feat(sklearn): refuse the label column as an input column

[kz930]

What changes were proposed in this PR?

Two families of sklearn operators let the column already chosen as the label be chosen a second time as an input column, and neither said anything while the workflow was being written.

Target Attribute names the label and Text Attribute names the columns Count Vectorizer tokenizes, on the fifty-one operators of the Sklearn and Sklearn Training groups. The generated code drops the target before the text pipeline reads its columns, so naming it there asks the pipeline for a column that is no longer present and the run ends with ValueError: A given column is not a column of the dataframe, which names neither field. Failing is right, since vectorizing the label would train a model on the answer, so the pairing is refused at compile time instead, in the same getOutputSchemas that already refuses Count Vectorizer on the two Gaussian Naive Bayes operators. Only while the vectorizer is on: with it off nothing reads text and the panel hides it, so a value left behind by an earlier configuration must not report the operator invalid.

Ground Truth Attribute Column names the label and Selected Features names the columns handed to fit, on the four advanced trainers. Here nothing failed at all. The features go to fit as they are named, so the ground truth among them is the answer given to the estimator as an input, and the model scores far better than what it learned deserves. On two hundred rows whose label is random noise, a KNN classifier reaches 0.685 train accuracy on the two real features and 0.995 once the label is included, and nothing in the run says why. selectedFeatures defaults to null, so the check reads it through an Option rather than assuming a list is there.

Both messages name the column and both fields, and say what to change.

Any related issues, documentation, discussions?

Closes #8010.

How was this PR tested?

SklearnModelOpDescSpec gains three cases: the target named as a text column is refused, text columns that are not the target are let through, and a stale text column is left alone while Count Vectorizer is off. SklearnMLOperatorDescriptorSpec gains two: the ground truth named as a feature is refused, and features that do not include it are let through. Both files already owned the assertions on the getOutputSchemas these checks live in. WorkflowOperator/test passes: 2381 tests.

Was this PR authored or co-authored using generative AI tooling?

Generated-by: Claude Code (Claude Opus 5)

[github-actions]

Automated Reviewer Suggestions

Based on the git blame history of the changed files, we recommend the following reviewers:

• Contributors with relevant context: @carloea2, @aglinxinyuan
  You can notify them by mentioning @carloea2, @aglinxinyuan in a comment.

[codecov-commenter]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 93.29%. Comparing base (9fe0657) to head (9582064).

Additional details and impacted files

@@             Coverage Diff              @@
##               main    #8012      +/-   ##
============================================
- Coverage     93.29%   93.29%   -0.01%     
+ Complexity     4678     4677       -1     
============================================
  Files          1179     1179              
  Lines         47713    47722       +9     
  Branches       5314     5316       +2     
============================================
+ Hits          44515    44520       +5     
- Misses         1732     1734       +2     
- Partials       1466     1468       +2     

Flag	Coverage Δ		*Carryforward flag
access-control-service	81.00% <ø> (ø)
agent-service	99.32% <ø> (ø)		Carriedforward from 9fe0657
amber	89.40% <100.00%> (-0.02%)	⬇️
computing-unit-managing-service	73.67% <ø> (ø)
config-service	86.73% <ø> (ø)
file-service	86.70% <ø> (ø)
frontend	95.77% <ø> (ø)		Carriedforward from 9fe0657
notebook-migration-service	79.31% <ø> (ø)
pyamber	97.78% <ø> (ø)		Carriedforward from 9fe0657
workflow-compiling-service	77.19% <ø> (ø)

*This pull request uses carry forward flags. Click here to find out more.

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[github-actions]

⚠️ Benchmark changes need a look

🟢 6 better · 🔴 1 worse · ⚪ 8 noise (<±5%) · 0 without baseline

Compared against main 9fe0657 benchmarked on this same runner, so the delta is largely free of cross-runner hardware noise. The "7d avg" column still reflects the gh-pages dashboard. Treat <±5% as noise unless repeated.

Dashboard · Run

	config	throughput	MB/s	latency	max Δ latest / 7d
🟢	bs=10 sw=10 sl=64	656	0.401	14,852/17,482/17,482 us	🟢 -27.5% / 🔴 +33.3%
🔴	bs=100 sw=10 sl=64	1,384	0.844	72,990/79,785/79,785 us	🟢 -23.8% / 🟢 -21.9%
🟢	bs=1000 sw=10 sl=64	1,552	0.947	641,148/694,608/694,608 us	🟢 -6.2% / 🟢 +29.5%

Baseline details

Latest main 9fe0657 from same runner

config	metric	PR	latest main	7d avg	Δ latest	Δ 7d
bs=10 sw=10 sl=64	throughput	656 tuples/sec	643 tuples/sec	894.86 tuples/sec	+2.0%	-26.7%
bs=10 sw=10 sl=64	MB/s	0.401 MB/s	0.392 MB/s	0.546 MB/s	+2.3%	-26.6%
bs=10 sw=10 sl=64	p50	14,852 us	14,519 us	11,139 us	+2.3%	+33.3%
bs=10 sw=10 sl=64	p95	17,482 us	24,118 us	13,843 us	-27.5%	+26.3%
bs=10 sw=10 sl=64	p99	17,482 us	24,118 us	16,839 us	-27.5%	+3.8%
bs=100 sw=10 sl=64	throughput	1,384 tuples/sec	1,376 tuples/sec	1,166 tuples/sec	+0.6%	+18.7%
bs=100 sw=10 sl=64	MB/s	0.844 MB/s	0.84 MB/s	0.711 MB/s	+0.5%	+18.6%
bs=100 sw=10 sl=64	p50	72,990 us	69,437 us	87,509 us	+5.1%	-16.6%
bs=100 sw=10 sl=64	p95	79,785 us	104,660 us	93,675 us	-23.8%	-14.8%
bs=100 sw=10 sl=64	p99	79,785 us	104,660 us	102,153 us	-23.8%	-21.9%
bs=1000 sw=10 sl=64	throughput	1,552 tuples/sec	1,564 tuples/sec	1,198 tuples/sec	-0.8%	+29.5%
bs=1000 sw=10 sl=64	MB/s	0.947 MB/s	0.955 MB/s	0.731 MB/s	-0.8%	+29.5%
bs=1000 sw=10 sl=64	p50	641,148 us	635,283 us	859,766 us	+0.9%	-25.4%
bs=1000 sw=10 sl=64	p95	694,608 us	740,491 us	902,897 us	-6.2%	-23.1%
bs=1000 sw=10 sl=64	p99	694,608 us	740,491 us	937,957 us	-6.2%	-25.9%

Raw CSV

config_idx,batch_size,schema_width,string_len,num_batches,total_ms,total_tuples,total_bytes,tuples_per_sec,mb_per_sec,lat_p50_us,lat_p95_us,lat_p99_us
0,10,10,64,20,304.79,200,128000,656,0.401,14851.65,17481.86,17481.86
1,100,10,64,20,1445.54,2000,1280000,1384,0.844,72989.51,79785.31,79785.31
2,1000,10,64,20,12883.49,20000,12800000,1552,0.947,641147.52,694608.26,694608.26

[kz930]

/request-review @carloea2 @aglinxinyuan