Giorgio Gilestro
ab3dc10587
- paper/pnas -> paper/manuscript (venue-neutral)
- configs/layer1 -> configs/inheritance, src/knowledge -> src/inheritance
(imported as `inheritance`), make layer1 -> make inheritance; layer2 alias dropped
- inheritance and trained-network bundles named after the manuscript figure
they feed (fig2_grounding_sweep, figS3_rebaselining, ...), or descriptively
where they feed none; configs keep their `experiment:` value so parquet
hashes are unchanged, only output.dir moves
- figure scripts, SI figure sources, notebooks, REPRODUCING.md, README and the
SI Methods/tables updated; make clean no longer deletes tracked manifests;
reproduce.sh hashes the s{seed}/ layouts too
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Y64o8FKP7rCuXzC48pxpMm
44 lines
1.6 KiB
TOML
44 lines
1.6 KiB
TOML
[project]
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name = "lamarckian-society"
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version = "0.1.0"
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description = "Layer 1 analytical core: knowledge transmission as a Wright-Fisher process."
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readme = "README.md"
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requires-python = ">=3.11"
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dependencies = [
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"numpy>=1.26",
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"scipy>=1.11",
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"pandas>=2.1",
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"pyarrow>=15",
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"matplotlib>=3.8",
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"pydantic>=2.5",
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"pyyaml>=6.0",
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]
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[project.optional-dependencies]
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dev = ["pytest>=8.0"]
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# Layer 1.5 neural existence proof. Torch is only needed from Stage C (RNN/VAE/MLP);
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# Stages A-B (synthetic sandbox + histogram bridge) are pure NumPy and run in the base env.
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# The default PyPI torch wheel is CUDA-enabled (cu13, matching the RTX A4000 driver).
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# Install with `uv sync --extra neural`.
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neural = ["torch>=2.2"]
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# The real-MNIST secondary-confirmation tier only. Install with `uv sync --extra mnist`.
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mnist = ["torchvision>=0.17"]
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# Layer 2 / LLM prototype (blueprint C2/C4): LoRA specialists + weight-space merging on a small
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# open-weight base. GPU; models download to the HF cache (outside the repo). `uv sync --extra llm`.
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llm = ["transformers>=4.44", "peft>=0.11", "datasets", "accelerate"]
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# Jupyter notebooks that walk through the biological model and regenerate every paper figure
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# from the committed artifacts. `uv sync --extra notebooks`.
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notebooks = ["jupyter>=1.0"]
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[build-system]
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requires = ["hatchling"]
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build-backend = "hatchling.build"
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# src-layout: src/inheritance/ (the inheritance model, importable as `inheritance`),
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# src/neural/ (trained networks) and src/llm/ (language models).
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[tool.hatch.build.targets.wheel]
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packages = ["src/inheritance", "src/neural", "src/llm"]
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[tool.pytest.ini_options]
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testpaths = ["tests"]
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addopts = "-q"
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