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>
37 lines
624 B
TOML
37 lines
624 B
TOML
[project]
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name = "giant"
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version = "0.1.0"
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description = "Geant4 step-function surrogate via conditional flow matching"
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readme = "README.md"
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requires-python = ">=3.12"
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dependencies = [
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"torch>=2.3",
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"numpy>=1.26",
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"pandas>=2.2",
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"pyarrow>=16",
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"typer>=0.12",
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]
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[project.optional-dependencies]
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dev = [
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"pytest>=8",
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]
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convert = [
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"uproot>=5.3",
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"awkward>=2.6",
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"polars>=1.0",
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]
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analysis = [
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"matplotlib>=3.8",
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]
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[project.scripts]
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giant = "giant.cli:app"
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[build-system]
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requires = ["hatchling"]
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build-backend = "hatchling.build"
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[tool.hatch.build.targets.wheel]
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packages = ["giant"]
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