process-4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import xgboost as xgb\n",
    "import pandas as pd\n",
    "from IPython.display import display #方便对数据进行可视化\n",
    "from sklearn.model_selection import train_test_split\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rmspe(y, y_hat):\n",
    "    #由于sales取了对数，这里要还原\n",
    "    y = np.exp(y) - 1\n",
    "    y_hat = np.exp(y_hat) - 1\n",
    "    \n",
    "    #由于有yi=0的情况，直接计算会得出无穷大的结果，所以需要处理一下\n",
    "    #定义一个y_rev来表示y的倒数，y为0时y_rev也赋值为0（因为在项目中提到“ Any day and store with 0 sales is ignored in scoring.“）\n",
    "    y_rev = np.zeros(y.shape, dtype = float)\n",
    "    n_zero = y != 0\n",
    "    y_rev[n_zero] = 1./y[n_zero]\n",
    "\n",
    "    rmspe = np.sqrt(np.mean(((y - y_hat)*y_rev)**2))\n",
    "    return rmspe\n",
    "\n",
    "\n",
    "#自定义一个评价函数\n",
    "\n",
    "def rmspe_feval(y_hat, dy):\n",
    "    y = dy.get_label()\n",
    "    rmspe_score = rmspe(y, y_hat)\n",
    "    return 'rmspe', rmspe_score\n",
    "#定义训练函数\n",
    "def train(param, dtrain, dval, num_round, feval_c):\n",
    "    train_time = time.clock()\n",
    "\n",
    "    model = xgb.train(\n",
    "        param, dtrain, num_round, feval = feval_c, \n",
    "        evals = [(dtrain, 'train'), (dval, 'val')], early_stopping_rounds = 100, \n",
    "        verbose_eval = True\n",
    "    )\n",
    "\n",
    "    train_time = time.clock() - train_time\n",
    "\n",
    "    print('The training time of the model is: {:.0f}s'.format(train_time))\n",
    "    \n",
    "    return model\n",
    "\n",
    "#定义一个打分函数\n",
    "def score(bst, dval):\n",
    "    y_pred = bst.predict(dval)\n",
    "#print(y_pred)\n",
    "    y_val = dval.get_label()\n",
    "    score = rmspe(y_val, y_pred)\n",
    "#print(score)\n",
    "    print('The rmspe of the model on validation data set is {:.6f}'.format(score))\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#从头开始处理数据\n",
    "\n",
    "# 导入训练数据\n",
    "train_data = pd.read_csv(\n",
    "    \"train.csv\", \n",
    "    low_memory=False)\n",
    "\n",
    "store_features = pd.read_csv(\"store.csv\")\n",
    "# 导入测试数据\n",
    "test_data = pd.read_csv(\"test.csv\")\n",
    "\n",
    "#融合\n",
    "train_data = pd.merge(train_data, store_features, on = 'Store')\n",
    "test_data = pd.merge(test_data, store_features, on = 'Store')\n",
    "\n",
    "train_data = train_data.drop('Customers', axis = 1)\n",
    "sales = train_data['Sales']\n",
    "features = train_data.drop('Sales', axis = 1)\n",
    "\n",
    "# 对字符串特征进行独热编码\n",
    "category = ['StoreType', 'Assortment', 'PromoInterval', 'StateHoliday']\n",
    "\n",
    "\n",
    "#首先将训练集和测试集合并，然后统一独热编码，然后再拆分为训练集和测试集\n",
    "features_test = test_data.drop('Id', axis = 1)\n",
    "data_merge = pd.concat([features, features_test], ignore_index = True)\n",
    "data_merge_pd = pd.get_dummies(data_merge, columns = category)\n",
    "\n",
    "# 需要把NA/NAN的数据转化成0\n",
    "data_merge_pd.fillna(0, inplace = True)\n",
    "\n",
    "#增加一步，增加‘Year', 'month', 'day'三个有序分类数据\n",
    "\n",
    "data_merge_pd['year'] = data_merge_pd['Date'].apply(lambda x: float(x.split('-')[0]))\n",
    "data_merge_pd['month'] = data_merge_pd['Date'].apply(lambda x: float(x.split('-')[1]))\n",
    "data_merge_pd['day'] = data_merge_pd['Date'].apply(lambda x: float(x.split('-')[2]))\n",
    "data_merge_pd = data_merge_pd.drop('Date', axis = 1)\n",
    "\n",
    "#拆分\n",
    "\n",
    "features_pd = data_merge_pd[0:features.shape[0]]\n",
    "features_test_pd = data_merge_pd[features.shape[0]:data_merge_pd.shape[0]]\n",
    "\n",
    "#下面将sales做log处理\n",
    "\n",
    "sales_log = np.log(train_data['Sales'] + 1) #+1是为了避免log（0）的情况同时使得数据从0开始\n",
    "\n",
    "\n",
    "X_train, X_val, y_train, y_val = train_test_split(\n",
    "    features_pd, sales_log, test_size = 0.2, random_state = 1)\n",
    "\n",
    "dtrain = xgb.DMatrix(X_train, label = y_train)\n",
    "dval = xgb.DMatrix(X_val, label = y_val)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-rmse:7.46056\tval-rmse:7.46389\ttrain-rmspe:0.910866\tval-rmspe:0.911281\n",
      "Multiple eval metrics have been passed: 'val-rmspe' will be used for early stopping.\n",
      "\n",
      "Will train until val-rmspe hasn't improved in 100 rounds.\n",
      "[1]\ttrain-rmse:7.3861\tval-rmse:7.38939\ttrain-rmspe:0.910842\tval-rmspe:0.911257\n",
      "[2]\ttrain-rmse:7.31241\tval-rmse:7.31566\ttrain-rmspe:0.910816\tval-rmspe:0.911232\n",
      "[3]\ttrain-rmse:7.23943\tval-rmse:7.24265\ttrain-rmspe:0.910789\tval-rmspe:0.911204\n",
      "[4]\ttrain-rmse:7.1672\tval-rmse:7.17038\ttrain-rmspe:0.91076\tval-rmspe:0.911175\n",
      "[5]\ttrain-rmse:7.09568\tval-rmse:7.09883\ttrain-rmspe:0.910728\tval-rmspe:0.911144\n",
      "[6]\ttrain-rmse:7.02488\tval-rmse:7.02799\ttrain-rmspe:0.910695\tval-rmspe:0.91111\n",
      "[7]\ttrain-rmse:6.95478\tval-rmse:6.95787\ttrain-rmspe:0.910659\tval-rmspe:0.911074\n",
      "[8]\ttrain-rmse:6.88561\tval-rmse:6.88867\ttrain-rmspe:0.910621\tval-rmspe:0.911036\n",
      "[9]\ttrain-rmse:6.81691\tval-rmse:6.81993\ttrain-rmspe:0.91058\tval-rmspe:0.910995\n",
      "[10]\ttrain-rmse:6.7489\tval-rmse:6.75189\ttrain-rmspe:0.910536\tval-rmspe:0.910951\n",
      "[11]\ttrain-rmse:6.68156\tval-rmse:6.68452\ttrain-rmspe:0.91049\tval-rmspe:0.910905\n",
      "[12]\ttrain-rmse:6.6149\tval-rmse:6.61782\ttrain-rmspe:0.91044\tval-rmspe:0.910855\n",
      "[13]\ttrain-rmse:6.54891\tval-rmse:6.55179\ttrain-rmspe:0.910388\tval-rmspe:0.910803\n",
      "[14]\ttrain-rmse:6.48358\tval-rmse:6.48643\ttrain-rmspe:0.910332\tval-rmspe:0.910747\n",
      "[15]\ttrain-rmse:6.42605\tval-rmse:6.42887\ttrain-rmspe:0.910279\tval-rmspe:0.910694\n",
      "[16]\ttrain-rmse:6.36195\tval-rmse:6.36474\ttrain-rmspe:0.910217\tval-rmspe:0.910632\n",
      "[17]\ttrain-rmse:6.29849\tval-rmse:6.30125\ttrain-rmspe:0.91015\tval-rmspe:0.910565\n",
      "[18]\ttrain-rmse:6.23569\tval-rmse:6.23842\ttrain-rmspe:0.910079\tval-rmspe:0.910494\n",
      "[19]\ttrain-rmse:6.17351\tval-rmse:6.17621\ttrain-rmspe:0.910004\tval-rmspe:0.910419\n",
      "[20]\ttrain-rmse:6.11194\tval-rmse:6.11461\ttrain-rmspe:0.909925\tval-rmspe:0.91034\n",
      "[21]\ttrain-rmse:6.051\tval-rmse:6.05364\ttrain-rmspe:0.909841\tval-rmspe:0.910256\n",
      "[22]\ttrain-rmse:5.99066\tval-rmse:5.99327\ttrain-rmspe:0.909752\tval-rmspe:0.910167\n",
      "[23]\ttrain-rmse:5.93111\tval-rmse:5.9337\ttrain-rmspe:0.909659\tval-rmspe:0.910074\n",
      "[24]\ttrain-rmse:5.87197\tval-rmse:5.87453\ttrain-rmspe:0.90956\tval-rmspe:0.909974\n",
      "[25]\ttrain-rmse:5.81366\tval-rmse:5.8162\ttrain-rmspe:0.909455\tval-rmspe:0.90987\n",
      "[26]\ttrain-rmse:5.75572\tval-rmse:5.75823\ttrain-rmspe:0.909345\tval-rmspe:0.909759\n",
      "[27]\ttrain-rmse:5.69835\tval-rmse:5.70084\ttrain-rmspe:0.909228\tval-rmspe:0.909643\n",
      "[28]\ttrain-rmse:5.64154\tval-rmse:5.644\ttrain-rmspe:0.909106\tval-rmspe:0.90952\n",
      "[29]\ttrain-rmse:5.58532\tval-rmse:5.58775\ttrain-rmspe:0.908976\tval-rmspe:0.909391\n",
      "[30]\ttrain-rmse:5.52986\tval-rmse:5.53227\ttrain-rmspe:0.908841\tval-rmspe:0.909255\n",
      "[31]\ttrain-rmse:5.47474\tval-rmse:5.47711\ttrain-rmspe:0.908698\tval-rmspe:0.909112\n",
      "[32]\ttrain-rmse:5.42037\tval-rmse:5.42273\ttrain-rmspe:0.908548\tval-rmspe:0.908962\n",
      "[33]\ttrain-rmse:5.36634\tval-rmse:5.36868\ttrain-rmspe:0.908391\tval-rmspe:0.908805\n",
      "[34]\ttrain-rmse:5.31305\tval-rmse:5.31537\ttrain-rmspe:0.908226\tval-rmspe:0.908639\n",
      "[35]\ttrain-rmse:5.26011\tval-rmse:5.2624\ttrain-rmspe:0.908052\tval-rmspe:0.908466\n",
      "[36]\ttrain-rmse:5.20772\tval-rmse:5.20998\ttrain-rmspe:0.907869\tval-rmspe:0.908283\n",
      "[37]\ttrain-rmse:5.15583\tval-rmse:5.15806\ttrain-rmspe:0.907679\tval-rmspe:0.908092\n",
      "[38]\ttrain-rmse:5.10445\tval-rmse:5.10666\ttrain-rmspe:0.907479\tval-rmspe:0.907892\n",
      "[39]\ttrain-rmse:5.05359\tval-rmse:5.05578\ttrain-rmspe:0.90727\tval-rmspe:0.907683\n",
      "[40]\ttrain-rmse:5.00325\tval-rmse:5.00541\ttrain-rmspe:0.907051\tval-rmspe:0.907464\n",
      "[41]\ttrain-rmse:4.95364\tval-rmse:4.95579\ttrain-rmspe:0.906823\tval-rmspe:0.907236\n",
      "[42]\ttrain-rmse:4.90431\tval-rmse:4.90643\ttrain-rmspe:0.906584\tval-rmspe:0.906997\n",
      "[43]\ttrain-rmse:4.85546\tval-rmse:4.85756\ttrain-rmspe:0.906334\tval-rmspe:0.906747\n",
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      "[46]\ttrain-rmse:4.71967\tval-rmse:4.7217\ttrain-rmspe:0.905562\tval-rmspe:0.905974\n",
      "[47]\ttrain-rmse:4.67293\tval-rmse:4.67495\ttrain-rmspe:0.90527\tval-rmspe:0.905682\n",
      "[48]\ttrain-rmse:4.62664\tval-rmse:4.62864\ttrain-rmspe:0.904965\tval-rmspe:0.905377\n",
      "[49]\ttrain-rmse:4.58058\tval-rmse:4.58255\ttrain-rmspe:0.904647\tval-rmspe:0.905058\n",
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      "[51]\ttrain-rmse:4.48984\tval-rmse:4.49177\ttrain-rmspe:0.90397\tval-rmspe:0.904382\n",
      "[52]\ttrain-rmse:4.44515\tval-rmse:4.44705\ttrain-rmspe:0.903612\tval-rmspe:0.904023\n",
      "[53]\ttrain-rmse:4.40092\tval-rmse:4.40281\ttrain-rmspe:0.903239\tval-rmspe:0.90365\n",
      "[54]\ttrain-rmse:4.35712\tval-rmse:4.35899\ttrain-rmspe:0.902852\tval-rmspe:0.903263\n",
      "[55]\ttrain-rmse:4.31376\tval-rmse:4.3156\ttrain-rmspe:0.902451\tval-rmspe:0.902861\n",
      "[56]\ttrain-rmse:4.27084\tval-rmse:4.27266\ttrain-rmspe:0.902034\tval-rmspe:0.902444\n",
      "[57]\ttrain-rmse:4.22834\tval-rmse:4.23014\ttrain-rmspe:0.901602\tval-rmspe:0.902011\n",
      "[58]\ttrain-rmse:4.18628\tval-rmse:4.18807\ttrain-rmspe:0.901154\tval-rmspe:0.901563\n",
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      "[60]\ttrain-rmse:4.10361\tval-rmse:4.10536\ttrain-rmspe:0.90021\tval-rmspe:0.900618\n",
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      "[63]\ttrain-rmse:3.9824\tval-rmse:3.98408\ttrain-rmspe:0.898663\tval-rmspe:0.89907\n",
      "[64]\ttrain-rmse:3.94281\tval-rmse:3.94447\ttrain-rmspe:0.898111\tval-rmspe:0.898518\n",
      "[65]\ttrain-rmse:3.90362\tval-rmse:3.90525\ttrain-rmspe:0.897542\tval-rmspe:0.897948\n",
      "[66]\ttrain-rmse:3.86481\tval-rmse:3.86643\ttrain-rmspe:0.896953\tval-rmspe:0.89736\n",
      "[67]\ttrain-rmse:3.82639\tval-rmse:3.82798\ttrain-rmspe:0.896347\tval-rmspe:0.896752\n",
      "[68]\ttrain-rmse:3.78836\tval-rmse:3.78993\ttrain-rmspe:0.89572\tval-rmspe:0.896125\n",
      "[69]\ttrain-rmse:3.75071\tval-rmse:3.75226\ttrain-rmspe:0.895073\tval-rmspe:0.895478\n",
      "[70]\ttrain-rmse:3.71344\tval-rmse:3.71497\ttrain-rmspe:0.894406\tval-rmspe:0.89481\n",
      "[71]\ttrain-rmse:3.67655\tval-rmse:3.67806\ttrain-rmspe:0.893718\tval-rmspe:0.894121\n",
      "[72]\ttrain-rmse:3.64003\tval-rmse:3.64152\ttrain-rmspe:0.893009\tval-rmspe:0.893412\n",
      "[73]\ttrain-rmse:3.60387\tval-rmse:3.60534\ttrain-rmspe:0.892278\tval-rmspe:0.892681\n",
      "[74]\ttrain-rmse:3.57213\tval-rmse:3.57359\ttrain-rmspe:0.891614\tval-rmspe:0.892016\n",
      "[75]\ttrain-rmse:3.53667\tval-rmse:3.53812\ttrain-rmspe:0.890842\tval-rmspe:0.891244\n",
      "[76]\ttrain-rmse:3.50155\tval-rmse:3.50297\ttrain-rmspe:0.89005\tval-rmspe:0.89045\n",
      "[77]\ttrain-rmse:3.46679\tval-rmse:3.46819\ttrain-rmspe:0.889233\tval-rmspe:0.889633\n",
      "[78]\ttrain-rmse:3.43238\tval-rmse:3.43376\ttrain-rmspe:0.888394\tval-rmspe:0.888793\n",
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      "[81]\ttrain-rmse:3.33136\tval-rmse:3.33269\ttrain-rmspe:0.885741\tval-rmspe:0.886137\n",
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      "[86]\ttrain-rmse:3.16976\tval-rmse:3.17103\ttrain-rmspe:0.880835\tval-rmspe:0.881227\n",
      "[87]\ttrain-rmse:3.13833\tval-rmse:3.13959\ttrain-rmspe:0.879777\tval-rmspe:0.880169\n",
      "[88]\ttrain-rmse:3.10723\tval-rmse:3.10847\ttrain-rmspe:0.878693\tval-rmspe:0.879084\n",
      "[89]\ttrain-rmse:3.07644\tval-rmse:3.07766\ttrain-rmspe:0.877584\tval-rmspe:0.877973\n",
      "[90]\ttrain-rmse:3.04596\tval-rmse:3.04717\ttrain-rmspe:0.876447\tval-rmspe:0.876835\n",
      "[91]\ttrain-rmse:3.01577\tval-rmse:3.01696\ttrain-rmspe:0.875288\tval-rmspe:0.875674\n",
      "[92]\ttrain-rmse:2.9859\tval-rmse:2.98708\ttrain-rmspe:0.8741\tval-rmspe:0.874485\n",
      "[93]\ttrain-rmse:2.95632\tval-rmse:2.95749\ttrain-rmspe:0.872886\tval-rmspe:0.87327\n",
      "[94]\ttrain-rmse:2.92718\tval-rmse:2.92834\ttrain-rmspe:0.871647\tval-rmspe:0.87203\n",
      "[95]\ttrain-rmse:2.8982\tval-rmse:2.89934\ttrain-rmspe:0.870379\tval-rmspe:0.87076\n",
      "[96]\ttrain-rmse:2.8695\tval-rmse:2.87062\ttrain-rmspe:0.869085\tval-rmspe:0.869465\n",
      "[97]\ttrain-rmse:2.84124\tval-rmse:2.84235\ttrain-rmspe:0.867765\tval-rmspe:0.868143\n",
      "[98]\ttrain-rmse:2.81311\tval-rmse:2.81421\ttrain-rmspe:0.866415\tval-rmspe:0.866792\n",
      "[99]\ttrain-rmse:2.78529\tval-rmse:2.78637\ttrain-rmspe:0.865036\tval-rmspe:0.865411\n",
      "[100]\ttrain-rmse:2.75774\tval-rmse:2.75881\ttrain-rmspe:0.863628\tval-rmspe:0.864001\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[101]\ttrain-rmse:2.73047\tval-rmse:2.73152\ttrain-rmspe:0.862192\tval-rmspe:0.862564\n",
      "[102]\ttrain-rmse:2.70348\tval-rmse:2.70452\ttrain-rmspe:0.860728\tval-rmspe:0.861098\n",
      "[103]\ttrain-rmse:2.67675\tval-rmse:2.67778\ttrain-rmspe:0.859237\tval-rmspe:0.859604\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1477]\ttrain-rmse:0.137808\tval-rmse:0.138052\ttrain-rmspe:0.178888\tval-rmspe:0.151534\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1574]\ttrain-rmse:0.133891\tval-rmse:0.134348\ttrain-rmspe:0.174307\tval-rmspe:0.147277\n",
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    },
    {
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1865]\ttrain-rmse:0.125094\tval-rmse:0.126243\ttrain-rmspe:0.164868\tval-rmspe:0.137876\n",
      "[1866]\ttrain-rmse:0.125071\tval-rmse:0.126221\ttrain-rmspe:0.164848\tval-rmspe:0.137851\n",
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      "[1911]\ttrain-rmse:0.123955\tval-rmse:0.125214\ttrain-rmspe:0.163536\tval-rmspe:0.136701\n",
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      "[1914]\ttrain-rmse:0.123899\tval-rmse:0.125169\ttrain-rmspe:0.163493\tval-rmspe:0.136656\n",
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      "[1918]\ttrain-rmse:0.123756\tval-rmse:0.125034\ttrain-rmspe:0.163364\tval-rmspe:0.136512\n",
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      "[1932]\ttrain-rmse:0.123269\tval-rmse:0.124597\ttrain-rmspe:0.162854\tval-rmspe:0.135932\n",
      "[1933]\ttrain-rmse:0.123249\tval-rmse:0.124586\ttrain-rmspe:0.162845\tval-rmspe:0.135915\n",
      "[1934]\ttrain-rmse:0.123185\tval-rmse:0.124528\ttrain-rmspe:0.162775\tval-rmspe:0.13585\n",
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      "[1938]\ttrain-rmse:0.123101\tval-rmse:0.124456\ttrain-rmspe:0.162696\tval-rmspe:0.135769\n",
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      "[1941]\ttrain-rmse:0.122992\tval-rmse:0.124351\ttrain-rmspe:0.16261\tval-rmspe:0.135646\n",
      "[1942]\ttrain-rmse:0.122972\tval-rmse:0.124333\ttrain-rmspe:0.162594\tval-rmspe:0.135628\n",
      "[1943]\ttrain-rmse:0.122956\tval-rmse:0.124318\ttrain-rmspe:0.162568\tval-rmspe:0.135615\n",
      "[1944]\ttrain-rmse:0.122931\tval-rmse:0.124295\ttrain-rmspe:0.162547\tval-rmspe:0.135591\n",
      "[1945]\ttrain-rmse:0.122927\tval-rmse:0.124292\ttrain-rmspe:0.162552\tval-rmspe:0.135589\n",
      "[1946]\ttrain-rmse:0.122907\tval-rmse:0.12428\ttrain-rmspe:0.162543\tval-rmspe:0.13558\n",
      "[1947]\ttrain-rmse:0.12289\tval-rmse:0.124263\ttrain-rmspe:0.162524\tval-rmspe:0.135566\n",
      "[1948]\ttrain-rmse:0.122866\tval-rmse:0.124247\ttrain-rmspe:0.162499\tval-rmspe:0.135549\n",
      "[1949]\ttrain-rmse:0.122861\tval-rmse:0.124243\ttrain-rmspe:0.162492\tval-rmspe:0.135546\n",
      "[1950]\ttrain-rmse:0.122843\tval-rmse:0.124231\ttrain-rmspe:0.16248\tval-rmspe:0.135531\n",
      "[1951]\ttrain-rmse:0.122818\tval-rmse:0.124208\ttrain-rmspe:0.162469\tval-rmspe:0.135509\n",
      "[1952]\ttrain-rmse:0.122796\tval-rmse:0.124189\ttrain-rmspe:0.162439\tval-rmspe:0.135489\n",
      "[1953]\ttrain-rmse:0.12275\tval-rmse:0.124146\ttrain-rmspe:0.162402\tval-rmspe:0.135443\n",
      "[1954]\ttrain-rmse:0.122729\tval-rmse:0.124127\ttrain-rmspe:0.162384\tval-rmspe:0.135401\n",
      "[1955]\ttrain-rmse:0.122685\tval-rmse:0.124085\ttrain-rmspe:0.162345\tval-rmspe:0.135356\n",
      "[1956]\ttrain-rmse:0.122667\tval-rmse:0.124073\ttrain-rmspe:0.162332\tval-rmspe:0.135344\n",
      "[1957]\ttrain-rmse:0.122645\tval-rmse:0.124058\ttrain-rmspe:0.162315\tval-rmspe:0.135327\n",
      "[1958]\ttrain-rmse:0.122599\tval-rmse:0.124015\ttrain-rmspe:0.162277\tval-rmspe:0.135283\n",
      "[1959]\ttrain-rmse:0.12258\tval-rmse:0.124005\ttrain-rmspe:0.162269\tval-rmspe:0.135271\n",
      "[1960]\ttrain-rmse:0.122575\tval-rmse:0.124\ttrain-rmspe:0.16226\tval-rmspe:0.135266\n",
      "[1961]\ttrain-rmse:0.122563\tval-rmse:0.12399\ttrain-rmspe:0.162206\tval-rmspe:0.135255\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1962]\ttrain-rmse:0.122528\tval-rmse:0.123956\ttrain-rmspe:0.162176\tval-rmspe:0.135223\n",
      "[1963]\ttrain-rmse:0.1225\tval-rmse:0.12394\ttrain-rmspe:0.162156\tval-rmspe:0.135206\n",
      "[1964]\ttrain-rmse:0.122476\tval-rmse:0.123924\ttrain-rmspe:0.162145\tval-rmspe:0.135189\n",
      "[1965]\ttrain-rmse:0.122461\tval-rmse:0.123914\ttrain-rmspe:0.162136\tval-rmspe:0.135179\n",
      "[1966]\ttrain-rmse:0.122431\tval-rmse:0.123886\ttrain-rmspe:0.162111\tval-rmspe:0.135158\n",
      "[1967]\ttrain-rmse:0.122405\tval-rmse:0.123863\ttrain-rmspe:0.162082\tval-rmspe:0.135123\n",
      "[1968]\ttrain-rmse:0.122386\tval-rmse:0.12385\ttrain-rmspe:0.16207\tval-rmspe:0.13511\n",
      "[1969]\ttrain-rmse:0.12237\tval-rmse:0.123836\ttrain-rmspe:0.162049\tval-rmspe:0.135096\n",
      "[1970]\ttrain-rmse:0.122348\tval-rmse:0.123821\ttrain-rmspe:0.16202\tval-rmspe:0.13508\n",
      "[1971]\ttrain-rmse:0.122326\tval-rmse:0.1238\ttrain-rmspe:0.161997\tval-rmspe:0.135058\n",
      "[1972]\ttrain-rmse:0.122307\tval-rmse:0.123788\ttrain-rmspe:0.161822\tval-rmspe:0.135036\n",
      "[1973]\ttrain-rmse:0.122298\tval-rmse:0.12378\ttrain-rmspe:0.161728\tval-rmspe:0.135029\n",
      "[1974]\ttrain-rmse:0.122263\tval-rmse:0.123747\ttrain-rmspe:0.161699\tval-rmspe:0.134992\n",
      "[1975]\ttrain-rmse:0.12225\tval-rmse:0.123735\ttrain-rmspe:0.161688\tval-rmspe:0.134979\n",
      "[1976]\ttrain-rmse:0.122237\tval-rmse:0.123723\ttrain-rmspe:0.161672\tval-rmspe:0.134969\n",
      "[1977]\ttrain-rmse:0.122221\tval-rmse:0.123707\ttrain-rmspe:0.16166\tval-rmspe:0.134953\n",
      "[1978]\ttrain-rmse:0.122199\tval-rmse:0.123686\ttrain-rmspe:0.161642\tval-rmspe:0.134937\n",
      "[1979]\ttrain-rmse:0.122193\tval-rmse:0.123681\ttrain-rmspe:0.161626\tval-rmspe:0.134931\n",
      "[1980]\ttrain-rmse:0.12217\tval-rmse:0.12366\ttrain-rmspe:0.161606\tval-rmspe:0.134882\n",
      "[1981]\ttrain-rmse:0.122147\tval-rmse:0.123641\ttrain-rmspe:0.161589\tval-rmspe:0.134865\n",
      "[1982]\ttrain-rmse:0.122129\tval-rmse:0.123625\ttrain-rmspe:0.161575\tval-rmspe:0.134845\n",
      "[1983]\ttrain-rmse:0.122122\tval-rmse:0.123619\ttrain-rmspe:0.161569\tval-rmspe:0.134838\n",
      "[1984]\ttrain-rmse:0.122077\tval-rmse:0.123575\ttrain-rmspe:0.161529\tval-rmspe:0.134793\n",
      "[1985]\ttrain-rmse:0.122069\tval-rmse:0.123568\ttrain-rmspe:0.161523\tval-rmspe:0.134786\n",
      "[1986]\ttrain-rmse:0.122034\tval-rmse:0.123537\ttrain-rmspe:0.161493\tval-rmspe:0.134754\n",
      "[1987]\ttrain-rmse:0.122013\tval-rmse:0.123523\ttrain-rmspe:0.161479\tval-rmspe:0.134745\n",
      "[1988]\ttrain-rmse:0.121999\tval-rmse:0.12351\ttrain-rmspe:0.161448\tval-rmspe:0.134731\n",
      "[1989]\ttrain-rmse:0.121981\tval-rmse:0.123493\ttrain-rmspe:0.161431\tval-rmspe:0.134714\n",
      "[1990]\ttrain-rmse:0.121964\tval-rmse:0.123479\ttrain-rmspe:0.161298\tval-rmspe:0.134691\n",
      "[1991]\ttrain-rmse:0.121943\tval-rmse:0.123463\ttrain-rmspe:0.161281\tval-rmspe:0.13467\n",
      "[1992]\ttrain-rmse:0.121917\tval-rmse:0.123441\ttrain-rmspe:0.16125\tval-rmspe:0.134641\n",
      "[1993]\ttrain-rmse:0.121889\tval-rmse:0.123427\ttrain-rmspe:0.161232\tval-rmspe:0.134624\n",
      "[1994]\ttrain-rmse:0.121862\tval-rmse:0.123404\ttrain-rmspe:0.161217\tval-rmspe:0.134584\n",
      "[1995]\ttrain-rmse:0.12185\tval-rmse:0.123392\ttrain-rmspe:0.161206\tval-rmspe:0.134571\n",
      "[1996]\ttrain-rmse:0.12183\tval-rmse:0.123373\ttrain-rmspe:0.161211\tval-rmspe:0.134546\n",
      "[1997]\ttrain-rmse:0.121792\tval-rmse:0.123336\ttrain-rmspe:0.16118\tval-rmspe:0.134508\n",
      "[1998]\ttrain-rmse:0.121765\tval-rmse:0.123311\ttrain-rmspe:0.161156\tval-rmspe:0.134481\n",
      "[1999]\ttrain-rmse:0.121711\tval-rmse:0.123255\ttrain-rmspe:0.161106\tval-rmspe:0.134419\n",
      "[2000]\ttrain-rmse:0.121683\tval-rmse:0.123229\ttrain-rmspe:0.161083\tval-rmspe:0.134396\n",
      "[2001]\ttrain-rmse:0.12165\tval-rmse:0.123198\ttrain-rmspe:0.161057\tval-rmspe:0.134365\n",
      "[2002]\ttrain-rmse:0.121633\tval-rmse:0.123182\ttrain-rmspe:0.161043\tval-rmspe:0.13435\n",
      "[2003]\ttrain-rmse:0.121598\tval-rmse:0.123151\ttrain-rmspe:0.161014\tval-rmspe:0.13432\n",
      "[2004]\ttrain-rmse:0.121585\tval-rmse:0.123139\ttrain-rmspe:0.160999\tval-rmspe:0.134308\n",
      "[2005]\ttrain-rmse:0.121561\tval-rmse:0.123118\ttrain-rmspe:0.160982\tval-rmspe:0.134291\n",
      "[2006]\ttrain-rmse:0.121534\tval-rmse:0.123093\ttrain-rmspe:0.160956\tval-rmspe:0.134265\n",
      "[2007]\ttrain-rmse:0.121513\tval-rmse:0.123083\ttrain-rmspe:0.160937\tval-rmspe:0.13425\n",
      "[2008]\ttrain-rmse:0.121496\tval-rmse:0.123069\ttrain-rmspe:0.16091\tval-rmspe:0.134233\n",
      "[2009]\ttrain-rmse:0.121483\tval-rmse:0.123059\ttrain-rmspe:0.160893\tval-rmspe:0.134222\n",
      "[2010]\ttrain-rmse:0.121471\tval-rmse:0.123048\ttrain-rmspe:0.160873\tval-rmspe:0.134209\n",
      "[2011]\ttrain-rmse:0.12143\tval-rmse:0.12301\ttrain-rmspe:0.16084\tval-rmspe:0.134172\n",
      "[2012]\ttrain-rmse:0.121393\tval-rmse:0.122976\ttrain-rmspe:0.160813\tval-rmspe:0.134138\n",
      "[2013]\ttrain-rmse:0.121377\tval-rmse:0.122963\ttrain-rmspe:0.160803\tval-rmspe:0.134123\n",
      "[2014]\ttrain-rmse:0.121363\tval-rmse:0.122949\ttrain-rmspe:0.160786\tval-rmspe:0.134109\n",
      "[2015]\ttrain-rmse:0.121345\tval-rmse:0.122936\ttrain-rmspe:0.160662\tval-rmspe:0.134095\n",
      "[2016]\ttrain-rmse:0.121312\tval-rmse:0.122904\ttrain-rmspe:0.160635\tval-rmspe:0.134038\n",
      "[2017]\ttrain-rmse:0.121306\tval-rmse:0.122899\ttrain-rmspe:0.160624\tval-rmspe:0.134033\n",
      "[2018]\ttrain-rmse:0.121276\tval-rmse:0.122872\ttrain-rmspe:0.160601\tval-rmspe:0.134008\n",
      "[2019]\ttrain-rmse:0.121258\tval-rmse:0.122854\ttrain-rmspe:0.160585\tval-rmspe:0.13399\n",
      "[2020]\ttrain-rmse:0.121243\tval-rmse:0.122842\ttrain-rmspe:0.160571\tval-rmspe:0.133979\n",
      "[2021]\ttrain-rmse:0.121231\tval-rmse:0.122832\ttrain-rmspe:0.160565\tval-rmspe:0.13397\n",
      "[2022]\ttrain-rmse:0.121216\tval-rmse:0.122818\ttrain-rmspe:0.160542\tval-rmspe:0.133958\n",
      "[2023]\ttrain-rmse:0.121192\tval-rmse:0.122795\ttrain-rmspe:0.160523\tval-rmspe:0.133938\n",
      "[2024]\ttrain-rmse:0.121173\tval-rmse:0.122777\ttrain-rmspe:0.160505\tval-rmspe:0.133882\n",
      "[2025]\ttrain-rmse:0.12113\tval-rmse:0.122736\ttrain-rmspe:0.160467\tval-rmspe:0.133834\n",
      "[2026]\ttrain-rmse:0.121114\tval-rmse:0.122721\ttrain-rmspe:0.160454\tval-rmspe:0.133818\n",
      "[2027]\ttrain-rmse:0.121102\tval-rmse:0.122711\ttrain-rmspe:0.160438\tval-rmspe:0.133809\n",
      "[2028]\ttrain-rmse:0.121084\tval-rmse:0.122698\ttrain-rmspe:0.160427\tval-rmspe:0.133797\n",
      "[2029]\ttrain-rmse:0.12106\tval-rmse:0.122678\ttrain-rmspe:0.160407\tval-rmspe:0.133776\n",
      "[2030]\ttrain-rmse:0.121046\tval-rmse:0.122667\ttrain-rmspe:0.160388\tval-rmspe:0.133759\n",
      "[2031]\ttrain-rmse:0.121017\tval-rmse:0.12264\ttrain-rmspe:0.16036\tval-rmspe:0.133733\n",
      "[2032]\ttrain-rmse:0.120978\tval-rmse:0.1226\ttrain-rmspe:0.160319\tval-rmspe:0.133688\n",
      "[2033]\ttrain-rmse:0.120952\tval-rmse:0.122575\ttrain-rmspe:0.160282\tval-rmspe:0.133642\n",
      "[2034]\ttrain-rmse:0.120922\tval-rmse:0.122548\ttrain-rmspe:0.160259\tval-rmspe:0.133613\n",
      "[2035]\ttrain-rmse:0.120893\tval-rmse:0.122522\ttrain-rmspe:0.160236\tval-rmspe:0.133586\n",
      "[2036]\ttrain-rmse:0.120887\tval-rmse:0.122515\ttrain-rmspe:0.160226\tval-rmspe:0.13358\n",
      "[2037]\ttrain-rmse:0.120865\tval-rmse:0.122497\ttrain-rmspe:0.160205\tval-rmspe:0.13356\n",
      "[2038]\ttrain-rmse:0.120848\tval-rmse:0.122481\ttrain-rmspe:0.160191\tval-rmspe:0.133547\n",
      "[2039]\ttrain-rmse:0.120826\tval-rmse:0.122462\ttrain-rmspe:0.160166\tval-rmspe:0.133527\n",
      "[2040]\ttrain-rmse:0.120812\tval-rmse:0.12245\ttrain-rmspe:0.160154\tval-rmspe:0.133514\n",
      "[2041]\ttrain-rmse:0.120793\tval-rmse:0.122433\ttrain-rmspe:0.160109\tval-rmspe:0.133494\n",
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      "[2047]\ttrain-rmse:0.120685\tval-rmse:0.12234\ttrain-rmspe:0.159882\tval-rmspe:0.133399\n",
      "[2048]\ttrain-rmse:0.12066\tval-rmse:0.122326\ttrain-rmspe:0.159866\tval-rmspe:0.13338\n",
      "[2049]\ttrain-rmse:0.120628\tval-rmse:0.122297\ttrain-rmspe:0.159838\tval-rmspe:0.133353\n",
      "[2050]\ttrain-rmse:0.120617\tval-rmse:0.122286\ttrain-rmspe:0.159827\tval-rmspe:0.133341\n",
      "[2051]\ttrain-rmse:0.120593\tval-rmse:0.122264\ttrain-rmspe:0.159808\tval-rmspe:0.133319\n",
      "[2052]\ttrain-rmse:0.120589\tval-rmse:0.122261\ttrain-rmspe:0.159802\tval-rmspe:0.133317\n",
      "[2053]\ttrain-rmse:0.120572\tval-rmse:0.122245\ttrain-rmspe:0.159789\tval-rmspe:0.133303\n",
      "[2054]\ttrain-rmse:0.120547\tval-rmse:0.12222\ttrain-rmspe:0.159768\tval-rmspe:0.133275\n",
      "[2055]\ttrain-rmse:0.120531\tval-rmse:0.122204\ttrain-rmspe:0.159749\tval-rmspe:0.133259\n",
      "[2056]\ttrain-rmse:0.120489\tval-rmse:0.122162\ttrain-rmspe:0.159714\tval-rmspe:0.133211\n",
      "[2057]\ttrain-rmse:0.120485\tval-rmse:0.122159\ttrain-rmspe:0.159723\tval-rmspe:0.133206\n",
      "[2058]\ttrain-rmse:0.12048\tval-rmse:0.122154\ttrain-rmspe:0.159732\tval-rmspe:0.133196\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2059]\ttrain-rmse:0.12046\tval-rmse:0.122141\ttrain-rmspe:0.159716\tval-rmspe:0.133185\n",
      "[2060]\ttrain-rmse:0.120442\tval-rmse:0.122126\ttrain-rmspe:0.1597\tval-rmspe:0.133163\n",
      "[2061]\ttrain-rmse:0.120421\tval-rmse:0.122115\ttrain-rmspe:0.159345\tval-rmspe:0.133144\n",
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      "[2063]\ttrain-rmse:0.120404\tval-rmse:0.122105\ttrain-rmspe:0.159334\tval-rmspe:0.133129\n",
      "[2064]\ttrain-rmse:0.120389\tval-rmse:0.122092\ttrain-rmspe:0.159322\tval-rmspe:0.133115\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2156]\ttrain-rmse:0.118584\tval-rmse:0.120505\ttrain-rmspe:0.156724\tval-rmspe:0.13125\n",
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     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
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     ]
    },
    {
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2738]\ttrain-rmse:0.110021\tval-rmse:0.113547\ttrain-rmspe:0.143379\tval-rmspe:0.123127\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2835]\ttrain-rmse:0.108906\tval-rmse:0.112709\ttrain-rmspe:0.141481\tval-rmspe:0.122188\n",
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    {
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3127]\ttrain-rmse:0.10582\tval-rmse:0.110485\ttrain-rmspe:0.135927\tval-rmspe:0.119598\n",
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    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3710]\ttrain-rmse:0.101085\tval-rmse:0.107445\ttrain-rmspe:0.128042\tval-rmspe:0.116215\n",
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     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4001]\ttrain-rmse:0.098956\tval-rmse:0.106253\ttrain-rmspe:0.124553\tval-rmspe:0.114863\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4098]\ttrain-rmse:0.098331\tval-rmse:0.105926\ttrain-rmspe:0.123666\tval-rmspe:0.114457\n",
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    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4389]\ttrain-rmse:0.096501\tval-rmse:0.105003\ttrain-rmspe:0.119702\tval-rmspe:0.113449\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4680]\ttrain-rmse:0.094845\tval-rmse:0.104268\ttrain-rmspe:0.116464\tval-rmspe:0.112579\n",
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    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4971]\ttrain-rmse:0.093312\tval-rmse:0.103633\ttrain-rmspe:0.113483\tval-rmspe:0.111831\n",
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    },
    {
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5262]\ttrain-rmse:0.091831\tval-rmse:0.103108\ttrain-rmspe:0.110628\tval-rmspe:0.111198\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5359]\ttrain-rmse:0.091356\tval-rmse:0.102953\ttrain-rmspe:0.109585\tval-rmspe:0.111035\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5651]\ttrain-rmse:0.089999\tval-rmse:0.102494\ttrain-rmspe:0.107653\tval-rmspe:0.110524\n",
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    {
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     ]
    },
    {
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     "text": [
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      "[5871]\ttrain-rmse:0.089044\tval-rmse:0.102199\ttrain-rmspe:0.106037\tval-rmspe:0.110185\n",
      "[5872]\ttrain-rmse:0.08904\tval-rmse:0.102197\ttrain-rmspe:0.106034\tval-rmspe:0.110178\n",
      "[5873]\ttrain-rmse:0.089038\tval-rmse:0.102195\ttrain-rmspe:0.106023\tval-rmspe:0.110177\n",
      "[5874]\ttrain-rmse:0.089035\tval-rmse:0.102194\ttrain-rmspe:0.106021\tval-rmspe:0.110176\n",
      "[5875]\ttrain-rmse:0.08903\tval-rmse:0.102191\ttrain-rmspe:0.106018\tval-rmspe:0.110173\n",
      "[5876]\ttrain-rmse:0.089024\tval-rmse:0.102189\ttrain-rmspe:0.10601\tval-rmspe:0.110171\n",
      "[5877]\ttrain-rmse:0.08902\tval-rmse:0.10219\ttrain-rmspe:0.106009\tval-rmspe:0.110172\n",
      "[5878]\ttrain-rmse:0.089017\tval-rmse:0.102189\ttrain-rmspe:0.106\tval-rmspe:0.11017\n",
      "[5879]\ttrain-rmse:0.089014\tval-rmse:0.102188\ttrain-rmspe:0.105995\tval-rmspe:0.110168\n",
      "[5880]\ttrain-rmse:0.08901\tval-rmse:0.102188\ttrain-rmspe:0.105993\tval-rmspe:0.110167\n",
      "[5881]\ttrain-rmse:0.089005\tval-rmse:0.102186\ttrain-rmspe:0.105988\tval-rmspe:0.110166\n",
      "[5882]\ttrain-rmse:0.089\tval-rmse:0.102183\ttrain-rmspe:0.105984\tval-rmspe:0.110164\n",
      "[5883]\ttrain-rmse:0.088996\tval-rmse:0.102181\ttrain-rmspe:0.105982\tval-rmspe:0.110163\n",
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      "[5889]\ttrain-rmse:0.088969\tval-rmse:0.102172\ttrain-rmspe:0.105965\tval-rmspe:0.110154\n",
      "[5890]\ttrain-rmse:0.088966\tval-rmse:0.102171\ttrain-rmspe:0.105963\tval-rmspe:0.110153\n",
      "[5891]\ttrain-rmse:0.088962\tval-rmse:0.102169\ttrain-rmspe:0.10595\tval-rmspe:0.110151\n",
      "[5892]\ttrain-rmse:0.088958\tval-rmse:0.102167\ttrain-rmspe:0.105947\tval-rmspe:0.11015\n",
      "[5893]\ttrain-rmse:0.088955\tval-rmse:0.102167\ttrain-rmspe:0.105943\tval-rmspe:0.110151\n",
      "[5894]\ttrain-rmse:0.088952\tval-rmse:0.102165\ttrain-rmspe:0.105941\tval-rmspe:0.11015\n",
      "[5895]\ttrain-rmse:0.088946\tval-rmse:0.102165\ttrain-rmspe:0.105938\tval-rmspe:0.110149\n",
      "[5896]\ttrain-rmse:0.08894\tval-rmse:0.102163\ttrain-rmspe:0.105933\tval-rmspe:0.110146\n",
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      "[5898]\ttrain-rmse:0.088934\tval-rmse:0.102163\ttrain-rmspe:0.105918\tval-rmspe:0.110149\n",
      "[5899]\ttrain-rmse:0.088928\tval-rmse:0.102161\ttrain-rmspe:0.105916\tval-rmspe:0.110153\n",
      "[5900]\ttrain-rmse:0.088926\tval-rmse:0.10216\ttrain-rmspe:0.105915\tval-rmspe:0.11015\n",
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      "[5902]\ttrain-rmse:0.088914\tval-rmse:0.102159\ttrain-rmspe:0.105898\tval-rmspe:0.110146\n",
      "[5903]\ttrain-rmse:0.088909\tval-rmse:0.102155\ttrain-rmspe:0.105896\tval-rmspe:0.110145\n",
      "[5904]\ttrain-rmse:0.088902\tval-rmse:0.102152\ttrain-rmspe:0.105894\tval-rmspe:0.110143\n",
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      "[5906]\ttrain-rmse:0.088893\tval-rmse:0.102152\ttrain-rmspe:0.10588\tval-rmspe:0.110141\n",
      "[5907]\ttrain-rmse:0.088888\tval-rmse:0.102152\ttrain-rmspe:0.105878\tval-rmspe:0.11014\n",
      "[5908]\ttrain-rmse:0.088883\tval-rmse:0.102152\ttrain-rmspe:0.105875\tval-rmspe:0.110138\n",
      "[5909]\ttrain-rmse:0.08888\tval-rmse:0.102151\ttrain-rmspe:0.105867\tval-rmspe:0.110137\n",
      "[5910]\ttrain-rmse:0.088877\tval-rmse:0.10215\ttrain-rmspe:0.105863\tval-rmspe:0.110136\n",
      "[5911]\ttrain-rmse:0.088872\tval-rmse:0.102147\ttrain-rmspe:0.105858\tval-rmspe:0.110136\n",
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      "[5919]\ttrain-rmse:0.088838\tval-rmse:0.102136\ttrain-rmspe:0.105758\tval-rmspe:0.110118\n",
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      "[5922]\ttrain-rmse:0.088824\tval-rmse:0.102131\ttrain-rmspe:0.105743\tval-rmspe:0.110116\n",
      "[5923]\ttrain-rmse:0.088821\tval-rmse:0.102129\ttrain-rmspe:0.105737\tval-rmspe:0.110113\n",
      "[5924]\ttrain-rmse:0.088813\tval-rmse:0.102126\ttrain-rmspe:0.105729\tval-rmspe:0.110108\n",
      "[5925]\ttrain-rmse:0.088809\tval-rmse:0.102124\ttrain-rmspe:0.105725\tval-rmspe:0.110108\n",
      "[5926]\ttrain-rmse:0.088802\tval-rmse:0.102123\ttrain-rmspe:0.105719\tval-rmspe:0.110105\n",
      "[5927]\ttrain-rmse:0.088798\tval-rmse:0.102122\ttrain-rmspe:0.105716\tval-rmspe:0.110104\n",
      "[5928]\ttrain-rmse:0.088793\tval-rmse:0.10212\ttrain-rmspe:0.105703\tval-rmspe:0.110102\n",
      "[5929]\ttrain-rmse:0.088786\tval-rmse:0.10212\ttrain-rmspe:0.105699\tval-rmspe:0.110101\n",
      "[5930]\ttrain-rmse:0.088779\tval-rmse:0.10212\ttrain-rmspe:0.105694\tval-rmspe:0.1101\n",
      "[5931]\ttrain-rmse:0.088777\tval-rmse:0.10212\ttrain-rmspe:0.105693\tval-rmspe:0.1101\n",
      "[5932]\ttrain-rmse:0.088774\tval-rmse:0.10212\ttrain-rmspe:0.105675\tval-rmspe:0.1101\n",
      "[5933]\ttrain-rmse:0.088771\tval-rmse:0.102119\ttrain-rmspe:0.105661\tval-rmspe:0.110098\n",
      "[5934]\ttrain-rmse:0.088769\tval-rmse:0.102119\ttrain-rmspe:0.10566\tval-rmspe:0.110098\n",
      "[5935]\ttrain-rmse:0.088767\tval-rmse:0.102118\ttrain-rmspe:0.105652\tval-rmspe:0.110096\n",
      "[5936]\ttrain-rmse:0.088765\tval-rmse:0.102117\ttrain-rmspe:0.105651\tval-rmspe:0.110096\n",
      "[5937]\ttrain-rmse:0.088756\tval-rmse:0.102114\ttrain-rmspe:0.105644\tval-rmspe:0.110091\n",
      "[5938]\ttrain-rmse:0.088752\tval-rmse:0.102111\ttrain-rmspe:0.105641\tval-rmspe:0.110091\n",
      "[5939]\ttrain-rmse:0.088743\tval-rmse:0.102104\ttrain-rmspe:0.105638\tval-rmspe:0.110088\n",
      "[5940]\ttrain-rmse:0.088734\tval-rmse:0.102103\ttrain-rmspe:0.105634\tval-rmspe:0.110089\n",
      "[5941]\ttrain-rmse:0.088729\tval-rmse:0.102103\ttrain-rmspe:0.105632\tval-rmspe:0.110088\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5942]\ttrain-rmse:0.088721\tval-rmse:0.102101\ttrain-rmspe:0.105629\tval-rmspe:0.110087\n",
      "[5943]\ttrain-rmse:0.088716\tval-rmse:0.102099\ttrain-rmspe:0.105626\tval-rmspe:0.110085\n",
      "[5944]\ttrain-rmse:0.08871\tval-rmse:0.102097\ttrain-rmspe:0.105622\tval-rmspe:0.110082\n",
      "[5945]\ttrain-rmse:0.088708\tval-rmse:0.102096\ttrain-rmspe:0.105622\tval-rmspe:0.110076\n",
      "[5946]\ttrain-rmse:0.088705\tval-rmse:0.102094\ttrain-rmspe:0.105617\tval-rmspe:0.110074\n",
      "[5947]\ttrain-rmse:0.088702\tval-rmse:0.102092\ttrain-rmspe:0.105602\tval-rmspe:0.110072\n",
      "[5948]\ttrain-rmse:0.088701\tval-rmse:0.102091\ttrain-rmspe:0.105601\tval-rmspe:0.110071\n",
      "[5949]\ttrain-rmse:0.088696\tval-rmse:0.10209\ttrain-rmspe:0.105567\tval-rmspe:0.11007\n",
      "[5950]\ttrain-rmse:0.088691\tval-rmse:0.102087\ttrain-rmspe:0.105562\tval-rmspe:0.110067\n",
      "[5951]\ttrain-rmse:0.088688\tval-rmse:0.102087\ttrain-rmspe:0.10556\tval-rmspe:0.110067\n",
      "[5952]\ttrain-rmse:0.088685\tval-rmse:0.102088\ttrain-rmspe:0.10538\tval-rmspe:0.110068\n",
      "[5953]\ttrain-rmse:0.088682\tval-rmse:0.102087\ttrain-rmspe:0.105364\tval-rmspe:0.110066\n",
      "[5954]\ttrain-rmse:0.088677\tval-rmse:0.102086\ttrain-rmspe:0.105349\tval-rmspe:0.110065\n",
      "[5955]\ttrain-rmse:0.088672\tval-rmse:0.102085\ttrain-rmspe:0.105345\tval-rmspe:0.110063\n",
      "[5956]\ttrain-rmse:0.08867\tval-rmse:0.102084\ttrain-rmspe:0.105343\tval-rmspe:0.110063\n",
      "[5957]\ttrain-rmse:0.088665\tval-rmse:0.102082\ttrain-rmspe:0.10534\tval-rmspe:0.110061\n",
      "[5958]\ttrain-rmse:0.088662\tval-rmse:0.10208\ttrain-rmspe:0.105336\tval-rmspe:0.11006\n",
      "[5959]\ttrain-rmse:0.088659\tval-rmse:0.102079\ttrain-rmspe:0.105335\tval-rmspe:0.110059\n",
      "[5960]\ttrain-rmse:0.088656\tval-rmse:0.102077\ttrain-rmspe:0.105332\tval-rmspe:0.110058\n",
      "[5961]\ttrain-rmse:0.088654\tval-rmse:0.102076\ttrain-rmspe:0.105327\tval-rmspe:0.110058\n",
      "[5962]\ttrain-rmse:0.088649\tval-rmse:0.102076\ttrain-rmspe:0.105325\tval-rmspe:0.110058\n",
      "[5963]\ttrain-rmse:0.088646\tval-rmse:0.102075\ttrain-rmspe:0.105323\tval-rmspe:0.110058\n",
      "[5964]\ttrain-rmse:0.088641\tval-rmse:0.102075\ttrain-rmspe:0.10532\tval-rmspe:0.110057\n",
      "[5965]\ttrain-rmse:0.088636\tval-rmse:0.102075\ttrain-rmspe:0.105316\tval-rmspe:0.110055\n",
      "[5966]\ttrain-rmse:0.088634\tval-rmse:0.102075\ttrain-rmspe:0.105315\tval-rmspe:0.110055\n",
      "[5967]\ttrain-rmse:0.088629\tval-rmse:0.102075\ttrain-rmspe:0.105309\tval-rmspe:0.110054\n",
      "[5968]\ttrain-rmse:0.088622\tval-rmse:0.102075\ttrain-rmspe:0.105306\tval-rmspe:0.110053\n",
      "[5969]\ttrain-rmse:0.088615\tval-rmse:0.102073\ttrain-rmspe:0.105299\tval-rmspe:0.11005\n",
      "[5970]\ttrain-rmse:0.088611\tval-rmse:0.102071\ttrain-rmspe:0.105291\tval-rmspe:0.110049\n",
      "[5971]\ttrain-rmse:0.088608\tval-rmse:0.10207\ttrain-rmspe:0.105288\tval-rmspe:0.110047\n",
      "[5972]\ttrain-rmse:0.088605\tval-rmse:0.102068\ttrain-rmspe:0.105281\tval-rmspe:0.110046\n",
      "[5973]\ttrain-rmse:0.088599\tval-rmse:0.102066\ttrain-rmspe:0.105278\tval-rmspe:0.110044\n",
      "[5974]\ttrain-rmse:0.088593\tval-rmse:0.102063\ttrain-rmspe:0.105275\tval-rmspe:0.11004\n",
      "[5975]\ttrain-rmse:0.088591\tval-rmse:0.102062\ttrain-rmspe:0.105273\tval-rmspe:0.110039\n",
      "[5976]\ttrain-rmse:0.088586\tval-rmse:0.10206\ttrain-rmspe:0.10527\tval-rmspe:0.110037\n",
      "[5977]\ttrain-rmse:0.088581\tval-rmse:0.10206\ttrain-rmspe:0.105266\tval-rmspe:0.110036\n",
      "[5978]\ttrain-rmse:0.088574\tval-rmse:0.102057\ttrain-rmspe:0.105255\tval-rmspe:0.110025\n",
      "[5979]\ttrain-rmse:0.088569\tval-rmse:0.102055\ttrain-rmspe:0.105249\tval-rmspe:0.110022\n",
      "[5980]\ttrain-rmse:0.088567\tval-rmse:0.102053\ttrain-rmspe:0.105247\tval-rmspe:0.11002\n",
      "[5981]\ttrain-rmse:0.088564\tval-rmse:0.102052\ttrain-rmspe:0.105244\tval-rmspe:0.110019\n",
      "[5982]\ttrain-rmse:0.088554\tval-rmse:0.10205\ttrain-rmspe:0.105121\tval-rmspe:0.110017\n",
      "[5983]\ttrain-rmse:0.088545\tval-rmse:0.102049\ttrain-rmspe:0.105115\tval-rmspe:0.110014\n",
      "[5984]\ttrain-rmse:0.088542\tval-rmse:0.102048\ttrain-rmspe:0.105108\tval-rmspe:0.110014\n",
      "[5985]\ttrain-rmse:0.088539\tval-rmse:0.102046\ttrain-rmspe:0.105105\tval-rmspe:0.110012\n",
      "[5986]\ttrain-rmse:0.088533\tval-rmse:0.102045\ttrain-rmspe:0.105098\tval-rmspe:0.110012\n",
      "[5987]\ttrain-rmse:0.08853\tval-rmse:0.102042\ttrain-rmspe:0.105095\tval-rmspe:0.110009\n",
      "[5988]\ttrain-rmse:0.088525\tval-rmse:0.10204\ttrain-rmspe:0.105088\tval-rmspe:0.110005\n",
      "[5989]\ttrain-rmse:0.088518\tval-rmse:0.102035\ttrain-rmspe:0.105082\tval-rmspe:0.110004\n",
      "[5990]\ttrain-rmse:0.088515\tval-rmse:0.102036\ttrain-rmspe:0.10508\tval-rmspe:0.110003\n",
      "[5991]\ttrain-rmse:0.088511\tval-rmse:0.102034\ttrain-rmspe:0.105076\tval-rmspe:0.110001\n",
      "[5992]\ttrain-rmse:0.088507\tval-rmse:0.102033\ttrain-rmspe:0.105074\tval-rmspe:0.110001\n",
      "[5993]\ttrain-rmse:0.088505\tval-rmse:0.102031\ttrain-rmspe:0.105073\tval-rmspe:0.11\n",
      "[5994]\ttrain-rmse:0.088496\tval-rmse:0.102032\ttrain-rmspe:0.105068\tval-rmspe:0.109998\n",
      "[5995]\ttrain-rmse:0.088491\tval-rmse:0.102029\ttrain-rmspe:0.105063\tval-rmspe:0.109996\n",
      "[5996]\ttrain-rmse:0.088488\tval-rmse:0.102027\ttrain-rmspe:0.10506\tval-rmspe:0.109994\n",
      "[5997]\ttrain-rmse:0.088484\tval-rmse:0.102026\ttrain-rmspe:0.105056\tval-rmspe:0.109992\n",
      "[5998]\ttrain-rmse:0.088479\tval-rmse:0.102025\ttrain-rmspe:0.105027\tval-rmspe:0.109992\n",
      "[5999]\ttrain-rmse:0.088474\tval-rmse:0.102024\ttrain-rmspe:0.105079\tval-rmspe:0.109984\n",
      "The training time of the model is: 14337s\n",
      "The rmspe of the model on validation data set is 0.109984\n"
     ]
    }
   ],
   "source": [
    "#设定参数\n",
    "param = {'max_depth': 10, 'eta': 0.01, 'silent': 0, 'objective': 'reg:linear','min_child_weight': 6,'colsample_bytree': 0.8, 'subsample':0.9}\n",
    "\n",
    "#设定迭代次数\n",
    "\n",
    "num_round = 6000\n",
    "#训练并打分\n",
    "model = train(param, dtrain, dval, num_round, rmspe_feval)\n",
    "score(model, dval)\n",
    "\n",
    "\n",
    "\n",
    "dtest = xgb.DMatrix(features_test_pd)\n",
    "sales_pred = model.predict(dtest)\n",
    "sales_pred = np.exp(sales_pred) - 1\n",
    "closed = (features_test_pd['Open'] == 0).values\n",
    "sales_pred[closed] = 0\n",
    "submission = pd.DataFrame({'Id':test_data['Id'], 'Sales': sales_pred})\n",
    "submission = submission.sort_values(by = 'Id')\n",
    "submission.to_csv('submit-test.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在需要画图看一下模型的特征重要性。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "xgb.plot_importance(model)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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