CIS490/training/models/_checkpoint.py

"""Schema-hashed checkpoint format.

Every saved model carries a sha256 of its input schema (the sorted
feature_names for summary models, the sorted channel_names for tensor
models). On load we recompute the schema hash from the live
``_features.py`` and refuse to load a checkpoint built against a
different schema. This is the difference between "the trained model
saw column 17 = guest.cpu_user" and "the live inference is feeding
column 17 = whatever-_features-now-puts-there."

A checkpoint is a JSON-serializable dict on disk. NN subclasses
serialize their torch state_dict separately as a sidecar ``.pt`` file
referenced from the JSON; GBT writes the XGBoost JSON directly.

Layout::

    artifacts/<name>.ckpt.json
    artifacts/<name>.pt          (torch sidecar; only for NN models)
    artifacts/<name>.xgb.json    (xgboost sidecar; only for GBT)

The JSON file is the source of truth for the schema header and the
loader uses it to know which sidecar to read.
"""
from __future__ import annotations

import hashlib
import json
from dataclasses import asdict, dataclass
from pathlib import Path
from typing import Any

import numpy as np

from training._features import (
    ALL_CHANNELS,
    PHASES,
    channel_in_deployment_mask,
    channel_names,
    in_deployment_mask,
)
from training.models import BaseModel, get_model
from training.models._base import StandardizeStats


CHECKPOINT_VERSION = 1


def summary_schema_hash() -> str:
    """sha256 of the sorted summary feature_names — what GBT and MLP see."""
    from training._features import feature_names_episode
    names = sorted(feature_names_episode())
    return hashlib.sha256("\n".join(names).encode()).hexdigest()


def tensor_schema_hash() -> str:
    """sha256 of the sorted channel_names — what CNN/GRU/LSTM/Transformer see."""
    names = sorted(channel_names())
    return hashlib.sha256("\n".join(names).encode()).hexdigest()


def expected_schema_hash(input_kind: str) -> str:
    if input_kind == "summary":
        return summary_schema_hash()
    if input_kind == "tensor":
        return tensor_schema_hash()
    raise ValueError(f"unknown input_kind: {input_kind}")


@dataclass
class CheckpointHeader:
    """Generic header — same for every model, written to the JSON file."""
    version: int
    name: str                           # registry name: "gbt" | "mlp" | "cnn" | ...
    mode: str                           # "realistic" | "oracle"
    input_kind: str                     # "summary" | "tensor"
    schema_hash: str
    n_classes: int
    phases: list[str]
    keep_mask: list[bool]
    standardize: dict
    sidecar: str                        # filename of .pt or .xgb.json
    pca_proj: list[list[float]] | None  # (n_keep_features_or_channels, 2) or None
    config: dict                        # model-specific config (depth, hidden, ...)
    train_meta: dict                    # split recipe + config + metric on val

    def to_dict(self) -> dict:
        return asdict(self)


def make_keep_mask(input_kind: str, mode: str) -> np.ndarray:
    """Per-feature or per-channel keep mask for the given mode."""
    if input_kind == "summary":
        full = in_deployment_mask()
    else:
        full = channel_in_deployment_mask()
    if mode == "realistic":
        return full
    if mode == "oracle":
        return np.ones_like(full)
    raise ValueError(f"unknown mode: {mode}")


def save_checkpoint(
    model: BaseModel,
    *,
    path: Path,                         # base path; .ckpt.json appended if absent
    name: str,
    mode: str,
    config: dict,
    train_meta: dict,
    pca_proj: np.ndarray | None = None,
) -> Path:
    """Persist a model + its schema header. Returns the JSON path."""
    base = Path(str(path).removesuffix(".ckpt.json"))
    base.parent.mkdir(parents=True, exist_ok=True)

    sidecar_filename = _write_sidecar(model, base=base)

    if model.standardize is None:
        raise ValueError("model.standardize must be fit before saving")
    if model.keep_mask is None:
        raise ValueError("model.keep_mask must be set before saving")

    header = CheckpointHeader(
        version=CHECKPOINT_VERSION,
        name=name,
        mode=mode,
        input_kind=model.input_kind,
        schema_hash=expected_schema_hash(model.input_kind),
        n_classes=model.n_classes,
        phases=list(PHASES[: model.n_classes]),
        keep_mask=[bool(b) for b in np.asarray(model.keep_mask).tolist()],
        standardize=model.standardize.to_dict(),
        sidecar=sidecar_filename,
        pca_proj=(pca_proj.tolist() if pca_proj is not None else None),
        config=config,
        train_meta=train_meta,
    )

    json_path = base.with_suffix(".ckpt.json")
    json_path.write_text(json.dumps(header.to_dict(), indent=2) + "\n")
    return json_path


def _write_sidecar(model: BaseModel, *, base: Path) -> str:
    """Persist the model-specific weights. Returns the sidecar filename.

    Each model subclass defines its own sidecar format and extension via
    ``save_sidecar(path)``. The framework picks the extension based on
    the model kind.
    """
    if model.__model_name__ == "gbt":
        path = base.with_suffix(".xgb.json")
    elif model.__model_name__ == "knn":
        path = base.with_suffix(".knn.pkl")
    else:
        path = base.with_suffix(".pt")
    model.save_sidecar(path)
    return path.name


def load_checkpoint(path: Path, *, device: str = "auto") -> BaseModel:
    """Load a checkpoint with schema verification.

    Raises if the schema hash does not match what ``_features.py``
    currently produces. This is the guarantee that a model only ever
    sees inputs in the layout it was trained on."""
    json_path = Path(str(path))
    if json_path.suffix != ".json":
        json_path = json_path.with_suffix(".ckpt.json")
    header = json.loads(json_path.read_text())

    if header.get("version") != CHECKPOINT_VERSION:
        raise ValueError(
            f"checkpoint version mismatch: file={header.get('version')} "
            f"expected={CHECKPOINT_VERSION}")

    expected = expected_schema_hash(header["input_kind"])
    if header["schema_hash"] != expected:
        raise ValueError(
            f"schema hash mismatch for {json_path}: "
            f"\n  file:    {header['schema_hash']}"
            f"\n  current: {expected}"
            f"\nThe channel/feature registry has changed since this model "
            f"was trained. Retrain or pin the registry."
        )

    cls = get_model(header["name"])
    sidecar = json_path.with_name(header["sidecar"])
    payload: dict[str, Any]
    if header["name"] in ("gbt", "knn"):
        # File-path loaders (XGBoost JSON, sklearn pickle); they open
        # the sidecar themselves rather than receiving torch tensors.
        payload = {"sidecar_path": str(sidecar)}
    else:
        import torch
        if device == "auto":
            device = "cuda" if torch.cuda.is_available() else "cpu"
        payload = torch.load(sidecar, map_location=device, weights_only=False)
        payload["_device"] = device
    return cls.from_checkpoint(header, payload, device=device)


def load_header(path: Path) -> dict:
    """Read just the JSON header (no weights). For inventories / registries."""
    p = Path(str(path))
    if p.suffix != ".json":
        p = p.with_suffix(".ckpt.json")
    return json.loads(p.read_text())