def standard_sample(make_hypothesis, make_data, skip=9, show=True, N=100, save_top='top.pkl', alsoprint='None', **kwargs):
"""
Just a simplified interface for sampling, allowing printing (showing), returning the top, and saving.
This is used by many examples, and is meant to easily allow running with a variety of parameters.
NOTE: This skip is a skip *only* on printing
**kwargs get passed to sampler
"""
if LOTlib.SIG_INTERRUPTED:
return TopN() # So we don't waste time!
h0 = make_hypothesis()
data = make_data()
best_hypotheses = TopN(N=N)
f = eval(alsoprint)
for i, h in enumerate(break_ctrlc(MHSampler(h0, data, **kwargs))):
best_hypotheses.add(h)
if show and i%(skip+1) == 0:
print i, \
h.posterior_score, \
h.prior, \
h.likelihood, \
f(h) if f is not None else '', \
qq(cleanFunctionNodeString(h))
if save_top is not None:
print "# Saving top hypotheses"
with open(save_top, 'w') as f:
pickle.dump(best_hypotheses, f)
return best_hypotheses
def run():
from LOTlib import lot_iter
from LOTlib.Inference.Proposals.RegenerationProposal import RegenerationProposal
#mp = MixtureProposal([RegenerationProposal(grammar), InsertDeleteProposal(grammar)] )
mp = RegenerationProposal(grammar)
from LOTlib.Hypotheses.LOTHypothesis import LOTHypothesis
h0 = LOTHypothesis(grammar, args=['x', 'y'], ALPHA=0.999, proposal_function=mp) # alpha here trades off with the amount of data. Currently assuming no noise, but that's not necessary
from LOTlib.Inference.MetropolisHastings import MHSampler
for h in lot_iter(MHSampler(h0, data, skip=100)):
print h.posterior_score, h.likelihood, h.prior, cleanFunctionNodeString(h)
def standard_sample(make_hypothesis, make_data, show_skip=9, show=True, N=100, save_top='top.pkl', alsoprint='None', **kwargs):
"""
Just a simplified interface for sampling, allowing printing (showing), returning the top, and saving.
This is used by many examples, and is meant to easily allow running with a variety of parameters.
NOTE: This skip is a skip *only* on printing
**kwargs get passed to sampler
"""
if LOTlib.SIG_INTERRUPTED:
return TopN() # So we don't waste time!
h0 = make_hypothesis()
data = make_data()
best_hypotheses = TopN(N=N)
f = eval(alsoprint)
sampler = MHSampler(h0, data, **kwargs)
# # TODO change acceptance temperature over times
# sampler.acceptance_temperature = 0.5
for i, h in enumerate(break_ctrlc(sampler)):
# if i % 10000 == 0 and i != 0:
# sampler.acceptance_temperature = min(1.0, sampler.acceptance_temperature+0.1)
# print '='*50
# print 'change acc temperature to', sampler.acceptance_temperature
best_hypotheses.add(h)
if show and i%(show_skip+1) == 0:
print i, \
h.posterior_score, \
h.prior, \
h.likelihood, \
f(h) if f is not None else '', \
qq(cleanFunctionNodeString(h))
if save_top is not None:
print "# Saving top hypotheses"
with open(save_top, 'w') as f:
pickle.dump(best_hypotheses, f)
return best_hypotheses
FunctionData(input=[ "n2", "n1" ], output=False),
FunctionData(input=[ "n2", "n2" ], output=False),
FunctionData(input=[ "n2", "p1" ], output=True),
FunctionData(input=[ "n2", "p2" ], output=True)]
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Standard exports
from LOTlib.Hypotheses.LOTHypothesis import LOTHypothesis
def make_ho(value=None):
return LOTHypothesis(grammar, value=value, args=['x', 'y'], ALPHA=0.999) # alpha here trades off with the amount of data. Currently assuming no noise, but that's not necessary
if __name__ == "__main__":
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Run mcmc
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
from LOTlib.Proposals.RegenerationProposal import *
#mp = MixtureProposal([RegenerationProposal(grammar), InsertDeleteProposal(grammar)] )
mp = RegenerationProposal(grammar)
h0 = LOTHypothesis(grammar, args=['x', 'y'], ALPHA=0.999, proposal_function=mp) # alpha here trades off with the amount of data. Currently assuming no noise, but that's not necessary
from LOTlib.Inference.MetropolisHastings import mh_sample
for h in mh_sample(h0, data, 4000000, skip=100):
print h.posterior_score, h.likelihood, h.prior, cleanFunctionNodeString(h)
print map( lambda d: h(*d.input), data)
print "\n"
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Standard exports
from LOTlib.Hypotheses.LOTHypothesis import LOTHypothesis
def make_ho(value=None):
return LOTHypothesis(
grammar, value=value, args=['x', 'y'], ALPHA=0.999
) # alpha here trades off with the amount of data. Currently assuming no noise, but that's not necessary
if __name__ == "__main__":
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Run mcmc
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
from LOTlib.Proposals.RegenerationProposal import *
#mp = MixtureProposal([RegenerationProposal(grammar), InsertDeleteProposal(grammar)] )
mp = RegenerationProposal(grammar)
h0 = LOTHypothesis(
grammar, args=['x', 'y'], ALPHA=0.999, proposal_function=mp
) # alpha here trades off with the amount of data. Currently assuming no noise, but that's not necessary
from LOTlib.Inference.MetropolisHastings import mh_sample
for h in mh_sample(h0, data, 4000000, skip=100):
print h.posterior_score, h.likelihood, h.prior, cleanFunctionNodeString(
h)
print map(lambda d: h(*d.input), data)
print "\n"
display_option_summary(options)
eval_data = None
if options.EVAL_DATA > 0:
eval_data = make_data(options.EVAL_DATA)
# choose the appropriate map function
args = list(itertools.product([make_hypothesis],[make_data], data_amounts * options.CHAINS) )
# set the output codec -- needed to display lambda to stdout
sys.stdout = codecs.getwriter('utf8')(sys.stdout)
seen = set()
for fs in MPI_unorderedmap(run, numpy.random.permutation(args)):
assert is_master_process()
for h in fs:
if h not in seen:
seen.add(h)
if eval_data is not None:
h.compute_posterior(eval_data) # evaluate on the big data
print h.prior, h.likelihood / options.EVAL_DATA, qq(cleanFunctionNodeString(h))
import pickle
with open(options.OUT_PATH, 'w') as f:
pickle.dump(seen, f)
if options.EVAL_DATA > 0:
eval_data = make_data(options.EVAL_DATA)
# choose the appropriate map function
args = list(itertools.product([make_hypothesis],[make_data], data_amounts * options.CHAINS) )
# set the output codec -- needed to display lambda to stdout
sys.stdout = codecs.getwriter('utf8')(sys.stdout)
seen = set()
for fs in MPI_unorderedmap(run, numpy.random.permutation(args)):
assert is_master_process()
for h in fs:
if h not in seen:
seen.add(h)
if eval_data is not None:
h.compute_posterior(eval_data) # evaluate on the big data
print h.posterior_score, h.prior, h.likelihood / options.EVAL_DATA, \
alsoprint(h) if alsoprint is not None else '',\
qq(cleanFunctionNodeString(h))
import pickle
with open(options.OUT_PATH, 'w') as f:
pickle.dump(seen, f)
eval_data = None
if options.EVAL_DATA > 0:
eval_data = make_data(options.EVAL_DATA)
# choose the appropriate map function
args = list(
itertools.product([make_hypothesis], [make_data],
data_amounts * options.CHAINS))
# set the output codec -- needed to display lambda to stdout
sys.stdout = codecs.getwriter('utf8')(sys.stdout)
seen = set()
for fs in MPI_unorderedmap(run, numpy.random.permutation(args)):
assert is_master_process()
for h in fs:
if h not in seen:
seen.add(h)
if eval_data is not None:
h.compute_posterior(eval_data) # evaluate on the big data
print h.posterior_score, h.prior, h.likelihood / options.EVAL_DATA, \
alsoprint(h) if alsoprint is not None else '',\
qq(cleanFunctionNodeString(h))
import pickle
with open(options.OUT_PATH, 'w') as f:
pickle.dump(seen, f)