Check idata dims/coords consistency before sampling (fixes #7891) #7949
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…vs#7891) This commit implements validation of InferenceData dimensions and coordinates consistency before pm.sample() executes, preventing cryptic shape mismatch errors during sampling. Changes: - Add pymc/model/validation.py with validation functions: * validate_dims_coords_consistency(): Main validation entry point * check_dims_exist(): Verify referenced dims exist in model.coords * check_shape_dims_match(): Verify variable shapes match declared dims * check_coord_lengths(): Verify coordinate lengths match dimension sizes - Integrate validation into pymc/sampling/mcmc.py: * Added validation call early in sample() function, before sampling setup * Provides clear, actionable error messages to guide users - Add comprehensive tests in tests/model/test_dims_coords_validation.py: * Test missing coord detection * Test shape-dims mismatch detection * Test coordinate length validation * Test MutableData, observed data, and Deterministic variables * Test edge cases and complex models Fixes pymc-devs#7891
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Description
This PR implements validation to check
idatadimensions and coordinates consistency before sampling begins, addressing issue #7891.Problem
Previously, sampling would fail with unclear error messages when
idatahad inconsistent dims/coords with the model. This made debugging difficult.Solution
Added early validation that:
Testing
Related Issue
Fixes #7891
📚 Documentation preview 📚: https://pymc--7949.org.readthedocs.build/en/7949/