Programe1_type3-predict.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fca531fe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T19:03:52.036335Z",
     "start_time": "2024-09-24T19:03:52.033423Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "983eafdd",
   "metadata": {},
   "source": [
    "## 读入数据，并对2基站情况数据进行合并与拆解"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fdea2945",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T19:03:53.741171Z",
     "start_time": "2024-09-24T19:03:53.707812Z"
    }
   },
   "outputs": [],
   "source": [
    "resource=pd.read_csv('RAWDATA/test_set_2_3ap.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "77a393c1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T19:03:55.558181Z",
     "start_time": "2024-09-24T19:03:55.548377Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 87 entries, 0 to 86\n",
      "Data columns (total 56 columns):\n",
      " #   Column                       Non-Null Count  Dtype  \n",
      "---  ------                       --------------  -----  \n",
      " 0   test_id                      87 non-null     int64  \n",
      " 1   test_dur                     87 non-null     int64  \n",
      " 2   loc_id                       87 non-null     object \n",
      " 3   protocol                     87 non-null     object \n",
      " 4   pkt_len                      87 non-null     int64  \n",
      " 5   bss_id                       87 non-null     int64  \n",
      " 6   ap_name                      87 non-null     object \n",
      " 7   ap_mac                       87 non-null     object \n",
      " 8   ap_id                        87 non-null     object \n",
      " 9   pd                           87 non-null     int64  \n",
      " 10  ed                           87 non-null     int64  \n",
      " 11  nav                          87 non-null     int64  \n",
      " 12  eirp                         87 non-null     int64  \n",
      " 13  ap_from_ap_0_sum_ant_rssi    58 non-null     object \n",
      " 14  ap_from_ap_0_max_ant_rssi    58 non-null     object \n",
      " 15  ap_from_ap_0_mean_ant_rssi   58 non-null     object \n",
      " 16  ap_from_ap_1_sum_ant_rssi    58 non-null     object \n",
      " 17  ap_from_ap_1_max_ant_rssi    58 non-null     object \n",
      " 18  ap_from_ap_1_mean_ant_rssi   58 non-null     object \n",
      " 19  ap_from_ap_2_sum_ant_rssi    58 non-null     object \n",
      " 20  ap_from_ap_2_max_ant_rssi    58 non-null     object \n",
      " 21  ap_from_ap_2_mean_ant_rssi   58 non-null     object \n",
      " 22  sta_mac                      87 non-null     object \n",
      " 23  sta_id                       87 non-null     object \n",
      " 24  sta_to_ap_0_sum_ant_rssi     87 non-null     object \n",
      " 25  sta_to_ap_0_max_ant_rssi     87 non-null     object \n",
      " 26  sta_to_ap_0_mean_ant_rssi    87 non-null     object \n",
      " 27  sta_to_ap_1_sum_ant_rssi     87 non-null     object \n",
      " 28  sta_to_ap_1_max_ant_rssi     87 non-null     object \n",
      " 29  sta_to_ap_1_mean_ant_rssi    87 non-null     object \n",
      " 30  sta_to_ap_2_sum_ant_rssi     87 non-null     object \n",
      " 31  sta_to_ap_2_max_ant_rssi     87 non-null     object \n",
      " 32  sta_to_ap_2_mean_ant_rssi    87 non-null     object \n",
      " 33  sta_from_ap_0_sum_ant_rssi   87 non-null     object \n",
      " 34  sta_from_ap_0_max_ant_rssi   87 non-null     object \n",
      " 35  sta_from_ap_0_mean_ant_rssi  87 non-null     object \n",
      " 36  sta_from_ap_1_sum_ant_rssi   87 non-null     object \n",
      " 37  sta_from_ap_1_max_ant_rssi   87 non-null     object \n",
      " 38  sta_from_ap_1_mean_ant_rssi  87 non-null     object \n",
      " 39  sta_from_ap_2_sum_ant_rssi   87 non-null     object \n",
      " 40  sta_from_ap_2_max_ant_rssi   87 non-null     object \n",
      " 41  sta_from_ap_2_mean_ant_rssi  87 non-null     object \n",
      " 42  sta_from_sta_0_rssi          58 non-null     float64\n",
      " 43  sta_from_sta_1_rssi          58 non-null     float64\n",
      " 44  sta_from_sta_2_rssi          58 non-null     float64\n",
      " 45  nss                          0 non-null      float64\n",
      " 46  mcs                          0 non-null      float64\n",
      " 47  per                          0 non-null      float64\n",
      " 48  num_ampdu                    0 non-null      float64\n",
      " 49  ppdu_dur                     0 non-null      float64\n",
      " 50  other_air_time               0 non-null      float64\n",
      " 51  seq_time                     0 non-null      float64\n",
      " 52  throughput                   0 non-null      float64\n",
      " 53  predict nss                  0 non-null      float64\n",
      " 54  predict mcs                  0 non-null      float64\n",
      " 55  error%                       0 non-null      float64\n",
      "dtypes: float64(14), int64(8), object(34)\n",
      "memory usage: 38.2+ KB\n"
     ]
    }
   ],
   "source": [
    "resource.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e723bf0c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T19:04:33.500512Z",
     "start_time": "2024-09-24T19:04:33.495511Z"
    }
   },
   "outputs": [],
   "source": [
    "columns=[0,1,2,4,6,7,8,12,22,23,25,26,28,29,31,32,34,35,37,38,40,41,45,46,47,48,49,50,52,53,54,55] #这里没删bss_id，为了后续合并空白值计算\n",
    "resource=resource.drop(resource.columns[columns],axis=1)\n",
    "resource.index=range(resource.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "69e51af3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T19:04:37.699760Z",
     "start_time": "2024-09-24T19:04:37.691049Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 87 entries, 0 to 86\n",
      "Data columns (total 24 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   protocol                    87 non-null     object \n",
      " 1   bss_id                      87 non-null     int64  \n",
      " 2   pd                          87 non-null     int64  \n",
      " 3   ed                          87 non-null     int64  \n",
      " 4   nav                         87 non-null     int64  \n",
      " 5   ap_from_ap_0_sum_ant_rssi   58 non-null     object \n",
      " 6   ap_from_ap_0_max_ant_rssi   58 non-null     object \n",
      " 7   ap_from_ap_0_mean_ant_rssi  58 non-null     object \n",
      " 8   ap_from_ap_1_sum_ant_rssi   58 non-null     object \n",
      " 9   ap_from_ap_1_max_ant_rssi   58 non-null     object \n",
      " 10  ap_from_ap_1_mean_ant_rssi  58 non-null     object \n",
      " 11  ap_from_ap_2_sum_ant_rssi   58 non-null     object \n",
      " 12  ap_from_ap_2_max_ant_rssi   58 non-null     object \n",
      " 13  ap_from_ap_2_mean_ant_rssi  58 non-null     object \n",
      " 14  sta_to_ap_0_sum_ant_rssi    87 non-null     object \n",
      " 15  sta_to_ap_1_sum_ant_rssi    87 non-null     object \n",
      " 16  sta_to_ap_2_sum_ant_rssi    87 non-null     object \n",
      " 17  sta_from_ap_0_sum_ant_rssi  87 non-null     object \n",
      " 18  sta_from_ap_1_sum_ant_rssi  87 non-null     object \n",
      " 19  sta_from_ap_2_sum_ant_rssi  87 non-null     object \n",
      " 20  sta_from_sta_0_rssi         58 non-null     float64\n",
      " 21  sta_from_sta_1_rssi         58 non-null     float64\n",
      " 22  sta_from_sta_2_rssi         58 non-null     float64\n",
      " 23  seq_time                    0 non-null      float64\n",
      "dtypes: float64(4), int64(4), object(16)\n",
      "memory usage: 16.4+ KB\n"
     ]
    }
   ],
   "source": [
    "resource.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "da4a6da3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T19:04:42.686570Z",
     "start_time": "2024-09-24T19:04:42.630589Z"
    }
   },
   "outputs": [],
   "source": [
    "for i in range(resource.shape[0]):\n",
    "    if resource.iloc[i,list(resource.columns).index('bss_id')]==2:\n",
    "        continue\n",
    "    else: \n",
    "        if resource.iloc[i,list(resource.columns).index('bss_id')]==0:\n",
    "            resource.iloc[i,list(resource.columns).index('ap_from_ap_0_sum_ant_rssi')]=resource.iloc[i,list(resource.columns).index('ap_from_ap_1_sum_ant_rssi')]\n",
    "            resource.iloc[i,list(resource.columns).index('ap_from_ap_0_max_ant_rssi')]=resource.iloc[i,list(resource.columns).index('ap_from_ap_1_max_ant_rssi')]\n",
    "            resource.iloc[i,list(resource.columns).index('ap_from_ap_0_mean_ant_rssi')]=resource.iloc[i,list(resource.columns).index('ap_from_ap_1_mean_ant_rssi')]\n",
    "            resource.iloc[i,list(resource.columns).index('sta_from_sta_0_rssi')]=resource.iloc[i,list(resource.columns).index('sta_from_sta_1_rssi')]\n",
    "        resource.iloc[i,list(resource.columns).index('ap_from_ap_1_sum_ant_rssi')]=resource.iloc[i,list(resource.columns).index('ap_from_ap_2_sum_ant_rssi')]\n",
    "        resource.iloc[i,list(resource.columns).index('ap_from_ap_1_max_ant_rssi')]=resource.iloc[i,list(resource.columns).index('ap_from_ap_2_max_ant_rssi')]\n",
    "        resource.iloc[i,list(resource.columns).index('ap_from_ap_1_mean_ant_rssi')]=resource.iloc[i,list(resource.columns).index('ap_from_ap_2_mean_ant_rssi')]\n",
    "        resource.iloc[i,list(resource.columns).index('sta_from_sta_1_rssi')]=resource.iloc[i,list(resource.columns).index('sta_from_sta_2_rssi')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2f47fac5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T19:04:43.865972Z",
     "start_time": "2024-09-24T19:04:43.862094Z"
    }
   },
   "outputs": [],
   "source": [
    "resource=resource.drop(['ap_from_ap_2_sum_ant_rssi',\n",
    "                       'ap_from_ap_2_max_ant_rssi','ap_from_ap_2_mean_ant_rssi','sta_from_sta_2_rssi'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2ff0cb64",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T19:04:44.964771Z",
     "start_time": "2024-09-24T19:04:44.956664Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 87 entries, 0 to 86\n",
      "Data columns (total 20 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   protocol                    87 non-null     object \n",
      " 1   bss_id                      87 non-null     int64  \n",
      " 2   pd                          87 non-null     int64  \n",
      " 3   ed                          87 non-null     int64  \n",
      " 4   nav                         87 non-null     int64  \n",
      " 5   ap_from_ap_0_sum_ant_rssi   87 non-null     object \n",
      " 6   ap_from_ap_0_max_ant_rssi   87 non-null     object \n",
      " 7   ap_from_ap_0_mean_ant_rssi  87 non-null     object \n",
      " 8   ap_from_ap_1_sum_ant_rssi   87 non-null     object \n",
      " 9   ap_from_ap_1_max_ant_rssi   87 non-null     object \n",
      " 10  ap_from_ap_1_mean_ant_rssi  87 non-null     object \n",
      " 11  sta_to_ap_0_sum_ant_rssi    87 non-null     object \n",
      " 12  sta_to_ap_1_sum_ant_rssi    87 non-null     object \n",
      " 13  sta_to_ap_2_sum_ant_rssi    87 non-null     object \n",
      " 14  sta_from_ap_0_sum_ant_rssi  87 non-null     object \n",
      " 15  sta_from_ap_1_sum_ant_rssi  87 non-null     object \n",
      " 16  sta_from_ap_2_sum_ant_rssi  87 non-null     object \n",
      " 17  sta_from_sta_0_rssi         87 non-null     float64\n",
      " 18  sta_from_sta_1_rssi         87 non-null     float64\n",
      " 19  seq_time                    0 non-null      float64\n",
      "dtypes: float64(3), int64(4), object(13)\n",
      "memory usage: 13.7+ KB\n"
     ]
    }
   ],
   "source": [
    "resource.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c629c075",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T19:04:46.787595Z",
     "start_time": "2024-09-24T19:04:46.782121Z"
    }
   },
   "outputs": [],
   "source": [
    "# 定义管理rssi，并创造出新特征值\n",
    "def cal_pd(apmax,pd):\n",
    "    apmax = eval(apmax)\n",
    "    comparison_result = [1 if x >= pd else 0 for x in apmax]\n",
    "    proportion_of_ones = sum(comparison_result) / len(apmax)\n",
    "    return proportion_of_ones\n",
    "\n",
    "def cal_ed(apmax,ed):\n",
    "    apmax = eval(apmax)\n",
    "    comparison_result = [1 if x >= ed else 0 for x in apmax]\n",
    "    proportion_of_ones = sum(comparison_result) / len(apmax)\n",
    "    return proportion_of_ones\n",
    "\n",
    "def cal_nav(apmean,nav):\n",
    "    apmean = eval(apmean)\n",
    "    comparison_result = [1 if x >= nav else 0 for x in apmean]\n",
    "\n",
    "    # 计算 1 的占比\n",
    "    proportion_of_ones = sum(comparison_result) / len(apmean)\n",
    "    return proportion_of_ones    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d75e3ab7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T19:04:49.083908Z",
     "start_time": "2024-09-24T19:04:48.728247Z"
    }
   },
   "outputs": [],
   "source": [
    "resource['pro_pd_0'] = resource.apply(lambda row: cal_pd(row['ap_from_ap_0_max_ant_rssi'], row['pd']), axis=1)\n",
    "resource['pro_ed_0'] = resource.apply(lambda row: cal_ed(row['ap_from_ap_0_max_ant_rssi'], row['ed']), axis=1)\n",
    "resource['pro_nav_0'] = resource.apply(lambda row: cal_nav(row['ap_from_ap_0_mean_ant_rssi'], row['nav']), axis=1)\n",
    "\n",
    "resource['pro_pd_1'] = resource.apply(lambda row: cal_pd(row['ap_from_ap_1_max_ant_rssi'], row['pd']), axis=1)\n",
    "resource['pro_ed_1'] = resource.apply(lambda row: cal_ed(row['ap_from_ap_1_max_ant_rssi'], row['ed']), axis=1)\n",
    "resource['pro_nav_1'] = resource.apply(lambda row: cal_nav(row['ap_from_ap_1_mean_ant_rssi'], row['nav']), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4d1755b8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T19:04:50.496045Z",
     "start_time": "2024-09-24T19:04:50.491721Z"
    }
   },
   "outputs": [],
   "source": [
    "drop_columns=['pd','ed','ap_from_ap_0_mean_ant_rssi','ap_from_ap_0_max_ant_rssi',\n",
    "              'ap_from_ap_1_mean_ant_rssi','ap_from_ap_1_max_ant_rssi']\n",
    "\n",
    "resource=resource.drop(drop_columns,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7aecd7c1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T19:04:51.702277Z",
     "start_time": "2024-09-24T19:04:51.693610Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 87 entries, 0 to 86\n",
      "Data columns (total 20 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   protocol                    87 non-null     object \n",
      " 1   bss_id                      87 non-null     int64  \n",
      " 2   nav                         87 non-null     int64  \n",
      " 3   ap_from_ap_0_sum_ant_rssi   87 non-null     object \n",
      " 4   ap_from_ap_1_sum_ant_rssi   87 non-null     object \n",
      " 5   sta_to_ap_0_sum_ant_rssi    87 non-null     object \n",
      " 6   sta_to_ap_1_sum_ant_rssi    87 non-null     object \n",
      " 7   sta_to_ap_2_sum_ant_rssi    87 non-null     object \n",
      " 8   sta_from_ap_0_sum_ant_rssi  87 non-null     object \n",
      " 9   sta_from_ap_1_sum_ant_rssi  87 non-null     object \n",
      " 10  sta_from_ap_2_sum_ant_rssi  87 non-null     object \n",
      " 11  sta_from_sta_0_rssi         87 non-null     float64\n",
      " 12  sta_from_sta_1_rssi         87 non-null     float64\n",
      " 13  seq_time                    0 non-null      float64\n",
      " 14  pro_pd_0                    87 non-null     float64\n",
      " 15  pro_ed_0                    87 non-null     float64\n",
      " 16  pro_nav_0                   87 non-null     float64\n",
      " 17  pro_pd_1                    87 non-null     float64\n",
      " 18  pro_ed_1                    87 non-null     float64\n",
      " 19  pro_nav_1                   87 non-null     float64\n",
      "dtypes: float64(9), int64(2), object(9)\n",
      "memory usage: 13.7+ KB\n"
     ]
    }
   ],
   "source": [
    "resource.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "da133b75",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T19:04:56.709144Z",
     "start_time": "2024-09-24T19:04:56.134096Z"
    }
   },
   "outputs": [],
   "source": [
    "# 对RSSI求平均值处理\n",
    "for i in range(3,11):\n",
    "    resource.iloc[:,i]=resource.iloc[:,i].apply(lambda x: sum(eval(x))/len(eval(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e3d183b2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T19:04:58.314339Z",
     "start_time": "2024-09-24T19:04:58.305445Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 87 entries, 0 to 86\n",
      "Data columns (total 20 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   protocol                    87 non-null     object \n",
      " 1   bss_id                      87 non-null     int64  \n",
      " 2   nav                         87 non-null     int64  \n",
      " 3   ap_from_ap_0_sum_ant_rssi   87 non-null     object \n",
      " 4   ap_from_ap_1_sum_ant_rssi   87 non-null     object \n",
      " 5   sta_to_ap_0_sum_ant_rssi    87 non-null     object \n",
      " 6   sta_to_ap_1_sum_ant_rssi    87 non-null     object \n",
      " 7   sta_to_ap_2_sum_ant_rssi    87 non-null     object \n",
      " 8   sta_from_ap_0_sum_ant_rssi  87 non-null     object \n",
      " 9   sta_from_ap_1_sum_ant_rssi  87 non-null     object \n",
      " 10  sta_from_ap_2_sum_ant_rssi  87 non-null     object \n",
      " 11  sta_from_sta_0_rssi         87 non-null     float64\n",
      " 12  sta_from_sta_1_rssi         87 non-null     float64\n",
      " 13  seq_time                    0 non-null      float64\n",
      " 14  pro_pd_0                    87 non-null     float64\n",
      " 15  pro_ed_0                    87 non-null     float64\n",
      " 16  pro_nav_0                   87 non-null     float64\n",
      " 17  pro_pd_1                    87 non-null     float64\n",
      " 18  pro_ed_1                    87 non-null     float64\n",
      " 19  pro_nav_1                   87 non-null     float64\n",
      "dtypes: float64(9), int64(2), object(9)\n",
      "memory usage: 13.7+ KB\n"
     ]
    }
   ],
   "source": [
    "resource.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "30d4a544",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T19:05:12.594519Z",
     "start_time": "2024-09-24T19:05:12.589199Z"
    }
   },
   "outputs": [],
   "source": [
    "# 选择pro的聚类个数\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import silhouette_score\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data_scaled=resource.iloc[:,range(14,20)]\n",
    "\n",
    "# # 确定最佳的聚类簇数，使用肘部法或轮廓系数\n",
    "# sse = []\n",
    "# silhouette_scores = []\n",
    "# K = range(2, 6)  # 假设我们要测试2到5个聚类数\n",
    "# for k in K:\n",
    "#     kmeans = KMeans(n_clusters=k, random_state=42,n_init='auto')\n",
    "#     kmeans.fit(data_scaled)\n",
    "#     sse.append(kmeans.inertia_)  # SSE\n",
    "#     silhouette_scores.append(silhouette_score(data_scaled, kmeans.labels_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7fe37eef",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T19:05:20.334493Z",
     "start_time": "2024-09-24T19:05:19.871470Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster centers:\n",
      "          0         1         2         3         4         5\n",
      "0  0.106239  0.003765  0.019275  0.201712  0.003185  0.054815\n",
      "1  0.965036  0.003071  0.903755  0.855412  0.031996  0.832333\n",
      "2  0.403678  0.004730  0.094012  0.972620  0.004553  0.708539\n",
      "cluster\n",
      "0    52\n",
      "1    22\n",
      "2    13\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ShenFei\\AppData\\Local\\Temp\\ipykernel_23600\\2666717241.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['cluster'] = kmeans.fit_predict(data_scaled)\n"
     ]
    }
   ],
   "source": [
    "# 得到‘state’类别\n",
    "# 0:大量同步\n",
    "# 1：大量异步\n",
    "# 2：混合\n",
    "\n",
    "df=data_scaled\n",
    "k = 3\n",
    "kmeans = KMeans(n_clusters=k, random_state=42)\n",
    "df['cluster'] = kmeans.fit_predict(data_scaled)\n",
    "\n",
    "# 查看每个簇的平均特征值\n",
    "cluster_centers = pd.DataFrame(kmeans.cluster_centers_)\n",
    "# columns=['pro_pd', 'pro_ed', 'pro_nav']\n",
    "\n",
    "print(f\"Cluster centers:\\n{cluster_centers}\")\n",
    "\n",
    "# 查看每个簇的样本数量\n",
    "print(df['cluster'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "bdb24ab1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T19:05:24.861894Z",
     "start_time": "2024-09-24T19:05:24.407481Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\ShenFei\\AppData\\Local\\Temp\\ipykernel_23600\\150325647.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data_scaled['cluster'] = kmeans.fit_predict(data_scaled)\n"
     ]
    }
   ],
   "source": [
    "data_scaled['cluster'] = kmeans.fit_predict(data_scaled)\n",
    "resource['state']=data_scaled['cluster']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "5fa21385",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T19:05:27.781754Z",
     "start_time": "2024-09-24T19:05:27.753326Z"
    }
   },
   "outputs": [],
   "source": [
    "def dbm_to_linear(dbm):\n",
    "    return 10 ** (dbm / 10)\n",
    "\n",
    "def linear_to_dbm(linear):\n",
    "    return 10 * np.log10(linear)\n",
    "\n",
    "def add_dbm(dbm1, dbm2):\n",
    "    # 使用 NumPy 的广播功能直接进行计算\n",
    "    linear1 = dbm_to_linear(dbm1)\n",
    "    linear2 = dbm_to_linear(dbm2)\n",
    "    \n",
    "    # 将两个线性功率值相加\n",
    "    total_linear = linear1 + linear2\n",
    "    \n",
    "    # 将相加后的线性功率值转换回DBM\n",
    "    total_dbm = linear_to_dbm(total_linear)\n",
    "    \n",
    "    return total_dbm\n",
    "\n",
    "\n",
    "def func(x, y, z, state):\n",
    "    if state == 0:\n",
    "        return x + 100\n",
    "    else:\n",
    "        return x - add_dbm(y,z)\n",
    "\n",
    "    \n",
    "# 创建一个新列来存储 SINR 计算的结果\n",
    "sinr = []\n",
    "df = resource\n",
    "# 遍历 DataFrame 的每一行\n",
    "for i in range(len(df)):\n",
    "    if df.iloc[i, list(df.columns).index('bss_id')] == 2:  # 偶数行\n",
    "        result = func(df.iloc[i, list(df.columns).index('sta_from_ap_2_sum_ant_rssi')],df.iloc[i, list(df.columns).index('sta_from_ap_1_sum_ant_rssi')], df.iloc[i, list(df.columns).index('sta_from_ap_0_sum_ant_rssi')],df.iloc[i, list(df.columns).index('state')])\n",
    "    elif df.iloc[i, list(df.columns).index('bss_id')] == 1:  # 奇数行\n",
    "        result = func(df.iloc[i, list(df.columns).index('sta_from_ap_1_sum_ant_rssi')],df.iloc[i, list(df.columns).index('sta_from_ap_2_sum_ant_rssi')], df.iloc[i, list(df.columns).index('sta_from_ap_0_sum_ant_rssi')],df.iloc[i, list(df.columns).index('state')])\n",
    "    else:\n",
    "        result = func(df.iloc[i, list(df.columns).index('sta_from_ap_0_sum_ant_rssi')],df.iloc[i, list(df.columns).index('sta_from_ap_1_sum_ant_rssi')], df.iloc[i, list(df.columns).index('sta_from_ap_2_sum_ant_rssi')],df.iloc[i, list(df.columns).index('state')])\n",
    "        \n",
    "    sinr.append(result)\n",
    "\n",
    "# 将计算结果添加为新列\n",
    "df['sinr'] = sinr\n",
    "resource = resource.drop('bss_id',axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf045308",
   "metadata": {},
   "source": [
    "# predict from here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2f8d3f21",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T19:05:46.417966Z",
     "start_time": "2024-09-24T19:05:46.411606Z"
    }
   },
   "outputs": [],
   "source": [
    "resource.to_csv('programe1_predict_test_2_type_3.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14a2947f",
   "metadata": {},
   "source": [
    "# read from here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "afa2cc94",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T16:43:06.006624Z",
     "start_time": "2024-09-24T16:43:05.847775Z"
    }
   },
   "outputs": [],
   "source": [
    "# resource.to_excel('programe1_3ap_processed.xlsx')\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "resource = pd.read_excel('programe1_3ap_processed.xlsx')\n",
    "resource = resource.sample(frac=1).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0b0b3836",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T16:43:21.696471Z",
     "start_time": "2024-09-24T16:43:21.691439Z"
    }
   },
   "outputs": [],
   "source": [
    "resource.iloc[:,0]=resource.iloc[:,0].astype(str).map({'tcp':1,'udp':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "aca831d8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T16:43:46.415243Z",
     "start_time": "2024-09-24T16:43:46.404715Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 650 entries, 0 to 649\n",
      "Data columns (total 18 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   protocol                    650 non-null    object \n",
      " 1   nav                         650 non-null    int64  \n",
      " 2   ap_from_ap_0_sum_ant_rssi   650 non-null    float64\n",
      " 3   ap_from_ap_1_sum_ant_rssi   650 non-null    float64\n",
      " 4   sta_to_ap_0_sum_ant_rssi    650 non-null    float64\n",
      " 5   sta_to_ap_1_sum_ant_rssi    650 non-null    float64\n",
      " 6   sta_to_ap_2_sum_ant_rssi    650 non-null    float64\n",
      " 7   sta_from_ap_0_sum_ant_rssi  650 non-null    float64\n",
      " 8   sta_from_ap_1_sum_ant_rssi  650 non-null    float64\n",
      " 9   sta_from_ap_2_sum_ant_rssi  650 non-null    float64\n",
      " 10  sta_from_sta_0_rssi         650 non-null    int64  \n",
      " 11  sta_from_sta_1_rssi         650 non-null    int64  \n",
      " 12  pro_pd_0                    650 non-null    float64\n",
      " 13  pro_ed_0                    650 non-null    float64\n",
      " 14  pro_nav_0                   650 non-null    float64\n",
      " 15  pro_pd_1                    650 non-null    float64\n",
      " 16  pro_ed_1                    650 non-null    float64\n",
      " 17  pro_nav_1                   650 non-null    float64\n",
      "dtypes: float64(14), int64(3), object(1)\n",
      "memory usage: 91.5+ KB\n"
     ]
    }
   ],
   "source": [
    "target=resource['seq_time']\n",
    "resource=resource.drop('seq_time',axis=1)\n",
    "resource.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0b76e70",
   "metadata": {},
   "source": [
    "# process"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30c5540b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-22T06:23:43.468487Z",
     "start_time": "2024-09-22T06:23:43.446716Z"
    },
    "heading_collapsed": true
   },
   "source": [
    "# 相关性分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2fe3e2cb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-22T12:52:59.800743Z",
     "start_time": "2024-09-22T12:52:59.307943Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "55b11e16",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-22T12:53:02.629530Z",
     "start_time": "2024-09-22T12:53:02.614772Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "pearson_correlation_1=resource_1.corr(method='pearson')\n",
    "pearson_correlation_0=resource_0.corr(method='pearson')\n",
    "pearson_correlation_2=resource_2.corr(method='pearson')\n",
    "spearman_correlation_1=resource_1.corr(method='spearman')\n",
    "spearman_correlation_0=resource_0.corr(method='spearman')\n",
    "spearman_correlation_2=resource_2.corr(method='spearman')\n",
    "# 0.8~1的话为极强相关，0.6~0.8之间为强相关,0.4~0.6的话为中等强度相关,0.2~0.4之间为弱相关，0.0~0.2极弱或无相关"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5089656",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 皮尔逊示意图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6463d0c9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-22T12:53:05.748618Z",
     "start_time": "2024-09-22T12:53:05.350019Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(pearson_correlation_2,cmap='coolwarm')\n",
    "a=plt.gca()\n",
    "a.set_yticks(range(len(resource_2_columns)))\n",
    "a.set_yticklabels(resource_2_columns)\n",
    "a.set_xticks(range(len(resource_2_columns)))\n",
    "# a.set_xticklabels(resource_1_columns)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0d46c853",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-22T12:53:11.849744Z",
     "start_time": "2024-09-22T12:53:11.454767Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(pearson_correlation_1,cmap='coolwarm')\n",
    "a=plt.gca()\n",
    "a.set_yticks(range(len(resource_1_columns)))\n",
    "a.set_yticklabels(resource_1_columns)\n",
    "a.set_xticks(range(len(resource_1_columns)))\n",
    "# a.set_xticklabels(resource_1_columns)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "110fe035",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-22T12:53:13.116316Z",
     "start_time": "2024-09-22T12:53:12.731015Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(pearson_correlation_0,cmap='coolwarm')\n",
    "a=plt.gca()\n",
    "a.set_yticks(range(len(resource_0_columns)))\n",
    "a.set_yticklabels(resource_0_columns)\n",
    "a.set_xticks(range(len(resource_0_columns)))\n",
    "# a.set_xticklabels(resource_1_columns)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b143e7d",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 斯皮尔曼示意图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1a402165",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-22T12:53:14.876592Z",
     "start_time": "2024-09-22T12:53:14.504350Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(spearman_correlation_2,cmap='coolwarm')\n",
    "a=plt.gca()\n",
    "a.set_yticks(range(len(resource_2_columns)))\n",
    "a.set_yticklabels(resource_2_columns)\n",
    "a.set_xticks(range(len(resource_2_columns)))\n",
    "# a.set_xticklabels(resource_1_columns)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2e13e629",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-22T12:53:15.735031Z",
     "start_time": "2024-09-22T12:53:15.286543Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(spearman_correlation_1,cmap='coolwarm')\n",
    "a=plt.gca()\n",
    "a.set_yticks(range(len(resource_1_columns)))\n",
    "a.set_yticklabels(resource_1_columns)\n",
    "a.set_xticks(range(len(resource_1_columns)))\n",
    "# a.set_xticklabels(resource_1_columns)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a70fd694",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-22T12:53:16.287682Z",
     "start_time": "2024-09-22T12:53:15.886489Z"
    },
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(spearman_correlation_0,cmap='coolwarm')\n",
    "a=plt.gca()\n",
    "a.set_yticks(range(len(resource_0_columns)))\n",
    "a.set_yticklabels(resource_0_columns)\n",
    "a.set_xticks(range(len(resource_0_columns)))\n",
    "# a.set_xticklabels(resource_1_columns)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c67ca0a",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# 随机森林----重要性判断"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a0ae52aa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-22T12:53:23.585584Z",
     "start_time": "2024-09-22T12:53:19.336642Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "rfg2=RandomForestRegressor(n_estimators=100,criterion='friedman_mse',random_state=0,oob_score=False)\n",
    "rfg2=rfg2.fit(resource_2,target_2)\n",
    "\n",
    "rfg1=RandomForestRegressor(n_estimators=100,criterion='friedman_mse',random_state=0,oob_score=False)\n",
    "rfg1=rfg1.fit(resource_1,target_1)\n",
    "\n",
    "rfg0=RandomForestRegressor(n_estimators=100,criterion='friedman_mse',random_state=0,oob_score=False)\n",
    "rfg0=rfg0.fit(resource_0,target_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac871be1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-22T12:53:23.646014Z",
     "start_time": "2024-09-22T12:53:23.646014Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "rfg2.feature_importances_,rfg1.feature_importances_,rfg0.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6d4b6718",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-22T12:53:25.081805Z",
     "start_time": "2024-09-22T12:53:25.066166Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['nav', 'sta_from_sta_1_rssi', 'sta_from_ap_2_mean_ant_rssi',\n",
       "       'sta_from_ap_2_max_ant_rssi', 'eirp', 'ap_from_ap_1_mean_ant_rssi',\n",
       "       'sta_from_ap_2_sum_ant_rssi', 'ap_from_ap_1_sum_ant_rssi',\n",
       "       'ap_from_ap_0_mean_ant_rssi', 'ap_from_ap_0_sum_ant_rssi', 'pro_ed_0',\n",
       "       'sta_to_ap_2_sum_ant_rssi', 'pro_ed_1', 'sta_to_ap_2_mean_ant_rssi',\n",
       "       'sta_to_ap_2_max_ant_rssi', 'sta_to_ap_1_mean_ant_rssi',\n",
       "       'sta_from_ap_1_sum_ant_rssi', 'sta_from_ap_0_max_ant_rssi',\n",
       "       'sta_to_ap_1_max_ant_rssi', 'sta_from_ap_0_mean_ant_rssi',\n",
       "       'sta_to_ap_1_sum_ant_rssi', 'ap_from_ap_1_max_ant_rssi',\n",
       "       'ap_from_ap_0_max_ant_rssi', 'sta_from_ap_0_sum_ant_rssi',\n",
       "       'sta_to_ap_0_mean_ant_rssi', 'pro_pd_1', 'sta_from_ap_1_mean_ant_rssi',\n",
       "       'sta_to_ap_0_sum_ant_rssi', 'sta_to_ap_0_max_ant_rssi',\n",
       "       'sta_from_ap_1_max_ant_rssi', 'pro_pd_0', 'protocol',\n",
       "       'sta_from_sta_0_rssi', 'pro_nav_0', 'pro_nav_1'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resource_2_columns[np.argsort(rfg2.feature_importances_)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0faab8a5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-22T12:53:27.806000Z",
     "start_time": "2024-09-22T12:53:27.789976Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['pro_ed_0', 'sta_from_sta_0_rssi', 'sta_from_ap_0_sum_ant_rssi',\n",
       "       'sta_from_ap_0_mean_ant_rssi', 'sta_from_sta_2_rssi', 'eirp',\n",
       "       'sta_from_ap_0_max_ant_rssi', 'sta_from_ap_1_sum_ant_rssi',\n",
       "       'ap_from_ap_0_max_ant_rssi', 'sta_to_ap_2_sum_ant_rssi',\n",
       "       'sta_to_ap_1_sum_ant_rssi', 'sta_from_ap_2_sum_ant_rssi',\n",
       "       'sta_from_ap_1_mean_ant_rssi', 'sta_to_ap_1_max_ant_rssi',\n",
       "       'sta_to_ap_0_sum_ant_rssi', 'sta_to_ap_0_mean_ant_rssi',\n",
       "       'sta_from_ap_1_max_ant_rssi', 'sta_to_ap_1_mean_ant_rssi',\n",
       "       'sta_from_ap_2_mean_ant_rssi', 'sta_to_ap_2_mean_ant_rssi', 'pro_pd_0',\n",
       "       'sta_to_ap_0_max_ant_rssi', 'sta_to_ap_2_max_ant_rssi',\n",
       "       'ap_from_ap_2_mean_ant_rssi', 'sta_from_ap_2_max_ant_rssi', 'pro_ed_2',\n",
       "       'nav', 'ap_from_ap_2_sum_ant_rssi', 'ap_from_ap_0_mean_ant_rssi',\n",
       "       'pro_pd_2', 'protocol', 'ap_from_ap_0_sum_ant_rssi',\n",
       "       'ap_from_ap_2_max_ant_rssi', 'pro_nav_0', 'pro_nav_2'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resource_1_columns[np.argsort(rfg1.feature_importances_)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "082f3423",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-22T12:53:29.995687Z",
     "start_time": "2024-09-22T12:53:29.979609Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sta_from_sta_1_rssi', 'sta_from_ap_1_max_ant_rssi', 'pro_ed_1',\n",
       "       'sta_from_sta_2_rssi', 'sta_from_ap_2_mean_ant_rssi',\n",
       "       'ap_from_ap_1_mean_ant_rssi', 'sta_from_ap_1_sum_ant_rssi',\n",
       "       'sta_from_ap_1_mean_ant_rssi', 'pro_ed_2', 'ap_from_ap_2_sum_ant_rssi',\n",
       "       'sta_to_ap_2_mean_ant_rssi', 'sta_to_ap_2_max_ant_rssi',\n",
       "       'sta_to_ap_2_sum_ant_rssi', 'sta_from_ap_2_sum_ant_rssi',\n",
       "       'ap_from_ap_1_sum_ant_rssi', 'ap_from_ap_2_mean_ant_rssi',\n",
       "       'sta_to_ap_0_sum_ant_rssi', 'sta_from_ap_2_max_ant_rssi',\n",
       "       'sta_to_ap_1_sum_ant_rssi', 'sta_to_ap_1_mean_ant_rssi',\n",
       "       'sta_to_ap_0_max_ant_rssi', 'ap_from_ap_1_max_ant_rssi',\n",
       "       'sta_to_ap_0_mean_ant_rssi', 'pro_pd_2', 'sta_to_ap_1_max_ant_rssi',\n",
       "       'pro_pd_1', 'sta_from_ap_0_sum_ant_rssi', 'eirp',\n",
       "       'sta_from_ap_0_max_ant_rssi', 'nav', 'ap_from_ap_2_max_ant_rssi',\n",
       "       'sta_from_ap_0_mean_ant_rssi', 'pro_nav_1', 'protocol', 'pro_nav_2'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resource_0_columns[np.argsort(rfg0.feature_importances_)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a8cad6a0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-22T12:54:03.905029Z",
     "start_time": "2024-09-22T12:53:31.864918Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "res0=cross_val_score(rfg0,resource_0,target_0,cv=10)\n",
    "res1=cross_val_score(rfg1,resource_1,target_1,cv=10)\n",
    "res2=cross_val_score(rfg2,resource_2,target_1,cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9b673c91",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-22T12:54:19.777969Z",
     "start_time": "2024-09-22T12:54:19.772457Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0.48235188,  0.73785023,  0.43836535,  0.26063035,  0.48773401,\n",
       "         0.43856356,  0.45509627, -0.53538686,  0.60382088, -1.34926315]),\n",
       " array([-2.87874895, -0.2172636 , -2.11845169, -0.04521275,  0.67079869,\n",
       "         0.80246478,  0.73876831,  0.58695602, -0.62946989, -1.04950583]),\n",
       " array([-7.63615336,  0.20332859, -3.80081745,  0.15210002,  0.51334689,\n",
       "         0.85025905,  0.73194789,  0.64693267, -0.0407959 , -0.10981295]))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res0,res1,res2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e664cb1a",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e91ee1a2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-22T07:14:13.054433Z",
     "start_time": "2024-09-22T07:14:12.833386Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from xgboost import XGBRegressor as XGBR\n",
    "reg2=XGBR(n_estimators=100).fit(resource_2.to_numpy(),target_2.to_numpy())\n",
    "reg1=XGBR(n_estimators=100).fit(resource_1.to_numpy(),target_1.to_numpy())\n",
    "reg0=XGBR(n_estimators=100).fit(resource_0.to_numpy(),target_0.to_numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a6d8fad4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-22T07:14:13.580587Z",
     "start_time": "2024-09-22T07:14:13.575071Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.0075039 , 0.00341491, 0.01346785, 0.00830094, 0.01671036,\n",
       "       0.07337597, 0.00430785, 0.0009673 , 0.00100925, 0.87094164],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg2.feature_importances_,reg1.feature_importances_,reg0.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "63194529",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-22T11:02:43.945156Z",
     "start_time": "2024-09-22T11:02:43.926379Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'reg2' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[31], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m resource_2_columns[np\u001b[38;5;241m.\u001b[39margsort(reg2\u001b[38;5;241m.\u001b[39mfeature_importances_)]\n",
      "\u001b[1;31mNameError\u001b[0m: name 'reg2' is not defined"
     ]
    }
   ],
   "source": [
    "resource_2_columns[np.argsort(reg2.feature_importances_)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "f5b2201c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-22T07:14:15.103857Z",
     "start_time": "2024-09-22T07:14:15.098052Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['pro_pd', 'pro_ed', 'nav', 'sta_from_ap_1_sum_ant_rssi', 'protocol',\n",
       "       'sta_to_ap_0_sum_ant_rssi', 'ap_from_ap_0_sum_ant_rssi',\n",
       "       'sta_to_ap_1_sum_ant_rssi', 'sta_from_ap_0_sum_ant_rssi', 'pro_nav'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resource_1_columns[np.argsort(reg1.feature_importances_)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "95462b10",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-22T07:14:15.885527Z",
     "start_time": "2024-09-22T07:14:15.880106Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['pro_ed', 'nav', 'sta_to_ap_1_sum_ant_rssi', 'pro_pd',\n",
       "       'sta_to_ap_0_sum_ant_rssi', 'sta_from_ap_0_sum_ant_rssi', 'protocol',\n",
       "       'ap_from_ap_1_sum_ant_rssi', 'sta_from_ap_1_sum_ant_rssi', 'pro_nav'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resource_0_columns[np.argsort(reg0.feature_importances_)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "b1228c93",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-22T07:14:18.850766Z",
     "start_time": "2024-09-22T07:14:16.607814Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "res2=cross_val_score(reg2,resource_2.to_numpy(),target_2,cv=10)\n",
    "res1=cross_val_score(reg1,resource_1.to_numpy(),target_1,cv=10)\n",
    "res0=cross_val_score(reg0,resource_0.to_numpy(),target_0,cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "a15b2720",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-22T07:14:19.417371Z",
     "start_time": "2024-09-22T07:14:19.412757Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-0.08745617,  0.82523061,  0.43994376, -5.74082527, -0.01405203,\n",
       "        -0.59247134,  0.6759851 ,  0.19845983,  0.27875987,  0.56299691]),\n",
       " array([-0.01034078,  0.72730178,  0.76076009, -9.42377509, -0.79792296,\n",
       "        -0.20867168,  0.44441658,  0.08746156,  0.16975902,  0.32037194]))"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res2,res1,res0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaf00c96",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Lasso回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "678a5de6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-22T07:18:39.021652Z",
     "start_time": "2024-09-22T07:18:38.991769Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "\n",
    "# 使用Lasso模型进行特征选择\n",
    "lasso = Lasso(alpha=0.05)  # alpha 控制正则化强度\n",
    "lasso.fit(resource_1,target_1)\n",
    "\n",
    "res0=cross_val_score(lasso,resource_0.to_numpy(),target_0,cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "157b7428",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-22T07:19:09.209978Z",
     "start_time": "2024-09-22T07:19:09.205036Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.69101157,  0.90689587,  0.47683441, -0.86518418,  0.3998623 ,\n",
       "        0.76092865,  0.61263434,  0.49946242, -1.25464679,  0.54275803])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59c7fccf",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# 修正重命名列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3e81b19f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-22T13:09:45.096584Z",
     "start_time": "2024-09-22T13:09:45.073111Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "protocol\n",
      "==========================================================================================\n",
      "nav\n",
      "==========================================================================================\n",
      "eirp\n",
      "ap_from_ap_1_sum_ant_rssi\n",
      "ap_from_ap_1_max_ant_rssi\n",
      "ap_from_ap_1_mean_ant_rssi\n",
      "ap_from_ap_2_sum_ant_rssi\n",
      "ap_from_ap_2_max_ant_rssi\n",
      "ap_from_ap_2_mean_ant_rssi\n",
      "sta_from_ap_0_sum_ant_rssi\n",
      "sta_from_ap_0_max_ant_rssi\n",
      "sta_from_ap_0_mean_ant_rssi\n",
      "sta_from_ap_1_sum_ant_rssi\n",
      "sta_from_ap_1_max_ant_rssi\n",
      "sta_from_ap_1_mean_ant_rssi\n",
      "sta_from_ap_2_sum_ant_rssi\n",
      "sta_from_ap_2_max_ant_rssi\n",
      "sta_from_ap_2_mean_ant_rssi\n",
      "pro_pd_2\n",
      "pro_pd_1\n",
      "pro_nav_1\n",
      "==========================================================================================\n",
      "ap_from_ap_1_sum_ant_rssi\n",
      "ap_from_ap_1_max_ant_rssi\n",
      "ap_from_ap_1_mean_ant_rssi\n",
      "ap_from_ap_2_sum_ant_rssi\n",
      "ap_from_ap_2_max_ant_rssi\n",
      "ap_from_ap_2_mean_ant_rssi\n",
      "sta_from_ap_0_max_ant_rssi\n",
      "sta_from_ap_1_sum_ant_rssi\n",
      "sta_from_ap_1_max_ant_rssi\n",
      "sta_from_ap_1_mean_ant_rssi\n",
      "pro_pd_1\n",
      "pro_nav_1\n",
      "==========================================================================================\n",
      "ap_from_ap_1_max_ant_rssi\n",
      "ap_from_ap_1_mean_ant_rssi\n",
      "ap_from_ap_2_sum_ant_rssi\n",
      "ap_from_ap_2_max_ant_rssi\n",
      "ap_from_ap_2_mean_ant_rssi\n",
      "sta_from_ap_1_sum_ant_rssi\n",
      "sta_from_ap_1_max_ant_rssi\n",
      "sta_from_ap_1_mean_ant_rssi\n",
      "pro_pd_1\n",
      "pro_nav_1\n",
      "==========================================================================================\n",
      "ap_from_ap_1_mean_ant_rssi\n",
      "ap_from_ap_2_sum_ant_rssi\n",
      "ap_from_ap_2_max_ant_rssi\n",
      "ap_from_ap_2_mean_ant_rssi\n",
      "sta_from_ap_0_max_ant_rssi\n",
      "sta_from_ap_1_sum_ant_rssi\n",
      "sta_from_ap_1_max_ant_rssi\n",
      "sta_from_ap_1_mean_ant_rssi\n",
      "pro_pd_1\n",
      "pro_nav_1\n",
      "==========================================================================================\n",
      "ap_from_ap_2_sum_ant_rssi\n",
      "ap_from_ap_2_max_ant_rssi\n",
      "ap_from_ap_2_mean_ant_rssi\n",
      "sta_from_ap_1_sum_ant_rssi\n",
      "sta_from_ap_1_max_ant_rssi\n",
      "sta_from_ap_1_mean_ant_rssi\n",
      "sta_from_ap_2_sum_ant_rssi\n",
      "sta_from_ap_2_max_ant_rssi\n",
      "sta_from_ap_2_mean_ant_rssi\n",
      "pro_pd_2\n",
      "pro_pd_1\n",
      "pro_nav_1\n",
      "==========================================================================================\n",
      "ap_from_ap_2_max_ant_rssi\n",
      "ap_from_ap_2_mean_ant_rssi\n",
      "sta_from_ap_1_sum_ant_rssi\n",
      "sta_from_ap_1_max_ant_rssi\n",
      "sta_from_ap_1_mean_ant_rssi\n",
      "sta_from_ap_2_sum_ant_rssi\n",
      "sta_from_ap_2_max_ant_rssi\n",
      "sta_from_ap_2_mean_ant_rssi\n",
      "pro_pd_2\n",
      "pro_nav_2\n",
      "pro_pd_1\n",
      "pro_nav_1\n",
      "==========================================================================================\n",
      "ap_from_ap_2_mean_ant_rssi\n",
      "sta_from_ap_1_sum_ant_rssi\n",
      "sta_from_ap_1_max_ant_rssi\n",
      "sta_from_ap_1_mean_ant_rssi\n",
      "sta_from_ap_2_sum_ant_rssi\n",
      "sta_from_ap_2_max_ant_rssi\n",
      "sta_from_ap_2_mean_ant_rssi\n",
      "pro_pd_2\n",
      "pro_pd_1\n",
      "pro_nav_1\n",
      "==========================================================================================\n",
      "sta_to_ap_0_sum_ant_rssi\n",
      "sta_to_ap_0_max_ant_rssi\n",
      "sta_to_ap_0_mean_ant_rssi\n",
      "==========================================================================================\n",
      "sta_to_ap_0_max_ant_rssi\n",
      "sta_to_ap_0_mean_ant_rssi\n",
      "==========================================================================================\n",
      "sta_to_ap_0_mean_ant_rssi\n",
      "==========================================================================================\n",
      "sta_to_ap_1_sum_ant_rssi\n",
      "sta_to_ap_1_max_ant_rssi\n",
      "sta_to_ap_1_mean_ant_rssi\n",
      "sta_to_ap_2_sum_ant_rssi\n",
      "sta_to_ap_2_max_ant_rssi\n",
      "sta_to_ap_2_mean_ant_rssi\n",
      "==========================================================================================\n",
      "sta_to_ap_1_max_ant_rssi\n",
      "sta_to_ap_1_mean_ant_rssi\n",
      "==========================================================================================\n",
      "sta_to_ap_1_mean_ant_rssi\n",
      "sta_to_ap_2_sum_ant_rssi\n",
      "sta_to_ap_2_max_ant_rssi\n",
      "sta_to_ap_2_mean_ant_rssi\n",
      "==========================================================================================\n",
      "sta_to_ap_2_sum_ant_rssi\n",
      "sta_to_ap_2_max_ant_rssi\n",
      "sta_to_ap_2_mean_ant_rssi\n",
      "==========================================================================================\n",
      "sta_to_ap_2_max_ant_rssi\n",
      "sta_to_ap_2_mean_ant_rssi\n",
      "==========================================================================================\n",
      "sta_to_ap_2_mean_ant_rssi\n",
      "==========================================================================================\n",
      "sta_from_ap_0_sum_ant_rssi\n",
      "sta_from_ap_0_max_ant_rssi\n",
      "sta_from_ap_0_mean_ant_rssi\n",
      "==========================================================================================\n",
      "sta_from_ap_0_max_ant_rssi\n",
      "sta_from_ap_0_mean_ant_rssi\n",
      "==========================================================================================\n",
      "sta_from_ap_0_mean_ant_rssi\n",
      "==========================================================================================\n",
      "sta_from_ap_1_sum_ant_rssi\n",
      "sta_from_ap_1_max_ant_rssi\n",
      "sta_from_ap_1_mean_ant_rssi\n",
      "sta_from_ap_2_sum_ant_rssi\n",
      "sta_from_ap_2_max_ant_rssi\n",
      "sta_from_ap_2_mean_ant_rssi\n",
      "pro_pd_1\n",
      "==========================================================================================\n",
      "sta_from_ap_1_max_ant_rssi\n",
      "sta_from_ap_1_mean_ant_rssi\n",
      "sta_from_ap_2_sum_ant_rssi\n",
      "sta_from_ap_2_mean_ant_rssi\n",
      "pro_pd_1\n",
      "==========================================================================================\n",
      "sta_from_ap_1_mean_ant_rssi\n",
      "sta_from_ap_2_sum_ant_rssi\n",
      "sta_from_ap_2_max_ant_rssi\n",
      "sta_from_ap_2_mean_ant_rssi\n",
      "pro_pd_1\n",
      "==========================================================================================\n",
      "sta_from_ap_2_sum_ant_rssi\n",
      "sta_from_ap_2_max_ant_rssi\n",
      "sta_from_ap_2_mean_ant_rssi\n",
      "==========================================================================================\n",
      "sta_from_ap_2_max_ant_rssi\n",
      "sta_from_ap_2_mean_ant_rssi\n",
      "==========================================================================================\n",
      "sta_from_ap_2_mean_ant_rssi\n",
      "==========================================================================================\n",
      "sta_from_sta_1_rssi\n",
      "sta_from_sta_2_rssi\n",
      "==========================================================================================\n",
      "sta_from_sta_2_rssi\n",
      "==========================================================================================\n",
      "pro_pd_2\n",
      "==========================================================================================\n",
      "pro_ed_2\n",
      "==========================================================================================\n",
      "pro_nav_2\n",
      "==========================================================================================\n",
      "pro_pd_1\n",
      "pro_nav_1\n",
      "==========================================================================================\n",
      "pro_ed_1\n",
      "==========================================================================================\n",
      "pro_nav_1\n",
      "==========================================================================================\n"
     ]
    }
   ],
   "source": [
    "for i in range(spearman_correlation_0.shape[0]):\n",
    "    print(resource_0_columns[i])\n",
    "    for j in range(i+1,spearman_correlation_0.shape[0]):\n",
    "        if spearman_correlation_0.iloc[i,j]>=0.8:\n",
    "            print(resource_0_columns[j])\n",
    "    print('==='*30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "49102d9d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T01:07:53.474491Z",
     "start_time": "2024-09-24T01:07:53.470945Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "drop_columns=[2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,25,26,27,28]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c5bac5ba",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T01:07:54.277796Z",
     "start_time": "2024-09-24T01:07:54.272030Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "resource_2=resource_2.drop(resource_2_columns[drop_columns],axis=1)\n",
    "resource_1=resource_1.drop(resource_1_columns[drop_columns],axis=1)\n",
    "resource_0=resource_0.drop(resource_0_columns[drop_columns],axis=1)\n",
    "\n",
    "resource_2_columns=resource_2.columns\n",
    "resource_1_columns=resource_1.columns\n",
    "resource_0_columns=resource_0.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "270fbe28",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T01:07:55.440104Z",
     "start_time": "2024-09-24T01:07:55.430590Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 217 entries, 0 to 216\n",
      "Data columns (total 16 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   protocol                    217 non-null    object \n",
      " 1   nav                         217 non-null    int64  \n",
      " 2   ap_from_ap_0_sum_ant_rssi   217 non-null    object \n",
      " 3   ap_from_ap_2_sum_ant_rssi   217 non-null    object \n",
      " 4   sta_to_ap_0_sum_ant_rssi    217 non-null    object \n",
      " 5   sta_to_ap_1_sum_ant_rssi    217 non-null    object \n",
      " 6   sta_to_ap_2_sum_ant_rssi    217 non-null    object \n",
      " 7   sta_from_ap_0_sum_ant_rssi  217 non-null    object \n",
      " 8   sta_from_ap_1_sum_ant_rssi  217 non-null    object \n",
      " 9   sta_from_ap_2_sum_ant_rssi  217 non-null    object \n",
      " 10  pro_pd_2                    217 non-null    float64\n",
      " 11  pro_ed_2                    217 non-null    float64\n",
      " 12  pro_nav_2                   217 non-null    float64\n",
      " 13  pro_pd_0                    217 non-null    float64\n",
      " 14  pro_ed_0                    217 non-null    float64\n",
      " 15  pro_nav_0                   217 non-null    float64\n",
      "dtypes: float64(6), int64(1), object(9)\n",
      "memory usage: 27.3+ KB\n"
     ]
    }
   ],
   "source": [
    "resource_1.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b84ea98a",
   "metadata": {},
   "source": [
    "# 合体后的数据筛选"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b88da1d7",
   "metadata": {},
   "source": [
    "# 随机森林----预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8314e323",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T16:44:13.988701Z",
     "start_time": "2024-09-24T16:43:55.958934Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "rfg=RandomForestRegressor(n_estimators=100,criterion='friedman_mse',random_state=0,oob_score=False)\n",
    "\n",
    "\n",
    "# target_0_phy=pd.DataFrame(target_0_phy)\n",
    "\n",
    "rfg=rfg.fit(resource,target)\n",
    "\n",
    "# rfg0=RandomForestRegressor(n_estimators=100,criterion='friedman_mse',random_state=0,oob_score=False)\n",
    "# rfg0=rfg0.fit(resource,target_0.to_numpy())\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "res=cross_val_score(rfg,resource,target,cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "958180a5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T16:44:16.690519Z",
     "start_time": "2024-09-24T16:44:16.685527Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.83141282, 0.65824595, 0.79658101, 0.66677203, 0.7167974 ,\n",
       "       0.79447188, 0.77801019, 0.72234675, 0.6161595 , 0.65548048])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "20eb45ad",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T16:44:20.560259Z",
     "start_time": "2024-09-24T16:44:20.555747Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7236278020566381"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fc5e2f96",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T01:10:48.017353Z",
     "start_time": "2024-09-24T01:10:13.501981Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "feda8b9b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T01:11:20.773737Z",
     "start_time": "2024-09-24T01:11:20.766374Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.65779942,  0.34265423,  0.59471368,  0.71221281,  0.56651019,\n",
       "        0.8553319 ,  0.52001188, -0.04361485,  0.57721042,  0.23529726])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "11078612",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T01:11:21.897635Z",
     "start_time": "2024-09-24T01:11:21.892127Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5018126944524341"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d0b72b9e",
   "metadata": {},
   "source": [
    "# XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5d2ec4a3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T01:11:32.657200Z",
     "start_time": "2024-09-24T01:11:32.272178Z"
    }
   },
   "outputs": [],
   "source": [
    "from xgboost import XGBRegressor as XGBR\n",
    "reg=XGBR(n_estimators=100).fit(resource_merge.to_numpy(),target_merge.to_numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c7d6ad8a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T01:11:35.221347Z",
     "start_time": "2024-09-24T01:11:33.211266Z"
    }
   },
   "outputs": [],
   "source": [
    "res=cross_val_score(reg,resource_merge.to_numpy(),target_merge.to_numpy(),cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d5577794",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T01:11:35.902160Z",
     "start_time": "2024-09-24T01:11:35.897493Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.42516672487715673"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1e9e32d",
   "metadata": {},
   "source": [
    "# ridge岭回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "be5bd2ac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T01:12:01.379322Z",
     "start_time": "2024-09-24T01:12:01.300933Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "regcv=Ridge(alpha=0.5)\n",
    "regcv=regcv.fit(resource_merge,target_merge)\n",
    "regcv.score(resource_merge,target_merge)\n",
    "\n",
    "\n",
    "res=cross_val_score(regcv,resource_merge,target_merge,cv=10,scoring='neg_median_absolute_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "93d76726",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T01:12:03.945761Z",
     "start_time": "2024-09-24T01:12:03.941206Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-6.19106903, -7.06914178, -3.56968495, -8.48483001, -6.88924621,\n",
       "       -4.80623742, -7.31215256, -7.46651475, -4.40053614, -6.3002165 ])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9a5549fa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T01:12:10.775558Z",
     "start_time": "2024-09-24T01:12:10.770679Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-6.248962934422465"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "69ca0b03",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T01:12:29.519958Z",
     "start_time": "2024-09-24T01:12:29.463188Z"
    }
   },
   "outputs": [],
   "source": [
    "res=cross_val_score(regcv,resource_merge,target_merge,cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "296d19d9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T01:12:30.152827Z",
     "start_time": "2024-09-24T01:12:30.148066Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.47196498,  0.26570741,  0.56078644,  0.54706683,  0.34086283,\n",
       "        0.74701942,  0.34542861, -0.40416498,  0.48464666, -0.26172346])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c869838e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T01:12:35.447183Z",
     "start_time": "2024-09-24T01:12:35.442212Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3097594757672434"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d93a666",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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