subspace/construction_b.py

"""Construction B — residual-from-normal-manifold lesion subspace, label-free.

Formalization §2: fit a low-rank normal-tissue subspace U_norm on a large normal CT bank
(PCA / coding rate). Lesion-relevant directions are the high-residual ones:
    r_i = || z_i - U_norm U_norm^T z_i ||,   L(x) = span{ z_i : r_i >= tau }.
Because lesions are rare, PCA over the whole bank approximates the normal manifold, so the
top-`rank` principal directions are U_norm and the lesion subspace is the residual (orthogonal
complement) where pathology concentrates.

membership_score(z) = residual norm ||(I - U U^T) z|| (HIGH residual => HIGH lesion score).
project(Z) = (I - U U^T) Z, the projection onto the lesion (residual) subspace. Label-free:
only token-feature geometry is used; tau is a quantile of residuals, not a label.
"""
from __future__ import annotations

import numpy as np
import torch

from data.leak_guard import subspace_construction_guard


class ResidualSubspace:
    def __init__(self, rank: int = 64, tau_quantile: float = 0.9,
                 reference_size: int = 200_000, seed: int = 0):
        self.rank = rank
        self.tau_quantile = tau_quantile
        self.reference_size = reference_size
        self.seed = seed
        self.U_norm_: torch.Tensor | None = None  # (d, rank) normal-manifold basis
        self.mean_: torch.Tensor | None = None
        self.tau_: float | None = None
        self.P_L_: torch.Tensor | None = None     # (d, d) residual projector I - UU^T

    def fit(self, token_bank: torch.Tensor) -> "ResidualSubspace":
        with subspace_construction_guard():  # no label/mask may be read in here
            X = token_bank.float()
            rng = np.random.default_rng(self.seed)
            if X.shape[0] > self.reference_size:
                idx = torch.from_numpy(
                    rng.choice(X.shape[0], self.reference_size, replace=False))
                Xs = X[idx]
            else:
                Xs = X
            self.mean_ = Xs.mean(dim=0, keepdim=True)
            Xc = Xs - self.mean_
            # PCA via SVD: top-`rank` right singular vectors = normal manifold U_norm
            _, _, Vt = torch.linalg.svd(Xc, full_matrices=False)
            U = Vt[: self.rank].T.contiguous()      # (d, rank)
            self.U_norm_ = U
            d = X.shape[1]
            self.P_L_ = torch.eye(d) - U @ U.T       # residual projector
            res = self._residual(Xs)
            self.tau_ = float(torch.quantile(res, self.tau_quantile))
        return self

    def _residual(self, Z: torch.Tensor) -> torch.Tensor:
        Zc = Z.float() - self.mean_
        proj = Zc @ self.U_norm_ @ self.U_norm_.T
        return (Zc - proj).norm(dim=1)

    def membership_score(self, Z: torch.Tensor) -> torch.Tensor:
        """Per-token lesion score = normal-manifold residual norm (higher => more lesion-like)."""
        assert self.U_norm_ is not None, "fit() first"
        return self._residual(Z).cpu()

    def membership_score_torch(self, Z: torch.Tensor, device=None) -> torch.Tensor:
        """GPU/torch residual scoring for large eval sets."""
        assert self.U_norm_ is not None, "fit() first"
        device = device or Z.device
        U = self.U_norm_.to(device)
        mean = self.mean_.to(device)
        Zc = Z.float().to(device) - mean
        proj = Zc @ U @ U.T
        return (Zc - proj).norm(dim=1).cpu()

    def project(self, Z: torch.Tensor) -> torch.Tensor:
        assert self.P_L_ is not None, "fit() first"
        return Z.float() @ self.P_L_.T.to(Z.device)