ValueError: Can't handle mix of multilabel-indicator and binary

Question

我将 Keras 与 scikit-learn 包装器一起使用。特别是，我想使用 GridSearchCV 进行超参数优化。

这是一个多 class 问题，即目标变量只能在一组 n class 中选择一个标签。例如，目标变量可以是 'Class1'、'Class2' ... 'Classn'.

# self._arch creates my model
nn = KerasClassifier(build_fn=self._arch, verbose=0)
clf = GridSearchCV(
  nn,
  param_grid={ ... },
  # I use f1 score macro averaged
  scoring='f1_macro',
  n_jobs=-1)

# self.fX is the data matrix
# self.fy_enc is the target variable encoded with one-hot format
clf.fit(self.fX.values, self.fy_enc.values)

问题是，当在交叉验证期间计算分数时，验证样本的真实标签被编码为 one-hot，而预测由于某种原因崩溃为二进制标签（当目标变量只有两个 classes）。例如，这是堆栈跟踪的最后一部分：

...........................................................................
/Users/fbrundu/.pyenv/versions/3.6.0/lib/python3.6/site-packages/sklearn/metrics/classification.py in _check_targets(y_true=array([[ 0.,  1.],
       [ 0.,  1.],
       [ 0... 0.,  1.],
       [ 0.,  1.],
       [ 0.,  1.]]), y_pred=array([1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1,...0, 1, 0, 0, 1, 0, 0, 0,
       0, 0, 0, 0, 1, 1]))
     77     if y_type == set(["binary", "multiclass"]):
     78         y_type = set(["multiclass"])
     79
     80     if len(y_type) > 1:
     81         raise ValueError("Can't handle mix of {0} and {1}"
---> 82                          "".format(type_true, type_pred))
        type_true = 'multilabel-indicator'
        type_pred = 'binary'
     83
     84     # We can't have more than one value on y_type => The set is no more needed
     85     y_type = y_type.pop()
     86

ValueError: Can't handle mix of multilabel-indicator and binary

如何指示 Keras/sklearn 在 one-hot 编码中返回预测？

Answer 1

根据 Vivek 的评论，我使用了原始的（不是单热编码的）目标数组，并且我配置了（在我的 Keras 模型中，见代码）损失 sparse_categorical_crossentropy，根据 the comments to this issue.

arch.compile(
  optimizer='sgd',
  loss='sparse_categorical_crossentropy',
  metrics=['accuracy'])

ValueError: Can't handle mix of multilabel-indicator and binary

ValueError: Can't handle mix of multilabel-indicator and binary

scikit-learn

keras

grid-search

one-hot-encoding

multiclass-classification