def test_sample(self):
# setup backend
dummy = 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)
# define a distance function
dist_calc = Euclidean(stat_calc)
# create fake observed data
y_obs = model.simulate(1)
# use the rejection sampling scheme
sampler = RejectionABC(model, dist_calc, dummy, seed=1)
journal = sampler.sample(y_obs, 10, 1, 0.1)
samples = journal.get_parameters()
# test shape of samples
samples_shape = np.shape(samples)
self.assertEqual(samples_shape, (10, 2))
# Compute posterior mean
self.assertEqual((np.average(np.asarray(
samples[:, 0])), np.average(np.asarray(samples[:, 1]))),
(1.6818856447333246, 8.4384177826766518))
class PenLogRegTests(unittest.TestCase):
def setUp(self):
self.prior = Uniform([-5.0, 5.0], [5.0, 10.0], seed=1)
self.model = Gaussian(self.prior, mu=1.1, sigma=1.0, seed=1)
self.stat_calc = Identity(degree=2, cross=0)
self.likfun = PenLogReg(self.stat_calc,
self.model,
n_simulate=100,
n_folds=10)
def test_likelihood(self):
#Checks whether wrong input type produces error message
self.assertRaises(TypeError, self.likfun.likelihood, 3.4, [2, 1])
self.assertRaises(TypeError, self.likfun.likelihood, [2, 4], 3.4)
# create observed data
y_obs = self.model.simulate(1)
# create fake simulated data
self.model.set_parameters(np.array([1.1, 1.0]))
y_sim = self.model.simulate(100)
comp_likelihood = self.likfun.likelihood(y_obs, y_sim)
expected_likelihood = 4.39965563272
# This checks whether it computes a correct value and dimension is right
self.assertLess(comp_likelihood - expected_likelihood, 10e-2)
class SemiautomaticTests(unittest.TestCase):
def setUp(self):
self.stat_calc = Identity(degree = 1, cross = 0)
# define prior and model
prior = Uniform([150, 5],[200, 25])
self.model = Gaussian(prior, seed = 1)
# define backend
self.backend = Backend()
# define statistics
self.statistics_cal = Identity(degree = 2, cross = False)
#Initialize summaryselection
self.summaryselection = Semiautomatic(self.model, self.statistics_cal, self.backend, n_samples = 1000, seed = 1)
def test_transformation(self):
#Transform statistics extraction
self.statistics_cal.statistics = lambda x, f2=self.summaryselection.transformation, f1=self.statistics_cal.statistics: f2(f1(x))
y_obs = self.model.simulate(10)
extracted_statistics_10 = self.statistics_cal.statistics(y_obs)
self.assertEqual(np.shape(extracted_statistics_10), (10,2))
y_obs = self.model.simulate(1)
extracted_statistics_1 = self.statistics_cal.statistics(y_obs)
self.assertLess(extracted_statistics_1[0,0] - 111.012664458, 10e-2)
self.assertLess(extracted_statistics_1[0,1] - (-63.224510811), 10e-2)
def setUp(self):
self.prior = Uniform([-5.0, 5.0], [5.0, 10.0], seed=1)
self.model = Gaussian(self.prior, mu=1.1, sigma=1.0, seed=1)
self.stat_calc = Identity(degree=2, cross=0)
self.likfun = PenLogReg(self.stat_calc,
self.model,
n_simulate=100,
n_folds=10)
def setUp(self):
self.stat_calc = Identity(degree = 1, cross = 0)
# define prior and model
prior = Uniform([150, 5],[200, 25])
self.model = Gaussian(prior, seed = 1)
# define backend
self.backend = Backend()
# define statistics
self.statistics_cal = Identity(degree = 2, cross = False)
#Initialize summaryselection
self.summaryselection = Semiautomatic(self.model, self.statistics_cal, self.backend, n_samples = 1000, seed = 1)
def infer_parameters():
# define observation for true parameters mean=170, std=15
y_obs = [
160.82499176, 167.24266737, 185.71695756, 153.7045709, 163.40568812,
140.70658699, 169.59102084, 172.81041696, 187.38782738, 179.66358934,
176.63417241, 189.16082803, 181.98288443, 170.18565017, 183.78493886,
166.58387299, 161.9521899, 155.69213073, 156.17867343, 144.51580379,
170.29847515, 197.96767899, 153.36646527, 162.22710198, 158.70012047,
178.53470703, 170.77697743, 164.31392633, 165.88595994, 177.38083686,
146.67058471763457, 179.41946565658628, 238.02751620619537,
206.22458790620766, 220.89530574344568, 221.04082532837026,
142.25301427453394, 261.37656571434275, 171.63761180867033,
210.28121820385866, 237.29130237612236, 175.75558340169619,
224.54340549862235, 197.42448680731226, 165.88273684581381,
166.55094082844519, 229.54308602661584, 222.99844054358519,
185.30223966014586, 152.69149367593846, 206.94372818527413,
256.35498655339154, 165.43140916577741, 250.19273595481803,
148.87781549665536, 223.05547559193792, 230.03418198709608,
146.13611923127021, 138.24716809523139, 179.26755740864527,
141.21704876815426, 170.89587081800852, 222.96391329259626,
188.27229523693822, 202.67075179617672, 211.75963110985992,
217.45423324370509
]
# define prior
from abcpy.distributions import Uniform
prior = Uniform([150, 5], [200, 25], seed=1)
# define the model
from abcpy.models import Gaussian
model = Gaussian(prior, seed=1)
# define statistics
from abcpy.statistics import Identity
statistics_calculator = Identity(degree=2, cross=False)
# define distance
from abcpy.distances import LogReg
distance_calculator = LogReg(statistics_calculator)
# define kernel
from abcpy.distributions import MultiStudentT
mean, cov, df = np.array([.0, .0]), np.eye(2), 3.
kernel = MultiStudentT(mean, cov, df, seed=1)
# define sampling scheme
from abcpy.inferences import PMCABC
sampler = PMCABC(model, distance_calculator, kernel, backend, seed=1)
# sample from scheme
T, n_sample, n_samples_per_param = 3, 250, 10
eps_arr = np.array([.75])
epsilon_percentile = 10
journal = sampler.sample(y_obs, T, eps_arr, n_sample, n_samples_per_param,
epsilon_percentile)
return journal
class GaussianTests(unittest.TestCase):
def setUp(self):
self.prior = Uniform([-1.0, 0.0], [1.0, 1.0], seed=1)
self.model = Gaussian(self.prior, 0, 1, seed=1)
def test_simulate(self):
samples = self.model.simulate(10)
self.assertIsInstance(samples, list)
expected_output = [1.6243453636632417, -0.61175641365007538, -0.5281717522634557, \
-1.0729686221561705, 0.86540762932467852, -2.3015386968802827, \
1.74481176421648, -0.76120690089510279, 0.31903909605709857, \
-0.24937037547741009]
self.assertEqual(samples, expected_output)
def test_get_parameters(self):
self.model.sample_from_prior()
params = self.model.get_parameters()
# test shape of parameters
param_len = len(params)
self.assertEqual(param_len, 2)
def test_set_parameters(self):
self.assertRaises(TypeError, self.model.set_parameters, 3.4)
self.assertFalse(self.model.set_parameters([1, 3, 2]))
self.assertFalse(self.model.set_parameters([2, -1]))
def setUp(self):
# find spark and initialize it
self.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
self.model = Gaussian(prior, mu=2.1, sigma=5.0, seed=1)
# define a distance function
stat_calc = Identity(degree=2, cross=0)
self.dist_calc = Euclidean(stat_calc)
# create fake observed data
self.observation = self.model.simulate(1)
# define kernel
mean = np.array([-13.0, .0, 7.0])
cov = np.eye(3)
self.kernel = MultiNormal(mean, cov, seed=1)
def setUp(self):
# define observation for true parameters mean=170, std=15
self.y_obs = [160.82499176]
self.model_array = [None] * 2
#Model 1: Gaussian
# define prior
prior = Uniform([150, 5], [200, 25])
# define the model
self.model_array[0] = Gaussian(prior, seed=1)
#Model 2: Student t
# define prior
prior = Uniform([150, 1], [200, 30])
# define the model
self.model_array[1] = Student_t(prior, seed=1)
# define statistics
self.statistics_calc = Identity(degree=2, cross=False)
# define backend
self.backend = Backend()
def setUp(self):
self.prior = Uniform([-1.0, 0.0], [1.0, 1.0], seed=1)
self.model = Gaussian(self.prior, 0, 1, seed=1)
def setUp(self):
self.prior = Uniform([-5.0, 5.0], [5.0, 10.0], seed=1)
self.model = Gaussian(self.prior, mu=1.1, sigma=1.0, seed=1)
self.stat_calc = Identity(degree=2, cross=0)
self.likfun = SynLiklihood(self.stat_calc)
class RSMCABCTests(unittest.TestCase):
def setUp(self):
# find spark and initialize it
self.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
self.model = Gaussian(prior, mu=2.1, sigma=5.0, seed=1)
# define a distance function
stat_calc = Identity(degree=2, cross=0)
self.dist_calc = Euclidean(stat_calc)
# create fake observed data
self.observation = self.model.simulate(1)
# define kernel
mean = np.array([-13.0, .0, 7.0])
cov = np.eye(3)
self.kernel = MultiNormal(mean, cov, seed=1)
def test_sample(self):
# use the RSMCABC scheme for T = 1
steps, n_sample, n_simulate = 1, 10, 1
sampler = RSMCABC(self.model,
self.dist_calc,
self.kernel,
self.backend,
seed=1)
journal = sampler.sample(self.observation, steps, n_sample, n_simulate)
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.assertEqual((mu_post_mean, sigma_post_mean), (,))
# use the RSMCABC scheme for T = 2
steps, n_sample, n_simulate = 2, 10, 1
sampler = RSMCABC(self.model,
self.dist_calc,
self.kernel,
self.backend,
seed=1)
journal = sampler.sample(self.observation, steps, n_sample, n_simulate)
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(mu_post_mean - (-0.349310337252), 10e-2)
self.assertLess(sigma_post_mean - 6.30221177368, 10e-2)
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)
class PMCABCTests(unittest.TestCase):
def setUp(self):
# find spark and initialize it
self.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
self.model = Gaussian(prior, mu=2.1, sigma=5.0, seed=1)
# define a distance function
stat_calc = Identity(degree=2, cross=0)
self.dist_calc = Euclidean(stat_calc)
# create fake observed data
self.observation = self.model.simulate(1)
# define kernel
mean = np.array([-13.0, .0, 7.0])
cov = np.eye(3)
self.kernel = MultiNormal(mean, cov, seed=1)
def test_calculate_weight(self):
n_samples = 2
rc = _RemoteContextPMCABC(self.backend, self.model, self.dist_calc,
self.kernel, self.observation, n_samples, 1)
theta = np.array([1.0])
weight = rc._calculate_weight(theta)
self.assertEqual(weight, 0.5)
accepted_parameters = np.array([[1.0], [1.0 + np.sqrt(2)]])
accepted_weights = np.array([[.5], [.5]])
accepted_cov_mat = np.array([[1.0]])
rc._update_broadcasts(self.backend, accepted_parameters,
accepted_weights, accepted_cov_mat)
weight = rc._calculate_weight(theta)
expected_weight = (2.0 * np.sqrt(2.0 * np.pi)) / (
(1 + np.exp(-1)) * 100)
self.assertEqual(weight, expected_weight)
def test_sample(self):
# use the PMCABC scheme for T = 1
T, n_sample, n_simulate, eps_arr, eps_percentile = 1, 10, 1, [.1], 10
sampler = PMCABC(self.model,
self.dist_calc,
self.kernel,
self.backend,
seed=1)
journal = sampler.sample(self.observation, T, eps_arr, n_sample,
n_simulate, eps_percentile)
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.assertEqual((mu_post_mean, sigma_post_mean), (,))
# use the PMCABC scheme for T = 2
T, n_sample, n_simulate, eps_arr, eps_percentile = 2, 10, 1, [.1,
.05], 10
sampler = PMCABC(self.model,
self.dist_calc,
self.kernel,
self.backend,
seed=1)
journal = sampler.sample(self.observation, T, eps_arr, n_sample,
n_simulate, eps_percentile)
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(mu_post_mean - 3.80593164247, 10e-2)
self.assertLess(sigma_post_mean - 7.21421951262, 10e-2)