"""Passenger inflow at bus stops in Montevideo city."""
from __future__ import annotations
import numpy as np
from stgraph.dataset.temporal.stgraph_temporal_dataset import STGraphTemporalDataset
[docs]class MontevideoBusDataLoader(STGraphTemporalDataset):
r"""Passenger inflow at bus stops in Montevideo city.
This dataset compiles hourly passenger inflow data for 11 key bus lines
in Montevideo, Uruguay, during October 2020. Focused on routes to the city
center, it encompasses bus stop vertices, interlinked by edges representing
connections with weights indicating road distances. The target variable
is passenger inflow, sourced from diverse data outlets within Montevideo's
Metropolitan Transportation System (STM).
This class provides functionality for loading, processing, and accessing the
Montevideo Bus dataset for use in deep learning tasks such as
passenger inflow prediction.
.. list-table:: gdata
:widths: 33 33 33
:header-rows: 1
* - num_nodes
- num_edges
- total_timestamps
* - 675
- 690
- 744
Example
-------
.. code-block:: python
from stgraph.dataset import MontevideoBusDataLoader
monte = MontevideoBusDataLoader(verbose=True)
num_nodes = monte.gdata["num_nodes"]
num_edges = monte.gdata["num_edges"]
total_timestamps = monte.gdata["total_timestamps"]
edge_list = monte.get_edges()
edge_weights = monte.get_edge_weights()
feats = monte.get_all_features()
targets = monte.get_all_targets()
Parameters
----------
verbose : bool, optional
Flag to control whether to display verbose info (default is False)
lags : int, optional
The number of time lags (default is 4)
cutoff_time : int, optional
The cutoff timestamp for the temporal dataset (default is None)
redownload : bool, optional (default is False)
Redownload the dataset online and save to cache
Attributes
----------
name : str
The name of the dataset.
gdata : dict
Graph meta data.
"""
def __init__(
self: MontevideoBusDataLoader,
verbose: bool = False,
lags: int = 4,
cutoff_time: int | None = None,
redownload: bool = False,
) -> None:
r"""Passenger inflow at bus stops in Montevideo city."""
super().__init__()
if not isinstance(lags, int):
raise TypeError("lags must be of type int")
if lags < 0:
raise ValueError("lags must be a positive integer")
if cutoff_time is not None and not isinstance(cutoff_time, int):
raise TypeError("cutoff_time must be of type int")
if cutoff_time is not None and cutoff_time < 0:
raise ValueError("cutoff_time must be a positive integer")
if cutoff_time is not None and cutoff_time <= lags:
raise ValueError("cutoff_time must be greater than lags")
self.name = "Montevideo_Bus"
self._url = "https://raw.githubusercontent.com/bfGraph/STGraph-Datasets/main/montevideobus.json"
self._verbose = verbose
self._lags = lags
self._cutoff_time = cutoff_time
if redownload and self._has_dataset_cache():
self._delete_cached_dataset()
if self._has_dataset_cache():
self._load_dataset()
else:
self._download_dataset()
self._save_dataset()
self._process_dataset()
def _process_dataset(self: MontevideoBusDataLoader) -> None:
self._set_total_timestamps()
self._set_num_nodes()
self._set_num_edges()
self._set_edges()
self._set_edge_weights()
self._set_features()
self._set_targets()
def _set_total_timestamps(self: MontevideoBusDataLoader) -> None:
r"""Set the total timestamps present in the dataset.
It sets the total timestamps present in the dataset into the
gdata attribute dictionary. It is the minimum of the cutoff time
choosen by the user and the total time periods present in the
original dataset.
"""
if self._cutoff_time is not None:
self.gdata["total_timestamps"] = min(
len(self._dataset["nodes"][0]["y"]),
self._cutoff_time,
)
else:
self.gdata["total_timestamps"] = len(self._dataset["nodes"][0]["y"])
def _set_num_nodes(self: MontevideoBusDataLoader) -> None:
r"""Set the total number of nodes present in the dataset."""
node_set = set()
max_node_id = 0
for edge in self._dataset["edges"]:
node_set.add(edge[0])
node_set.add(edge[1])
max_node_id = max(max_node_id, edge[0], edge[1])
if max_node_id != len(node_set) - 1:
raise ValueError("Node ID labelling is not continuous")
self.gdata["num_nodes"] = len(node_set)
def _set_num_edges(self: MontevideoBusDataLoader) -> None:
r"""Set the total number of edges present in the dataset."""
self.gdata["num_edges"] = len(self._dataset["edges"])
def _set_edges(self: MontevideoBusDataLoader) -> None:
r"""Set the edge list of the dataset."""
self._edge_list = [(edge[0], edge[1]) for edge in self._dataset["edges"]]
def _set_edge_weights(self: MontevideoBusDataLoader) -> None:
r"""Set the edge weights of the dataset."""
edges = self._dataset["edges"]
edge_weights = self._dataset["weights"]
comb_edge_list = [
(edges[i][0], edges[i][1], edge_weights[i]) for i in range(len(edges))
]
comb_edge_list.sort(key=lambda x: (x[1], x[0]))
self._edge_weights = np.array([edge_det[2] for edge_det in comb_edge_list])
def _set_features(self: MontevideoBusDataLoader) -> None:
r"""Calculate and set the feature attributes."""
features = [
np.array(node.get("X").get(feature_var)[: self.gdata["total_timestamps"]])
for node in self._dataset["nodes"]
for feature_var in ["y"]
]
stacked_features = np.stack(features).T
standardized_features = (
stacked_features - np.mean(stacked_features, axis=0)
) / np.std(stacked_features, axis=0)
self._all_features = np.array(
[
standardized_features[i : i + self._lags, :].T
for i in range(len(standardized_features) - self._lags)
],
)
def _set_targets(self: MontevideoBusDataLoader) -> None:
r"""Calculate and set the target attributes."""
targets = []
for node in self._dataset["nodes"]:
y = node.get("y")[: self.gdata["total_timestamps"]]
targets.append(np.array(y))
stacked_targets = np.stack(targets).T
standardized_targets = (
stacked_targets - np.mean(stacked_targets, axis=0)
) / np.std(stacked_targets, axis=0)
self._all_targets = np.array(
[
standardized_targets[i + self._lags, :].T
for i in range(len(standardized_targets) - self._lags)
],
)
[docs] def get_edges(self: MontevideoBusDataLoader) -> list:
r"""Return the edge list."""
return self._edge_list
[docs] def get_edge_weights(self: MontevideoBusDataLoader) -> np.ndarray:
r"""Return the edge weights."""
return self._edge_weights
[docs] def get_all_targets(self: MontevideoBusDataLoader) -> np.ndarray:
r"""Return the targets for each timestamp."""
return self._all_targets
[docs] def get_all_features(self: MontevideoBusDataLoader) -> np.ndarray:
r"""Return the features for each timestamp."""
return self._all_features