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Posted to notifications@texera.apache.org by "kz930 (via GitHub)" <gi...@apache.org> on 2026/08/12 20:41:55 UTC

[I] Gaussian Naive Bayes offers a Count Vectorizer switch that always ends the run [texera]

kz930 opened a new issue, #7597:
URL: https://github.com/apache/texera/issues/7597

### What happened?

The two Gaussian Naive Bayes operators, `Gaussian Naive Bayes` and `Training: Gaussian Naive Bayes`, expose a `Count Vectorizer` switch that cannot succeed. Turning it on ends the execution with an error raised from inside scikit-learn: `TypeError: Sparse data was passed for X, but dense data is required. Use '.toarray()' to convert to a dense numpy array.`

`CountVectorizer` emits a scipy sparse matrix, and `GaussianNB` validates its input without `accept_sparse` because it estimates a per-feature mean and variance, which reads every cell including the zeros. The refusal is deliberate on scikit-learn's side: densifying a text matrix with a large vocabulary is what would exhaust memory.

This is specific to `GaussianNB`, not a property of the switch. Fitting `make_pipeline(CountVectorizer(), Estimator())` on the same documents across all 26 estimators the Sklearn and Sklearn Training groups use, only `GaussianNB` raises; the other 25, including `MultinomialNB`, `BernoulliNB` and `ComplementNB`, accept the sparse matrix. So exactly two operators carry a switch that has no working configuration.

The switch is declared once on the shared base, `SklearnModelOpDesc.scala:54`, and both families splice it into the pipeline the same way (`SklearnClassifierOpDesc.scala:48`, `SklearnTrainingOpDesc.scala:47`), so every operator in both groups inherits it whether or not its estimator can use it.

Expected: a combination that cannot work should not be offered. Hiding or disabling `Count Vectorizer` on these two operators would match how the family already handles an impossible pairing, since `Tfidf Transformer` and `Text Attribute` are hidden when `Count Vectorizer` is off. Failing at compile time with a message naming the estimator and pointing at the Multinomial, Bernoulli and Complement variants would also resolve it. Silently densifying would not: it reintroduces the memory cost scikit-learn refuses for a reason.

### How to reproduce?

Upload a CSV with a text column and a label:

```
note,y
great excellent good,1
awful terrible bad,0
```

Build `CSV File Scan` to `Gaussian Naive Bayes`, set Target Attribute to `y`, turn on `Count Vectorizer`, set Text Attribute to `note`, and run. The execution stops and the operator console shows the TypeError. `Training: Gaussian Naive Bayes` fails the same way on the same file.

### Version/Branch

1.3.0-incubating-SNAPSHOT (main)

### Relevant log output

```shell
TypeError: Sparse data was passed for X, but dense data is required. Use '.toarray()' to convert to a dense numpy array.
```

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Re: [I] Gaussian Naive Bayes offers a Count Vectorizer switch that always ends the run [texera]

Posted by "aglinxinyuan (via GitHub)" <gi...@apache.org>.

aglinxinyuan closed issue #7597: Gaussian Naive Bayes offers a Count Vectorizer switch that always ends the run
URL: https://github.com/apache/texera/issues/7597


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Re: [I] Gaussian Naive Bayes offers a Count Vectorizer switch that always ends the run [texera]

Posted by "github-actions[bot] (via GitHub)" <gi...@apache.org>.

github-actions[bot] commented on issue #7597:
URL: https://github.com/apache/texera/issues/7597#issuecomment-5272563347

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