在jupyter notebook中用准确率召回率F1分数AUC4个指标分别给rt3 模型和uerroberta-base-finetuned-dianping-chinese模型绘制雷达图的每一步程序

以下是在Jupyter Notebook中使用准确率、召回率、F1分数和AUC四个指标绘制雷达图的步骤：

1. 导入必要的库和模块：

import numpy as np
import matplotlib.pyplot as plt

2. 定义函数来绘制雷达图：

def plot_radar(categories, values, title):
    N = len(categories)
    angles = np.linspace(0, 2 * np.pi, N, endpoint=False).tolist()
    values += values[:1]
    angles += angles[:1]
    
    fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(polar=True))
    ax.plot(angles, values, linewidth=1, linestyle='solid')
    ax.fill(angles, values, alpha=0.25)
    ax.set_xticks(angles[:-1])
    ax.set_xticklabels(categories)
    ax.yaxis.grid(True)
    ax.set_title(title, size=20, pad=10)
    plt.show()

3. 定义一个包含准确率、召回率、F1分数和AUC值的字典：

metrics = {
    'Accuracy': [accuracy_rt3, accuracy_uer],
    'Recall': [recall_rt3, recall_uer],
    'F1 Score': [f1_score_rt3, f1_score_uer],
    'AUC': [auc_rt3, auc_uer]
}

4. 定义雷达图的类别和数值：

categories = list(metrics.keys())
values_rt3 = metrics['Accuracy'][0], metrics['Recall'][0], metrics['F1 Score'][0], metrics['AUC'][0]
values_uer = metrics['Accuracy'][1], metrics['Recall'][1], metrics['F1 Score'][1], metrics['AUC'][1]

5. 调用绘制雷达图的函数分别绘制rt3模型和uer/roberta-base-finetuned-dianping-chinese模型的雷达图：

plot_radar(categories, values_rt3, 'rt3 Model')
plot_radar(categories, values_uer, 'uer/roberta-base-finetuned-dianping-chinese Model')

注意：在以上代码中，accuracy_rt3、accuracy_uer、recall_rt3、recall_uer、f1_score_rt3、f1_score_uer、auc_rt3和auc_uer都是具体的指标数值，需要根据实际情况进行替换