lars
a867fc4aae
Provides stratified marginal comparisons, joint-structure checks (correlation matrices, physically-coupled pairwise plots, direction alignment), and physical-constraint validation (unit-norm directions, non-negative raw targets) for a trained model's generated samples, building on the aggregate marginal/KL check already in giant.validate. Supports two entry points: live sampling against a checkpoint + val data (load_model_bundle/collect_samples), or loading a precomputed `giant predict --coord local` parquet directly (load_predicted_local) without needing the checkpoint at all. Predict output is now tagged with parquet schema metadata so the loader can verify a file's format and reject coord=global or untagged files with a clear error instead of guessing from column names. Also extends the config git-hash mismatch warning (added for --config loading) to checkpoint loading: both `giant predict` and analysis.load_model_bundle now look for a config.toml next to the checkpoint and warn (without failing) if it was generated from a different git commit. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
21 lines
502 B
Python
21 lines
502 B
Python
X_DIM = 9
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LOCAL_TARGET_NAMES = [
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"log_step_length",
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"log_delta_e",
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"log_edep",
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"post_dx",
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"post_dy",
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"post_dz",
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"travel_dx",
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"travel_dy",
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"travel_dz",
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]
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# Parquet schema metadata written by `giant predict` and checked by
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# giant.analysis loaders, so a file's format can be verified without
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# guessing from its column names.
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PREDICT_COORD_METADATA_KEY = "giant.predict.coord"
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PREDICT_SCHEMA_VERSION_KEY = "giant.predict.schema_version"
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PREDICT_SCHEMA_VERSION = "1"
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