Kaggle.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "85c90c61",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(r\"D:\\data_set_for_deepLearning\\wtbdata_245days.csv\") #,header = None,nrows = 5\n",
    "\n",
    "#change time to float\n",
    "def time_of_day(x):\n",
    "    return (int(x[:2])+int(x[3:])/60)\n",
    "df[\"Tmstamp\"]=df[\"Tmstamp\"].astype(str).apply(time_of_day,2) \n",
    "\n",
    "#Sum_2days_Patv\n",
    "df['Sum_2days_Patv'] = df['Patv'].cumsum()\n",
    "df['Sum_2days_Patv']=df['Sum_2days_Patv'][288:].reset_index(drop=True)-df['Sum_2days_Patv'][:-288].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "9aeddcbe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "正在划分数据集\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "print(\"正在划分数据集\")\n",
    "df=df.iloc[1:,:] #第一行很多NAN\n",
    "df=df.iloc[:-288,:]#去除最后的尾巴NAN\n",
    "y=df['Sum_2days_Patv']\n",
    "X=df.iloc[:,:-1]\n",
    "\n",
    "train_X,test_X,train_y,test_y = train_test_split(X,y,test_size=0.3,random_state=17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "a37348a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TurbID</th>\n",
       "      <th>Day</th>\n",
       "      <th>Tmstamp</th>\n",
       "      <th>Wspd</th>\n",
       "      <th>Wdir</th>\n",
       "      <th>Etmp</th>\n",
       "      <th>Itmp</th>\n",
       "      <th>Ndir</th>\n",
       "      <th>Pab1</th>\n",
       "      <th>Pab2</th>\n",
       "      <th>Pab3</th>\n",
       "      <th>Prtv</th>\n",
       "      <th>Patv</th>\n",
       "      <th>Sum_2days_Patv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>6.17</td>\n",
       "      <td>-3.99</td>\n",
       "      <td>30.73</td>\n",
       "      <td>41.80</td>\n",
       "      <td>25.92</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>494.66</td>\n",
       "      <td>198862.920000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>6.27</td>\n",
       "      <td>-2.18</td>\n",
       "      <td>30.60</td>\n",
       "      <td>41.63</td>\n",
       "      <td>20.91</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>509.76</td>\n",
       "      <td>199852.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>6.42</td>\n",
       "      <td>-0.73</td>\n",
       "      <td>30.52</td>\n",
       "      <td>41.52</td>\n",
       "      <td>20.91</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>542.53</td>\n",
       "      <td>200853.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0.89</td>\n",
       "      <td>30.49</td>\n",
       "      <td>41.38</td>\n",
       "      <td>20.91</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>509.36</td>\n",
       "      <td>201848.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>6.10</td>\n",
       "      <td>-1.03</td>\n",
       "      <td>30.47</td>\n",
       "      <td>41.22</td>\n",
       "      <td>20.91</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>482.21</td>\n",
       "      <td>202866.390000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>4727227</th>\n",
       "      <td>134</td>\n",
       "      <td>243</td>\n",
       "      <td>23.166667</td>\n",
       "      <td>10.98</td>\n",
       "      <td>-1.96</td>\n",
       "      <td>-5.11</td>\n",
       "      <td>-0.67</td>\n",
       "      <td>345.57</td>\n",
       "      <td>8.82</td>\n",
       "      <td>8.82</td>\n",
       "      <td>8.82</td>\n",
       "      <td>136.49</td>\n",
       "      <td>1152.60</td>\n",
       "      <td>159075.250002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4727228</th>\n",
       "      <td>134</td>\n",
       "      <td>243</td>\n",
       "      <td>23.333333</td>\n",
       "      <td>11.82</td>\n",
       "      <td>-3.18</td>\n",
       "      <td>-5.46</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>345.57</td>\n",
       "      <td>13.87</td>\n",
       "      <td>13.87</td>\n",
       "      <td>13.87</td>\n",
       "      <td>84.43</td>\n",
       "      <td>681.65</td>\n",
       "      <td>159067.390002</td>\n",
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       "    <tr>\n",
       "      <th>4727229</th>\n",
       "      <td>134</td>\n",
       "      <td>243</td>\n",
       "      <td>23.500000</td>\n",
       "      <td>11.91</td>\n",
       "      <td>-1.42</td>\n",
       "      <td>-5.21</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>345.57</td>\n",
       "      <td>10.69</td>\n",
       "      <td>10.69</td>\n",
       "      <td>10.69</td>\n",
       "      <td>145.72</td>\n",
       "      <td>1118.35</td>\n",
       "      <td>158948.400002</td>\n",
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       "      <th>4727230</th>\n",
       "      <td>134</td>\n",
       "      <td>243</td>\n",
       "      <td>23.666667</td>\n",
       "      <td>11.86</td>\n",
       "      <td>-0.95</td>\n",
       "      <td>-5.40</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>345.57</td>\n",
       "      <td>13.94</td>\n",
       "      <td>13.94</td>\n",
       "      <td>13.94</td>\n",
       "      <td>89.56</td>\n",
       "      <td>683.49</td>\n",
       "      <td>159365.800002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4727231</th>\n",
       "      <td>134</td>\n",
       "      <td>243</td>\n",
       "      <td>23.833333</td>\n",
       "      <td>11.72</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-5.23</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>345.57</td>\n",
       "      <td>10.90</td>\n",
       "      <td>10.90</td>\n",
       "      <td>10.90</td>\n",
       "      <td>120.18</td>\n",
       "      <td>1026.93</td>\n",
       "      <td>159520.360002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4727231 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         TurbID  Day    Tmstamp   Wspd  Wdir   Etmp   Itmp    Ndir   Pab1  \\\n",
       "1             1    1   0.166667   6.17 -3.99  30.73  41.80   25.92   1.00   \n",
       "2             1    1   0.333333   6.27 -2.18  30.60  41.63   20.91   1.00   \n",
       "3             1    1   0.500000   6.42 -0.73  30.52  41.52   20.91   1.00   \n",
       "4             1    1   0.666667   6.25  0.89  30.49  41.38   20.91   1.00   \n",
       "5             1    1   0.833333   6.10 -1.03  30.47  41.22   20.91   1.00   \n",
       "...         ...  ...        ...    ...   ...    ...    ...     ...    ...   \n",
       "4727227     134  243  23.166667  10.98 -1.96  -5.11  -0.67  345.57   8.82   \n",
       "4727228     134  243  23.333333  11.82 -3.18  -5.46  -0.54  345.57  13.87   \n",
       "4727229     134  243  23.500000  11.91 -1.42  -5.21  -0.42  345.57  10.69   \n",
       "4727230     134  243  23.666667  11.86 -0.95  -5.40  -0.38  345.57  13.94   \n",
       "4727231     134  243  23.833333  11.72  0.04  -5.23  -0.37  345.57  10.90   \n",
       "\n",
       "          Pab2   Pab3    Prtv     Patv  Sum_2days_Patv  \n",
       "1         1.00   1.00   -0.25   494.66   198862.920000  \n",
       "2         1.00   1.00   -0.24   509.76   199852.170000  \n",
       "3         1.00   1.00   -0.26   542.53   200853.280000  \n",
       "4         1.00   1.00   -0.23   509.36   201848.150000  \n",
       "5         1.00   1.00   -0.27   482.21   202866.390000  \n",
       "...        ...    ...     ...      ...             ...  \n",
       "4727227   8.82   8.82  136.49  1152.60   159075.250002  \n",
       "4727228  13.87  13.87   84.43   681.65   159067.390002  \n",
       "4727229  10.69  10.69  145.72  1118.35   158948.400002  \n",
       "4727230  13.94  13.94   89.56   683.49   159365.800002  \n",
       "4727231  10.90  10.90  120.18  1026.93   159520.360002  \n",
       "\n",
       "[4727231 rows x 14 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "6b47e158",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11808866e48>]"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x=list(df.iloc[:int(4727231/134),13])\n",
    "y=np.arange(len(x))\n",
    "plt.plot(y,x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "eab8daa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,\n",
       "        27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,\n",
       "        40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,\n",
       "        53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,  65,\n",
       "        66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,  78,\n",
       "        79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,  91,\n",
       "        92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103, 104,\n",
       "       105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n",
       "       118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n",
       "       131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n",
       "       144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n",
       "       157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n",
       "       170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n",
       "       183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n",
       "       196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n",
       "       209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n",
       "       222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n",
       "       235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245], dtype=int64)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Day'].unique() #一共245天数 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "aaa301e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightgbm as lgb\n",
    "\n",
    "# 创建成lgb特征的数据集格式\n",
    "lgb_train = lgb.Dataset(train_X, train_y)\n",
    "lgb_eval = lgb.Dataset(test_X, test_y, reference=lgb_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "348059d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start training...\n",
      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.099242 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 3074\n",
      "[LightGBM] [Info] Number of data points in the train set: 3309061, number of used features: 13\n",
      "[LightGBM] [Info] Start training from score 98574.715652\n",
      "[1]\tvalid_0's l2: 4.72949e+09\tvalid_0's auc: 0.738508\n",
      "Training until validation scores don't improve for 5 rounds\n",
      "[2]\tvalid_0's l2: 4.55412e+09\tvalid_0's auc: 0.751476\n",
      "[3]\tvalid_0's l2: 4.37896e+09\tvalid_0's auc: 0.749898\n",
      "[4]\tvalid_0's l2: 4.23358e+09\tvalid_0's auc: 0.754859\n",
      "[5]\tvalid_0's l2: 4.0801e+09\tvalid_0's auc: 0.753272\n",
      "[6]\tvalid_0's l2: 3.92894e+09\tvalid_0's auc: 0.755633\n",
      "[7]\tvalid_0's l2: 3.81475e+09\tvalid_0's auc: 0.777335\n",
      "[8]\tvalid_0's l2: 3.70752e+09\tvalid_0's auc: 0.777812\n",
      "[9]\tvalid_0's l2: 3.59651e+09\tvalid_0's auc: 0.777166\n",
      "[10]\tvalid_0's l2: 3.48157e+09\tvalid_0's auc: 0.773502\n",
      "[11]\tvalid_0's l2: 3.37605e+09\tvalid_0's auc: 0.772539\n",
      "[12]\tvalid_0's l2: 3.30767e+09\tvalid_0's auc: 0.767634\n",
      "[13]\tvalid_0's l2: 3.24377e+09\tvalid_0's auc: 0.767576\n",
      "Early stopping, best iteration is:\n",
      "[8]\tvalid_0's l2: 3.70752e+09\tvalid_0's auc: 0.777812\n"
     ]
    }
   ],
   "source": [
    "# 将参数写成字典下形式\n",
    "params = {\n",
    "    'task': 'train',\n",
    "    'boosting_type': 'gbdt',  # 设置提升类型\n",
    "    'objective': 'regression', # 目标函数\n",
    "    'metric': {'l2', 'auc'},  # 评估函数\n",
    "    'num_leaves': 31,   # 叶子节点数\n",
    "    'learning_rate': 0.05,  # 学习速率\n",
    "    'feature_fraction': 0.9, # 建树的特征选择比例\n",
    "    'bagging_fraction': 0.8, # 建树的样本采样比例\n",
    "    'bagging_freq': 5,  # k 意味着每 k 次迭代执行bagging\n",
    "    'verbose': 1 # <0 显示致命的, =0 显示错误 (警告), >0 显示信息\n",
    "}\n",
    "\n",
    "print('Start training...')\n",
    "# 训练 cv and train\n",
    "gbm = lgb.train(params,lgb_train,num_boost_round=20,valid_sets=lgb_eval,early_stopping_rounds=5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "d864cfd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "368797    -0.396325\n",
       "3885432    0.575354\n",
       "1136982   -0.577361\n",
       "2243701    1.672583\n",
       "788650    -0.451062\n",
       "             ...   \n",
       "601765     1.332846\n",
       "2743718    0.525136\n",
       "1571321   -0.096510\n",
       "733381     1.972341\n",
       "1139964    0.330156\n",
       "Name: Sum_2days_Patv, Length: 1418170, dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看相对误差\n",
    "(gbm.predict(test_X, num_iteration=gbm.best_iteration)-test_y)/test_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "50b55d76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         -0.396325\n",
       "1          0.575354\n",
       "2         -0.577361\n",
       "3          1.672583\n",
       "4         -0.451062\n",
       "             ...   \n",
       "1389336    1.332846\n",
       "1389337    0.525136\n",
       "1389338   -0.096510\n",
       "1389339    1.972341\n",
       "1389340    0.330156\n",
       "Name: Sum_2days_Patv, Length: 1389341, dtype: float64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "7dd0157b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         -0.396325\n",
       "1          0.575354\n",
       "2         -0.577361\n",
       "3          1.672583\n",
       "4         -0.451062\n",
       "             ...   \n",
       "1389336    1.332846\n",
       "1389337    0.525136\n",
       "1389338   -0.096510\n",
       "1389339    1.972341\n",
       "1389340    0.330156\n",
       "Name: Sum_2days_Patv, Length: 1389341, dtype: float64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=((gbm.predict(test_X, num_iteration=gbm.best_iteration)-test_y)/test_y).dropna(how='all')\n",
    "a=a.reset_index(drop=True)\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30c1cb5f",
   "metadata": {},
   "source": [
    "$∑_i Patv_i$"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:pytorch]",
   "language": "python",
   "name": "conda-env-pytorch-py"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
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