Closed as not planned
Description
Describe the workflow you want to enable
Currently when fitting with a narwhals DataFrame, the feature names do not pass through because it does not implement a __dataframe__ method.
Example:
import narwhals as nw
import pandas as pd
import polars as pl
from sklearn.preprocessing import StandardScaler
df_pd = pd.DataFrame({"a": [0, 1, 2], "b": [3, 4, 5]})
df_pl = pl.DataFrame(df_pd)
df_nw = nw.from_native(df_pd)
s_pd, s_pl, s_nw = StandardScaler(), StandardScaler(), StandardScaler()
s_pd.fit(df_pd)
s_pl.fit(df_pl)
s_nw.fit(df_nw)
print(s_pd.feature_names_in_)
print(s_pl.feature_names_in_)
print(s_nw.feature_names_in_)Expected output
['a' 'b']
['a' 'b']
['a' 'b']
Actual output
['a' 'b']
['a' 'b']
AttributeError: 'StandardScaler' object has no attribute 'feature_names_in_'
All other attributes on s_nw are what I'd expect.
Describe your proposed solution
This should be easy enough to implement by adding another check within sklearn.utils.validation._get_feature_names:
- Add
_is_narwhals_dfmethod, borrowing logic from_is_pandas_df
def _is_narwhals_df(X):
"""Return True if the X is a narwhals dataframe."""
try:
nw = sys.modules["narwhals"]
except KeyError:
return False
return isinstance(X, nw.DataFrame)- Add an additional check to
_get_feature_names:
elif _is_narwhals_df(X):
feature_names = np.asarray(X.columns, dtype=object)Describe alternatives you've considered, if relevant
No response