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[ASTGCN之1个特征]解读(torch)之参数读取和数据读入(一)

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一、目录

[ASTGCN之1个特征]解读(torch)之参数读取和数据读入(一)

; 二、configparser和argparse

不了解的和遇见参数 读取报错的：见博文《【configparser】参数读取》 和 《【argparse】参数配置》
configurations

  • PEMS04_astgcn.conf的内容展示

[ASTGCN之1个特征]解读(torch)之参数读取和数据读入(一)

; 三、 prepareData.py 文件

该文件是进行参数读取和数据预处理的.

导入库

import os
import numpy as np
import argparse
import configparser

3.1 参数读入 


parser = argparse.ArgumentParser()
parser.add_argument("--config", default='configurations/PEMS08_astgcn.conf', type=str,
                    help="configuration file path")
args = parser.parse_args()
config = configparser.ConfigParser()
print('Read configuration file: %s' % (args.config))
config.read(args.config)
data_config = config['Data']
training_config = config['Training']

adj_filename = data_config['adj_filename']
graph_signal_matrix_filename = data_config['graph_signal_matrix_filename']

if config.has_option('Data', 'id_filename'):
    id_filename = data_config['id_filename']
else:
    id_filename = None

num_of_vertices = int(data_config['num_of_vertices'])
points_per_hour = int(data_config['points_per_hour'])
num_for_predict = int(data_config['num_for_predict'])
len_input = int(data_config['len_input'])
dataset_name = data_config['dataset_name']
num_of_weeks = int(training_config['num_of_weeks'])
num_of_days = int(training_config['num_of_days'])
num_of_hours = int(training_config['num_of_hours'])

3.2 变量解析

  • 应用到模型中的变量对照表

代码变量含义解析原文变量举例num_of_vertices网络中顶点个数170points_per_hour每个小时中观察的时间点个数12num_for_predict预测未来多少个时间点结果12len_input12num_of_weeks20num_of_days20num_of_hours21

  • 应用到模型控制的变量对照表

代码变量默认值含义解析作用args.config’configurations/PEMS04_astgcn.conf’文件路径用于读取模型参数的路径configconfigparser实例化并读取参数后里面包含了两个节,节中包含大量变量data_configconfig[“Data”]表示config中节[“Data”] (里面包含众多变量)training_configconfig[“Training”]表示config中节[“Training”] (里面包含众多变量)adj_file’./data/PEMS04/distance.csv’文件路径用于读取邻接矩阵graph_signal_matrix_filename’./data/PEMS04/PEMS04.npz’文件路径用于读取图信号矩阵dataset_name数据集名称

四、函数 read_and_generate_dataset

  • 读取数据的代码
all_data = read_and_generate_dataset(graph_signal_matrix_filename, 0, 0, num_of_hours, num_for_predict, points_per_hour=points_per_hour, save=True)
  • 函数定义
def read_and_generate_dataset(graph_signal_matrix_filename,
                              num_of_weeks, num_of_days,
                              num_of_hours, num_for_predict,
                             points_per_hour=12, save=False):
    '''
    Parameters
    ----------
    graph_signal_matrix_filename: str, path of graph signal matrix file
    num_of_weeks: int,0
    num_of_days:  int,0
    num_of_hours: int,1
    num_for_predict: int, 12
    points_per_hour: int, default 12, depends on data
    save:bool,True
    Returns
    ----------
    feature: np.ndarray,
             shape is (num_of_samples, num_of_depend * points_per_hour,
                       num_of_vertices, num_of_features)
    target: np.ndarray,
            shape is (num_of_samples, num_of_vertices, num_for_predict)
    '''

    data_seq = np.load(graph_signal_matrix_filename)['data']

    all_samples = []
    for idx in range(data_seq.shape[0]):
        sample = get_sample_indices(data_seq, num_of_weeks, num_of_days,
                                    num_of_hours, idx, num_for_predict,
                                    points_per_hour)

        if ((sample[0] is None) and (sample[1] is None) and (sample[2] is None)):
            continue

        week_sample, day_sample, hour_sample, target = sample

        sample = []

        if num_of_weeks > 0:
            week_sample = np.expand_dims(week_sample, axis=0).transpose((0, 2, 3, 1))
            sample.append(week_sample)

        if num_of_days > 0:
            day_sample = np.expand_dims(day_sample, axis=0).transpose((0, 2, 3, 1))
            sample.append(day_sample)

        if num_of_hours > 0:
            hour_sample = np.expand_dims(hour_sample, axis=0).transpose((0, 2, 3, 1))
            sample.append(hour_sample)

        target = np.expand_dims(target, axis=0).transpose((0, 2, 3, 1))[:, :, 0, :]
        sample.append(target)

        time_sample = np.expand_dims(np.array([idx]), axis=0)
        sample.append(time_sample)

        all_samples.append(
            sample)

    split_line1 = int(len(all_samples) * 0.6)
    split_line2 = int(len(all_samples) * 0.8)

    training_set = [np.concatenate(i, axis=0)
                    for i in zip(*all_samples[:split_line1])]
    validation_set = [np.concatenate(i, axis=0)
                      for i in zip(*all_samples[split_line1: split_line2])]
    testing_set = [np.concatenate(i, axis=0)
                   for i in zip(*all_samples[split_line2:])]

    train_x = np.concatenate(training_set[:-2], axis=-1)
    val_x = np.concatenate(validation_set[:-2], axis=-1)
    test_x = np.concatenate(testing_set[:-2], axis=-1)

    train_target = training_set[-2]
    val_target = validation_set[-2]
    test_target = testing_set[-2]

    train_timestamp = training_set[-1]
    val_timestamp = validation_set[-1]
    test_timestamp = testing_set[-1]

    (stats, train_x_norm, val_x_norm, test_x_norm) = normalization(train_x, val_x, test_x)

    all_data = {
        'train': {
            'x': train_x_norm,
            'target': train_target,
            'timestamp': train_timestamp,
        },
        'val': {
            'x': val_x_norm,
            'target': val_target,
            'timestamp': val_timestamp,
        },
        'test': {
            'x': test_x_norm,
            'target': test_target,
            'timestamp': test_timestamp,
        },
        'stats': {
            '_mean': stats['_mean'],
            '_std': stats['_std'],
        }
    }
    print('train x:', all_data['train']['x'].shape)
    print('train target:', all_data['train']['target'].shape)
    print('train timestamp:', all_data['train']['timestamp'].shape)
    print()
    print('val x:', all_data['val']['x'].shape)
    print('val target:', all_data['val']['target'].shape)
    print('val timestamp:', all_data['val']['timestamp'].shape)
    print()
    print('test x:', all_data['test']['x'].shape)
    print('test target:', all_data['test']['target'].shape)
    print('test timestamp:', all_data['test']['timestamp'].shape)
    print()
    print('train data _mean :', stats['_mean'].shape, stats['_mean'])
    print('train data _std :', stats['_std'].shape, stats['_std'])

    if save:
        file = os.path.basename(graph_signal_matrix_filename).split('.')[0]
        dirpath = os.path.dirname(graph_signal_matrix_filename)
        filename = os.path.join(dirpath, file + '_r' + str(num_of_hours) + '_d' + str(num_of_days) + '_w' + str(num_of_weeks)) + '_astcgn'
        print('save file:', filename)
        np.savez_compressed(filename,
                            train_x=all_data['train']['x'], train_target=all_data['train']['target'],
                            train_timestamp=all_data['train']['timestamp'],
                            val_x=all_data['val']['x'], val_target=all_data['val']['target'],
                            val_timestamp=all_data['val']['timestamp'],
                            test_x=all_data['test']['x'], test_target=all_data['test']['target'],
                            test_timestamp=all_data['test']['timestamp'],
                            mean=all_data['stats']['_mean'], std=all_data['stats']['_std']
                            )
    return all_data

函数解析：
注释1：数据读入
np.load(graph_signal_matrix_filename)['data'] :读取data数据
读取的是 .npz文件的时候，通常里面至少包含一个array 数组。
1. 查看里面包含几个数组内容的方法：

  data = np.load(graph_signal_matrix_filename)
  print(data.files)

out: ['data']
包含一个数组，该数组名为 "data"
2. 取出数组的方法
data["data"]
3. data_seq.shape为 (17856, 170, 3)当PEMS08的时候，分别表示(序列长度，顶点个数，顶点的特征个数)。提醒：这里的序列长度，其实时按照时间顺序排列的。
注释二：生成滑动窗口

伪代码

[ASTGCN之1个特征]解读(torch)之参数读取和数据读入(一)

读完原理图，可以阅读section 5，section6.

; A. 原理图

利用滑动窗口生成新序列的原理图。右侧顶部是一个连续时间的序列，滑动窗口设为4，当窗口每走一步(也可能走unit步后)获得一个”窗口数据”，直到最后一个窗口。将所有的”窗口数据”在axis=0轴合并成为一个新的数据。(见左图)

[ASTGCN之1个特征]解读(torch)之参数读取和数据读入(一)
  1. 调用函数 get_sample_indices获得week,day,hour,target的样本切片，函数解析见section 5. 切片的shape=(序列某长度，顶点个数，特征个数)
  2. 对

B.数据合成演示图

[ASTGCN之1个特征]解读(torch)之参数读取和数据读入(一)
; 注释三：构造train\val\test数据
  • split_line1

五、 prepareData.py 中的函数 get_sample_indices

5.1 该函数的调用

[ASTGCN之1个特征]解读(torch)之参数读取和数据读入(一)
  • 可以看到每个idx –>调用一次函数

; 5.2 函数介绍

在本函数中，可以获得周样本数据，日样本数据，时样本数据。在本函数中，首先按照原理图的思想，获得小方块组的索引列表(这一步见section 6 函数 search_data),之后再通过 np.concatenate进行数据合并，获得最终的样本。

最终样本的shape=(窗口大小× \times × 小方块组的个数，顶点个数，特征个数)

以时样本为例，进行说明

[ASTGCN之1个特征]解读(torch)之参数读取和数据读入(一)
5.4 函数代码 
def get_sample_indices(data_sequence, num_of_weeks, num_of_days, num_of_hours,
                       label_start_idx, num_for_predict, points_per_hour=12):
    '''
    Parameters
    ----------
    data_sequence: np.ndarray
                   shape is (sequence_length, num_of_vertices, num_of_features)=（序列长度，顶点个数，特征个数）
    num_of_weeks :int, 0
    num_of_days  :int, 0
    num_of_hours: int, 1
    label_start_idx: int, the first index of predicting target，0~16992
    num_for_predict: int, 12 ,num_timesteps_output,标签中的滑动窗口大小==预测的数据长度
    points_per_hour: int, default 12, number of points per hour每小时分为12个阶段，每间隔5分钟记录一次数据。或着说两个小方块的时间距离为5分钟。
    Returns
    ----------
    week_sample: np.ndarray周样本
                 shape is (num_of_weeks * points_per_hour,
                           num_of_vertices, num_of_features)
    day_sample: np.ndarray日样本
                 shape is (num_of_days * points_per_hour,
                           num_of_vertices, num_of_features)
    hour_sample: np.ndarray时样本
                 shape is (num_of_hours * points_per_hour,
                           num_of_vertices, num_of_features)
    target: np.ndarray 标签样本
            shape is (num_for_predict, num_of_vertices, num_of_features)
    '''
    week_sample, day_sample, hour_sample = None, None, None

    if label_start_idx + num_for_predict > data_sequence.shape[0]:

        return week_sample, day_sample, hour_sample, None

    if num_of_weeks > 0:
        week_indices = search_data(data_sequence.shape[0], num_of_weeks,
                                   label_start_idx, num_for_predict,
                                   7 * 24, points_per_hour)

        if not week_indices:
            return None, None, None, None

        week_sample = np.concatenate([data_sequence[i: j]
                                      for i, j in week_indices], axis=0)

    if num_of_days > 0:
        day_indices = search_data(data_sequence.shape[0], num_of_days,
                                  label_start_idx, num_for_predict,
                                  24, points_per_hour)

        if not day_indices:
            return None, None, None, None

        day_sample = np.concatenate([data_sequence[i: j]
                                     for i, j in day_indices], axis=0)

    if num_of_hours > 0:
        hour_indices = search_data(data_sequence.shape[0], num_of_hours,
                                   label_start_idx, num_for_predict,
                                   1, points_per_hour)

        if not hour_indices:
            return None, None, None, None

        hour_sample = np.concatenate([data_sequence[i: j]
                                      for i, j in hour_indices], axis=0)

    target = data_sequence[label_start_idx: label_start_idx + num_for_predict]

    return week_sample, day_sample, hour_sample, target

六、函数 search_data

在这个函数中获取每个窗口(滑动生成的)索引的首尾，假设在原理图中获得新数据的shape=(20,4,170,3),表示获得20个窗口，每个窗口的范围为4.也就是如原理图中所展示的具有20个小方块组(4个为一组)，该函数返回的是每个小方块组的第一个和最后一个的索引的组合序列。
重要提醒：

  1. 在原理图中，我们使窗口每次滑动一步，在本函数中我们使窗口每次滑动 points_per_hour * units个步伐.

  2. 在原理图中，我们的窗口大小假设为4(方便作图)，在本函数中窗口大小固定为 points_per_hour,在本文中普遍默认为12.

  3. 在原理图中，我们的窗口滑动是沿时间轴的方向移动的，而在本函数中窗口时逆时间轴的方向移动的。当滑动窗口的起始索引start_idx

6.1 假设num_of_days=1

调用函数的命令

[ASTGCN之1个特征]解读(torch)之参数读取和数据读入(一)
; 6.2 假设 
def search_data(sequence_length, num_of_depend, label_start_idx,
                num_for_predict, units, points_per_hour):
    '''
    Parameters
    ----------
    sequence_length: int, length of all history data序列的长度
    num_of_depend: int,
    label_start_idx: int, the first index of predicting target
    num_for_predict: int, the number of points will be predicted for each sample
    units: int, week: 7 * 24, day: 24, recent(hour): 1
    points_per_hour: int, number of points per hour, depends on data
    Returns
    ----------
    list[(start_idx, end_idx)]
    '''

    if points_per_hour < 0:
        raise ValueError("points_per_hour should be greater than 0!")

    if label_start_idx + num_for_predict > sequence_length:
        return None

    x_idx = []
    for i in range(1, num_of_depend + 1):
        start_idx = label_start_idx - points_per_hour * units * i
        end_idx = start_idx + num_for_predict
        if start_idx >= 0:
            x_idx.append((start_idx, end_idx))
        else:
            return None

    if len(x_idx) != num_of_depend:
        return None

    return x_idx[::-1]

七、 函数 normalization

对样本train,val,test的数据进行标准化。标准化的方法： 减均值除以标准差。

def normalization(train, val, test):
    '''
    Parameters
    ----------
    train, val, test: np.ndarray (B,N,F,T)
    Returns
    ----------
    stats: dict, two keys: mean and std
    train_norm, val_norm, test_norm: np.ndarray,
                                     shape is the same as original
    '''
    assert train.shape[1:] == val.shape[1:] and val.shape[1:] == test.shape[1:]
    mean = train.mean(axis=(0,1,3), keepdims=True)
    std = train.std(axis=(0,1,3), keepdims=True)
    print('mean.shape:',mean.shape)
    print('std.shape:',std.shape)

    def normalize(x):
        return (x - mean) / std

    train_norm = normalize(train)
    val_norm = normalize(val)
    test_norm = normalize(test)

    return {'_mean': mean, '_std': std}, train_norm, val_norm, test_norm

八、测试read_and_generate_dataset

  1. 调用该函数
all_data = read_and_generate_dataset(graph_signal_matrix_filename,
                                     2, 1, 2, num_for_predict,
                                     points_per_hour=points_per_hour, save=True)
  1. idx : int , 0~16994，每个idx对应调用get_sample_indices
for idx in range(data_seq.shape[0]):
    sample = get_sample_indices(data_seq, num_of_weeks, num_of_days,
                                num_of_hours, idx, num_for_predict,
                                points_per_hour)

通过 3,4,5中可以看出idx=max(7 ∗ 24 ∗ 12 ∗ 724127 ∗24 ∗12 ∗num_of_weeks, 24 ∗ 12 ∗ 241224 ∗12 ∗num_of_days,12num_of_hours),才可以正常的取值。

  1. num_of_weeks=2 >0: 调用search_data
week_indices = search_data(data_sequence.shape[0], num_of_weeks,
                                   label_start_idx, num_for_predict,
                                   7 * 24, points_per_hour)

7 ∗ 24 ∗ 12 = 2016 72412=2016 7 ∗24 ∗12 =2016

idxstart_idx_1end_idx_1start_idx_2end_idx_2return0->2015
− 2016 → − 1 -2016\to -1 −2016 →−1

None2016-012-2016-2014None403120152017-111None403220162028012[(0, 12), (2016, 2028)]

  • 数据拼接： week_sample=np.concatenate([dataseq[0:12],dataseq[2016:2018],axis=0) ,其shape=(24,307,12)

  • num_of_days=2>0: 调用search_data

day_indices = search_data(data_sequence.shape[0], num_of_days,
                          label_start_idx, num_for_predict,
                          24, points_per_hour)

idxstart_idx_1end_idx_1start_idx_2end_idx_2return403237443775634563468[(3456, 3468), (3744, 3756)]

  • 数据拼接： day_sample=np.concatenate([dataseq[3456:3468],dataseq[3744:3756]],axis=0),其shape=(24,207,12)

  • num_of_days=2 >0: 调用search_data

hour_indices = search_data(data_sequence.shape[0], num_of_hours,
                           label_start_idx, num_for_predict,
                           1, points_per_hour)

idxstart_idx_1end_idx_1start_idx_2end_idx_2return40324020403240084020[(4008, 4020), (4020, 4032)]

  • 数据拼接： np.concatenate([dataseq[4008:4020],[4020,4032]],axis=0)，其shape=(24,307,12)

后篇博文见：
【ASTGCN】代码解读(torch)之train_ASTGCN_r（二）

【ASTGCN】模型解读(torch)之模型框架(三)

Original: https://blog.csdn.net/panbaoran913/article/details/124332937
Author: panbaoran913
Title: [ASTGCN之1个特征]解读(torch)之参数读取和数据读入(一)

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