def test_fit_0hp(self):
# carry out fit (this test is designed to fall back on the fit method of the Study class)
S = bl.HyperStudy()
S.loadData(np.array([1, 2, 3, 4, 5]))
S.setOM(
bl.om.Gaussian('mean', bl.cint(0, 6, 20), 'sigma',
bl.oint(0, 2, 20)))
S.setTM(bl.tm.Static())
S.fit()
# test parameter distributions
np.testing.assert_allclose(
S.getParameterDistributions('mean', density=False)[1][:, 5],
[0.00707902, 0.00707902, 0.00707902, 0.00707902, 0.00707902],
rtol=1e-05,
err_msg='Erroneous posterior distribution values.')
# test parameter mean values
np.testing.assert_allclose(S.getParameterMeanValues('mean'),
[3., 3., 3., 3., 3.],
rtol=1e-05,
err_msg='Erroneous posterior mean values.')
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence,
-16.1946904707,
decimal=5,
err_msg='Erroneous log-evidence value.')
def test_fit_2hp(self):
# carry out fit
S = bl.HyperStudy()
S.loadData(np.array([1, 2, 3, 4, 5]))
S.setOM(
bl.om.Gaussian('mean', bl.cint(0, 6, 20), 'sigma',
bl.oint(0, 2, 20)))
T = bl.tm.CombinedTransitionModel(
bl.tm.GaussianRandomWalk('sigma',
bl.cint(0, 0.2, 2),
target='mean'),
bl.tm.RegimeSwitch('log10pMin', [-3, -1]))
S.setTM(T)
S.fit()
# test parameter distributions
np.testing.assert_allclose(
S.getParameterDistributions('mean', density=False)[1][:, 5], [
5.80970506e-03, 1.12927905e-01, 4.44501254e-02, 1.00250119e-02,
1.72751309e-05
],
rtol=1e-05,
err_msg='Erroneous posterior distribution values.')
# test parameter mean values
np.testing.assert_allclose(
S.getParameterMeanValues('mean'),
[0.96492471, 2.09944204, 2.82451616, 3.72702495, 5.0219119],
rtol=1e-05,
err_msg='Erroneous posterior mean values.')
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence,
-10.7601875492,
decimal=5,
err_msg='Erroneous log-evidence value.')
# test hyper-parameter distribution
x, p = S.getHyperParameterDistribution('sigma')
np.testing.assert_allclose(
np.array([x, p]), [[0., 0.2], [0.48943645, 0.51056355]],
rtol=1e-05,
err_msg='Erroneous values in hyper-parameter distribution.')
# test joint hyper-parameter distribution
x, y, p = S.getJointHyperParameterDistribution(['log10pMin', 'sigma'])
np.testing.assert_allclose(
np.array([x, y]), [[-3., -1.], [0., 0.2]],
rtol=1e-05,
err_msg='Erroneous parameter values in joint hyper-parameter '
'distribution.')
np.testing.assert_allclose(
p, [[0.00701834, 0.0075608], [0.48241812, 0.50300274]],
rtol=1e-05,
err_msg='Erroneous probability values in joint hyper-parameter '
'distribution.')
def test_save_load(self):
S = bl.HyperStudy()
S.loadData(np.array([1, 2, 3, 4, 5]))
S.setOM(
bl.om.Gaussian('mean', bl.cint(0, 6, 20), 'sigma',
bl.oint(0, 2, 20)))
S.setTM(bl.tm.Static())
S.fit()
bl.save('study.bl', S)
S = bl.load('study.bl')
def test_deterministic(self):
S = bl.HyperStudy()
S.loadData(np.array([1, 2, 3, 4, 5]))
def linear(t, a=[1, 2]):
return 0.5 + 0.2*a*t
L = bl.om.Poisson('rate', bl.oint(0, 6, 100))
T = bl.tm.Deterministic(linear, target='rate')
S.set(L, T)
S.fit()
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence, -9.4050089375418136, decimal=5,
err_msg='Erroneous log-evidence value.')
def test_statichyperparameter(self):
S = bl.HyperStudy()
S.loadData(np.array([1, 2, 3, 4, 5]))
S.setOM(bl.om.Poisson('rate', bl.oint(0, 6, 50)))
S.setTM(
bl.tm.GaussianRandomWalk('sigma',
bl.cint(0, 0.2, 5),
target='rate'))
S.fit()
p = S.eval('exp(0.99*log(sigma))+1 > 1.1')
np.testing.assert_almost_equal(
p,
0.60696006616644793,
decimal=5,
err_msg='Erroneous parsing result for inequality.')
def test_fit_1hp(self):
# carry out fit
S = bl.HyperStudy()
S.loadData(np.array([1, 2, 3, 4, 5]))
S.setOM(
bl.om.Gaussian('mean',
bl.cint(0, 6, 20),
'sigma',
bl.oint(0, 2, 20),
prior=lambda m, s: 1 / s**3))
S.setTM(
bl.tm.GaussianRandomWalk('sigma',
bl.cint(0, 0.2, 2),
target='mean'))
S.fit()
# test parameter distributions
np.testing.assert_allclose(
S.getParameterDistributions('mean', density=False)[1][:, 5],
[0.01042107, 0.00766233, 0.00618352, 0.00554651, 0.00548637],
rtol=1e-05,
err_msg='Erroneous posterior distribution values.')
# test parameter mean values
np.testing.assert_allclose(
S.getParameterMeanValues('mean'),
[2.88534505, 2.93135361, 3., 3.06864639, 3.11465495],
rtol=1e-05,
err_msg='Erroneous posterior mean values.')
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence,
-16.0629517262,
decimal=5,
err_msg='Erroneous log-evidence value.')
# test hyper-parameter distribution
x, p = S.getHyperParameterDistribution('sigma')
print(np.array([x, p]))
np.testing.assert_allclose(
np.array([x, p]), [[0., 0.2], [0.43828499, 0.56171501]],
rtol=1e-05,
err_msg='Erroneous values in hyper-parameter distribution.')
def test_fit_hyperprior_sympy(self):
# carry out fit
S = bl.HyperStudy()
S.loadData(np.array([1, 2, 3, 4, 5]))
S.setOM(
bl.om.Gaussian('mean',
bl.cint(0, 6, 20),
'sigma',
bl.oint(0, 2, 20),
prior=lambda m, s: 1 / s**3))
S.setTM(
bl.tm.GaussianRandomWalk('sigma',
bl.cint(0, 0.2, 2),
target='mean',
prior=stats.Exponential('e', 1.)))
S.fit()
# test parameter distributions
np.testing.assert_allclose(
S.getParameterDistributions('mean', density=False)[1][:, 5],
[0.01012545, 0.00761074, 0.00626273, 0.00568207, 0.00562725],
rtol=1e-05,
err_msg='Erroneous posterior distribution values.')
# test parameter mean values
np.testing.assert_allclose(
S.getParameterMeanValues('mean'),
[2.89548676, 2.93742566, 3., 3.06257434, 3.10451324],
rtol=1e-05,
err_msg='Erroneous posterior mean values.')
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence,
-17.0866290887,
decimal=5,
err_msg='Erroneous log-evidence value.')
# test hyper-parameter distribution
x, p = S.getHyperParameterDistribution('sigma')
np.testing.assert_allclose(
np.array([x, p]), [[0., 0.2], [0.487971, 0.512029]],
rtol=1e-05,
err_msg='Erroneous values in hyper-parameter distribution.')
def test_fit_hyperprior_function(self):
# carry out fit
S = bl.HyperStudy()
S.loadData(np.array([1, 2, 3, 4, 5]))
S.setOM(
bl.om.Gaussian('mean',
bl.cint(0, 6, 20),
'sigma',
bl.oint(0, 2, 20),
prior=lambda m, s: 1 / s**3))
S.setTM(
bl.tm.GaussianRandomWalk('sigma',
bl.cint(0.1, 0.3, 2),
target='mean',
prior=lambda s: 1. / s))
S.fit()
# test parameter distributions
np.testing.assert_allclose(
S.getParameterDistributions('mean', density=False)[1][:, 5],
[0.04071021, 0.00783661, 0.00527211, 0.00484169, 0.00480379],
rtol=1e-05,
err_msg='Erroneous posterior distribution values.')
# test parameter mean values
np.testing.assert_allclose(
S.getParameterMeanValues('mean'),
[2.68460027, 2.81872578, 3., 3.18127422, 3.31539973],
rtol=1e-05,
err_msg='Erroneous posterior mean values.')
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence,
-15.9898700147,
decimal=5,
err_msg='Erroneous log-evidence value.')
# test hyper-parameter distribution
x, p = S.getHyperParameterDistribution('sigma')
np.testing.assert_allclose(
np.array([x, p]), [[0.1, 0.3], [0.61609973, 0.38390027]],
rtol=1e-05,
err_msg='Erroneous values in hyper-parameter distribution.')
def test_fit_hyperprior_array(self):
# carry out fit
S = bl.HyperStudy()
S.loadData(np.array([1, 2, 3, 4, 5]))
S.setOM(
bl.om.Gaussian('mean',
bl.cint(0, 6, 20),
'sigma',
bl.oint(0, 2, 20),
prior=lambda m, s: 1 / s**3))
S.setTM(
bl.tm.GaussianRandomWalk('sigma',
bl.cint(0, 0.2, 2),
target='mean',
prior=np.array([0.2, 0.8])))
S.fit()
# test parameter distributions
np.testing.assert_allclose(
S.getParameterDistributions('mean', density=False)[1][:, 5],
[0.01205759, 0.00794796, 0.00574501, 0.00479608, 0.00470649],
rtol=1e-05,
err_msg='Erroneous posterior distribution values.')
# test parameter mean values
np.testing.assert_allclose(
S.getParameterMeanValues('mean'),
[2.82920111, 2.89773902, 3., 3.10226098, 3.17079889],
rtol=1e-05,
err_msg='Erroneous posterior mean values.')
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence,
-15.9915077133,
decimal=5,
err_msg='Erroneous log-evidence value.')
# test hyper-parameter distribution
x, p = S.getHyperParameterDistribution('sigma')
np.testing.assert_allclose(
np.array([x, p]), [[0., 0.2], [0.16322581, 0.83677419]],
rtol=1e-05,
err_msg='Erroneous values in hyper-parameter distribution.')
