huangfu
745868a456
Group samples by identical inputs before splitting, add target-aware stratification options, and cover the behavior with tests so repeated-input rows no longer leak across train, validation, and test sets. Made-with: Cursor
194 lines
6.2 KiB
Python
194 lines
6.2 KiB
Python
"""最小冒烟测试：模型 shape 与单 epoch 训练不报错。"""
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from __future__ import annotations
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import json
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from pathlib import Path
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import numpy as np
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import torch
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import yaml
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from src.config import load_config
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from src.data import INPUT_COLUMNS
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from src.data import load_raw_txt
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from src.model import MLPRegressor
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from src.preprocess import prepare_training_data
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from src.trainer import fit
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def _write_synthetic_txt(path: Path, n: int = 64) -> None:
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rng = np.random.default_rng(0)
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x = rng.normal(size=(n, 8))
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y = np.zeros((n, 3))
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y[:, 0] = rng.normal(size=n)
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y[:, 1] = rng.normal(size=n)
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# 第 11 列 V_pi 落在默认物理门控 [0, 500] 内
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y[:, 2] = rng.uniform(1.0, 400.0, size=n)
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mat = np.hstack([x, y])
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lines = [",".join(str(v) for v in row) for row in mat]
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path.write_text("\n".join(lines), encoding="utf-8")
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def test_mlp_forward_shape() -> None:
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m = MLPRegressor(8, [16, 16], 3, batchnorm=False, dropout=0.0, residual=False)
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x = torch.randn(5, 8)
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y = m(x)
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assert y.shape == (5, 3)
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def test_one_epoch_training_pipeline(tmp_path: Path) -> None:
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data_txt = tmp_path / "data.txt"
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_write_synthetic_txt(data_txt, n=80)
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cfg_dict = {
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"data_path": str(data_txt),
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"split_ratios": [0.7, 0.15, 0.15],
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"random_seed": 1,
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"remove_duplicate_rows": False,
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"outlier_strategy": "none",
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"outlier_apply_to": "targets",
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"outlier_config": {
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"iqr_k": 1.5,
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"zscore_threshold": 4.0,
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"quantile_lower": 0.001,
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"quantile_upper": 0.999,
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},
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"remove_nonpositive_vpi": False,
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"filter_v_pi_range": True,
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"v_pi_min": 0.0,
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"v_pi_max": 500.0,
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"model": {
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"input_dim": 8,
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"hidden_dims": [32, 32],
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"output_dim": 3,
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"batchnorm": False,
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"dropout": 0.0,
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"residual": False,
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},
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"optimizer": {"name": "adamw", "lr": 0.01, "weight_decay": 0.0},
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"scheduler": {
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"type": "cosine",
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"plateau_factor": 0.5,
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"plateau_patience": 10,
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"plateau_min_lr": 1e-6,
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},
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"training": {
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"batch_size": 16,
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"epochs": 1,
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"early_stopping_patience": 1,
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"num_workers": 0,
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},
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"loss": {"type": "huber", "huber_delta": 1.0, "target_weights": [1.0, 1.0, 1.0]},
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"output_dir": str(tmp_path / "results"),
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}
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cfg_path = tmp_path / "cfg.yaml"
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cfg_path.write_text(yaml.safe_dump(cfg_dict), encoding="utf-8")
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cfg = load_config(cfg_path)
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df = load_raw_txt(data_txt)
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run_dir = tmp_path / "run0"
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run_dir.mkdir()
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(run_dir / "figures").mkdir()
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(run_dir / "checkpoints").mkdir()
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bundle = prepare_training_data(df, cfg, run_dir)
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device = torch.device("cpu")
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model = MLPRegressor(
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input_dim=cfg.model.input_dim,
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hidden_dims=cfg.model.hidden_dims,
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output_dim=cfg.model.output_dim,
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batchnorm=cfg.model.batchnorm,
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dropout=cfg.model.dropout,
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residual=cfg.model.residual,
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).to(device)
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fit(model, cfg, bundle.train_loader, bundle.val_loader, run_dir, device)
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assert (run_dir / "checkpoints" / "best.pt").is_file()
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meta = json.loads((run_dir / "cleaning_meta.json").read_text(encoding="utf-8"))
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assert meta["n_train"] > 0
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def test_grouped_split_keeps_same_inputs_together(tmp_path: Path) -> None:
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data_txt = tmp_path / "grouped_data.txt"
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rng = np.random.default_rng(7)
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rows = []
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base_inputs = rng.normal(size=(24, 8))
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for x in base_inputs:
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for _ in range(3):
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y0 = float(x[0] * 2.0 + rng.normal(scale=0.01))
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y1 = float(x[1] * -1.5 + rng.normal(scale=0.01))
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y2 = float(abs(x[2]) * 20.0 + 10.0 + rng.normal(scale=0.1))
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rows.append(np.concatenate([x, [y0, y1, y2]]))
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mat = np.asarray(rows, dtype=float)
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data_txt.write_text(
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"\n".join(",".join(str(v) for v in row) for row in mat),
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encoding="utf-8",
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)
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cfg_dict = {
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"data_path": str(data_txt),
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"split_ratios": [0.7, 0.15, 0.15],
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"random_seed": 3,
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"split_mode": "grouped_stratified",
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"split_stratify_target": "V_pi",
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"split_stratify_bins": 6,
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"remove_duplicate_rows": False,
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"outlier_strategy": "none",
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"outlier_apply_to": "targets",
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"outlier_config": {
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"iqr_k": 1.5,
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"zscore_threshold": 4.0,
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"quantile_lower": 0.001,
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"quantile_upper": 0.999,
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},
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"remove_nonpositive_vpi": False,
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"filter_v_pi_range": True,
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"v_pi_min": 0.0,
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"v_pi_max": 500.0,
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"model": {
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"input_dim": 8,
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"hidden_dims": [16, 16],
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"output_dim": 3,
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"batchnorm": False,
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"dropout": 0.0,
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"residual": False,
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},
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"optimizer": {"name": "adamw", "lr": 0.01, "weight_decay": 0.0},
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"scheduler": {
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"type": "cosine",
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"plateau_factor": 0.5,
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"plateau_patience": 10,
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"plateau_min_lr": 1e-6,
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},
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"training": {
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"batch_size": 16,
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"epochs": 1,
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"early_stopping_patience": 1,
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"num_workers": 0,
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},
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"loss": {"type": "huber", "huber_delta": 1.0, "target_weights": [1.0, 1.0, 1.0]},
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"output_dir": str(tmp_path / "results"),
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}
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cfg_path = tmp_path / "cfg_grouped.yaml"
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cfg_path.write_text(yaml.safe_dump(cfg_dict), encoding="utf-8")
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cfg = load_config(cfg_path)
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df = load_raw_txt(data_txt)
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run_dir = tmp_path / "run_grouped"
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run_dir.mkdir()
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bundle = prepare_training_data(df, cfg, run_dir)
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split_data = json.loads((run_dir / "split_indices.json").read_text(encoding="utf-8"))
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split_name_by_row = {}
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for split_name, indices in split_data.items():
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for idx in indices:
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split_name_by_row[int(idx)] = split_name
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cleaned = df.reset_index(drop=True)
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for _, sub in cleaned.groupby(INPUT_COLUMNS, dropna=False):
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assigned = {split_name_by_row[int(i)] for i in sub.index.to_list()}
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assert len(assigned) == 1
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assert len(bundle.X_train) > 0
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