562 lines
23 KiB
Python
562 lines
23 KiB
Python
#!/usr/bin/python
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# -*- coding: UTF-8 -*-
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"""
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@author:Marques
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@file:generate_label_11.0.py
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@email:admin@marques22.com
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@email:2021022362@m.scnu.edu.cn
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@time:2022/09/05
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"""
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# 14.0
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# 手动均衡数量
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# 13.0
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# 限制选择部分数据集，先做测试
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# 12.0
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# 置不可用事件的片段为上限，不可用片段设置为背景，不记录事件
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# 10.0
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# 使用提出质量差的信号
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# 9.0
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# 增加 最新的质量标签 未使用
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# 8.0
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# 生成 除低通气所有事件标签
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# 尝试过步进两秒 会造成不足两秒的数据被抛弃，造成较多误判，但是可以考虑囊括这部分
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# 采用 30秒数据 移动 1秒 将所有呼吸暂停标注为1 低通气为0 正常为0
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# 预处理操作 为 50Hz陷波滤波器去工频 外加 20Hz的低通滤波器 这个20Hz要看BCG信号的频谱范围
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# 先提剔除极端值
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# 数值大于最高基准线或最低基准线
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# type1： average:1800 low:1200 high:2400
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# type2: average:2400 low:1800 high:3000
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# 过多片段会造成平均值偏移
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# TODO
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# 加入体动标签，计算除体动外的平均值
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# 最后降采为100hz
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import time
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import logging
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import numpy as np
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import pandas as pd
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from pathlib import Path
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from datetime import datetime
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import yaml
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from pathos import multiprocessing
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from tqdm import tqdm
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# 数据集 和 标签 位置
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bcg_numpy_data_path = Path(r"/home/marques/code/marques/apnea/dataset/BCG_100hz_lowpass50/")
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bcg_label_path = Path(r"/home/marques/code/marques/apnea/dataset/BCG_label_0616/")
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# BCG 记录开始时间
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bcg_start_time = np.loadtxt(Path(r"/home/marques/code/marques/apnea/dataset/start_time.csv"), delimiter=', ',
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dtype=object)
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bcg_start_time = dict(zip(bcg_start_time[:, 0], bcg_start_time[:, 1]))
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# 读取每个数据集路径
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all_numpy_dataset = list(bcg_numpy_data_path.rglob("*.npy"))
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all_numpy_dataset.sort()
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# 划分后的数据集保存路径
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# dataset_save_path = Path(r"/home/marques/code/marques/apnea/dataset/dataset/dataset0623_300_30_30/")
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dataset_save_path = Path(r"./dataset/")
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dataset_save_path.mkdir(exist_ok=True)
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# 设置日志
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logger = logging.getLogger()
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logger.setLevel(logging.NOTSET)
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realtime = time.strftime('%Y%m%d%H%M', time.localtime(time.time()))
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fh = logging.FileHandler(dataset_save_path / (realtime + ".log"), mode='w')
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fh.setLevel(logging.NOTSET)
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# fh.setFormatter(logging.Formatter("%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s"))
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fh.setFormatter(logging.Formatter("%(message)s"))
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logger.addHandler(fh)
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ch = logging.StreamHandler()
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ch.setLevel(logging.NOTSET)
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ch.setFormatter(logging.Formatter("%(message)s"))
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logger.addHandler(ch)
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# all_label = []
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# 输出统计数据标题栏
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# logger.info("sampNo".center(8) +
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# "hpy_num".center(8) + "hpy_time".center(10) +
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# "csa_num".center(8) + "csa_time".center(10) +
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# "osa_num".center(8) + "osa_time".center(10) +
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# "msa_num".center(8) + "msa_time".center(10)
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# )
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logger.info("sampNo".center(8) + ',' +
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"train_num".center(10) + ',' + "train_P".center(10) + ',' + "train_N".center(10) + ',' +
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"valid_num".center(10) + ',' + "valid_P".center(10) + ',' + "valid_N".center(10) + ',' +
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"test_num".center(10) + ',' + "test_P".center(10) + ',' + "test_N".center(10) + ',' +
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"train_eve".center(10) + ',' + "valid_eve".center(10) + ',' + "test_eve".center(10)
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)
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base_random_seed = 42
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window_second = 30
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step_second = 1
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valid_ratio = 0.1
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test_ratio = 0.1
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normal_event_quality_label = 0
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# valid_ratio = 5000
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# test_ratio = 10000
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assert ((valid_ratio + test_ratio) < 1 and 0 < valid_ratio < 1 and 0 < test_ratio < 1) or (
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valid_ratio > 1 and valid_ratio > 1), "验证集与测试集输入应同时为比例或数量"
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# dataset sampNo for test
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with open("./settings.yaml") as f:
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hyp = yaml.load(f, Loader=yaml.SafeLoader)
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select_dataset = hyp["select_sampno"]
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# 需要置成0的片段，前面不一定补零，还有可能上万
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disable_segment = {
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'221': [[0, 10000]],
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'670': [[0, 20000]],
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'683': [[0, 20000]],
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'704': [[0, 26000]],
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'726': [[0, 20000]],
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'736': [[0, 47000]],
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'933': [[0, 773560]],
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'935': [[0, 26600]],
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'952': [[0, 17000]],
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'955': [[0, 78000]],
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'961': [[0, 107000]],
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'962': [[0, 15100]],
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'966': [[0, 13120]],
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'967': [[0, 44000]],
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'1006': [[0, 60000]],
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'1009': [[0, 1000]],
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'1010': [[0, 49000]],
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'1296': [[0, 27000]],
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'1300': [[0, 33800]],
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'1301': [[0, 14000]],
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'1302': [[0, 5600]],
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'1374': [[0, 1000]],
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'1478': [[0, 998000]],
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}
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# 生成数据集主函数
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def generate_label(No, dataset_path):
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"""
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:param dataset_path: 数据集路径
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:return:
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"""
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# 获取数据编号
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sampNo = dataset_path.stem.split("samp")[0]
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# 标签路径
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label_path = bcg_label_path / f"export{sampNo}_all.csv"
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if not label_path.exists():
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raise FileNotFoundError(f"{label_path} not exist")
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if not dataset_path.exists():
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raise Exception(f"{dataset_path} not exists")
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# 加载数据集
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select_numpy = np.load(dataset_path)
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select_numpy_len = len(select_numpy)
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# 开头不合理片段置零
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if sampNo in disable_segment.keys():
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for sp, ep in disable_segment[sampNo]:
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select_numpy[sp:ep] = 0
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# 剔除质量差信号
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if sampNo == "670":
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select_numpy = select_numpy[:17195 * 100]
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# 获取前面补了多少0
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not_zero_point = 0
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for num in select_numpy:
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if num > 10:
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break
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not_zero_point += 1
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not_zero_point //= 100
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# 读取标签
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label_csv = pd.read_csv(label_path, encoding='gbk')
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label_csv["new_start"] = label_csv["new_start"].astype("int")
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label_csv["new_end"] = label_csv["new_end"].astype("int")
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label_csv["Duration"] = label_csv["Duration"].astype("int")
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label_csv["new_label"] = label_csv["new_label"].fillna("2")
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label_csv["new_label"] = label_csv["new_label"].astype("int")
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# 剔除质量不好的样本
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# drop_csv = label_csv[
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# (label_csv["Event type"].isin(["Central apnea", "Obstructive apnea"])) & (label_csv["new_label"] == 2)]
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# label_csv = label_csv.drop(drop_csv.index)
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# 事件片段与背景片段， 每个背景长度均为设定窗长
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segment_labels = []
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negative_labels = []
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hpy_num = csa_num = osa_num = msa_num = 0
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hpy_time = csa_time = osa_time = msa_time = 0
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# 遍历全部事件并统计
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for i in range(len(label_csv)):
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# 进行LabelEncoder
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label = label_csv.iloc[i, :]
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# 如果事件在补零片段，则不添加到事件列表
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if label["new_end"] < not_zero_point:
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continue
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if sampNo == "670" and label["new_start"] > 17195:
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continue
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if label["new_end"] - label["new_start"] < 10:
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continue
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# 将事件添加到事件列表
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if label["Event type"] == "Hypopnea":
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label_type = 1
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hpy_num += 1
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hpy_time += label["new_end"] - label["new_start"]
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# 将低通气添加到背景 好像不用专门加入到负样本事件中？
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# negative_labels.append(
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# [sampNo, i, label_type, normal_event_quality_label, label["new_start"], label["new_end"]])
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continue
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elif label["Event type"] == "Central apnea":
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label_type = 2
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csa_num += 1
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csa_time += label["new_end"] - label["new_start"]
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elif label["Event type"] == "Obstructive apnea":
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label_type = 3
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osa_num += 1
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osa_time += label["new_end"] - label["new_start"]
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# MSA 认为是OSA
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elif label["Event type"] == "Mixed apnea":
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label_type = 3
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msa_num += 1
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msa_time += label["new_end"] - label["new_start"]
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else:
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continue
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# label_type = 0
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if label["new_end"] - label["new_start"] > label["Duration"] + 20:
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print(sampNo, label)
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# 格式为 样本编号 第几个事件 标签 开始事件 结束事件
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segment_labels.append([sampNo, i, label_type, label["new_label"], label["new_start"], label["new_end"]])
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# logger.info(sampNo.center(8) +
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# str(hpy_num).center(8) + str(hpy_time).center(10) +
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# str(csa_num).center(8) + str(csa_time).center(10) +
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# str(osa_num).center(8) + str(osa_time).center(10) +
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# str(msa_num).center(8) + str(msa_time).center(10))
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# 设置随机树种子
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random_seed = base_random_seed + int(sampNo)
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# 对于无事件的样本，直接将所有的片段
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if len(segment_labels) == 0:
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# 剔除补零片段（把开始点移动到补零结束）
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normal_SP = not_zero_point
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# 开头至少满足一个窗长
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if normal_SP < window_second:
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normal_SP = window_second
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# 结束点为样本总长 除以 采样率
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normal_EP = select_numpy_len // 100
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label_type = 0
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# 正常时间编号为秒数 除以 30，即epoch
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negative_labels += [[sampNo, 10000 + normal_SP // 30, label_type, normal_event_quality_label, SP1,
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SP1 + window_second] for SP1 in
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range(normal_SP - window_second + step_second, normal_EP - window_second + step_second,
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window_second)]
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# 对于有事件的样本
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# 遍历事件，获取事件之间的背景片段
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for index in range(len(segment_labels) + 1):
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# 前一个事件的结尾 与 下一事件开头即为背景
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# 对于开头的无事件片段，则设定开始事件为0
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if index == 0:
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normal_SP = 0
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# 非开头片段，开始点为上一个事件的结尾
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else:
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# 加一秒 作为 缓冲
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normal_SP = segment_labels[index - 1][-1] + 1
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# 最后一个事件则取到样本片段结尾
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if index == len(segment_labels):
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normal_EP = select_numpy_len // 100 - window_second
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# 否则结束事件取 本事件开头
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else:
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# 减一秒 作为 缓冲
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normal_EP = segment_labels[index][-2] - 1
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# 剔除包含开头补零的片段
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if normal_EP < not_zero_point:
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continue
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# 剔除开头不足30s的正常片段
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if normal_SP < window_second:
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continue
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label_type = 0
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# 将背景事件按照滑窗距离逐个加入到背景事件中
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temp_1 = [[sampNo, 10000 + normal_SP // 30, label_type, normal_event_quality_label, SP1, SP1 + window_second]
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for SP1 in range(normal_SP - window_second, normal_EP - window_second, window_second)]
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negative_labels += temp_1
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train_label, valid_label, test_label = [], [], []
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# assert (valid_ratio + test_ratio) < 1 <= len(segment_labels), f"{sampNo}训练集与测试集数量应小于总数据集数量{len(segment_labels)}"
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# 对于测试数据全部直接保存
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if int(sampNo) in select_dataset:
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event_label = np.zeros(select_numpy_len // 100)
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quality_label = np.zeros(select_numpy_len // 100)
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# 用于存储事件标签
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for PN, segmentNo, label_type, new_label, SP, EP in segment_labels:
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event_label[SP:EP] = label_type
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test_label = []
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# 剔除补零片段
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normal_SP = not_zero_point
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if normal_SP < window_second:
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normal_SP = window_second
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normal_EP = select_numpy_len // 100
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# 分成指定窗长的滑窗片段
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test_label += [
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[sampNo, SP1 // 30, int(event_label[SP1 + window_second - step_second]),
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int(quality_label[SP1 + window_second - step_second]),
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SP1, SP1 + window_second] for SP1 in range(normal_SP - window_second + step_second,
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normal_EP - window_second + step_second, step_second)]
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logger.info(sampNo.center(8) + ',' +
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str(0).center(10) + ',' + str(0).center(10) + ',' + str(0).center(10) + ',' +
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str(0).center(10) + ',' + str(0).center(10) + ',' + str(0).center(10) + ',' +
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str(len(test_label)).center(10) + ',' +
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str(sum(np.array(test_label)[:, 2].astype(int) > 1) if len(test_label) != 0 else 0).center(10) +
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',' + str(sum(np.array(test_label)[:, 2].astype(int) < 1) if len(test_label) != 0 else 0).center(
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10) + ',' + str(0).center(10) + ',' + str(0).center(10) + ',' + str(len(segment_labels)).center(10)
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)
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df2.loc[No] = [sampNo,
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str(0), str(0), str(0),
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str(0), str(0), str(0),
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str(len(test_label)),
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str(sum(np.array(test_label)[:, 2].astype(int) > 1) if len(test_label) != 0 else 0),
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str(sum(np.array(test_label)[:, 2].astype(int) < 1) if len(test_label) != 0 else 0),
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str(0), str(0), str(len(segment_labels))]
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# np.save(dataset_save_path / f"{sampNo}_{step_second}s_all_{window_second}s_sa_test2_label.npy",
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# np.array(test_label))
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df1 = pd.DataFrame(data=test_label,
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columns=["sampNo", "index", "label_type", "new_label", "SP", "EP"])
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df1.to_csv(dataset_save_path / f"{sampNo}_{step_second}s_focal_{window_second}s_sa_all_label.csv",
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index=False)
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train_label, valid_label, test_label = [], [], []
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# 对于训练与验证集样本
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if True:
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# 打乱片段顺序
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np.random.seed(random_seed)
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np.random.shuffle(segment_labels)
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np.random.shuffle(negative_labels)
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# 获取训练集、验证集、测试集分到事件个数
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if 0 < valid_ratio < 1:
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train_segment_num = int(len(segment_labels) * (1 - valid_ratio - test_ratio))
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valid_segment_num = int(len(segment_labels) * (1 - test_ratio))
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else:
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train_segment_num = len(segment_labels) - valid_ratio - test_ratio
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valid_segment_num = valid_ratio
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# 分别将各事件切分为30s, 步进1秒的片段
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for index in range(train_segment_num):
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PN, segmentNo, label_type, new_label, SP, EP = segment_labels[index]
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train_label += [[PN, segmentNo, label_type, new_label, SP1, SP1 + window_second] for SP1 in
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range(SP - window_second, EP - window_second + step_second, step_second)]
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for index in range(train_segment_num, valid_segment_num):
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PN, segmentNo, label_type, new_label, SP, EP = segment_labels[index]
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valid_label += [[PN, segmentNo, label_type, new_label, SP1, SP1 + window_second] for SP1 in
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range(SP - window_second, EP - window_second + step_second, step_second)]
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for index in range(valid_segment_num, len(segment_labels)):
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PN, segmentNo, label_type, new_label, SP, EP = segment_labels[index]
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test_label += [[PN, segmentNo, label_type, new_label, SP1, SP1 + window_second] for SP1 in
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range(SP - window_second, EP - window_second + step_second * step_second)]
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# 计算片段和事件个数
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train_num, valid_num, test_num = len(train_label), len(valid_label), len(test_label)
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train_eve, valid_eve, test_eve = train_segment_num, (valid_segment_num - train_segment_num), (
|
||
len(segment_labels) - valid_segment_num)
|
||
|
||
# 数据集补偿
|
||
# if train_num < 300:
|
||
# train_num = 300 - train_num
|
||
#
|
||
# if valid_num < 300:
|
||
# valid_num = 300 - valid_num
|
||
#
|
||
# if test_num < 300:
|
||
# test_num = 300 - test_num
|
||
|
||
# 获取训练集、验证集、测试集分到背景个数
|
||
# if 0 < valid_ratio < 1:
|
||
# train_eve2 = int(len(negative_labels) * (1 - valid_ratio - test_ratio))
|
||
# valid_eve2 = int(len(negative_labels) * valid_ratio)
|
||
# else:
|
||
# train_eve2 = len(negative_labels) - valid_ratio - test_ratio
|
||
# valid_eve2 = valid_ratio
|
||
#
|
||
# test_eve2 = len(negative_labels) - train_eve2 - valid_eve2
|
||
# 直接补充到足够个数的背景事件
|
||
train_eve2 = max(train_eve, 300)
|
||
valid_eve2 = max(valid_eve, 40)
|
||
test_eve2 = max(test_eve, 40)
|
||
|
||
# 强制背景数量
|
||
# train_eve2 = train_eve
|
||
# valid_eve2 = valid_eve
|
||
# test_eve2 = test_eve
|
||
|
||
# 添加背景事件数量
|
||
for sampNo, index, label_type, new_label, normal_SP, normal_EP in negative_labels[:train_eve2]:
|
||
label_type = int(label_type)
|
||
train_label += [[sampNo, 10000 + normal_SP // 30, label_type, new_label, SP1, SP1 + window_second] for SP1
|
||
in
|
||
range(normal_SP, normal_EP, step_second)]
|
||
|
||
for sampNo, index, label_type, new_label, normal_SP, normal_EP in negative_labels[
|
||
train_eve2: train_eve2 + valid_eve2]:
|
||
label_type = int(label_type)
|
||
valid_label += [[sampNo, 10000 + normal_SP // 30, label_type, new_label, SP1, SP1 + window_second] for SP1
|
||
in
|
||
range(normal_SP, normal_EP, step_second)]
|
||
|
||
for sampNo, index, label_type, new_label, normal_SP, normal_EP in negative_labels[
|
||
train_eve2 + valid_eve2:train_eve2 + valid_eve2 + test_eve2]:
|
||
label_type = int(label_type)
|
||
test_label += [[sampNo, 10000 + normal_SP // 30, label_type, new_label, SP1, SP1 + window_second] for SP1 in
|
||
range(normal_SP, normal_EP, step_second)]
|
||
|
||
logger.info(sampNo.center(8) + ',' +
|
||
str(len(train_label)).center(10) + ',' +
|
||
str(sum(np.array(train_label)[:, 2].astype(int) > 1) if len(train_label) != 0 else 0).center(
|
||
10) + ',' +
|
||
str(sum(np.array(train_label)[:, 2].astype(int) < 1) if len(train_label) != 0 else 0).center(
|
||
10) + ',' +
|
||
str(len(valid_label)).center(10) + ',' +
|
||
str(sum(np.array(valid_label)[:, 2].astype(int) > 1) if len(valid_label) != 0 else 0).center(
|
||
10) + ',' +
|
||
str(sum(np.array(valid_label)[:, 2].astype(int) < 1) if len(valid_label) != 0 else 0).center(
|
||
10) + ',' +
|
||
str(len(test_label)).center(10) + ',' +
|
||
str(sum(np.array(test_label)[:, 2].astype(int) > 1) if len(test_label) != 0 else 0).center(
|
||
10) + ',' +
|
||
str(sum(np.array(test_label)[:, 2].astype(int) < 1) if len(test_label) != 0 else 0).center(
|
||
10) + ',' +
|
||
str(train_eve).center(10) + ',' + str(valid_eve).center(10) + ',' + str(test_eve).center(10)
|
||
)
|
||
|
||
df2.loc[No] = [sampNo.center(8),
|
||
str(len(train_label)),
|
||
str(sum(np.array(train_label)[:, 2].astype(int) > 1) if len(train_label) != 0 else 0),
|
||
str(sum(np.array(train_label)[:, 2].astype(int) < 1) if len(train_label) != 0 else 0),
|
||
str(len(valid_label)),
|
||
str(sum(np.array(valid_label)[:, 2].astype(int) > 1) if len(valid_label) != 0 else 0),
|
||
str(sum(np.array(valid_label)[:, 2].astype(int) < 1) if len(valid_label) != 0 else 0),
|
||
str(len(test_label)),
|
||
str(sum(np.array(test_label)[:, 2].astype(int) > 1) if len(test_label) != 0 else 0),
|
||
str(sum(np.array(test_label)[:, 2].astype(int) < 1) if len(test_label) != 0 else 0),
|
||
str(train_eve), str(valid_eve), str(test_eve).center(10)]
|
||
|
||
def label_check(label_list):
|
||
temp_list = []
|
||
for sampNo, index, label_type, new_label, SP, EP in label_list:
|
||
if EP - SP < window_second:
|
||
print(sampNo, index, label_type, SP, EP)
|
||
temp_list.append([sampNo, index, label_type, new_label, SP, EP])
|
||
|
||
if SP < 0:
|
||
print(sampNo, index, label_type, SP, EP)
|
||
temp_list.append([sampNo, index, label_type, new_label, SP, EP])
|
||
|
||
if len(select_numpy[SP * 100:EP * 100]) != window_second * 100:
|
||
print(sampNo, index, label_type, SP, EP, len(select_numpy[SP * 100:EP * 100]))
|
||
temp_list.append([sampNo, index, label_type, new_label, SP, EP])
|
||
|
||
for j in temp_list:
|
||
label_list.remove(j)
|
||
|
||
label_check(train_label)
|
||
label_check(valid_label)
|
||
label_check(test_label)
|
||
for sampNo, index, label_type, new_label, SP, EP in train_label:
|
||
if EP - SP < window_second:
|
||
print(sampNo, index, label_type, new_label, SP, EP)
|
||
|
||
if SP < 0:
|
||
print(sampNo, index, label_type, new_label, SP, EP)
|
||
|
||
if len(select_numpy[SP * 100:EP * 100]) != window_second * 100:
|
||
print(sampNo, index, label_type, new_label, SP, EP, len(select_numpy[SP * 100:EP * 100]))
|
||
|
||
df1 = pd.DataFrame(data=train_label,
|
||
columns=["sampNo", "index", "label_type", "new_label", "SP", "EP"])
|
||
df1.to_csv(dataset_save_path / f"{sampNo}_{step_second}s_focal_{window_second}s_sa_train_label.csv",
|
||
index=False)
|
||
|
||
df1 = pd.DataFrame(data=valid_label,
|
||
columns=["sampNo", "index", "label_type", "new_label", "SP", "EP"])
|
||
df1.to_csv(dataset_save_path / f"{sampNo}_{step_second}s_focal_{window_second}s_sa_valid_label.csv",
|
||
index=False)
|
||
|
||
df1 = pd.DataFrame(data=test_label,
|
||
columns=["sampNo", "index", "label_type", "new_label", "SP", "EP"])
|
||
df1.to_csv(dataset_save_path / f"{sampNo}_{step_second}s_focal_{window_second}s_sa_test_label.csv", index=False)
|
||
|
||
# np.save(dataset_save_path / f"{sampNo}_{step_second}s_focal_{window_second}s_sa_train_label.npy",
|
||
# np.array(train_label))
|
||
# np.save(dataset_save_path / f"{sampNo}_{step_second}s_focal_{window_second}s_sa_valid_label.npy",
|
||
# np.array(valid_label))
|
||
# np.save(dataset_save_path / f"{sampNo}_{step_second}s_focal_{window_second}s_sa_test_label.npy",
|
||
# np.array(test_label))
|
||
|
||
|
||
if __name__ == '__main__':
|
||
# pool = multiprocessing.Pool(processes=44)
|
||
# pool.map(generate_label, list(all_numpy_dataset))
|
||
# pool.close()
|
||
# pool.join()
|
||
|
||
df2 = pd.DataFrame(data=None,
|
||
columns=["sampNo",
|
||
"train_num", "train_P", "train_N",
|
||
"valid_num", "valid_P", "valid_N",
|
||
"test_num", "test_P", "test_N",
|
||
"train_eve", "valid_eve", "test_eve"])
|
||
|
||
temp = []
|
||
for one_dataset in all_numpy_dataset:
|
||
if int(one_dataset.stem.split("samp")[0]) in [*select_dataset]:
|
||
temp.append(one_dataset)
|
||
# for one_dataset in temp:
|
||
# all_numpy_dataset.remove(one_dataset)
|
||
|
||
for No, one_dataset in enumerate(temp):
|
||
generate_label(No, one_dataset)
|
||
|
||
df2.to_csv(dataset_save_path / (realtime + ".csv"), index=False)
|
||
# generate_label(all_numpy_dataset[0])
|