Practise 1 - House Price Regression.ipynb

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   "source": [
    "## House Prices - Advanced Regression Techniques\n",
    "Predict sales prices and practice feature engineering, RFs, and gradient boosting\n",
    "\n",
    "https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques"
   ]
  },
  {
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   "execution_count": 1,
   "id": "cf53001c-c4b8-4273-9959-dbc1b6d5b079",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from d2l import torch as d2l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8a5567ef-0c21-492a-8210-7fea9d417635",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1460, 81) (1459, 80)\n"
     ]
    }
   ],
   "source": [
    "train_data = pd.read_csv(\"./data/house-prices-advanced-regression-techniques/train.csv\")\n",
    "test_data = pd.read_csv(\"./data/house-prices-advanced-regression-techniques/test.csv\")\n",
    "\n",
    "print(train_data.shape,test_data.shape)"
   ]
  },
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       "  <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>YrSold</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>2008</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>2007</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>2008</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>2006</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  YrSold SaleType SaleCondition  \\\n",
       "0   1          60       RL         65.0    2008       WD        Normal   \n",
       "1   2          20       RL         80.0    2007       WD        Normal   \n",
       "2   3          60       RL         68.0    2008       WD        Normal   \n",
       "3   4          70       RL         60.0    2006       WD       Abnorml   \n",
       "\n",
       "   SalePrice  \n",
       "0     208500  \n",
       "1     181500  \n",
       "2     223500  \n",
       "3     140000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "train_data.iloc[0:4,[0,1,2,3,-4,-3,-2,-1]]"
   ]
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       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
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       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
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       "      <th>2</th>\n",
       "      <td>1463</td>\n",
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       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
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       "      <th>3</th>\n",
       "      <td>1464</td>\n",
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       "      <td>78.0</td>\n",
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       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
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       "     Id  MSSubClass MSZoning  LotFrontage  MoSold  YrSold SaleType  \\\n",
       "0  1461          20       RH         80.0       6    2010       WD   \n",
       "1  1462          20       RL         81.0       6    2010       WD   \n",
       "2  1463          60       RL         74.0       3    2010       WD   \n",
       "3  1464          60       RL         78.0       6    2010       WD   \n",
       "\n",
       "  SaleCondition  \n",
       "0        Normal  \n",
       "1        Normal  \n",
       "2        Normal  \n",
       "3        Normal  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "test_data.iloc[0:4,[0,1,2,3,-4,-3,-2,-1]]"
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.602858</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.157619</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.363335</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
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       "     YrSold SaleType SaleCondition\n",
       "1 -0.602858       WD        Normal\n",
       "2  0.157619       WD        Normal\n",
       "3 -1.363335       WD       Abnorml"
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   ],
   "source": [
    "all_features = pd.concat((train_data.iloc[:, 1:-1], test_data.iloc[:, 1:]))\n",
    "# 正则化 (均值为0 方差为1)\n",
    "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",
    "all_features[numeric_features] = all_features[numeric_features].fillna(0)\n",
    "all_features.iloc[[1,2,3],[-3,-2,-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f5652796-daf6-453d-9fe4-39b627b1cb87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2919, 331)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_features = pd.get_dummies(all_features,dummy_na=True)\n",
    "all_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8ff9cec3-132f-4c94-9b5a-8e318c1ec969",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[208500.],\n",
       "        [181500.],\n",
       "        [223500.],\n",
       "        ...,\n",
       "        [266500.],\n",
       "        [142125.],\n",
       "        [147500.]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_num = train_data.shape[0]\n",
    "train_features = torch.tensor(all_features[:train_num].values,dtype=torch.float32)\n",
    "test_features = torch.tensor(all_features[train_num:].values,dtype=torch.float32)\n",
    "train_labels = torch.tensor(train_data.iloc[:,-1].values.reshape((-1,1)),dtype=torch.float32)\n",
    "train_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f5ddf806-5747-4269-a220-d0efc4ae22d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "loss = torch.nn.MSELoss()\n",
    "\n",
    "def log_rmse(net,features,labels):\n",
    "    cliped_pred = torch.clamp(net(features),1,float('inf'))\n",
    "    log_loss = torch.sqrt(loss(torch.log(cliped_pred),torch.log(labels)))\n",
    "    return log_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c9f9dfb3-060c-45fc-8c85-b821d185dcfc",
   "metadata": {},
   "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",
    "    train_iter = d2l.load_array((train_features, train_labels), batch_size)\n",
    "    optimizer = torch.optim.Adam(net.parameters(),\n",
    "                                 lr = learning_rate,\n",
    "                                 weight_decay = weight_decay)\n",
    "    for epoch in range(num_epochs):\n",
    "        for X, y in train_iter:\n",
    "            optimizer.zero_grad()\n",
    "            l = loss(net(X), y)\n",
    "            l.backward()\n",
    "            optimizer.step()\n",
    "        train_ls.append(log_rmse(net, train_features, train_labels).detach())\n",
    "        if test_labels is not None:\n",
    "            test_ls.append(log_rmse(net, test_features, test_labels).detach())\n",
    "    return train_ls, test_ls\n",
    "\n",
    "def get_k_fold_data(k, i, X, y):\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], 0)\n",
    "            y_train = torch.cat([y_train, y_part], 0)\n",
    "    return X_train, y_train, X_valid, y_valid\n",
    "\n",
    "def k_fold(k, X_train, y_train, num_epochs, learning_rate, weight_decay,\n",
    "           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()\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.plot(list(range(1, num_epochs + 1)), [train_ls, valid_ls],\n",
    "                     xlabel='epoch', ylabel='rmse', xlim=[1, num_epochs],\n",
    "                     legend=['train', 'valid'], yscale='log')\n",
    "        print(f'折{i + 1}，训练log rmse{float(train_ls[-1]):f}, '\n",
    "              f'验证log rmse{float(valid_ls[-1]):f}')\n",
    "    return train_l_sum / k, valid_l_sum / k\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee037c9f-b9e2-41c9-94b8-aa229b1deac6",
   "metadata": {},
   "source": [
    "### Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "48c3ec55-91f1-4e31-a662-51b867edc594",
   "metadata": {
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "折1，训练log rmse0.169758, 验证log rmse0.156537\n",
      "折2，训练log rmse0.162371, 验证log rmse0.191815\n",
      "折3，训练log rmse0.163923, 验证log rmse0.168563\n",
      "折4，训练log rmse0.167659, 验证log rmse0.154543\n",
      "折5，训练log rmse0.163499, 验证log rmse0.183020\n",
      "5-折验证: 平均训练log rmse: 0.165442, 平均验证log rmse: 0.170896\n"
     ]
    },
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    "def get_net():\n",
    "    net = nn.Sequential(nn.Linear(train_features.shape[1],1))\n",
    "    #net = nn.Sequential(nn.Linear(train_features.shape[1],256),nn.Linear(256,1))\n",
    "    return net\n",
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    "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,\n",
    "                          weight_decay, batch_size)\n",
    "print(f'{k}-折验证: 平均训练log rmse: {float(train_l):f}, '\n",
    "      f'平均验证log rmse: {float(valid_l):f}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68c773ad-7fd0-429a-9d10-051e2c9a8721",
   "metadata": {},
   "source": [
    "过拟合程度很低，效果良好。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c6d9322-4a8e-4142-adf2-20cf52aa59db",
   "metadata": {},
   "source": [
    "## Multilayer Perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "edc80f65-13bf-414f-861f-500887606c0b",
   "metadata": {
    "editable": false,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "折1，训练log rmse0.135577, 验证log rmse0.155352\n",
      "折2，训练log rmse0.123091, 验证log rmse0.176229\n",
      "折3，训练log rmse0.141807, 验证log rmse0.195642\n",
      "折4，训练log rmse0.126948, 验证log rmse0.168006\n",
      "折5，训练log rmse0.114000, 验证log rmse0.167879\n",
      "5-折验证: 平均训练log_rmse: 0.128284, 平均验证log_rmse: 0.172622\n"
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   "source": [
    "def get_net():\n",
    "    #net = nn.Sequential(nn.Linear(train_features.shape[1],1))\n",
    "    net = nn.Sequential(nn.Linear(train_features.shape[1],256),nn.Linear(256,1))\n",
    "    for m in net:\n",
    "        if type(m) == nn.Linear:\n",
    "            nn.init.xavier_normal_(m.weight)\n",
    "    return net\n",
    "\n",
    "k, num_epochs, lr, weight_decay, batch_size = 5, 30, 0.4, 10, 64\n",
    "train_l, valid_l = k_fold(k, train_features, train_labels, num_epochs, lr,\n",
    "                          weight_decay, batch_size)\n",
    "print(f'{k}-折验证: 平均训练log_rmse: {float(train_l):f}, 平均验证log_rmse: {float(valid_l):f}')\n"
   ]
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  {
   "cell_type": "markdown",
   "id": "cdb80fa3-a7fa-4de8-8541-6ac9ad58efd9",
   "metadata": {},
   "source": [
    "Xavier初始化的多层感知机，在有权重衰减的情况下也能取得较好的效果，但出现了较为明显的过拟合，证明多层感知机对这个数据集过于复杂"
   ]
  },
  {
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