{
"cells": [
{
"cell_type": "markdown",
"id": "cfbc8b83",
"metadata": {
"id": "cfbc8b83"
},
"source": [
"# **模型訓練（分類問題）**\n",
"此份程式碼會講解針對分類型任務在模型訓練上需要注意的細節。\n",
"\n",
"## 本章節內容大綱\n",
"* ### 二元分類問題\n",
" * ### [創建資料集／載入資料集（Dataset Creating/ Loading）](#DatasetCreating/Loading)\n",
" * ### [資料前處理（Data Preprocessing）](#DataPreprocessing)\n",
" * ### [模型建置（Model Building）](#ModelBuilding)\n",
" * ### [模型訓練（Model Training）](#ModelTraining)\n",
" * ### [模型評估（Model Evaluation）](#ModelEvaluation)\n",
"* ### 多元分類問題\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "6c1ac997",
"metadata": {
"id": "6c1ac997"
},
"source": [
"## 匯入套件"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1e78cf9f",
"metadata": {
"id": "1e78cf9f"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from tqdm.auto import tqdm\n",
"\n",
"# PyTorch 相關套件\n",
"import torch\n",
"import torch.nn as nn"
]
},
{
"cell_type": "markdown",
"id": "979f67f3",
"metadata": {
"id": "979f67f3"
},
"source": [
"\n",
"## 創建資料集／載入資料集（Dataset Creating / Loading）"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "MuTlaAi9FzqJ",
"metadata": {
"id": "MuTlaAi9FzqJ"
},
"outputs": [],
"source": [
"# 上傳資料\n",
"!wget -q https://github.com/TA-aiacademy/course_3.0/releases/download/DL/Data_part2.zip\n",
"!unzip -q Data_part2.zip"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5cbef01a",
"metadata": {
"id": "5cbef01a"
},
"outputs": [],
"source": [
"train_df = pd.read_csv('./Data/FilmComment_train.csv')\n",
"test_df = pd.read_csv('./Data/FilmComment_test.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9c5cca18",
"metadata": {
"id": "9c5cca18"
},
"outputs": [],
"source": [
"train_df.head()"
]
},
{
"cell_type": "markdown",
"id": "5d6bfd0d",
"metadata": {
"id": "5d6bfd0d"
},
"source": [
"* #### 電影評論資料集\n",
"訓練集，測試集分別為 6250，2500 筆，9997 種常用字詞，若在同一則評論中出現該字詞為 1，若否則為 0，y_label 標記評價正面與否。"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "32082d9c",
"metadata": {
"id": "32082d9c"
},
"outputs": [],
"source": [
"X_df = train_df.iloc[:, :-1].values\n",
"y_df = train_df.y_label.values"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "55e1bc9b",
"metadata": {
"id": "55e1bc9b"
},
"outputs": [],
"source": [
"X_test = test_df.iloc[:, :-1].values\n",
"y_test = test_df.y_label.values"
]
},
{
"cell_type": "markdown",
"id": "f2133a95",
"metadata": {
"id": "f2133a95"
},
"source": [
"\n",
"## 資料前處理（Data Preprocessing）"
]
},
{
"cell_type": "markdown",
"id": "858fc2ed",
"metadata": {
"id": "858fc2ed"
},
"source": [
"* ### 資料正規化（Data Normalization）\n",
"由於此資料集的數值範圍都介於 0-1，並且皆是以相同意義轉換特徵值，因此也可以使用原始的數值作為訓練資料。"
]
},
{
"cell_type": "markdown",
"id": "f18e67ea",
"metadata": {
"id": "f18e67ea"
},
"source": [
"* ### 資料切分（Data Splitting）"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6fe5be4a",
"metadata": {
"id": "6fe5be4a"
},
"outputs": [],
"source": [
"# train, valid/test dataset split\n",
"from sklearn.model_selection import train_test_split\n",
"X_train, X_valid, y_train, y_valid = train_test_split(X_df, y_df, test_size=0.2, random_state=5566)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "74f1ed43",
"metadata": {
"id": "74f1ed43"
},
"outputs": [],
"source": [
"print(f'X_train shape: {X_train.shape}')\n",
"print(f'X_valid shape: {X_valid.shape}')\n",
"print(f'y_train shape: {y_train.shape}')\n",
"print(f'y_valid shape: {y_valid.shape}')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "nQsKsEMdZ7ZR",
"metadata": {
"id": "nQsKsEMdZ7ZR"
},
"outputs": [],
"source": [
"# Build torch dataset and dataloader\n",
"from torch.utils.data import TensorDataset, DataLoader\n",
"\n",
"BATCH_SIZE = 512\n",
"\n",
"train_dataset = TensorDataset(torch.from_numpy(X_train).float(),\n",
" torch.from_numpy(y_train).unsqueeze(1).float())\n",
"valid_dataset = TensorDataset(torch.from_numpy(X_valid).float(),\n",
" torch.from_numpy(y_valid).unsqueeze(1).float())\n",
"\n",
"train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
"valid_loader = DataLoader(valid_dataset, batch_size=BATCH_SIZE, shuffle=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "Z11U2tHZdYXr",
"metadata": {
"id": "Z11U2tHZdYXr"
},
"outputs": [],
"source": [
"for x, y in train_loader:\n",
" print(x.shape, y.shape, y[:10])\n",
" break"
]
},
{
"cell_type": "markdown",
"id": "88a51105",
"metadata": {
"id": "88a51105"
},
"source": [
"\n",
"## 模型建置（Model Building）"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "OADvRuBYaGEc",
"metadata": {
"id": "OADvRuBYaGEc"
},
"outputs": [],
"source": [
"torch.manual_seed(5566)\n",
"\n",
"model = nn.Sequential(\n",
" nn.Linear(X_train.shape[1], 16),\n",
" nn.ReLU(),\n",
" nn.Linear(16, 16),\n",
" nn.ReLU(),\n",
" nn.Linear(16, 1),\n",
" nn.Sigmoid(),\n",
")\n",
"\n",
"print(model)"
]
},
{
"cell_type": "markdown",
"id": "3a358982",
"metadata": {
"id": "3a358982"
},
"source": [
"## 模型訓練（Model training）"
]
},
{
"cell_type": "markdown",
"id": "2fd7204c",
"metadata": {
"id": "2fd7204c"
},
"source": [
"* ### 設定模型訓練時，所需的優化器 (optimizer)、損失函數 (loss function)、評估指標 (metrics)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cKqRy_ZWae3Z",
"metadata": {
"id": "cKqRy_ZWae3Z"
},
"outputs": [],
"source": [
"optimizer = torch.optim.RMSprop(model.parameters(), lr=0.001)\n",
"loss_fn = nn.BCELoss()\n",
"\n",
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
"print(f'device: {device}')\n",
"model = model.to(device)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "i84dnY6MatnT",
"metadata": {
"id": "i84dnY6MatnT"
},
"outputs": [],
"source": [
"def train_epoch(model, optimizer, loss_fn, train_dataloader, val_dataloader):\n",
" # 訓練一輪\n",
" model.train()\n",
" total_train_loss = 0\n",
" total_train_correct = 0\n",
" for x, y in tqdm(train_dataloader, leave=False):\n",
" x, y = x.to(device), y.to(device) # 將資料移至GPU\n",
" y_pred = model(x) # 計算預測值\n",
" loss = loss_fn(y_pred, y) # 計算誤差\n",
" optimizer.zero_grad() # 梯度歸零\n",
" loss.backward() # 反向傳播計算梯度\n",
" optimizer.step() # 更新模型參數\n",
"\n",
" total_train_loss += loss.item()\n",
" total_train_correct += ((y_pred > 0.5) == (y > 0.5)).sum().item()\n",
"\n",
" # 驗證一輪\n",
" model.eval()\n",
" total_val_loss = 0\n",
" total_val_correct = 0\n",
" # 關閉梯度計算以加速\n",
" with torch.no_grad():\n",
" for x, y in val_dataloader:\n",
" x, y = x.to(device), y.to(device)\n",
" y_pred = model(x)\n",
" loss = loss_fn(y_pred, y)\n",
" total_val_loss += loss.item()\n",
" total_val_correct += ((y_pred > 0.5) == (y > 0.5)).sum().item()\n",
"\n",
" avg_train_loss = total_train_loss / len(train_dataloader)\n",
" avg_train_acc = total_train_correct / len(train_dataloader.dataset)\n",
" avg_val_loss = total_val_loss / len(val_dataloader)\n",
" avg_val_acc = total_val_correct / len(val_dataloader.dataset)\n",
" return avg_train_loss, avg_val_loss, avg_train_acc, avg_val_acc"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "z2uvE80Havvy",
"metadata": {
"id": "z2uvE80Havvy"
},
"outputs": [],
"source": [
"train_loss_log = []\n",
"val_loss_log = []\n",
"train_acc_log = []\n",
"val_acc_log = []\n",
"for epoch in tqdm(range(20)):\n",
" avg_train_loss, avg_val_loss, avg_train_acc, avg_val_acc = train_epoch(model, optimizer, loss_fn, train_loader, valid_loader)\n",
" train_loss_log.append(avg_train_loss)\n",
" val_loss_log.append(avg_val_loss)\n",
" train_acc_log.append(avg_train_acc)\n",
" val_acc_log.append(avg_val_acc)\n",
" print(f'Epoch: {epoch}, Train Loss: {avg_train_loss:.3f}, Val Loss: {avg_val_loss:.3f} | Train Acc: {avg_train_acc:.3f}, Val Acc: {avg_val_acc:.3f}')"
]
},
{
"cell_type": "markdown",
"id": "49d3db7d",
"metadata": {
"id": "49d3db7d"
},
"source": [
"\n",
"## 模型評估（Model Evaluation）"
]
},
{
"cell_type": "markdown",
"id": "cb66d982",
"metadata": {
"id": "cb66d982"
},
"source": [
"* ### 視覺化訓練過程的評估指標 （Visualization）"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3e7050e9",
"metadata": {
"id": "3e7050e9"
},
"outputs": [],
"source": [
"plt.figure(figsize=(15, 4))\n",
"plt.subplot(1, 2, 1)\n",
"plt.plot(range(len(train_loss_log)), train_loss_log, label='train_loss')\n",
"plt.plot(range(len(val_loss_log)), val_loss_log, label='valid_loss')\n",
"plt.xlabel('Epochs')\n",
"plt.ylabel('Binary crossentropy')\n",
"plt.legend()\n",
"\n",
"plt.subplot(1, 2, 2)\n",
"plt.plot(range(len(train_acc_log)), train_acc_log, label='train_acc')\n",
"plt.plot(range(len(val_acc_log)), val_acc_log, label='valid_acc')\n",
"plt.xlabel('Epochs')\n",
"plt.ylabel('Accuracy')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "daaebf99",
"metadata": {
"id": "daaebf99"
},
"source": [
"* ### 模型預測（Model predictions）"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "79b4465f",
"metadata": {
"id": "79b4465f"
},
"outputs": [],
"source": [
"# predict all test data\n",
"model.eval()\n",
"\n",
"y_pred = []\n",
"with torch.no_grad():\n",
" for x, _ in tqdm(valid_loader):\n",
" x = x.to(device)\n",
" y_pred.append(model(x))\n",
"\n",
"y_pred = torch.cat(y_pred).cpu()\n",
"y_pred_class = (y_pred > 0.5).squeeze(1).int()\n",
"\n",
"print(y_pred_class, y_pred_class.shape)"
]
},
{
"cell_type": "markdown",
"id": "8273fa68",
"metadata": {
"id": "8273fa68"
},
"source": [
"* ### 視覺化結果"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4a8c906c",
"metadata": {
"id": "4a8c906c"
},
"outputs": [],
"source": [
"plt.figure(figsize=(15, 4))\n",
"plt.scatter(range(y_pred.shape[0]), y_pred)\n",
"plt.hlines(0.5, 0, y_pred.shape[0], colors='red', label='y=0.5')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "73309200",
"metadata": {
"id": "73309200"
},
"source": [
"----------------\n",
"此範例是二元分類，y 的表示方式可用一維陣列，分別以 0, 1 表示兩個類別（正面，負面評價）\n",
"\n",
"\n",
"**若是多元分類又該如何表示？** pytorch中以**整數值**代表類別(0, 1, ... n)\n",
"\n",
"**對訓練有何影響？**\n",
"跟 y 最直接相關的就是 Loss function，使用torch.nn.CrossEntropyLoss()\n",
"\n",
"\n",
"* 預測值(y_pred)：每筆資料n類別預測值\n",
"* 解答(y): 0~n-1 的整數值代表解答類別\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "ccb031e0",
"metadata": {
"id": "ccb031e0"
},
"source": [
"----------------------------"
]
},
{
"cell_type": "markdown",
"id": "23ba626a",
"metadata": {
"id": "23ba626a"
},
"source": [
"## 多元分類（Multi-class classification）"
]
},
{
"cell_type": "markdown",
"id": "e5e7adb5",
"metadata": {
"id": "e5e7adb5"
},
"source": [
"### 創建資料集／載入資料集（Dataset Creating / Loading）"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2ada5ae4",
"metadata": {
"id": "2ada5ae4"
},
"outputs": [],
"source": [
"train_df = pd.read_csv('./Data/FilmComment_train.csv')\n",
"test_df = pd.read_csv('./Data/FilmComment_test.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5fa1ba27",
"metadata": {
"id": "5fa1ba27"
},
"outputs": [],
"source": [
"X_df = train_df.iloc[:, :-1].values\n",
"y_df = train_df.y_label.values"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1db3602b",
"metadata": {
"id": "1db3602b"
},
"outputs": [],
"source": [
"X_test = test_df.iloc[:, :-1].values\n",
"y_test = test_df.y_label.values"
]
},
{
"cell_type": "markdown",
"id": "47419c59",
"metadata": {
"id": "47419c59"
},
"source": [
"\n",
"## 資料前處理（Data Preprocessing）"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "94cd8466",
"metadata": {
"id": "94cd8466"
},
"outputs": [],
"source": [
"# train, valid/test dataset split\n",
"from sklearn.model_selection import train_test_split\n",
"X_train, X_valid, y_train, y_valid = train_test_split(X_df, y_df,\n",
" test_size=0.2,\n",
" random_state=5566)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b85e17bf",
"metadata": {
"id": "b85e17bf"
},
"outputs": [],
"source": [
"print(f'X_train shape: {X_train.shape}')\n",
"print(f'X_valid shape: {X_valid.shape}')\n",
"print(f'y_train shape: {y_train.shape}')\n",
"print(f'y_valid shape: {y_valid.shape}')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "pcuSsMtHhIFw",
"metadata": {
"id": "pcuSsMtHhIFw"
},
"outputs": [],
"source": [
"# Build torch dataset and dataloader\n",
"from torch.utils.data import TensorDataset, DataLoader\n",
"\n",
"BATCH_SIZE = 512\n",
"\n",
"train_dataset = TensorDataset(torch.from_numpy(X_train).float(),\n",
" torch.from_numpy(y_train).long())\n",
"valid_dataset = TensorDataset(torch.from_numpy(X_valid).float(),\n",
" torch.from_numpy(y_valid).long())\n",
"\n",
"train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
"valid_loader = DataLoader(valid_dataset, batch_size=BATCH_SIZE, shuffle=False)\n",
"\n",
"for x, y in train_loader:\n",
" print(x.shape, y.shape, y[:10])\n",
" break"
]
},
{
"cell_type": "markdown",
"id": "80924567",
"metadata": {
"id": "80924567"
},
"source": [
"### 模型建置（Model Building）"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8391cbd3",
"metadata": {
"id": "8391cbd3"
},
"outputs": [],
"source": [
"torch.manual_seed(5566)\n",
"\n",
"# 不需要sigmoid, 分別輸出類別0, 1的值\n",
"model = nn.Sequential(\n",
" nn.Linear(X_train.shape[1], 16),\n",
" nn.ReLU(),\n",
" nn.Linear(16, 16),\n",
" nn.ReLU(),\n",
" nn.Linear(16, 2),\n",
")\n",
"\n",
"print(model)"
]
},
{
"cell_type": "markdown",
"id": "f9ceea97",
"metadata": {
"id": "f9ceea97"
},
"source": [
"### 模型訓練（Model training）"
]
},
{
"cell_type": "markdown",
"id": "bb5925b9",
"metadata": {
"id": "bb5925b9"
},
"source": [
"* #### 設定模型訓練時，所需的優化器 (optimizer)、損失函數 (loss function)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "_vqU2S13hs6N",
"metadata": {
"id": "_vqU2S13hs6N"
},
"outputs": [],
"source": [
"optimizer = torch.optim.RMSprop(model.parameters(), lr=0.001)\n",
"loss_fn = nn.CrossEntropyLoss() # 多元分類損失函數\n",
"\n",
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
"print(f'device: {device}')\n",
"model = model.to(device)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "321b5sOqh5a9",
"metadata": {
"id": "321b5sOqh5a9"
},
"outputs": [],
"source": [
"def train_epoch(model, optimizer, loss_fn, train_dataloader, val_dataloader):\n",
" # 訓練一輪\n",
" model.train()\n",
" total_train_loss = 0\n",
" total_train_correct = 0\n",
" for x, y in tqdm(train_dataloader, leave=False):\n",
" x, y = x.to(device), y.to(device) # 將資料移至GPU\n",
" y_pred = model(x) # 計算預測值\n",
" loss = loss_fn(y_pred, y) # 計算誤差\n",
" optimizer.zero_grad() # 梯度歸零\n",
" loss.backward() # 反向傳播計算梯度\n",
" optimizer.step() # 更新模型參數\n",
"\n",
" total_train_loss += loss.item()\n",
" # 利用argmax計算最大值是第n個類別，與解答比對是否相同\n",
" total_train_correct += ((y_pred.argmax(dim=1) == y).sum().item())\n",
"\n",
" # 驗證一輪\n",
" model.eval()\n",
" total_val_loss = 0\n",
" total_val_correct = 0\n",
" # 關閉梯度計算以加速\n",
" with torch.no_grad():\n",
" for x, y in val_dataloader:\n",
" x, y = x.to(device), y.to(device)\n",
" y_pred = model(x)\n",
" loss = loss_fn(y_pred, y)\n",
" total_val_loss += loss.item()\n",
" # 利用argmax計算最大值是第n個類別，與解答比對是否相同\n",
" total_val_correct += ((y_pred.argmax(dim=1) == y).sum().item())\n",
"\n",
" avg_train_loss = total_train_loss / len(train_dataloader)\n",
" avg_train_acc = total_train_correct / len(train_dataloader.dataset)\n",
" avg_val_loss = total_val_loss / len(val_dataloader)\n",
" avg_val_acc = total_val_correct / len(val_dataloader.dataset)\n",
"\n",
" return avg_train_loss, avg_val_loss, avg_train_acc, avg_val_acc"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "sk_Qvl2uiP4U",
"metadata": {
"id": "sk_Qvl2uiP4U"
},
"outputs": [],
"source": [
"train_loss_log = []\n",
"val_loss_log = []\n",
"train_acc_log = []\n",
"val_acc_log = []\n",
"for epoch in tqdm(range(20)):\n",
" avg_train_loss, avg_val_loss, avg_train_acc, avg_val_acc = train_epoch(model, optimizer, loss_fn, train_loader, valid_loader)\n",
" train_loss_log.append(avg_train_loss)\n",
" val_loss_log.append(avg_val_loss)\n",
" train_acc_log.append(avg_train_acc)\n",
" val_acc_log.append(avg_val_acc)\n",
" print(f'Epoch: {epoch}, Train Loss: {avg_train_loss:.3f}, Val Loss: {avg_val_loss:.3f} | Train Acc: {avg_train_acc:.3f}, Val Acc: {avg_val_acc:.3f}')"
]
},
{
"cell_type": "markdown",
"id": "f7941459",
"metadata": {
"id": "f7941459"
},
"source": [
"### 模型評估（Model evalutation）"
]
},
{
"cell_type": "markdown",
"id": "983598de",
"metadata": {
"id": "983598de"
},
"source": [
"* #### 視覺化訓練過程的評估指標 （Visualization）"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "yqbJg154iXLv",
"metadata": {
"id": "yqbJg154iXLv"
},
"outputs": [],
"source": [
"plt.figure(figsize=(15, 4))\n",
"plt.subplot(1, 2, 1)\n",
"plt.plot(range(len(train_loss_log)), train_loss_log, label='train_loss')\n",
"plt.plot(range(len(val_loss_log)), val_loss_log, label='valid_loss')\n",
"plt.xlabel('Epochs')\n",
"plt.ylabel('Binary crossentropy')\n",
"plt.legend()\n",
"\n",
"plt.subplot(1, 2, 2)\n",
"plt.plot(range(len(train_acc_log)), train_acc_log, label='train_acc')\n",
"plt.plot(range(len(val_acc_log)), val_acc_log, label='valid_acc')\n",
"plt.xlabel('Epochs')\n",
"plt.ylabel('Accuracy')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "faf85f9f",
"metadata": {
"id": "faf85f9f"
},
"source": [
"* ### 模型預測（Model predictions）"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ed795618",
"metadata": {
"id": "ed795618"
},
"outputs": [],
"source": [
"# predict all test data\n",
"model.eval()\n",
"\n",
"y_pred = []\n",
"with torch.no_grad():\n",
" for x, _ in tqdm(valid_loader):\n",
" x = x.to(device)\n",
" y_pred.append(model(x))\n",
"\n",
"y_pred = torch.cat(y_pred).cpu()\n",
"y_pred_class = y_pred.argmax(dim=1)\n",
"\n",
"print(y_pred_class, y_pred_class.shape)"
]
},
{
"cell_type": "markdown",
"id": "702cec23",
"metadata": {
"id": "702cec23"
},
"source": [
"### Quiz\n",
"請試著利用 Data/pkgo_train.csv 做多元分類問題，預測五個種類的 pokemon，並調整模型（網路層數、神經元數目）得到更高的準確度。\n",
"\n",
"pkgo_train 為 Pokemon go 中 pokemon 出沒狀態描述的資料集，欄位說明如下：\n",
"* latitude, longitude: 位置（經緯度）\n",
"* local.xx: 時間（擷取格式 mm-dd'T'hh-mm-ss.ms'Z'）\n",
"* appearedTimeOfDay: night, evening, afternoon, morning 四種時段\n",
"* appearedHour/Minute: 當地小時／分鐘\n",
"* appearedDayOfWeek: Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday\n",
"* appearedDay/Month: 當地日期／月份\n",
"* terrainType: 地形種類\n",
"* closeToWater: 是否接近水源（100 公尺內）\n",
"* city: 城市\n",
"* continent: 洲別\n",
"* weather: 天氣種類（Foggy Clear, PartlyCloudy, MostlyCloudy, Overcast, Rain, BreezyandOvercast, LightRain, Drizzle, BreezyandPartlyCloudy, HeavyRain, BreezyandMostlyCloudy, Breezy, Windy, WindyandFoggy, Humid, Dry, WindyandPartlyCloudy, DryandMostlyCloudy, DryandPartlyCloudy, DrizzleandBreezy, LightRainandBreezy, HumidandPartlyCloudy, HumidandOvercast, RainandWindy）\n",
"* temperature: 攝氏溫度\n",
"* windSpeed: 風速（km/h）\n",
"* windBearing: 風向\n",
"* pressure: 氣壓\n",
"* sunrise/sunsetXX: 日出日落相關訊息\n",
"* population_density: 人口密集度\n",
"* urban/suburban/midurban/rural: 出沒過的地點城市程度（人口密集度小於 200 為 rural, 大於等於 200 且小於 400 為 midUrban, 大於等於400 且小於 800 為 subUrban, 大於 800 為 urban）\n",
"* gymDistanceKm: 最近道館的距離\n",
"* gymInxx: 道館是否在指定距離內\n",
"* cooc1-cooc151: 是否有其他 pokemon 在 24 小時內，出現在周圍 100 公尺之內\n",
"* category: 種類"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "523db00d",
"metadata": {
"id": "523db00d"
},
"outputs": [],
"source": []
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"provenance": []
},
"gpuClass": "standard",
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.12"
}
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"nbformat": 4,
"nbformat_minor": 5
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