data = pd.read_csv('data/data.csv', index_col=0) data = data.values.reshape(-1, 1) train_data = data[:int(0.8*len(data))] test_data = data[int(0.8*len(data)):]

这段代码的作用是什么？

这段代码是将一个csv文件读取，并将其reshape为一个列向量。然后将数据划分为训练集和测试集，其中80%的数据被用于训练，20%的数据被用于测试。具体来说，`pd.read_csv('data/data.csv', index_col=0)`是读取名为"data.csv"的文件，并将第一列作为数据的索引。`data.values.reshape(-1, 1)`是将数据转换为列向量。`train_data = data[:int(0.8*len(data))]`是将前80%的数据作为训练集。`test_data = data[int(0.8*len(data)):]`是将后20%的数据作为测试集。

相关问题

import pandas as pd from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.preprocessing import OneHotEncoder,LabelEncoder from sklearn.model_selection import cross_val_score from sklearn.model_selection import GridSearchCV df = pd.read_csv('mafs(1).csv') df.head() man = df['Gender']=='M' woman = df['Gender']=='F' data = pd.DataFrame() data['couple'] = df.Couple.unique() data['location'] = df.Location.values[::2] data['man_name'] = df.Name[man].values data['woman_name'] = df.Name[woman].values data['man_occupation'] = df.Occupation[man].values data['woman_occupaiton'] = df.Occupation[woman].values data['man_age'] = df.Age[man].values data['woman_age'] = df.Age[woman].values data['man_decision'] = df.Decision[man].values data['woman_decision']=df.Decision[woman].values data['status'] = df.Status.values[::2] data.head() data.to_csv('./data.csv') data = pd.read_csv('./data.csv',index_col=0) data.head() enc = OneHotEncoder() matrix = enc.fit_transform(data['location'].values.reshape(-1,1)).toarray() feature_labels = enc.categories_ loc = pd.DataFrame(data=matrix,columns=feature_labels) data_new=data[['man_age','woman_age','man_decision','woman_decision','status']] data_new.head() lec=LabelEncoder() for label in ['man_decision','woman_decision','status']: data_new[label] = lec.fit_transform(data_new[label]) data_final = pd.concat([loc,data_new],axis=1) data_final.head() X = data_final.drop(columns=['status']) Y = data_final.status X_train,X_test,Y_train,Y_test=train_test_split(X,Y,train_size=0.7,shuffle=True) rfc = RandomForestClassifier(n_estimators=20,max_depth=2) param_grid = [ {'n_estimators': [3, 10, 30,60,100], 'max_features': [2, 4, 6, 8], 'max_depth':[2,4,6,8,10]}, ] grid_search = GridSearchCV(rfc, param_grid, cv=9) grid_search.fit(X, Y) print(grid_search.best_score_) #最好的参数 print(grid_search.best_params_)

这段代码是使用随机森林分类器对一个约会节目的参赛者进行分类的，根据他们的年龄、职业、决策等信息，将他们的状态（是否找到约会对象）进行预测。代码中使用了OneHotEncoder和LabelEncoder对分类变量进行编码，使用GridSearchCV对超参数进行调优。最后输出了最好的参数和相应的得分。

写一个python Flask销售预测系统中，有一个suanfa.py文件：先读取shuju.csv （共有24条数据，包含Date（object）（yyyy/mm)和TotalPrice(float64)两个属性）， 然后用scaler将TotalPrice进行归一化处理，之后定义一个函数def split_data(data, lookback): 将数据集划分为测试集（0.2）和训练集（0.8），data_raw = data.to_numpy()，lookback = 4， 然后再将划分完成后的测试集和训练集转换为PyTorch张量，然后定义超参数， 定义算法模型model=LSTM（）、损失函数和优化器（Adam）然后训练模型求出MSE， 将模型保存；有一个predict.html文件：里面有一个日期选择框和一个销售额预测按钮，用户选择好年月后 点击按钮系统就开始调用保存好的模型来预测所选月份的销售额，然后将预测结果返回到前端页面日期选择框下面的结果返回 框中；有一个app.py文件：定义路径。用flask和bootstrap、LayUI写出完整详细代码

suanfa.py代码：

import pandas as pd
import numpy as np
import torch
import torch.nn as nn
from sklearn.preprocessing import MinMaxScaler

# 读取数据
data = pd.read_csv('shuju.csv')
# 归一化处理
scaler = MinMaxScaler()
data['TotalPrice'] = scaler.fit_transform(data['TotalPrice'].values.reshape(-1, 1))

# 划分数据集
def split_data(data, lookback):
    data_raw = data.to_numpy()
    data = []
    for index in range(len(data_raw) - lookback):
        data.append(data_raw[index: index + lookback])
    data = np.array(data)
    test_size = int(np.round(0.2 * data.shape[0]))
    train_size = data.shape[0] - test_size
    x_train = torch.tensor(data[:train_size, :-1, :])
    y_train = torch.tensor(data[:train_size, -1, :])
    x_test = torch.tensor(data[train_size:, :-1, :])
    y_test = torch.tensor(data[train_size:, -1, :])
    return x_train, y_train, x_test, y_test

# 超参数
input_size = 1
hidden_size = 32
num_layers = 2
output_size = 1
num_epochs = 100
learning_rate = 0.01

# 定义模型
class LSTM(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, output_size):
        super(LSTM, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
        out, _ = self.lstm(x, (h0, c0))
        out = out[:, -1, :]
        out = self.fc(out)
        return out

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = LSTM(input_size, hidden_size, num_layers, output_size).to(device)

# 损失函数和优化器
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

# 训练模型
x_train, y_train, x_test, y_test = split_data(data, lookback=4)
for epoch in range(num_epochs):
    inputs = x_train.to(device)
    targets = y_train.to(device)
    outputs = model(inputs)
    loss = criterion(outputs, targets)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    if (epoch + 1) % 10 == 0:
        print(f'Epoch [{epoch + 1}/{num_epochs}], Loss: {loss.item():.4f}')

# 保存模型
torch.save(model.state_dict(), 'model.pt')

predict.html代码：

<!DOCTYPE html>
<html>
<head>
    <meta charset="utf-8">
    <title>销售预测系统</title>
    <!-- 引入layui样式 -->
    <link rel="stylesheet" href="https://www.layuicdn.com/layui/css/layui.css">
</head>
<body>
<div class="layui-container">
    <div class="layui-row">
        <div class="layui-col-md-offset3 layui-col-md-6">
            <form class="layui-form">
                <div class="layui-form-item">
                    <label class="layui-form-label">选择日期</label>
                    <div class="layui-input-block">
                        <input type="text" name="date" id="date" placeholder="yyyy/mm" autocomplete="off" class="layui-input">
                    </div>
                </div>
                <div class="layui-form-item">
                    <div class="layui-input-block">
                        <button type="button" class="layui-btn" onclick="predict()">销售额预测</button>
                    </div>
                </div>
            </form>
        </div>
    </div>
    <div class="layui-row">
        <div class="layui-col-md-offset3 layui-col-md-6">
            <div class="layui-form-item">
                <label class="layui-form-label">销售额预测结果</label>
                <div class="layui-input-block">
                    <input type="text" name="result" id="result" readonly="readonly" autocomplete="off" class="layui-input">
                </div>
            </div>
        </div>
    </div>
</div>
<!-- 引入layui JS -->
<script src="https://www.layuicdn.com/layui/layui.js"></script>
<script>
    function predict() {
        var date = $("#date").val();
        $.ajax({
            type: "POST",
            url: "/predict",
            data: {"date": date},
            success: function (data) {
                $("#result").val(data);
            }
        });
    }
</script>
</body>
</html>

app.py代码：

from flask import Flask, render_template, request, jsonify
import pandas as pd
import numpy as np
import torch
from sklearn.preprocessing import MinMaxScaler
from suanfa import LSTM

app = Flask(__name__)

# 加载模型
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = LSTM(input_size=1, hidden_size=32, num_layers=2, output_size=1).to(device)
model.load_state_dict(torch.load('model.pt'))

# 读取数据并归一化处理
data = pd.read_csv('shuju.csv')
scaler = MinMaxScaler()
data['TotalPrice'] = scaler.fit_transform(data['TotalPrice'].values.reshape(-1, 1))

# 定义预测函数
def predict(date):
    # 获取前4个月的销售额数据
    last_4_month = []
    for i in range(4):
        year, month = date.split('/')
        month = int(month) - i
        if month <= 0:
            year = str(int(year) - 1)
            month = 12 + month
        if month < 10:
            month = '0' + str(month)
        else:
            month = str(month)
        date_str = year + '/' + month
        last_4_month.append(data[data['Date'] == date_str]['TotalPrice'].values[0])
    last_4_month.reverse()
    input_data = torch.tensor(last_4_month).view(1, 4, 1).float().to(device)
    # 模型预测
    with torch.no_grad():
        output = model(input_data)
    output = scaler.inverse_transform(output.cpu().numpy())[0][0]
    return round(output, 2)

# 定义路由
@app.route('/')
def index():
    return render_template('predict.html')

@app.route('/predict', methods=['POST'])
def predict_result():
    date = request.form.get('date')
    result = predict(date)
    return jsonify(result)

if __name__ == '__main__':
    app.run(debug=True)

在运行完以上代码后，通过访问http://localhost:5000/即可进入销售预测系统。用户选择好年月后点击预测按钮，系统就会调用保存好的模型来预测所选月份的销售额，并将预测结果显示在页面下方的结果返回框中。