def test_inequality(self):
S = bl.Study()
S.loadData(np.array([1, 2, 3, 4, 5]))
S.setOM(bl.om.Poisson('rate', bl.oint(0, 6, 50)))
S.setTM(bl.tm.Static())
S.fit()
S2 = bl.Study()
S2.loadData(np.array([1, 2, 3, 4, 5]))
S2.setOM(bl.om.Poisson('rate2', bl.oint(0, 6, 50)))
S2.setTM(bl.tm.GaussianRandomWalk('sigma', 0.2, target='rate2'))
S2.fit()
P = bl.Parser(S, S2)
P('log(rate2*2*1.2) + 4 + rate^2 > 20', t=3)
np.testing.assert_almost_equal(
P('log(rate2@1*2*1.2) + 4 + rate@2^2 > 20'),
0.162262091093,
decimal=5,
err_msg='Erroneous parsing result for inequality.')
np.testing.assert_almost_equal(
P('log(rate2*2*1.2) + 4 + rate^2 > 20', t=3),
0.163699467863,
decimal=5,
err_msg=
'Erroneous parsing result for inequality with fixed timestamp.')
def test_fit_prior_function(self):
# carry out fit
S = bl.Study()
S.loadData(np.array([1, 2, 3, 4, 5]))
S.setOM(
bl.om.Poisson('rate', bl.oint(0, 6, 1000), prior=lambda x: 1. / x))
S.setTM(bl.tm.GaussianRandomWalk('sigma', 0.1, target='rate'))
S.fit()
# test parameter distributions
np.testing.assert_allclose(
S.getParameterDistributions('rate')[1][:, 250],
[0.0007529, 0.00070742, 0.00066273, 0.00063262, 0.00062968],
rtol=1e-05,
err_msg='Erroneous posterior distribution values.')
# test parameter mean values
np.testing.assert_allclose(
S.getParameterMeanValues('rate'),
[2.77252114, 2.78251864, 2.79475018, 2.80541289, 2.81072838],
rtol=1e-05,
err_msg='Erroneous posterior mean values.')
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence,
-11.3966589329,
decimal=5,
err_msg='Erroneous log-evidence value.')
def test_fit_prior_sympy(self):
# carry out fit
S = bl.Study()
S.loadData(np.array([1, 2, 3, 4, 5]))
S.setOM(
bl.om.Poisson('rate',
bl.oint(0, 6, 1000),
prior=stats.Exponential('expon', 1.)))
S.setTM(bl.tm.GaussianRandomWalk('sigma', 0.1, target='rate'))
S.fit()
# test parameter distributions
np.testing.assert_allclose(
S.getParameterDistributions('rate')[1][:, 250],
[0.0020607, 0.0019692, 0.00187339, 0.0018053, 0.00179584],
rtol=1e-05,
err_msg='Erroneous posterior distribution values.')
# test parameter mean values
np.testing.assert_allclose(
S.getParameterMeanValues('rate'),
[2.25427356, 2.26949283, 2.28527551, 2.29704214, 2.30024139],
rtol=1e-05,
err_msg='Erroneous posterior mean values.')
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence,
-11.1819034242,
decimal=5,
err_msg='Erroneous log-evidence value.')
def test_fit_0hp(self):
# carry out fit
S = bl.Study()
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')[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_1hp(self):
# carry out fit
S = bl.Study()
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.GaussianRandomWalk('sigma', 0.1, target='mean'))
S.fit()
# test parameter distributions
np.testing.assert_allclose(
S.getParameterDistributions('mean')[1][:, 5],
[0.00722368, 0.00712209, 0.00702789, 0.00696926, 0.00696322],
rtol=1e-05,
err_msg='Erroneous posterior distribution values.')
# test parameter mean values
np.testing.assert_allclose(
S.getParameterMeanValues('mean'),
[2.99313985, 2.99573566, 3., 3.00426434, 3.00686015],
rtol=1e-05,
err_msg='Erroneous posterior mean values.')
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence,
-16.1865343702,
decimal=5,
err_msg='Erroneous log-evidence value.')
def test_fit_2hp(self):
# carry out fit
S = bl.Study()
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', 0.1, target='mean'),
bl.tm.RegimeSwitch('log10pMin', -3))
S.setTM(T)
S.fit()
# test parameter distributions
np.testing.assert_allclose(
S.getParameterDistributions('mean')[1][:, 5],
[0.02976422, 0.15404218, 0.10859567, 0.02553673, 0.00054109],
rtol=1e-05,
err_msg='Erroneous posterior distribution values.')
# test parameter mean values
np.testing.assert_allclose(
S.getParameterMeanValues('mean'),
[1.08288559, 2.24388932, 2.38033179, 2.98934128, 4.64547841],
rtol=1e-05,
err_msg='Erroneous posterior mean values.')
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence,
-14.3305753098,
decimal=5,
err_msg='Erroneous log-evidence value.')
def test_fit_prior_array(self):
# carry out fit
S = bl.Study()
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=np.ones((20, 20))))
S.setTM(bl.tm.GaussianRandomWalk('sigma', 0.1, target='mean'))
S.fit()
# test parameter distributions
np.testing.assert_allclose(
S.getParameterDistributions('mean')[1][:, 5],
[0.04317995, 0.04296549, 0.04275526, 0.04262151, 0.04262491],
rtol=1e-05,
err_msg='Erroneous posterior distribution values.')
# test parameter mean values
np.testing.assert_allclose(
S.getParameterMeanValues('mean'),
[2.66415455, 2.66519273, 2.66664847, 2.66788051, 2.66828383],
rtol=1e-05,
err_msg='Erroneous posterior mean values.')
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence,
-10.9827282104,
decimal=5,
err_msg='Erroneous log-evidence value.')
def test_fit_prior_function(self):
# carry out fit
S = bl.Study()
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))
S.setTM(bl.tm.GaussianRandomWalk('sigma', 0.1, target='mean'))
S.fit()
# test parameter distributions
np.testing.assert_allclose(
S.getParameterDistributions('mean')[1][:, 5],
[0.01591204, 0.01579036, 0.01567361, 0.01559665, 0.01558591],
rtol=1e-05,
err_msg='Erroneous posterior distribution values.')
# test parameter mean values
np.testing.assert_allclose(
S.getParameterMeanValues('mean'),
[2.99576496, 2.99741879, 3., 3.00258121, 3.00423504],
rtol=1e-05,
err_msg='Erroneous posterior mean values.')
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence,
-11.9842221343,
decimal=5,
err_msg='Erroneous log-evidence value.')
def test_fit_1hp(self):
# carry out fit
S = bl.Study()
S.loadData(np.array([1, 2, 3, 4, 5]))
S.setOM(bl.om.Poisson('rate'))
S.setTM(bl.tm.GaussianRandomWalk('sigma', 0.1, target='rate'))
S.fit()
# test parameter distributions
np.testing.assert_allclose(
S.getParameterDistributions('rate')[1][:, 250],
[0.00055206, 0.00051683, 0.00048233, 0.00045903, 0.00045629],
rtol=1e-05,
err_msg='Erroneous posterior distribution values.')
# test parameter mean values
np.testing.assert_allclose(
S.getParameterMeanValues('rate'),
[2.97583695, 2.98402851, 2.99501907, 3.00527058, 3.01126784],
rtol=1e-05,
err_msg='Erroneous posterior mean values.')
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence,
-10.4337420351,
decimal=5,
err_msg='Erroneous log-evidence value.')
def test_fit_prior_sympy(self):
# carry out fit
S = bl.Study()
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=[stats.Uniform('u', 0, 6),
stats.Exponential('e', 2.)]))
S.setTM(bl.tm.GaussianRandomWalk('sigma', 0.1, target='mean'))
S.fit()
# test parameter distributions
np.testing.assert_allclose(
S.getParameterDistributions('mean')[1][:, 5],
[0.00909976, 0.0089861, 0.00887967, 0.00881235, 0.00880499],
rtol=1e-05,
err_msg='Erroneous posterior distribution values.')
# test parameter mean values
np.testing.assert_allclose(
S.getParameterMeanValues('mean'),
[2.9942575, 2.99646768, 3., 3.00353232, 3.0057425],
rtol=1e-05,
err_msg='Erroneous posterior mean values.')
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence,
-12.4324853153,
decimal=5,
err_msg='Erroneous log-evidence value.')
def test_fit_2hp(self):
# carry out fit
S = bl.Study()
S.loadData(np.array([1, 2, 3, 4, 5]))
S.setOM(bl.om.Poisson('rate'))
T = bl.tm.CombinedTransitionModel(
bl.tm.GaussianRandomWalk('sigma', 0.1, target='rate'),
bl.tm.RegimeSwitch('log10pMin', -3))
S.setTM(T)
S.fit()
# test parameter distributions
np.testing.assert_allclose(
S.getParameterDistributions('rate')[1][:, 250],
[0.00052993, 0.00050302, 0.00047929, 0.00045883, 0.00045629],
rtol=1e-05,
err_msg='Erroneous posterior distribution values.')
# test parameter mean values
np.testing.assert_allclose(
S.getParameterMeanValues('rate'),
[2.87337277, 2.9265807, 2.98217424, 3.00439557, 3.01127175],
rtol=1e-05,
err_msg='Erroneous posterior mean values.')
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence,
-10.4342948181,
decimal=5,
err_msg='Erroneous log-evidence value.')
def test_optimize(self):
# carry out fit
S = bl.Study()
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', 1.07, target='mean'),
bl.tm.RegimeSwitch('log10pMin', -3.90))
S.setTM(T)
S.optimize()
# test parameter distributions
np.testing.assert_allclose(S.getParameterDistributions('mean', density=False)[1][:, 5],
[4.52572851e-04, 1.67790320e-03, 2.94525791e-07, 1.49841548e-08, 1.10238422e-09],
rtol=1e-05, err_msg='Erroneous posterior distribution values.')
# test parameter mean values
np.testing.assert_allclose(S.getParameterMeanValues('mean'),
[0.95899404, 1.93816557, 2.99999968, 4.06183394, 5.04100612],
rtol=1e-05, err_msg='Erroneous posterior mean values.')
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence, -8.01030368139, decimal=5,
err_msg='Erroneous log-evidence value.')
# test optimized hyper-parameter values
np.testing.assert_almost_equal(S.getHyperParameterValue('sigma'), 1.06576569677, decimal=5,
err_msg='Erroneous log-evidence value.')
np.testing.assert_almost_equal(S.getHyperParameterValue('log10pMin'), -4.04001476542, decimal=5,
err_msg='Erroneous log-evidence value.')
def test_optimize(self):
# carry out fit
S = bl.Study()
S.loadData(np.array([1, 2, 3, 4, 5]))
S.setOM(bl.om.Poisson('rate', bl.oint(0, 6, 1000), prior=stats.Exponential('expon', 1.)))
T = bl.tm.CombinedTransitionModel(bl.tm.GaussianRandomWalk('sigma', 2.1, target='rate'),
bl.tm.RegimeSwitch('log10pMin', -3))
S.setTM(T)
S.optimize()
# test parameter distributions
np.testing.assert_allclose(S.getParameterDistributions('rate', density=False)[1][:, 250],
[0.00181567, 0.00213315, 0.00091028, 0.00041154, 0.00090885],
rtol=1e-05, err_msg='Erroneous posterior distribution values.')
# test parameter mean values
np.testing.assert_allclose(S.getParameterMeanValues('rate'),
[1.01204314, 2.25763551, 3.24176817, 3.74634864, 3.12632199],
rtol=1e-05, err_msg='Erroneous posterior mean values.')
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence, -9.47362827569, decimal=5,
err_msg='Erroneous log-evidence value.')
# test optimized hyper-parameter values
np.testing.assert_almost_equal(S.getHyperParameterValue('sigma'), 2.11216289063, decimal=5,
err_msg='Erroneous log-evidence value.')
np.testing.assert_almost_equal(S.getHyperParameterValue('log10pMin'), -3.0, decimal=5,
err_msg='Erroneous log-evidence value.')
def test_fit_0hp(self):
# carry out fit
S = bl.Study()
S.loadData(np.array([1, 2, 3, 4, 5]))
S.setOM(bl.om.Poisson('rate'))
S.setTM(bl.tm.Static())
S.fit()
# test parameter distributions
np.testing.assert_allclose(
S.getParameterDistributions('rate')[1][:, 250],
[0.00046598, 0.00046598, 0.00046598, 0.00046598, 0.00046598],
rtol=1e-05,
err_msg='Erroneous posterior distribution values.')
# test parameter mean values
np.testing.assert_allclose(
S.getParameterMeanValues('rate'),
[2.99835794, 2.99835794, 2.99835794, 2.99835794, 2.99835794],
rtol=1e-05,
err_msg='Erroneous posterior mean values.')
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence,
-10.4463425036,
decimal=5,
err_msg='Erroneous log-evidence value.')
def test_fit_prior_array(self):
# carry out fit
S = bl.Study()
S.loadData(np.array([1, 2, 3, 4, 5]))
S.setOM(bl.om.Poisson('rate', bl.oint(0, 6, 1000),
prior=np.ones(1000)))
S.setTM(bl.tm.GaussianRandomWalk('sigma', 0.1, target='rate'))
S.fit()
# test parameter distributions
np.testing.assert_allclose(
S.getParameterDistributions('rate')[1][:, 250],
[0.00048679, 0.00045124, 0.00041767, 0.00039717, 0.00039976],
rtol=1e-04,
err_msg='Erroneous posterior distribution values.')
# test parameter mean values
np.testing.assert_allclose(
S.getParameterMeanValues('rate'),
[2.81716591, 2.82337653, 2.83204058, 2.83944083, 2.84187612],
rtol=1e-05,
err_msg='Erroneous posterior mean values.')
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence,
-10.0866227472,
decimal=5,
err_msg='Erroneous log-evidence value.')
def test_distribution(self):
S = bl.Study()
S.loadData(np.array([1, 2, 3, 4, 5]))
S.setOM(bl.om.Poisson('rate', bl.oint(0, 6, 50)))
S.setTM(bl.tm.Static())
S.fit()
S2 = bl.Study()
S2.loadData(np.array([1, 2, 3, 4, 5]))
S2.setOM(bl.om.Poisson('rate2', bl.oint(0, 6, 50)))
S2.setTM(bl.tm.GaussianRandomWalk('sigma', 0.2, target='rate2'))
S2.fit()
P = bl.Parser(S, S2)
x, p = P('log(rate2@1*2*1.2)+ 4 + rate@2^2')
np.testing.assert_allclose(p[100:105],
[0.00643873, 0.00618468, 0.00466452, 0.00314371, 0.00365816],
rtol=1e-05, err_msg='Erroneous derived probability distribution.')
def test_gaussianmean(self):
S = bl.Study()
S.loadData(np.array([[1, 0.5], [0, 0.4], [1, 0.3], [0, 0.2], [0, 0.1]]))
L = bl.om.GaussianMean('mu', bl.oint(0, 1, 100))
T = bl.tm.Static()
S.set(L, T)
S.fit()
np.testing.assert_almost_equal(S.logEvidence, -6.3333705075036226, decimal=5,
err_msg='Erroneous log-evidence value.')
def test_bernoulli(self):
S = bl.Study()
S.loadData(np.array([1, 0, 1, 0, 0]))
L = bl.om.Bernoulli('p', bl.oint(0, 1, 100))
T = bl.tm.Static()
S.set(L, T)
S.fit()
np.testing.assert_almost_equal(S.logEvidence, -4.3494298741972859, decimal=5,
err_msg='Erroneous log-evidence value.')
def test_laplace(self):
S = bl.Study()
S.load(np.array([1, 0, 1, 0, 0]))
L = bl.om.Laplace('mu', None, 'b', None)
T = bl.tm.Static()
S.set(L, T)
S.fit()
np.testing.assert_almost_equal(S.logEvidence, -10.658573159, decimal=5,
err_msg='Erroneous log-evidence value.')
def test_poisson(self):
S = bl.Study()
S.loadData(np.array([1, 0, 1, 0, 0]))
L = bl.om.Poisson('rate', bl.oint(0, 1, 100))
T = bl.tm.Static()
S.set(L, T)
S.fit()
np.testing.assert_almost_equal(S.logEvidence, -4.433708287229158, decimal=5,
err_msg='Erroneous log-evidence value.')
def test_gaussian(self):
S = bl.Study()
S.loadData(np.array([1, 0, 1, 0, 0]))
L = bl.om.Gaussian('mu', bl.oint(0, 1, 100), 'std', bl.oint(0, 1, 100))
T = bl.tm.Static()
S.set(L, T)
S.fit()
np.testing.assert_almost_equal(S.logEvidence, -12.430583625665736, decimal=5,
err_msg='Erroneous log-evidence value.')
def test_scaledar1(self):
S = bl.Study()
S.loadData(np.array([1, 0, 1, 0, 0]))
L = bl.om.ScaledAR1('rho', bl.oint(-1, 1, 100), 'sigma', bl.oint(0, 1, 100))
T = bl.tm.Static()
S.set(L, T)
S.fit()
np.testing.assert_almost_equal(S.logEvidence, -4.4178639067800738, decimal=5,
err_msg='Erroneous log-evidence value.')
def test_whitenoise(self):
S = bl.Study()
S.loadData(np.array([1, 0, 1, 0, 0]))
L = bl.om.WhiteNoise('std', bl.oint(0, 1, 100))
T = bl.tm.Static()
S.set(L, T)
S.fit()
np.testing.assert_almost_equal(S.logEvidence, -6.8161638661444073, decimal=5,
err_msg='Erroneous log-evidence value.')
def test_notequal(self):
S = bl.Study()
S.loadData(np.array([1, 2, 3, 4, 5]))
L = bl.om.Poisson('rate', bl.oint(0, 6, 100))
T = bl.tm.NotEqual('p_min', -3)
S.set(L, T)
S.fit()
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence, -10.569099863134156, decimal=5,
err_msg='Erroneous log-evidence value.')
def test_gaussianrandomwalk(self):
S = bl.Study()
S.loadData(np.array([1, 2, 3, 4, 5]))
L = bl.om.Poisson('rate', bl.oint(0, 6, 100))
T = bl.tm.GaussianRandomWalk('sigma', 0.2, target='rate')
S.set(L, T)
S.fit()
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence, -10.323144246611964, decimal=5,
err_msg='Erroneous log-evidence value.')
def test_alphastablerandomwalk(self):
S = bl.Study()
S.loadData(np.array([1, 2, 3, 4, 5]))
L = bl.om.Poisson('rate', bl.oint(0, 6, 100))
T = bl.tm.AlphaStableRandomWalk('c', 0.2, 'alpha', 1.5, target='rate')
S.set(L, T)
S.fit()
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence, -10.122384638661309, decimal=5,
err_msg='Erroneous log-evidence value.')
def test_changepoint(self):
S = bl.Study()
S.loadData(np.array([1, 2, 3, 4, 5]))
L = bl.om.Poisson('rate', bl.oint(0, 6, 100))
T = bl.tm.ChangePoint('t_change', 2)
S.set(L, T)
S.fit()
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence, -12.894336092378385, decimal=5,
err_msg='Erroneous log-evidence value.')
def test_static(self):
S = bl.Study()
S.loadData(np.array([1, 2, 3, 4, 5]))
L = bl.om.Poisson('rate', bl.oint(0, 6, 100))
T = bl.tm.Static()
S.set(L, T)
S.fit()
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence, -10.372209708143769, decimal=5,
err_msg='Erroneous log-evidence value.')
def test_regimeswitch(self):
S = bl.Study()
S.loadData(np.array([1, 2, 3, 4, 5]))
L = bl.om.Poisson('rate', bl.oint(0, 6, 100))
T = bl.tm.RegimeSwitch('p_min', -3)
S.set(L, T)
S.fit()
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence, -10.372866559561402, decimal=5,
err_msg='Erroneous log-evidence value.')
def test_independent(self):
S = bl.Study()
S.loadData(np.array([1, 2, 3, 4, 5]))
L = bl.om.Poisson('rate', bl.oint(0, 6, 100))
T = bl.tm.Independent()
S.set(L, T)
S.fit()
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence, -11.087360077190617, decimal=5,
err_msg='Erroneous log-evidence value.')