python - 计算多类的sklearn.roc_auc_score

Question

我想计算我的分类器的 AUC、精确度和准确度。我在做监督学习:

这是我的工作代码。此代码适用于二进制类，但不适用于多类。请假设您有一个包含二进制类的数据框:

sample_features_dataframe = self._get_sample_features_dataframe()
labeled_sample_features_dataframe = retrieve_labeled_sample_dataframe(sample_features_dataframe)
labeled_sample_features_dataframe, binary_class_series, multi_class_series = self._prepare_dataframe_for_learning(labeled_sample_features_dataframe)

k = 10
k_folds = StratifiedKFold(binary_class_series, k)
for train_indexes, test_indexes in k_folds:
    train_set_dataframe = labeled_sample_features_dataframe.loc[train_indexes.tolist()]
    test_set_dataframe = labeled_sample_features_dataframe.loc[test_indexes.tolist()]

    train_class = binary_class_series[train_indexes]
    test_class = binary_class_series[test_indexes]
    selected_classifier = RandomForestClassifier(n_estimators=100)
    selected_classifier.fit(train_set_dataframe, train_class)
    predictions = selected_classifier.predict(test_set_dataframe)
    predictions_proba = selected_classifier.predict_proba(test_set_dataframe)

    roc += roc_auc_score(test_class, predictions_proba[:,1])
    accuracy += accuracy_score(test_class, predictions)
    recall += recall_score(test_class, predictions)
    precision += precision_score(test_class, predictions)

最后，我当然将结果除以 K，以获得平均 AUC、精度等。这段代码工作正常。但是，我无法为多类计算相同的值:

    train_class = multi_class_series[train_indexes]
    test_class = multi_class_series[test_indexes]

    selected_classifier = RandomForestClassifier(n_estimators=100)
    selected_classifier.fit(train_set_dataframe, train_class)

    predictions = selected_classifier.predict(test_set_dataframe)
    predictions_proba = selected_classifier.predict_proba(test_set_dataframe)

我发现对于多类我必须为平均值添加参数“weighted”。

    roc += roc_auc_score(test_class, predictions_proba[:,1], average="weighted")

我得到一个错误:raise ValueError("{0} format is not supported".format(y_type))

ValueError:不支持多类格式

Answer 1

您不能将 roc_auc 用作多类模型的单个汇总指标。如果你愿意，你可以计算每类 roc_auc，如

roc = {label: [] for label in multi_class_series.unique()}
for label in multi_class_series.unique():
    selected_classifier.fit(train_set_dataframe, train_class == label)
    predictions_proba = selected_classifier.predict_proba(test_set_dataframe)
    roc[label] += roc_auc_score(test_class, predictions_proba[:,1])

然而，更常见的是使用 sklearn.metrics.confusion_matrix 来评估多类模型的性能。