egutierrez
dabc945eda
Extrae al registry funciones del proyecto interno footprint_aurgi: - core (6): slugify_ascii, normalize_for_join, cp_provincia_es, infer_provincia_from_cp, safe_read_csv_fallback, csv_to_parquet_duckdb - geo puras (7): haversine_km, point_in_ring, point_in_polygon, point_in_polygons_bbox, polygon_bbox, extent_with_padding, distance_bucket - geo I/O (4): load_geojson_polygons, load_boundary_gdf, add_basemap_osm, add_basemap_with_timeout - valhalla client (4): valhalla_route, valhalla_isochrone, valhalla_isochrones_async, valhalla_matrix_1_to_n - datascience stats (7): trimmed_mean, geometric_mean, detect_distribution_type, best_central_tendency, summary_stats, kde_density_levels, alpha_shape_concave_hull - datascience fuzzy (3): fuzzy_merge_adaptive (rapidfuzz), words_to_dataset, remove_words_from_column - datascience viz (2): plot_kde_2d, plot_heatmap_log - infra (4): compress_pdf_ghostscript, render_table_page_pdfpages, add_header_logo, osm2pgsql_ingest - pipelines (4): setup_geo_stack_docker, compute_centers_reachability, generate_isochrones_by_zone, count_points_per_zone - types geo (4): LonLat, BBox, IsochroneRequest, Centro Incluye: - apps/footprint_geo_stack/ (PostGIS + Martin + Valhalla via docker-compose) - 131/132 tests pasan (1 skip esperado: osm2pgsql en PATH) - Issue tracker dev/issues/0052-footprint-aurgi-extraction.md - Atribucion uniforme: source_repo internal:footprint_aurgi, source_license internal-aurgi - Build con 9 agentes en paralelo (8 wave 1 + 1 wave 2 pipelines) Tambien commitea trabajo previo no commiteado: aggregate_extraction_results, chunk_with_overlap, clean_pdf_text, merge_entity_aliases, extract_graph_gliner2, extract_relations_mrebel, extract_triples_spacy_es, gliner2/mrebel/marianmt/rebel/spacy_es load_model, parse_rebel_output, translate_es_to_en, issue 0050/0051. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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name, kind, lang, domain, version, purity, signature, description, tags, uses_functions, uses_types, returns, returns_optional, error_type, imports, params, output, tested, tests, test_file_path, file_path, notes
| name | kind | lang | domain | version | purity | signature | description | tags | uses_functions | uses_types | returns | returns_optional | error_type | imports | params | output | tested | tests | test_file_path | file_path | notes | ||||||||||||||||||||||||
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| align_relations_to_entities | function | py | datascience | 1.0.0 | pure | def align_relations_to_entities(triplets: list[dict], entity_names: list[str]) -> list[dict] | Filtra y alinea triplets REBEL/mREBEL a nombres canonicos de entidades. Para cada triplet, resuelve head y tail contra entity_names con match exacto case-insensitive o substring (gana el nombre mas largo). Descarta triplets donde algun lado no resuelve o head==tail. |
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false |
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lista de dicts con claves from (str), kind (str), to (str), head_type (str), tail_type (str). from/to son valores tomados verbatim de entity_names. | true |
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python/functions/datascience/tests/test_align_relations_to_entities.py | python/functions/datascience/align_relations_to_entities.py | Funcion pura. Compone con parse_rebel_output: el output de parse_rebel_output entra como triplets, y entity_names viene de [e.name for e in entities] del contexto de extraccion. Estrategia de matching: 1. Exacto case-insensitive (O(1) via dict) 2. Substring bidireccional: entity in span O span in entity (itera por longitud DESC) Esto cubre casos como mREBEL emitiendo "esta en Bilbao" cuando la entidad es "Bilbao", o "Banco Santander S.A." cuando la entidad canonizada es "Banco Santander". |
Ejemplo
from python.functions.datascience.parse_rebel_output import parse_rebel_output
from python.functions.datascience.align_relations_to_entities import align_relations_to_entities
decoded = "tp_XX<triplet> Pablo Isla <per> Inditex <org> employer"
triplets = parse_rebel_output(decoded)
entities = ["Pablo Isla", "Inditex", "A Coruna"]
aligned = align_relations_to_entities(triplets, entities)
# [{'from': 'Pablo Isla', 'kind': 'employer', 'to': 'Inditex',
# 'head_type': 'per', 'tail_type': 'org'}]
Estrategia de matching
- Exacto case-insensitive:
"inditex"=="Inditex". - Substring bidireccional: la entidad esta contenida en el span del modelo, o el span del modelo esta contenido en el nombre de la entidad. Cuando varias entidades encajan, gana la mas larga (mas especifica).
Notas
- No hace fuzzy matching (Levenshtein, etc.) — la precision sobre el recall es preferida en el contexto de grafos de conocimiento.
- Para mejorar recall: normalizar entity_names antes de llamar (quitar siglas, tildes).
- Los triplets con
from == to(self-loops) se descartan siempre.