feat(browser): auto-commit con 178 cambios

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

python/functions/datascience/association_matrix_test.py

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+"""Tests para association_matrix."""
+
+from datascience import association_matrix
+
+
+def _find_pair(pairs, a, b):
+    """Devuelve el par (a, b) sin importar el orden en que aparezca, o None."""
+    for p in pairs:
+        if {p["a"], p["b"]} == {a, b}:
+            return p
+    return None
+
+
+def test_two_correlated_numerics_strong_pearson():
+    columns = {
+        "size": {"values": [1, 2, 3, 4, 5, 6, 7, 8], "type": "numeric"},
+        "price": {
+            "values": [2.1, 4.0, 5.9, 8.1, 10.0, 12.2, 13.8, 16.1],
+            "type": "numeric",
+        },
+    }
+    result = association_matrix(columns, strong_threshold=0.5)
+
+    pair = _find_pair(result["pairs"], "size", "price")
+    assert pair is not None
+    assert pair["method"] == "pearson/spearman"
+    assert abs(pair["value"]) > 0.95
+    assert "pearson" in pair["extra"] and "spearman" in pair["extra"]
+    # El par fuertemente correlado aparece en strong.
+    assert _find_pair(result["strong"], "size", "price") is not None
+
+
+def test_numeric_explained_by_category_strong_correlation_ratio():
+    columns = {
+        "region": {
+            "values": ["N", "N", "S", "S", "E", "E", "W", "W"],
+            "type": "categorical",
+        },
+        "score": {
+            "values": [10.0, 11.0, 50.0, 49.0, 90.0, 91.0, 30.0, 31.0],
+            "type": "numeric",
+        },
+    }
+    result = association_matrix(columns, strong_threshold=0.5)
+
+    pair = _find_pair(result["pairs"], "region", "score")
+    assert pair is not None
+    assert pair["method"] == "correlation_ratio"
+    # La categoria explica casi toda la varianza de la numerica.
+    assert pair["value"] > 0.9
+    assert _find_pair(result["strong"], "region", "score") is not None
+
+
+def test_independent_pair_not_strong():
+    # x e y construidos para ser practicamente independientes (sin relacion).
+    columns = {
+        "x": {"values": [1, 2, 1, 2, 1, 2, 1, 2], "type": "numeric"},
+        "y": {"values": [5, 5, 5, 5, 5, 5, 5, 6], "type": "numeric"},
+    }
+    result = association_matrix(columns, strong_threshold=0.5)
+
+    pair = _find_pair(result["pairs"], "x", "y")
+    assert pair is not None
+    # Ni la metrica principal ni la MI superan el umbral fuerte.
+    assert abs(pair["value"]) < 0.5
+    assert pair["extra"]["mi"] < 0.5
+    assert _find_pair(result["strong"], "x", "y") is None
+
+
+def test_empty_dict_does_not_crash():
+    result = association_matrix({})
+    assert result["pairs"] == []
+    assert result["strong"] == []
+    assert "methods_legend" in result
+    assert "pearson" in result["methods_legend"]
+
+
+def test_single_column_returns_empty():
+    columns = {"only": {"values": [1, 2, 3, 4], "type": "numeric"}}
+    result = association_matrix(columns)
+    assert result["pairs"] == []
+    assert result["strong"] == []