diff --git a/src/pyhf/infer/intervals/upper_limits.py b/src/pyhf/infer/intervals/upper_limits.py index 601189cdbb..8f0e41382a 100644 --- a/src/pyhf/infer/intervals/upper_limits.py +++ b/src/pyhf/infer/intervals/upper_limits.py @@ -15,7 +15,8 @@ def __dir__(): def _interp(x, xp, fp): tb, _ = get_backend() - return tb.astensor(np.interp(x, xp.tolist(), fp.tolist())) + # xp has already been turned into a list at this point + return tb.astensor(np.interp(x, xp, fp.tolist())) def toms748_scan( @@ -79,19 +80,29 @@ def toms748_scan( def f_cached(poi): if poi not in cache: - cache[poi] = hypotest( + # FIXME: scipy.optimize.toms748 still operates on floats, + # not any form of ndarray, so want everything in the + # cache to be a float. + # This may change with the Python array API standard + # in the future. + cls_obs, cls_exp_band = hypotest( poi, data, model, return_expected_set=True, **hypotest_kwargs, ) + cache[poi] = (float(cls_obs), [float(x) for x in cls_exp_band]) return cache[poi] def f(poi, level, limit=0): # Use integers for limit so we don't need a string comparison # limit == 0: Observed # else: expected + + # Arrays are not hashable types, so cast to float + poi = float(poi) + return ( f_cached(poi)[0] - level if limit == 0 @@ -194,7 +205,9 @@ def linear_grid_scan( obs = tb.astensor([[r[0]] for r in results]) exp = tb.astensor([[r[1][idx] for idx in range(5)] for r in results]) - result_array = tb.concatenate([obs, exp], axis=1).T + # TODO: Can use `.T` after TensorFlow support is removed. + result_array = tb.transpose(tb.concatenate([obs, exp], axis=1)) + result_array = tb.tolist(result_array) # observed limit and the (0, +-1, +-2)sigma expected limits limits = [_interp(level, result_array[idx][::-1], scan[::-1]) for idx in range(6)] diff --git a/tests/test_infer.py b/tests/test_infer.py index 0ccd072b94..596a982118 100644 --- a/tests/test_infer.py +++ b/tests/test_infer.py @@ -23,7 +23,7 @@ def check_uniform_type(in_list): ) -def test_toms748_scan(tmp_path, hypotest_args): +def test_toms748_scan(backend, tmp_path, hypotest_args): """ Test the upper limit toms748 scan returns the correct structure and values """ @@ -53,11 +53,12 @@ def test_toms748_scan(tmp_path, hypotest_args): for i in range(5) ] ) - assert observed_cls == pytest.approx(0.05) + # FIXME: Remove float cast after TensorFlow support removed + assert float(observed_cls) == pytest.approx(0.05) assert expected_cls == pytest.approx(0.05) -def test_toms748_scan_bounds_extension(hypotest_args): +def test_toms748_scan_bounds_extension(backend, hypotest_args): """ Test the upper limit toms748 scan bounds can correctly extend to bracket the CLs level """ @@ -72,18 +73,20 @@ def test_toms748_scan_bounds_extension(hypotest_args): data, model, 3, 5, rtol=1e-8 ) - assert observed_limit == pytest.approx(observed_limit_default) + # FIXME: Remove float cast after TensorFlow support removed + assert float(observed_limit) == pytest.approx(observed_limit_default) assert np.allclose(np.asarray(expected_limits), np.asarray(expected_limits_default)) # Force bounds_up to expand observed_limit, expected_limits = pyhf.infer.intervals.upper_limits.toms748_scan( data, model, 0, 1, rtol=1e-8 ) - assert observed_limit == pytest.approx(observed_limit_default) + # FIXME: Remove float cast after TensorFlow support removed + assert float(observed_limit) == pytest.approx(observed_limit_default) assert np.allclose(np.asarray(expected_limits), np.asarray(expected_limits_default)) -def test_upper_limit_against_auto(hypotest_args): +def test_upper_limit_against_auto(backend, hypotest_args): """ Test upper_limit linear scan and toms748_scan return similar results """ @@ -97,11 +100,13 @@ def test_upper_limit_against_auto(hypotest_args): ) obs_linear, exp_linear = results_linear # Can't expect these to be much closer given the low granularity of the linear scan - assert obs_auto == pytest.approx(obs_linear, abs=0.1) + # FIXME: Remove float cast after TensorFlow support removed + assert float(obs_auto) == pytest.approx(obs_linear, abs=0.1) assert np.allclose(exp_auto, exp_linear, atol=0.1) -def test_upper_limit(hypotest_args): +@pytest.mark.skip_numpy_minuit +def test_upper_limit(backend, hypotest_args): """ Check that the default return structure of pyhf.infer.hypotest is as expected """ @@ -110,22 +115,27 @@ def test_upper_limit(hypotest_args): results = pyhf.infer.intervals.upper_limits.upper_limit(data, model, scan=scan) assert len(results) == 2 observed_limit, expected_limits = results - assert observed_limit == pytest.approx(1.0262704738584554) - assert expected_limits == pytest.approx( - [0.65765653, 0.87999725, 1.12453992, 1.50243428, 2.09232927] + # FIXME: Remove float cast after TensorFlow support removed + assert float(observed_limit) == pytest.approx(1.0262704738584554) + # FIXME: Can use expected_limits == pytest.approx([...]) after TensorFlow support removed + assert np.allclose( + expected_limits, [0.65765653, 0.87999725, 1.12453992, 1.50243428, 2.09232927] ) # tighter relative tolerance needed for macos results = pyhf.infer.intervals.upper_limits.upper_limit(data, model, rtol=1e-6) assert len(results) == 2 observed_limit, expected_limits = results - assert observed_limit == pytest.approx(1.01156939) - assert expected_limits == pytest.approx( - [0.55988001, 0.75702336, 1.06234693, 1.50116923, 2.05078596] + # FIXME: Remove float cast after TensorFlow support removed + assert float(observed_limit) == pytest.approx(1.01156939) + # FIXME: Can use expected_limits == pytest.approx([...]) after TensorFlow support removed + assert np.allclose( + expected_limits, [0.55988001, 0.75702336, 1.06234693, 1.50116923, 2.05078596] ) -def test_upper_limit_with_kwargs(hypotest_args): +@pytest.mark.skip_numpy_minuit +def test_upper_limit_with_kwargs(backend, hypotest_args): """ Check that the default return structure of pyhf.infer.hypotest is as expected """ @@ -136,9 +146,11 @@ def test_upper_limit_with_kwargs(hypotest_args): ) assert len(results) == 2 observed_limit, expected_limits = results - assert observed_limit == pytest.approx(1.0262704738584554) - assert expected_limits == pytest.approx( - [0.65765653, 0.87999725, 1.12453992, 1.50243428, 2.09232927] + # FIXME: Remove float cast after TensorFlow support removed + assert float(observed_limit) == pytest.approx(1.0262704738584554) + # FIXME: Can use expected_limits == pytest.approx([...]) after TensorFlow support removed + assert np.allclose( + expected_limits, [0.65765653, 0.87999725, 1.12453992, 1.50243428, 2.09232927] ) # linear_grid_scan @@ -147,9 +159,11 @@ def test_upper_limit_with_kwargs(hypotest_args): ) assert len(results) == 3 observed_limit, expected_limits, (_scan, point_results) = results - assert observed_limit == pytest.approx(1.0262704738584554) - assert expected_limits == pytest.approx( - [0.65765653, 0.87999725, 1.12453992, 1.50243428, 2.09232927] + # FIXME: Remove float cast after TensorFlow support removed + assert float(observed_limit) == pytest.approx(1.0262704738584554) + # FIXME: Can use expected_limits == pytest.approx([...]) after TensorFlow support removed + assert np.allclose( + expected_limits, [0.65765653, 0.87999725, 1.12453992, 1.50243428, 2.09232927] ) assert _scan.tolist() == scan.tolist() assert len(_scan) == len(point_results) @@ -160,9 +174,11 @@ def test_upper_limit_with_kwargs(hypotest_args): ) assert len(results) == 3 observed_limit, expected_limits, (_scan, point_results) = results - assert observed_limit == pytest.approx(1.01156939) - assert expected_limits == pytest.approx( - [0.55988001, 0.75702336, 1.06234693, 1.50116923, 2.05078596] + # FIXME: Remove float cast after TensorFlow support removed + assert float(observed_limit) == pytest.approx(1.01156939) + # FIXME: Can use expected_limits == pytest.approx([...]) after TensorFlow support removed + assert np.allclose( + expected_limits, [0.55988001, 0.75702336, 1.06234693, 1.50116923, 2.05078596] )