tests/test_quality_gate_multidimensional.py

from __future__ import annotations

import inspect
import math

import pytest

from backend.app.services import quality_gate
from backend.app.services.quality_gate import pass_quality_gate


def test_quality_gate_reports_per_feature_kl_for_matrix_inputs() -> None:
    original = [
        [0.10, 0.90],
        [0.20, 0.80],
        [0.30, 0.70],
        [0.40, 0.60],
        [0.50, 0.50],
    ]
    synthetic = [
        [0.11, 0.88],
        [0.21, 0.78],
        [0.31, 0.68],
        [0.41, 0.58],
        [0.51, 0.48],
    ]

    result = pass_quality_gate(original, synthetic, kl_threshold=0.5)

    assert result["kl_aggregation"] == "mean_per_feature"
    assert len(result["per_feature_kl_divergence"]) == 2
    assert result["kl_divergence"] >= 0.0


def test_quality_gate_source_does_not_flatten_vector_matrices() -> None:
    assert "reshape(-1)" not in inspect.getsource(quality_gate)


def test_quality_gate_handles_empty_nonfinite_and_shape_errors() -> None:
    empty = pass_quality_gate([], [], kl_threshold=0.5)
    assert empty["accepted"] is True
    assert empty["kl_divergence"] == 0.0
    assert empty["mmd"] == 0.0

    noisy = pass_quality_gate(
        [[0.1, float("nan")], [float("inf"), 0.4]],
        [[0.2, 0.3], [0.5, float("-inf")]],
        kl_threshold=10.0,
    )
    assert math.isfinite(float(noisy["kl_divergence"]))
    assert math.isfinite(float(noisy["mmd"]))

    with pytest.raises(ValueError, match="same feature dimension"):
        pass_quality_gate([[0.1, 0.2]], [[0.1]], kl_threshold=0.5)