在imdb10star1wnb.csv数据集上绘制有三个分类的PR曲线代码

以下是在imdb10star1wnb.csv数据集上绘制三个分类的PR曲线的Python代码：

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import precision_recall_curve
from sklearn.metrics import average_precision_score

# 读取数据集
data = pd.read_csv("imdb10star1wnb.csv")

# 分割数据集为训练集和测试集

# 定义特征和标签
X = data.drop(columns=["sentiment"])
y = data["sentiment"]

# 分割数据集
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, test_size=0.2)

# 训练模型
from sklearn.linear_model import LogisticRegression
model = LogisticRegression(max_iter=10000)
model.fit(X_train, y_train)

# 预测测试集
y_pred = model.predict(X_test)

# 计算PR曲线
precision = dict()
recall = dict()
average_precision = dict()
for i in range(3):
    precision[i], recall[i], _ = precision_recall_curve(y_test, y_pred, pos_label=i)
    average_precision[i] = average_precision_score(y_test, y_pred, pos_label=i)

# 绘制PR曲线
colors = ['navy', 'turquoise', 'darkorange']
for i, color in zip(range(3), colors):
    plt.plot(recall[i], precision[i], color=color, lw=2,
             label='Precision-recall curve of class {0} (area = {1:0.2f})'
             ''.format(i, average_precision[i]))

plt.xlabel("Recall")
plt.ylabel("Precision")
plt.legend(loc="best")
plt.title("Precision-Recall curve")
plt.show()

这段代码将数据集分割为训练集和测试集，然后使用逻辑回归模型进行训练和预测。接着，使用 `precision_recall_curve` 和 `average_precision_score` 函数计算三个分类的PR曲线和平均精度。最后，使用 `matplotlib` 库将PR曲线绘制出来。