def test_sample(self):
# use the PMCABC scheme for T = 1
T, n_sample, n_simulate, eps_arr, eps_percentile = 1, 10, 1, [10], 10
sampler = PMCABC([self.model], [self.dist_calc], self.backend, seed=1)
journal = sampler.sample([self.observation], T, eps_arr, n_sample,
n_simulate, eps_percentile)
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(mu_post_mean - 0.03713, 10e-2)
self.assertLess(sigma_post_mean - 7.727, 10e-2)
#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, [10,
5], 10
sampler = PMCABC([self.model], [self.dist_calc], self.backend, seed=1)
sampler.sample_from_prior(rng=np.random.RandomState(1))
journal = sampler.sample([self.observation], T, eps_arr, n_sample,
n_simulate, eps_percentile)
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(mu_post_mean - 0.9356, 10e-2)
self.assertLess(sigma_post_mean - 7.819, 10e-2)
self.assertFalse(journal.number_of_simulations == 0)
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)
def infer_parameters_pmcabc():
# 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)
# define distance
from abcpy.distances import Euclidean
distance_calculator = Euclidean(statistics_calculator)
# define sampling scheme
from abcpy.inferences import PMCABC
sampler = PMCABC([height_weight_model], [distance_calculator], backend, seed=1)
# sample from scheme
T, n_sample, n_samples_per_param = 2, 10, 1
eps_arr = np.array([10000])
epsilon_percentile = 95
print('PMCABC Inferring')
journal = sampler.sample([y_obs], T, eps_arr, n_sample, n_samples_per_param, epsilon_percentile)
return journal
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])
# define the model
model = Gaussian(prior)
# 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)
# define backend
from abcpy.backends import BackendDummy as Backend
backend = Backend()
# define sampling scheme
from abcpy.inferences import PMCABC
sampler = PMCABC(model, distance_calculator, kernel, backend)
# 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
def infer_parameters(backend,
steps=3,
n_sample=250,
n_samples_per_param=10,
logging_level=logging.WARN):
logging.basicConfig(level=logging_level)
# define observation for true parameters mean=170, std=15
height_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.continuousmodels import Uniform
mu = Uniform([[150], [200]], name='mu')
sigma = Uniform([[5], [25]], name='sigma')
# define the model
from abcpy.continuousmodels import Normal
height = Normal([mu, sigma], name='height')
# 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, seed=42)
# define sampling scheme
from abcpy.inferences import PMCABC
sampler = PMCABC([height], [distance_calculator], backend, seed=1)
# sample from scheme
eps_arr = np.array([.75])
epsilon_percentile = 10
journal = sampler.sample([height_obs], steps, eps_arr, n_sample,
n_samples_per_param, epsilon_percentile)
return journal
def test_restart_from_journal(self):
# test with value of eps_arr_2 > percentile of distances
n_sample, n_simulate, eps_arr, eps_percentile = 10, 1, [10, 5], 10
# 2 steps with intermediate journal:
sampler = PMCABC([self.model], [self.dist_calc], self.backend, seed=1)
sampler.sample_from_prior(rng=np.random.RandomState(1))
journal_intermediate = sampler.sample([self.observation], 1,
[eps_arr[0]], n_sample,
n_simulate, eps_percentile)
journal_intermediate.save("tmp.jnl")
journal_final_1 = sampler.sample([self.observation],
1, [eps_arr[1]],
n_sample,
n_simulate,
eps_percentile,
journal_file="tmp.jnl")
# 2 steps directly
sampler = PMCABC([self.model], [self.dist_calc], self.backend, seed=1)
sampler.sample_from_prior(rng=np.random.RandomState(1))
journal_final_2 = sampler.sample([self.observation], 2, eps_arr,
n_sample, n_simulate, eps_percentile)
self.assertEqual(journal_final_1.configuration["epsilon_arr"],
journal_final_2.configuration["epsilon_arr"])
self.assertEqual(journal_final_1.posterior_mean()['mu'],
journal_final_2.posterior_mean()['mu'])
# test with value of eps_arr_2 < percentile of distances
n_sample, n_simulate, eps_arr, eps_percentile = 10, 1, [10, 1], 10
# 2 steps with intermediate journal:
sampler = PMCABC([self.model], [self.dist_calc], self.backend, seed=1)
sampler.sample_from_prior(rng=np.random.RandomState(1))
journal_intermediate = sampler.sample([self.observation], 1,
[eps_arr[0]], n_sample,
n_simulate, eps_percentile)
journal_intermediate.save("tmp.jnl")
journal_final_1 = sampler.sample([self.observation],
1, [eps_arr[1]],
n_sample,
n_simulate,
eps_percentile,
journal_file="tmp.jnl")
# 2 steps directly
sampler = PMCABC([self.model], [self.dist_calc], self.backend, seed=1)
sampler.sample_from_prior(rng=np.random.RandomState(1))
journal_final_2 = sampler.sample([self.observation], 2, eps_arr,
n_sample, n_simulate, eps_percentile)
self.assertEqual(journal_final_1.configuration["epsilon_arr"],
journal_final_2.configuration["epsilon_arr"])
self.assertEqual(journal_final_1.posterior_mean()['mu'],
journal_final_2.posterior_mean()['mu'])
def infer_parameters():
# define backend
# Note, the dummy backend does not parallelize the code!
from abcpy.backends import BackendDummy as Backend
backend = Backend()
# define observation for true parameters mean=170, std=15
height_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.continuousmodels import Uniform
mu = Uniform([[150], [200]], )
sigma = Uniform([[5], [25]], )
# define the model
from abcpy.continuousmodels import Normal
height = Normal([mu, sigma], )
# define statistics
from abcpy.statistics import Identity
statistics_calculator = Identity(degree=3, cross=True)
# Learn the optimal summary statistics using Semiautomatic summary selection
from abcpy.summaryselections import Semiautomatic
summary_selection = Semiautomatic([height],
statistics_calculator,
backend,
n_samples=1000,
n_samples_per_param=1,
seed=1)
# Redefine the statistics function
statistics_calculator.statistics = lambda x, f2=summary_selection.transformation, \
f1=statistics_calculator.statistics: f2(f1(x))
# define distance
from abcpy.distances import LogReg
distance_calculator = LogReg(statistics_calculator)
# define kernel
from abcpy.perturbationkernel import DefaultKernel
kernel = DefaultKernel([mu, sigma])
# define sampling scheme
from abcpy.inferences import PMCABC
sampler = PMCABC([height], [distance_calculator], backend, kernel, 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([height_obs], T, eps_arr, n_sample,
n_samples_per_param, epsilon_percentile)
return journal
def run(self,
jobID,
n_sample,
steps,
epsilon_init,
epsilon_percentile,
save_output=True,
parallelize=False):
assert self._prior_set is True
if parallelize:
backend = BackendMPI()
else:
backend = BackendDummy()
steps_minus_1 = steps - 1
epsilon_init = [epsilon_init] + [None] * steps_minus_1
sim = Simulator(self, self.to_sample_list, self.priors_over_hood)
sampler = PMCABC([sim], [self._distance_calc], backend, seed=1)
journal_filename = self.output_folder + 'journal_' + jobID
if os.path.exists(journal_filename):
f = open(journal_filename, 'rb')
journal_init = pickle.load(f)
f.close()
print('loading from journal file..')
stat = journal_init.get_distances()
print(
str(epsilon_percentile) +
'th percentile of initial distances: ',
np.percentile(stat, epsilon_percentile))
else:
print('first_iteration...')
journal_init = None
journal = sampler.sample([self._obs],
steps,
epsilon_init,
n_sample,
1,
epsilon_percentile,
journal_class=journal_init)
stat = journal.get_distances()
print(
str(epsilon_percentile) + 'th percentile of new distances: ',
np.percentile(stat, epsilon_percentile))
print('obtained ' + str(n_sample) + ' samples from ' +
str(journal.number_of_simulations[0]) + ' realizations')
if save_output:
f = open(journal_filename, 'wb')
pickle.dump(journal, f)
f.close()
self._prior_set = False
return journal
def infer_parameters(steps=2, n_sample=50, n_samples_per_param=1, logging_level=logging.WARN):
"""Perform inference for this example.
Parameters
----------
steps : integer, optional
Number of iterations in the sequential PMCABC algorithm ("generations"). The default value is 3
n_samples : integer, optional
Number of posterior samples to generate. The default value is 250.
n_samples_per_param : integer, optional
Number of data points in each simulated data set. The default value is 10.
Returns
-------
abcpy.output.Journal
A journal containing simulation results, metadata and optionally intermediate results.
"""
logging.basicConfig(level=logging_level)
# define backend
# Note, the dummy backend does not parallelize the code!
from abcpy.backends import BackendDummy as Backend
backend = Backend()
# define observation for true parameters mean=170, std=15
height_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.continuousmodels import Uniform
mu = Uniform([[150], [200]], name="mu")
sigma = Uniform([[5], [25]], name="sigma")
# define the model
from abcpy.continuousmodels import Normal
height = Normal([mu, sigma], )
# 1) generate simulations from prior
from abcpy.inferences import DrawFromPrior
draw_from_prior = DrawFromPrior([height], backend=backend)
# notice the use of the `.sample_par_sim_pairs` method rather than `.sample` to obtain data suitably formatted
# for the summary statistics learning routines
parameters, simulations = draw_from_prior.sample_par_sim_pairs(100, n_samples_per_param=1)
# if you want to use the test loss to do early stopping in the training:
parameters_val, simulations_val = draw_from_prior.sample_par_sim_pairs(100, n_samples_per_param=1)
# discard the mid dimension (n_samples_per_param, as the StatisticsLearning classes use that =1)
simulations = simulations.reshape(simulations.shape[0], simulations.shape[2])
simulations_val = simulations_val.reshape(simulations_val.shape[0], simulations_val.shape[2])
# 2) now train the NNs with the different methods with the generated data
from abcpy.statistics import Identity
identity = Identity() # to apply before computing the statistics
logging.info("semiNN")
from abcpy.statisticslearning import SemiautomaticNN, TripletDistanceLearning
semiNN = SemiautomaticNN([height], identity, backend=backend, parameters=parameters,
simulations=simulations, parameters_val=parameters_val, simulations_val=simulations_val,
early_stopping=True, # early stopping
seed=1, n_epochs=10, scale_samples=False, use_tqdm=False)
logging.info("triplet")
triplet = TripletDistanceLearning([height], identity, backend=backend, parameters=parameters,
simulations=simulations, parameters_val=parameters_val,
simulations_val=simulations_val,
early_stopping=True, # early stopping
seed=1, n_epochs=10, scale_samples=True, use_tqdm=False)
# 3) save and re-load NNs:
# get the statistics from the already fit StatisticsLearning object 'semiNN':
learned_seminn_stat = semiNN.get_statistics()
learned_triplet_stat = triplet.get_statistics()
# this has a save net method:
learned_seminn_stat.save_net("seminn_net.pth")
# if you used `scale_samples=True` in learning the NNs, need to provide a path where pickle stores the scaler too:
learned_triplet_stat.save_net("triplet_net.pth", path_to_scaler="scaler.pkl")
# to reload: need to use the Neural Embedding statistics fromFile; this needs to know which kind of NN it is using;
# need therefore to pass either the input/output size (it data size and number parameters) or the network class if
# that was specified explicitly in the StatisticsLearning class. Check the docstring for NeuralEmbedding.fromFile
# for more details.
from abcpy.statistics import NeuralEmbedding
learned_seminn_stat_loaded = NeuralEmbedding.fromFile("seminn_net.pth", input_size=1, output_size=2)
learned_triplet_stat_loaded = NeuralEmbedding.fromFile("triplet_net.pth", input_size=1, output_size=2,
path_to_scaler="scaler.pkl")
# 4) you can optionally rescale the different summary statistics be their standard deviation on a reference dataset
# of simulations. To do this, it is enough to pass at initialization the reference dataset, and the rescaling will
# be applied every time the statistics is computed on some simulation or observation.
learned_triplet_stat_loaded = NeuralEmbedding.fromFile("triplet_net.pth", input_size=1, output_size=2,
path_to_scaler="scaler.pkl",
reference_simulations=simulations_val)
# 5) perform inference
# define distance
from abcpy.distances import Euclidean
distance_calculator = Euclidean(learned_seminn_stat_loaded)
# define kernel
from abcpy.perturbationkernel import DefaultKernel
kernel = DefaultKernel([mu, sigma])
# define sampling scheme
from abcpy.inferences import PMCABC
sampler = PMCABC([height], [distance_calculator], backend, kernel, seed=1)
eps_arr = np.array([500]) # starting value of epsilon; the smaller, the slower the algorithm.
# at each iteration, take as epsilon the epsilon_percentile of the distances obtained by simulations at previous
# iteration from the observation
epsilon_percentile = 10
journal = sampler.sample([height_obs], steps, eps_arr, n_sample, n_samples_per_param, epsilon_percentile)
return journal
def infer_parameters(steps=3,
n_sample=250,
n_samples_per_param=10,
logging_level=logging.WARN):
"""Perform inference for this example.
Parameters
----------
steps : integer, optional
Number of iterations in the sequential PMCABC algoritm ("generations"). The default value is 3
n_samples : integer, optional
Number of posterior samples to generate. The default value is 250.
n_samples_per_param : integer, optional
Number of data points in each simulated data set. The default value is 10.
Returns
-------
abcpy.output.Journal
A journal containing simulation results, metadata and optionally intermediate results.
"""
logging.basicConfig(level=logging_level)
# 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.continuousmodels import Uniform
mu = Uniform([[150], [200]], name="mu")
sigma = Uniform([[5], [25]], name="sigma")
# define the model
model = Gaussian([mu, sigma], name='height')
# 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, seed=42)
# define backend
from abcpy.backends import BackendDummy as Backend
backend = Backend()
# define sampling scheme
from abcpy.inferences import PMCABC
sampler = PMCABC([model], [distance_calculator], backend, seed=1)
# sample from scheme
eps_arr = np.array([.75])
epsilon_percentile = 10
journal = sampler.sample([y_obs], steps, eps_arr, n_sample,
n_samples_per_param, epsilon_percentile)
return journal
def infer_parameters(steps=3,
n_sample=250,
n_samples_per_param=10,
logging_level=logging.WARN):
"""Perform inference for this example.
Parameters
----------
steps : integer, optional
Number of iterations in the sequential PMCABC algoritm ("generations"). The default value is 3
n_samples : integer, optional
Number of posterior samples to generate. The default value is 250.
n_samples_per_param : integer, optional
Number of data points in each simulated data set. The default value is 10.
Returns
-------
abcpy.output.Journal
A journal containing simulation results, metadata and optionally intermediate results.
"""
logging.basicConfig(level=logging_level)
# define backend
# Note, the dummy backend does not parallelize the code!
from abcpy.backends import BackendDummy as Backend
backend = Backend()
# define observation for true parameters mean=170, std=15
height_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.continuousmodels import Uniform
mu = Uniform([[150], [200]], name="mu")
sigma = Uniform([[5], [25]], name="sigma")
# define the model
from abcpy.continuousmodels import Normal
height = Normal([mu, sigma], )
# define statistics
from abcpy.statistics import Identity
statistics_calculator = Identity(degree=3, cross=True)
# Learn the optimal summary statistics using Semiautomatic summary selection
from abcpy.statisticslearning import Semiautomatic
statistics_learning = Semiautomatic([height],
statistics_calculator,
backend,
n_samples=1000,
n_samples_per_param=1,
seed=1)
# Redefine the statistics function
new_statistics_calculator = statistics_learning.get_statistics()
# Learn the optimal summary statistics using SemiautomaticNN summary selection;
# we use 200 samples as a validation set for early stopping:
from abcpy.statisticslearning import SemiautomaticNN
statistics_learning = SemiautomaticNN([height],
statistics_calculator,
backend,
n_samples=1000,
n_samples_val=200,
n_samples_per_param=1,
seed=1,
early_stopping=True)
# Redefine the statistics function
new_statistics_calculator = statistics_learning.get_statistics()
# define distance
from abcpy.distances import Euclidean
distance_calculator = Euclidean(new_statistics_calculator)
# define kernel
from abcpy.perturbationkernel import DefaultKernel
kernel = DefaultKernel([mu, sigma])
# define sampling scheme
from abcpy.inferences import PMCABC
sampler = PMCABC([height], [distance_calculator], backend, kernel, seed=1)
eps_arr = np.array([
500
]) # starting value of epsilon; the smaller, the slower the algorithm.
# at each iteration, take as epsilon the epsilon_percentile of the distances obtained by simulations at previous
# iteration from the observation
epsilon_percentile = 10
journal = sampler.sample([height_obs], steps, eps_arr, n_sample,
n_samples_per_param, epsilon_percentile)
return journal
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 the teacher student ratio, depending on the yearly budget of the school
from abcpy.continuousmodels import Uniform, Normal
school_budget = Uniform([[1], [10]], name = 'school_budget')
# The average class size of a certain school
class_size = Normal([[800*school_budget], [1]], name = 'class_size')
# The number of teachers in the school
no_teacher = Normal([[20*school_budget], [1]], name = 'no_teacher')
# The grade a student would receive without any bias
grade_without_additional_effects = Normal([[4.5], [0.25]], name = 'grade_without_additional_effects')
# The grade a student of a certain school receives
final_grade = grade_without_additional_effects - .001 * class_size + .02 * no_teacher
# 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]], name = 'schol_without_additional_effects')
# A quantity determining whether a student receives a scholarship, including his social teacher_student_ratio
final_scholarship = scholarship_without_additional_effects + .03 * no_teacher
# 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.distances import Euclidean
distance_calculator_final_grade = Euclidean(statistics_calculator_final_grade)
distance_calculator_final_scholarship = Euclidean(statistics_calculator_final_scholarship)
# Define a backend
from abcpy.backends import BackendDummy as Backend
backend = Backend()
# Define kernels
from abcpy.perturbationkernel import MultivariateNormalKernel, MultivariateStudentTKernel
kernel_1 = MultivariateNormalKernel([school_budget,\
scholarship_without_additional_effects, grade_without_additional_effects])
kernel_2 = MultivariateStudentTKernel([class_size, no_teacher], df=3)
# Join the defined kernels
from abcpy.perturbationkernel import JointPerturbationKernel
kernel = JointPerturbationKernel([kernel_1, kernel_2])
# Define sampling parameters
T, n_sample, n_samples_per_param = 3, 250, 10
eps_arr = np.array([.75])
epsilon_percentile = 10
# Define sampler
from abcpy.inferences import PMCABC
sampler = PMCABC([final_grade, final_scholarship], [distance_calculator, distance_calculator], backend, kernel)
# Sample
journal = sampler.sample([y_obs_grades, y_obs_scholarship], T, eps_arr, n_sample, n_samples_per_param, epsilon_percentile)
def ABC_inference(algorithm,
model,
observation,
distance_calculator,
eps,
n_samples,
n_steps,
backend,
seed=None,
full_output=0,
**kwargs):
"""NB: eps represents initial value of epsilon for PMCABC and SABC; represents the single eps value for RejectionABC
and represents the final value for SMCABC."""
start = time()
if algorithm == "PMCABC":
sampler = PMCABC([model], [distance_calculator], backend, seed=seed)
jrnl = sampler.sample([[observation]],
n_steps,
np.array([eps]),
n_samples=n_samples,
full_output=full_output,
**kwargs)
if algorithm == "APMCABC":
sampler = APMCABC([model], [distance_calculator], backend, seed=seed)
jrnl = sampler.sample([[observation]],
n_steps,
n_samples=n_samples,
full_output=full_output,
**kwargs)
elif algorithm == "SABC":
sampler = SABC([model], [distance_calculator], backend, seed=seed)
jrnl = sampler.sample([[observation]],
n_steps,
eps,
n_samples=n_samples,
full_output=full_output,
**kwargs)
elif algorithm == "RejectionABC":
sampler = RejectionABC([model], [distance_calculator],
backend,
seed=seed)
jrnl = sampler.sample([[observation]],
eps,
n_samples=n_samples,
full_output=full_output,
**kwargs)
elif algorithm == "SMCABC":
# for this usually a larger number of steps is required. alpha can be left to 0.95, covFactor=2 and
# resample=None. epsilon_final is instead important to fix!
sampler = SMCABC([model], [distance_calculator], backend, seed=seed)
# sample(observations, steps, n_samples=10000, n_samples_per_param=1, epsilon_final=0.1, alpha=0.95,
# covFactor=2, resample=None, full_output=0, which_mcmc_kernel=0, journal_file=None)
jrnl = sampler.sample([[observation]],
n_steps,
n_samples=n_samples,
full_output=full_output,
epsilon_final=eps,
**kwargs)
print("It took ", time() - start, " seconds.")
return jrnl