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# LargeST_protocol/tasks/general_task.yaml
# ============================================================
# Purpose:
# - This file contains only processing rules shared by all tasks and naming conventions.
# Recommended reading order for each task.yaml:
# 1) Start with sample in each task.yaml: what constitutes one sample.
# 2) Read inputs: what the model can access at inference time.
# 3) Read prediction: what the model should predict.
# 4) Finally, read leakage: which columns must be excluded from model inputs.
# ============================================================
general_defaults:
# ------------------------------------------------------------
# Default input processing rules.
# Note: these are processing defaults, not input field defaults.
# These rules specify how to process time series, tabular, or graph inputs when a task uses them,
# but do not automatically add any input fields to a task.
# ------------------------------------------------------------
input_processing:
tabular_static:
# Unique identifier column for sensors / road segments in LargeST.
# This column is used only to join labels, splits, and metadata.
id_column: ID
time_series:
# LargeST historical traffic flow data typically uses timestamp as the time axis and sensor IDs as columns.
timestamp_column: timestamp
id_column: ID
timezone: UTC
sampling:
# LargeST raw historical traffic flow data typically has a 15-minute resolution.
# If your h5 file has been preprocessed to a different resolution, override this in the specific task.
native_rate: 15min
# By default, the task resolution matches the native resolution.
# For example, override this to 1h in task.yaml for a ranking task with hourly decisions.
task_rate: 15min
# Use this aggregation method when task_rate is coarser than native_rate, for example 15min -> 1h.
# mean is suitable for average traffic flow; sum is suitable for cumulative flow counts, subject to the data semantics.
downsample_method: mean
# Use this interpolation method when task_rate is finer than native_rate, for example 15min -> 5min. This task configuration is generally not recommended.
# ffill forward-fills using the most recent observation.
upsample_method: ffill
graph:
# Graph inputs are static by default, for example LargeST's adj.npy.
# Future dynamic graphs, such as event-induced or construction-impact graphs, can be configured in individual tasks.
directed: false
weighted: true
# ------------------------------------------------------------
# Standard split names.
# Use these standard values in splits.csv to avoid mixing train/val/valid/dev.
# ------------------------------------------------------------
split_names:
train: train
validation: val
test: test
# ------------------------------------------------------------
# Inheritance rules.
# A task can set inherit_general: true to inherit this file's processing defaults.
# Note: inheritance only affects default rules such as input_processing / columns / split_names,
# and does not automatically inherit any specific input fields.
# ------------------------------------------------------------
inheritance:
default_inherit_general: true
merge_strategy: recursive_override
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