def data_split(data, rate): train_l = int(len(data) * rate) test_l = len(data) - train_l """打乱数据集并且划分""" train_set, test_set = torch.utils.data.random_split(data, [train_l, test_l]) return train_set, test_set

这是一个数据集划分函数，用于将数据集按照给定比例划分为训练集和测试集。输入参数 data 是一个 PyTorch 数据集对象，rate 是训练集所占比例，取值范围为 (0, 1)。在函数内部，首先计算出训练集和测试集的样本数量，然后调用 PyTorch 中的 torch.utils.data.random_split() 函数将数据集随机划分为训练集和测试集，这个函数返回的是两个新的数据集对象。最后将训练集和测试集分别返回。

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: ```python 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: ```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: ```python 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) ```

实现C4.5算法，验证算法的正确性，并将算法应用于C:/Users/Administrator/Desktop/Bank-data

C4.5算法是一种常用的决策树算法，用于分类和预测。具体实现步骤如下： 1. 数据预处理：将数据集按照一定比例分为训练集和测试集，并将数据离散化。 2. 特征选择：计算每个特征的信息增益或信息增益比，选择信息增益或信息增益比最大的特征作为划分属性。 3. 决策树生成：以选定的划分属性作为根节点，将数据集分为多个子集，并递归生成子树。 4. 决策树剪枝：通过预测错误率进行剪枝，得到最终的决策树。下面是Python实现C4.5算法的代码： ```python import numpy as np import pandas as pd from math import log # 定义节点类 class Node: def __init__(self, feature, label): self.feature = feature # 划分属性 self.label = label # 叶节点类别 self.child = {} # 子节点字典 # 计算信息熵 def cal_entropy(data): n = len(data) label_count = {} for i in range(n): label = data[i][-1] if label not in label_count.keys(): label_count[label] = 0 label_count[label] += 1 entropy = 0.0 for key in label_count: p = float(label_count[key]) / n entropy -= p * log(p, 2) return entropy # 计算信息增益 def cal_info_gain(data, feature): n = len(data) feature_count = {} for i in range(n): value = data[i][feature] if value not in feature_count.keys(): feature_count[value] = 0 feature_count[value] += 1 feature_entropy = 0.0 for key in feature_count: p = float(feature_count[key]) / n sub_data = [example for example in data if example[feature] == key] feature_entropy += p * cal_entropy(sub_data) info_gain = cal_entropy(data) - feature_entropy return info_gain # 计算信息增益比 def cal_info_gain_ratio(data, feature): n = len(data) feature_count = {} for i in range(n): value = data[i][feature] if value not in feature_count.keys(): feature_count[value] = 0 feature_count[value] += 1 feature_entropy = 0.0 split_info = 0.0 for key in feature_count: p = float(feature_count[key]) / n sub_data = [example for example in data if example[feature] == key] feature_entropy += p * cal_entropy(sub_data) split_info -= p * log(p, 2) if split_info == 0: return 0 info_gain_ratio = (cal_entropy(data) - feature_entropy) / split_info return info_gain_ratio # 选择最优划分属性 def choose_best_feature(data, feature_list, algorithm='ID3'): best_feature = -1 best_gain = 0.0 for i in feature_list: if algorithm == 'ID3': info_gain = cal_info_gain(data, i) elif algorithm == 'C4.5': info_gain = cal_info_gain_ratio(data, i) if info_gain > best_gain: best_gain = info_gain best_feature = i return best_feature # 划分数据集 def split_data(data, feature): sub_data = [] for example in data: if example[feature] == key: reduced_example = example[:feature] reduced_example.extend(example[feature+1:]) sub_data.append(reduced_example) return sub_data # 构建决策树 def create_tree(data, feature_list, algorithm='ID3'): label_list = [example[-1] for example in data] # 如果数据集中所有实例属于同一类别，则返回单节点树 if label_list.count(label_list[0]) == len(label_list): return Node(None, label_list[0]) # 如果特征集为空，则返回单节点树，其中类别为数据集中实例数最多的类别 if len(feature_list) == 0: label_count = {} for i in range(len(label_list)): if label_list[i] not in label_count.keys(): label_count[label_list[i]] = 0 label_count[label_list[i]] += 1 max_count = 0 for key in label_count: if label_count[key] > max_count: max_count = label_count[key] max_label = key return Node(None, max_label) # 选择最优划分属性 best_feature = choose_best_feature(data, feature_list, algorithm) # 如果最优划分属性的信息增益或信息增益比小于阈值，则返回单节点树，其中类别为数据集中实例数最多的类别 if best_feature == -1: label_count = {} for i in range(len(label_list)): if label_list[i] not in label_count.keys(): label_count[label_list[i]] = 0 label_count[label_list[i]] += 1 max_count = 0 for key in label_count: if label_count[key] > max_count: max_count = label_count[key] max_label = key return Node(None, max_label) # 构建子树 feature_list.remove(best_feature) node = Node(best_feature, None) feature_values = [example[best_feature] for example in data] unique_values = set(feature_values) for value in unique_values: sub_data = split_data(data, best_feature, value) sub_feature_list = feature_list[:] node.child[value] = create_tree(sub_data, sub_feature_list, algorithm) return node # 决策树分类 def classify(tree, feature_labels, test_data): first_str = list(tree.child.keys())[0] second_dict = tree.child[first_str] feature_index = feature_labels.index(tree.feature) for key in second_dict.keys(): if test_data[feature_index] == key: if type(second_dict[key]).__name__ == 'Node': class_label = classify(second_dict[key], feature_labels, test_data) else: class_label = second_dict[key] return class_label # 计算预测错误率 def cal_error_rate(tree, feature_labels, test_data_set): error_count = 0 for test_data in test_data_set: if classify(tree, feature_labels, test_data) != test_data[-1]: error_count += 1 error_rate = float(error_count) / len(test_data_set) return error_rate # 主函数 if __name__ == '__main__': # 读取数据 data = pd.read_csv('C:/Users/Administrator/Desktop/Bank-data.csv') # 数据预处理 data = data.dropna() # 去掉缺失值 data = data.sample(frac=1).reset_index(drop=True) # 随机打乱数据集 n = len(data) m = len(data.columns) - 1 train_data = data[:int(0.7*n)] test_data = data[int(0.7*n):] train_data_set = train_data.values.tolist() test_data_set = test_data.values.tolist() # 特征选择 feature_list = list(range(m)) # 构建决策树 algorithm = 'C4.5' tree = create_tree(train_data_set, feature_list, algorithm) # 输出决策树 print(tree) # 预测测试集并计算错误率 feature_labels = list(data.columns[:-1]) error_rate = cal_error_rate(tree, feature_labels, test_data_set) print('错误率：', error_rate) ``` 以上代码中，我们以银行客户数据集为例，利用C4.5算法构建决策树，并对模型进行测试并计算错误率。

def data_split(data, rate): train_l = int(len(data) * rate) test_l = len(data) - train_l """打乱数据集并且划分""" train_set, test_set = torch.utils.data.random_split(data, [train_l, test_l]) return train_set, test_set

实现C4.5算法，验证算法的正确性，并将算法应用于C:/Users/Administrator/Desktop/Bank-data

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