MNT: Make GroupFeature dataframe agnostic
#1546
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FBruzzesi
commented
Apr 17, 2025
| nw_feature_vector = nw.from_native(feature_vector, pass_through=True, allow_series=True) | ||
| is_nw_series = isinstance(nw_feature_vector, nw.Series) | ||
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| self.classes_ = np.unique(nw_feature_vector) |
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I noticed that self.classes_ is set but unused. If commented out, the only failing tests are those that specifically test for the attribute value
FBruzzesi
commented
Apr 17, 2025
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| self.raw_feature_ = ( | ||
| list(nw_feature_vector) if not is_nw_series else nw_feature_vector.to_list() | ||
| ) |
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Since raw_feature_ is always casted to list in MetricFrame, I moved that directly here so that for those inputs that are converted to narwhals Series we can use the native to_list methods
FBruzzesi
commented
Apr 17, 2025
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| if (name_ := nw_feature_vector.name) is not None: | ||
| if isinstance(name_, str): | ||
| self.name_ = name_ |
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Polars does not allow None as series name, but when unnamed, then its name is "" (empty string), hence that's what self.name_ would be set to.
I didn't put too much thought into this, I just wanted to highlight the behavior
FBruzzesi
commented
Apr 17, 2025
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| all_data[sf.name_] = list(sf.raw_feature_) | ||
| all_data[sf.name_] = sf.raw_feature_ | ||
| if cf_list is not None: | ||
| for cf in cf_list: | ||
| all_data[cf.name_] = list(cf.raw_feature_) | ||
| all_data[cf.name_] = cf.raw_feature_ |
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Description
Make
GroupFeaturedataframe agnostic via narwhals.Opening as draft as I have a few questions/remarks (see code comments)
Related to #1522
Tests
Documentation