egutierrez
63a9cb5273
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>
53 lines
2.1 KiB
Markdown
53 lines
2.1 KiB
Markdown
---
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name: diff_relations
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kind: function
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lang: py
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domain: datascience
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version: "1.0.0"
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purity: pure
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signature: "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"
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description: "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."
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tags: [diff, relations, graph, snapshot, operations, comparison, datascience]
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uses_functions: []
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uses_types: []
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returns: []
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returns_optional: false
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error_type: ""
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imports: []
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tested: true
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tests:
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- "relacion añadida"
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- "relacion eliminada"
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- "relacion con metadata modificada (mismo source/target/type, distinto weight)"
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- "key compuesta funciona correctamente"
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test_file_path: "python/functions/datascience/diff_relations_test.py"
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file_path: "python/functions/datascience/diff_relations.py"
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---
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## Ejemplo
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```python
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before = [
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{"source_id": "A", "target_id": "B", "relation_type": "knows", "weight": 1.0},
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{"source_id": "B", "target_id": "C", "relation_type": "owns", "weight": 0.5},
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]
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after = [
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{"source_id": "A", "target_id": "B", "relation_type": "knows", "weight": 2.0},
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{"source_id": "C", "target_id": "D", "relation_type": "knows", "weight": 1.0},
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]
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result = diff_relations(before, after)
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# result["added"] -> [{"source_id": "C", "target_id": "D", ...}]
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# result["removed"] -> [{"source_id": "B", "target_id": "C", ...}]
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# result["modified"] -> [{"key": "A|B|knows", "changes": {"weight": {"old": 1.0, "new": 2.0}}}]
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# result["unchanged"] -> 0
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```
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## Notas
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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.
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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.
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Complemento natural de `diff_entities_py_datascience` para comparar grafos completos entre ejecuciones de pipelines.
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