def test_sample(self):
# setup backend
backend = BackendDummy()
# define a uniform prior distribution
mu = Uniform([[-5.0], [5.0]], name='mu')
sigma = Uniform([[0.0], [10.0]], name='sigma')
# define a Gaussian model
self.model = Normal([mu,sigma])
# define sufficient statistics for the model
stat_calc = Identity(degree = 2, cross = 0)
# create fake observed data
#y_obs = self.model.forward_simulate(1, np.random.RandomState(1))[0].tolist()
y_obs = [np.array(9.8)]
# Define the likelihood function
likfun = SynLiklihood(stat_calc)
T, n_sample, n_samples_per_param = 1, 10, 100
sampler = PMC([self.model], [likfun], backend, seed = 1)
journal = sampler.sample([y_obs], T, n_sample, n_samples_per_param, covFactors = np.array([.1,.1]), iniPoints = None)
mu_post_sample, sigma_post_sample, post_weights = np.array(journal.get_parameters()['mu']), np.array(journal.get_parameters()['sigma']), np.array(journal.get_weights())
# Compute posterior mean
mu_post_mean, sigma_post_mean = np.average(mu_post_sample, weights=post_weights, axis=0), np.average(sigma_post_sample, weights=post_weights, axis=0)
# test shape of sample
mu_sample_shape, sigma_sample_shape, weights_sample_shape = np.shape(mu_post_sample), np.shape(mu_post_sample), np.shape(post_weights)
self.assertEqual(mu_sample_shape, (10,1))
self.assertEqual(sigma_sample_shape, (10,1))
self.assertEqual(weights_sample_shape, (10,1))
self.assertLess(abs(mu_post_mean - (-3.402868)), 1e-3)
self.assertLess(abs(sigma_post_mean - 6.212), 1e-3)
self.assertFalse(journal.number_of_simulations == 0)
# use the PMC scheme for T = 2
T, n_sample, n_samples_per_param = 2, 10, 100
sampler = PMC([self.model], [likfun], backend, seed = 1)
journal = sampler.sample([y_obs], T, n_sample, n_samples_per_param, covFactors = np.array([.1,.1]), iniPoints = None)
mu_post_sample, sigma_post_sample, post_weights = np.array(journal.get_parameters()['mu']), np.array(journal.get_parameters()['sigma']), np.array(journal.get_weights())
# Compute posterior mean
mu_post_mean, sigma_post_mean = np.average(mu_post_sample, weights=post_weights, axis=0), np.average(sigma_post_sample, weights=post_weights, axis=0)
# test shape of sample
mu_sample_shape, sigma_sample_shape, weights_sample_shape = np.shape(mu_post_sample), np.shape(mu_post_sample), np.shape(post_weights)
self.assertEqual(mu_sample_shape, (10,1))
self.assertEqual(sigma_sample_shape, (10,1))
self.assertEqual(weights_sample_shape, (10,1))
self.assertLess(abs(mu_post_mean - (-3.03325763) ), 1e-3)
self.assertLess(abs(sigma_post_mean - 6.92124735), 1e-3)
self.assertFalse(journal.number_of_simulations == 0)
def infer_parameters_pmc():
# define observation for true parameters mean=170, 65
rng = np.random.RandomState(seed=1)
y_obs = [np.array(rng.multivariate_normal([170, 65], np.eye(2), 1).reshape(2, ))]
# define prior
from abcpy.continuousmodels import Uniform
mu0 = Uniform([[150], [200]], name="mu0")
mu1 = Uniform([[25], [100]], name="mu1")
# define the model
height_weight_model = NestedBivariateGaussian([mu0, mu1])
# define statistics
from abcpy.statistics import Identity
statistics_calculator = Identity(degree=2, cross=False)
from abcpy.approx_lhd import SynLikelihood
approx_lhd = SynLikelihood(statistics_calculator)
# define sampling scheme
from abcpy.inferences import PMC
sampler = PMC([height_weight_model], [approx_lhd], backend, seed=2)
# sample from scheme
T, n_sample, n_samples_per_param = 2, 10, 10
print('PMC Inferring')
journal = sampler.sample([y_obs], T, n_sample, n_samples_per_param)
return journal
namefile_postfix + ".jnl")
perform_ABC = False
print("\n Using previosly generated journal.")
except FileNotFoundError:
perform_ABC = True
else:
perform_ABC = True
if perform_ABC:
print("Performing inference...")
start = time()
# steps, n_samples = 10000, n_samples_per_param = 100
start = time()
jrnl = sampler.sample([x_obs],
steps,
n_samples=n_samples,
n_samples_per_param=n_samples_per_param,
full_output=1 if full_output else 0)
end = time()
print("It took {:.4f} seconds".format(end - start))
running_time_inference[obs_index] = end - start
np.save(
inference_folder + "running_time_inference" +
namefile_postfix_no_index, running_time_inference)
jrnl.save(inference_folder + "jrnl" + namefile_postfix + ".jnl")
params, weights = extract_params_and_weights_from_journal(jrnl)
means = extract_posterior_mean_from_journal(jrnl)
params_subsample, weights_subsample = subsample_trace_and_weights(
params, weights, size=subsample_size)
if perform_postprocessing:
def infer_parameters():
# The data corresponding to model_1 defined below
grades_obs = [
3.872486707973337, 4.6735380808674405, 3.9703538990858376,
4.11021272048805, 4.211048655421368, 4.154817956586653,
4.0046893064392695, 4.01891381384729, 4.123804757702919,
4.014941267301294, 3.888174595940634, 4.185275142948246,
4.55148774469135, 3.8954427675259016, 4.229264035335705,
3.839949451328312, 4.039402553532825, 4.128077814241238,
4.361488645531874, 4.086279074446419, 4.370801602256129,
3.7431697332475466, 4.459454162392378, 3.8873973643008255,
4.302566721487124, 4.05556051626865, 4.128817316703757,
3.8673704442215984, 4.2174459453805015, 4.202280254493361,
4.072851400451234, 3.795173229398952, 4.310702877332585,
4.376886328810306, 4.183704734748868, 4.332192463368128,
3.9071312388426587, 4.311681374107893, 3.55187913252144,
3.318878360783221, 4.187850500877817, 4.207923106081567,
4.190462065625179, 4.2341474252986036, 4.110228694304768,
4.1589891480847765, 4.0345604687633045, 4.090635481715123,
3.1384654393449294, 4.20375641386518, 4.150452690356067,
4.015304457401275, 3.9635442007388195, 4.075915739179875,
3.5702080541929284, 4.722333310410388, 3.9087618197155227,
4.3990088006390735, 3.968501165774181, 4.047603645360087,
4.109184340976979, 4.132424805281853, 4.444358334346812,
4.097211737683927, 4.288553086265748, 3.8668863066511303,
3.8837108501541007
]
# The prior information changing the class size and social background, depending on school location
from abcpy.continuousmodels import Uniform, Normal
school_location = Uniform([[0.2], [0.3]], )
# The average class size of a certain school
class_size = Normal([[school_location], [0.1]], )
# The social background of a student
background = Normal([[school_location], [0.1]], )
# The grade a student would receive without any bias
grade_without_additional_effects = Normal([[4.5], [0.25]], )
# The grade a student of a certain school receives
final_grade = grade_without_additional_effects - class_size - background
# The data corresponding to model_2 defined below
scholarship_obs = [
2.7179657436207805, 2.124647285937229, 3.07193407853297,
2.335024761813643, 2.871893855192, 3.4332002458233837,
3.649996835818173, 3.50292335102711, 2.815638168018455,
2.3581613289315992, 2.2794821846395568, 2.8725835459926503,
3.5588573782815685, 2.26053126526137, 1.8998143530749971,
2.101110815311782, 2.3482974964831573, 2.2707679029919206,
2.4624550491079225, 2.867017757972507, 3.204249152084959,
2.4489542437714213, 1.875415915801106, 2.5604889644872433,
3.891985093269989, 2.7233633223405205, 2.2861070389383533,
2.9758813233490082, 3.1183403287267755, 2.911814060853062,
2.60896794303205, 3.5717098647480316, 3.3355752461779824,
1.99172284546858, 2.339937680892163, 2.9835630207301636,
2.1684912355975774, 3.014847335983034, 2.7844122961916202,
2.752119871525148, 2.1567428931391635, 2.5803629307680644,
2.7326646074552103, 2.559237193255186, 3.13478196958166,
2.388760269933492, 3.2822443541491815, 2.0114405441787437,
3.0380056368041073, 2.4889680313769724, 2.821660164621084,
3.343985964873723, 3.1866861970287808, 4.4535037154856045,
3.0026333138006027, 2.0675706089352612, 2.3835301730913185,
2.584208398359566, 3.288077633446465, 2.6955853384148183,
2.918315169739928, 3.2464814419322985, 2.1601516779909433,
3.231003347780546, 1.0893224045062178, 0.8032302688764734,
2.868438615047827
]
# A quantity that determines whether a student will receive a scholarship
scholarship_without_additional_effects = Normal([[2], [0.5]], )
# A quantity determining whether a student receives a scholarship, including his social background
final_scholarship = scholarship_without_additional_effects + 3 * background
# Define a summary statistics for final grade and final scholarship
from abcpy.statistics import Identity
statistics_calculator_final_grade = Identity(degree=2, cross=False)
statistics_calculator_final_scholarship = Identity(degree=3, cross=False)
# Define a distance measure for final grade and final scholarship
from abcpy.approx_lhd import SynLikelihood
approx_lhd_final_grade = SynLikelihood(statistics_calculator_final_grade)
approx_lhd_final_scholarship = SynLikelihood(
statistics_calculator_final_scholarship)
# Define a backend
from abcpy.backends import BackendDummy as Backend
backend = Backend()
# Define a perturbation kernel
from abcpy.perturbationkernel import DefaultKernel
kernel = DefaultKernel([school_location, class_size, grade_without_additional_effects, \
background, scholarship_without_additional_effects])
# Define sampling parameters
T, n_sample, n_samples_per_param = 3, 250, 10
# Define sampler
from abcpy.inferences import PMC
sampler = PMC([final_grade, final_scholarship], \
[approx_lhd_final_grade, approx_lhd_final_scholarship], backend, kernel)
# Sample
journal = sampler.sample([grades_obs, scholarship_obs], T, n_sample,
n_samples_per_param)
def test_sample(self):
# setup backend
backend = BackendDummy()
# define a uniform prior distribution
mu = Uniform([[-5.0], [5.0]], name='mu')
sigma = Uniform([[0.0], [10.0]], name='sigma')
# define a Gaussian model
self.model = Normal([mu, sigma])
# define sufficient statistics for the model
stat_calc = Identity(degree=2, cross=0)
# create fake observed data
#y_obs = self.model.forward_simulate(1, np.random.RandomState(1))[0].tolist()
y_obs = [np.array(9.8)]
# Define the likelihood function
likfun = SynLiklihood(stat_calc)
T, n_sample, n_samples_per_param = 1, 10, 100
sampler = PMC([self.model], [likfun], backend, seed=1)
journal = sampler.sample([y_obs],
T,
n_sample,
n_samples_per_param,
covFactors=np.array([.1, .1]),
iniPoints=None)
mu_post_sample, sigma_post_sample, post_weights = np.array(
journal.get_parameters()['mu']), np.array(
journal.get_parameters()['sigma']), np.array(
journal.get_weights())
# Compute posterior mean
mu_post_mean, sigma_post_mean = journal.posterior_mean(
)['mu'], journal.posterior_mean()['sigma']
# test shape of sample
mu_sample_shape, sigma_sample_shape, weights_sample_shape = (len(mu_post_sample), mu_post_sample[0].shape[1]), \
(len(sigma_post_sample),
sigma_post_sample[0].shape[1]), post_weights.shape
self.assertEqual(mu_sample_shape, (10, 1))
self.assertEqual(sigma_sample_shape, (10, 1))
self.assertEqual(weights_sample_shape, (10, 1))
self.assertLess(abs(mu_post_mean - (-3.3711206204663764)), 1e-3)
self.assertLess(abs(sigma_post_mean - 6.518520667688998), 1e-3)
self.assertFalse(journal.number_of_simulations == 0)
# use the PMC scheme for T = 2
T, n_sample, n_samples_per_param = 2, 10, 100
sampler = PMC([self.model], [likfun], backend, seed=1)
journal = sampler.sample([y_obs],
T,
n_sample,
n_samples_per_param,
covFactors=np.array([.1, .1]),
iniPoints=None)
mu_post_sample, sigma_post_sample, post_weights = np.array(
journal.get_parameters()['mu']), np.array(
journal.get_parameters()['sigma']), np.array(
journal.get_weights())
# Compute posterior mean
mu_post_mean, sigma_post_mean = journal.posterior_mean(
)['mu'], journal.posterior_mean()['sigma']
# test shape of sample
mu_sample_shape, sigma_sample_shape, weights_sample_shape = (len(mu_post_sample), mu_post_sample[0].shape[1]), \
(len(sigma_post_sample),
sigma_post_sample[0].shape[1]), post_weights.shape
self.assertEqual(mu_sample_shape, (10, 1))
self.assertEqual(sigma_sample_shape, (10, 1))
self.assertEqual(weights_sample_shape, (10, 1))
self.assertLess(abs(mu_post_mean - (-2.970827684425406)), 1e-3)
self.assertLess(abs(sigma_post_mean - 6.82165619013458), 1e-3)
self.assertFalse(journal.number_of_simulations == 0)
def test_sample(self):
# setup backend
backend = BackendDummy()
# define a uniform prior distribution
lb = np.array([-5, 0])
ub = np.array([5, 10])
prior = Uniform(lb, ub, seed=1)
# define a Gaussian model
model = Gaussian(prior, mu=2.1, sigma=5.0, seed=1)
# define sufficient statistics for the model
stat_calc = Identity(degree=2, cross=0)
# create fake observed data
y_obs = model.simulate(1)
# Define the likelihood function
likfun = SynLiklihood(stat_calc)
# use the PMC scheme for T = 1
mean = np.array([-13.0, .0, 7.0])
cov = np.eye(3)
kernel = MultiNormal(mean, cov, seed=1)
T, n_sample, n_samples_per_param = 1, 10, 100
sampler = PMC(model, likfun, kernel, backend, seed=1)
journal = sampler.sample(y_obs,
T,
n_sample,
n_samples_per_param,
covFactor=np.array([.1, .1]),
iniPoints=None)
samples = (journal.get_parameters(), journal.get_weights())
# Compute posterior mean
mu_post_sample, sigma_post_sample, post_weights = np.array(
samples[0][:, 0]), np.array(samples[0][:,
1]), np.array(samples[1][:,
0])
mu_post_mean, sigma_post_mean = np.average(
mu_post_sample,
weights=post_weights), np.average(sigma_post_sample,
weights=post_weights)
# test shape of sample
mu_sample_shape, sigma_sample_shape, weights_sample_shape = np.shape(
mu_post_sample), np.shape(mu_post_sample), np.shape(post_weights)
self.assertEqual(mu_sample_shape, (10, ))
self.assertEqual(sigma_sample_shape, (10, ))
self.assertEqual(weights_sample_shape, (10, ))
self.assertLess(abs(mu_post_mean - (-1.48953333102)), 1e-10)
self.assertLess(abs(sigma_post_mean - 6.50695612708), 1e-10)
# use the PMC scheme for T = 2
T, n_sample, n_samples_per_param = 2, 10, 100
sampler = PMC(model, likfun, kernel, backend, seed=1)
journal = sampler.sample(y_obs,
T,
n_sample,
n_samples_per_param,
covFactor=np.array([.1, .1]),
iniPoints=None)
samples = (journal.get_parameters(), journal.get_weights())
# Compute posterior mean
mu_post_sample, sigma_post_sample, post_weights = np.asarray(
samples[0][:, 0]), np.asarray(samples[0][:, 1]), np.asarray(
samples[1][:, 0])
mu_post_mean, sigma_post_mean = np.average(
mu_post_sample,
weights=post_weights), np.average(sigma_post_sample,
weights=post_weights)
# test shape of sample
mu_sample_shape, sigma_sample_shape, weights_sample_shape = np.shape(
mu_post_sample), np.shape(mu_post_sample), np.shape(post_weights)
self.assertEqual(mu_sample_shape, (10, ))
self.assertEqual(sigma_sample_shape, (10, ))
self.assertEqual(weights_sample_shape, (10, ))
self.assertLess(abs(mu_post_mean - (-1.4033145848)), 1e-10)
self.assertLess(abs(sigma_post_mean - 7.05175546876), 1e-10)