moved_repo_first_update
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rpotter6298
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import Any, Optional, Protocol
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import pandas as pd
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@dataclass
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class PatientSplit:
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"""Patient-disjoint split definition for a fold."""
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train: pd.DataFrame
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val: pd.DataFrame
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holdout: Optional[pd.DataFrame] = None
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@dataclass
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class LoaderBundle:
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"""All loaders needed by a training run."""
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train: Any
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val: Any
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holdout: Optional[Any] = None
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@dataclass
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class FoldResult:
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"""Normalized fold output from trainer implementations."""
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fold: int
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metrics: dict[str, Any]
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artifacts: dict[str, Any]
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class SplitManager(Protocol):
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def build_plans(
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self,
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*,
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clinical: Any,
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args: Any,
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profile: Optional[Any] = None,
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) -> list[PatientSplit]:
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...
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class GraphFactory(Protocol):
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def build(
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self,
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*,
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clinical: Any,
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args: Any,
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fold: int,
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profile: Optional[Any] = None,
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) -> Any:
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...
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class LoaderFactory(Protocol):
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def build(
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self,
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*,
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clinical: Any,
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split: PatientSplit,
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args: Any,
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fold: int,
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profile: Optional[Any] = None,
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) -> LoaderBundle:
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...
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class Trainer(Protocol):
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def fit(
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self,
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*,
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graph: Any,
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loaders: LoaderBundle,
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args: Any,
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fold: int,
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profile: Optional[Any] = None,
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) -> FoldResult:
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...
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class NetworkManager:
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"""
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V2 orchestration entrypoint.
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This class is intentionally small and modular:
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- split policy is delegated to a SplitManager
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- graph assembly is delegated to a GraphFactory
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- dataloaders are delegated to a LoaderFactory
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- train/eval/checkpoint lifecycle is delegated to a Trainer
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"""
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def __init__(
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self,
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*,
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clinical: Any,
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args: Any,
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split_manager: SplitManager,
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graph_factory: GraphFactory,
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loader_factory: LoaderFactory,
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trainer: Trainer,
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profile: Optional[Any] = None,
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) -> None:
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self.clinical = clinical
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self.args = args
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self.split_manager = split_manager
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self.graph_factory = graph_factory
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self.loader_factory = loader_factory
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self.trainer = trainer
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self.profile = profile
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self._split_plans: Optional[list[PatientSplit]] = None
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def run_fold(self, fold: int) -> FoldResult:
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plans = self._get_split_plans()
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if fold < 0 or fold >= len(plans):
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raise IndexError(f"Requested fold {fold} but only {len(plans)} fold plans are available")
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split = plans[fold]
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self._validate_patient_disjointness(split)
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self._validate_labels(split)
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graph = self.graph_factory.build(
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clinical=self.clinical,
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args=self.args,
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fold=fold,
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profile=self.profile,
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)
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loaders = self.loader_factory.build(
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clinical=self.clinical,
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split=split,
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args=self.args,
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fold=fold,
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profile=self.profile,
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)
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return self.trainer.fit(
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graph=graph,
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loaders=loaders,
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args=self.args,
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fold=fold,
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profile=self.profile,
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)
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def run_all_folds(self, n_splits: Optional[int] = None) -> list[FoldResult]:
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plans = self._get_split_plans()
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max_folds = len(plans)
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if n_splits is None:
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n = max_folds
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else:
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n = int(n_splits)
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if n < 1:
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raise ValueError("n_splits must be >= 1")
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if n > max_folds:
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raise ValueError(f"Requested {n} folds but only {max_folds} fold plans are available")
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return [self.run_fold(fold) for fold in range(n)]
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def _get_split_plans(self) -> list[PatientSplit]:
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if self._split_plans is None:
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self._split_plans = self.split_manager.build_plans(
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clinical=self.clinical,
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args=self.args,
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profile=self.profile,
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)
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if not self._split_plans:
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raise ValueError("SplitManager returned no fold plans")
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return self._split_plans
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def _validate_patient_disjointness(self, split: PatientSplit) -> None:
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train_ids = self._patient_ids(split.train)
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val_ids = self._patient_ids(split.val)
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holdout_ids = self._patient_ids(split.holdout) if split.holdout is not None else set()
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if train_ids & val_ids:
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overlap = sorted(train_ids & val_ids)[:10]
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raise ValueError(f"Patient leakage between train/val: {overlap}")
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if train_ids & holdout_ids:
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overlap = sorted(train_ids & holdout_ids)[:10]
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raise ValueError(f"Patient leakage between train/holdout: {overlap}")
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if val_ids & holdout_ids:
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overlap = sorted(val_ids & holdout_ids)[:10]
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raise ValueError(f"Patient leakage between val/holdout: {overlap}")
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def _validate_labels(self, split: PatientSplit) -> None:
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label_col = getattr(self.clinical, "label_col", None)
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if not label_col:
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return
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for name, df in (("train", split.train), ("val", split.val), ("holdout", split.holdout)):
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if df is None:
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continue
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if label_col not in df.columns:
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raise ValueError(f"{name} split is missing label column {label_col!r}")
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@staticmethod
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def _patient_ids(df: Optional[pd.DataFrame]) -> set[Any]:
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if df is None or df.empty:
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return set()
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if "Patient ID" not in df.columns:
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raise ValueError("Split dataframes must include 'Patient ID'")
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return set(df["Patient ID"].tolist())
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