def test_single_parameter_linear_adjustment():
"""A regression test against values obtained in the notebook."""
seed = 20170616
n_obs = 50
batch_size = 100
mu, sigma = (5, 1)
# Hyperparameters
mu0, sigma0 = (10, 100)
y_obs = gauss.Gauss(
mu, sigma, n_obs=n_obs, batch_size=1, random_state=np.random.RandomState(seed))
sim_fn = partial(gauss.Gauss, sigma=sigma, n_obs=n_obs)
# Posterior
n = y_obs.shape[1]
mu1 = (mu0 / sigma0**2 + y_obs.sum() / sigma**2) / (1 / sigma0**2 + n / sigma**2)
sigma1 = (1 / sigma0**2 + n / sigma**2)**(-0.5)
# Model
m = elfi.ElfiModel()
elfi.Prior('norm', mu0, sigma0, model=m, name='mu')
elfi.Simulator(sim_fn, m['mu'], observed=y_obs, name='Gauss')
elfi.Summary(lambda x: x.mean(axis=1), m['Gauss'], name='S1')
elfi.Distance('euclidean', m['S1'], name='d')
res = elfi.Rejection(m['d'], output_names=['S1'], seed=seed).sample(1000, threshold=1)
adj = elfi.adjust_posterior(model=m, sample=res, parameter_names=['mu'], summary_names=['S1'])
assert np.allclose(_statistics(adj.outputs['mu']), (4.9772879640569778, 0.02058680115402544))
def Perfrom_ABC(self,save = False):
sigma_prior = elfi.Prior('uniform',self.start_prior,self.end_prior)
y0 = self.simulator(self.sigma0)
sim = elfi.Simulator(self.simulator,sigma_prior,observed=y0)
S1 = elfi.Summary(self.var,sim)
S2 = elfi.Summary(self.max_min_diff,sim)
S3 = elfi.Summary(self.PDP_diff_mean,sim)
S4 = elfi.Summary(self.PDP_diff_var,sim)
S5 = elfi.Summary(self.mean,sim)
sumstats = []
sumstats.append(S1)
sumstats.append(S2)
sumstats.append(S3)
sumstats.append(S4)
sumstats.append(S5)
output_names = 'S1,S2,S3,S4,S5'
output_list = ['S1','S2','S3','S4','S5']
if self.distance == 'euclidean':
d = elfi.Distance('euclidean',*sumstats)
elif self.distance == 'seuclidean':
d = elfi.Distance('seuclidean',*sumstats,V = None)
if self.mode == 'SMC':
rej_temp = elfi.Rejection(d.model['d'],output_names = output_list)
rej = elfi.SMC(d.model['d'],output_names = rej_temp.output_names)
quantile_list = list(np.repeat(self.quantile,self.T))
res_sample = rej.sample(self.N,quantile_list)
try:
adj_res = elfi.adjust_posterior(res_sample.T, d.model, output_list)
except:
print('Error in calculation of adjustment')
adj_res = 'NaN'
if self.mode == 'Rejection':
rej = elfi.Rejection(d.model['d'],output_names = output_list)
res_sample = rej.sample(self.N,self.quantile)
try:
adj_res = elfi.adjust_posterior(res_sample.T, d.model, output_list)
except:
print('Error in calculation of adjustment')
adj_res = 'NaN'
dat = res_sample.samples_array[:,-1]
dat_grid = np.linspace(self.start_prior,self.end_prior,self.N)
kde = kde_scipy(dat,dat_grid,bandwidth = 1)
self.data_dict = {'theta':res_sample.samples_array[:,-1],\
'summariers':np.array([res_sample.outputs['S1'],res_sample.outputs['S2'],\
res_sample.outputs['S3'],res_sample.outputs['S4'],res_sample.outputs['S5']]),\
'True sim': y0, 'true summaries' : np.array([self.var(y0),\
self.max_min_diff(y0),self.PDP_diff_mean(y0),self.PDP_diff_var(y0),self.mean(y0),]),\
'kde' : kde, 'kde_grid':dat_grid,'sigma0': self.sigma0,\
'elfi adjust' : adj_res,\
'elfi results' : res_sample}
if save == True:
try:
string = str(self.quantile)[-1]
f = open('examples/Pickle_data_%s_q_%s_sig0_%d_summaries_%s_distance_%s'%(self.mode,string,int(self.sigma0),output_names,self.distance),'wb')
p.dump(self.data_dict,f)
f.close()
except:
print('Error in saving')
return self.data_dict
