python Micro-average

在Python中，用于计算微平均的函数是f1_score。可以使用以下代码来计算微平均值：

from sklearn.metrics import f1_score

# 假设test_labels是真实标签，preds是预测标签
micro_avg = f1_score(test_labels, preds, average='micro')

在这个例子中，f1_score函数的average参数被设置为'micro'，这意味着它将计算微平均。微平均在处理不平衡数据集时特别有用，其中数据集中的类别数量差异很大。微平均考虑每个样本的贡献相等，并计算出一个整体的F1分数。此外，对于不同样本数量的类别，宏平均和微平均有不同的使用场景，可以根据需要进行选择。

相关问题

使用micro-average方法绘制ROC曲线 demo

这里给出一个使用micro-average方法绘制ROC曲线的demo，使用Python的sklearn库来实现：

from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt
import numpy as np

# 假设有3个类别，每个类别有4个样本，预测结果为类别1的概率、类别2的概率、类别3的概率
y_score = np.array([[0.9, 0.1, 0.0], [0.3, 0.6, 0.1], [0.8, 0.2, 0.0], [0.2, 0.5, 0.3],
                    [0.1, 0.8, 0.1], [0.2, 0.3, 0.5], [0.4, 0.5, 0.1], [0.1, 0.6, 0.3],
                    [0.6, 0.2, 0.2], [0.3, 0.4, 0.3], [0.1, 0.7, 0.2], [0.2, 0.3, 0.5]])

# 标签为0/1/2，对应三个类别
y_true = np.array([0, 1, 0, 1, 2, 2, 1, 0, 2, 1, 0, 2]) 

# 将标签转化为one-hot编码
y_true_onehot = np.zeros((y_true.shape[0], 3))
for i in range(y_true.shape[0]):
    y_true_onehot[i][y_true[i]] = 1

# 计算总体的TPR和FPR
fpr, tpr, _ = roc_curve(y_true_onehot.ravel(), y_score.ravel())
roc_auc = auc(fpr, tpr)

# 绘制ROC曲线
plt.figure()
plt.plot(fpr, tpr, color='darkorange', lw=2, label='micro-average ROC curve (area = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic example')
plt.legend(loc="lower right")
plt.show()

运行以上代码，即可绘制出使用micro-average方法绘制的ROC曲线。

修正下列代码y_test=np.array(y_test) y_score=np.array(y_score) fpr = dict() tpr = dict() roc_auc = dict() for i in range(n_classes): # 遍历三个类别 fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i]) roc_auc[i] = auc(fpr[i], tpr[i]) # Compute micro-average ROC curve and ROC area（方法二） fpr["micro"], tpr["micro"], _ = roc_curve(y_test.ravel(), y_score.ravel()) roc_auc["micro"] = auc(fpr["micro"], tpr["micro"]) # Compute macro-average ROC curve and ROC area（方法一） # First aggregate all false positive rates all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)])) # Then interpolate all ROC curves at this points mean_tpr = np.zeros_like(all_fpr) for i in range(n_classes): mean_tpr += interp(all_fpr, fpr[i], tpr[i]) # Finally average it and compute AUC mean_tpr /= n_classes fpr["macro"] = all_fpr tpr["macro"] = mean_tpr roc_auc["macro"] = auc(fpr["macro"], tpr["macro"]) # Plot all ROC curves lw=2 plt.figure() plt.plot(fpr["micro"], tpr["micro"], label='micro-average ROC curve (area = {0:0.2f})' ''.format(roc_auc["micro"]), color='deeppink', linestyle=':', linewidth=4) plt.plot(fpr["macro"], tpr["macro"], label='macro-average ROC curve (area = {0:0.2f})' ''.format(roc_auc["macro"]), color='navy', linestyle=':', linewidth=4) colors = cycle(['aqua', 'darkorange', 'cornflowerblue']) for i, color in zip(range(n_classes), colors): plt.plot(fpr[i], tpr[i], color=color, lw=lw, label='ROC curve of class {0} (area = {1:0.2f})' ''.format(i, roc_auc[i])) plt.plot([0, 1], [0, 1], 'k--', lw=lw) plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.title('Some extension of Receiver operating characteristic to multi-class') plt.legend(loc="lower right") plt.show()

以下是修正后的代码：

y_test = np.array(y_test)
y_score = np.array(y_score)

fpr = dict()
tpr = dict()
roc_auc = dict()

for i in range(n_classes):
    # 遍历三个类别
    fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])
    roc_auc[i] = auc(fpr[i], tpr[i])

# Compute micro-average ROC curve and ROC area（方法二）
fpr["micro"], tpr["micro"], _ = roc_curve(y_test.ravel(), y_score.ravel())
roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])

# Compute macro-average ROC curve and ROC area（方法一）
# First aggregate all false positive rates
all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))

# Then interpolate all ROC curves at this points
mean_tpr = np.zeros_like(all_fpr)

for i in range(n_classes):
    mean_tpr += interp(all_fpr, fpr[i], tpr[i])

# Finally average it and compute AUC
mean_tpr /= n_classes

fpr["macro"] = all_fpr
tpr["macro"] = mean_tpr
roc_auc["macro"] = auc(fpr["macro"], tpr["macro"])

# Plot all ROC curves
lw = 2

plt.figure()
plt.plot(fpr["micro"], tpr["micro"],
         label='micro-average ROC curve (area = {0:0.2f})'
               ''.format(roc_auc["micro"]),
         color='deeppink', linestyle=':', linewidth=4)

plt.plot(fpr["macro"], tpr["macro"],
         label='macro-average ROC curve (area = {0:0.2f})'
               ''.format(roc_auc["macro"]),
         color='navy', linestyle=':', linewidth=4)

colors = cycle(['aqua', 'darkorange', 'cornflowerblue'])

for i, color in zip(range(n_classes), colors):
    plt.plot(fpr[i], tpr[i], color=color, lw=lw,
             label='ROC curve of class {0} (area = {1:0.2f})'
             ''.format(i, roc_auc[i]))

plt.plot([0, 1], [0, 1], 'k--', lw=lw)
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Some extension of Receiver operating characteristic to multi-class')
plt.legend(loc="lower right")
plt.show()