Question

亲爱的Stack Overflow用户，

我使用sklearn训练用于文本挖掘的多标签SVM（具有概率）。对于每个条目，我没有单个目标标签，而是列表。这些目标通过MultiLabelBinarizer进行转换：

vect = TfidfVectorizer()
x_train = vect.fit_transform(training_texts)

mlb = MultiLabelBinarizer()
training_targets_mlb = mlb.fit_transform(training_targets)

clf = OneVsRestClassifier(SVC(kernel='linear', probability=True)
clf.fit(x_train, training_targets_mlb)

将sklearn升级到最新版本0.19之后，上面的代码仍然可以正常工作，但是下面的交叉验证代码（在我认为的0.18中有效）现在会引发错误：

from sklearn.model_selection import cross_val_predict
cv_scores = cross_val_predict(estimator=clf,
                              X=vect.fit_transform(texts),
                              y=mlb.fit_transform(targets),
                              cv=sklearn.model_selection.KFold(shuffle=True, n_splits=5),
                              method='predict_proba')

错误：

File "/usr/local/lib/python2.7/dist-packages/sklearn/model_selection/_validation.py", line 647, in cross_val_predict
    y = le.fit_transform(y)
  File "/usr/local/lib/python2.7/dist-packages/sklearn/preprocessing/label.py", line 111, in fit_transform
    y = column_or_1d(y, warn=True)
  File "/usr/local/lib/python2.7/dist-packages/sklearn/utils/validation.py", line 583, in column_or_1d
    raise ValueError("bad input shape {0}".format(shape))
ValueError: bad input shape (18165, 25)

这是预期的行为，即我不应该在这里传递数组吗？如果是这样，我将如何进行交叉验证？

感谢您的帮助！

scikit-learn：多标签交叉验证在v0.19中不再起作用

0 个答案: