Don't Overfit! II——gluon.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "比赛：https://www.kaggle.com/c/dont-overfit-ii"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-12T02:47:15.145271Z",
     "start_time": "2019-02-12T02:47:13.289369Z"
    }
   },
   "outputs": [],
   "source": [
    "import zipfile\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from mxnet import autograd, gluon, init, nd\n",
    "from mxnet.gluon import data as gdata, loss as gloss, nn\n",
    "import d2lzh as d2l\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "root = 'E:/Data/Kaggle'\n",
    "dataDir = f'{root}/dont-overfit-ii'\n",
    "Z = zipfile.ZipFile(f'{dataDir}.zip')\n",
    "Z.extractall(dataDir)\n",
    "\n",
    "trainset = pd.read_csv(f'{dataDir}/train.csv', index_col='id')\n",
    "testset = pd.read_csv(f'{dataDir}/test.csv', index_col='id')\n",
    "submit = pd.read_csv(f'{dataDir}/sample_submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-12T02:24:46.906406Z",
     "start_time": "2019-02-12T02:24:46.876550Z"
    }
   },
   "outputs": [
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       "<p>5 rows × 301 columns</p>\n",
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      "text/plain": [
       "    target      0      1      2      3      4      5      6      7      8  \\\n",
       "id                                                                          \n",
       "0      1.0 -0.098  2.165  0.681 -0.614  1.309 -0.455 -0.236  0.276 -2.246   \n",
       "1      0.0  1.081 -0.973 -0.383  0.326 -0.428  0.317  1.172  0.352  0.004   \n",
       "2      1.0 -0.523 -0.089 -0.348  0.148 -0.022  0.404 -0.023 -0.172  0.137   \n",
       "3      1.0  0.067 -0.021  0.392 -1.637 -0.446 -0.725 -1.035  0.834  0.503   \n",
       "4      1.0  2.347 -0.831  0.511 -0.021  1.225  1.594  0.585  1.509 -0.012   \n",
       "\n",
       "    ...      290    291    292    293    294    295    296    297    298  \\\n",
       "id  ...                                                                    \n",
       "0   ...    0.867  1.347  0.504 -0.649  0.672 -2.097  1.051 -0.414  1.038   \n",
       "1   ...   -0.165 -1.695 -1.257  1.359 -0.808 -1.624 -0.458 -1.099 -0.936   \n",
       "2   ...    0.013  0.263 -1.222  0.726  1.444 -1.165 -1.544  0.004  0.800   \n",
       "3   ...   -0.404  0.640 -0.595 -0.966  0.900  0.467 -0.562 -0.254 -0.533   \n",
       "4   ...    0.898  0.134  2.415 -0.996 -1.006  1.378  1.246  1.478  0.428   \n",
       "\n",
       "      299  \n",
       "id         \n",
       "0  -1.065  \n",
       "1   0.973  \n",
       "2  -1.211  \n",
       "3   0.238  \n",
       "4   0.253  \n",
       "\n",
       "[5 rows x 301 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-12T02:25:25.663946Z",
     "start_time": "2019-02-12T02:25:25.656949Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(250, 301)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainset.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "训练数据集包括 250 个样本、300 个特征和 1 个标签。我们将所有的训练数据和测试数据的 300 个特征按样本连结。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-12T03:28:45.992365Z",
     "start_time": "2019-02-12T03:28:45.820239Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "all_features = pd.concat((trainset.iloc[:, 1:], testset), sort=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-12T02:28:54.262881Z",
     "start_time": "2019-02-12T02:28:54.178881Z"
    }
   },
   "source": [
    "## 预处理数据\n",
    "\n",
    "我们对连续数值的特征做标准化（standardization）：设该特征在整个数据集上的均值为 $\\mu$，标准差为 $\\sigma$。那么，我们可以将该特征的每个值先减去 $\\mu$ 再除以 $\\sigma$ 得到标准化后的每个特征值。对于缺失的特征值，我们将其替换成该特征的均值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-12T03:28:53.914086Z",
     "start_time": "2019-02-12T03:28:53.427086Z"
    }
   },
   "outputs": [],
   "source": [
    "all_features = all_features.apply(lambda x: (x - x.mean()) / (x.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-12T03:40:57.202028Z",
     "start_time": "2019-02-12T03:40:57.139045Z"
    }
   },
   "outputs": [],
   "source": [
    "n_train = trainset.shape[0]\n",
    "train_features = nd.array(all_features[:n_train].values)\n",
    "test_features = nd.array(all_features[n_train:].values)\n",
    "train_labels = nd.array(trainset.target.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-12T03:40:57.960242Z",
     "start_time": "2019-02-12T03:40:57.956244Z"
    }
   },
   "outputs": [],
   "source": [
    "loss = gloss.SoftmaxCrossEntropyLoss()\n",
    "\n",
    "def get_net():\n",
    "    net = nn.Sequential()\n",
    "    net.add(nn.Dense(2))\n",
    "    net.initialize()\n",
    "    return net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-12T03:40:58.575445Z",
     "start_time": "2019-02-12T03:40:58.567446Z"
    }
   },
   "outputs": [],
   "source": [
    "batch_size = 8\n",
    "net = get_net()\n",
    "learning_rate = 1e-3\n",
    "train_iter = gdata.DataLoader(gdata.ArrayDataset(train_features, train_labels), batch_size, shuffle=True)\n",
    "trainer = gluon.Trainer(net.collect_params(), 'adam', {\n",
    "        'learning_rate': learning_rate, 'wd': weight_decay})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-12T03:44:48.421604Z",
     "start_time": "2019-02-12T03:44:41.008980Z"
    }
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   ],
   "source": [
    "for epoch in range(num_epochs):\n",
    "    for X, y in train_iter:\n",
    "        with autograd.record():\n",
    "            l = loss(net(X), y).mean(0)\n",
    "        l.backward()\n",
    "        trainer.step(1)\n",
    "        print(l.asscalar())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-12T03:42:01.051827Z",
     "start_time": "2019-02-12T03:42:01.023727Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "[0.00494241]\n",
       "<NDArray 1 @cpu(0)>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss(net(X), y).mean(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-12T03:45:02.638477Z",
     "start_time": "2019-02-12T03:45:02.632538Z"
    }
   },
   "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 = gdata.DataLoader(gdata.ArrayDataset(train_features, train_labels), batch_size, shuffle=True)\n",
    "    # 这里使用了Adam优化算法\n",
    "    trainer = gluon.Trainer(net.collect_params(), 'adam', {\n",
    "        'learning_rate': learning_rate, 'wd': weight_decay})\n",
    "    for epoch in range(num_epochs):\n",
    "        for X, y in train_iter:\n",
    "            with autograd.record():\n",
    "                l = loss(net(X), y).mean(0)\n",
    "            l.backward()\n",
    "            trainer.step(1)\n",
    "        train_ls.append(l.asscalar())\n",
    "        if test_labels is not None:\n",
    "            test_ls.append(l.asscalar())\n",
    "    return train_ls, test_ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-12T03:45:03.395278Z",
     "start_time": "2019-02-12T03:45:03.390283Z"
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   },
   "outputs": [],
   "source": [
    "def get_k_fold_data(k, i, X, y):\n",
    "    '''\n",
    "    k Fold 验证\n",
    "    '''\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 = nd.concat(X_train, X_part, dim=0)\n",
    "            y_train = nd.concat(y_train, y_part, dim=0)\n",
    "    return X_train, y_train, X_valid, y_valid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-12T03:45:04.353597Z",
     "start_time": "2019-02-12T03:45:04.343611Z"
    }
   },
   "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()\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'\n",
    "              % (i, train_ls[-1], valid_ls[-1]))\n",
    "    return train_l_sum / k, valid_l_sum / k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-12T12:35:13.048984Z",
     "start_time": "2019-02-12T12:35:12.077384Z"
    }
   },
   "outputs": [
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     "text": [
      "fold 0, train rmse 0.000000, valid rmse 0.000000\n",
      "fold 1, train rmse 0.000000, valid rmse 0.000000\n",
      "fold 2, train rmse 0.000000, valid rmse 0.000000\n",
      "fold 3, train rmse 4.065372, valid rmse 4.065372\n",
      "fold 4, train rmse 0.578632, valid rmse 0.578632\n",
      "5-fold validation: avg train rmse 0.928801, avg valid rmse 0.928801\n"
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    "k, num_epochs, lr, weight_decay, batch_size = 5, 10, 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('%d-fold validation: avg train rmse %f, avg valid rmse %f'\n",
    "      % (k, train_l, valid_l))"
   ]
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