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53e13eb | 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 | """LightGBM multi-horizon CGM forecaster, packaged for the HF Hub.
One repo holds four feature ablations (``cgm``, ``insulin``, ``carbs``,
``all``); each ablation has 12 boosters (one per 5-minute horizon) stored as
LightGBM ``Booster.save_model`` text files under
``boosters/<ablation>/horizon_<NN>.txt``. The active ablation is selected at
load time via ``ablation=`` on ``AutoConfig`` / ``AutoModel`` ``from_pretrained``.
Usage::
from transformers import AutoConfig, AutoModel
cfg = AutoConfig.from_pretrained(
"anonymous-4FAD/LightGBM", trust_remote_code=True, ablation="cgm")
model = AutoModel.from_pretrained(
"anonymous-4FAD/LightGBM", 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
from huggingface_hub import snapshot_download
from transformers import PretrainedConfig, PreTrainedModel
_HUB_DOWNLOAD_KWARGS = (
"cache_dir",
"force_download",
"local_files_only",
"proxies",
"revision",
"token",
)
class LightGBMMultiHorizonConfig(PretrainedConfig):
"""Config for the multi-horizon LightGBM forecaster."""
model_type = "lightgbm_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 LightGBMMultiHorizonModel(PreTrainedModel):
"""Wraps 12 LightGBM boosters (one per horizon) behind a transformers API.
Holds no torch parameters; the boosters live in ``self._boosters`` after
``from_pretrained`` and run on CPU.
"""
config_class = LightGBMMultiHorizonConfig
main_input_name = "features"
_tied_weights_keys: dict = None
_no_split_modules: list = []
def __init__(self, config: LightGBMMultiHorizonConfig):
super().__init__(config)
# Sentinel buffer so ``model.to(device)`` and ``state_dict()`` don't choke.
self.register_buffer("_dummy", torch.zeros(1))
self._boosters: list = []
def _init_weights(self, module):
# No torch params to initialize.
pass
@classmethod
def from_pretrained(
cls,
pretrained_model_name_or_path,
*model_args,
config=None,
ablation: Optional[str] = None,
**kwargs,
):
kwargs.pop("trust_remote_code", None)
kwargs.pop("_from_auto", None)
kwargs.pop("_commit_hash", None)
kwargs.pop("subfolder", 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 = LightGBMMultiHorizonConfig.from_pretrained(
pretrained_model_name_or_path, **config_kwargs
)
elif ablation is not None:
config.ablation = ablation
model = cls(config)
if os.path.isdir(str(pretrained_model_name_or_path)):
local_dir = str(pretrained_model_name_or_path)
else:
local_dir = snapshot_download(
repo_id=str(pretrained_model_name_or_path),
allow_patterns=[
"config.json",
f"boosters/{config.ablation}/horizon_*.txt",
],
**hub_kwargs,
)
booster_dir = os.path.join(local_dir, "boosters", config.ablation)
if not os.path.isdir(booster_dir):
raise FileNotFoundError(
f"Missing boosters directory for ablation {config.ablation!r}: {booster_dir}"
)
# Imported lazily so the package is only required for inference.
import lightgbm as lgb
boosters = []
for h in range(config.horizon_length):
path = os.path.join(booster_dir, f"horizon_{h:02d}.txt")
if not os.path.isfile(path):
raise FileNotFoundError(f"Missing booster: {path}")
boosters.append(lgb.Booster(model_file=path))
model._boosters = boosters
model.eval()
return model
def forward(self, features) -> torch.Tensor:
if isinstance(features, torch.Tensor):
x = features.detach().cpu().numpy().astype(np.float32, copy=False)
else:
x = np.asarray(features, dtype=np.float32)
if not self._boosters:
raise RuntimeError(
"LightGBM boosters are not loaded. Construct the model via "
"from_pretrained()."
)
cols = [b.predict(x) for b in self._boosters]
out = np.stack(cols, axis=-1).astype(np.float32, copy=False)
return torch.as_tensor(out)
def predict(self, timestamps, cgm, insulin, carbs) -> np.ndarray:
"""Run inference for a benchmark.py-style batch.
See the corresponding ``predict`` on the Ridge model for the input
contract; output is ``(B, horizon_length)``.
"""
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,
)
out = self.forward(features)
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 matching ``feature_names`` order.
See ``hub/ridge/model.py`` for the lag convention (``CGM_t0`` = oldest,
``CGM_t<history_length-1>`` = newest within the window).
"""
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:]
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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