101 lines
3.3 KiB
Python
101 lines
3.3 KiB
Python
from matplotlib import pyplot as plt
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import seaborn as sns
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import datetime
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import matplotlib.figure as figure
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import matplotlib.backends.backend_agg as agg
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from Load_process.file_processing import Process_File
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def plot_history(Losses, Accuracys, Save_Root, File_Name):
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File = Process_File()
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plt.figure(figsize=(16,4))
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plt.subplot(1,2,1)
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# 修正維度不匹配問題
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train_losses = Losses[0]
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val_losses = Losses[1]
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# 分別繪製訓練損失和驗證損失
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train_epochs = range(1, len(train_losses) + 1)
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plt.plot(train_epochs, train_losses, label='Train')
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val_epochs = range(1, len(val_losses) + 1)
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plt.plot(val_epochs, val_losses, label='Validation')
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plt.ylabel('Losses')
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plt.xlabel('epoch')
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plt.legend(loc='upper left')
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plt.title('Model Loss')
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if Accuracys is not None:
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plt.subplot(1,2,2)
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train_acc = Accuracys[0]
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val_acc = Accuracys[1]
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# 分別繪製訓練準確率和驗證準確率
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train_epochs_acc = range(1, len(train_acc) + 1)
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plt.plot(train_epochs_acc, train_acc, label='Train')
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val_epochs_acc = range(1, len(val_acc) + 1)
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plt.plot(val_epochs_acc, val_acc, label='Validation')
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plt.ylabel('Accuracies')
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plt.xlabel('epoch')
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plt.legend(loc='upper left')
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plt.title('Model Accuracy')
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File.JudgeRoot_MakeDir(Save_Root)
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modelfiles = File.Make_Save_Root(f"{str(File_Name)}.png", Save_Root)
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plt.savefig(modelfiles)
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plt.close("all") # 關閉圖表
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def draw_heatmap(matrix, Save_Root, File_Name, index): # 二分類以上混淆矩陣做法
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File = Process_File()
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# 创建热图
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fig = figure.Figure(figsize=(6, 4))
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canvas = agg.FigureCanvasAgg(fig)
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Ax = fig.add_subplot(111)
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sns.heatmap(matrix, square = True, annot = True, fmt = 'd', linecolor = 'white', cmap = "Purples", ax = Ax)#画热力图，cmap表示设定的颜色集
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File.JudgeRoot_MakeDir(Save_Root)
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modelfiles = File.Make_Save_Root(f"{File_Name}-{str(index)}.png", Save_Root)
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# confusion.figure.savefig(modelfiles)
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# 设置图像参数
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Ax.set_title(f"{File_Name} confusion matrix")
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Ax.set_xlabel("X-Predict label of the model")
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Ax.set_ylabel("Y-True label of the model")
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# 保存图像到文件中
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canvas.print_figure(modelfiles)
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def Confusion_Matrix_of_Two_Classification(Matrix, Save_Root, File_Name, index):
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File = Process_File()
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fx = sns.heatmap(Matrix, annot=True, cmap='turbo')
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# labels the title and x, y axis of plot
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fx.set_title('Plotting Confusion Matrix using Seaborn\n\n')
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fx.set_xlabel('answer Values ')
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fx.set_ylabel('Predicted Values')
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# 根据矩阵维度动态设置标签
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n_classes = Matrix.shape[0]
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# 如果是2类问题，使用False/True标签
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if n_classes == 2:
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labels = ['False', 'True']
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else:
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# 对于多类问题，使用数字标签
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labels = [str(i) for i in range(n_classes)]
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fx.xaxis.set_ticklabels(labels)
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fx.yaxis.set_ticklabels(labels)
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File.JudgeRoot_MakeDir(Save_Root)
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modelfiles = File.Make_Save_Root(f"{File_Name}-{str(index)}.png", Save_Root)
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plt.savefig(modelfiles)
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plt.close("all") # 關閉圖表
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pass |