python - 将 sklearn 函数应用于 pandas 数据帧会给出 ValueError ("Unknown label type: %r"% y)

Question

以下代码给出错误信息:

    >>> import pandas as pd
    >>> from sklearn import preprocessing, svm
    >>> df = pd.DataFrame({"a": [0,1,2], "b":[0,1,2], "c": [0,1,2]})
    >>> clf = svm.SVC()
    >>> df = df.apply(lambda x: preprocessing.scale(x))
    >>> clf.fit(df[["a", "b"]], df["c"])
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
      File "C:\Users\Alexander\Anaconda\lib\site-packages\sklearn\svm\base.py", lin
     151, in fit
        y = self._validate_targets(y)
      File "C:\Users\Alexander\Anaconda\lib\site-packages\sklearn\svm\base.py", lin
     515, in _validate_targets
        check_classification_targets(y)
      File "C:\Users\Alexander\Anaconda\lib\site-packages\sklearn\utils\multiclass.
    y", line 173, in check_classification_targets
        raise ValueError("Unknown label type: %r" % y)
    ValueError: Unknown label type: 0   -1.224745
    1    0.000000
    2    1.224745
    Name: c, dtype: float64

pandas DataFrame 的 dtype 不是对象，所以应用 sklearn svm 函数应该没问题，但由于某些原因它无法识别分类标签。是什么导致了这个问题？

Answer 1

问题是在缩放步骤之后，标签是浮点值，这不是有效的标签类型；如果您转换为 int 或 str 它应该可以工作:

In [32]: clf.fit(df[["a", "b"]], df["c"].astype(int))
Out[32]: 
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',
  max_iter=-1, probability=False, random_state=None, shrinking=True,
  tol=0.001, verbose=False)