tab-realdata-hub 0.1.4

Manifest-backed real-data ingestion and OpenML materialization for tabular workflows

tab-realdata-hub

tab-realdata-hub materializes external tabular data sources into the manifest-backed packed-shard contract consumed by tab-foundry.

tab-realdata-hub is the sole owner of that manifest contract. The parquet manifest is the stable index layer, and richer evolving dataset/provenance fields live in metadata.ndjson. Downstream consumers are expected to read through this package rather than reimplementing compatibility shims.

Install from the upstream git tag with:

python -m pip install "tab-realdata-hub @ git+https://github.com/bensonlee5/tab-realdata-hub.git@v0.1.2"

For repo-local development:

uv sync

The v1 surface is OpenML-first:

• build pinned OpenML bundle JSON from known task pools or live discovery
• materialize bundle tasks into packed shards plus manifest parquet
• inspect manifest-backed datasets through a stable library and CLI surface

Example:

uv sync

.venv/bin/tab-realdata-hub bundle build-openml \
  --out-path bundles/many_class_v1.json \
  --bundle-name many_class_v1 \
  --version 1 \
  --task-source tabarena_v0_1 \
  --min-classes 2 \
  --max-features 10 \
  --max-classes 10 \
  --max-missing-pct 10.0

.venv/bin/tab-realdata-hub materialize openml-bundle \
  --bundle-path bundles/many_class_v1.json \
  --out-root outputs/openml/many_class_v1

.venv/bin/tab-realdata-hub manifest inspect \
  --manifest outputs/openml/many_class_v1/manifest.parquet

The repo now tracks two hub-owned classification validation bundles for tab-foundry under src/tab_realdata_hub/bench/:

• nanotabpfn_openml_classification_medium_v1.json
• nanotabpfn_openml_classification_large_v1.json

The current TF-RD-010 contract is:

• medium: no-missing multiclass validation with max_features=10, min_classes=3, max_classes=10, and min_minority_class_pct=2.5
• large: allow-missing multiclass validation with max_features=20, max_missing_pct=5.0, min_classes=3, max_classes=10, and min_minority_class_pct=2.5

Refresh the checked-in bundle definitions from the pinned tabarena_v0_1 source with:

.venv/bin/tab-realdata-hub bundle build-openml \
  --out-path src/tab_realdata_hub/bench/nanotabpfn_openml_classification_medium_v1.json \
  --bundle-name nanotabpfn_openml_classification_medium \
  --version 1 \
  --task-source tabarena_v0_1 \
  --new-instances 200 \
  --max-features 10 \
  --min-classes 3 \
  --max-classes 10 \
  --max-missing-pct 0.0 \
  --min-minority-class-pct 2.5

.venv/bin/tab-realdata-hub bundle build-openml \
  --out-path src/tab_realdata_hub/bench/nanotabpfn_openml_classification_large_v1.json \
  --bundle-name nanotabpfn_openml_classification_large \
  --version 1 \
  --task-source tabarena_v0_1 \
  --new-instances 200 \
  --max-features 20 \
  --min-classes 3 \
  --max-classes 10 \
  --max-missing-pct 5.0 \
  --min-minority-class-pct 2.5

Materialize the checked-in bundle definitions into the manifest paths consumed downstream by tab-foundry with:

.venv/bin/tab-realdata-hub materialize openml-bundle \
  --bundle-path src/tab_realdata_hub/bench/nanotabpfn_openml_classification_medium_v1.json \
  --out-root data/manifests/bench/nanotabpfn_openml_classification_medium_v1

.venv/bin/tab-realdata-hub materialize openml-bundle \
  --bundle-path src/tab_realdata_hub/bench/nanotabpfn_openml_classification_large_v1.json \
  --out-root data/manifests/bench/nanotabpfn_openml_classification_large_v1

Inspect the resulting manifests with:

.venv/bin/tab-realdata-hub manifest inspect \
  --manifest data/manifests/bench/nanotabpfn_openml_classification_medium_v1/manifest.parquet

.venv/bin/tab-realdata-hub manifest inspect \
  --manifest data/manifests/bench/nanotabpfn_openml_classification_large_v1/manifest.parquet