參考：PR曲線和F1-score 評價指標(biāo)相關(guān) - 知乎
sklearn官方文檔：Precision-Recall — scikit-learn 1.0.2 documentation
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多標(biāo)簽設(shè)置中的PR曲線

??????? 查了好多文檔，但依舊看得稀里糊涂。上面兩個鏈接講的非常清楚了，所以在這里記錄一下。具體原理不多說，看上面鏈接就行，下面直接上代碼：

一、數(shù)據(jù)說明：

????????n_classes，標(biāo)簽類別總數(shù)

??????? Y_test, 真實標(biāo)簽（one-hot形式，轉(zhuǎn)換為nudarry格式）

????????y_score，預(yù)測結(jié)果得分（轉(zhuǎn)換為nudarry格式）

二、導(dǎo)入：

import numpy as np from sklearn.metrics import precision_recall_curve,average_precision_score,PrecisionRecallDisplayimport matplotlib.pyplot as plt from itertools import cycle

三、代碼：

def plot_pr_multi_label(n_classes, Y_test, y_score):# For each classprecision = dict()recall = dict()average_precision = dict()for i in range(n_classes):precision[i], recall[i], _ = precision_recall_curve(Y_test[:, i], y_score[:, i])average_precision[i] = average_precision_score(Y_test[:, i], y_score[:, i])# print(recall)# print(average_precision)# A "micro-average": quantifying score on all classes jointlyprecision["micro"], recall["micro"], _ = precision_recall_curve(Y_test.ravel(), y_score.ravel())average_precision["micro"] = average_precision_score(Y_test, y_score, average="micro")# print(precision)# print(average_precision)# 繪制平均PR曲線display = PrecisionRecallDisplay(recall=recall["micro"], precision=precision["micro"],average_precision=average_precision["micro"], )display.plot()_ = display.ax_.set_title("Micro-averaged over all classes")# 繪制每個類的PR曲線和 iso-f1 曲線# setup plot detailscolors = cycle(["navy", "turquoise", "darkorange", "cornflowerblue", "teal"])_, ax = plt.subplots(figsize=(7, 8))f_scores = np.linspace(0.2, 0.8, num=4)lines, labels = [], []for f_score in f_scores:x = np.linspace(0.01, 1)y = f_score * x / (2 * x - f_score)(l,) = plt.plot(x[y >= 0], y[y >= 0], color="gray", alpha=0.2)plt.annotate("f1={0:0.1f}".format(f_score), xy=(0.9, y[45] + 0.02))display = PrecisionRecallDisplay(recall=recall["micro"], precision=precision["micro"],average_precision=average_precision["micro"], )display.plot(ax=ax, name="Micro-average precision-recall", color="gold")for i, color in zip(range(n_classes), colors):display = PrecisionRecallDisplay(recall=recall[i], precision=precision[i],average_precision=average_precision[i], )display.plot(ax=ax, name=f"Precision-recall for class {i}", color=color)# add the legend for the iso-f1 curveshandles, labels = display.ax_.get_legend_handles_labels()handles.extend([l])labels.extend(["iso-f1 curves"])# print(l)# print(handles)# print(labels)# set the legend and the axesax.set_xlim([0.0, 1.0])ax.set_ylim([0.0, 1.05])ax.legend(handles=handles, labels=labels, loc="best")ax.set_title("Extension of Precision-Recall curve to multi-class")# plt.show()

四、結(jié)果:

(1) 平均精度

（2）每個類的PR曲線和 iso-f1 曲線

總結(jié)

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