def do_wPred():
for _ in range(10):
pred = wPred([0.5],
psamps,
model.num,
model.data,
returnMuSigma=True,
addResidVar=True,
returnRlz=True)
model = setup_model(data)
nsamp = int(matfile['nsamp'])
nburn = int(matfile['nburn'])
if lamWOs_init > 0:
model.params.lamWOs.val = np.array([[lamWOs_init]])
t_start = time()
model.do_mcmc(nburn + nsamp)
t_end = time()
#%% Test Predict
np.random.seed(42)
z = model.get_samples(0, sampleset=[1, 2, 3, 4, 5], flat=True)
#pred=wPred([0.5,0.5],z,model.num,model.data,returnMuSigma=True)
#pred=wPred(np.array([0.5,0.5,0.6,0.6]).reshape(2,2),z,model.num,model.data,returnMuSigma=True)
pred = wPred([0.5, 0.5], z, model.num, model.data, returnMuSigma=True)
print('Samples are:')
print(pred.w)
print('Mu are:')
print(pred.mu)
print('Sigma are:')
print(pred.sigma)
'''
# Matlab code:
rng(42,'twister');
pred=gPredict([0.5,0.5],p.pvals(1:5),p.model,p.data,'returnMuSigma',1);
pred.w, pred.Myhat,pred.Syhat
'''
#%% Plot Predict
#import importlib
def test_predict_univ_sim_only(self):
np.random.seed(42)
show_figs = True
# Open data from matlab
script_path = os.path.dirname(os.path.realpath(__file__))
mat_fn = '%s/data/univ_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 univ_sim_only_mcmc_test.mat \n')
matfile = scipy.io.loadmat(mat_fn)
else:
print('Generating matfile univ_sim_only_mcmc_test.mat \n')
# Run matlab code, then open data from matlab
try:
eng = matlab.engine.start_matlab()
eng.cd(script_path)
eng.addpath('matlab/', nargout=0)
eng.univ_sim_only_mcmc_test(nargout=0)
eng.quit()
matfile = scipy.io.loadmat('mat_fn')
except Exception as e:
print(e)
print('make sure matlab.engine installed')
y = matfile['y']
x = matfile['x']
t = matfile['t']
data = SepiaData(x_sim=x, t_sim=t, y_sim=y)
data.standardize_y()
data.transform_xt()
print(data)
model = setup_model(data)
nsamp = int(matfile['nsamp'])
nburn = int(matfile['nburn'])
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'])
# Creates dict with each sampled variable name as key, array of samples (nsamp, ...) as value
samples = model.get_samples(nburn)
log_post = np.array(model.params.lp.mcmc.draws)
np.random.seed(42)
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'].squeeze())
print('Sigma are:')
print(pred.sigma.squeeze())
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()))
# Prediction with multiple realizations
np.random.seed(42)
sampleset = np.arange(100, 1001, 100) - 1
samples = model.get_samples(sampleset=sampleset)
nq = 10
t = np.linspace(0, 1, nq)
xpred = np.column_stack((np.ones((nq, 1)) * 0.5, t))
pred_plot = wPred(xpred, samples, model.num, model.data)
print('pred_plot_w are:')
print(pred_plot.w.squeeze()[0, :])
print('Matlab pred_plot_w are:')
print(matfile['pred_plot_w'].squeeze()[0, :])
print('Checking predicted realizations for plotting...')
# Apparently numerics come into play here, need to turn down the rtol on 'close'
self.assertTrue(
np.allclose(matfile['pred_plot_w'].squeeze(),
pred_plot.w.squeeze(),
rtol=1e-3))
print('Done.')
if show_figs:
import matplotlib.pyplot as plt
plt.figure()
plt.plot(data.sim_data.t_trans, data.sim_data.y_std)
plt.plot(np.tile(t, (len(sampleset), 1)), np.squeeze(pred_plot.w),
'.')
plt.show()
def test_predict_sim_from_multi_obs(self):
show_figs = True
exclude_burnin = True
n_pc = 2
seed = 42.
lamWOs_init = 50000. # use 0 to use default lamWOs initial value
nsamp = 100
nburn = 0
# Open data from matlab
script_path = os.path.dirname(os.path.realpath(__file__))
mat_fn = '%s/data/multi_sim_and_obs_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_and_obs_mcmc_test.mat \n'
)
matfile = scipy.io.loadmat(mat_fn)
else:
print('Generating matfile multi_sim_and_obs_mcmc_test.mat \n')
# Run matlab code, then open data from matlab
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_and_obs_mcmc_test(nsamp,
nburn,
seed,
lamWOs_init,
n_pc,
0,
nargout=0)
eng.quit()
matfile = scipy.io.loadmat(
'%s/data/multi_sim_and_obs_mcmc_test.mat' % script_path)
except Exception as e:
print(e)
print('make sure matlab.engine installed')
nburn = int(matfile['nburn'])
nsamp = int(matfile['nsamp'])
y_sim = matfile['y'].T
y_ind_sim = matfile['y_ind'].squeeze()
xt_sim = matfile['x']
y_obs = matfile['y_obs']
y_ind_obs = matfile['y_ind_obs'].squeeze()
x_obs = matfile['x_obs']
data = SepiaData(x_sim=xt_sim[:, 0][:, None],
t_sim=xt_sim[:, 1][:, None],
y_sim=y_sim,
y_ind_sim=y_ind_sim,
x_obs=x_obs,
y_obs=y_obs,
y_ind_obs=y_ind_obs)
data.standardize_y()
data.transform_xt()
data.create_K_basis(n_pc=n_pc)
data.create_D_basis(D=matfile['Dobs'].T)
print(data)
np.random.seed(int(seed))
model = setup_model(data)
if lamWOs_init > 0:
model.params.lamWOs.val = np.array([[lamWOs_init]])
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(int(seed))
psamps = model.get_samples(0, sampleset=[0, 1, 2, 3, 4], flat=True)
pred = wPred([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))
print('Done.')
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.')