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"""Train end-to-end, frozen-probe, and fine-tuned downstream classifiers."""
from __future__ import annotations
import argparse
import copy
import json
import random
import time
from pathlib import Path
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from models import AutoEncoder, Classifier, build_encoder
def load_npz(path: Path):
import numpy as np # noqa: PLC0415
with np.load(path, allow_pickle=False) as data:
x = torch.tensor(data["x"], dtype=torch.float32)
y = torch.tensor(data["y"] + 1, dtype=torch.long)
train_key = "train_idx" if "train_idx" in data else "natural_train_idx"
keys = {"train": train_key, "val": "val_idx", "test": "test_idx"}
splits = {name: torch.tensor(data[key], dtype=torch.long) for name, key in keys.items()}
return x, y, splits
def make_loader(x, y, idx, batch_size, shuffle, num_workers=0, pin_memory=False):
return DataLoader(
TensorDataset(x[idx], y[idx]),
batch_size=batch_size,
shuffle=shuffle,
num_workers=num_workers,
pin_memory=pin_memory,
)
def sample_label_budget(y, train_idx, budget: float, seed: int):
if budget >= 0.999:
return train_idx
if budget <= 0:
raise ValueError("label budget must be positive.")
import numpy as np # noqa: PLC0415
rng = np.random.default_rng(seed)
train_np = train_idx.cpu().numpy()
y_np = y.cpu().numpy()
chosen = []
for cls in sorted(set(y_np[train_np].tolist())):
cls_idx = train_np[y_np[train_np] == cls]
if len(cls_idx) == 0:
continue
n = max(1, int(round(len(cls_idx) * budget)))
chosen.append(rng.choice(cls_idx, size=min(n, len(cls_idx)), replace=False))
if not chosen:
raise ValueError("label budget produced an empty training split.")
return torch.tensor(np.sort(np.concatenate(chosen)), dtype=torch.long)
def count_parameters(model):
total = sum(p.numel() for p in model.parameters())
trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)
return {"total": total, "trainable": trainable}
def classification_metrics(logits, y):
pred = logits.argmax(dim=1)
acc = (pred == y).float().mean().item()
precisions = []
recalls = []
f1s = []
for cls in range(3):
tp = ((pred == cls) & (y == cls)).sum().item()
fp = ((pred == cls) & (y != cls)).sum().item()
fn = ((pred != cls) & (y == cls)).sum().item()
recall = tp / max(tp + fn, 1)
precision = tp / max(tp + fp, 1)
precisions.append(precision)
recalls.append(recall)
f1s.append(2 * precision * recall / max(precision + recall, 1e-12))
class_names = ("down", "stationary", "up")
details = {
f"precision_{name}": float(precisions[index])
for index, name in enumerate(class_names)
}
details.update(
{
f"recall_{name}": float(recalls[index])
for index, name in enumerate(class_names)
}
)
details.update(
{
f"f1_{name}": float(f1s[index])
for index, name in enumerate(class_names)
}
)
return {
"accuracy": acc,
"balanced_accuracy": sum(recalls) / 3,
"macro_f1": sum(f1s) / 3,
**details,
}
def evaluate(model, loader, device):
model.eval()
logits_all = []
y_all = []
with torch.no_grad():
for x, y in loader:
logits_all.append(model(x.to(device)).cpu())
y_all.append(y)
return classification_metrics(torch.cat(logits_all), torch.cat(y_all))
def train_epoch(model, loader, optimizer, device, criterion):
model.train()
total = 0.0
n = 0
for x, y in loader:
x, y = x.to(device), y.to(device)
optimizer.zero_grad(set_to_none=True)
loss = criterion(model(x), y)
loss.backward()
optimizer.step()
total += float(loss.item()) * len(y)
n += len(y)
return total / max(n, 1)
def level_weights(device: torch.device, dtype: torch.dtype, decay: float = 0.85) -> torch.Tensor:
near_to_far = torch.pow(
torch.full((10,), decay, device=device, dtype=dtype),
torch.arange(10, device=device, dtype=dtype),
)
weights = torch.cat([near_to_far.flip(0), near_to_far, near_to_far.flip(0), near_to_far])
return weights / weights.mean()
def structure_regularizer(recon: torch.Tensor) -> torch.Tensor:
prices = recon[:, :, :20]
bid = prices[:, :, :10]
ask = prices[:, :, 10:20]
bid_order = torch.relu(bid[:, :, :-1] - bid[:, :, 1:]).mean()
ask_order = torch.relu(ask[:, :, :-1] - ask[:, :, 1:]).mean()
spread = torch.relu(bid[:, :, -1] - ask[:, :, 0]).mean()
return bid_order + ask_order + spread
def reconstruction_loss(pred: torch.Tensor, target: torch.Tensor, loss_name: str, structure_weight: float) -> torch.Tensor:
mse = torch.mean((pred - target) ** 2)
if loss_name == "mse":
return mse
if loss_name == "wmse":
weights = level_weights(pred.device, pred.dtype)
return torch.mean((pred - target) ** 2 * weights)
if loss_name == "mse_structure":
return mse + structure_weight * structure_regularizer(pred)
if loss_name in {"lobench", "wmse_structure"}:
weights = level_weights(pred.device, pred.dtype)
wmse = torch.mean((pred - target) ** 2 * weights)
return wmse + structure_weight * structure_regularizer(pred)
raise ValueError(f"unknown pretrain_loss: {loss_name}")
def pretrain_encoder(model_name, latent_dim, train_loader, val_loader, cfg, device, ckpt_path):
meta_path = ckpt_path.with_suffix(".pretrain.json")
if ckpt_path.exists() and meta_path.exists():
return json.loads(meta_path.read_text(encoding="utf-8")) | {"cached": True}
encoder = build_encoder(model_name, latent_dim)
decoder_name = cfg.get("pretrain_decoder", "mlp")
ae = AutoEncoder(
encoder,
latent_dim=latent_dim,
decoder_name=decoder_name,
decoder_layers=int(cfg.get("pretrain_decoder_layers", 2)),
decoder_heads=int(cfg.get("pretrain_decoder_heads", 8)),
decoder_d_model=int(cfg.get("pretrain_decoder_d_model", latent_dim)),
).to(device)
opt = torch.optim.AdamW(ae.parameters(), lr=cfg["learning_rate"], weight_decay=cfg["weight_decay"])
loss_name = cfg.get("pretrain_loss", "mse")
structure_weight = float(cfg.get("pretrain_structure_weight", 0.1))
best = float("inf")
best_epoch = 0
history = []
epochs_without_improvement = 0
patience = int(cfg.get("pretrain_patience", 0))
started = time.perf_counter()
for epoch in range(1, int(cfg["pretrain_epochs"]) + 1):
epoch_started = time.perf_counter()
ae.train()
train_loss = 0.0
train_mse = 0.0
train_n = 0
for x, _ in train_loader:
x = x.to(device)
opt.zero_grad(set_to_none=True)
pred = ae(x)
loss = reconstruction_loss(pred, x, loss_name, structure_weight)
loss.backward()
opt.step()
train_loss += float(loss.item()) * len(x)
train_mse += float(torch.mean((pred.detach() - x) ** 2).item()) * len(x)
train_n += len(x)
val = 0.0
val_mse = 0.0
n = 0
ae.eval()
with torch.no_grad():
for x, _ in val_loader:
x = x.to(device)
pred = ae(x)
batch_loss = reconstruction_loss(pred, x, loss_name, structure_weight).item()
batch_mse = torch.mean((pred - x) ** 2).item()
val += batch_loss * len(x)
val_mse += batch_mse * len(x)
n += len(x)
val /= max(n, 1)
val_mse /= max(n, 1)
history.append(
{
"epoch": epoch,
"train_loss": train_loss / max(train_n, 1),
"val_loss": val,
"train_mse": train_mse / max(train_n, 1),
"val_mse": val_mse,
f"train_{loss_name}": train_loss / max(train_n, 1),
f"val_{loss_name}": val,
"epoch_seconds": time.perf_counter() - epoch_started,
}
)
if val < best:
best = val
best_epoch = epoch
torch.save(ae.encoder.state_dict(), ckpt_path)
epochs_without_improvement = 0
else:
epochs_without_improvement += 1
if patience and epochs_without_improvement >= patience:
break
meta = {
"decoder": decoder_name,
"pretrain_loss": loss_name,
"pretrain_structure_weight": structure_weight,
"best_val_loss": best,
f"best_val_{loss_name}": best,
"best_val_mse": history[best_epoch - 1]["val_mse"] if best_epoch else None,
"best_epoch": best_epoch,
"seconds": time.perf_counter() - started,
"history": history,
"cached": False,
}
meta_path.write_text(json.dumps(meta, indent=2), encoding="utf-8")
return meta
def set_seed(seed: int) -> None:
import os # noqa: PLC0415
os.environ.setdefault("CUBLAS_WORKSPACE_CONFIG", ":4096:8")
random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
# Fail loudly if a final evidence run reaches a nondeterministic CUDA
# kernel. Repeated seeds are meaningful only when the execution policy is
# itself recorded and enforced.
torch.use_deterministic_algorithms(True, warn_only=False)
try:
import numpy as np # noqa: PLC0415
np.random.seed(seed)
except Exception:
pass
def run(args):
cfg = json.loads(Path(args.config).read_text(encoding="utf-8"))
train_cfg = cfg["training"]
out_dir = Path(cfg["results_dir"])
out_dir.mkdir(parents=True, exist_ok=True)
pretrain_stock = args.pretrain_stock or args.stock
windows = Path(cfg["processed_dir"]) / f"{args.stock}_pilot.npz"
x, y, splits = load_npz(windows)
set_seed(args.seed)
device = torch.device(train_cfg["device"] if torch.cuda.is_available() else "cpu")
batch_size = int(train_cfg["batch_size"])
num_workers = int(train_cfg.get("num_workers", 0))
pin_memory = device.type == "cuda"
label_budget = float(args.label_budget)
downstream_train_idx = sample_label_budget(y, splits["train"], label_budget, args.seed)
pretrain_train_loader = make_loader(x, y, splits["train"], batch_size, shuffle=True, num_workers=num_workers, pin_memory=pin_memory)
train_loader = make_loader(x, y, downstream_train_idx, batch_size, shuffle=True, num_workers=num_workers, pin_memory=pin_memory)
val_loader = make_loader(x, y, splits["val"], batch_size, shuffle=False, num_workers=num_workers, pin_memory=pin_memory)
test_loader = make_loader(x, y, splits["test"], batch_size, shuffle=False, num_workers=num_workers, pin_memory=pin_memory)
latent_dim = int(train_cfg["latent_dim"])
decoder_name = train_cfg.get("pretrain_decoder", "mlp")
pretrain_loss_name = train_cfg.get("pretrain_loss", "mse")
encoder = build_encoder(args.model, latent_dim)
loss_tag = "" if pretrain_loss_name == "mse" else f"_loss{pretrain_loss_name}"
ckpt = out_dir / f"{pretrain_stock}_{args.model}_{decoder_name}{loss_tag}_seed{args.seed}_encoder.pt"
pretrain = None
if args.setting in {"frozen_probe", "fine_tune"}:
source_train_loader = pretrain_train_loader
source_val_loader = val_loader
if pretrain_stock != args.stock:
source_windows = Path(cfg["processed_dir"]) / f"{pretrain_stock}_pilot.npz"
sx, sy, source_splits = load_npz(source_windows)
source_train_loader = make_loader(
sx,
sy,
source_splits["train"],
batch_size,
shuffle=True,
num_workers=num_workers,
pin_memory=pin_memory,
)
source_val_loader = make_loader(
sx,
sy,
source_splits["val"],
batch_size,
shuffle=False,
num_workers=num_workers,
pin_memory=pin_memory,
)
pretrain = pretrain_encoder(args.model, latent_dim, source_train_loader, source_val_loader, train_cfg, device, ckpt)
encoder.load_state_dict(torch.load(ckpt, map_location="cpu"))
if args.setting in {"frozen_probe", "random_frozen"}:
for p in encoder.parameters():
p.requires_grad = False
model = Classifier(encoder, latent_dim=latent_dim).to(device)
parameters = count_parameters(model)
opt = torch.optim.AdamW(
[p for p in model.parameters() if p.requires_grad],
lr=train_cfg["learning_rate"],
weight_decay=train_cfg["weight_decay"],
)
criterion = nn.CrossEntropyLoss()
history = []
train_started = time.perf_counter()
best_val_macro_f1 = -1.0
best_epoch = 0
best_state = copy.deepcopy(model.state_dict())
epochs_without_improvement = 0
patience = int(train_cfg.get("downstream_patience", 0))
for epoch in range(1, int(train_cfg["downstream_epochs"]) + 1):
epoch_started = time.perf_counter()
loss = train_epoch(model, train_loader, opt, device, criterion)
metrics = evaluate(model, val_loader, device)
if metrics["macro_f1"] > best_val_macro_f1:
best_val_macro_f1 = metrics["macro_f1"]
best_epoch = epoch
best_state = copy.deepcopy(model.state_dict())
epochs_without_improvement = 0
else:
epochs_without_improvement += 1
history.append(
{
"epoch": epoch,
"loss": loss,
"epoch_seconds": time.perf_counter() - epoch_started,
**{f"val_{k}": v for k, v in metrics.items()},
}
)
if patience and epochs_without_improvement >= patience:
break
downstream_seconds = time.perf_counter() - train_started
model.load_state_dict(best_state)
result = {
"stock": args.stock,
"pretrain_stock": pretrain_stock,
"model": args.model,
"setting": args.setting,
"seed": args.seed,
"label_budget": label_budget,
"label_horizon": cfg.get("data", {}).get("label_horizon"),
"label_threshold": cfg.get("data", {}).get("label_threshold"),
"train_samples": int(len(downstream_train_idx)),
"full_train_samples": int(len(splits["train"])),
"pretrain_decoder": decoder_name,
"pretrain": pretrain,
"pretrain_cached": bool(pretrain.get("cached")) if pretrain else False,
"parameters": parameters,
"best_val_macro_f1": best_val_macro_f1,
"best_epoch": best_epoch,
"downstream_seconds": downstream_seconds,
"total_seconds": downstream_seconds + (pretrain["seconds"] if pretrain else 0.0),
"val": evaluate(model, val_loader, device),
"test": evaluate(model, test_loader, device),
"history": history,
}
budget_tag = f"_budget{int(round(label_budget * 100)):03d}" if label_budget < 0.999 else ""
source_tag = f"_from{pretrain_stock}" if pretrain_stock != args.stock else ""
out_file = out_dir / f"{args.stock}_{args.model}_{args.setting}{source_tag}_seed{args.seed}{budget_tag}.json"
out_file.write_text(json.dumps(result, indent=2), encoding="utf-8")
print(json.dumps(result["test"], indent=2))
print(f"wrote {out_file}")
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--config", required=True)
parser.add_argument("--stock", required=True)
parser.add_argument("--model", required=True)
parser.add_argument("--setting", choices=["end_to_end", "random_frozen", "frozen_probe", "fine_tune"], required=True)
parser.add_argument("--seed", type=int, required=True)
parser.add_argument("--label-budget", type=float, default=1.0)
parser.add_argument("--pretrain-stock", default=None)
return parser.parse_args()
if __name__ == "__main__":
run(parse_args())
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