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Added DropNullColumn transformer to remove columns that contain only nulls #1115
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Hi @rcap107 ! I made a first pass and have a few comments :
- Personnally I like the name
DropNullColumn
, I think it’s clear what it does ! - I would rename the file
_drop_null.py
- Make sure you
pre-commit run --all-files
before pushing, it seems to be what’s breaking the CI for you here - I think
is_all_null
could be placed in theDropNullColumn
file if it’s only used there for now, but I could also see it being in_common.py
skrub/_dataframe/_common.py
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@@ -1187,3 +1208,15 @@ def with_columns(df, **new_cols): | |||
cols = {col_name: col(df, col_name) for col_name in column_names(df)} | |||
cols.update({n: make_column_like(df, c, n) for n, c in new_cols.items()}) | |||
return make_dataframe_like(df, cols) | |||
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|||
@dispatch | |||
def drop(obj, col): |
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I don’t know if drop
is necessary, you could directly use skrub selectors:
df = s.select(df, ~s.cols(col))
skrub/_table_vectorizer.py
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@@ -191,6 +192,9 @@ class TableVectorizer(TransformerMixin, BaseEstimator): | |||
similar functionality to what is offered by scikit-learn's | |||
:class:`~sklearn.compose.ColumnTransformer`. | |||
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drop_null_columns : bool, default=False |
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Do we want it to be True
by default ?
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That should be discussed with others I think
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I vote for true by default -- there's nothing we can learn from a completely empty column.
if it is False by default, I think it should be set to True in the tabular_learner
skrub/tests/test_dropnulls.py
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main_table_dropped = ns.drop(main_table_dropped, "value_nan") | ||
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# Don't drop null columns | ||
tv = TableVectorizer(drop_null_columns=False) |
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This test needs to go in the TV test file IMO
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I can move it 👍
Hi @TheooJ, thanks a lot for the comments! I'll address them and update the PR 👍 |
skrub/tests/test_drop_nulls.py
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# assert_array_equal( | ||
# sbd.to_numpy(sbd.col(drop_null_table, "value_almost_null")), | ||
# np.array(["almost", None, None]), |
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Not sure how to write this check so that it works with either pandas or polars
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You could use df_module
as a fixture in the test by adding it to the arguments, then comparing series instead of numpy arrays:
df_module.assert_column_equal(
sbd.col(drop_null_table, "value_almost_null"),
df_module.make_column("value_almost_null", ["almost", None, None]),
)
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Test would look like
def test_single_column(drop_null_table, df_module):
"""Check that null columns are dropped and non-null columns are kept."""
dn = DropNullColumn()
assert dn.fit_transform(drop_null_table["value_nan"]) == []
assert dn.fit_transform(drop_null_table["value_null"]) == []
df_module.assert_column_equal(
sbd.col(drop_null_table, "idx"), df_module.make_column("idx", [1, 2, 3])
)
df_module.assert_column_equal(
sbd.col(drop_null_table, "value_almost_nan"),
df_module.make_column("value_almost_nan", [2.5, np.nan, np.nan]),
)
df_module.assert_column_equal(
sbd.col(drop_null_table, "value_almost_null"),
df_module.make_column("value_almost_null", ["almost", None, None]),
)
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This also circumvents that depending on the version of pandas, null values are not treated the same
the failure in the min-deps environment is not related to this pr; the fix is in #1122 |
skrub/_table_vectorizer.py
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@@ -536,6 +542,9 @@ def add_step(steps, transformer, cols, allow_reject=False): | |||
cols = s.all() - self._specific_columns | |||
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self._preprocessors = [CheckInputDataFrame()] | |||
if self.drop_null_columns: | |||
add_step(self._preprocessors, DropNullColumn(), cols, allow_reject=True) |
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- we may want to insert it after CleanNullStrings? so that if the column becomes full of nulls after converting
"N/A"
tonull
it will be dropped. also it's not important but your transformer never raises a RejectColumn exception soallow_reject
has no effect you don't need it here and can leave the default
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I added it after CleanNullStrings, but I think I did it in an ugly way, maybe it can be fixed
Nevermind, I'll just raise the warning by default. I was thinking that maybe it would be possible to only raise a warning in verbose mode (if it's even a thing), but in the end I just went with a different solution.
We also need to decide whether it's "warn and drop", or "warn and keep", and explain the behavior in the documentation. |
skrub/tests/test_table_vectorizer.py
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@@ -778,11 +777,7 @@ def test_drop_null_column(): | |||
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# Raise exception if a null column is found | |||
with pytest.raises( |
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This test is still failing because the TableVectorizer is not raising the correct exception and I don't know how to make it do that.
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here raise a ValueError instead of RejectColumn
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rejectcolumn is a way to signify to the tablevectorizer "I'm not the right transformer for this column, don't apply me here".
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Gotcha, fixed
I have updated the code to have "warn and keep" as the default behavior, I think it's the version that makes the most sense. At the moment the only problem I have is that I don't know how to raise the proper exception from TableVectorizer. In DropNull I am raising RejectColumn, but then I don't know how to propagate it correctly, |
I find it a bit weird to have a DropColumnIfNull that does not drop the column and just raises an exception. maybe it should be named something like CheckNulls or something? |
I'm not sure I understand -- in any case the tablevectorizer chooses the estimators during fit so the schema of the output can change every week in this scenario. eg if a column has one more unique value than the previous week it can change from a one-hot encoding to a gap encoding. or for example if you had |
so if you want consistent output schema (number of columns, names and types) across training the tablevetorizer is not what you want anyway |
Yes, I agree this sounds weird. I recognize dropping NaN columns –which are usually useless– looks good. What about having the transformer drop by default, but allowing the user to pass other options as arguments in the
Right, I was pointing out that even outside of the It's true though that this issue broadly applies to the |
Following today's discussion with @Vincent-Maladiere and @jeromedockes, I reverted the changes back to the old version (with the simple flag) |
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LGTM! thanks again @rcap107
(the codecov report is bogus)
I'll let @Vincent-Maladiere do a final review & merge
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In our IRL discussion, haven't we agreed on a threshold for the null ratio in the column (which is 1.0 by default)?
Apart from that, and to keep in mind for later, the items we discussed were:
- The ability to freeze TableVectorizer column-to-transformer mapping. It would help to obtain consistent results for retraining in an automated environment, and having more sensible errors to debug.
- Decoupling the check/cleaning part of the TableVectorizer (which comes before the vectorizing part) so that it can be used as a standalone object wherever.
we definitely did; I had understood it would be tackled in a separate PR +1 for points 1. and 2. -- let's open a separate issue for 1. and there is #925 for 2. |
I also understood that the threshold would be added in a separate issue |
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LGTM then, thanks @rcap107 :)
yay 🎉 !! thanks @rcap107 ! |
🎉 |
fixes #1110
DropNullColumn (provisional name) takes as input a column, and drops it if all the values are nulls or nans. TableVectorizer was also updated with a
drop_null_columns
flag set toFalse
by default; if the flag is set toTrue
, the DropNullColumn is added as a processing step for all columns.I've also added
drop
andis_all_null
to_common.py
, though I am not sure if they should go there. Maybeis_all_null
can stay in theDropNullColumn
file.The test I wrote passes, but I'm not sure if it's good enough.
The documentation is still missing.