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
parent 7d100e7f3e
commit 763e06c127
178 changed files with 19917 additions and 317 deletions
@@ -0,0 +1,150 @@
+"""Tests para summarize_table_duckdb."""
+
+import duckdb
+import pytest
+
+from .summarize_table_duckdb import summarize_table_duckdb
+
+
+@pytest.fixture
+def db(tmp_path):
+    """Crea una DuckDB temporal con numerica + categorica + nulls + id unico."""
+    path = str(tmp_path / "eda_test.duckdb")
+    con = duckdb.connect(path)
+    con.execute(
+        "CREATE TABLE ventas ("
+        "  id INTEGER,"          # unico, sin nulls -> possible_id
+        "  region VARCHAR,"      # categorica baja cardinalidad
+        "  total DOUBLE,"        # numerica con un null
+        "  pais VARCHAR"         # constante
+        ")"
+    )
+    con.execute(
+        "INSERT INTO ventas VALUES "
+        "(1, 'norte', 120.5, 'ES'), "
+        "(2, 'sur',   80.0,  'ES'), "
+        "(3, 'norte', NULL,  'ES'), "
+        "(4, 'este',  45.25, 'ES')"
+    )
+    con.close()
+    return path
+
+
+def test_shape_y_metadatos_tabla(db):
+    res = summarize_table_duckdb(db, "ventas")
+    assert res["status"] == "ok"
+    profile = res["profile"]
+
+    # Claves del TableProfile presentes.
+    for key in (
+        "table", "source", "profiled_at", "n_rows", "n_cols", "size_bytes",
+        "duplicate_rows", "duplicate_pct", "constant_cols", "all_null_cols",
+        "null_cell_pct", "type_breakdown", "columns", "correlations",
+        "key_candidates", "quality_score", "llm", "models",
+    ):
+        assert key in profile, f"falta clave {key} en TableProfile"
+
+    assert profile["table"] == "ventas"
+    assert profile["source"] == "duckdb"
+    assert profile["n_rows"] == 4
+    assert profile["n_cols"] == 4
+    assert len(profile["columns"]) == 4
+    assert profile["key_candidates"] == []
+    assert profile["quality_score"] is None
+    assert profile["correlations"] is None
+
+
+def test_column_profile_shape(db):
+    profile = summarize_table_duckdb(db, "ventas")["profile"]
+    by_name = {c["name"]: c for c in profile["columns"]}
+
+    for col in profile["columns"]:
+        for key in (
+            "name", "physical_type", "inferred_type", "semantic_type", "count",
+            "n_rows", "null_count", "null_pct", "empty_count", "empty_pct",
+            "distinct_count", "unique_pct", "flags", "quality_score",
+            "numeric", "categorical", "datetime",
+        ):
+            assert key in col, f"falta clave {key} en ColumnProfile {col['name']}"
+
+    # id: numerica, sin nulls, unica.
+    assert by_name["id"]["inferred_type"] == "numeric"
+    assert by_name["id"]["null_count"] == 0
+    assert by_name["id"]["count"] == 4
+    assert by_name["id"]["distinct_count"] == 4
+    assert "possible_id" in by_name["id"]["flags"]
+
+    # region: categorica baja cardinalidad.
+    assert by_name["region"]["inferred_type"] == "categorical"
+    assert by_name["region"]["distinct_count"] == 3
+
+    # total: numerica con un null. count no-nulo = 3.
+    total = by_name["total"]
+    assert total["inferred_type"] == "numeric"
+    assert total["null_count"] == 1
+    assert total["count"] == 3
+    assert total["numeric"] is not None
+    # SUMMARIZE rellena min/max/mean/std/p25/p50/p75; el resto queda en None.
+    assert total["numeric"]["min"] == pytest.approx(45.25)
+    assert total["numeric"]["max"] == pytest.approx(120.5)
+    assert total["numeric"]["mean"] is not None
+    assert total["numeric"]["skew"] is None
+    assert total["numeric"]["histogram"] is None
+    assert total["numeric"]["p99"] is None
+
+    # pais: constante -> flag constant + aparece en constant_cols.
+    assert "constant" in by_name["pais"]["flags"]
+    assert "pais" in profile["constant_cols"]
+
+
+def test_distinct_no_excede_filas(db):
+    """distinct_count exacto: nunca supera n_rows ni unique_pct pasa de 1.0.
+
+    Regresion: SUMMARIZE.approx_unique (HyperLogLog) sobreestimaba y reportaba
+    mas distintos que filas en tablas pequenas, inflando unique_pct > 1.0 y
+    disparando flags possible_id falsos.
+    """
+    profile = summarize_table_duckdb(db, "ventas")["profile"]
+    n_rows = profile["n_rows"]
+    for col in profile["columns"]:
+        assert col["distinct_count"] <= n_rows, (
+            f"{col['name']}: distinct_count {col['distinct_count']} > n_rows {n_rows}"
+        )
+        assert col["unique_pct"] <= 1.0, (
+            f"{col['name']}: unique_pct {col['unique_pct']} > 1.0"
+        )
+
+
+def test_columna_unica_da_possible_id(db):
+    """Una columna con todos los valores unicos -> unique_pct == 1.0 + possible_id."""
+    profile = summarize_table_duckdb(db, "ventas")["profile"]
+    by_name = {c["name"]: c for c in profile["columns"]}
+
+    # id: 4 valores distintos sobre 4 filas, sin nulls.
+    idc = by_name["id"]
+    assert idc["distinct_count"] == 4
+    assert idc["unique_pct"] == 1.0
+    assert "possible_id" in idc["flags"]
+
+
+def test_type_breakdown(db):
+    profile = summarize_table_duckdb(db, "ventas")["profile"]
+    tb = profile["type_breakdown"]
+    assert set(tb.keys()) == {
+        "numeric", "categorical", "datetime", "text", "boolean"
+    }
+    assert tb["numeric"] == 2          # id, total
+    assert tb["categorical"] == 2      # region, pais
+    assert tb["datetime"] == 0
+    assert tb["boolean"] == 0
+
+
+def test_tabla_invalida_devuelve_error(db):
+    res = summarize_table_duckdb(db, "ventas; DROP TABLE ventas")
+    assert res["status"] == "error"
+    assert "invalido" in res["error"]
+
+
+def test_tabla_inexistente_devuelve_error(db):
+    res = summarize_table_duckdb(db, "no_existe")
+    assert res["status"] == "error"