sklearn one_hot向量輸出維度第2維大小的本質

問題起源：在pipeline轉換時，突然出現one_hot向量輸出維度爲第2維爲1。通過多次驗證，發現第2維都是2而非1？問題在哪裏呢，最後發現第2維爲2和第2維爲1是輸入不同導致的？

case1:

y = np.array([1,0,1,1]).reshape(-1,1)
json_pritimive = "sklearn.preprocessing.OneHotEncoder"
obj = AIPrimitive()
obj.load_from_json(json_pritimive)
obj.fit(X_fit=y)
obj.produce(X_produce=y)

sklearn.preprocessing.OneHotEncoder fit start
sklearn.preprocessing.OneHotEncoder fit over
sklearn.preprocessing.OneHotEncoder produce start
sklearn.preprocessing.OneHotEncoder produce over
array([[0., 1.],
       [1., 0.],
       [0., 1.],
       [0., 1.]])

case2:

y = np.array([1,1,1,1]).reshape(-1,1)
json_pritimive = "sklearn.preprocessing.OneHotEncoder"
obj = AIPrimitive()
obj.load_from_json(json_pritimive)
obj.fit(X_fit=y)
obj.produce(X_produce=y)

sklearn.preprocessing.OneHotEncoder fit start
sklearn.preprocessing.OneHotEncoder fit over
sklearn.preprocessing.OneHotEncoder produce start
sklearn.preprocessing.OneHotEncoder produce over
array([[1.],
       [1.],
       [1.],
       [1.]])

case3:

y = np.array([1,1,2,0]).reshape(-1,1)
json_pritimive = "sklearn.preprocessing.OneHotEncoder"
obj = AIPrimitive()
obj.load_from_json(json_pritimive)
obj.fit(X_fit=y)
obj.produce(X_produce=y)

sklearn.preprocessing.OneHotEncoder fit start
sklearn.preprocessing.OneHotEncoder fit over
sklearn.preprocessing.OneHotEncoder produce start
sklearn.preprocessing.OneHotEncoder produce over
array([[0., 1., 0.],
       [0., 1., 0.],
       [0., 0., 1.],
       [1., 0., 0.]])

從這裏可以看出axis=1的大小是由類別數量決定的，也符合one_hot向量的本質。我的理解偏離本質，固化的認爲第2維大小爲2，而不是其他值。