feat(operator): export the estimators and the Hugging Face models - #8368
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kz930 wants to merge 1 commit into
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feat(operator): export the estimators and the Hugging Face models#8368kz930 wants to merge 1 commit into
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The scikit-learn estimators are fitted on one port and score on the other, so the script holds both frames and narrows each by the same rule: a fit and a score taken on different columns would compare two different models. The four Hugging Face models declare the column types they take, and the iris regression keeps the row when a petal measurement is empty rather than ending the run on it. Sklearn Prediction and Sklearn Testing are reported as unverifiable rather than exported blind: each consumes a fitted model on an input port, and a fixture written from the JVM cannot carry a live Python object. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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This was referenced Sep 2, 2026
Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## main #8368 +/- ##
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- Coverage 94.11% 94.11% -0.01%
+ Complexity 4811 4802 -9
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Files 1197 1197
Lines 48813 48804 -9
Branches 5906 5906
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- Hits 45939 45930 -9
Misses 1420 1420
Partials 1454 1454
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📊 Arrow Flight E2E bench(no arrow-flight-e2e.csv in artifact) |
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What changes were proposed in this PR?
The scikit-learn estimators are fitted on one port and score on the other, so
the script holds both frames and narrows each by the same rule: a fit and a
score taken on different columns would compare two different models.
The four Hugging Face models declare the column types they take, and the iris
regression keeps the row when a petal measurement is empty rather than ending
the run on it.
Sklearn Prediction and Sklearn Testing are reported as unverifiable rather
than exported blind: each consumes a fitted model on an input port, and a
fixture written from the JVM cannot carry a live Python object.
Any related issues, documentation, discussions?
Part of #8325, 16 of 20; that issue lists the set in order. It needs #8327 for the trait, so it does not compile until that lands, and the rows these operators add to the verification runner follow with the harness rather than as whole new files here.
Closes #8316, closes #8056.
How was this PR tested?
Each operator asserts the block it emits in its own spec. Once the harness lands, each is also run through the engine and through its generated script, on every configuration its schema offers, and the two answers compared.
Was this PR authored or co-authored using generative AI tooling?
Generated-by: Claude Code (Opus 5)