test(workflow-operator): add unit test coverage for Sklearn Naive Bayes descriptors

[aglinxinyuan]

What changes were proposed in this PR?

Pin behavior of the four previously-untested Sklearn Naive Bayes classifier descriptors in common/workflow-operator. No production-code changes.

Spec	Source class	Tests
SklearnBernoulliNaiveBayesOpDescSpec	SklearnBernoulliNaiveBayesOpDesc	5
SklearnComplementNaiveBayesOpDescSpec	SklearnComplementNaiveBayesOpDesc	5
SklearnGaussianNaiveBayesOpDescSpec	SklearnGaussianNaiveBayesOpDesc	5
SklearnMultinomialNaiveBayesOpDescSpec	SklearnMultinomialNaiveBayesOpDesc	5

Behavior pinned

Surface	Contract
operatorInfo	exact model name + Sklearn <name> Operator description; Sklearn group; training/testing input ports + one blocking output
field defaults	countVectorizer/tfidfTransformer false; target/text null
getOutputSchemas	model_name (STRING) + model (BINARY) keyed by the declared output port
generatePythonCode	imports and instantiates the matching sklearn estimator (e.g. BernoulliNB) via make_pipeline
Round-trip	config fields preserved through the polymorphic LogicalOp base, with the correct operatorType discriminator

Any related issues, documentation, discussions?

Part of the ongoing workflow-operator unit-test coverage effort.

How was this PR tested?

• sbt "WorkflowOperator/testOnly *SklearnBernoulliNaiveBayesOpDescSpec *SklearnComplementNaiveBayesOpDescSpec *SklearnGaussianNaiveBayesOpDescSpec *SklearnMultinomialNaiveBayesOpDescSpec" — 20 tests, all green
• sbt "WorkflowOperator/Test/scalafmtCheck" and sbt "WorkflowOperator/scalafixAll --check" — clean
• CI to confirm

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

Generated-by: Claude Code (Opus 4.8 [1M context])

[github-actions]

Automated Reviewer Suggestions

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

• No candidates found from git blame history.

[codecov-commenter]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 54.60%. Comparing base (1c580e5) to head (7a72636).

Additional details and impacted files

@@            Coverage Diff            @@
##               main    #5925   +/-   ##
=========================================
  Coverage     54.60%   54.60%           
+ Complexity     2927     2925    -2     
=========================================
  Files          1109     1109           
  Lines         42828    42828           
  Branches       4608     4608           
=========================================
  Hits          23385    23385           
+ Misses        18081    18080    -1     
- Partials       1362     1363    +1     

Flag	Coverage Δ		*Carryforward flag
access-control-service	70.44% <ø> (ø)
agent-service	34.36% <ø> (ø)		Carriedforward from 1c580e5
amber	56.73% <ø> (ø)
computing-unit-managing-service	1.65% <ø> (ø)
config-service	57.35% <ø> (ø)
file-service	58.59% <ø> (ø)
frontend	48.31% <ø> (ø)		Carriedforward from 1c580e5
pyamber	90.20% <ø> (ø)		Carriedforward from 1c580e5
python	90.76% <ø> (ø)		Carriedforward from 1c580e5
workflow-compiling-service	58.69% <ø> (ø)

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

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[Copilot]

Pull request overview

Adds Scala unit tests in common/workflow-operator to pin the current behavior/contract of the four Sklearn Naive Bayes operator descriptors (Bernoulli, Complement, Gaussian, Multinomial) without changing production code.

Changes:

• Introduces new AnyFlatSpec test suites validating operatorInfo (name/description/group + port shape) and default config values.
• Verifies getOutputSchemas emits the expected model_name (STRING) and model (BINARY) schema on the declared output port.
• Pins generatePythonCode output (sklearn estimator import + pipeline usage) and JSON polymorphic round-trip via LogicalOp using the operatorType discriminator.

Reviewed changes

Copilot reviewed 4 out of 4 changed files in this pull request and generated no comments.

File	Description
common/workflow-operator/src/test/scala/org/apache/texera/amber/operator/sklearn/SklearnBernoulliNaiveBayesOpDescSpec.scala	Adds contract/unit tests for BernoulliNB descriptor behavior and JSON round-trip.
common/workflow-operator/src/test/scala/org/apache/texera/amber/operator/sklearn/SklearnComplementNaiveBayesOpDescSpec.scala	Adds contract/unit tests for ComplementNB descriptor behavior and JSON round-trip.
common/workflow-operator/src/test/scala/org/apache/texera/amber/operator/sklearn/SklearnGaussianNaiveBayesOpDescSpec.scala	Adds contract/unit tests for GaussianNB descriptor behavior and JSON round-trip.
common/workflow-operator/src/test/scala/org/apache/texera/amber/operator/sklearn/SklearnMultinomialNaiveBayesOpDescSpec.scala	Adds contract/unit tests for MultinomialNB descriptor behavior and JSON round-trip.

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[github-actions]

⚠️ Benchmark changes need a look

🟢 4 better · 🔴 1 worse · ⚪ 10 noise (<±5%) · 0 without baseline

Compared against main 1c580e5 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	389	0.238	25,780/33,058/33,058 us	🔴 +10.0% / 🔴 +8.4%
🟢	bs=100 sw=10 sl=64	833	0.508	116,571/139,934/139,934 us	🟢 -11.4% / 🔴 -6.7%
⚪	bs=1000 sw=10 sl=64	935	0.57	1,069,345/1,118,882/1,118,882 us	⚪ within ±5% / 🔴 -10.3%

Baseline details

Latest main 1c580e5 from same runner

config	metric	PR	latest main	7d avg	Δ latest	Δ 7d
bs=10 sw=10 sl=64	throughput	389 tuples/sec	395 tuples/sec	410.82 tuples/sec	-1.5%	-5.3%
bs=10 sw=10 sl=64	MB/s	0.238 MB/s	0.241 MB/s	0.251 MB/s	-1.2%	-5.1%
bs=10 sw=10 sl=64	p50	25,780 us	23,427 us	23,785 us	+10.0%	+8.4%
bs=10 sw=10 sl=64	p95	33,058 us	36,524 us	34,980 us	-9.5%	-5.5%
bs=10 sw=10 sl=64	p99	33,058 us	36,524 us	34,980 us	-9.5%	-5.5%
bs=100 sw=10 sl=64	throughput	833 tuples/sec	819 tuples/sec	891.94 tuples/sec	+1.7%	-6.6%
bs=100 sw=10 sl=64	MB/s	0.508 MB/s	0.5 MB/s	0.544 MB/s	+1.6%	-6.7%
bs=100 sw=10 sl=64	p50	116,571 us	119,613 us	112,277 us	-2.5%	+3.8%
bs=100 sw=10 sl=64	p95	139,934 us	158,005 us	139,802 us	-11.4%	+0.1%
bs=100 sw=10 sl=64	p99	139,934 us	158,005 us	139,802 us	-11.4%	+0.1%
bs=1000 sw=10 sl=64	throughput	935 tuples/sec	920 tuples/sec	1,041 tuples/sec	+1.6%	-10.2%
bs=1000 sw=10 sl=64	MB/s	0.57 MB/s	0.561 MB/s	0.635 MB/s	+1.6%	-10.3%
bs=1000 sw=10 sl=64	p50	1,069,345 us	1,084,616 us	972,714 us	-1.4%	+9.9%
bs=1000 sw=10 sl=64	p95	1,118,882 us	1,133,084 us	1,023,057 us	-1.3%	+9.4%
bs=1000 sw=10 sl=64	p99	1,118,882 us	1,133,084 us	1,023,057 us	-1.3%	+9.4%

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
0,10,10,64,20,513.76,200,128000,389,0.238,25779.92,33057.68,33057.68
1,100,10,64,20,2400.65,2000,1280000,833,0.508,116571.14,139933.82,139933.82
2,1000,10,64,20,21400.53,20000,12800000,935,0.570,1069345.18,1118882.16,1118882.16