| |
| """Generate Croissant 1.1 + minimal RAI metadata for BehaviorBench.""" |
| import hashlib |
| import json |
| from pathlib import Path |
| import pyarrow as pa |
| import pyarrow.parquet as pq |
|
|
| ROOT = Path(__file__).resolve().parents[1] |
| BASE = "https://huggingface.co/datasets/Salesforce/BehaviorBench" |
| RAW = BASE + "/resolve/main/" |
|
|
|
|
| def field_type(t): |
| """Return (Croissant type, repeated) for validator-safe scalar fields. |
| |
| Current mlcroissant record generation cannot reliably cast nested Parquet |
| struct values (including list<struct<...>>). Those columns remain present |
| in the distributed Parquet files but are omitted from RecordSet fields. |
| """ |
| repeated = False |
| if pa.types.is_list(t) or pa.types.is_large_list(t): |
| t = t.value_type |
| repeated = True |
| if pa.types.is_boolean(t): return "sc:Boolean", repeated |
| if pa.types.is_integer(t): return "sc:Integer", repeated |
| if pa.types.is_floating(t) or pa.types.is_decimal(t): return "sc:Float", repeated |
| if pa.types.is_string(t) or pa.types.is_large_string(t): return "sc:Text", repeated |
| return None |
|
|
|
|
| def sha256_file(path, chunk_size=8 * 1024 * 1024): |
| digest = hashlib.sha256() |
| with path.open("rb") as stream: |
| for chunk in iter(lambda: stream.read(chunk_size), b""): |
| digest.update(chunk) |
| return digest.hexdigest() |
|
|
|
|
| def canonical_files(): |
| files=[] |
| for top in (ROOT/"dataset", ROOT/"benchmark"): |
| for p in top.rglob("*.parquet"): |
| |
| if "evaluation/sources" in p.as_posix(): continue |
| files.append(p) |
| return sorted(files) |
|
|
|
|
| def main(): |
| distributions=[]; recordsets=[] |
| for p in canonical_files(): |
| rel=p.relative_to(ROOT).as_posix(); fid=rel |
| distributions.append({ |
| "@type":"cr:FileObject", "@id":fid, |
| "name":rel, "contentUrl":RAW+rel, |
| "encodingFormat":"application/x-parquet", |
| "sha256":sha256_file(p), |
| }) |
| pf=pq.ParquetFile(p); schema=pf.schema_arrow |
| rid=BASE+"#records/"+rel.removesuffix(".parquet") |
| fields=[] |
| for f in schema: |
| type_info = field_type(f.type) |
| if type_info is None: |
| continue |
| data_type, repeated = type_info |
| field = { |
| "@type":"cr:Field", "@id":rid+"/"+f.name, |
| "name":f.name, "dataType":data_type, |
| "source":{"fileObject":fid,"extract":{"column":f.name}}, |
| } |
| if repeated: |
| field["repeated"] = True |
| fields.append(field) |
| recordsets.append({ |
| "@type":"cr:RecordSet", "@id":rid, "name":rel, |
| "description":f"Rows from {rel} ({pf.metadata.num_rows:,} records).", |
| "field":fields, |
| }) |
|
|
| doc={ |
| "@context":{ |
| "@language":"en", "@vocab":"https://schema.org/", |
| "sc":"https://schema.org/", "cr":"http://mlcommons.org/croissant/", |
| "rai":"http://mlcommons.org/croissant/RAI/", |
| "dct":"http://purl.org/dc/terms/", "prov":"http://www.w3.org/ns/prov#", |
| "arrayShape":"cr:arrayShape", "citeAs":"cr:citeAs", |
| "column":"cr:column", "conformsTo":"dct:conformsTo", |
| "containedIn":"cr:containedIn", "data":{"@id":"cr:data","@type":"@json"}, |
| "dataType":{"@id":"cr:dataType","@type":"@vocab"}, |
| "description":{"@container":"@language"}, |
| "equivalentProperty":"cr:equivalentProperty", |
| "examples":{"@id":"cr:examples","@type":"@json"}, |
| "extract":"cr:extract", "field":"cr:field", |
| "fileObject":"cr:fileObject", "fileProperty":"cr:fileProperty", |
| "fileSet":"cr:fileSet", "format":"cr:format", "includes":"cr:includes", |
| "isArray":"cr:isArray", "isLiveDataset":"cr:isLiveDataset", |
| "jsonPath":"cr:jsonPath", "key":"cr:key", "md5":"cr:md5", |
| "name":{"@container":"@language"}, "parentField":"cr:parentField", |
| "path":"cr:path", "recordSet":"cr:recordSet", "references":"cr:references", |
| "regex":"cr:regex", "repeated":"cr:repeated", "replace":"cr:replace", |
| "samplingRate":"cr:samplingRate", "separator":"cr:separator", |
| "source":"cr:source", "subField":"cr:subField", "transform":"cr:transform" |
| }, |
| "@type":"sc:Dataset", |
| "name":"BehaviorBench", |
| "alternateName":"BehaviorBench: Modeling Real-World User Decisions from Behavioral Traces", |
| "description":"A benchmark derived from public Polymarket and Polygon behavioral traces for wallet-level Belief and Trade prediction under Direct, Profile, and Retrieval interfaces.", |
| "url":BASE, |
| "license":"https://www.apache.org/licenses/LICENSE-2.0", |
| "conformsTo":"http://mlcommons.org/croissant/1.1", |
| "citeAs":"BehaviorBench: Modeling Real-World User Decisions from Behavioral Traces (NeurIPS 2026 E&D submission).", |
| "version":"1.0.0", |
| "datePublished":"2026", |
| "inLanguage":"en", |
| "keywords":["behavioral modeling","user modeling","prediction markets","Polymarket","personalization","benchmark"], |
| "isAccessibleForFree":True, |
| "distribution":distributions, |
| "recordSet":recordsets, |
| "rai:dataLimitations":"Observable wallet behavior is an imperfect proxy for internal belief or preference. The sample represents active Polymarket wallets rather than the general population. Belief confidence is concentrated at 0 and 1; ties do not uniquely identify direction. Trade values use estimated mark-price conversion. Generalization beyond this platform and block range is not established.", |
| "rai:dataBiases":"Activity filtering favors active wallets; automated strategies, market makers, hedgers, and arbitrageurs may be overrepresented. Market topics and participation are time-dependent. Generated profiles inherit biases from their generator models.", |
| "rai:personalSensitiveInformation":"The data contain public pseudonymous blockchain wallet addresses and behavioral traces that may support sensitive inferences about financial interests, risk preferences, politics, or sports. No verified real-world identities or demographic attributes are included. Deanonymization and consequential use are not intended.", |
| "rai:dataUseCases":"Intended for research evaluation of personalized behavioral prediction, sequence modeling, profile representations, and retrieval augmentation. Not intended for identity attribution, financial advice, surveillance, credit/employment decisions, law enforcement, or targeting individuals.", |
| "rai:dataSocialImpact":"Potential benefits include reproducible evaluation of personalized systems. Risks include surveillance, profiling, deanonymization, manipulation, and overclaiming psychological inference from financial behavior. Mitigations are documented in docs/RESPONSIBLE_AI.md.", |
| "rai:hasSyntheticData":True, |
| "rai:dataCollection":"Derived from public Polymarket and Polygon on-chain records covering released block heights approximately 30,000,825 through 80,000,000. The release contains processed source tables rather than a complete raw chain archive.", |
| "rai:dataReleaseMaintenance":"Version material corrections in CHANGELOG.md; regenerate checksums and Croissant metadata for each release.", |
| "rai:machineAnnotationTools":"Belief and Trade labels are deterministic transformations of holdings and transactions. Profile interface fields are language-model-generated summaries and retain generator-model metadata where available.", |
| "prov:wasDerivedFrom":[{"@type":"sc:Dataset","name":"Public Polymarket and Polygon records","url":"https://polymarket.com/"}], |
| "prov:wasGeneratedBy":[ |
| {"@type":"sc:CreateAction","name":"Belief construction","description":"Aggregate wallet-market holdings into stance and normalized position imbalance, then create fixed per-wallet temporal splits."}, |
| {"@type":"sc:CreateAction","name":"Trade construction","description":"Order transactions by block/log index, reconstruct position changes, derive increase/decrease/close labels and estimated USDC amount, then create fixed per-wallet temporal splits."}, |
| {"@type":"sc:CreateAction","name":"Benchmark interfaces","description":"Render Direct prompts, model-generated Profile representations, and Retrieval contexts from support wallets excluded from the experiment set."} |
| ] |
| } |
| (ROOT/"croissant.json").write_text(json.dumps(doc,ensure_ascii=False,indent=2)+"\n") |
| print(f"Wrote {len(distributions)} distributions and {len(recordsets)} record sets") |
|
|
| if __name__=="__main__": main() |
|
|
