Python利用Seaborn绘制多标签的混淆矩阵 Python利用Seaborn绘制多标签的混

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 snsfrom matplotlib import pyplot as pltfrom scipy.special import softmaxfrom sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score, f1_scoresns.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，F1acc = 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1sum_true = np.expand_dims(np.sum(cf_matrix, axis=1), axis=1)precision_matrix = cf_matrix / sum_trueax = 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1sum_pred = np.expand_dims(np.sum(cf_matrix, axis=0), axis=0)recall_matrix = cf_matrix / sum_predax = 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_matrixb = precision_matrix + recall_matrixf1_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：
【Python利用Seaborn绘制多标签的混淆矩阵】