def bad_y_exp_complex_sim(n=[12, 13, 9, 17, 11, 11, 13, 8, 15], n_mis=2):
# generate full data
d = data.complex_hierarchical_data(n)
# make some data missing
y_w_missing = [y_j.copy() for y_j in d['y']]
for k in range(n_mis):
j = random.choice(range(d['J']))
i = random.choice(range(d['n'][j]))
y_w_missing[j][i] = pl.nan
# generate model
m = models.complex_hierarchical_model(y_w_missing, d['X'], d['t'])
# replace data prediction with wrong exponent
m['y_exp'] = [mc.Lambda('y_exp_%d'%j, lambda mu=m['mu'], beta=m['beta'], gamma=m['gamma'], t=m['t'], j=j: mu[j] - pl.exp(beta[j])*t[j] - pl.exp(gamma[j])*t[j]**3) for j in range(m['J'])]
@mc.potential
def y_obs(y_exp=m['y_exp'], sigma=m['sigma'], y=m['y']):
logp = 0.
for j in range(m['J']):
missing = pl.isnan(y[j])
logp += mc.normal_like(y[j][~missing], y_exp[j][~missing], sigma[j]**-2)
return logp
m['y_obs'] = y_obs
m['y_pred'] = [mc.Normal('y_pred_%d'%j, m['y_exp'][j], m['sigma'][j]**-2) for j in range(m['J'])]
# fit model with Normal Approx
na = mc.NormApprox(m)
na.fit(method='fmin_powell', verbose=True)
na.sample(5000)
return d, m
def good_complex_sim(n=[12, 13, 9, 17, 11, 11, 13, 8, 15], n_mis=2):
# generate full data
d = data.complex_hierarchical_data(n)
# make some data missing
y_w_missing = [y_j.copy() for y_j in d['y']]
for k in range(n_mis):
j = random.choice(range(d['J']))
i = random.choice(range(d['n'][j]))
y_w_missing[j][i] = pl.nan
# generate model
m = models.complex_hierarchical_model(y_w_missing, d['X'], d['t'])
# fit model with Normal Approx
na = mc.NormApprox(m)
na.fit(method='fmin_powell', verbose=True)
na.sample(5000)
return d, m
def test_complex_model(self):
d = data.complex_hierarchical_data([10,11,12])
vars = models.complex_hierarchical_model(d['y'], d['X'], d['t'])
assert 'mu' in vars
def test_complex_model(self):
d = data.complex_hierarchical_data([10, 11, 12])
vars = models.complex_hierarchical_model(d['y'], d['X'], d['t'])
assert 'mu' in vars