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c4135cc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 | """Ridge multi-horizon CGM forecaster, packaged for the HF Hub.
One repo holds four feature ablations (``cgm``, ``insulin``, ``carbs``, ``all``)
as separate ``model_<ablation>.safetensors`` files. The active ablation is
selected at load time via the ``ablation=`` kwarg passed through ``AutoConfig``
or ``AutoModel`` ``from_pretrained``.
Usage::
from transformers import AutoConfig, AutoModel
cfg = AutoConfig.from_pretrained(
"anonymous-4FAD/Ridge", trust_remote_code=True, ablation="cgm")
model = AutoModel.from_pretrained(
"anonymous-4FAD/Ridge", trust_remote_code=True, config=cfg)
preds = model.predict(timestamps_ns, cgm, insulin, carbs) # (B, 12)
"""
from __future__ import annotations
import math
import os
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from transformers import PretrainedConfig, PreTrainedModel
_HUB_DOWNLOAD_KWARGS = (
"cache_dir",
"force_download",
"local_files_only",
"proxies",
"revision",
"subfolder",
"token",
)
class RidgeMultiHorizonConfig(PretrainedConfig):
"""Config for the multi-horizon Ridge forecaster.
The same repo serves four ablations (``cgm``, ``insulin``, ``carbs``,
``all``); the currently active one is ``self.ablation``.
"""
model_type = "ridge_multihorizon"
def __init__(
self,
ablation: str = "all",
ablations: Optional[list] = None,
history_length: int = 24,
horizon_length: int = 12,
feature_names_by_ablation: Optional[dict] = None,
n_features_by_ablation: Optional[dict] = None,
target_names: Optional[list] = None,
**kwargs,
):
if ablations is None:
ablations = ["cgm", "insulin", "carbs", "all"]
if ablation not in ablations:
raise ValueError(
f"ablation must be one of {ablations}, got {ablation!r}"
)
self.ablation = ablation
self.ablations = list(ablations)
self.history_length = int(history_length)
self.horizon_length = int(horizon_length)
self.feature_names_by_ablation = feature_names_by_ablation or {}
self.n_features_by_ablation = n_features_by_ablation or {}
self.target_names = list(target_names or [])
super().__init__(**kwargs)
@property
def n_features(self) -> int:
if self.n_features_by_ablation:
return int(self.n_features_by_ablation[self.ablation])
return len(self.feature_names_by_ablation[self.ablation])
@property
def feature_names(self) -> list:
return list(self.feature_names_by_ablation[self.ablation])
class RidgeMultiHorizonModel(PreTrainedModel):
"""Multi-output Ridge regressor over standardized tabular features.
Holds only buffers (``scaler_mean``, ``scaler_scale``, ``coef``,
``intercept``); there are no trainable parameters.
"""
config_class = RidgeMultiHorizonConfig
main_input_name = "features"
_tied_weights_keys: dict = None
_no_split_modules: list = []
def __init__(self, config: RidgeMultiHorizonConfig):
super().__init__(config)
n_feat = config.n_features
n_horiz = config.horizon_length
self.register_buffer("scaler_mean", torch.zeros(n_feat))
self.register_buffer("scaler_scale", torch.ones(n_feat))
self.register_buffer("coef", torch.zeros(n_horiz, n_feat))
self.register_buffer("intercept", torch.zeros(n_horiz))
def _init_weights(self, module):
# No trainable parameters; values come from safetensors.
pass
def forward(self, features: torch.Tensor) -> torch.Tensor:
x = (features.to(self.coef.dtype) - self.scaler_mean) / self.scaler_scale
return x @ self.coef.T + self.intercept
@classmethod
def from_pretrained(
cls,
pretrained_model_name_or_path,
*model_args,
config=None,
ablation: Optional[str] = None,
**kwargs,
):
# Drop transformers-internal markers we don't need to act on.
kwargs.pop("trust_remote_code", None)
kwargs.pop("_from_auto", None)
kwargs.pop("_commit_hash", None)
hub_kwargs = {k: kwargs.pop(k) for k in _HUB_DOWNLOAD_KWARGS if k in kwargs}
if config is None:
config_kwargs = dict(hub_kwargs)
if ablation is not None:
config_kwargs["ablation"] = ablation
config = RidgeMultiHorizonConfig.from_pretrained(
pretrained_model_name_or_path, **config_kwargs
)
elif ablation is not None:
config.ablation = ablation
model = cls(config)
weights_filename = f"model_{config.ablation}.safetensors"
if os.path.isdir(str(pretrained_model_name_or_path)):
weights_path = os.path.join(
str(pretrained_model_name_or_path), weights_filename)
if not os.path.isfile(weights_path):
raise FileNotFoundError(
f"Expected {weights_filename} in {pretrained_model_name_or_path}"
)
else:
weights_path = hf_hub_download(
repo_id=str(pretrained_model_name_or_path),
filename=weights_filename,
**hub_kwargs,
)
state = load_file(weights_path)
missing, unexpected = model.load_state_dict(state, strict=False)
if missing:
raise RuntimeError(
f"{weights_filename} is missing buffers required by the model: {missing}"
)
if unexpected:
# Not fatal, but worth surfacing in case a checkpoint has stale keys.
print(
f"RidgeMultiHorizonModel: ignoring unexpected keys in "
f"{weights_filename}: {unexpected}"
)
model.eval()
return model
@torch.no_grad()
def predict(self, timestamps, cgm, insulin, carbs) -> np.ndarray:
"""Run inference for a benchmark.py-style batch.
Args:
timestamps: int64 ns timestamps, shape ``(B, T_in)``.
cgm: float CGM values, shape ``(B, T_in)``.
insulin: float insulin values, shape ``(B, T_in)`` (used only if
the active ablation requires Insulin features).
carbs: float carb values, shape ``(B, T_in)`` (used only if the
active ablation requires Carbs features).
Returns:
``(B, horizon_length)`` numpy array of predicted CGM values.
"""
features = _build_tabular_features(
timestamps=np.asarray(timestamps),
cgm=np.asarray(cgm, dtype=np.float64),
insulin=np.asarray(insulin, dtype=np.float64),
carbs=np.asarray(carbs, dtype=np.float64),
feature_names=self.config.feature_names,
history_length=self.config.history_length,
)
device = self.coef.device
x = torch.as_tensor(features, dtype=self.coef.dtype, device=device)
out = self.forward(x)
return out.detach().cpu().numpy()
def _build_tabular_features(
*,
timestamps: np.ndarray,
cgm: np.ndarray,
insulin: np.ndarray,
carbs: np.ndarray,
feature_names: list,
history_length: int,
) -> np.ndarray:
"""Assemble a (B, F) feature matrix in the order given by ``feature_names``.
Convention: ``CGM_t<i>`` means the i-th *most recent* sample within the
last ``history_length`` steps, i.e. ``CGM_t0`` = oldest in the window,
``CGM_t<history_length-1>`` = newest. Same convention applies to
``Insulin_t<i>`` / ``Carbs_t<i>``. ``hour_sin`` / ``hour_cos`` are derived
from the most recent input timestamp (UTC hour-of-day).
"""
if cgm.shape[-1] < history_length:
raise ValueError(
f"Need at least {history_length} CGM samples, got {cgm.shape[-1]}"
)
cgm_h = cgm[..., -history_length:]
insulin_h = insulin[..., -history_length:]
carbs_h = carbs[..., -history_length:]
# Hour-of-day from the most recent input timestamp (ns since epoch).
last_ts = np.asarray(timestamps)[..., -1].astype(np.int64)
hours = (last_ts // 3_600_000_000_000) % 24
hour_sin = np.sin(2.0 * math.pi * hours / 24.0)
hour_cos = np.cos(2.0 * math.pi * hours / 24.0)
columns = []
for name in feature_names:
if name.startswith("CGM_t"):
i = int(name.split("_t", 1)[1])
columns.append(cgm_h[..., i])
elif name.startswith("Insulin_t"):
i = int(name.split("_t", 1)[1])
columns.append(insulin_h[..., i])
elif name.startswith("Carbs_t"):
i = int(name.split("_t", 1)[1])
columns.append(carbs_h[..., i])
elif name == "hour_sin":
columns.append(hour_sin)
elif name == "hour_cos":
columns.append(hour_cos)
else:
raise ValueError(f"Unknown feature column: {name!r}")
return np.stack(columns, axis=-1).astype(np.float32)
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