From ead579b25c12f08270d5e698c6082993bcc4060a Mon Sep 17 00:00:00 2001 From: RYDE-WORK Date: Sat, 28 Feb 2026 18:29:07 +0800 Subject: [PATCH] Fallback --- lnp_ml/modeling/benchmark.py | 19 +++---------------- 1 file changed, 3 insertions(+), 16 deletions(-) diff --git a/lnp_ml/modeling/benchmark.py b/lnp_ml/modeling/benchmark.py index 42f2ff5..6ac6b20 100644 --- a/lnp_ml/modeling/benchmark.py +++ b/lnp_ml/modeling/benchmark.py @@ -1,7 +1,6 @@ """Benchmark 脚本：在 baseline 论文公开的 CV 划分上评估模型（仅 delivery 任务）""" import json -import math from pathlib import Path from typing import Dict, List, Optional @@ -10,7 +9,6 @@ import pandas as pd import torch import torch.nn as nn from torch.utils.data import DataLoader -from torch.optim.lr_scheduler import LambdaLR, CosineAnnealingLR, SequentialLR from loguru import logger from tqdm import tqdm from sklearn.metrics import mean_squared_error, r2_score @@ -160,7 +158,6 @@ def train_fold( weight_decay: float = 1e-5, epochs: int = 50, patience: int = 10, - warmup_epochs: int = 3, config: Optional[Dict] = None, ) -> Dict: """训练单个 fold""" @@ -169,19 +166,9 @@ def train_fold( logger.info(f"{'='*60}") optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay) - - warmup_scheduler = LambdaLR( - optimizer, lr_lambda=lambda epoch: (epoch + 1) / warmup_epochs + scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( + optimizer, mode="min", factor=0.5, patience=5 ) - cosine_scheduler = CosineAnnealingLR( - optimizer, T_max=epochs - warmup_epochs - ) - scheduler = SequentialLR( - optimizer, - schedulers=[warmup_scheduler, cosine_scheduler], - milestones=[warmup_epochs], - ) - early_stopping = EarlyStopping(patience=patience) best_val_loss = float("inf") @@ -211,7 +198,7 @@ def train_fold( "lr": current_lr, }) - scheduler.step() + scheduler.step(val_metrics["loss"]) if val_metrics["loss"] < best_val_loss: best_val_loss = val_metrics["loss"]