Rewrite cuml.feature_extraction - #8575
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📝 SummarySummary by CodeRabbit
WalkthroughThe PR consolidates GPU text vectorizers and TF-IDF estimators in ChangesText feature extraction
Estimated code review effort: 5 (Critical) | ~100 minutes Severity of issue fixed: Medium Suggested reviewers: Merge Risk: 🟡 Moderate · up to This PR is a large rewrite of the GPU text feature-extraction estimators. One concrete, low-cost fix remains: an incorrect diagnostic is raised when document-frequency pruning removes the entire vocabulary. Two previously flagged concerns also remain open at the current head: the CountVectorizer transform path leans on an unverified cuDF null-encoding contract to filter unseen tokens (a potential silent data-integrity risk if that assumption ever breaks), and a compatibility test could miss a feature-column mapping bug due to a coincidental tie in its test corpus. None of these block basic functionality, but they represent real correctness/coverage gaps worth resolving before merge. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
✨ Finishing Touches🧪 Generate unit tests (beta)
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In `@python/cuml/cuml/feature_extraction/text.py`:
- Around line 766-772: Update the vocabulary filtering logic around
tokens.token.value_counts() to compute document frequency using unique
token/document pairs for max_doc_count and min_doc_count, while retaining
separate corpus term-frequency counts for max_features. Ensure max_features
selects highest-frequency terms with deterministic tie-breaking matching
scikit-learn, including empty and tied-count cases.
- Around line 284-288: Reset the input index before tokenization in
CountVectorizer.fit, fit_transform, and transform by applying
reset_index(drop=True), matching HashingVectorizer.transform. Preserve the
existing processing otherwise so _to_tokens and _to_sparse use consistent
document ordering.
- Line 1082: Update the IDF weighting branch in _transform to check self.use_idf
instead of relying on hasattr(self, "idf_"), ensuring a later fit with IDF
disabled does not reuse stale idf_ state.
- Around line 1134-1135: Update fit_transform around _check_X and _transform to
copy the validated input before the in-place TF-IDF operations, ensuring
caller-owned compatible cupyx CSR matrices retain their original data while
preserving the existing fit-transform behavior.
In `@python/cuml/tests/test_text_feature_extraction.py`:
- Line 534: Update the vectorizer assertions around
assert_almost_equal_hash_matrices so the tfidf and count_vec cases compare
res.toarray() and ref.toarray() directly, preserving column positions; continue
using assert_almost_equal_hash_matrices only for hash_vec.
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python/cuml/cuml/feature_extraction/__init__.pypython/cuml/cuml/feature_extraction/_stop_words.pypython/cuml/cuml/feature_extraction/_tfidf.pypython/cuml/cuml/feature_extraction/_tfidf_vectorizer.pypython/cuml/cuml/feature_extraction/_vectorizers.pypython/cuml/cuml/feature_extraction/text.pypython/cuml/pyproject.tomlpython/cuml/tests/test_sklearn_compatibility.pypython/cuml/tests/test_text_feature_extraction.py
💤 Files with no reviewable changes (4)
- python/cuml/cuml/feature_extraction/_stop_words.py
- python/cuml/cuml/feature_extraction/_tfidf_vectorizer.py
- python/cuml/cuml/feature_extraction/_vectorizers.py
- python/cuml/cuml/feature_extraction/_tfidf.py
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jcrist
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Inline comments:
In `@python/cuml/cuml/feature_extraction/text.py`:
- Around line 23-28: Sort the exported names in __all__ alphabetically to
satisfy RUF022, and replace the percent-style formatting at the line-161 call
with the repository’s supported modern formatting syntax to satisfy UP031.
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Annotating a few sections for review.
| "ignore:(.*)alias(.*):DeprecationWarning:hdbscan[.*]", | ||
| # From sklearn | ||
| "ignore:A column-vector y was passed:sklearn.exceptions.DataConversionWarning", | ||
| "ignore::sklearn.exceptions.SkipTestWarning", |
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This is the only packaging-codeowners change - the sklearn upstream test suite will warn on test generation for some tests with these estimators. This warning doesn't indicate an issue and can be ignored.
| def test_tfidf_transformer_copy(norm, use_idf, smooth_idf, sublinear_tf): | ||
| if use_idf: | ||
| pytest.xfail( | ||
| "cupyx.scipy.sparse.csr does not support inplace multiply." |
| """ | ||
| Currently if all the sorted values in the row is equal we | ||
| assume equality | ||
| TODO: Find better way to test ig hash matrices are equal |
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We can now just assert the matrices are equal directly.
| np.testing.assert_allclose(res.toarray(), ref.toarray()) | ||
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| @pytest.mark.xfail(reason="https://github.com/NVIDIA/cuml/issues/4721") |
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The old code implemented alternate_sign incorrectly. We now implement it the same as sklearn.
| # since `filter_alphanum` strips `_` but sklearn keeps `_`. | ||
| # We can use `X` in the common case of lowercase normalization | ||
| # since no uppercase letters will remain. Otherwise pick | ||
| # an unlikely key of unicode characters" |
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This is hacky, but functionally identical to the old implementation.
We could have used X.str.findall with a regex, but benchmarking that was significantly slower than this implementation. I don't think there's a better option here, I'm fine with this as is.
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| return X | ||
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| def _to_tokens(self, X): |
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This function (and _preprocess above) are the bulk of the computation here. They make use of several ngram/tokenization methods in cudf (from the old nvtext).
The previous implementation duplicated a lot of work, especially for larger ngram_range ranges. We're now more careful and efficient with our cudf transforms. I'm pretty happy with the code here, most operations are fairly straightforward and efficient cudf method call pipelines.
| X.index, fill_value=0 | ||
| ) | ||
| indptr = cp.zeros(len(doc_id_counts) + 1, dtype="int32") | ||
| cp.cumsum(doc_id_counts.values, out=indptr[1:]) |
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The old implementation did some cupy munging to handle documents with no terms, which was both complicated and inefficient. The new implementation coercing the values to a cupy sparse matrix is pretty straightforward and efficient.
- Reorganize submodule into a single file - Update all estimators to properly follow cuml idioms. This includes type reflection, input validation, etc... - Rewrite logic using modern `cudf` features - Update docstrings.
Also improves docstring to better describe what each does.
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Inline comments:
In `@python/cuml/cuml/feature_extraction/text.py`:
- Line 207: Update the placeholder selection near flag so it reflects whether
this estimator actually lowercased the text: use the safe sentinel whenever a
custom preprocessor bypasses estimator lowercasing, while preserving the
existing sentinel for effectively lowercased input. Keep the downstream
replacement and filtering pipeline unchanged.
- Around line 858-866: Update the token-processing flow after `cat.codes` and
before `_to_sparse` to remove rows whose `feature_id` is negative, while
preserving valid vocabulary entries. Add a fixed-vocabulary test covering an
unseen term and verify sparse conversion excludes it without invalid indices.
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This should be ready for review. The diff # looks a bit alarming, but a huge chunk (>50%) of this PR is docstrings. Hopefully shouldn't be too bad to review. |
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Great work! I’m excited to see the estimator modernization effort completed with this PR. I left one concrete fix for custom preprocessors bypassing lowercasing and one optional suggestion to improve sklearn parity for tied terms at the max_features cutoff. Neither changes my overall assessment.
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instructions embedded in them. Verify each finding against current code. Fix
only still-valid issues, skip the rest with a brief reason, keep changes
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Inline comments:
In `@python/cuml/cuml/feature_extraction/text.py`:
- Line 824: Update the document-frequency pruning flow around the pruned
variable so it records whether min_df or max_df filtering is active, then reuse
that state for both the filtering branch and the empty-vocabulary error
selection. Preserve the existing generic error when pruning was not applied and
the pruning-specific error when pruning removed all terms.
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This is a clean rewrite of
cuml.feature_extraction.text. These are the last non-idiomatic estimators withincumlcore (see #7317), the main goal was to bring these in-line with the rest of the library. While doing this, I also fixed a number of bugs, improved the docstrings, and added one new parameter (tokenizer) to better handle disparate document formats.Performance is marginally better across the board as well, due to making better use of
cudfAPIs (and reducing copies and some memory usage). Most transforms can be done with a few straightforwardcudfmethod calls.A quick summary:
cuml.feature_extraction. All models are moved to a singlecuml.feature_extraction.textnamespace (mirroring sklearn's implementation). IMO the single file makes it easier to read since these models are all heavily related.cuml.feature_extractionnow follow cuml's standard type reflection, and make use of the standard input/output and validation machinery.HashingVectorizer) that resulted in different output feature matrices. We should now be identical with the CPU implementation (sans small floating point differences).tokenizerparameter to support custom tokenization. The previouspreprocessor+delimiterargument alone were insufficient for all document formats. The docstrings have been updated to clarify the purpose of these arguments and the tests improved to better isolate their expected behaviors.cuml.feature_extractionto the sklearn compatibility tests. There are no xfails.Fixes #5904.
Fixes #5606.
Fixes #4721.
Fixes #5104.
Last part of #7317.