2.3.1 气温预测 复杂步骤.ipynb

{"cells":[{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>year</th>\n","      <th>month</th>\n","      <th>day</th>\n","      <th>temp_2</th>\n","      <th>temp_1</th>\n","      <th>average</th>\n","      <th>actual</th>\n","      <th>friend</th>\n","      <th>week_Fri</th>\n","      <th>week_Mon</th>\n","      <th>week_Sat</th>\n","      <th>week_Sun</th>\n","      <th>week_Thurs</th>\n","      <th>week_Tues</th>\n","      <th>week_Wed</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2016</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>45</td>\n","      <td>45</td>\n","      <td>45.6</td>\n","      <td>45</td>\n","      <td>29</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2016</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>44</td>\n","      <td>45</td>\n","      <td>45.7</td>\n","      <td>44</td>\n","      <td>61</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2016</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>45</td>\n","      <td>44</td>\n","      <td>45.8</td>\n","      <td>41</td>\n","      <td>56</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2016</td>\n","      <td>1</td>\n","      <td>4</td>\n","      <td>44</td>\n","      <td>41</td>\n","      <td>45.9</td>\n","      <td>40</td>\n","      <td>53</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2016</td>\n","      <td>1</td>\n","      <td>5</td>\n","      <td>41</td>\n","      <td>40</td>\n","      <td>46.0</td>\n","      <td>44</td>\n","      <td>41</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   year  month  day  temp_2  temp_1  average  actual  friend  week_Fri  \\\n","0  2016      1    1      45      45     45.6      45      29         1   \n","1  2016      1    2      44      45     45.7      44      61         0   \n","2  2016      1    3      45      44     45.8      41      56         0   \n","3  2016      1    4      44      41     45.9      40      53         0   \n","4  2016      1    5      41      40     46.0      44      41         0   \n","\n","   week_Mon  week_Sat  week_Sun  week_Thurs  week_Tues  week_Wed  \n","0         0         0         0           0          0         0  \n","1         0         1         0           0          0         0  \n","2         0         0         1           0          0         0  \n","3         1         0         0           0          0         0  \n","4         0         0         0           0          1         0  "]},"execution_count":2,"metadata":{},"output_type":"execute_result"}],"source":["# 复杂步骤构建预测模型\n","import numpy as np\n","import torch \n","import pandas as pd\n","import seaborn as sb\n","import sklearn.preprocessing as preprocessing\n","import warnings\n","warnings.filterwarnings('ignore')\n","\n","features = pd.read_csv('/GitRepos/data/temps.csv')\n","# onehot code for non-number element\n","features = pd.get_dummies(features)\n","features.head()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# get labels y\n","labels = np.array(features['actual'])\n","\n","# drop colum y\n","features = features.drop(['year','actual'],axis=1)\n","\n","feature_list = list(features.columns)\n","features = np.array(features)\n","\n","# 特征之间尺度不一致 要进行数据标准化\n","features = preprocessing.StandardScaler().fit_transform(features)\n","features.shape\n","# 构建模型\n","x = torch.tensor(features,dtype=float)\n","y = torch.tensor(labels,dtype=float)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n","weights = torch.randn((13,128), dtype = float, requires_grad = True)\n","bias = torch.randn(128,dtype = float,requires_grad=True)\n","weights2 = torch.randn((128,1),dtype=float,requires_grad=True)\n","bias2 = torch.randn(1,dtype=float,requires_grad=True)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["lr = 0.01\n","losses = []\n","\n","for epoch in range(1000):\n","    epoch += 1\n","    # foreward\n","    hidden = x.mm(weights) + bias\n","    # activation\n","    hidden = torch.relu(hidden)\n","    # prediction\n","    pred = hidden.mm(weights2) + bias2\n","    # loss\n","    loss = torch.mean((pred - y) ** 2)\n","    # backward\n","    loss.backward()\n","    # print\n","    losses.append(loss)\n","    if epoch % 50 == 0:\n","        print('loss {}',loss)\n","    # update\n","    weights.data.add_( -1*weights.grad.data*lr)\n","    bias.data.add_(-1*bias.grad.data)\n","    weights2.data.add_(-1*weights2.grad.data*lr)\n","    bias2.data.add_(-1*bias2.grad.data*lr)\n","    # zero grad\n","    weights.grad.data.zero_()\n","    bias.grad.data.zero_()\n","    weights2.grad.data.zero_()\n","    bias2.grad.data.zero_()"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["epoch100,loss153.16565235310688\n","epoch200,loss145.4522265618731\n","epoch300,loss143.31167657225615\n","epoch400,loss142.16882361241758\n","epoch500,loss141.4581892625563\n","epoch600,loss140.9701484926021\n","epoch700,loss140.6301150676974\n","epoch800,loss140.37422479840046\n","epoch900,loss140.1762781490014\n","epoch1000,loss140.0193163378169\n"]}],"source":["from pandas.core.arrays.sparse import dtype\n","import torch\n","import torch.nn as nn\n","import pandas as pd\n","import numpy as np\n","import sklearn.preprocessing as preprocessing\n","\n","features = pd.read_csv('d:/GitRepos/data/temps.csv')\n","features = pd.get_dummies(features)\n","y_labels = np.array(features['actual'])\n","features = features.drop(['friend','actual'],axis = 1)\n","features = np.array(features)\n","\n","features = preprocessing.StandardScaler().fit_transform(features)\n","features[:5]\n","\n","x_train = torch.tensor(features,dtype=float)\n","y_train = torch.tensor(y_labels,dtype=float)\n","\n","batch_size = 32\n","\n","weights = torch.randn((13,128),dtype=float,requires_grad=True)\n","bias = torch.randn(128,dtype=float,requires_grad=True)\n","weights2 = torch.randn((128,1),dtype = float,requires_grad=True)\n","bias2 = torch.randn(1,dtype=float,requires_grad=True)\n","\n","lr = 0.001\n","for epoch in range(1000):\n","    epoch += 1\n","    #前向\n","    hidden = x_train.mm(weights) + bias\n","    activation = torch.relu(hidden)\n","    #预测\n","    pred = activation.mm(weights2) + bias2\n","    #loss\n","    loss = torch.mean((pred - y_train)**2)\n","    #grad,error 'Nonetype' without backward()\n","    loss.backward()\n","    if epoch % 100 == 0:\n","        print('epoch{},loss{}'.format(epoch,loss))\n","    #update\n","    weights.data.add_(-1*weights.grad.data*lr)\n","    bias.data.add_(-1*bias.grad.data*lr)\n","    weights2.data.add_(-1*weights2.grad.data*lr)\n","    bias2.data.add_(-1*bias2.grad.data*lr)\n","    #zero grad\n","    weights.grad.data.zero_()\n","    bias.grad.data.zero_()\n","    weights2.grad.data.zero_()\n","    bias2.grad.data.zero_()\n"]}],"metadata":{"kernelspec":{"display_name":"pytorch","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":"3.9.13 (main, Oct 13 2022, 21:23:06) [MSC v.1916 64 bit (AMD64)]"},"orig_nbformat":4,"vscode":{"interpreter":{"hash":"e32618030883af29da10316c76c83da9a02f65dadf9a9b09d160d0d4f5840e5a"}}},"nbformat":4,"nbformat_minor":2}