Commit Graph

7 Commits

Author SHA1 Message Date
Egutierrez c4cff5ed5b feat(eda): render de models en markdown + PDF DB-level para profile_database (H4,H9)
- H4: render_eda_markdown anade seccion Modelos (PCA/KMeans/normalidad/outliers);
  render_eda_pdf formatea models/series/caveats como tablas (no str(dict) crudo)
- H9: profile_database gana flag emit_pdf -> PDF movil DB-level (resumen tablas +
  join graph) via render_eda_pdf_relational; clave report_pdf_path
- aditivos y retrocompatibles (flags default False). 38 tests verdes

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-29 04:05:38 +02:00
Egutierrez 7ac69ab4fb feat(eda): series temporales + rigor anti-data-mining + PDF movil + /eda + benchmark issues
Bloque del grupo eda (sesion ausente EDA-benchmark):
- 8 funciones nuevas: adf_kpss_stationarity, acf_pacf, stl_decompose, to_returns,
  fdr_correction, suggest_reexpression, exploratory_caveats, render_eda_pdf
- integracion: profile_table (run_series, emit_pdf), association_matrix (FDR Benjamini-Hochberg),
  render_eda_markdown (secciones series/reexpresion/caveats)
- slash commands /eda y /capitulos
- issues 0173-0177: mejoras del /eda derivadas del benchmark sobre 12 datasets reales
  (outlier_pct x100, periodo estacional, FK inference, render models, tipos id-like)

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-29 03:34:01 +02:00
egutierrez 02301aaed3 feat(datascience): auto-commit con 5 cambios
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-06-28 18:16:24 +02:00
egutierrez 32c7336bf6 feat(infra): auto-commit con 56 cambios
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-06-21 14:22:55 +02:00
egutierrez 763e06c127 feat(browser): auto-commit con 178 cambios
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-06-20 18:22:23 +02:00
egutierrez e1e9bb7499 feat(shell): auto-commit con 31 cambios
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-06-14 23:55:16 +02:00
egutierrez 95959f713c feat: funciones Python para core, cybersecurity, datascience y finance
Agrega funciones Python reutilizables organizadas por dominio:
- core: composicion funcional (pipe, compose, map, filter, reduce, etc.)
- cybersecurity: analisis de amenazas y puertos
- datascience: estadisticas y deteccion de outliers
- finance: indicadores tecnicos y analisis financiero
2026-03-29 00:13:50 +01:00