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stgat_data.py
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stgat_data.py
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import os.path as osp
import pickle as pkl
import sys
import pandas as pd
import numpy as np
import torch
from cogdl.data import Dataset, Graph
from cogdl.utils import remove_self_loops, download_url, untar, coalesce, MAE, CrossEntropyLoss
import os
import scipy.sparse as sp
from sklearn.preprocessing import StandardScaler
from datetime import datetime
import geopy.distance # to compute distances between stations
import glob
from tqdm import tqdm
import warnings
from numpy.core.umath_tests import inner1d
def files_exist(files):
return all([osp.exists(f) for f in files])
def PeMS_lane_columns(x):
tmp = [
'lane_N_samples_{}'.format(x),
'lane_N_flow_{}'.format(x),
'lane_N_avg_occ_{}'.format(x),
'lane_N_avg_speed_{}'.format(x),
'lane_N_observed_{}'.format(x)
]
return tmp
def raw_data_processByNumNodes(raw_dir, num_nodes, meta_file_name):
PeMS_daily = os.path.join(f'{raw_dir}', '*')
PeMS_metadata = os.path.join(f'{raw_dir}', meta_file_name)
output_dir = os.path.join(f'{raw_dir}')
# Parameters
outcome_var = 'avg_speed'
files = glob.glob(PeMS_daily)
files.remove(glob.glob(PeMS_metadata)[0])
PeMS_columns = ['timestamp', 'station', 'district', 'freeway_num',
'direction_travel', 'lane_type', 'station_length',
'samples', 'perc_observed', 'total_flow', 'avg_occupancy',
'avg_speed']
#PeMS_lane_columns = lambda x: ['lane_N_samples_{}'.format(x),
# 'lane_N_flow_{}'.format(x),
# 'lane_N_avg_occ_{}'.format(x),
# 'lane_N_avg_speed_{}'.format(x),
# 'lane_N_observed_{}'.format(x)]
PeMS_all_columns = PeMS_columns.copy()
for i in range(1, 9):
PeMS_all_columns += PeMS_lane_columns(i)
# Randomly select stations to build the dataset
np.random.seed(42)
station_file = files[0]
station_file_content = pd.read_csv(station_file, header=0, names=PeMS_all_columns)
station_file_content = station_file_content[PeMS_columns]
station_file_content = station_file_content.dropna(subset=[outcome_var])
unique_stations = station_file_content['station'].unique()
selected_stations = np.random.choice(unique_stations, size=num_nodes, replace=False)
# Build two-months of data for the selected stations/nodes
station_data = pd.DataFrame({col: []} for col in PeMS_columns)
for station_file in tqdm(files):
# Get file date
file_date_str = station_file.split(os.path.sep)[-1].split('.')[0]
file_date = datetime(int(file_date_str.split('_')[-3]), int(file_date_str.split('_')[-2]),
int(file_date_str.split('_')[-1]))
# Check if weekday
if file_date.weekday() < 5:
# Read CSV
station_file_content = pd.read_csv(
station_file, header=0, names=PeMS_all_columns)
# Keep only columns of interest
station_file_content = station_file_content[PeMS_columns]
# Keep stations
station_file_content = station_file_content[
station_file_content['station'].isin(selected_stations)]
# Append to dataset
station_data = pd.concat([station_data, station_file_content])
# Drop the 11 rows with missing values
station_data = station_data.dropna(subset=['timestamp', outcome_var])
station_data.head()
station_data.shape
station_metadata = pd.read_table(PeMS_metadata)
station_metadata = station_metadata[['ID', 'Latitude', 'Longitude']]
# Filter for selected stations
station_metadata = station_metadata[station_metadata['ID'].isin(selected_stations)]
station_metadata.head()
# Keep only the required columns (time interval, station ID and the outcome variable)
station_data = station_data[['timestamp', 'station', outcome_var]]
station_data[outcome_var] = pd.to_numeric(station_data[outcome_var])
# Reshape the dataset and aggregate the traffic speeds in each time interval
V = station_data.pivot_table(index=['timestamp'], columns=['station'], values=outcome_var, aggfunc='mean')
V.head()
V.shape
# Compute distances
distances = pd.crosstab(station_metadata.ID, station_metadata.ID, normalize=True)
distances_std = []
for station_i in selected_stations:
for station_j in selected_stations:
if station_i == station_j:
distances.at[station_j, station_i] = 0
else:
# Compute distance between stations
station_i_meta = station_metadata[station_metadata['ID'] == station_i]
station_j_meta = station_metadata[station_metadata['ID'] == station_j]
if np.isnan(station_i_meta['Latitude'].values[0]) or np.isnan(station_i_meta['Longitude'].values[0]) or np.isnan(station_j_meta['Latitude'].values[0]) or np.isnan(station_j_meta['Longitude'].values[0]):
d_ij = 0
else:
d_ij = geopy.distance.geodesic(
(station_i_meta['Latitude'].values[0], station_i_meta['Longitude'].values[0]),
(station_j_meta['Latitude'].values[0], station_j_meta['Longitude'].values[0])).m
distances.at[station_j, station_i] = d_ij
distances_std.append(d_ij)
distances_std = np.std(distances_std)
distances.head()
W = pd.crosstab(station_metadata.ID, station_metadata.ID, normalize=True)
epsilon = 0.1
sigma = distances_std
for station_i in selected_stations:
for station_j in selected_stations:
if station_i == station_j:
W.at[station_j, station_i] = 0
else:
# Compute distance between stations
d_ij = distances.loc[station_j, station_i]
# Compute weight w_ij
w_ij = np.exp(-d_ij ** 2 / sigma ** 2)
if w_ij >= epsilon:
W.at[station_j, station_i] = w_ij
W.head()
# Save to file
V = V.fillna(V.mean())
V.to_csv(os.path.join(output_dir, 'V_{}.csv'.format(num_nodes)), index=True)
W.to_csv(os.path.join(output_dir, 'W_{}.csv'.format(num_nodes)), index=False)
station_metadata.to_csv(os.path.join(output_dir, 'station_meta_{}.csv'.format(num_nodes)), index=False)
def read_stgat_data(folder, num_nodes):
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
W = pd.read_csv(osp.join(folder, "W_{}.csv".format(num_nodes)))
T_V = pd.read_csv(osp.join(folder, "V_{}.csv".format(num_nodes)))
V = T_V.drop('timestamp',axis=1)
num_samples, num_nodes = V.shape
scaler = StandardScaler()
# format graph for pyg layer inputs
G = sp.coo_matrix(W)
edge_index = torch.tensor(np.array([G.row, G.col]), dtype=torch.int64).to(device)
edge_weight = torch.tensor(G.data).float().to(device)
data = Graph()
data.num_nodes = num_nodes
data.num_samples = num_samples
data.edge_index = edge_index
data.edge_weight = edge_weight
data.scaler = scaler
data.V = V
data.W = W
data.timestamp = T_V['timestamp']
data.node_ids = V.columns
return data
class STGATDataset(Dataset):
def __init__(self, root, name, num_stations, meta_file_name):
self.name = name
self.meta_file_name = meta_file_name
self.url = "https://cloud.tsinghua.edu.cn/f/5af7ea1a7d064c5ba6c8/?dl=1"
self.url_test = "https://cloud.tsinghua.edu.cn/f/a39effe167df447eab80/?dl=1"
self.num_stations = num_stations
super(STGATDataset, self).__init__(root)
self.data = torch.load(self.processed_paths[0])
self.num_nodes = self.data.num_nodes
@property
def raw_file_names(self):
names = ["station_meta_{}.csv".format(self.num_stations), "V_{}.csv".format(self.num_stations), "W_{}.csv".format(self.num_stations)]
return names
@property
def processed_file_names(self):
return ["data.pt"]
def get(self, idx):
assert idx == 0
return self.data
def download(self):
# if os.path.exists(self.raw_dir+r'\PeMS_20210501_20210630'): # pragma: no cover
# TODO: Auto Traffic pipeline support
# if os.path.exists(self.raw_dir): # auto_traffic
# return
_test_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
test_path = _test_path[:(len(_test_path)-len("/cogdl"))]+"/tests/test_stgat/"
if os.path.exists(test_path):
download_url(self.url_test, self.raw_dir, name=self.name + ".zip")
else:
download_url(self.url, self.raw_dir, name=self.name + ".zip")
untar(self.raw_dir, self.name + ".zip")
def process(self):
files = self.raw_paths
if not files_exist(files):
raw_data_processByNumNodes(self.raw_dir, self.num_stations, self.meta_file_name)
data = read_stgat_data(self.raw_dir, self.num_stations)
torch.save(data, self.processed_paths[0])
def __repr__(self):
return "{}".format(self.name)
def get_evaluator(self):
return MAE()
def get_loss_fn(self):
return torch.nn.MSELoss()
class PeMS_Dataset(STGATDataset):
def __init__(self, data_path="data"):
dataset = "pems-stgat"
path = osp.join(data_path, dataset)
super(PeMS_Dataset, self).__init__(path, dataset, num_stations=288, meta_file_name= 'd07_text_meta.txt')