[bot] Merge master/1dcacb66 into rel/dev - #1748
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…ion detail `check_filters` compares each filter category by exact equality on a canonical form, so a `filter_date_score` of False can mean the period differed, the granularity differed, or the filter hung off a different dataset. The evaluation detail carried only the boolean, which made a finished run undiagnosable: the committed report says the filters did not match but not how, and the only way to find out was to re-run the question live. Add `scoring.normalized_filters`, exposing the same canonical form the comparison uses, grouped by the three scored categories. Carry expected and actual through `EvaluationResult` into `VisualizationEvaluator`'s detail dict, mirroring the existing `expected_metric_uris`/`actual_metric_uris` pair, and print a diff-on-mismatch under each category in the agentic failure summary. Measured on one MIC evaluation corpus, `filter_date_score` is the single largest failure at 33% of 364 visualization evaluations, so this is the check most in need of an explanation and the one that had none. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
…-viz results dict(_NO_FILTERS) only copies the outer dict -- every no-visualization EvaluationResult shared the same three module-level lists in actual_filters, so mutating one result's list would leak into every other result built from _NO_FILTERS. Give each result its own lists. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
…-detail feat(gooddata-eval): report compared filters in visualization evaluation detail
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## rel/dev #1748 +/- ##
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Coverage 80.15% 80.16%
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Files 272 272
Lines 19111 19120 +9
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+ Hits 15319 15327 +8
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🚀 Automated PR to perform merge from master into rel/dev with changes up to 1dcacb6 (created by https://github.com/gooddata/gooddata-python-sdk/actions/runs/32371136128).