写一个auc的测试代码

好的，下面是一个简单的AUC测试代码：

import numpy as np

def evaluate_AUC(predictions, targets):
    """
    Computes the Area Under Curve (AUC) score for binary classification.

    Args:
        predictions (np.array): predicted class probabilities, shape (num_examples,)
        targets (np.array): true class labels, shape (num_examples,)

    Returns:
        float: AUC score
    """
    # Sort examples by predicted probability
    sorted_indices = np.argsort(-predictions)
    sorted_predictions = predictions[sorted_indices]
    sorted_targets = targets[sorted_indices]

    # Compute true positive and false positive rates at different thresholds
    num_positives = np.sum(sorted_targets == 1)
    num_negatives = np.sum(sorted_targets == 0)
    tp_rate = np.zeros(num_positives+1)
    fp_rate = np.zeros(num_positives+1)
    tp = 0
    fp = 0
    for i in range(len(sorted_targets)):
        if sorted_targets[i] == 1:
            tp += 1
        else:
            fp += 1
        tp_rate[tp] = tp / num_positives
        fp_rate[tp] = fp / num_negatives

    # Compute AUC as the area under the ROC curve
    auc = np.trapz(tp_rate, fp_rate)

    return auc

这个代码实现了一个简单的AUC评估函数，输入模型对样本的预测概率值和目标标签，返回AUC得分。请注意，此代码并未经过完整测试，可能存在错误或不足之处。