Initial release: E1 genus probe package moved out of the E01 workflow
- src/custom_models torch modules, store, training, metrics, runner and CLI (package-relative imports for pip installability) - test suite (49 tests) + conftest with fast synthetic-store fixtures - uv-managed dev env (pyproject + CPU torch index), hatchling build - README: uv for the package, pip install into conda envs for the workflow
This commit is contained in:
Tom Kasper
@@ -0,0 +1,571 @@
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"""Generalised train/validate/test CLI for the E1 genus probe.
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Subcommands: ``build`` (resolve a budget), ``store`` (build a signal
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store from pod5+labels or a synthetic corpus), ``train`` (a full run,
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optionally a shuffled-label control), ``validate`` / ``test`` (evaluate a
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run directory's checkpoint, ``test`` optionally attaching a trap store),
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``gate`` (verdict over run dirs) and ``configs`` (config round-trip).
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Every subcommand seeds first (GATE ZERO §2.3); each returns exit code 0
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on success and 1 on usage/runtime errors. ``train`` accepts a previous
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run's ``config.json`` as a base with any flag overriding it, so variants
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(windows ablation, holdout robustness, retraining) stay reproducible.
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"""
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from __future__ import annotations
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import argparse
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import json
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import sys
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from collections.abc import Sequence
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from pathlib import Path
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from typing import Any
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from .config import (
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DataConfig,
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ExtractConfig,
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ModelConfig,
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RunConfig,
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TrainConfig,
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load_config,
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save_config,
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)
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from .runner import evaluate_gate, evaluate_test, evaluate_val, train_run
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def _add_model_args(parser: argparse.ArgumentParser, required: bool) -> None:
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"""Register the model-request flags on a subparser.
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Args:
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parser: Target subparser.
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required: Whether ``--arch``/``--budget`` must be given (the
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``build`` command demands them; ``train`` falls back to
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defaults/base-config values).
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"""
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parser.add_argument("--arch", choices=["cnn", "linatt"], required=required)
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parser.add_argument("--budget", type=int, required=required)
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parser.add_argument("--stride", type=int, default=None)
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parser.add_argument("--d-embed", type=int, default=None)
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parser.add_argument("--budget-tol", type=float, default=None)
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parser.add_argument("--n-heads", type=int, default=None)
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def _add_data_args(parser: argparse.ArgumentParser) -> None:
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"""Register the split-configuration flags on a subparser.
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All default to ``None`` so only explicitly passed flags override a
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base config (tri-state booleans use ``default=None``).
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Args:
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parser: Target subparser.
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"""
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parser.add_argument("--genera", default=None)
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parser.add_argument("--val-frac", type=float, default=None)
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parser.add_argument("--test-frac", type=float, default=None)
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parser.add_argument("--holdout-runs", action="store_true", default=None)
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parser.add_argument("--min-test-windows", type=int, default=None)
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parser.add_argument("--data-seed", type=int, default=None)
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def _add_train_args(parser: argparse.ArgumentParser) -> None:
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"""Register the training-semantics flags on a subparser.
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Args:
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parser: Target subparser.
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"""
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parser.add_argument("--batch", type=int, default=None)
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parser.add_argument("--lr", type=float, default=None)
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parser.add_argument("--weight-decay", type=float, default=None)
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parser.add_argument("--epochs", type=int, default=None)
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parser.add_argument("--warmup", type=float, default=None)
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parser.add_argument("--smoothing", type=float, default=None)
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parser.add_argument("--amp", choices=["off", "bf16", "fp16"], default=None)
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parser.add_argument("--grad-clip", type=float, default=None)
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parser.add_argument(
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"--metric", choices=["val_recall_macro", "val_loss"], default=None
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)
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parser.add_argument("--patience", type=int, default=None)
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parser.add_argument("--workers", type=int, default=None)
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parser.add_argument("--device", default=None)
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parser.add_argument("--seed", type=int, default=None)
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parser.add_argument("--deterministic", action="store_true", default=None)
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def _pick(value: Any, base: Any, default: Any) -> Any:
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"""Three-way option resolution: flag > base config > default.
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Args:
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value: The parsed CLI flag (``None`` when not passed).
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base: The corresponding value from a base config (``None``
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without one).
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default: The spec default used last.
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Returns:
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The first non-``None`` of the three.
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"""
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if value is not None:
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return value
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if base is not None:
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return base
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return default
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def _parse_genera(raw: str | None) -> tuple[str, ...]:
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"""Parse a comma-separated genus list.
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Args:
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raw: e.g. ``"Bacillus,Listeria,Staphylococcus"``; ``None`` means
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no restriction (use every genus in the store).
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Returns:
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The trimmed non-empty names as a tuple.
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Raises:
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ValueError: If the string contains no non-empty names.
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"""
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if raw is None:
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return ()
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genera = tuple(g.strip() for g in raw.split(",") if g.strip())
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if not genera:
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raise ValueError("--genera parsed to an empty list")
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return genera
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def _run_config_from_args(args: argparse.Namespace) -> RunConfig:
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"""Assemble a :class:`RunConfig` from flags plus optional base config.
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Resolution order per field: explicit flag, then the base config's
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value (when ``--config`` was given), then the spec default.
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``--control`` or ``--stage control`` marks the run as the
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shuffled-label control.
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Args:
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args: Parsed ``train`` arguments.
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Returns:
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The fully resolved run configuration.
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Raises:
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ValueError: If the base file is not a ``RunConfig`` or neither
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flags nor base supply ``--store`` / ``--out-dir``.
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"""
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base: RunConfig | None = None
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if args.config:
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base = load_config(args.config)
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if not isinstance(base, RunConfig):
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raise ValueError(f"{args.config} does not hold a RunConfig")
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base_model = base.model if base else None
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base_data = base.data if base else None
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base_train = base.train if base else None
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model = ModelConfig(
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arch=_pick(args.arch, base_model.arch if base_model else None, "cnn"),
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param_budget=_pick(
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args.budget, base_model.param_budget if base_model else None, 1_000_000
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),
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stride=_pick(args.stride, base_model.stride if base_model else None, 4),
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d_embed=_pick(args.d_embed, base_model.d_embed if base_model else None, 256),
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budget_tol=_pick(
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args.budget_tol, base_model.budget_tol if base_model else None, 0.10
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),
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n_heads=_pick(args.n_heads, base_model.n_heads if base_model else None, 4),
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)
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data = DataConfig(
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genera=(
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_parse_genera(args.genera)
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if args.genera is not None
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else (base_data.genera if base_data else ())
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),
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val_fraction=_pick(
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args.val_frac, base_data.val_fraction if base_data else None, 0.10
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),
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test_fraction=_pick(
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args.test_frac, base_data.test_fraction if base_data else None, 0.10
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),
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holdout_runs=_pick(
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args.holdout_runs, base_data.holdout_runs if base_data else None, False
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),
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min_test_windows_per_genus=_pick(
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args.min_test_windows,
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base_data.min_test_windows_per_genus if base_data else None,
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300,
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),
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seed=_pick(args.data_seed, base_data.seed if base_data else None, 0),
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)
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stage = base.stage if base else "arch_ladder"
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if args.control:
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stage = "control"
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elif args.stage:
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stage = args.stage
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device = _pick(args.device, base_train.device if base_train else None, None)
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if device in ("auto", ""):
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device = None
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train = TrainConfig(
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batch_size=_pick(args.batch, base_train.batch_size if base_train else None, 256),
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lr=_pick(args.lr, base_train.lr if base_train else None, 3e-4),
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weight_decay=_pick(
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args.weight_decay, base_train.weight_decay if base_train else None, 0.01
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),
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max_epochs=_pick(
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args.epochs, base_train.max_epochs if base_train else None, 40
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),
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warmup_frac=_pick(
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args.warmup, base_train.warmup_frac if base_train else None, 0.05
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),
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label_smoothing=_pick(
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args.smoothing, base_train.label_smoothing if base_train else None, 0.0
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),
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amp=_pick(args.amp, base_train.amp if base_train else None, "bf16"),
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grad_clip=_pick(
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args.grad_clip, base_train.grad_clip if base_train else None, 1.0
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),
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early_stop_metric=_pick(
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args.metric, base_train.early_stop_metric if base_train else None, "val_recall_macro"
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),
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patience=_pick(args.patience, base_train.patience if base_train else None, 5),
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num_workers=_pick(
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args.workers, base_train.num_workers if base_train else None, 4
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),
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device=device,
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seed=_pick(args.seed, base_train.seed if base_train else None, 0),
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deterministic=_pick(
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args.deterministic, base_train.deterministic if base_train else None, True
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),
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)
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store_dir = args.store or (base.store_dir if base else None)
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if store_dir is None:
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raise ValueError("--store (or a base --config with a store_dir) is required")
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out_dir = args.out_dir or (base.out_dir if base else None)
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if out_dir is None:
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raise ValueError("--out-dir (or a base --config with an out_dir) is required")
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run_id = args.run_id or (base.run_id if base else "") or ""
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return RunConfig(
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run_id=run_id,
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stage=stage,
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store_dir=Path(store_dir),
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data=data,
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model=model,
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train=train,
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out_dir=Path(out_dir),
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notes=args.notes or (base.notes if base else "") or "",
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)
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def _cmd_build(args: argparse.Namespace) -> int:
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"""Handle ``build``: resolve a budget and print the realised geometry.
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Args:
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args: Parsed arguments (arch, budget, stride, d-embed,
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budget-tol, n-heads, n-classes, seed).
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Returns:
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Exit code 0 on success.
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"""
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from .models import build_probe, count_params
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from .seed import seed_everything
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seed_everything(args.seed)
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cfg = ModelConfig(
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arch=args.arch,
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param_budget=args.budget,
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stride=args.stride if args.stride is not None else 4,
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d_embed=args.d_embed if args.d_embed is not None else 256,
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budget_tol=args.budget_tol if args.budget_tol is not None else 0.10,
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n_heads=args.n_heads if args.n_heads is not None else 4,
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)
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probe, realised = build_probe(cfg, args.n_classes)
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payload = {
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"arch": realised.arch,
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"param_budget": realised.param_budget,
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"params_realised": realised.params_realised,
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"stride": realised.stride,
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"patch_len": min(4, realised.stride),
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"d_model": realised.d_model,
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"n_layers": realised.n_layers,
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"n_heads": realised.n_heads,
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"d_embed": realised.d_embed,
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"n_classes": args.n_classes,
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"count_params": count_params(probe),
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}
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print(json.dumps(payload, indent=2, sort_keys=True))
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return 0
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def _cmd_store(args: argparse.Namespace) -> int:
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"""Handle ``store``: build a signal store and print its meta.
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Args:
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args: Parsed arguments; either ``--synthetic`` or both ``--pod5``
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and ``--labels`` must be given, plus windowing flags.
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Returns:
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Exit code 0 on success.
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Raises:
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ValueError: On an incomplete mode selection (no ``--synthetic``
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and no pod5/labels pair).
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"""
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from .store import extract_store, load_labels, synthetic_store
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out = Path(args.out)
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if args.synthetic:
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meta = synthetic_store(
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out_dir=out,
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n_genera=args.n_genera,
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reads_per_genus=args.reads_per_genus,
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window_samples=args.window_samples,
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period=args.period,
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noise=args.noise,
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seed=args.seed,
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shard_windows=args.shard_windows,
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genus_prefix=args.genus_prefix,
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)
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elif args.pod5 and args.labels:
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cfg = ExtractConfig(
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pod5_paths=tuple(Path(p) for p in args.pod5),
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window_samples=args.window_samples,
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skip_head_samples=args.skip_head,
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max_windows_per_read=args.max_windows_per_read,
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min_read_samples=args.min_read_samples,
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norm=args.norm,
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dtype=args.dtype,
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shard_windows=args.shard_windows,
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seed=args.seed,
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)
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labels = load_labels(Path(args.labels))
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meta = extract_store(cfg, labels, out)
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else:
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raise ValueError(
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"store needs either --synthetic or both --pod5 PATHS... and --labels PARQUET"
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)
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print(json.dumps(meta, indent=2, sort_keys=True, default=str))
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return 0
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def _cmd_train(args: argparse.Namespace) -> int:
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"""Handle ``train``: run one training run and print its summary.
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Args:
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args: Parsed arguments (see :func:`_run_config_from_args`).
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Returns:
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Exit code 0 on success.
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"""
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cfg = _run_config_from_args(args)
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realised, result, _ = train_run(cfg)
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tail = result.history.row(result.history.height - 1, named=True)
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metric = (
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tail["val_recall_macro"]
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if realised.train.early_stop_metric == "val_recall_macro"
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else tail["val_loss"]
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)
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run_dir = realised.out_dir / realised.run_id
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print(
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json.dumps(
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{
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"run_id": realised.run_id,
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"run_dir": str(run_dir),
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"arch": realised.model.arch,
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"d_model": realised.model.d_model,
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"n_layers": realised.model.n_layers,
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"params_realised": realised.model.params_realised,
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"best_epoch": result.best_epoch,
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f"best_{realised.train.early_stop_metric}": metric,
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"seconds": round(result.seconds, 1),
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},
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indent=2,
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sort_keys=True,
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)
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)
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return 0
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def _cmd_validate(args: argparse.Namespace) -> int:
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"""Handle ``validate``: evaluate a run checkpoint on its val split.
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Args:
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args: Parsed arguments (``--run-dir``, optional ``--store``,
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``--n-boot``).
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Returns:
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Exit code 0 on success.
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"""
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payload = evaluate_val(
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Path(args.run_dir), store_dir=Path(args.store) if args.store else None, n_boot=args.n_boot
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)
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print(json.dumps(payload, indent=2, sort_keys=True))
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return 0
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def _cmd_test(args: argparse.Namespace) -> int:
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"""Handle ``test``: evaluate a run checkpoint on its test split.
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Args:
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args: Parsed arguments (``--run-dir``, optional ``--store`` and
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``--trap-store``, ``--n-boot``).
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Returns:
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Exit code 0 on success.
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"""
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payload = evaluate_test(
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Path(args.run_dir),
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store_dir=Path(args.store) if args.store else None,
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trap_store_dir=Path(args.trap_store) if args.trap_store else None,
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n_boot=args.n_boot,
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)
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print(json.dumps(payload, indent=2, sort_keys=True))
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return 0
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def _cmd_gate(args: argparse.Namespace) -> int:
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"""Handle ``gate``: compute and print the E1-v2 gate verdict.
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Args:
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args: Parsed arguments (``--runs``, ``--control``,
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``--chance-tol``).
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Returns:
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Exit code 0 on success.
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"""
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verdict = evaluate_gate([Path(r) for r in args.runs], Path(args.control), args.chance_tol)
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print(json.dumps(verdict.__dict__, indent=2, sort_keys=True))
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return 0
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def _cmd_configs(args: argparse.Namespace) -> int:
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"""Handle ``configs``: load a config JSON and re-save it elsewhere.
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A round-trip helper proving ``load_config(save_config(c)) == c`` and
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letting users materialise a base config from an existing run.
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||||
|
||||
Args:
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args: Parsed arguments (``--config`` source, ``--out``
|
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destination).
|
||||
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||||
Returns:
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||||
Exit code 0 on success.
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||||
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||||
"""
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cfg = load_config(args.config)
|
||||
save_config(cfg, Path(args.out))
|
||||
print(f"wrote {args.out}")
|
||||
return 0
|
||||
|
||||
|
||||
def build_parser() -> argparse.ArgumentParser:
|
||||
"""Construct the full CLI parser with all subcommands.
|
||||
|
||||
Returns:
|
||||
The configured ``argparse.ArgumentParser`` (``--help`` lists
|
||||
every subcommand and flag).
|
||||
|
||||
"""
|
||||
parser = argparse.ArgumentParser(
|
||||
prog="custom_models",
|
||||
description="E1 genus probe: torch models and train/validate/test wrapper",
|
||||
)
|
||||
sub = parser.add_subparsers(dest="command", required=True)
|
||||
|
||||
p_build = sub.add_parser("build", help="resolve a param budget into a realised probe")
|
||||
_add_model_args(p_build, required=True)
|
||||
p_build.add_argument("--n-classes", type=int, default=11)
|
||||
p_build.add_argument("--seed", type=int, default=0)
|
||||
p_build.set_defaults(func=_cmd_build)
|
||||
|
||||
p_store = sub.add_parser("store", help="build a signal store (pod5+labels or synthetic)")
|
||||
p_store.add_argument("--out", required=True)
|
||||
p_store.add_argument("--synthetic", action="store_true")
|
||||
p_store.add_argument("--n-genera", type=int, default=11)
|
||||
p_store.add_argument("--reads-per-genus", type=int, default=200)
|
||||
p_store.add_argument("--period", type=int, default=None)
|
||||
p_store.add_argument("--noise", type=float, default=0.15)
|
||||
p_store.add_argument("--genus-prefix", default="genus_")
|
||||
p_store.add_argument("--pod5", nargs="+", default=None)
|
||||
p_store.add_argument("--labels", default=None)
|
||||
p_store.add_argument("--window-samples", type=int, default=12_000)
|
||||
p_store.add_argument("--skip-head", type=int, default=500)
|
||||
p_store.add_argument("--min-read-samples", type=int, default=12_500)
|
||||
p_store.add_argument("--max-windows-per-read", type=int, default=1)
|
||||
p_store.add_argument("--norm", choices=["median_iqr", "median_mad"], default="median_iqr")
|
||||
p_store.add_argument("--dtype", choices=["float16", "float32"], default="float16")
|
||||
p_store.add_argument("--shard-windows", type=int, default=8_192)
|
||||
p_store.add_argument("--seed", type=int, default=0)
|
||||
p_store.set_defaults(func=_cmd_store)
|
||||
|
||||
p_train = sub.add_parser("train", help="train a probe on a signal store")
|
||||
p_train.add_argument("--store", default=None)
|
||||
p_train.add_argument("--out-dir", default=None)
|
||||
p_train.add_argument("--config", default=None)
|
||||
p_train.add_argument("--run-id", default=None)
|
||||
p_train.add_argument(
|
||||
"--stage",
|
||||
choices=["arch_ladder", "windows_ablation", "robustness", "control", "extra"],
|
||||
default=None,
|
||||
)
|
||||
p_train.add_argument("--control", action="store_true", default=None)
|
||||
p_train.add_argument("--notes", default=None)
|
||||
_add_model_args(p_train, required=False)
|
||||
_add_data_args(p_train)
|
||||
_add_train_args(p_train)
|
||||
p_train.set_defaults(func=_cmd_train)
|
||||
|
||||
p_validate = sub.add_parser("validate", help="evaluate a run checkpoint on its val split")
|
||||
p_validate.add_argument("--run-dir", required=True)
|
||||
p_validate.add_argument("--store", default=None)
|
||||
p_validate.add_argument("--n-boot", type=int, default=10_000)
|
||||
p_validate.set_defaults(func=_cmd_validate)
|
||||
|
||||
p_test = sub.add_parser("test", help="evaluate a run checkpoint on its test split")
|
||||
p_test.add_argument("--run-dir", required=True)
|
||||
p_test.add_argument("--store", default=None)
|
||||
p_test.add_argument("--trap-store", default=None)
|
||||
p_test.add_argument("--n-boot", type=int, default=10_000)
|
||||
p_test.set_defaults(func=_cmd_test)
|
||||
|
||||
p_gate = sub.add_parser("gate", help="gate verdict over run dirs against a control run")
|
||||
p_gate.add_argument("--runs", nargs="+", required=True)
|
||||
p_gate.add_argument("--control", required=True)
|
||||
p_gate.add_argument("--chance-tol", type=float, default=1.5)
|
||||
p_gate.set_defaults(func=_cmd_gate)
|
||||
|
||||
p_configs = sub.add_parser("configs", help="round-trip a config JSON (save_config/load_config)")
|
||||
p_configs.add_argument("--config", required=True)
|
||||
p_configs.add_argument("--out", required=True)
|
||||
p_configs.set_defaults(func=_cmd_configs)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
def main(argv: Sequence[str] | None = None) -> int:
|
||||
"""CLI entry point (also ``python -m custom_models``).
|
||||
|
||||
Args:
|
||||
argv: Argument list without the program name; ``None`` uses
|
||||
``sys.argv``.
|
||||
|
||||
Returns:
|
||||
0 on success; 1 on usage or runtime errors (message printed to
|
||||
stderr).
|
||||
|
||||
"""
|
||||
parser = build_parser()
|
||||
args = parser.parse_args(list(argv) if argv is not None else None)
|
||||
try:
|
||||
return int(args.func(args))
|
||||
except (ValueError, RuntimeError, FileNotFoundError, ImportError) as exc:
|
||||
print(f"error: {exc}", file=sys.stderr)
|
||||
return 1
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
Reference in New Issue
Block a user