feat(machine-learning): export the Machine Learning Scorer - #8367
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The scorer reads one label through two columns, the actual and the predicted, and reports the metrics a task admits. The export says which task it is scoring the same way the operator does, so the two paths select the same metrics from the same pair. Two behaviour fixes travel with it. It reports the settings it cannot score rather than failing deep inside the generated Python, and it scores the rows an empty cell leaves usable rather than ending the run on one. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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carloea2
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Sep 4, 2026
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The scorer export and metric handling look good.
kz930
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September 4, 2026 17:12
…anded over The scorer dropped the rows missing either column by rebinding the name it was given. The translator names a variable per output PORT, so anything else drawn from the same upstream would go on to read the already-filtered table, and a single-branch workflow never notices. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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What changes were proposed in this PR?
The scorer reads one label through two columns, the actual and the predicted,
and reports the metrics a task admits. The export says which task it is
scoring the same way the operator does, so the two paths select the same
metrics from the same pair.
Two behaviour fixes travel with it. It reports the settings it cannot score
rather than failing deep inside the generated Python, and it scores the rows
an empty cell leaves usable rather than ending the run on one.
Any related issues, documentation, discussions?
Part of #8325, 15 of 21; 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 #8302, closes #8058.
Closes #8421, the task this change is the whole of.
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)