我有两个文件run.py 和model.py 其中model.py如下所示： class EGAE(torch.nn.Module): def clustering(self): epsilon = torch.tensor(10**-7).to(self.device) indicator = self.indicator / self.indicator.norm(dim=1).reshape((self.data_size, -1)).max(epsilon) indicator = indicator.detach().cpu().numpy() km = KMeans(n_clusters=self.n_clusters).fit(indicator) prediction = km.predict(indicator) acc, nmi, ari, f1 = cal_clustering_metric(self.labels.cpu().numpy(), prediction) return acc, nmi, ari, f1 现在我要在run.py文件中调用model.py中EGAE类里面的clustering中的prediction，请问怎么调用

首先，在run.py文件中导入EGAE类所在的model.py文件：

from model import EGAE

接着，实例化EGAE类并调用clustering方法：

# 假设data和labels为你的输入数据和标签
model = EGAE()
prediction = model.clustering(data, labels)

需要注意的是，clustering方法需要传入输入数据和标签作为参数。如果你的EGAE类中已经包含了这些参数，可以不用在调用时传入。

相关问题

我有两个文件run.py 和model.py 其中model.py如下所示： class EGAE(torch.nn.Module): def clustering(self): epsilon = torch.tensor(10**-7).to(self.device) indicator = self.indicator / self.indicator.norm(dim=1).reshape((self.data_size, -1)).max(epsilon) indicator = indicator.detach().cpu().numpy() km = KMeans(n_clusters=self.n_clusters).fit(indicator) prediction = km.predict(indicator) acc, nmi, ari, f1 = cal_clustering_metric(self.labels.cpu().numpy(), prediction) return acc, nmi, ari, f1 现在我要在run.py文件中调用model.py中EGAE类里面的clustering方法，并且输出clustering方法中prediction

可以按照以下步骤在run.py中调用model.py中EGAE类的clustering方法，并输出prediction：

1. 导入需要的库和模块：

import torch
from model import EGAE
from sklearn.cluster import KMeans

2. 加载已经训练好的模型和数据：

# 加载数据
data = ...
labels = ...

# 加载模型
model = EGAE(...)
model.load_state_dict(torch.load("model.pth"))
model.eval()

3. 调用EGAE类中的clustering方法，并输出prediction：

# 调用clustering方法
with torch.no_grad():
    acc, nmi, ari, f1, prediction = model.clustering(data, labels)

# 输出prediction
print(prediction)

其中，需要注意以下几点：

- 在调用EGAE类中的clustering方法时，需要传入data和labels作为参数；
- 在调用模型之前，需要先加载训练好的权重，并将模型设置为eval模式；
- 在调用clustering方法时，需要使用with torch.no_grad()语句块来禁用梯度计算，以提高推理速度；
- EGAE类中的clustering方法需要返回acc、nmi、ari、f1和prediction这五个值，需要将其全部返回并接收；

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:

import pandas as pd
import numpy as np
import torch
from torch import nn
from torch.utils.data import Dataset, DataLoader
from sklearn.preprocessing import MinMaxScaler

# Read csv file
data = pd.read_csv('shuju.csv')

# Normalize the data
scaler = MinMaxScaler()
data['TotalPrice'] = scaler.fit_transform(data[['TotalPrice']])

# Split data into train and test sets
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)
    train_size = int(len(data) * 0.8)
    train_data = data[:train_size, :]
    test_data = data[train_size:, :]
    return train_data, test_data

train_data, test_data = split_data(data, lookback=4)

# Convert to PyTorch tensors
train_data = torch.from_numpy(train_data).type(torch.Tensor)
test_data = torch.from_numpy(test_data).type(torch.Tensor)

# Define hyperparameters
input_size = 1
hidden_size = 2
num_layers = 1
output_size = 1
learning_rate = 0.01
num_epochs = 200

# Define LSTM model
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).requires_grad_()
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).requires_grad_()
        out, (hn, cn) = self.lstm(x, (h0.detach(), c0.detach()))
        out = self.fc(out[:, -1, :])
        return out

model = LSTM(input_size, hidden_size, num_layers, output_size)

# Define loss function and optimizer
criterion = torch.nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

# Train the model
for epoch in range(num_epochs):
    outputs = model(train_data)
    optimizer.zero_grad()
    loss = criterion(outputs, train_data[:, -1, :])
    loss.backward()
    optimizer.step()
    if epoch % 10 == 0:
        print("Epoch: %d, loss: %1.5f" % (epoch, loss.item()))

# Save the model
torch.save(model, 'model.pt')

predict.html:

<!DOCTYPE html>
<html>
<head>
	<title>Predict sales</title>
	<link rel="stylesheet" type="text/css" href="{{ url_for('static', filename='layui/css/layui.css') }}">
	<script type="text/javascript" src="{{ url_for('static', filename='layui/layui.js') }}"></script>
</head>
<body>
	<div class="layui-container">
		<div class="layui-row">
			<div class="layui-col-md-offset4 layui-col-md-4">
				<h2 class="layui-text-center">Predict sales</h2>
				<form class="layui-form" action="">
					<div class="layui-form-item">
						<label class="layui-form-label">Date</label>
						<div class="layui-input-inline">
							<input type="text" name="date" id="date" class="layui-input" placeholder="yyyy/mm">
						</div>
					</div>
					<div class="layui-form-item">
						<div class="layui-input-block">
							<button type="button" class="layui-btn layui-btn-normal" onclick="predict()">Predict</button>
						</div>
					</div>
				</form>
				<div class="layui-text-center">
					<h3>Predicted sales:</h3>
					<h4 id="result"></h4>
				</div>
			</div>
		</div>
	</div>
	<script type="text/javascript">
		function predict() {
			var date = document.getElementById("date").value;
			if (date === "") {
				layer.msg("Please enter a date");
				return;
			}
			var year = parseInt(date.split("/")[0]);
			var month = parseInt(date.split("/")[1]);
			if (isNaN(year) || isNaN(month)) {
				layer.msg("Invalid date format");
				return;
			}
			if (month < 1 || month > 12) {
				layer.msg("Invalid month");
				return;
			}
			var data = [[
				[{{ test_data[-1, :][0] }}],
				[{{ test_data[-2, :][0] }}],
				[{{ test_data[-3, :][0] }}],
				[{{ test_data[-4, :][0] }}]
			]];
			var model = "{{ url_for('static', filename='model.pt') }}";
			$.ajax({
				type: "POST",
				url: "{{ url_for('predict') }}",
				data: JSON.stringify({
					"year": year,
					"month": month,
					"data": data,
					"model": model
				}),
				contentType: "application/json; charset=utf-8",
				dataType: "json",
				success: function(result) {
					document.getElementById("result").innerHTML = result.predicted_sales.toFixed(2);
				},
				error: function(xhr, textStatus, errorThrown) {
					layer.msg("Error: " + xhr.responseText);
				}
			});
		}
	</script>
</body>
</html>

app.py:

import os
import json
import torch
from flask import Flask, render_template, request

app = Flask(__name__)
app.config['SECRET_KEY'] = os.urandom(24)

class SalesDataset(Dataset):
    def __init__(self, data):
        self.data = data

    def __len__(self):
        return len(self.data)

    def __getitem__(self, index):
        x = self.data[index, :-1]
        y = self.data[index, -1]
        return x, y

@app.route('/')
def index():
    return render_template('predict.html')

@app.route('/predict', methods=['POST'])
def predict():
    year = request.json['year']
    month = request.json['month']
    data = torch.from_numpy(np.array(request.json['data'])).type(torch.Tensor)
    model = torch.load(request.json['model'])
    predicted_sales = model(data).item()
    return json.dumps({'predicted_sales': predicted_sales})

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