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ano6060
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anonymous-ride-sample

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RIDE Representative Sample

This repository provides a representative sample of the RIDE dataset collection for reviewer inspection. It is intended to make the data schemas, file formats, and construction pipeline easy to inspect without downloading the full releases. It is not intended for reporting benchmark results.

Sample Contents

The sample contains:

  • a Silver sample under silver/
  • a Gold sample under gold/

How This Sample Was Created

The Silver sample was created from the full RIDE Silver release by retaining the static tables and the first month of event and journey data:

  • silver/static/
  • silver/events/events_202301.parquet
  • silver/journeys/journeys_202301.parquet

The Gold sample was generated from the Silver sample using the same public RIDE gold-construction scripts used to create the full Gold Lite and Gold Standard releases.

The Gold sample uses a January 2023 temporal split:

  • training snapshot period: 2023-01-01 to 2023-01-20
  • test snapshot period: 2023-01-21 to 2023-01-31
  • number of training snapshots: 200
  • number of test snapshots: 100
  • number of future target events: 15
  • random seed: 42
  • idle time before first planned event: 5 minutes
  • idle time after last observed event: 5 minutes

The generated Gold sample includes the benchmark core and model-ready representations created by the same pipeline as the full releases. This preserves the schemas and construction logic of RIDE while keeping the sample small enough for inspection.

Reproduction Commands

The Gold sample core was generated with:

python -m scripts.dataset.gold.create_gold_dataset_core \
  --start-train-day 2023-01-01 \
  --end-train-day 2023-01-20 \
  --start-test-day 2023-01-21 \
  --end-test-day 2023-01-31 \
  --n-train 200 \
  --n-test 100 \
  --n-future 15 \
  --idle-time-beg 5 \
  --idle-time-end 5 \
  --seed 42 \
  --events-dir data/ride-sample/silver/events \
  --journeys-dir data/ride-sample/silver/journeys \
  --output-root data/ride-sample/gold/core

Model-specific datasets were generated with:

python -m scripts.dataset.gold.create_tabular_data \
  --silver-dir data/ride-sample/silver \
  --dataset-core-spec data/ride-sample/gold/core/dataset_core_spec.yaml \
  --output-dir data/ride-sample/gold/tabular

python -m scripts.dataset.gold.create_sequential_data \
  --silver-dir data/ride-sample/silver \
  --dataset-core-spec data/ride-sample/gold/core/dataset_core_spec.yaml \
  --output-dir data/ride-sample/gold/sequential

python -m scripts.dataset.gold.create_gnn_data \
  --silver-dir data/ride-sample/silver \
  --dataset-core-spec data/ride-sample/gold/core/dataset_core_spec.yaml \
  --output-dir data/ride-sample/gold/gnn \
  --graph-chunk-size 100

python -m scripts.dataset.gold.create_graph_event_data \
  --silver-dir data/ride-sample/silver \
  --dataset-core-spec data/ride-sample/gold/core/dataset_core_spec.yaml \
  --output-dir data/ride-sample/gold/graph_event
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