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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| extract_triples_spacy_es | function | py | datascience | 1.0.0 | impure | def extract_triples_spacy_es(text: str, nlp: Any) -> dict | Extraccion OpenIE schema-less en castellano via reglas de dependencia spaCy. Detecta patrones sujeto-verbo-objeto con el lemma del verbo como relacion (sin vocabulario fijo). Tambien extrae entidades NER (PER, ORG, LOC, MISC). |
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false | error_go_core |
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Dict con 'text' (input), 'triples' (lista de {subject, relation, object, verb_form, object_dep, prep}), 'entities' (lista de {text, label}) y 'elapsed_s'. La relacion es el lemma del verbo, opcionalmente sufijado con preposicion (_en, _con) o modo pasivo ([pass]). | true |
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python/functions/datascience/tests/test_extract_triples_spacy_es.py | python/functions/datascience/extract_triples_spacy_es.py | LICENSE: spaCy es MIT. Modelo es_core_news_md es CC BY-SA 4.0. Uso comercial permitido con atribucion. Validado en notebook 09 del analisis gliner_glirel_tuning. Complementa a extract_graph_gliner2: GLiNER2 usa vocabulario fijo de relaciones pero mayor precision; spaCy OpenIE usa lemmas verbales (sin vocabulario fijo) pero requiere post-filtrado manual. impure: invoca inferencia del modelo (side effect computacional). El nlp se inyecta externamente para permitir cache y reutilizacion. Relaciones compuestas: 'fundar_en' (fundar + preposicion 'en'), 'ser_nombrado[pass]' (pasiva), 'trabajar_con' (trabajar + 'con'). |
Ejemplo
from datascience.spacy_es_load_model import spacy_es_load_model
from datascience.extract_triples_spacy_es import extract_triples_spacy_es
nlp = spacy_es_load_model()
result = extract_triples_spacy_es(
"Amancio Ortega fundo Inditex en 1985 en La Coruna.",
nlp=nlp,
)
# result["triples"]:
# [{"subject": "Amancio Ortega", "relation": "fundar", "object": "Inditex", ...},
# {"subject": "Amancio Ortega", "relation": "fundar_en", "object": "1985", ...},
# {"subject": "Amancio Ortega", "relation": "fundar_en", "object": "La Coruna", ...}]