Python利用Seaborn绘制多标签的混淆矩阵

Seaborn - 绘制多标签的混淆矩阵、召回、精准、F1

导入seaborn\matplotlib\scipy\sklearn等包：

import seaborn as sns
from matplotlib import pyplot as plt
from scipy.special import softmax
from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score, f1_score

sns.set_theme(color_codes=True)

从dataframe中，获取y_true(真实标签)和y_pred(预测标签)：

y_true = df["target"]
y_pred = df['prediction']

计算验证数据整体的准确率acc、精准率precision、召回率recall、F1，使用加权模式average=‘weighted’：

# 准确率acc，精准precision，召回recall，F1
acc = accuracy_score(df["target"], df['prediction'])
precision = precision_score(y_true, y_pred, average='weighted')
recall = recall_score(y_true, y_pred, average='weighted')
f1 = f1_score(y_true, y_pred, average='weighted')
print(f'[Info] acc: {acc}, precision: {precision}, recall: {recall}, f1: {f1}')

计算混淆矩阵：

# 横坐标是真实类别数，纵坐标是预测类别数
cf_matrix = confusion_matrix(y_true, y_pred)

5类矩阵的绘制方案，混淆矩阵、百分比的混淆矩阵、召回矩阵、精准矩阵、F1矩阵：

• 混淆矩阵是计数，百分比的混淆矩阵是占比

• 召回矩阵是，每行的和是1，每行代表真实类别数，占比就是召回

• 精准矩阵是，每列的和是1，每列代表预测列表数，占比就是精准

• F1矩阵是按照 2PR/(P+R)，注意为0的情况，需要补0，使用np.divide(a, b, out=np.zeros_like(a), where=(b != 0))

代码如下：

# 横坐标是真实类别数，纵坐标是预测类别数
cf_matrix = confusion_matrix(y_true, y_pred)

figure, axes = plt.subplots(2, 2, figsize=(16*1.25, 16))

# 混淆矩阵
ax = sns.heatmap(cf_matrix, annot=True, fmt='g', ax=axes[0][0], cmap='Blues')
ax.title.set_text("Confusion Matrix")
ax.set_xlabel("y_pred")
ax.set_ylabel("y_true")
# plt.savefig(csv_path.replace(".csv", "_cf_matrix.png"))
# plt.show()

# 混淆矩阵 - 百分比
cf_matrix = confusion_matrix(y_true, y_pred)
ax = sns.heatmap(cf_matrix / np.sum(cf_matrix), annot=True, ax=axes[0][1], fmt='.2％', cmap='Blues')
ax.title.set_text("Confusion Matrix (percent)")
ax.set_xlabel("y_pred")
ax.set_ylabel("y_true")
# plt.savefig(csv_path.replace(".csv", "_cf_matrix_p.png"))
# plt.show()

# 召回矩阵，行和为1
sum_true = np.expand_dims(np.sum(cf_matrix, axis=1), axis=1)
precision_matrix = cf_matrix / sum_true
ax = sns.heatmap(precision_matrix, annot=True, fmt='.2％', ax=axes[1][0], cmap='Blues')
ax.title.set_text("Precision Matrix")
ax.set_xlabel("y_pred")
ax.set_ylabel("y_true")
# plt.savefig(csv_path.replace(".csv", "_recall.png"))
# plt.show()

# 精准矩阵，列和为1
sum_pred = np.expand_dims(np.sum(cf_matrix, axis=0), axis=0)
recall_matrix = cf_matrix / sum_pred
ax = sns.heatmap(recall_matrix, annot=True, fmt='.2％', ax=axes[1][1], cmap='Blues')
ax.title.set_text("Recall Matrix")
ax.set_xlabel("y_pred")
ax.set_ylabel("y_true")
# plt.savefig(csv_path.replace(".csv", "_precision.png"))
# plt.show()

# 绘制4张图
plt.autoscale(enable=False)
plt.savefig(csv_path.replace(".csv", "_all.png"), bbox_inches='tight', pad_inches=0.2)
plt.show()

# F1矩阵
a = 2 * precision_matrix * recall_matrix
b = precision_matrix + recall_matrix
f1_matrix = np.divide(a, b, out=np.zeros_like(a), where=(b != 0))
ax = sns.heatmap(f1_matrix, annot=True, fmt='.2％', cmap='Blues')
ax.title.set_text("F1 Matrix")
ax.set_xlabel("y_pred")
ax.set_ylabel("y_true")
plt.savefig(csv_path.replace(".csv", "_f1.png"))
plt.show()

输出混淆矩阵、混淆矩阵(百分比)、召回矩阵、精准矩阵：

F1 Score：

来源：https://blog.csdn.net/caroline_wendy/article/details/125796474