[aglinxinyuan]

What changes were proposed in this PR?

The 51 per-model sklearn descriptor specs (25 classifiers, 26 training-only descriptors) each repeated the same five tests. A new test trait, SklearnModelOpDescBehaviors, registers them. Each spec now makes one sklearnClassifier(...) or sklearnTrainer(...) call with its own model name and sklearn import written out as literals.

Surface	Pinned
operatorInfo	name, description, group, input port names, one blocking output
config defaults	countVectorizer, tfidfTransformer, target, text
getOutputSchemas	model_name STRING and model BINARY on the declared output port
generatePythonCode	the estimator import, make_pipeline, the model name
JSON	the operatorType discriminator and a round-trip through LogicalOp

Test names are unchanged (<Descriptor>[.<member>] should ...). The seven specs with extra tests (the naive Bayes family and the decision tree) keep them. Expected values stay literal in each spec, so a renamed model or a changed import still fails that model's own spec. sklearnClassifier only accepts a SklearnClassifierOpDesc and sklearnTrainer only a SklearnTrainingOpDesc, so a descriptor cannot be checked against the wrong family's ports and group.

Any related issues, documentation, discussions?

Closes #9201.

How was this PR tested?

• sbt "WorkflowOperator/testOnly org.apache.texera.amber.operator.sklearn.*": 58 suites, 431 tests pass. These are the same 431 test names as on main, compared from the JUnit XML reports before and after.
• Mutation check: a wrong model name in SklearnBaggingOpDescSpec fails its operatorInfo and generatePythonCode tests, and a wrong import in SklearnTrainingKNNOpDescSpec fails its generatePythonCode test; the other tests in both specs pass.
• sbt "WorkflowOperator/Test/scalafmtCheck" "WorkflowOperator/Test/scalafix --check"

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

Generated-by: Claude Code (Claude Opus 5.5)

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Automated Reviewer Suggestions

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

• Contributors with relevant context: @kz930
  You can notify them by mentioning @kz930 in a comment.

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Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 91.98%. Comparing base (92e86d4) to head (49580b5).
✅ All tests successful. No failed tests found.

Additional details and impacted files

@@            Coverage Diff            @@
##               main    #9202   +/-   ##
=========================================
  Coverage     91.98%   91.98%           
  Complexity     5083     5083           
=========================================
  Files          1285     1285           
  Lines         56094    56094           
  Branches       7137     7137           
=========================================
  Hits          51599    51599           
  Misses         2742     2742           
  Partials       1753     1753           

Flag	Coverage Δ		*Carryforward flag
access-control-service	77.44% <ø> (ø)
agent-service	99.16% <ø> (ø)		Carriedforward from 92e86d4
amber	86.43% <ø> (ø)
computing-unit-managing-service	64.02% <ø> (ø)
config-service	87.61% <ø> (ø)
file-service	82.29% <ø> (ø)
frontend	96.47% <ø> (ø)		Carriedforward from 92e86d4
notebook-migration-service	83.77% <ø> (ø)
pyamber	98.54% <ø> (ø)		Carriedforward from 92e86d4
workflow-compiling-service	74.25% <ø> (ø)

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

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✅ No material benchmark regressions detected

🟢 4 better · 🔴 0 worse · ⚪ 11 noise (<±5%) · 0 without baseline

Compared against main 92e86d4 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	441	0.269	21,589/30,356/30,356 us	🟢 -11.9% / 🔴 +120.3%
🟢	bs=100 sw=10 sl=64	955	0.583	101,355/151,457/151,457 us	🟢 -9.1% / 🔴 +62.6%
⚪	bs=1000 sw=10 sl=64	1,157	0.706	862,756/917,989/917,989 us	⚪ within ±5% / 🔴 -11.3%

Baseline details

Latest main 92e86d4 from same runner

config	metric	PR	latest main	7d avg	Δ latest	Δ 7d
bs=10 sw=10 sl=64	throughput	441 tuples/sec	446 tuples/sec	941.58 tuples/sec	-1.1%	-53.2%
bs=10 sw=10 sl=64	MB/s	0.269 MB/s	0.273 MB/s	0.575 MB/s	-1.5%	-53.2%
bs=10 sw=10 sl=64	p50	21,589 us	22,457 us	10,991 us	-3.9%	+96.4%
bs=10 sw=10 sl=64	p95	30,356 us	34,467 us	13,776 us	-11.9%	+120.3%
bs=10 sw=10 sl=64	p99	30,356 us	34,467 us	16,872 us	-11.9%	+79.9%
bs=100 sw=10 sl=64	throughput	955 tuples/sec	941 tuples/sec	1,264 tuples/sec	+1.5%	-24.5%
bs=100 sw=10 sl=64	MB/s	0.583 MB/s	0.574 MB/s	0.772 MB/s	+1.6%	-24.4%
bs=100 sw=10 sl=64	p50	101,355 us	96,544 us	86,073 us	+5.0%	+17.8%
bs=100 sw=10 sl=64	p95	151,457 us	166,612 us	93,135 us	-9.1%	+62.6%
bs=100 sw=10 sl=64	p99	151,457 us	166,612 us	111,124 us	-9.1%	+36.3%
bs=1000 sw=10 sl=64	throughput	1,157 tuples/sec	1,177 tuples/sec	1,304 tuples/sec	-1.7%	-11.3%
bs=1000 sw=10 sl=64	MB/s	0.706 MB/s	0.718 MB/s	0.796 MB/s	-1.7%	-11.3%
bs=1000 sw=10 sl=64	p50	862,756 us	849,212 us	841,986 us	+1.6%	+2.5%
bs=1000 sw=10 sl=64	p95	917,989 us	895,649 us	881,107 us	+2.5%	+4.2%
bs=1000 sw=10 sl=64	p99	917,989 us	895,649 us	918,208 us	+2.5%	-0.0%

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
0,10,10,64,20,453.96,200,128000,441,0.269,21589.13,30355.77,30355.77
1,100,10,64,20,2094.13,2000,1280000,955,0.583,101355.31,151456.96,151456.96
2,1000,10,64,20,17280.97,20000,12800000,1157,0.706,862756.23,917989.17,917989.17