def test_bayesian_TN_trace(pm):
fd = woehler.determine_fractures(data, 1e7).fatigue_data
bayes = woehler.Bayesian(fd)
bayes._common_analysis()
bayes._nsamples = 1000
bayes._TN_trace()
pm.HalfNormal.assert_called_with('stdev', sigma=1.3)
assert pm.Normal.call_count == 2
expected_mu = 5.294264482012933
expected_sigma = 0.2621494419382026
assert pm.Normal.call_args_list[0][0] == ('mu', )
np.testing.assert_almost_equal(pm.Normal.call_args_list[0][1]['mu'],
expected_mu,
decimal=9)
np.testing.assert_almost_equal(pm.Normal.call_args_list[0][1]['sigma'],
expected_sigma,
decimal=9)
assert pm.Normal.call_args_list[1][0] == ('y', )
observed = pm.Normal.call_args_list[1][1][
'observed'] # Consider switch to kwargs property when py3.7 is dropped
np.testing.assert_almost_equal(observed.mean(), expected_mu, decimal=9)
np.testing.assert_almost_equal(observed.std(), expected_sigma, decimal=9)
pm.sample.assert_called_with(1000,
target_accept=0.99,
random_seed=None,
chains=3,
tune=1000)
def test_bayesian_mock(_slope_trace, _TN_trace, _SD_TS_trace):
expected = pd.Series({
'SD_50': 100.,
'1/TS': 1.12,
'k_1': 7.0,
'ND_50': 1e6,
'1/TN': 5.3
}).sort_index()
expected_slope_trace = {
'x': np.array([0.0, -8.0, -6.0]),
'Intercept': np.array([0.0, 19., 21.])
}
expected_SD_TS_trace = {
'SD_50': np.array([0.0, 150., 50]),
'TS_50': np.array([0.0, 1.22, 1.02])
}
_slope_trace.__call__(
).get_values.side_effect = lambda key: expected_slope_trace[key]
_TN_trace.__call__().get_values.return_value = np.array([0.0, 5.4, 5.2])
_SD_TS_trace.__call__(
).get_values.side_effect = lambda key: expected_SD_TS_trace[key]
fd = woehler.determine_fractures(data, 1e7).fatigue_data
wc = woehler.Bayesian(fd).analyze(nsamples=10).sort_index()
pd.testing.assert_series_equal(wc, expected)
np.testing.assert_allclose(wc.to_numpy(), expected.to_numpy())
def test_bayesian_full():
expected = pd.Series({
'SD_50': 340.,
'1/TS': 1.12,
'k_1': 7.0,
'ND_50': 400000.,
'1/TN': 5.3
}).sort_index()
fd = woehler.determine_fractures(data, 1e7).fatigue_data
wc = woehler.Bayesian(fd).analyze(random_seed=4223,
progressbar=False).sort_index()
pd.testing.assert_index_equal(wc.index, expected.index)
np.testing.assert_allclose(wc.to_numpy(), expected.to_numpy(), rtol=1e-1)
def test_bayesian_slope_trace(pm):
fd = woehler.determine_fractures(data, 1e7).fatigue_data
bayes = woehler.Bayesian(fd)
bayes._nsamples = 1000
bayes._slope_trace()
formula, data_dict = pm.glm.GLM.from_formula.call_args[0]
assert formula == 'y ~ x'
pd.testing.assert_series_equal(data_dict['x'], np.log10(fd.fractures.load))
np.testing.assert_array_equal(data_dict['y'],
np.log10(fd.fractures.cycles.to_numpy()))
family = pm.glm.GLM.from_formula.call_args[1][
'family'] # Consider switch to kwargs property when py3.7 is dropped
assert family is pm.glm.families.StudentT()
pm.sample.assert_called_with(1000,
target_accept=0.99,
random_seed=None,
chains=2,
tune=1000)
def test_bayesian_SD_TS_trace_mock(pm, tt):
def check_likelihood(l, var):
assert var == tt.as_tensor_variable.return_value
assert isinstance(l.likelihood,
woehler.analyzers.likelihood.Likelihood)
np.testing.assert_array_equal(l.likelihood._fd, fd)
return 'foovar'
fd = woehler.determine_fractures(data, 1e7).fatigue_data
inf_load_mean = fd.infinite_zone.load.mean()
inf_load_std = fd.infinite_zone.load.std()
with mock.patch.object(woehler.Bayesian._LogLike,
'__call__',
autospec=True) as loglike_call:
loglike_call.side_effect = check_likelihood
bayes = woehler.Bayesian(fd)
bayes._nsamples = 1000
bayes._SD_TS_trace()
pm.Normal.assert_called_once_with('SD_50',
mu=inf_load_mean,
sigma=inf_load_std * 5)
pm.Lognormal.assert_called_once_with('TS_50',
mu=np.log10(1.1),
sigma=np.log10(0.5))
tt.as_tensor_variable.assert_called_once_with(
[pm.Normal.return_value, pm.Lognormal.return_value])
pm.Potential.assert_called_once_with('likelihood', 'foovar')
pm.sample.assert_called_with(1000,
cores=1,
chains=3,
random_seed=None,
discard_tuned_samples=True,
tune=1000)