c语言建网站,wordpress 防爬,WordPress支持多少文章,广东企业品牌网站建设价格回归的流程与分类基本一致#xff0c;只需要把评估指标改动一下就行。回归输出的是损失曲线、R^2曲线、训练集预测值与真实值折线图、测试集预测值散点图与真实值折线图。输出效果如下#xff1a; 注意#xff1a;预测值与真实值图像处理为按真实值排序#xff0c;图中呈现…回归的流程与分类基本一致只需要把评估指标改动一下就行。回归输出的是损失曲线、R^2曲线、训练集预测值与真实值折线图、测试集预测值散点图与真实值折线图。输出效果如下 注意预测值与真实值图像处理为按真实值排序图中呈现的升序与数据集趋势无关。
代码如下
from functools import partial
import numpy as np
import pandas as pd
from sklearn.preprocessing import label_binarize
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, confusion_matrix, roc_curve, r2_scoreimport torch
import torch.nn as nn
from torch.utils.data import DataLoader, TensorDataset, Dataset
from visdom import Visdomfrom typing import Union, Optional
from sklearn.base import TransformerMixin
from torch.optim.optimizer import Optimizerdef regress(data: tuple[Union[np.ndarray, Dataset], Union[np.ndarray, Dataset]],model: nn.Module,optimizer: Optimizer,criterion: nn.Module,scaler: Optional[TransformerMixin] None,batch_size: int 64,epochs: int 10,device: Optional[torch.device] None
) - nn.Module:回归任务的训练函数。:param data: 形如(X,y)的np.ndarray类型及形如(train_data,test_data)的torch.utils.data.Dataset类型:param model: 回归模型:param optimizer: 优化器:param criterion: 损失函数:param scaler: 数据标准化器:param batch_size: 批大小:param epochs: 训练轮数:param device: 训练设备:return: 训练好的回归模型if isinstance(data[0], np.ndarray):X, y data# 分离训练集和测试集指定随机种子以便复现X_train, X_test, y_train, y_test train_test_split(X, y, test_size0.2, random_state42)# 数据标准化if scaler is not None:X_train scaler.fit_transform(X_train)X_test scaler.transform(X_test)# 转换为tensorX_train torch.from_numpy(X_train.astype(np.float32))X_test torch.from_numpy(X_test.astype(np.float32))y_train torch.from_numpy(y_train.astype(np.float32))y_test torch.from_numpy(y_test.astype(np.float32))# 将X和y封装成TensorDatasettrain_dataset TensorDataset(X_train, y_train)test_dataset TensorDataset(X_test, y_test)elif isinstance(data[0], Dataset):train_dataset, test_dataset dataelse:raise ValueError(Unsupported data type)train_loader DataLoader(datasettrain_dataset,batch_sizebatch_size,shuffleTrue,num_workers2,)test_loader DataLoader(datasettest_dataset,batch_sizebatch_size,shuffleTrue,num_workers2,)model.to(device)vis Visdom()# 训练模型for epoch in range(epochs):for step, (batch_x_train, batch_y_train) in enumerate(train_loader):batch_x_train batch_x_train.to(device)batch_y_train batch_y_train.to(device)# 前向传播output model(batch_x_train)loss criterion(output, batch_y_train)# 反向传播optimizer.zero_grad()loss.backward()optimizer.step()niter epoch * len(train_loader) step 1 # 计算迭代次数if niter % 100 0:# 评估模型model.eval()with torch.no_grad():eval_dict {test_loss: [],test_r2: [],y_test: [],y_pred: [],}for batch_x_test, batch_y_test in test_loader:batch_x_test batch_x_test.to(device)batch_y_test batch_y_test.to(device)test_output model(batch_x_test)test_predicted_tuple (batch_y_test.numpy(), test_output.numpy())# 计算并记录损失、R^2、真实值、预测值eval_dict[test_loss].append(criterion(test_output, batch_y_test))eval_dict[test_r2].append(r2_score(*test_predicted_tuple))eval_dict[y_test].append(batch_y_test)eval_dict[y_pred].append(test_output)# 画出损失曲线vis.line(Xtorch.ones((1, 2)) * (niter // 100),Ytorch.stack((loss, torch.mean(torch.tensor(eval_dict[test_loss])))).unsqueeze(0),winloss,updateappend,optsdict(titleLoss, legend[train_loss, test_loss]),)# 画出R^2曲线train_r2 r2_score(batch_y_train.numpy(), output.numpy())vis.line(Xtorch.ones((1, 2)) * (niter // 100),Ytorch.tensor((train_r2, np.mean(eval_dict[test_r2]))).unsqueeze(0),winR^2,updateappend,optsdict(titleR^2, legend[train_R^2, test_R^2], ytickmin0, ytickmax1),)# 画出训练集预测值和真实值折线图sorted_train_idx torch.argsort(batch_y_train) # 按真实值排序vis.line(Xtorch.arange(batch_size).repeat(2, 1).t(),Ytorch.stack((batch_y_train[sorted_train_idx], output[sorted_train_idx]), dim1),winbatch_train_line,optsdict(titlePredicted vs. Actual (Train Set), legend[Actual, Predicted]),)# 画出测试集预测值散点图和真实值折线图x list(range(len(y_test)))y_test torch.cat(eval_dict[y_test])y_pred torch.cat(eval_dict[y_pred])sorted_test_idx torch.argsort(y_test)vis._send({data: [{x: x, y: y_test[sorted_test_idx].tolist(), type: custom, mode: lines, name: Actual},{x: x, y: y_pred[sorted_test_idx].tolist(), type: custom, mode: markers, name: Predicted, marker: {size: 3}}],win: test_line,layout: {title: Predicted vs. Actual (Test Set)},})return model
