完成实验五；实验四notebook增加数据集目录描述

Lab5/utils.py

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+import math
+import torch
+from torch.utils import data
+import torch.nn as nn
+from matplotlib import pyplot as plt
+import numpy as np
+import time
+
+
+def mse_fn(y, pred):
+    return np.mean((np.array(y) - np.array(pred)) ** 2)
+
+
+def mae_fn(y, pred):
+    return np.mean(np.abs(np.array(y) - np.array(pred)))
+
+
+def mape_fn(y, pred):
+    mask = y != 0
+    y = y[mask]
+    pred = pred[mask]
+    mape = np.abs((y - pred) / y)
+    mape = np.mean(mape) * 100
+    return mape
+
+
+def eval(y, pred):
+    y = y.cpu().numpy()
+    pred = pred.cpu().numpy()
+    mse = mse_fn(y, pred)
+    rmse = math.sqrt(mse)
+    mae = mae_fn(y, pred)
+    mape = mape_fn(y, pred)
+    return [rmse, mae, mape]
+
+
+# 测试函数（用于分类）
+def test(net, data_iter, loss_fn, denormalize_fn, device='cpu'):
+    rmse, mae, mape = 0, 0, 0
+    batch_count = 0
+    total_loss = 0.0
+    net.eval()
+    for seqs, targets in data_iter:
+        seqs = seqs.to(device).float()
+        targets = targets.to(device).float()
+        y_hat = net(seqs)
+        loss = loss_fn(y_hat, targets)
+
+        targets = denormalize_fn(targets)
+        y_hat = denormalize_fn(y_hat)
+        a, b, c = eval(targets.detach(), y_hat.detach())
+        rmse += a
+        mae += b
+        mape += c
+        total_loss += loss.detach().cpu().numpy().tolist()
+        batch_count += 1
+    return [rmse / batch_count, mae / batch_count, mape / batch_count], total_loss / batch_count
+
+
+def train(net, train_iter, val_iter, test_iter, loss_fn, denormalize_fn, optimizer, num_epoch,
+          early_stop=10, device='cpu', num_print_epoch_round=0):
+    train_loss_lst = []
+    val_loss_lst = []
+    train_score_lst = []
+    val_score_lst = []
+    epoch_time = []
+
+    best_epoch = 0
+    best_val_rmse = 9999
+    early_stop_flag = 0
+    for epoch in range(num_epoch):
+        net.train()
+        epoch_loss = 0
+        batch_count = 0
+        batch_time = []
+        rmse, mae, mape = 0, 0, 0
+        for seqs, targets in train_iter:
+            batch_s = time.time()
+            seqs = seqs.to(device).float()
+            targets = targets.to(device).float()
+            optimizer.zero_grad()
+            y_hat = net(seqs)
+            loss = loss_fn(y_hat, targets)
+            loss.backward()
+            optimizer.step()
+
+            targets = denormalize_fn(targets)
+            y_hat = denormalize_fn(y_hat)
+            a, b, c = eval(targets.detach(), y_hat.detach())
+            rmse += a
+            mae += b
+            mape += c
+            epoch_loss += loss.detach().cpu().numpy().tolist()
+            batch_count += 1
+
+            batch_time.append(time.time() - batch_s)
+
+        train_loss = epoch_loss / batch_count
+        train_loss_lst.append(train_loss)
+        train_score_lst.append([rmse/batch_count, mae/batch_count, mape/batch_count])
+
+        # 验证集
+        val_score, val_loss = test(net, val_iter, loss_fn, denormalize_fn, device)
+        val_score_lst.append(val_score)
+        val_loss_lst.append(val_loss)
+
+        epoch_time.append(np.array(batch_time).sum())
+
+        # 打印本轮训练结果
+        if num_print_epoch_round > 0 and (epoch+1) % num_print_epoch_round == 0:
+            print(
+                f"Epoch [{epoch + 1}/{num_epoch}],",
+                f"Train Loss: {train_loss:.4f},",
+                f"Train RMSE: {train_score_lst[-1][0]:.4f},",
+                f"Val Loss: {val_loss:.4f},",
+                f"Val RMSE: {val_score[0]:.6f},",
+                f"Time Use: {epoch_time[-1]:.3f}s"
+            )
+
+        # 早停
+        if val_score[0] < best_val_rmse:
+            best_val_rmse = val_score[0]
+            best_epoch = epoch
+            early_stop_flag = 0
+        else:
+            early_stop_flag += 1
+            if early_stop_flag == early_stop:
+                print(f'The model has not been improved for {early_stop} rounds. Stop early!')
+                break
+
+    # 输出最终训练结果
+    print(
+        f'Final result:',
+        f'Get best validation rmse {np.array(val_score_lst)[:, 0].min():.4f} at epoch {best_epoch},',
+        f'Total time {np.array(epoch_time).sum():.2f}s'
+    )
+
+    # 计算测试集效果
+    test_score, test_loss = test(net, test_iter, loss_fn, denormalize_fn, device)
+    print(
+        'Test result:',
+        f'Test RMSE: {test_score[0]},',
+        f'Test MAE: {test_score[1]},',
+        f'Test MAPE: {test_score[2]}'
+    )
+    return train_loss_lst, val_loss_lst, train_score_lst, val_score_lst, epoch
+
+
+def visualize(num_epochs, train_data, test_data, x_label='epoch', y_label='loss'):
+    x = np.arange(0, num_epochs + 1).astype(dtype=np.int32)
+    plt.figure(figsize=(5, 3.5))
+    plt.plot(x, train_data, label=f"train_{y_label}", linewidth=1.5)
+    plt.plot(x, test_data, label=f"val_{y_label}", linewidth=1.5)
+    plt.xlabel(x_label)
+    plt.ylabel(y_label)
+    plt.legend()
+    plt.show()
+
+
+def plot_metric(score_log):
+    score_log = np.array(score_log)
+
+    plt.figure(figsize=(13, 3.5))
+    plt.subplot(1, 3, 1)
+    plt.plot(score_log[:, 0], c='#d28ad4')
+    plt.ylabel('RMSE')
+
+    plt.subplot(1, 3, 2)
+    plt.plot(score_log[:, 1], c='#e765eb')
+    plt.ylabel('MAE')
+
+    plt.subplot(1, 3, 3)
+    plt.plot(score_log[:, 2], c='#6b016d')
+    plt.ylabel('MAPE')
+
+    plt.show()