def setUp(self, m=100, n=1, nt_sim=50, nt_obs=20, n_theta=3, n_basis=5, sig_n=0.1, seed=42):
n_hier = 3
self.hier_idx = np.array([[0, 0, 0]])
#self.hier_idx = np.array([[1, 1, 1], [2, -1, 2]]) # TODO this fails for multivariate; cant use for univariate now
multi_data_list = []
univ_data_list = []
for si in range(n_hier):
multi_data_dict = generate_data.generate_multi_sim_and_obs(m=m, n=n, nt_sim=nt_sim, nt_obs=nt_obs,
n_theta=n_theta, n_basis=n_basis,
sig_n=sig_n, seed=seed)
univ_data_dict = generate_data.generate_univ_sim_and_obs(m=m, n=n, sig_n=sig_n, seed=seed)
d = SepiaData(t_sim=univ_data_dict['t_sim'], y_sim=univ_data_dict['y_sim'], y_obs=univ_data_dict['y_obs'])
d.transform_xt()
d.standardize_y()
univ_data_list.append(d)
d = SepiaData(t_sim=multi_data_dict['t_sim'], y_sim=multi_data_dict['y_sim'],
y_ind_sim=multi_data_dict['y_ind_sim'], y_obs=multi_data_dict['y_obs'],
y_ind_obs=multi_data_dict['y_ind_obs'])
d.transform_xt()
d.standardize_y()
d.create_K_basis(5)
d.create_D_basis('constant')
multi_data_list.append(d)
self.univ_model_list = [SepiaModel(d) for d in univ_data_list]
self.multi_model_list = [SepiaModel(d) for d in multi_data_list]
def test_univariate_sim_and_obs(self):
"""
Tests univiariate sim and obs where we pass in both x and t.
"""
m = 700 # number of simulated observations
p = 3 # dimension of x (sim/obs inputs)
q = 2 # dimension of t (extra sim inputs)
n = 5 # number of observed observations
x_sim = np.random.uniform(-1, 3, (m, p))
t = np.random.uniform(-10, 10, (m, q))
x_obs = np.random.uniform(-1.5, 3.5, (n, p))
y_sim = 5 * np.random.normal(0, 1, m) + 2
y_obs = 5 * np.random.normal(0, 1, n) + 1
d = SepiaData(x_sim=x_sim,
y_sim=y_sim,
t_sim=t,
x_obs=x_obs,
y_obs=y_obs)
print('Testing univariate sim and obs SepiaData...')
print(d)
self.assertTrue(d.obs_data is not None)
self.assertTrue(not d.sim_only)
self.assertTrue(d.scalar_out)
d.transform_xt()
self.assertTrue(np.all(np.min(d.sim_data.x_trans, 0) == 0))
self.assertTrue(np.all(np.max(d.sim_data.x_trans, 0) == 1))
self.assertTrue(np.all(np.min(d.sim_data.t_trans, 0) == 0))
self.assertTrue(np.all(np.max(d.sim_data.t_trans, 0) == 1))
d.transform_xt(-10, 10)
self.assertTrue(np.all(np.min(d.sim_data.x_trans, 0) == -10))
self.assertTrue(np.all(np.max(d.sim_data.x_trans, 0) == 10))
self.assertTrue(np.all(np.min(d.sim_data.t_trans, 0) == -10))
self.assertTrue(np.all(np.max(d.sim_data.t_trans, 0) == 10))
d.standardize_y(center=False, scale=False)
self.assertEqual(d.sim_data.orig_y_sd, 1)
self.assertEqual(d.sim_data.orig_y_mean, 0)
self.assertTrue(np.allclose(d.sim_data.y, d.sim_data.y_std))
self.assertEqual(d.obs_data.orig_y_sd, 1)
self.assertEqual(d.obs_data.orig_y_mean, 0)
self.assertTrue(np.allclose(d.obs_data.y, d.obs_data.y_std))
d.standardize_y(scale='columnwise')
self.assertTrue(np.allclose(d.sim_data.orig_y_sd, 5, rtol=0.1))
self.assertTrue(np.allclose(d.sim_data.orig_y_mean, 2, rtol=0.1))
self.assertTrue(np.allclose(np.mean(d.sim_data.y_std, 0), 0, rtol=0.1))
self.assertTrue(np.allclose(np.std(d.sim_data.y_std, 0), 1, rtol=0.1))
self.assertTrue(d.sim_data.y.shape == d.sim_data.y_std.shape)
self.assertTrue(np.allclose(d.obs_data.orig_y_sd, 5, rtol=0.1))
self.assertTrue(np.allclose(d.obs_data.orig_y_mean, 2, rtol=0.1))
self.assertTrue(d.obs_data.y.shape == d.obs_data.y_std.shape)
d.create_K_basis(10)
d.create_D_basis()
def test_multivariate_sim_only_x_only(self):
"""
Tests setup for multivariate sim only where we only use an x input, not t.
"""
m = 700 # number of simulated observations
p = 3 # dimension of x (simulation inputs)
ell = 1000 # dimension of y output
pu = 3 # number of PCs
y_ind = np.linspace(0, 100, ell)
K_true = np.vstack([
0.5 * (np.sin(y_ind) + 1),
np.square(-y_ind + 50) / 2500, y_ind / 100
])
y = np.transpose(
np.log(1 + y_ind)[:, None] + np.dot(
K_true.T, 2 * np.array([1, 0.5, 0.2])[:, None] *
np.random.normal(0, 1, (pu, m))))
x = 0.5 * np.random.uniform(-1, 3, (m, p))
d = SepiaData(x_sim=x, y_sim=y, t_sim=None, y_ind_sim=y_ind)
print('Testing multivariate sim-only SepiaData...')
print(d)
self.assertTrue(d.obs_data is None)
self.assertTrue(d.sim_only)
self.assertTrue(not d.scalar_out)
d.transform_xt()
self.assertTrue(np.all(np.min(d.sim_data.x_trans, 0) == 0))
self.assertTrue(np.all(np.max(d.sim_data.x_trans, 0) == 1))
d.transform_xt(-10, 10)
self.assertTrue(np.all(np.min(d.sim_data.x_trans, 0) == -10))
self.assertTrue(np.all(np.max(d.sim_data.x_trans, 0) == 10))
d.standardize_y(center=False, scale=False)
self.assertEqual(d.sim_data.orig_y_sd, 1)
self.assertEqual(d.sim_data.orig_y_mean, 0)
self.assertTrue(np.allclose(d.sim_data.y, d.sim_data.y_std))
d.standardize_y(scale='columnwise')
self.assertTrue(
np.allclose(d.sim_data.orig_y_mean,
np.log(1 + y_ind),
rtol=0.1,
atol=0.5))
self.assertTrue(np.allclose(np.std(d.sim_data.y_std, 0), 1, rtol=0.1))
self.assertTrue(np.allclose(np.mean(d.sim_data.y_std, 0), 0, rtol=0.1))
self.assertTrue(d.sim_data.y.shape == d.sim_data.y_std.shape)
d.create_K_basis(3)
self.assertTrue(d.sim_data.K.shape == (pu, ell))
d.create_D_basis()
print(d)
def test_predict_multi_sim_only(self):
show_figs = True
exclude_burnin = True
n_pc = 2
seed = 42
lamWOs_init = 50000. # use 0 to use default lamWOs initial value
list_to_sample = [
1, 2, 3, 4
] # 1-based indexing for matlab; order is [betaU, lamUz, lamWs, lamWOs]
nsamp = 100
nburn = 10
# Open data from matlab
script_path = os.path.dirname(os.path.realpath(__file__))
mat_fn = '%s/data/multi_sim_only_mcmc_test.mat' % script_path
if os.path.isfile(mat_fn):
# if the matlab data is already in place, just load that
print(
'Found matfile, loading from multi_sim_only_mcmc_test.mat \n')
matfile = scipy.io.loadmat(mat_fn)
else:
print('Generating matfile multi_sim_only_mcmc_test.mat \n')
# Run matlab code, then open data from matlab
list_to_sample = [
1, 2, 3, 4
] # 1-based indexing for matlab; order is [betaU, lamUz, lamWs, lamWOs]
script_path = os.path.dirname(os.path.realpath(__file__))
# Run matlab code, then open data from matlab
try:
eng = matlab.engine.start_matlab()
eng.cd(script_path)
eng.addpath('matlab/', nargout=0)
eng.multi_sim_only_mcmc_test(nsamp,
nburn,
list_to_sample,
seed,
lamWOs_init,
n_pc,
nargout=0)
eng.quit()
matfile = scipy.io.loadmat(
'%s/data/multi_sim_only_mcmc_test.mat' % script_path)
except Exception as e:
print(e)
print('make sure matlab.engine installed')
y = matfile['y'].T
y_ind = matfile['y_ind'].T
x = matfile['x']
data = SepiaData(x_sim=x[:, 0][:, None],
t_sim=x[:, 1][:, None],
y_sim=y,
y_ind_sim=y_ind)
data.standardize_y()
data.transform_xt()
data.create_K_basis(n_pc=n_pc)
print(data)
np.random.seed(int(seed))
model = setup_model(data)
if lamWOs_init > 0:
model.params.lamWOs.val = np.array([[lamWOs_init]])
model.params.mcmcList = [
model.params.mcmcList[i - 1] for i in list_to_sample
]
t_start = time()
model.do_mcmc(nburn + nsamp)
t_end = time()
print('Python mcmc time %0.3g s' % (t_end - t_start))
print('Matlab mcmc time %0.3g s' % matfile['mcmc_time'])
np.random.seed(seed)
psamps = model.get_samples(0, sampleset=[0, 1, 2, 3, 4], flat=True)
pred = wPred([0.5, 0.5],
psamps,
model.num,
model.data,
returnMuSigma=True)
print('Samples are:')
print(pred.w.squeeze())
print('Matlab Samples are:')
print(matfile['pred_w'].squeeze())
print('Mu are:')
print(pred.mu.squeeze())
print('Matlab Mu are:')
print(matfile['pred_Myhat'])
print('Sigma are:')
print(pred.sigma.squeeze().squeeze().reshape(10, 2).T)
print('Matlab Sigma are:')
print(matfile['pred_Syhat'].squeeze())
print('Checking predicted realizations...')
self.assertTrue(
np.allclose(matfile['pred_w'].squeeze(), pred.w.squeeze()))
print('Checking predicted means...')
self.assertTrue(
np.allclose(matfile['pred_Myhat'].squeeze(), pred.mu.squeeze()))
print('Checking predicted sigmas...')
self.assertTrue(
np.allclose(matfile['pred_Syhat'].squeeze(),
pred.sigma.squeeze().reshape(10, 2).T))
pred_arv = wPred([0.5, 0.5],
psamps,
model.num,
model.data,
addResidVar=True,
returnMuSigma=True)
print('Add Residual Variance test')
print('Samples are:')
print(pred_arv.w.squeeze())
print('Matlab Samples are:')
print(matfile['pred_arv_w'].squeeze())
print('Mu are:')
print(pred_arv.mu.squeeze())
print('Matlab Mu are:')
print(matfile['pred_arv_Myhat'])
print('Sigma are:')
print(pred_arv.sigma.squeeze().squeeze().reshape(10, 2).T)
print('Matlab Sigma are:')
print(matfile['pred_arv_Syhat'].squeeze())
print('Checking predicted realizations...')
self.assertTrue(
np.allclose(matfile['pred_arv_w'].squeeze(), pred_arv.w.squeeze()))
print('Checking predicted means...')
self.assertTrue(
np.allclose(matfile['pred_arv_Myhat'].squeeze(),
pred_arv.mu.squeeze()))
print('Checking predicted sigmas...')
self.assertTrue(
np.allclose(matfile['pred_arv_Syhat'].squeeze(),
pred_arv.sigma.squeeze().reshape(10, 2).T))
print('Done.')
