Sklearn ROC AUC 分数：ValueError：y 应该是一个一维数组，而是一个形状为 (15, 2) 的数组

Question

我有这个带有 target 的数据集LULUS，它是一个不平衡数据集。我试图打印roc auc分数，如果我可以为我的数据的每个折叠，但在每个折叠不知何故它总是引发错误说ValueError: y should be a 1d array, got an array of shape (15, 2) instead.。我有点困惑我做错了哪一部分，因为我的做法与文档中的完全一样。经过几次折叠，我发现如果只有一个标签，它不会打印分数，但它会返回关于一维数组的第二种类型的错误。

merged_df = pd.read_csv(r'C:\...\merged.csv')

num_columns = merged_df.select_dtypes(include=['float64']).columns
cat_columns = merged_df.select_dtypes(include=['object']).drop(['TARGET','NAMA'], axis=1).columns

numeric_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='mean')),
    ('scaler', StandardScaler())])

categorical_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='most_frequent')),
    ('label', OneHotEncoder(handle_unknown='ignore'))])

preprocessor = ColumnTransformer(
    transformers=[
        ('num', numeric_transformer, num_columns),
        ('cat', categorical_transformer, cat_columns)])

X = merged_df.drop(['TARGET','Unnamed: 0'],1)
y = merged_df['TARGET']

X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2)

X_train = X_train.drop(['NIM', 'NAMA'],1)
X_test = X_test.drop(['NIM', 'NAMA'],1)

rf = Pipeline(steps=[('preprocessor', preprocessor),
                     ('classifier',tree.DecisionTreeClassifier(class_weight='balanced', criterion='entropy'))])

rf.fit(X_train, y_train)

pred = rf.predict(X_test)

y_proba = rf.predict_proba(X_test)

from sklearn.model_selection import KFold

kf = KFold(n_splits=10)

for train, test in kf.split(X):
    X_train, X_test = X.loc[train], X.loc[test]
    y_train, y_test = y.loc[train], y.loc[test]
    model = rf.fit(X_train, y_train)
    y_proba = model.predict_proba(X_test)
    try:
        print(roc_auc_score(y_test, y_proba,average='weighted', multi_class='ovr'))
    except ValueError:
        pass

在电子表格中查看我的数据

Answer 1

您的输出model.predict_proba()是一个包含 2 列的矩阵，每个类别一列。要计算 roc，您需要提供正类的概率：

使用示例数据集：

from sklearn.datasets import make_classification
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split

X, y = make_classification(n_classes=2)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.33, random_state=42)
rf = RandomForestClassifier()
model = rf.fit(X_train, y_train)
y_proba = model.predict_proba(X_test)

它看起来像这样：

array([[0.69, 0.31],
       [0.13, 0.87],
       [0.94, 0.06],
       [0.94, 0.06],
       [0.07, 0.93]])

然后做：

roc_auc_score(y_test, y_proba[:,1])