Programe2-type_2_重构.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0bf9af82",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T16:30:05.050164Z",
     "start_time": "2024-09-24T16:30:04.549431Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5e31f6b2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T16:30:06.340174Z",
     "start_time": "2024-09-24T16:30:06.283920Z"
    }
   },
   "outputs": [],
   "source": [
    "part1=pd.read_csv('RAWDATA/training_set_2ap_loc0_nav82.csv')\n",
    "part2=pd.read_csv('RAWDATA/training_set_2ap_loc0_nav86.csv')\n",
    "part3=pd.read_csv('RAWDATA/training_set_2ap_loc1_nav82.csv')\n",
    "part4=pd.read_csv('RAWDATA/training_set_2ap_loc1_nav86_new.csv')\n",
    "part5=pd.read_csv('RAWDATA/training_set_2ap_loc2_nav82.csv')\n",
    "\n",
    "resource=pd.concat([part1,part2,part3,part4,part5],axis=0)\n",
    "resource=resource.iloc[:390,:]    #删掉异常的行值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b35ae5fd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T16:30:08.272596Z",
     "start_time": "2024-09-24T16:30:08.262055Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 390 entries, 0 to 77\n",
      "Data columns (total 43 columns):\n",
      " #   Column                       Non-Null Count  Dtype  \n",
      "---  ------                       --------------  -----  \n",
      " 0   test_id                      390 non-null    int64  \n",
      " 1   test_dur                     390 non-null    int64  \n",
      " 2   loc_id                       390 non-null    object \n",
      " 3   protocol                     390 non-null    object \n",
      " 4   pkt_len                      390 non-null    int64  \n",
      " 5   bss_id                       390 non-null    int64  \n",
      " 6   ap_name                      390 non-null    object \n",
      " 7   ap_mac                       390 non-null    object \n",
      " 8   ap_id                        390 non-null    object \n",
      " 9   pd                           390 non-null    int64  \n",
      " 10  ed                           390 non-null    int64  \n",
      " 11  nav                          390 non-null    int64  \n",
      " 12  eirp                         390 non-null    int64  \n",
      " 13  ap_from_ap_0_sum_ant_rssi    195 non-null    object \n",
      " 14  ap_from_ap_0_max_ant_rssi    195 non-null    object \n",
      " 15  ap_from_ap_0_mean_ant_rssi   195 non-null    object \n",
      " 16  ap_from_ap_1_sum_ant_rssi    195 non-null    object \n",
      " 17  ap_from_ap_1_max_ant_rssi    195 non-null    object \n",
      " 18  ap_from_ap_1_mean_ant_rssi   195 non-null    object \n",
      " 19  sta_mac                      390 non-null    object \n",
      " 20  sta_id                       390 non-null    object \n",
      " 21  sta_to_ap_0_sum_ant_rssi     390 non-null    object \n",
      " 22  sta_to_ap_0_max_ant_rssi     390 non-null    object \n",
      " 23  sta_to_ap_0_mean_ant_rssi    390 non-null    object \n",
      " 24  sta_to_ap_1_sum_ant_rssi     390 non-null    object \n",
      " 25  sta_to_ap_1_max_ant_rssi     390 non-null    object \n",
      " 26  sta_to_ap_1_mean_ant_rssi    390 non-null    object \n",
      " 27  sta_from_ap_0_sum_ant_rssi   390 non-null    object \n",
      " 28  sta_from_ap_0_max_ant_rssi   390 non-null    object \n",
      " 29  sta_from_ap_0_mean_ant_rssi  390 non-null    object \n",
      " 30  sta_from_ap_1_sum_ant_rssi   390 non-null    object \n",
      " 31  sta_from_ap_1_max_ant_rssi   390 non-null    object \n",
      " 32  sta_from_ap_1_mean_ant_rssi  390 non-null    object \n",
      " 33  sta_from_sta_0_rssi          195 non-null    float64\n",
      " 34  sta_from_sta_1_rssi          195 non-null    float64\n",
      " 35  nss                          390 non-null    float64\n",
      " 36  mcs                          390 non-null    float64\n",
      " 37  per                          390 non-null    float64\n",
      " 38  num_ampdu                    390 non-null    float64\n",
      " 39  ppdu_dur                     390 non-null    float64\n",
      " 40  other_air_time               390 non-null    float64\n",
      " 41  seq_time                     390 non-null    float64\n",
      " 42  throughput                   390 non-null    float64\n",
      "dtypes: float64(10), int64(8), object(25)\n",
      "memory usage: 134.1+ KB\n"
     ]
    }
   ],
   "source": [
    "resource.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "47ed8091",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T16:30:09.610508Z",
     "start_time": "2024-09-24T16:30:09.605769Z"
    }
   },
   "outputs": [],
   "source": [
    "columns=[0,1,2,4,6,7,8,12,19,20,22,23,25,26,28,29,31,32,37,38,39,40,42]\n",
    "resource=resource.drop(resource.columns[columns],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3c6d76ba",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T16:30:10.992262Z",
     "start_time": "2024-09-24T16:30:10.984477Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 390 entries, 0 to 77\n",
      "Data columns (total 20 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   protocol                    390 non-null    object \n",
      " 1   bss_id                      390 non-null    int64  \n",
      " 2   pd                          390 non-null    int64  \n",
      " 3   ed                          390 non-null    int64  \n",
      " 4   nav                         390 non-null    int64  \n",
      " 5   ap_from_ap_0_sum_ant_rssi   195 non-null    object \n",
      " 6   ap_from_ap_0_max_ant_rssi   195 non-null    object \n",
      " 7   ap_from_ap_0_mean_ant_rssi  195 non-null    object \n",
      " 8   ap_from_ap_1_sum_ant_rssi   195 non-null    object \n",
      " 9   ap_from_ap_1_max_ant_rssi   195 non-null    object \n",
      " 10  ap_from_ap_1_mean_ant_rssi  195 non-null    object \n",
      " 11  sta_to_ap_0_sum_ant_rssi    390 non-null    object \n",
      " 12  sta_to_ap_1_sum_ant_rssi    390 non-null    object \n",
      " 13  sta_from_ap_0_sum_ant_rssi  390 non-null    object \n",
      " 14  sta_from_ap_1_sum_ant_rssi  390 non-null    object \n",
      " 15  sta_from_sta_0_rssi         195 non-null    float64\n",
      " 16  sta_from_sta_1_rssi         195 non-null    float64\n",
      " 17  nss                         390 non-null    float64\n",
      " 18  mcs                         390 non-null    float64\n",
      " 19  seq_time                    390 non-null    float64\n",
      "dtypes: float64(5), int64(4), object(11)\n",
      "memory usage: 64.0+ KB\n"
     ]
    }
   ],
   "source": [
    "resource.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9d6f5865",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T16:30:12.216731Z",
     "start_time": "2024-09-24T16:30:12.213856Z"
    }
   },
   "outputs": [],
   "source": [
    "# resource.to_excel('programe2.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3f2c6674",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T16:30:13.435531Z",
     "start_time": "2024-09-24T16:30:13.323989Z"
    }
   },
   "outputs": [],
   "source": [
    "for i in range(resource.shape[0]):\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')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f2f5fae7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T16:30:14.849452Z",
     "start_time": "2024-09-24T16:30:14.844993Z"
    }
   },
   "outputs": [],
   "source": [
    "resource=resource.drop(['ap_from_ap_1_sum_ant_rssi','ap_from_ap_1_max_ant_rssi',\n",
    "                       'ap_from_ap_1_mean_ant_rssi','sta_from_sta_1_rssi'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8c13a6cc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T16:30:15.857835Z",
     "start_time": "2024-09-24T16:30:15.849322Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 390 entries, 0 to 77\n",
      "Data columns (total 16 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   protocol                    390 non-null    object \n",
      " 1   bss_id                      390 non-null    int64  \n",
      " 2   pd                          390 non-null    int64  \n",
      " 3   ed                          390 non-null    int64  \n",
      " 4   nav                         390 non-null    int64  \n",
      " 5   ap_from_ap_0_sum_ant_rssi   390 non-null    object \n",
      " 6   ap_from_ap_0_max_ant_rssi   390 non-null    object \n",
      " 7   ap_from_ap_0_mean_ant_rssi  390 non-null    object \n",
      " 8   sta_to_ap_0_sum_ant_rssi    390 non-null    object \n",
      " 9   sta_to_ap_1_sum_ant_rssi    390 non-null    object \n",
      " 10  sta_from_ap_0_sum_ant_rssi  390 non-null    object \n",
      " 11  sta_from_ap_1_sum_ant_rssi  390 non-null    object \n",
      " 12  sta_from_sta_0_rssi         390 non-null    float64\n",
      " 13  nss                         390 non-null    float64\n",
      " 14  mcs                         390 non-null    float64\n",
      " 15  seq_time                    390 non-null    float64\n",
      "dtypes: float64(4), int64(4), object(8)\n",
      "memory usage: 51.8+ KB\n"
     ]
    }
   ],
   "source": [
    "resource.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "91621e79",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T16:30:17.336188Z",
     "start_time": "2024-09-24T16:30:17.331222Z"
    }
   },
   "outputs": [],
   "source": [
    "# 定义管理rssi中异常值数据，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": 12,
   "id": "f0ccde03",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T16:30:18.588668Z",
     "start_time": "2024-09-24T16:30:18.413498Z"
    }
   },
   "outputs": [],
   "source": [
    "resource['pro_pd'] = resource.apply(lambda row: cal_pd(row['ap_from_ap_0_max_ant_rssi'], row['pd']), axis=1)\n",
    "resource['pro_ed'] = resource.apply(lambda row: cal_ed(row['ap_from_ap_0_max_ant_rssi'], row['ed']), axis=1)\n",
    "resource['pro_nav'] = resource.apply(lambda row: cal_nav(row['ap_from_ap_0_mean_ant_rssi'], row['nav']), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d17cc8e9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T16:30:19.807085Z",
     "start_time": "2024-09-24T16:30:19.802061Z"
    }
   },
   "outputs": [],
   "source": [
    "drop_columns=['pd','ed','ap_from_ap_0_mean_ant_rssi','ap_from_ap_0_max_ant_rssi']\n",
    "\n",
    "# drop_columns=['pd','ed','ap_from_ap_0_mean_ant_rssi','ap_from_ap_0_max_ant_rssi'\n",
    "#               ,'sta_to_ap_0_max_ant_rssi','sta_to_ap_0_mean_ant_rssi'\n",
    "#               ,'sta_to_ap_1_max_ant_rssi','sta_to_ap_1_mean_ant_rssi'\n",
    "#              ,'sta_from_ap_0_max_ant_rssi','sta_from_ap_0_mean_ant_rssi'\n",
    "#              ,'sta_from_ap_1_max_ant_rssi','sta_from_ap_1_mean_ant_rssi']\n",
    "\n",
    "resource=resource.drop(drop_columns,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6dfeb29a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T16:30:21.054366Z",
     "start_time": "2024-09-24T16:30:21.045271Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 390 entries, 0 to 77\n",
      "Data columns (total 15 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   protocol                    390 non-null    object \n",
      " 1   bss_id                      390 non-null    int64  \n",
      " 2   nav                         390 non-null    int64  \n",
      " 3   ap_from_ap_0_sum_ant_rssi   390 non-null    object \n",
      " 4   sta_to_ap_0_sum_ant_rssi    390 non-null    object \n",
      " 5   sta_to_ap_1_sum_ant_rssi    390 non-null    object \n",
      " 6   sta_from_ap_0_sum_ant_rssi  390 non-null    object \n",
      " 7   sta_from_ap_1_sum_ant_rssi  390 non-null    object \n",
      " 8   sta_from_sta_0_rssi         390 non-null    float64\n",
      " 9   nss                         390 non-null    float64\n",
      " 10  mcs                         390 non-null    float64\n",
      " 11  seq_time                    390 non-null    float64\n",
      " 12  pro_pd                      390 non-null    float64\n",
      " 13  pro_ed                      390 non-null    float64\n",
      " 14  pro_nav                     390 non-null    float64\n",
      "dtypes: float64(7), int64(2), object(6)\n",
      "memory usage: 48.8+ KB\n"
     ]
    }
   ],
   "source": [
    "resource.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a8065e2a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T16:30:22.595648Z",
     "start_time": "2024-09-24T16:30:22.241762Z"
    }
   },
   "outputs": [],
   "source": [
    "# 对RSSI求平均值处理\n",
    "for i in range(3,8):\n",
    "    resource.iloc[:,i]=resource.iloc[:,i].apply(lambda x: sum(eval(x))/len(eval(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "864cc6c6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T16:30:25.550778Z",
     "start_time": "2024-09-24T16:30:24.888311Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max value: 0.044444444444444446\n",
      "Min value: 0.0\n",
      "Mean value: 0.002331550769270887\n",
      "Standard Deviation: 0.0059889756477766775\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pro_ed的分布情况\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming df is your DataFrame and 'pro_ed' is the column of interest\n",
    "pro_ed = resource['pro_ed']\n",
    "\n",
    "# Calculate basic statistics\n",
    "max_value = pro_ed.max()\n",
    "min_value = pro_ed.min()\n",
    "mean_value = pro_ed.mean()\n",
    "std_dev = pro_ed.std()\n",
    "\n",
    "# Display basic statistics\n",
    "print(f\"Max value: {max_value}\")\n",
    "print(f\"Min value: {min_value}\")\n",
    "print(f\"Mean value: {mean_value}\")\n",
    "print(f\"Standard Deviation: {std_dev}\")\n",
    "\n",
    "# Plot a histogram to visualize the distribution of 'pro_ed'\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.hist(pro_ed, bins=10, color='skyblue', edgecolor='black')\n",
    "plt.title('Histogram of pro_ed')\n",
    "plt.xlabel('pro_ed values')\n",
    "plt.ylabel('Frequency')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "16392b3e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T16:30:31.857706Z",
     "start_time": "2024-09-24T16:30:28.557948Z"
    }
   },
   "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=2.\n",
      "  warnings.warn(\n",
      "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=2.\n",
      "  warnings.warn(\n",
      "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=2.\n",
      "  warnings.warn(\n",
      "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=2.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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[:,[12,13,14]]\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",
    "    kmeans.fit(data_scaled)\n",
    "    sse.append(kmeans.inertia_)  # SSE\n",
    "    silhouette_scores.append(silhouette_score(data_scaled, kmeans.labels_))\n",
    "\n",
    "# 绘制肘部法图像\n",
    "plt.figure(figsize=(10, 4))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(K, sse, 'bo-')\n",
    "plt.title('Elbow Method for Optimal k')\n",
    "plt.xlabel('Number of clusters (k)')\n",
    "plt.ylabel('SSE')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(K, silhouette_scores, 'bo-')\n",
    "plt.title('Silhouette Score for k')\n",
    "plt.xlabel('Number of clusters (k)')\n",
    "plt.ylabel('Silhouette Score')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ddfceb85",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T16:30:40.435660Z",
     "start_time": "2024-09-24T16:30:39.734835Z"
    }
   },
   "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=2.\n",
      "  warnings.warn(\n",
      "C:\\Users\\ShenFei\\AppData\\Local\\Temp\\ipykernel_12240\\1486243090.py:10: 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster centers:\n",
      "          0         1         2\n",
      "0  0.897140  0.003123  0.924633\n",
      "1  0.063595  0.001679  0.034990\n",
      "2  0.770176  0.001579  0.150017\n",
      "cluster\n",
      "0    179\n",
      "1    170\n",
      "2     41\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 得到‘state’类别\n",
    "# 0:大量同步\n",
    "# 1：大量异步\n",
    "# 2：混合\n",
    "\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())\n",
    "\n",
    "# # 可视化聚类结果\n",
    "# plt.scatter(df['pro_nav'], df['pro_pd'], c=df['cluster'], cmap='viridis', label='Cluster')\n",
    "# plt.colorbar()\n",
    "# plt.xlabel('pro_nav')\n",
    "# plt.ylabel('pro_pd')\n",
    "# plt.title('KMeans Clustering of WLAN Communication States')\n",
    "# plt.show()\n",
    "# 3D plot of the clustering results\n",
    "fig = plt.figure(figsize=(10, 7))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "# Plot the clusters in 3D\n",
    "scatter = ax.scatter(df['pro_nav'], df['pro_pd'], df['pro_ed'], c=df['cluster'], cmap='viridis')\n",
    "\n",
    "# Set labels and title\n",
    "ax.set_xlabel('pro_nav')\n",
    "ax.set_ylabel('pro_pd')\n",
    "ax.set_zlabel('pro_ed')\n",
    "ax.set_title('3D KMeans Clustering of WLAN Communication States')\n",
    "\n",
    "# Add a color bar\n",
    "plt.colorbar(scatter)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d99cb1af",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T16:30:43.566595Z",
     "start_time": "2024-09-24T16:30:43.562912Z"
    }
   },
   "outputs": [],
   "source": [
    "resource['state']=df['cluster']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57dc84da",
   "metadata": {},
   "source": [
    "## 进行均值处理与SINR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "aa34983e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T16:30:45.620292Z",
     "start_time": "2024-09-24T16:30:45.611752Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 390 entries, 0 to 77\n",
      "Data columns (total 16 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   protocol                    390 non-null    object \n",
      " 1   bss_id                      390 non-null    int64  \n",
      " 2   nav                         390 non-null    int64  \n",
      " 3   ap_from_ap_0_sum_ant_rssi   390 non-null    object \n",
      " 4   sta_to_ap_0_sum_ant_rssi    390 non-null    object \n",
      " 5   sta_to_ap_1_sum_ant_rssi    390 non-null    object \n",
      " 6   sta_from_ap_0_sum_ant_rssi  390 non-null    object \n",
      " 7   sta_from_ap_1_sum_ant_rssi  390 non-null    object \n",
      " 8   sta_from_sta_0_rssi         390 non-null    float64\n",
      " 9   nss                         390 non-null    float64\n",
      " 10  mcs                         390 non-null    float64\n",
      " 11  seq_time                    390 non-null    float64\n",
      " 12  pro_pd                      390 non-null    float64\n",
      " 13  pro_ed                      390 non-null    float64\n",
      " 14  pro_nav                     390 non-null    float64\n",
      " 15  state                       390 non-null    int32  \n",
      "dtypes: float64(7), int32(1), int64(2), object(6)\n",
      "memory usage: 50.3+ KB\n"
     ]
    }
   ],
   "source": [
    "resource.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1d6225a6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T16:30:55.263194Z",
     "start_time": "2024-09-24T16:30:55.225910Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      36.107692\n",
      "1      30.800000\n",
      "2      36.107692\n",
      "3      30.800000\n",
      "4      35.445588\n",
      "         ...    \n",
      "73    100.575000\n",
      "74     97.450000\n",
      "75    101.800000\n",
      "76     97.450000\n",
      "77    101.800000\n",
      "Name: sinr, Length: 390, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 信干比采取dbm直接相减得到db\n",
    "def func(x, y, state):\n",
    "    if state == 0:\n",
    "        return x + 100\n",
    "    else:\n",
    "        return x - y\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')] == 1:  # ap1\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_0_sum_ant_rssi')],df.iloc[i, list(df.columns).index('state')])\n",
    "    else:  # ap2\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('state')])\n",
    "    sinr.append(result)\n",
    "\n",
    "# 将计算结果添加为新列\n",
    "df['sinr'] = sinr\n",
    "\n",
    "# 输出结果\n",
    "print(df['sinr'])\n",
    "resource = resource.drop('bss_id',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a301daf2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T16:30:57.416009Z",
     "start_time": "2024-09-24T16:30:57.411824Z"
    }
   },
   "outputs": [],
   "source": [
    "resource = resource.loc[resource['sinr'] > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2151b493",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T16:31:00.735365Z",
     "start_time": "2024-09-24T16:31:00.730899Z"
    }
   },
   "outputs": [],
   "source": [
    "resource.iloc[:,0]=resource.iloc[:,0].astype(str).map({'tcp':1,'udp':0})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4e209b2",
   "metadata": {},
   "source": [
    "# read from here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ddd7bc5e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-25T01:31:49.863894Z",
     "start_time": "2024-09-25T01:31:48.884202Z"
    }
   },
   "outputs": [],
   "source": [
    "# resource.to_excel('programe2_2ap_processed.xlsx')\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "resource = pd.read_excel('programe2_2ap_processed.xlsx')\n",
    "resource = resource.sample(frac=1).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c6cafcdb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-25T01:31:52.000466Z",
     "start_time": "2024-09-25T01:31:51.992267Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 379 entries, 0 to 378\n",
      "Data columns (total 16 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   protocol                    379 non-null    int64  \n",
      " 1   nav                         379 non-null    int64  \n",
      " 2   ap_from_ap_0_sum_ant_rssi   379 non-null    float64\n",
      " 3   sta_to_ap_0_sum_ant_rssi    379 non-null    float64\n",
      " 4   sta_to_ap_1_sum_ant_rssi    379 non-null    float64\n",
      " 5   sta_from_ap_0_sum_ant_rssi  379 non-null    float64\n",
      " 6   sta_from_ap_1_sum_ant_rssi  379 non-null    float64\n",
      " 7   sta_from_sta_0_rssi         379 non-null    int64  \n",
      " 8   nss                         379 non-null    int64  \n",
      " 9   mcs                         379 non-null    int64  \n",
      " 10  seq_time                    379 non-null    float64\n",
      " 11  pro_pd                      379 non-null    float64\n",
      " 12  pro_ed                      379 non-null    float64\n",
      " 13  pro_nav                     379 non-null    float64\n",
      " 14  state                       379 non-null    int64  \n",
      " 15  sinr                        379 non-null    float64\n",
      "dtypes: float64(10), int64(6)\n",
      "memory usage: 47.5 KB\n"
     ]
    }
   ],
   "source": [
    "resource.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "92144d68",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-25T01:31:54.224479Z",
     "start_time": "2024-09-25T01:31:54.216530Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 379 entries, 0 to 378\n",
      "Data columns (total 16 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   protocol                    379 non-null    int64  \n",
      " 1   nav                         379 non-null    int64  \n",
      " 2   ap_from_ap_0_sum_ant_rssi   379 non-null    float64\n",
      " 3   sta_to_ap_0_sum_ant_rssi    379 non-null    float64\n",
      " 4   sta_to_ap_1_sum_ant_rssi    379 non-null    float64\n",
      " 5   sta_from_ap_0_sum_ant_rssi  379 non-null    float64\n",
      " 6   sta_from_ap_1_sum_ant_rssi  379 non-null    float64\n",
      " 7   sta_from_sta_0_rssi         379 non-null    int64  \n",
      " 8   nss                         379 non-null    int64  \n",
      " 9   mcs                         379 non-null    int64  \n",
      " 10  seq_time                    379 non-null    float64\n",
      " 11  pro_pd                      379 non-null    float64\n",
      " 12  pro_ed                      379 non-null    float64\n",
      " 13  pro_nav                     379 non-null    float64\n",
      " 14  state                       379 non-null    int64  \n",
      " 15  sinr                        379 non-null    float64\n",
      "dtypes: float64(10), int64(6)\n",
      "memory usage: 47.5 KB\n"
     ]
    }
   ],
   "source": [
    "target=resource[['nss','mcs']]\n",
    "# resource=resource.drop(['nss','mcs'],axis=1)\n",
    "resource.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b35e0cd",
   "metadata": {},
   "source": [
    "# 构造nss,mcs字典"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d8bf0ea9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-25T01:31:56.916520Z",
     "start_time": "2024-09-25T01:31:56.911820Z"
    }
   },
   "outputs": [],
   "source": [
    "nss_mcs_dict=dict()\n",
    "phy=[8.6,17.2,25.8,34.4,51.6,68.8,77.4,86.0,103.2,114.7,129.0,143.4,17.2,34.4,51.6,68.8,103.2,137.6,154.9,172.1,206.5,\n",
    "    229.4,258.1,286.8]\n",
    "num=0\n",
    "\n",
    "for i in range(1,3):\n",
    "    for j in range(0,12):\n",
    "        nss_mcs_dict[(i,j)]=phy[num]\n",
    "        num+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3677b61b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-23T11:42:51.592488Z",
     "start_time": "2024-09-23T11:42:51.588488Z"
    },
    "heading_collapsed": true
   },
   "source": [
    "# 相关性分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70bd3dc2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-23T14:03:41.530205Z",
     "start_time": "2024-09-23T14:03:41.530205Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "pearson_correlation=resource.corr(method='pearson')\n",
    "spearman_correlation=resource.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\n",
    "plt.imshow(pearson_correlation,cmap='coolwarm')\n",
    "a=plt.gca()\n",
    "a.set_yticks(range(len(resource_columns)))\n",
    "a.set_yticklabels(resource_columns)\n",
    "a.set_xticks(range(len(resource_columns)))\n",
    "# a.set_xticklabels(resource_1_columns)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72f431a3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-23T14:03:41.531096Z",
     "start_time": "2024-09-23T14:03:41.531096Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "plt.imshow(spearman_correlation,cmap='coolwarm')\n",
    "a=plt.gca()\n",
    "a.set_yticks(range(len(resource_columns)))\n",
    "a.set_yticklabels(resource_columns)\n",
    "a.set_xticks(range(len(resource_columns)))\n",
    "# a.set_xticklabels(resource_1_columns)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65e0e5da",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-23T13:41:46.578226Z",
     "start_time": "2024-09-23T13:41:46.516015Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1e8a0428",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-23T14:04:29.828033Z",
     "start_time": "2024-09-23T14:04:29.819515Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 345 entries, 0 to 76\n",
      "Data columns (total 14 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   protocol                    345 non-null    object \n",
      " 1   nav                         345 non-null    int64  \n",
      " 2   ap_from_ap_0_sum_ant_rssi   345 non-null    object \n",
      " 3   sta_to_ap_0_sum_ant_rssi    345 non-null    object \n",
      " 4   sta_to_ap_1_sum_ant_rssi    345 non-null    object \n",
      " 5   sta_from_ap_0_sum_ant_rssi  345 non-null    object \n",
      " 6   sta_from_ap_1_sum_ant_rssi  345 non-null    object \n",
      " 7   sta_from_sta_0_rssi         345 non-null    float64\n",
      " 8   seq_time                    345 non-null    float64\n",
      " 9   pro_pd                      345 non-null    float64\n",
      " 10  pro_ed                      345 non-null    float64\n",
      " 11  pro_nav                     345 non-null    float64\n",
      " 12  state                       345 non-null    int32  \n",
      " 13  sinr                        345 non-null    float64\n",
      "dtypes: float64(6), int32(1), int64(1), object(6)\n",
      "memory usage: 39.1+ KB\n"
     ]
    }
   ],
   "source": [
    "resource.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "02ae225c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-23T14:04:32.151957Z",
     "start_time": "2024-09-23T14:04:32.144922Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 345 entries, 0 to 76\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype\n",
      "---  ------  --------------  -----\n",
      " 0   nss     345 non-null    int64\n",
      " 1   mcs     345 non-null    int64\n",
      "dtypes: int64(2)\n",
      "memory usage: 8.1 KB\n"
     ]
    }
   ],
   "source": [
    "target.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dbbff14",
   "metadata": {},
   "source": [
    "# SINR_总情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b13f76cd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-25T01:32:11.506619Z",
     "start_time": "2024-09-25T01:32:03.014269Z"
    }
   },
   "outputs": [],
   "source": [
    "resource=resource.drop('sta_from_sta_0_rssi',axis=1)\n",
    "resource=resource.drop(['mcs','nss'],axis=1)\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "rfg0=RandomForestRegressor(n_estimators=100,criterion='friedman_mse',random_state=0,oob_score=False)\n",
    "\n",
    "target_phy=[]\n",
    "for idx,s in target.iterrows():\n",
    "    target_phy.append(nss_mcs_dict[s['nss'],s['mcs']])\n",
    "\n",
    "# target_0_phy=pd.DataFrame(target_0_phy)\n",
    "\n",
    "rfg0=rfg0.fit(resource,target_phy)\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(rfg0,resource,target_phy,cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e1cb378c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-25T01:32:17.823509Z",
     "start_time": "2024-09-25T01:32:17.816213Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.91014529, 0.97066302, 0.83193871, 0.95018807, 0.96852579,\n",
       "       0.75103629, 0.94168775, 0.80378873, 0.95456839, 0.83780847])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c7eaa10d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-25T01:32:24.216036Z",
     "start_time": "2024-09-25T01:32:24.211125Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8920350511848583"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ce12d387",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-25T01:38:33.082342Z",
     "start_time": "2024-09-25T01:38:30.676180Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\model_selection\\_split.py:725: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=10.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# resource=resource.drop(['mcs','nss'],axis=1)\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rfg0=RandomForestClassifier(n_estimators=100,criterion='gini',random_state=0,oob_score=False)\n",
    "\n",
    "# target_phy=[]\n",
    "# for idx,s in target.iterrows():\n",
    "#     target_phy.append(nss_mcs_dict[s['nss'],s['mcs']])\n",
    "\n",
    "# target_0_phy=pd.DataFrame(target_0_phy)\n",
    "\n",
    "rfg0=rfg0.fit(resource,target.loc[:,'mcs'])\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(rfg0,resource,target.loc[:,'mcs'],cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ddb2153c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-25T01:38:34.635071Z",
     "start_time": "2024-09-25T01:38:34.630034Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.84210526, 0.92105263, 0.89473684, 0.92105263, 0.89473684,\n",
       "       0.92105263, 0.86842105, 0.94736842, 0.92105263, 0.97297297])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6d48ccf6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-25T01:38:37.030222Z",
     "start_time": "2024-09-25T01:38:37.025134Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9104551920341395"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "29c80433",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-25T01:38:41.913153Z",
     "start_time": "2024-09-25T01:38:41.904904Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 379 entries, 0 to 378\n",
      "Data columns (total 13 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   protocol                    379 non-null    int64  \n",
      " 1   nav                         379 non-null    int64  \n",
      " 2   ap_from_ap_0_sum_ant_rssi   379 non-null    float64\n",
      " 3   sta_to_ap_0_sum_ant_rssi    379 non-null    float64\n",
      " 4   sta_to_ap_1_sum_ant_rssi    379 non-null    float64\n",
      " 5   sta_from_ap_0_sum_ant_rssi  379 non-null    float64\n",
      " 6   sta_from_ap_1_sum_ant_rssi  379 non-null    float64\n",
      " 7   seq_time                    379 non-null    float64\n",
      " 8   pro_pd                      379 non-null    float64\n",
      " 9   pro_ed                      379 non-null    float64\n",
      " 10  pro_nav                     379 non-null    float64\n",
      " 11  state                       379 non-null    int64  \n",
      " 12  sinr                        379 non-null    float64\n",
      "dtypes: float64(10), int64(3)\n",
      "memory usage: 38.6 KB\n"
     ]
    }
   ],
   "source": [
    "resource.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59ff83a1",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# 分成三份"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0cb49de4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T16:31:24.727492Z",
     "start_time": "2024-09-24T16:31:24.720491Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "child=resource.groupby('state')\n",
    "\n",
    "child_0=list(child)[0][1] #完全同步\n",
    "child_1=list(child)[0][1] # 大量异步\n",
    "child_2=list(child)[0][1]  # 混合情况\n",
    "\n",
    "child_0.index=range(child_0.shape[0])\n",
    "child_1.index=range(child_1.shape[0])\n",
    "child_2.index=range(child_2.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4fd497e8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T16:31:26.912955Z",
     "start_time": "2024-09-24T16:31:26.905933Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# 删除列并取出目标值\n",
    "target_0=child_0[['nss','mcs']]\n",
    "target_1=child_1[['nss','mcs']]\n",
    "target_2=child_2[['nss','mcs']]\n",
    "\n",
    "child_0=child_0.drop(['mcs','nss','state'],axis=1)\n",
    "child_1=child_1.drop(['mcs','nss','state'],axis=1)\n",
    "child_2=child_2.drop(['mcs','nss','state'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dd2c6dc",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# child_0情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2d5285a1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T16:31:46.049702Z",
     "start_time": "2024-09-24T16:31:39.196882Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "rfg0=RandomForestRegressor(n_estimators=100,criterion='friedman_mse',random_state=0,oob_score=False)\n",
    "\n",
    "target_0_phy=[]\n",
    "for idx,s in target_0.iterrows():\n",
    "    target_0_phy.append(nss_mcs_dict[s['nss'],s['mcs']])\n",
    "\n",
    "# target_0_phy=pd.DataFrame(target_0_phy)\n",
    "\n",
    "rfg0=rfg0.fit(child_0,target_0_phy)\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(rfg0,child_0,target_0_phy,cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8fd73406",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T16:31:47.417650Z",
     "start_time": "2024-09-24T16:31:47.413070Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.015199409085189375"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e914b1d6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T16:31:49.831382Z",
     "start_time": "2024-09-24T16:31:49.826064Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.        ,  0.        ,  0.        , -0.03219007,\n",
       "        0.84663934,  0.581401  ,  0.        , -1.17453336, -0.06932281])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ba4351a",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# child_1情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "14d3075e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T16:29:11.782236Z",
     "start_time": "2024-09-24T16:29:05.410524Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "rfg1=RandomForestRegressor(n_estimators=100,criterion='friedman_mse',random_state=0,oob_score=False)\n",
    "\n",
    "target_1_phy=[]\n",
    "for idx,s in target_1.iterrows():\n",
    "    target_1_phy.append(nss_mcs_dict[s['nss'],s['mcs']])\n",
    "\n",
    "\n",
    "rfg1=rfg1.fit(child_1,target_1_phy)\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(rfg1,child_1,target_1_phy,cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6805ab35",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T16:29:12.901948Z",
     "start_time": "2024-09-24T16:29:12.897417Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.        ,  0.        ,  0.        , -0.03219007,\n",
       "        0.84663934,  0.581401  ,  0.        , -1.17453336, -0.06932281])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "64150679",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T01:55:44.931545Z",
     "start_time": "2024-09-24T01:55:44.927035Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.015199409085189375"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eeba6a45",
   "metadata": {},
   "source": [
    "# child_2情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38fdb19c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# rfg1=RandomForestRegressor(n_estimators=100,criterion='friedman_mse',random_state=0,oob_score=False)\n",
    "\n",
    "# target_1_phy=[]\n",
    "# for idx,s in target_1.iterrows():\n",
    "#     target_1_phy.append(nss_mcs_dict[s['nss'],s['mcs']])\n",
    "\n",
    "\n",
    "# rfg1=rfg1.fit(child_1,target_1_phy)\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(rfg1,child_1,target_1_phy,cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "79bbe5e1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T01:55:51.273138Z",
     "start_time": "2024-09-24T01:55:49.551334Z"
    }
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "''",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\pandas\\core\\indexes\\base.py:3790\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3789\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3790\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m   3791\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[1;32mindex.pyx:152\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mindex.pyx:181\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:7080\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:7088\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: ''",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[30], line 4\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mensemble\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m RandomForestClassifier\n\u001b[0;32m      3\u001b[0m rfg1\u001b[38;5;241m=\u001b[39mRandomForestRegressor(n_estimators\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m100\u001b[39m,criterion\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgini\u001b[39m\u001b[38;5;124m'\u001b[39m,random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,oob_score\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m----> 4\u001b[0m rfg1\u001b[38;5;241m=\u001b[39mrfg1\u001b[38;5;241m.\u001b[39mfit(child_1,target_1[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m      6\u001b[0m \u001b[38;5;66;03m# rfg0=RandomForestRegressor(n_estimators=100,criterion='friedman_mse',random_state=0,oob_score=False)\u001b[39;00m\n\u001b[0;32m      7\u001b[0m \u001b[38;5;66;03m# rfg0=rfg0.fit(resource,target_0.to_numpy())\u001b[39;00m\n\u001b[0;32m      8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodel_selection\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m cross_val_score\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\pandas\\core\\frame.py:3896\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3894\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m   3895\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 3896\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mget_loc(key)\n\u001b[0;32m   3897\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m   3898\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\pandas\\core\\indexes\\base.py:3797\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3792\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[0;32m   3793\u001b[0m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[0;32m   3794\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[0;32m   3795\u001b[0m     ):\n\u001b[0;32m   3796\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[1;32m-> 3797\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m   3798\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m   3799\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m   3800\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m   3801\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[0;32m   3802\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[1;31mKeyError\u001b[0m: ''"
     ]
    }
   ],
   "source": [
    "# from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# rfg1=RandomForestRegressor(n_estimators=100,criterion='gini',random_state=0,oob_score=False)\n",
    "# rfg1=rfg1.fit(child_1,target_1[''])\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(rfg1,child_1,target_1,cv=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f06a2bb6",
   "metadata": {},
   "source": [
    "# predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "ed35d4a0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T19:50:54.718318Z",
     "start_time": "2024-09-24T19:50:54.677830Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "predict = pd.read_excel('programe2_test_1_2ap_processed.xlsx')\n",
    "predict=predict.drop(['nss','mcs'],axis=1)\n",
    "predict=predict.drop('sta_from_sta_0_rssi',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "d33bdeae",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T19:50:56.216609Z",
     "start_time": "2024-09-24T19:50:56.208526Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 80 entries, 0 to 79\n",
      "Data columns (total 13 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   protocol                    80 non-null     int64  \n",
      " 1   nav                         80 non-null     int64  \n",
      " 2   ap_from_ap_0_sum_ant_rssi   80 non-null     float64\n",
      " 3   sta_to_ap_0_sum_ant_rssi    80 non-null     float64\n",
      " 4   sta_to_ap_1_sum_ant_rssi    80 non-null     float64\n",
      " 5   sta_from_ap_0_sum_ant_rssi  80 non-null     float64\n",
      " 6   sta_from_ap_1_sum_ant_rssi  80 non-null     float64\n",
      " 7   seq_time                    80 non-null     float64\n",
      " 8   pro_pd                      80 non-null     float64\n",
      " 9   pro_ed                      80 non-null     float64\n",
      " 10  pro_nav                     80 non-null     float64\n",
      " 11  state                       80 non-null     int64  \n",
      " 12  sinr                        80 non-null     float64\n",
      "dtypes: float64(10), int64(3)\n",
      "memory usage: 8.3 KB\n"
     ]
    }
   ],
   "source": [
    "predict.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "99dc2e87",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-24T19:51:02.790147Z",
     "start_time": "2024-09-24T19:51:02.757380Z"
    }
   },
   "outputs": [],
   "source": [
    "res=rfg0.predict(predict)\n",
    "pd.DataFrame(res).to_excel('programe2_test1_type2_res_mcs.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b818078",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "toc": {
   "base_numbering": 1,
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