feat(browser): auto-commit con 178 cambios

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

python/functions/datascience/pca_explained_test.py

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+"""Tests para pca_explained."""
+
+from pca_explained import pca_explained
+
+
+def test_pc1_concentra_varianza_con_columnas_colineales():
+    # x e y son casi colineales (y = 2x + ruido minimo); z es independiente.
+    n = 50
+    x = [float(i) for i in range(n)]
+    y = [2.0 * i + (0.01 if i % 2 == 0 else -0.01) for i in range(n)]
+    z = [float((i * 7) % 13) for i in range(n)]
+
+    result = pca_explained({"x": x, "y": y, "z": z}, n_components=2)
+
+    assert result["n_components"] == 2
+    assert result["n_rows_used"] == n
+    assert result["n_features"] == 3
+    # Con dos columnas casi colineales, PC1 debe concentrar mucha varianza.
+    assert result["explained_variance_ratio"][0] > 0.6
+    # Cumulative es monotona creciente.
+    assert result["cumulative"][-1] >= result["cumulative"][0]
+    assert len(result["projection"]) == n
+
+
+def test_una_sola_columna_numerica_datos_insuficientes():
+    result = pca_explained({"x": [1.0, 2.0, 3.0, 4.0, 5.0]})
+
+    assert result["n_components"] == 0
+    assert result["explained_variance_ratio"] == []
+    assert result["note"] == "datos insuficientes"
+
+
+def test_pocas_filas_validas_datos_insuficientes():
+    # Solo 2 filas validas (la tercera tiene un None) -> insuficiente.
+    result = pca_explained({"a": [1.0, 2.0, None], "b": [4.0, 5.0, 6.0]})
+
+    assert result["n_components"] == 0
+    assert result["note"] == "datos insuficientes"