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