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new file: Figure_1.png

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deleted:    best_model.pth
	new file:   checkpoints/forward/best_tcn_model.pt
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	modified:   src/__pycache__/config.cpython-310.pyc
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	modified:   src/__pycache__/dataset.cpython-310.pyc
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	modified:   src/__pycache__/model.cpython-310.pyc
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	modified:   src/config.py
	modified:   src/dataset.py
	modified:   src/evaluate.py
	modified:   src/model.py
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	modified:   src/train.py
	renamed:    src/__init__.py -> src_old/__init__.py
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	new file:   src_old/config.py
	new file:   src_old/dataset.py
	new file:   src_old/evaluate.py
	new file:   src_old/model.py
	new file:   src_old/train.py
	deleted:    test_results.png
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CrbnsCat10n
2026-05-04 18:18:44 +08:00
parent c65e47e6b0
commit dcc023cc04
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import os
import glob
import pandas as pd
import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
from sklearn.preprocessing import StandardScaler
from config import *
class MultiOutputStandardizer:
"""Per-channel standardization that ignores missing labels via masks."""
def __init__(self, n_outputs):
self.n_outputs = n_outputs
self.mean_ = np.zeros(n_outputs, dtype=np.float32)
self.scale_ = np.ones(n_outputs, dtype=np.float32)
self.fitted = False
def fit(self, y_sequences, mask_sequences):
means = []
scales = []
for c in range(self.n_outputs):
valid_values = []
for y_seq, m_seq in zip(y_sequences, mask_sequences):
valid = m_seq[:, c] > 0.5
if np.any(valid):
valid_values.append(y_seq[valid, c])
if len(valid_values) == 0:
means.append(0.0)
scales.append(1.0)
continue
vals = np.concatenate(valid_values, axis=0)
mean = float(np.mean(vals))
std = float(np.std(vals))
if std < 1e-6:
std = 1.0
means.append(mean)
scales.append(std)
self.mean_ = np.asarray(means, dtype=np.float32)
self.scale_ = np.asarray(scales, dtype=np.float32)
self.fitted = True
def transform(self, y):
if not self.fitted:
raise RuntimeError("MultiOutputStandardizer must be fitted before transform.")
return (y - self.mean_) / self.scale_
def inverse_transform(self, y):
if not self.fitted:
raise RuntimeError("MultiOutputStandardizer must be fitted before inverse_transform.")
return y * self.scale_ + self.mean_
class BuildingDataset(Dataset):
def __init__(self, file_paths, seq_len, step_size, scaler_X=None, scaler_Y=None, fit_scaler=False):
self.seq_len = seq_len
self.X_data = []
self.Y_data = []
self.M_data = []
self.scaler_X = scaler_X if scaler_X is not None else StandardScaler()
self.scaler_Y = scaler_Y if scaler_Y is not None else MultiOutputStandardizer(len(OUTPUT_SENSORS))
raw_X = []
raw_Y = []
raw_M = []
for f in file_paths:
# 读取数据
df = pd.read_csv(f)
# 使用长表格式: code, type, time, value1, value2, value3
# 提取 012 的输入轴作为基准
df_in = df[df['code'] == INPUT_SENSOR][['time', INPUT_AXIS]].rename(columns={INPUT_AXIS: 'input_signal'})
if len(df_in) == 0:
# 若无输入传感器(如自由衰减数据)，则补零
df_in = pd.DataFrame({'time': df['time'].unique()})
df_in['input_signal'] = 0.0
# 提取 007~011 的 Z 轴并按时间戳逐步合并 (使用 left join 确保以 df_in 的时间为基准)
df_merged = df_in
for sens in OUTPUT_SENSORS:
df_out_sens = df[df['code'] == sens][['time', OUTPUT_AXIS]].rename(columns={OUTPUT_AXIS: f'out_{sens}'})
df_merged = pd.merge(df_merged, df_out_sens, on='time', how='left')
df_merged = df_merged.sort_values('time').reset_index(drop=True)
if len(df_merged) == 0:
print(f"Warning: Skipping file {f} due to no overlapping timestamps across required sensors.")
continue
x_seq = df_merged['input_signal'].values.reshape(-1, 1).astype(np.float32)
# 提取所有 target 传感器列与可用性掩码
out_cols = [f'out_{sens}' for sens in OUTPUT_SENSORS]
y_seq = np.zeros((len(df_merged), len(OUTPUT_SENSORS)), dtype=np.float32)
m_seq = np.zeros((len(df_merged), len(OUTPUT_SENSORS)), dtype=np.float32)
for c, col in enumerate(out_cols):
series = df_merged[col]
observed = ~series.isna()
m_seq[:, c] = observed.astype(np.float32)
if observed.any():
filled = series.interpolate(method='linear').bfill().ffill()
y_seq[:, c] = filled.fillna(0.0).values.astype(np.float32)
else:
y_seq[:, c] = 0.0
raw_X.append(x_seq)
raw_Y.append(y_seq)
raw_M.append(m_seq)
if len(raw_X) == 0:
raise ValueError("未能从文件中构造出有效序列，请检查数据路径与传感器编码配置。")
# 拼接所有文件数据进行 fit
X_all = np.vstack(raw_X)
if fit_scaler:
self.scaler_X.fit(X_all)
self.scaler_Y.fit(raw_Y, raw_M)
# 切分窗口
for x_seq, y_seq, m_seq in zip(raw_X, raw_Y, raw_M):
x_seq_scaled = self.scaler_X.transform(x_seq)
y_seq_scaled = self.scaler_Y.transform(y_seq)
y_seq_scaled = np.where(m_seq > 0.5, y_seq_scaled, 0.0).astype(np.float32)
for i in range(0, len(x_seq_scaled) - seq_len + 1, step_size):
x_win = x_seq_scaled[i:i+seq_len]
y_win = y_seq_scaled[i:i+seq_len]
m_win = m_seq[i:i+seq_len]
if np.sum(m_win) <= 0:
continue
self.X_data.append(x_win)
self.Y_data.append(y_win)
self.M_data.append(m_win)
self.X_data = np.array(self.X_data)
self.Y_data = np.array(self.Y_data)
self.M_data = np.array(self.M_data)
def __len__(self):
return len(self.X_data)
def __getitem__(self, idx):
return (
torch.tensor(self.X_data[idx], dtype=torch.float32),
torch.tensor(self.Y_data[idx], dtype=torch.float32),
torch.tensor(self.M_data[idx], dtype=torch.float32),
)
def get_dataloaders(condition='Non_TMD', include_free_vib=False):
"""
condition: 'Non_TMD' 或者是 'TMD'
include_free_vib: 是否在训练集中加入自由振动与自由衰减数据
"""
base_dir = os.path.join(DATA_DIR, condition)
# 手动区分的子目录
train_files = glob.glob(os.path.join(base_dir, 'train', '*.csv'))
if include_free_vib:
train_files += glob.glob(os.path.join(base_dir, 'free_vib', '*.csv'))
val_files = glob.glob(os.path.join(base_dir, 'val', '*.csv'))
test_files = glob.glob(os.path.join(base_dir, 'test', '*.csv'))
print(f"[{condition}] Train files: {len(train_files)}, Val files: {len(val_files)}, Test files: {len(test_files)}")
if len(train_files) == 0:
raise ValueError(f"错误: 在 {base_dir}/train 目录下未找到训练文件！请检查路径是否正确。")
if len(val_files) == 0:
raise ValueError(f"错误: 在 {base_dir}/val 目录下未找到验证文件！请检查路径是否正确。")
train_dataset = BuildingDataset(train_files, SEQ_LEN, STEP_SIZE, fit_scaler=True)
val_dataset = BuildingDataset(val_files, SEQ_LEN, STEP_SIZE,
scaler_X=train_dataset.scaler_X,
scaler_Y=train_dataset.scaler_Y, fit_scaler=False)
# 若某条件（如 TMD）下没有 test 数据，可以处理一下防止报错
test_loader = None
if len(test_files) > 0:
test_dataset = BuildingDataset(test_files, SEQ_LEN, STEP_SIZE,
scaler_X=train_dataset.scaler_X,
scaler_Y=train_dataset.scaler_Y, fit_scaler=False)
test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False)
train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=False)
return train_loader, val_loader, test_loader, train_dataset.scaler_X, train_dataset.scaler_Y