def test_user_warning_trapz():
    y_pred = np.array([0.04, 0.04, 0.10])
    y_true = np.array([1., -1., 1.])
    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter("always")
        average_precision(y_true, y_pred, integration='trapz')
        assert issubclass(w[-1].category, UserWarning)
        assert "sorting method used" in str(w[-1].message)
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def test_user_warning_trapz():
    y_pred = np.array([0.04, 0.04, 0.10])
    y_true = np.array([1., -1., 1.])
    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter("always")
        average_precision(y_true, y_pred, integration='trapz')
        assert issubclass(w[-1].category, UserWarning)
        assert "sorting method used" in str(w[-1].message)
def test_non_trivial_perfect_ap_voc2010():
    y_pred = np.array([0.82, 0.75, 0.60, 0.90])
    y_true = np.array([1., 1., 0., 1.])
    ap = average_precision(y_true, y_pred, integration='voc2010')
    reference = 1.
    assert abs(ap - reference) < 1e-6
def test_non_trivial_ap_trapz():
    y_pred = np.array([0.25, 0.45, 0.60, 0.90])
    y_true = np.array([1., 1., 0., 1.])
    ap = average_precision(y_true, y_pred, integration='trapz')
    reference = 0.7638888888888888
    assert abs(ap - reference) < 1e-6
def test_perfect_pos_predictions_voc2010():
    y_pred = np.array([0.92, 0.99, 0.97])
    y_true = np.array([1., 1., 1.])
    ap = average_precision(y_true, y_pred, integration='voc2010')
    reference = 1.
    assert abs(ap - reference) < 1e-6
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def test_non_trivial_perfect_ap_voc2010():
    y_pred = np.array([0.82, 0.75, 0.60, 0.90])
    y_true = np.array([1., 1., 0., 1.])
    ap = average_precision(y_true, y_pred, integration='voc2010')
    reference = 1.
    assert abs(ap - reference) < 1e-6
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def test_non_trivial_ap_trapz():
    y_pred = np.array([0.25, 0.45, 0.60, 0.90])
    y_true = np.array([1., 1., 0., 1.])
    ap = average_precision(y_true, y_pred, integration='trapz')
    reference = 0.7638888888888888
    assert abs(ap - reference) < 1e-6
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def test_perfect_pos_predictions_voc2010():
    y_pred = np.array([0.92, 0.99, 0.97])
    y_true = np.array([1., 1., 1.])
    ap = average_precision(y_true, y_pred, integration='voc2010')
    reference = 1.
    assert abs(ap - reference) < 1e-6