Closed
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 unique_labels function. Because the dtypes become object dtype when converted to numpy arrays we get ValueError: Mix type of y not allowed, got types {'binary', 'unknown'}:
Repro with sklearn 1.2.1
import pandas as pd
import pytest
from sklearn.utils.multiclass import unique_labels
for dtype in ["Int64", "Float64", "boolean"]:
y_true = pd.Series([1, 0, 0, 1, 0, 1, 1, 0, 1], dtype=dtype)
y_predicted = pd.Series([0, 0, 1, 1, 0, 1, 1, 1, 1], dtype="int64")
with pytest.raises(ValueError, match="Mix type of y not allowed, got types"):
unique_labels(y_true, y_predicted)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.utils.multiclass import unique_labels
for dtype in ["int64", "float64", "bool"]:
y_true = pd.Series([1, 0, 0, 1, 0, 1, 1, 0, 1], dtype=dtype)
y_predicted = pd.Series([0, 0, 1, 1, 0, 1, 1, 1, 1], dtype="int64")
unique_labels(y_true, y_predicted)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 unique_labels.
Additional context
No response