Source code for torch_geometric_temporal.dataset.metr_la

import os
import zipfile
import numpy as np
import torch
from torch_geometric.utils import dense_to_sparse
from six.moves import urllib
from ..signal import StaticGraphTemporalSignal

[docs]class METRLADatasetLoader(object): """A traffic forecasting dataset based on Los Angeles Metropolitan traffic conditions. The dataset contains traffic readings collected from 207 loop detectors on highways in Los Angeles County in aggregated 5 minute intervals for 4 months between March 2012 to June 2012. For further details on the version of the sensor network and discretization see: `"Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting" <>`_ """ def __init__(self, raw_data_dir=os.path.join(os.getcwd(), "data")): super(METRLADatasetLoader, self).__init__() self.raw_data_dir = raw_data_dir self._read_web_data() def _download_url(self, url, save_path): # pragma: no cover with urllib.request.urlopen(url) as dl_file: with open(save_path, "wb") as out_file: out_file.write( def _read_web_data(self): url = "" # Check if zip file is in data folder from working directory, otherwise download if not os.path.isfile( os.path.join(self.raw_data_dir, "") ): # pragma: no cover if not os.path.exists(self.raw_data_dir): os.makedirs(self.raw_data_dir) self._download_url(url, os.path.join(self.raw_data_dir, "")) if not os.path.isfile( os.path.join(self.raw_data_dir, "adj_mat.npy") ) or not os.path.isfile( os.path.join(self.raw_data_dir, "node_values.npy") ): # pragma: no cover with zipfile.ZipFile( os.path.join(self.raw_data_dir, ""), "r" ) as zip_fh: zip_fh.extractall(self.raw_data_dir) A = np.load(os.path.join(self.raw_data_dir, "adj_mat.npy")) X = np.load(os.path.join(self.raw_data_dir, "node_values.npy")).transpose( (1, 2, 0) ) X = X.astype(np.float32) # Normalise as in DCRNN paper (via Z-Score Method) means = np.mean(X, axis=(0, 2)) X = X - means.reshape(1, -1, 1) stds = np.std(X, axis=(0, 2)) X = X / stds.reshape(1, -1, 1) self.A = torch.from_numpy(A) self.X = torch.from_numpy(X) def _get_edges_and_weights(self): edge_indices, values = dense_to_sparse(self.A) edge_indices = edge_indices.numpy() values = values.numpy() self.edges = edge_indices self.edge_weights = values def _generate_task(self, num_timesteps_in: int = 12, num_timesteps_out: int = 12): """Uses the node features of the graph and generates a feature/target relationship of the shape (num_nodes, num_node_features, num_timesteps_in) -> (num_nodes, num_timesteps_out) predicting the average traffic speed using num_timesteps_in to predict the traffic conditions in the next num_timesteps_out Args: num_timesteps_in (int): number of timesteps the sequence model sees num_timesteps_out (int): number of timesteps the sequence model has to predict """ indices = [ (i, i + (num_timesteps_in + num_timesteps_out)) for i in range(self.X.shape[2] - (num_timesteps_in + num_timesteps_out) + 1) ] # Generate observations features, target = [], [] for i, j in indices: features.append((self.X[:, :, i : i + num_timesteps_in]).numpy()) target.append((self.X[:, 0, i + num_timesteps_in : j]).numpy()) self.features = features self.targets = target
[docs] def get_dataset( self, num_timesteps_in: int = 12, num_timesteps_out: int = 12 ) -> StaticGraphTemporalSignal: """Returns data iterator for METR-LA dataset as an instance of the static graph temporal signal class. Return types: * **dataset** *(StaticGraphTemporalSignal)* - The METR-LA traffic forecasting dataset. """ self._get_edges_and_weights() self._generate_task(num_timesteps_in, num_timesteps_out) dataset = StaticGraphTemporalSignal( self.edges, self.edge_weights, self.features, self.targets ) return dataset