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Introduce strongly-typed dataclasses for model configuration: - Dimensions, Labels, Anchoring, EstimationOptions, TransitionInfo - FactorEndogenousInfo, EndogenousFactorsInfo This improves type safety and enables IDE autocompletion while keeping user-facing model_dict as a plain dictionary. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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Replace dict fields with frozendict in frozen dataclasses to ensure true immutability: - Labels.aug_periods_to_periods - Labels.aug_stages_to_stages - Anchoring.outcomes - TransitionInfo.param_names, individual_functions, function_names - EndogenousFactorsInfo.aug_periods_to_aug_period_meas_types, factor_info 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Update process_model() to return a ProcessedModel frozen dataclass and update all consumers to use attribute access instead of dict access. This provides: - Better type safety with explicit typed fields - Immutability via frozen dataclass - IDE autocomplete support - Clear documentation of the model structure 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
…c so that config.TEST_DATA_DIR is valid also for skillmodels the package (as opposed to the project).
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| ## Dimensions | ||
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| The `Dimensions` dataclass contains integer values for model dimensions: |
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These section can now replaced with autodoc / autoclass; That's one of the benefits of strong typing! Results are best if attribute docstrings come right after the attribute like here
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Limitation of jb book for now, we can convert once available.
src/skillmodels/types.py
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| def _make_immutable(value: object) -> object: |
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I don't know where this is used but I tend to keep dicts and lists for data that should not be represented as a dataclass. It's more intuitive for most Python users and using tuples for lists of variables does not work well with pandas.
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It is used to convert internally-used containers to be immutable. Does not affect user-facing code (that would be typed as Mapping/Sequence) and it is easy to work around pandas. I value peace of mind provided by immutable types much higher 😇
…h all defaults set right there; get rid of remaining mutable types being passed around.
…e where we could do so.
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Refactor model configuration to use frozen dataclasses instead of dicts
Introduce strongly-typed dataclasses for model configuration:
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