Description
Describe the workflow you want to enable
I would like to be able to pass the nullable pandas dtypes ("Int64", "Float64", "boolean") into sklearn's LabelBinarizer. Because the dtypes become object dtype when converted to numpy arrays we get ValueError: Unknown label type::
Repro with sklearn 1.2.1:
import pandas as pd
import pytest
from sklearn.preprocessing import LabelBinarizer
for dtype in ["Int64", "Float64", "boolean"]:
y_true = pd.Series([1, 0, 0, 1, 0, 1, 1, 0, 1], dtype=dtype)
lb = LabelBinarizer()
with pytest.raises(ValueError, match="Unknown label type:"):
lb.fit(y_true.unique())Describe your proposed solution
We should get the same behavior as when int64, float64, and bool dtypes are used, which is no error:
import pandas as pd
from sklearn.preprocessing import LabelBinarizer
for dtype in ["int64", "float64", "bool"]:
y_true = pd.Series([1, 0, 0, 1, 0, 1, 1, 0, 1], dtype=dtype)
lb = LabelBinarizer()
lb.fit(y_true.unique())
y_one_hot_true = lb.transform(y_true)Describe alternatives you've considered, if relevant
Our current workaround is to convert the data to numpy arrays with the corresponding dtype that works prior to passing it into LabelBinarizer
Additional context
No response