python实现 P-R曲线类，不用sklearn

你可以使用Python来实现P-R曲线类，以下是一个简单的示例代码：

import matplotlib.pyplot as plt

class PRCurve:
    def __init__(self, y_true, y_scores):
        self.y_true = y_true
        self.y_scores = y_scores

    def precision_recall_curve(self):
        thresholds = sorted(set(self.y_scores), reverse=True)
        precisions = []
        recalls = []

        for threshold in thresholds:
            y_pred = [1 if score >= threshold else 0 for score in self.y_scores]
            true_positives = sum([1 for yt, yp in zip(self.y_true, y_pred) if yt == 1 and yp == 1])
            false_positives = sum([1 for yt, yp in zip(self.y_true, y_pred) if yt == 0 and yp == 1])
            false_negatives = sum([1 for yt, yp in zip(self.y_true, y_pred) if yt == 1 and yp == 0])

            precision = true_positives / (true_positives + false_positives) if (true_positives + false_positives) > 0 else 0
            recall = true_positives / (true_positives + false_negatives) if (true_positives + false_negatives) > 0 else 0

            precisions.append(precision)
            recalls.append(recall)

        return precisions, recalls

    def plot(self):
        precisions, recalls = self.precision_recall_curve()
        plt.plot(recalls, precisions, marker='.')
        plt.xlabel('Recall')
        plt.ylabel('Precision')
        plt.title('P-R Curve')
        plt.show()

使用示例：

# 实例化 PRCurve 类，传入真实标签和预测分数
y_true = [1, 0, 1, 1, 0, 0, 1]
y_scores = [0.9, 0.6, 0.8, 0.7, 0.4, 0.3, 0.5]
pr_curve = PRCurve(y_true, y_scores)

# 绘制 P-R 曲线
pr_curve.plot()

这段代码实现了一个P-R曲线类，其中`y_true`是真实的标签，`y_scores`是预测的分数。`precision_recall_curve`方法计算并返回精确率和召回率的列表，`plot`方法用于绘制P-R曲线。你可以根据你的实际需求进行修改和优化。