feat(browser): auto-commit con 178 cambios

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

python/functions/datascience/run_eda_models_test.py

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+"""Tests para run_eda_models."""
+
+import numpy as np
+
+from run_eda_models import run_eda_models
+
+
+def _numeric(values: list) -> dict:
+    """Envuelve una lista como columna numerica del perfil."""
+    return {"values": values, "type": "numeric"}
+
+
+def test_three_numeric_columns_runs_all_models():
+    # Tres columnas con estructura latente: x e y correlacionadas, z ruido.
+    rng = np.random.default_rng(0)
+    n = 120
+    x = rng.normal(0.0, 1.0, n)
+    y = x * 2.0 + rng.normal(0.0, 0.3, n)
+    z = rng.normal(5.0, 1.0, n)
+
+    columns = {
+        "x": _numeric(x.tolist()),
+        "y": _numeric(y.tolist()),
+        "z": _numeric(z.tolist()),
+    }
+
+    result = run_eda_models(columns)
+
+    assert result["n_numeric_cols"] == 3
+    assert result["note"] == ""
+
+    # PCA presente y con varianza explicada.
+    assert result["pca"] is not None
+    assert result["pca"]["n_components"] >= 1
+    assert len(result["pca"]["explained_variance_ratio"]) >= 1
+
+    # KMeans presente con un k elegido.
+    assert result["kmeans"] is not None
+    assert result["kmeans"]["best_k"] >= 2
+
+    # Outliers presente (puede ser 0 outliers, pero el bloque existe).
+    assert result["outliers"] is not None
+    assert "n_outliers" in result["outliers"]
+
+    # Normality presente, una entrada por columna numerica.
+    assert result["normality"] is not None
+    assert set(result["normality"].keys()) == {"x", "y", "z"}
+    for col in ("x", "y", "z"):
+        assert result["normality"][col]["n"] == n
+
+
+def test_single_numeric_column_note_and_normality_only():
+    rng = np.random.default_rng(7)
+    values = rng.normal(10.0, 2.0, 100).tolist()
+    columns = {
+        "only": _numeric(values),
+        "label": {"values": ["a"] * 100, "type": "categorical"},
+    }
+
+    result = run_eda_models(columns)
+
+    assert result["n_numeric_cols"] == 1
+    assert result["note"] == "insuficientes columnas numericas para modelos multivariantes"
+
+    # Multivariantes en None.
+    assert result["pca"] is None
+    assert result["kmeans"] is None
+    assert result["outliers"] is None
+
+    # Normality univariante si se ejecuta con una sola columna.
+    assert result["normality"] is not None
+    assert "only" in result["normality"]
+    assert result["normality"]["only"]["n"] == 100
+
+
+def test_flags_disable_models():
+    rng = np.random.default_rng(1)
+    n = 60
+    columns = {
+        "a": _numeric(rng.normal(0, 1, n).tolist()),
+        "b": _numeric(rng.normal(0, 1, n).tolist()),
+    }
+
+    result = run_eda_models(
+        columns,
+        run_pca=False,
+        run_kmeans=False,
+        run_isolation=False,
+        run_normality=False,
+    )
+
+    assert result["n_numeric_cols"] == 2
+    assert result["pca"] is None
+    assert result["kmeans"] is None
+    assert result["outliers"] is None
+    assert result["normality"] is None
+    assert result["note"] == ""
+
+
+def test_no_numeric_columns_returns_note_and_no_normality():
+    columns = {
+        "cat": {"values": ["x", "y", "z"], "type": "categorical"},
+    }
+
+    result = run_eda_models(columns)
+
+    assert result["n_numeric_cols"] == 0
+    assert result["note"] == "insuficientes columnas numericas para modelos multivariantes"
+    assert result["pca"] is None
+    # run_normality True pero no hay columnas numericas -> None.
+    assert result["normality"] is None