFP: Clashes in Nepal Reportedly Leave 500 Ma...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CETIS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91580</th>\n",
       "      <td>201602180049</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>209</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"PKK attack on oil pipeline cost KRG $100mn: S...</td>\n",
       "      <td>\"BRIEF: Oil pipeline blown up in Turkey [Trend...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>START Primary Collection</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73834</th>\n",
       "      <td>201412070129</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>217</td>\n",
       "      <td>United States</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"Man Convicted of Starting Massive Da Vinci Fi...</td>\n",
       "      <td>\"Man Pleads Guilty, Gets 15 Years In Prison Fo...</td>\n",
       "      <td>\"Man accused of Da Vinci apartment arson was a...</td>\n",
       "      <td>START Primary Collection</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>0</td>\n",
       "      <td>-9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>199808070002</td>\n",
       "      <td>1998</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>104</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Laura Myers, “U.S. promises to hit back if any...</td>\n",
       "      <td>“Kenya; New Twist in bomb Tragedy,” Africa New...</td>\n",
       "      <td>“Blast toll tops 200,” Evening Herald (Plymout...</td>\n",
       "      <td>CETIS</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>199808070002, 199808070003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6285</th>\n",
       "      <td>200202150002</td>\n",
       "      <td>2002</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>45</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>“Two Bombs in Riohacha Cause Property Damage W...</td>\n",
       "      <td>“Highlights: Colombian Guerrilla/Paramilitary ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CETIS</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>0</td>\n",
       "      <td>-9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69379</th>\n",
       "      <td>201408230034</td>\n",
       "      <td>2014</td>\n",
       "      <td>8</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>200</td>\n",
       "      <td>Syria</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>Casualty numbers for this incident conflict ac...</td>\n",
       "      <td>\"Jihadists killed in new push to take Syria ai...</td>\n",
       "      <td>\"Third ISIL Attempt to Seize Tabaqa Airport Fa...</td>\n",
       "      <td>\"Syrian Army Inflict 'Heavy Losses' on ISIL at...</td>\n",
       "      <td>START Primary Collection</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96079</th>\n",
       "      <td>201606120025</td>\n",
       "      <td>2016</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>110</td>\n",
       "      <td>Lebanon</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>Casualty numbers for this incident conflict ac...</td>\n",
       "      <td>\"CCTV footage holds clues to bombing,\" The Dai...</td>\n",
       "      <td>\"Bomb explodes outside Lebanese Blom Bank in B...</td>\n",
       "      <td>\"Lebanon's interior minister says Beirut blast...</td>\n",
       "      <td>START Primary Collection</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 135 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            eventid  iyear  imonth  iday approxdate  extended resolution  \\\n",
       "4968   200107240001   2001       7    24        NaN         0        NaT   \n",
       "5619   200109110004   2001       9    11        NaN         0        NaT   \n",
       "5620   200109110005   2001       9    11        NaN         0        NaT   \n",
       "8840   200403210001   2004       3    21        NaN         0        NaT   \n",
       "91580  201602180049   2016       2    17        NaN         0        NaT   \n",
       "73834  201412070129   2014      12     7        NaN         0        NaT   \n",
       "564    199808070002   1998       8     7        NaN         0        NaT   \n",
       "6285   200202150002   2002       2    15        NaN         0        NaT   \n",
       "69379  201408230034   2014       8    23        NaN         0        NaT   \n",
       "96079  201606120025   2016       6    12        NaN         0        NaT   \n",
       "\n",
       "       country    country_txt  region  ...  \\\n",
       "4968       186      Sri Lanka       6  ...   \n",
       "5619       217  United States       1  ...   \n",
       "5620       217  United States       1  ...   \n",
       "8840       141          Nepal       6  ...   \n",
       "91580      209         Turkey      10  ...   \n",
       "73834      217  United States       1  ...   \n",
       "564        104          Kenya      11  ...   \n",
       "6285        45       Colombia       3  ...   \n",
       "69379      200          Syria      10  ...   \n",
       "96079      110        Lebanon      10  ...   \n",
       "\n",
       "                                                addnotes  \\\n",
       "4968                                                 NaN   \n",
       "5619   This attack was one of four related incidents ...   \n",
       "5620   This attack was one of four related incidents ...   \n",
       "8840   The Agence France Presse reported that there w...   \n",
       "91580                                                NaN   \n",
       "73834                                                NaN   \n",
       "564                                                  NaN   \n",
       "6285                                                 NaN   \n",
       "69379  Casualty numbers for this incident conflict ac...   \n",
       "96079  Casualty numbers for this incident conflict ac...   \n",
       "\n",
       "                                                  scite1  \\\n",
       "4968   Peter Foster, Richard Allyne, Vilma Wimaladasa...   \n",
       "5619   United States Government, The 9/11 Commission ...   \n",
       "5620   United States Government, The 9/11 Commission ...   \n",
       "8840   Kedar Man Sing, “2 Deadly clashes in Nepal as ...   \n",
       "91580  \"PKK attack on oil pipeline cost KRG $100mn: S...   \n",
       "73834  \"Man Convicted of Starting Massive Da Vinci Fi...   \n",
       "564    Laura Myers, “U.S. promises to hit back if any...   \n",
       "6285   “Two Bombs in Riohacha Cause Property Damage W...   \n",
       "69379  \"Jihadists killed in new push to take Syria ai...   \n",
       "96079  \"CCTV footage holds clues to bombing,\" The Dai...   \n",
       "\n",
       "                                                  scite2  \\\n",
       "4968   \"Tamil Tigers hit Sri Lanka airport; Suicide b...   \n",
       "5619   Lindsay Kines, “United States on high alert af...   \n",
       "5620   Lindsay Kines, “United States on high alert af...   \n",
       "8840   “AFP: Clashes in Nepal Reportedly Leave 500 Ma...   \n",
       "91580  \"BRIEF: Oil pipeline blown up in Turkey [Trend...   \n",
       "73834  \"Man Pleads Guilty, Gets 15 Years In Prison Fo...   \n",
       "564    “Kenya; New Twist in bomb Tragedy,” Africa New...   \n",
       "6285   “Highlights: Colombian Guerrilla/Paramilitary ...   \n",
       "69379  \"Third ISIL Attempt to Seize Tabaqa Airport Fa...   \n",
       "96079  \"Bomb explodes outside Lebanese Blom Bank in B...   \n",
       "\n",
       "                                                  scite3  \\\n",
       "4968   \"Attack on airbase, airport leaves 14 dead, 13...   \n",
       "5619   Joe Frolick, “Hijackers Ram Two Airliners Into...   \n",
       "5620   Joe Frolick, “Hijackers Ram Two Airliners Into...   \n",
       "8840                                                 NaN   \n",
       "91580                                                NaN   \n",
       "73834  \"Man accused of Da Vinci apartment arson was a...   \n",
       "564    “Blast toll tops 200,” Evening Herald (Plymout...   \n",
       "6285                                                 NaN   \n",
       "69379  \"Syrian Army Inflict 'Heavy Losses' on ISIL at...   \n",
       "96079  \"Lebanon's interior minister says Beirut blast...   \n",
       "\n",
       "                       dbsource  INT_LOG  INT_IDEO INT_MISC INT_ANY  \\\n",
       "4968                      CETIS        0         1        1       1   \n",
       "5619                      CETIS        0         1        0       1   \n",
       "5620                      CETIS        0         1        0       1   \n",
       "8840                      CETIS        0         0        0       0   \n",
       "91580  START Primary Collection        0         0        0       0   \n",
       "73834  START Primary Collection       -9        -9        0      -9   \n",
       "564                       CETIS        0         1        1       1   \n",
       "6285                      CETIS       -9        -9        0      -9   \n",
       "69379  START Primary Collection        0         1        0       1   \n",
       "96079  START Primary Collection        0         1        0       1   \n",
       "\n",
       "                                                 related  \n",
       "4968                                                 NaN  \n",
       "5619   200109110004, 200109110005, 200109110006, 2001...  \n",
       "5620   200109110005, 200109110004, 200109110006, 2001...  \n",
       "8840                                                 NaN  \n",
       "91580                                                NaN  \n",
       "73834                                                NaN  \n",
       "564                           199808070002, 199808070003  \n",
       "6285                                                 NaN  \n",
       "69379                                                NaN  \n",
       "96079                                                NaN  \n",
       "\n",
       "[10 rows x 135 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "def pca_analyze(data):\n",
    "    print(\"\\n原始数据:\\n\",data)\n",
    "    #归一化\n",
    "    data = (data-data.mean())/data.std()  \n",
    "    print(\"\\n归一化之后的数据:\\n\",pd.DataFrame(data))\n",
    "    # 皮尔森相关系数\n",
    "    data_corr=data.corr()\n",
    "    print(\"\\n相关系数:\\n\",pd.DataFrame(data_corr))\n",
    "    #热力图\n",
    "    cmap = cm.Blues\n",
    "    sns.set()\n",
    "    plt.figure(figsize=(10, 10))\n",
    "    ax = sns.heatmap(data=data_corr,square=True,cmap=cmap, vmin=0, vmax=1) \n",
    "    plt.title('correlation coefficient--headmap')\n",
    "    ax.set_yticks(range(len(data_corr.columns)))\n",
    "    ax.set_yticklabels(data_corr.columns)\n",
    "    ax.set_xticks(range(len(data_corr)))\n",
    "    ax.set_xticklabels(data_corr.columns)\n",
    "    plt.show()\n",
    "    #pca降维，选择解释度和为0.95的因子\n",
    "    print(\"\\n选择累计贡献率大于等于0.95的因子\")\n",
    "    pca = PCA(n_components=0.95)\n",
    "    df_d = pca.fit_transform(data)\n",
    "    df_ev = pca.explained_variance_\n",
    "    df_evr = pca.explained_variance_ratio_\n",
    "    df_evr_sum = np.cumsum(pca.explained_variance_ratio_)\n",
    "    df_fa = pd.DataFrame(\n",
    "            {'特征值': df_ev, '方差贡献率': df_evr,'方差累计贡献率': df_evr_sum})\n",
    "    print(df_fa)\n",
    "    k1_spss = pca.components_ / np.sqrt(pca.explained_variance_.reshape(-1, 1))  # 成分得分系数矩阵\n",
    "    k1_spss = pd.DataFrame(k1_spss.T)\n",
    "    print(\"\\n成分得分系数矩阵: \\n\",k1_spss)\n",
    "    tmp_columns = []\n",
    "    for index in range(df_ev.shape[0]):\n",
    "       tmp = \"factor\" + str(index + 1)\n",
    "       tmp_columns.append(tmp)\n",
    "    k1_spss.columns = tmp_columns\n",
    "    print(\"\\n主成分得分\",k1_spss)\n",
    "    print(\"\\n==============采用熵权法计算各个因子的权重===============\")\n",
    "    fa_t_score = np.dot(np.mat(data), np.mat(k1_spss))\n",
    "    w = get_entropy_weight(fa_t_score.T)\n",
    "    print(\"\\n熵权法求得的权重为：\\n\",w)\n",
    "    fa_t_score = np.dot(fa_t_score,w)\n",
    "    fa_t_score = pd.DataFrame(fa_t_score.T)\n",
    "    print(\"\\n各个样本得分如下：\\n\",fa_t_score)\n",
    "    fa_t_score.columns = ['综合得分']\n",
    "    fa_t_score.insert(0, 'ID', range(0, data.shape[0]))\n",
    "    top10 = fa_t_score.sort_values(by='综合得分', ascending=False).head(10)\n",
    "    index = top10.ID\n",
    "    print(\"\\n前十大事件综合得分：\\n\", fa_t_score.sort_values(by='综合得分', ascending=False).head(10))\n",
    "    return fa_t_score,index\n",
    "fa_t_score,index = pca_analyze(df)\n",
    "terrorism_data.iloc[index,:]"
   ]
  }
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