egutierrez
837563c3ba
Datascience: aggregate_by_group, deduplicate_entities/relations, detect_drift, diff_entities/relations, extract_entities/relations_llm, hotness_score, melt, merge_graphs, pivot, build_entity/relation_schema_prompt. Finance: avellaneda_stoikov_quotes, generate_gbm_prices, generate_taker_order, hawkes_intensity + módulo finance.py. Cybersecurity: envelope_encrypt/decrypt + módulo cybersecurity.py. Pipelines: extraction_pipeline, monte_carlo_market, run_market_sim. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2.1 KiB
2.1 KiB
name, kind, lang, domain, version, purity, signature, description, tags, uses_functions, uses_types, returns, returns_optional, error_type, imports, tested, tests, test_file_path, file_path
| name | kind | lang | domain | version | purity | signature | description | tags | uses_functions | uses_types | returns | returns_optional | error_type | imports | tested | tests | test_file_path | file_path | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| diff_relations | function | py | datascience | 1.0.0 | pure | def diff_relations(before: list[dict], after: list[dict], key: tuple[str, str, str] = ('source_id', 'target_id', 'relation_type'), ignore_fields: list[str] | None = None, compare_fields: list[str] | None = None) -> dict | Compara relaciones entre dos snapshots usando key compuesta (source_id, target_id, relation_type). Detecta relaciones añadidas, eliminadas y modificadas con detalle campo a campo. |
|
false | true |
|
python/functions/datascience/diff_relations_test.py | python/functions/datascience/diff_relations.py |
Ejemplo
before = [
{"source_id": "A", "target_id": "B", "relation_type": "knows", "weight": 1.0},
{"source_id": "B", "target_id": "C", "relation_type": "owns", "weight": 0.5},
]
after = [
{"source_id": "A", "target_id": "B", "relation_type": "knows", "weight": 2.0},
{"source_id": "C", "target_id": "D", "relation_type": "knows", "weight": 1.0},
]
result = diff_relations(before, after)
# result["added"] -> [{"source_id": "C", "target_id": "D", ...}]
# result["removed"] -> [{"source_id": "B", "target_id": "C", ...}]
# result["modified"] -> [{"key": "A|B|knows", "changes": {"weight": {"old": 1.0, "new": 2.0}}}]
# result["unchanged"] -> 0
Notas
La key compuesta se serializa como source_id|target_id|relation_type. Si alguno de los campos clave no existe en la relacion, se usa string vacio.
Misma semantica que diff_entities_py_datascience pero adaptada para relaciones donde no hay un ID unico — la identidad se define por los tres campos de la key.
Complemento natural de diff_entities_py_datascience para comparar grafos completos entre ejecuciones de pipelines.