egutierrez
81e8597d21
Implementa chapters/modelos.py (build_modelos / CHAPTER_VERSION) consumiendo
profile['models'] {pca,kmeans,outliers,normality} de run_eda_models. Render
markdown estructurado con bloques anti-corte:
- Intro de normalizacion z-score: por que se estandariza antes de PCA/KMeans (MUST-8.3).
- PCA: scree plot (varianza explicada + acumulada, un solo eje Y) + tablas de
varianza y cargas principales (SHOULD-8.4).
- Segmentacion KMeans: scatter PCA coloreado por cluster con centroides, en su
propia pagina/slide (MUST-8.1); tabla de tamaños; micro-analisis LLM por
cluster con titulo, cada entrada indivisible (MUST-8.2).
- Isolation Forest: explicacion de la deteccion multivariante de outliers y del
umbral + conteos (MUST-8.3).
- Normalidad: tabla por columna (Jarque-Bera / D'Agostino / Shapiro), pagina sola.
El scatter coloreado y los titulos LLM no estan en el TableProfile, asi que el
capitulo los toma de ctx (cluster_projection precomputado, o raw_numeric para
calcular project_clusters_2d en vivo, o cluster_titles/run_cluster_llm para el
micro-analisis), igual que overview lee head_rows; degrada honesto con una Note
cuando faltan. Devuelve None si el profile no trae bloque models renderizable.
Tests self-contained (sin DuckDB/sklearn/LLM/red): golden PDF+PPTX, edges
(profile None/vacio/insuficiente, kmeans sin proyeccion), anti-corte (tabla de
normalidad de 40 columnas parte repitiendo cabecera sin perder ninguna). 8/8.
Suite del nucleo render_automatic_eda_pdf/pptx sigue verde.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
499 lines
21 KiB
Python
499 lines
21 KiB
Python
"""Models chapter (MODELOS) — cheap unsupervised models, rendered as markdown.
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Builds the *Modelos* chapter of an AutomaticEDA document from the ``models``
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block of a TableProfile (``run_eda_models`` output: ``{pca, kmeans, outliers,
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normality}``). It renders, as structured markdown/tables/figures that the core
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paginator never cuts:
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1. **Normalization note** — every multivariate model below standardizes the
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columns with z-score first; the chapter explains why (different scales would
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otherwise dominate distance/variance).
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2. **PCA** — a scree plot (explained + cumulative variance, single Y axis) plus
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variance and top-loadings tables.
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3. **KMeans segments** — a PCA scatter **coloured by cluster** (its own
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page/slide), the cluster-size table, and a per-cluster LLM micro-analysis
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with a title for each segment.
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4. **Isolation Forest outliers** — a short explanation of how anomalous rows are
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isolated multivariately and how the threshold is chosen, plus the counts.
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5. **Normality** — per-column Jarque-Bera / D'Agostino / Shapiro verdicts.
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The raw numeric data needed to colour the cluster scatter is **not** in the
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TableProfile, so — exactly like ``overview`` reads ``head_rows`` from ``ctx`` —
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this chapter looks for the cluster projection / raw numeric columns in ``ctx``
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(or in ``profile``) and degrades honestly when they are absent: it falls back to
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the uncoloured ``pca.projection`` with a note, or omits the scatter entirely.
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ctx keys this chapter consumes (all optional):
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cluster_projection : dict — a pre-computed ``project_clusters_2d`` result
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(``points``/``labels``/``centers_2d``/``cluster_profiles``/...). Used
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directly when present (forward-compatible with the calculation phase).
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raw_numeric : dict — ``{col: [values]}`` raw numeric columns; when present
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and ``cluster_projection`` is not, the chapter calls
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``project_clusters_2d`` live to build points + aligned labels.
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cluster_titles : list — pre-computed ``[{cluster, title, description}]``
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(a ``describe_clusters_llm`` ``clusters`` list). Used for the per-cluster
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micro-analysis without an LLM call (offline/tests).
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run_cluster_llm : bool — when True and ``cluster_titles`` is absent, call
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``describe_clusters_llm`` live on the cluster profiles.
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cluster_llm_model : str — model id for the live LLM call.
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Contract: build_<id>(profile, ctx) -> Chapter | None ; CHAPTER_VERSION = "x.y.z".
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"""
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from __future__ import annotations
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from .. import model
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CHAPTER_VERSION = "1.0.0"
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CHAPTER_ID = "modelos"
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CHAPTER_TITLE = "Modelos"
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# Tableau-10 palette (matplotlib's default cycle) — used both for the matplotlib
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# scatter and to keep the legend/colours stable per cluster index.
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_CLUSTER_COLORS = [
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"#4e79a7", "#f28e2b", "#e15759", "#76b7b2", "#59a14f",
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"#edc948", "#b07aa1", "#ff9da7", "#9c755f", "#bab0ac",
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]
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# --------------------------------------------------------------------------- #
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# Formatting helpers (mirror the overview chapter's defensive style).
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# --------------------------------------------------------------------------- #
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def _fmt_num(value, decimals: int = 3) -> str:
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if value is None:
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return "—"
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if isinstance(value, bool):
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return "sí" if value else "no"
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if isinstance(value, int):
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return f"{value:,}".replace(",", ".")
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if isinstance(value, float):
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if value != value: # NaN
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return "NaN"
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if value in (float("inf"), float("-inf")):
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return str(value)
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text = f"{value:.{decimals}f}".rstrip("0").rstrip(".")
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return text if text else "0"
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return model._safe_str(value)
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def _fmt_pct_ratio(value, decimals: int = 1) -> str:
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"""Format a 0..1 ratio as a percentage."""
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if value is None:
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return "—"
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try:
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return f"{float(value) * 100:.{decimals}f}%"
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except (TypeError, ValueError):
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return model._safe_str(value)
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def _fmt_pct_already(value, decimals: int = 2) -> str:
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"""Format a value that is *already* a 0..100 percentage."""
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if value is None:
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return "—"
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try:
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return f"{float(value):.{decimals}f}%"
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except (TypeError, ValueError):
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return model._safe_str(value)
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def _is_dict(v) -> bool:
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return isinstance(v, dict)
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# --------------------------------------------------------------------------- #
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# Cluster projection: prefer a pre-computed result, else compute it live, else
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# fall back to the uncoloured PCA projection.
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# --------------------------------------------------------------------------- #
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def _resolve_cluster_projection(profile: dict, ctx: dict):
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"""Return (projection_dict_or_None, source_label).
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Order: ctx/profile['cluster_projection'] (pre-computed) → live
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project_clusters_2d on ctx/profile['raw_numeric'] → None.
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"""
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pre = ctx.get("cluster_projection") or profile.get("cluster_projection")
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models = profile.get("models") if _is_dict(profile.get("models")) else {}
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if not pre and _is_dict(models):
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pre = models.get("cluster_projection")
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if _is_dict(pre) and pre.get("points"):
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return pre, "precomputed"
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raw = ctx.get("raw_numeric") or profile.get("raw_numeric")
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if _is_dict(raw) and raw:
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try:
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# Import the submodule's function explicitly (avoid the package
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# attribute shadowing the function with the same-named module).
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from datascience.project_clusters_2d import project_clusters_2d
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proj = project_clusters_2d(raw)
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if _is_dict(proj) and proj.get("points"):
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return proj, "live"
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except Exception: # noqa: BLE001 — never break the chapter.
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return None, "none"
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return None, "none"
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def _cluster_titles(profile: dict, ctx: dict, projection: dict):
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"""Return a list of {cluster, title, description} for the segments.
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Order: ctx['cluster_titles'] (pre-computed) → live describe_clusters_llm when
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ctx['run_cluster_llm'] and we have cluster_profiles → derived titles from the
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distinctive features → None.
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"""
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pre = ctx.get("cluster_titles")
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if isinstance(pre, list) and pre:
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return [c for c in pre if _is_dict(c)]
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profiles = (projection or {}).get("cluster_profiles") or []
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feats = (projection or {}).get("feature_names") or []
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if ctx.get("run_cluster_llm") and profiles:
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try:
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from datascience.describe_clusters_llm import describe_clusters_llm
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out = describe_clusters_llm(
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profiles, feats,
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model=ctx.get("cluster_llm_model", "claude-haiku-4-5-20251001"))
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clusters = (out or {}).get("clusters")
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if isinstance(clusters, list) and clusters:
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return [c for c in clusters if _is_dict(c)]
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except Exception: # noqa: BLE001
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pass
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# Derived fallback: name each cluster by its distinctive features.
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if profiles:
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derived = []
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for p in profiles:
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if not _is_dict(p):
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continue
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cid = p.get("cluster", len(derived))
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dist = p.get("distinctive") or []
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label = ", ".join(model._safe_str(d) for d in dist[:2]) if dist else ""
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title = f"Segmento {cid}" + (f" — {label}" if label else "")
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derived.append({"cluster": cid, "title": title, "description": ""})
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if derived:
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return derived
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return None
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# --------------------------------------------------------------------------- #
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# Figure builders (lazy: matplotlib only imported when the renderer draws them).
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# --------------------------------------------------------------------------- #
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def _make_scree(pca: dict):
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"""Return a zero-arg callable drawing the PCA scree plot, or None."""
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evr = pca.get("explained_variance_ratio") or []
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cum = pca.get("cumulative") or []
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if not evr:
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return None
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def _draw():
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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comps = list(range(1, len(evr) + 1))
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fig, ax = plt.subplots(figsize=(7.0, 4.2))
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ax.bar(comps, evr, color="#4e79a7", alpha=0.85,
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label="Varianza explicada")
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if cum:
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ax.plot(comps[:len(cum)], cum, color="#e15759", marker="o",
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linewidth=1.8, label="Acumulada")
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ax.set_xlabel("Componente principal")
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ax.set_ylabel("Proporción de varianza")
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ax.set_xticks(comps)
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ax.set_ylim(0, 1.0)
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ax.grid(axis="y", color="#dddddd", linewidth=0.6)
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ax.legend(loc="best", fontsize=8, frameon=False)
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ax.set_title("Varianza explicada por componente (PCA)", fontsize=10)
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fig.tight_layout()
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return fig
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return _draw
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def _make_cluster_scatter(projection: dict):
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"""Return a zero-arg callable drawing the cluster scatter, or None."""
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points = projection.get("points") or []
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labels = projection.get("labels") or []
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if not points or len(points) != len(labels):
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return None
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centers = projection.get("centers_2d") or []
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explained = projection.get("explained_2d") or []
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def _draw():
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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fig, ax = plt.subplots(figsize=(7.0, 5.2))
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uniq = sorted(set(int(l) for l in labels))
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for cl in uniq:
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xs = [p[0] for p, l in zip(points, labels) if int(l) == cl]
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ys = [p[1] for p, l in zip(points, labels) if int(l) == cl]
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color = _CLUSTER_COLORS[cl % len(_CLUSTER_COLORS)]
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ax.scatter(xs, ys, s=14, c=color, alpha=0.7, linewidths=0,
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label=f"Cluster {cl} (n={len(xs)})")
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for cl, c in enumerate(centers):
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color = _CLUSTER_COLORS[cl % len(_CLUSTER_COLORS)]
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ax.scatter([c[0]], [c[1]], s=180, c=color, marker="X",
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edgecolors="black", linewidths=1.2, zorder=5)
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xlab, ylab = "PC1", "PC2"
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if len(explained) >= 2:
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xlab = f"PC1 ({_fmt_pct_ratio(explained[0])} var.)"
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ylab = f"PC2 ({_fmt_pct_ratio(explained[1])} var.)"
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ax.set_xlabel(xlab)
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ax.set_ylabel(ylab)
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ax.set_title("Segmentos KMeans proyectados sobre el plano PCA",
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fontsize=10)
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ax.grid(color="#eeeeee", linewidth=0.5)
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ax.legend(loc="best", fontsize=8, frameon=True, framealpha=0.9)
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fig.tight_layout()
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return fig
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return _draw
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# --------------------------------------------------------------------------- #
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# Section builders. Each returns a list of blocks (possibly empty).
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# --------------------------------------------------------------------------- #
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def _normalization_intro() -> list:
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text = (
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"Estos modelos son **no supervisados**: buscan estructura latente sin "
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"una variable objetivo. Antes de aplicarlos, todas las columnas "
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"numéricas se **estandarizan con z-score** (cada valor menos la media, "
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"dividido por la desviación típica). Sin esta normalización, una "
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"variable con escala grande (p.ej. ingresos en euros) dominaría las "
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"distancias y la varianza frente a otra de escala pequeña (p.ej. un "
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"ratio entre 0 y 1), sesgando tanto el PCA como el KMeans. Tras la "
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"estandarización todas las variables pesan por igual."
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)
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return [model.Heading(text="Modelos no supervisados", level=1),
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model.Markdown(text=text)]
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def _pca_section(pca: dict) -> list:
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if not _is_dict(pca) or not pca.get("explained_variance_ratio"):
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return []
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blocks = [model.Heading(text="PCA — varianza explicada", level=2)]
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n_used = pca.get("n_rows_used")
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n_feat = pca.get("n_features")
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intro = (
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f"El PCA resume {_fmt_num(n_feat)} variables numéricas en componentes "
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f"ortogonales ordenados por la varianza que capturan "
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f"({_fmt_num(n_used)} filas usadas tras eliminar nulos). El gráfico de "
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"sedimentación (scree) muestra cuánta varianza aporta cada componente y "
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"su acumulado: un codo marca cuántos componentes bastan."
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)
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blocks.append(model.Markdown(text=intro))
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scree = _make_scree(pca)
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if scree is not None:
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blocks.append(model.Figure(
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make=scree, caption="Varianza explicada y acumulada por componente."))
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evr = pca.get("explained_variance_ratio") or []
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cum = pca.get("cumulative") or []
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rows = []
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for i, v in enumerate(evr):
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acc = cum[i] if i < len(cum) else None
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rows.append([f"PC{i + 1}", _fmt_pct_ratio(v), _fmt_pct_ratio(acc)])
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if rows:
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blocks.append(model.DataTable(
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header=["Componente", "Varianza", "Acumulada"], rows=rows,
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title="Varianza por componente"))
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# Top loadings: keep the strongest features per component (capped).
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loadings = pca.get("top_loadings") or []
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if loadings:
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per_comp: dict = {}
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for ld in loadings:
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if not _is_dict(ld):
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continue
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comp = ld.get("component")
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per_comp.setdefault(comp, [])
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if len(per_comp[comp]) < 4:
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per_comp[comp].append(ld)
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rows = []
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for comp in sorted(per_comp.keys(), key=lambda x: (x is None, x)):
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for ld in per_comp[comp]:
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rows.append([f"PC{int(comp) + 1}" if comp is not None else "—",
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model._safe_str(ld.get("feature")),
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_fmt_num(ld.get("loading"))])
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if rows:
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blocks.append(model.DataTable(
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header=["Componente", "Variable", "Carga"], rows=rows,
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title="Cargas principales (top por componente)",
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note="Cargas con mayor valor absoluto: qué variables definen "
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"cada eje."))
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return blocks
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def _kmeans_section(kmeans: dict, projection: dict, titles) -> list:
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has_km = _is_dict(kmeans) and kmeans.get("best_k")
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has_proj = _is_dict(projection) and projection.get("points")
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if not has_km and not has_proj:
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return []
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blocks = [model.Heading(text="Segmentación (KMeans)", level=2)]
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best_k = (projection or {}).get("best_k") or (kmeans or {}).get("best_k")
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sil = (projection or {}).get("silhouette")
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if sil is None:
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sil = (kmeans or {}).get("silhouette")
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intro = (
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f"KMeans agrupa las filas en **{_fmt_num(best_k)} segmentos** elegidos "
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"automáticamente maximizando el coeficiente de *silhouette* "
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f"(**{_fmt_num(sil)}**, rango −1 a 1: cuanto más alto, segmentos más "
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"compactos y separados). Los segmentos se proyectan sobre el plano de "
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"los dos primeros componentes principales para visualizarlos."
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)
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blocks.append(model.Markdown(text=intro))
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if has_proj:
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scatter = _make_cluster_scatter(projection)
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if scatter is not None:
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blocks.append(model.Figure(
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make=scatter,
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caption="Cada punto es una fila coloreada por su segmento "
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"KMeans; las «X» son los centroides."))
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else:
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blocks.append(model.Note(
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"Proyección de clusters no dibujable (puntos y etiquetas "
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"desalineados)."))
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else:
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# We have kmeans stats but no aligned points+labels to colour by.
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blocks.append(model.Note(
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"Scatter coloreado por segmento no disponible: el perfil no incluye "
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"la proyección con etiquetas alineadas (pásala en "
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"ctx['cluster_projection'] o las columnas crudas en "
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"ctx['raw_numeric'] para colorear el plano PCA)."))
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# Cluster sizes table.
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sizes = (projection or {}).get("cluster_sizes") or (kmeans or {}).get("cluster_sizes") or []
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total = sum(s for s in sizes if isinstance(s, (int, float))) or 0
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if sizes:
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rows = []
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for i, s in enumerate(sizes):
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pct = (s / total) if total else None
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rows.append([f"Cluster {i}", _fmt_num(s), _fmt_pct_ratio(pct)])
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blocks.append(model.DataTable(
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header=["Segmento", "Tamaño", "% del total"], rows=rows,
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title="Tamaño de cada segmento"))
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# Per-cluster LLM micro-analysis (each entry kept indivisible as one block).
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if titles:
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blocks.append(model.Heading(text="Interpretación de los segmentos",
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level=3))
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for t in titles:
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if not _is_dict(t):
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continue
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cid = t.get("cluster")
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title = model._safe_str(t.get("title")) or f"Cluster {cid}"
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desc = model._safe_str(t.get("description"))
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line = f"**Cluster {cid} — {title}.**"
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if desc:
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line += " " + desc
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blocks.append(model.Markdown(text=line))
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return blocks
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def _outliers_section(outliers: dict) -> list:
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if not _is_dict(outliers) or outliers.get("n_outliers") is None:
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return []
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if outliers.get("note") and not outliers.get("n_rows_used"):
|
||
# insufficient data — nothing meaningful to show.
|
||
return []
|
||
blocks = [model.Heading(text="Detección de anomalías (Isolation Forest)",
|
||
level=2)]
|
||
explain = (
|
||
"**Isolation Forest** detecta filas anómalas de forma *multivariante*: "
|
||
"construye árboles que parten el espacio con cortes aleatorios y mide "
|
||
"cuántos cortes hacen falta para aislar cada fila. Las filas raras "
|
||
"(combinaciones de valores poco frecuentes considerando **todas las "
|
||
"columnas a la vez**, no una sola) se aíslan con muy pocos cortes y "
|
||
"obtienen un score bajo. El **umbral** de decisión separa las filas "
|
||
"normales de las anómalas según la contaminación esperada del modelo: "
|
||
"una fila es outlier cuando su score queda por debajo de ese umbral."
|
||
)
|
||
blocks.append(model.Markdown(text=explain))
|
||
blocks.append(model.KVTable(rows=[
|
||
("Filas analizadas", _fmt_num(outliers.get("n_rows_used"))),
|
||
("Outliers detectados", _fmt_num(outliers.get("n_outliers"))),
|
||
("% outliers", _fmt_pct_already(outliers.get("outlier_pct"))),
|
||
("Umbral de decisión", _fmt_num(outliers.get("threshold"), 4)),
|
||
], title="Anomalías multivariantes"))
|
||
return blocks
|
||
|
||
|
||
def _normality_section(normality: dict) -> list:
|
||
if not _is_dict(normality) or not normality:
|
||
return []
|
||
header = ["Columna", "Jarque-Bera (p)", "D'Agostino (p)", "Shapiro (p)",
|
||
"¿Normal?"]
|
||
rows = []
|
||
for col, res in normality.items():
|
||
if not _is_dict(res):
|
||
continue
|
||
jb = res.get("jarque_bera") if _is_dict(res.get("jarque_bera")) else {}
|
||
da = res.get("dagostino") if _is_dict(res.get("dagostino")) else {}
|
||
sh = res.get("shapiro") if _is_dict(res.get("shapiro")) else {}
|
||
is_norm = res.get("is_normal")
|
||
if res.get("note") and is_norm is None and not jb:
|
||
rows.append([model._safe_str(col), "—", "—", "—",
|
||
model._safe_str(res.get("note"))])
|
||
continue
|
||
rows.append([
|
||
model._safe_str(col),
|
||
_fmt_num(jb.get("p"), 4) if jb else "—",
|
||
_fmt_num(da.get("p"), 4) if da else "—",
|
||
_fmt_num(sh.get("p"), 4) if sh else "—",
|
||
"sí" if is_norm else ("no" if is_norm is not None else "—"),
|
||
])
|
||
if not rows:
|
||
return []
|
||
return [
|
||
model.Heading(text="Normalidad de las variables", level=2),
|
||
model.Markdown(text=(
|
||
"Tests de hipótesis de normalidad por columna (hipótesis nula: la "
|
||
"muestra proviene de una distribución normal). Se marca **normal** "
|
||
"cuando el p-valor supera 0,05 (no se rechaza la nula). Pocas "
|
||
"variables reales son estrictamente normales; esto orienta qué "
|
||
"transformaciones o tests robustos aplicar después.")),
|
||
model.DataTable(header=header, rows=rows,
|
||
title="Pruebas de normalidad"),
|
||
]
|
||
|
||
|
||
# --------------------------------------------------------------------------- #
|
||
# Entry point.
|
||
# --------------------------------------------------------------------------- #
|
||
def build_modelos(profile: dict, ctx: dict):
|
||
"""Build the MODELOS Chapter, or None if there are no models to show."""
|
||
profile = profile or {}
|
||
ctx = ctx or {}
|
||
if not isinstance(profile, dict):
|
||
return None
|
||
models = profile.get("models")
|
||
if not _is_dict(models):
|
||
return None
|
||
|
||
pca = models.get("pca") if _is_dict(models.get("pca")) else None
|
||
kmeans = models.get("kmeans") if _is_dict(models.get("kmeans")) else None
|
||
outliers = models.get("outliers") if _is_dict(models.get("outliers")) else None
|
||
normality = models.get("normality") if _is_dict(models.get("normality")) else None
|
||
|
||
projection, _src = _resolve_cluster_projection(profile, ctx)
|
||
titles = _cluster_titles(profile, ctx, projection) if (
|
||
(kmeans and kmeans.get("best_k")) or (projection and projection.get("points"))
|
||
) else None
|
||
|
||
sections = []
|
||
sections += _pca_section(pca) if pca else []
|
||
sections += _kmeans_section(kmeans, projection, titles)
|
||
sections += _outliers_section(outliers) if outliers else []
|
||
sections += _normality_section(normality) if normality else []
|
||
|
||
if not sections:
|
||
return None # models block present but nothing renderable.
|
||
|
||
blocks = _normalization_intro() + sections
|
||
return model.Chapter(id=CHAPTER_ID, title=CHAPTER_TITLE,
|
||
version=CHAPTER_VERSION, blocks=blocks)
|