{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.16 实战Kaggle比赛：房价预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.2.0\n"
     ]
    }
   ],
   "source": [
    "# 如果没有安装pandas，则反注释下面一行\n",
    "# !pip install pandas\n",
    "\n",
    "%matplotlib inline\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sys\n",
    "sys.path.append(\"..\") \n",
    "import d2lzh_pytorch as d2l\n",
    "\n",
    "print(torch.__version__)\n",
    "torch.set_default_tensor_type(torch.FloatTensor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.16.2 获取和读取数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data = pd.read_csv('../../data/kaggle_house/train.csv')\n",
    "test_data = pd.read_csv('../../data/kaggle_house/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 81)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1459, 80)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage SaleType SaleCondition  SalePrice\n",
       "0   1          60       RL         65.0       WD        Normal     208500\n",
       "1   2          20       RL         80.0       WD        Normal     181500\n",
       "2   3          60       RL         68.0       WD        Normal     223500\n",
       "3   4          70       RL         60.0       WD       Abnorml     140000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.iloc[0:4, [0, 1, 2, 3, -3, -2, -1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_features = pd.concat((train_data.iloc[:, 1:-1], test_data.iloc[:, 1:]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.16.3 预处理数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "numeric_features = all_features.dtypes[all_features.dtypes != 'object'].index\n",
    "all_features[numeric_features] = all_features[numeric_features].apply(\n",
    "    lambda x: (x - x.mean()) / (x.std()))\n",
    "# 标准化后，每个数值特征的均值变为0，所以可以直接用0来替换缺失值\n",
    "all_features[numeric_features] = all_features[numeric_features].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2919, 331)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dummy_na=True将缺失值也当作合法的特征值并为其创建指示特征\n",
    "all_features = pd.get_dummies(all_features, dummy_na=True)\n",
    "all_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_train = train_data.shape[0]\n",
    "train_features = torch.tensor(all_features[:n_train].values, dtype=torch.float)\n",
    "test_features = torch.tensor(all_features[n_train:].values, dtype=torch.float)\n",
    "train_labels = torch.tensor(train_data.SalePrice.values, dtype=torch.float).view(-1, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.16.4 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "loss = torch.nn.MSELoss()\n",
    "\n",
    "def get_net(feature_num):\n",
    "    net = nn.Linear(feature_num, 1)\n",
    "    for param in net.parameters():\n",
    "        nn.init.normal_(param, mean=0, std=0.01)\n",
    "    return net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def log_rmse(net, features, labels):\n",
    "    with torch.no_grad():\n",
    "        # 将小于1的值设成1，使得取对数时数值更稳定\n",
    "        clipped_preds = torch.max(net(features), torch.tensor(1.0))\n",
    "        rmse = torch.sqrt(loss(clipped_preds.log(), labels.log()))\n",
    "    return rmse.item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def train(net, train_features, train_labels, test_features, test_labels,\n",
    "          num_epochs, learning_rate, weight_decay, batch_size):\n",
    "    train_ls, test_ls = [], []\n",
    "    dataset = torch.utils.data.TensorDataset(train_features, train_labels)\n",
    "    train_iter = torch.utils.data.DataLoader(dataset, batch_size, shuffle=True)\n",
    "    # 这里使用了Adam优化算法\n",
    "    optimizer = torch.optim.Adam(params=net.parameters(), lr=learning_rate, weight_decay=weight_decay) \n",
    "    net = net.float()\n",
    "    for epoch in range(num_epochs):\n",
    "        for X, y in train_iter:\n",
    "            l = loss(net(X.float()), y.float())\n",
    "            optimizer.zero_grad()\n",
    "            l.backward()\n",
    "            optimizer.step()\n",
    "        train_ls.append(log_rmse(net, train_features, train_labels))\n",
    "        if test_labels is not None:\n",
    "            test_ls.append(log_rmse(net, test_features, test_labels))\n",
    "    return train_ls, test_ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.16.5 $K$折交叉验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_k_fold_data(k, i, X, y):\n",
    "    # 返回第i折交叉验证时所需要的训练和验证数据\n",
    "    assert k > 1\n",
    "    fold_size = X.shape[0] // k\n",
    "    X_train, y_train = None, None\n",
    "    for j in range(k):\n",
    "        idx = slice(j * fold_size, (j + 1) * fold_size)\n",
    "        X_part, y_part = X[idx, :], y[idx]\n",
    "        if j == i:\n",
    "            X_valid, y_valid = X_part, y_part\n",
    "        elif X_train is None:\n",
    "            X_train, y_train = X_part, y_part\n",
    "        else:\n",
    "            X_train = torch.cat((X_train, X_part), dim=0)\n",
    "            y_train = torch.cat((y_train, y_part), dim=0)\n",
    "    return X_train, y_train, X_valid, y_valid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def k_fold(k, X_train, y_train, num_epochs,\n",
    "           learning_rate, weight_decay, batch_size):\n",
    "    train_l_sum, valid_l_sum = 0, 0\n",
    "    for i in range(k):\n",
    "        data = get_k_fold_data(k, i, X_train, y_train)\n",
    "        net = get_net(X_train.shape[1])\n",
    "        train_ls, valid_ls = train(net, *data, num_epochs, learning_rate,\n",
    "                                   weight_decay, batch_size)\n",
    "        train_l_sum += train_ls[-1]\n",
    "        valid_l_sum += valid_ls[-1]\n",
    "        if i == 0:\n",
    "            d2l.semilogy(range(1, num_epochs + 1), train_ls, 'epochs', 'rmse',\n",
    "                         range(1, num_epochs + 1), valid_ls,\n",
    "                         ['train', 'valid'])\n",
    "        print('fold %d, train rmse %f, valid rmse %f' % (i, train_ls[-1], valid_ls[-1]))\n",
    "    return train_l_sum / k, valid_l_sum / k"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.16.6 模型选择"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fold 0, train rmse 0.170585, valid rmse 0.156860\n",
      "fold 1, train rmse 0.162552, valid rmse 0.190944\n",
      "fold 2, train rmse 0.164199, valid rmse 0.168767\n",
      "fold 3, train rmse 0.168698, valid rmse 0.154873\n",
      "fold 4, train rmse 0.163213, valid rmse 0.183080\n",
      "5-fold validation: avg train rmse 0.165849, avg valid rmse 0.170905\n"
     ]
    },
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       "<matplotlib.figure.Figure at 0x10b283d30>"
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     "metadata": {},
     "output_type": "display_data"
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   "source": [
    "k, num_epochs, lr, weight_decay, batch_size = 5, 100, 5, 0, 64\n",
    "train_l, valid_l = k_fold(k, train_features, train_labels, num_epochs, lr, weight_decay, batch_size)\n",
    "print('%d-fold validation: avg train rmse %f, avg valid rmse %f' % (k, train_l, valid_l))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.16.7 预测并在Kaggle提交结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def train_and_pred(train_features, test_features, train_labels, test_data,\n",
    "                   num_epochs, lr, weight_decay, batch_size):\n",
    "    net = get_net(train_features.shape[1])\n",
    "    train_ls, _ = train(net, train_features, train_labels, None, None,\n",
    "                        num_epochs, lr, weight_decay, batch_size)\n",
    "    d2l.semilogy(range(1, num_epochs + 1), train_ls, 'epochs', 'rmse')\n",
    "    print('train rmse %f' % train_ls[-1])\n",
    "    preds = net(test_features).detach().numpy()\n",
    "    test_data['SalePrice'] = pd.Series(preds.reshape(1, -1)[0])\n",
    "    submission = pd.concat([test_data['Id'], test_data['SalePrice']], axis=1)\n",
    "    # submission.to_csv('./submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train rmse 0.162085\n"
     ]
    },
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