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Dive-into-DL-PyTorch/code/chapter03_DL-basics/3.16_kaggle-house-price.ipynb

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{
"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": [
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" <th></th>\n",
" <th>Id</th>\n",
" <th>MSSubClass</th>\n",
" <th>MSZoning</th>\n",
" <th>LotFrontage</th>\n",
" <th>SaleType</th>\n",
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" 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",
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"metadata": {},
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"source": [
"train_data.iloc[0:4, [0, 1, 2, 3, -3, -2, -1]]"
]
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{
"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": [
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"data": {
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},
"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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"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",
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]
},
{
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"metadata": {},
"source": [
"## 3.16.7 预测并在Kaggle提交结果"
]
},
{
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"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)"
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},
{
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