Python MHSampler.acceptance_ratio Examples

Example #1

from LOTlib import break_ctrlc

from LOTlib.Inference.GrammarInference.FullGrammarHypothesis import FullGrammarHypothesis

from LOTlib.Inference.Samplers.MetropolisHastings import MHSampler

h0 = FullGrammarHypothesis(counts, L, GroupLength, prior_offset, NYes, NTrials,
                           Output)
mhs = MHSampler(h0, [], 100000, skip=0)

for s, h in break_ctrlc(enumerate(mhs)):

    print mhs.acceptance_ratio(), h.prior, h.likelihood,\
          h.value['alpha'].value[0], h.value['beta'].value[0],\
          h.value['prior_temperature'].value, h.value['likelihood_temperature'].value,\
          'RULES',\
          ' '.join([str(x) for x in h.value['rulep']['BOOL'].value ]),\
          ' '.join([str(x) for x in h.value['rulep']['PREDICATE'].value ]),\
          ' '.join([str(x) for x in h.value['rulep']['START'].value ]),\
          ' '.join([str(x) for x in h.value['rulep']['SET'].value ])

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Run gradient ascent
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# from LOTlib import break_ctrlc
#
# from LOTlib.Inference.GrammarInference.FullGrammarHypothesis import FullGrammarHypothesis
#
#
# from LOTlib.Inference.GrammarInference.GradientGrammarInference import GrammarGradient
#
# h0 = FullGrammarHypothesis(counts, L, GroupLength, prior_offset, NYes, NTrials, Output)

Example #2

File: Run.py Project: joshrule/LOTlib

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
from LOTlib import break_ctrlc

from LOTlib.Inference.GrammarInference.FullGrammarHypothesis import FullGrammarHypothesis

from LOTlib.Inference.Samplers.MetropolisHastings import MHSampler

h0 = FullGrammarHypothesis(counts, L, GroupLength, prior_offset, NYes, NTrials, Output)
mhs = MHSampler(h0, [], 100000, skip=0)

for s, h in break_ctrlc(enumerate(mhs)):

    print mhs.acceptance_ratio(), h.prior, h.likelihood,\
          h.value['alpha'].value[0], h.value['beta'].value[0],\
          h.value['prior_temperature'].value, h.value['likelihood_temperature'].value,\
          'RULES',\
          ' '.join([str(x) for x in h.value['rulep']['BOOL'].value ]),\
          ' '.join([str(x) for x in h.value['rulep']['PREDICATE'].value ]),\
          ' '.join([str(x) for x in h.value['rulep']['START'].value ]),\
          ' '.join([str(x) for x in h.value['rulep']['SET'].value ])


# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Run gradient ascent
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# from LOTlib import break_ctrlc
#
# from LOTlib.Inference.GrammarInference.FullGrammarHypothesis import FullGrammarHypothesis
#
#
# from LOTlib.Inference.GrammarInference.GradientGrammarInference import GrammarGradient
#

Example #3

File: Run.py Project: joshrule/LOTlib

print "# Computed counts for each hypothesis & nonterminal"

from LOTlib.Inference.GrammarInference.SimpleGrammarHypothesis import SimpleGrammarHypothesis
from LOTlib.Inference.GrammarInference.FullGrammarHypothesis import FullGrammarHypothesis

from LOTlib.Inference.Samplers.MetropolisHastings import MHSampler

h0 = SimpleGrammarHypothesis(counts, L, GroupLength, prior_offset, NYes, NTrials, Output)
# h0 = FullGrammarHypothesis(counts, L, GroupLength, prior_offset, NYes, NTrials, Output)

writ = []
mhs = MHSampler(h0, [], 100, skip=500)
for s, h in break_ctrlc(enumerate(mhs)):


    if isinstance(h, SimpleGrammarHypothesis):
        a = str(mhs.acceptance_ratio()) + ',' + str(h.prior) + ',' + str(h.likelihood) +  ',BOOLS,' +\
            ','.join([str(x) for x in h.value['BOOL'].value ]) + ',PREDS,' + ','.join([str(x) for x in h.value['PREDICATE'].value ])
    else:
        assert isinstance(h, FullGrammarHypothesis)
        a = str(mhs.acceptance_ratio()) + ',' + str(h.prior) + ',' + str(h.likelihood) +  ',' + \
        str(h.value['alpha'].value[0]) + ',' + str(h.value['beta'].value[0]) + ',' + \
        str(h.value['prior_temperature']) + ',' + str(h.value['likelihood_temperature'])  + ',RULES,' +\
            ','.join([str(x) for x in h.value['rulep']['PREDICATE'].value ])
    print a
    writ.append(a)

with open('Viz/' +MODEL + '.csv', 'w') as f:
    f.writelines('\n'.join(writ))

Example #4

File: Run.py Project: TerryLew/BinLOTlib

print "# Computed counts for each hypothesis & nonterminal"

from LOTlib.Inference.GrammarInference.SimpleGrammarHypothesis import SimpleGrammarHypothesis
from LOTlib.Inference.GrammarInference.FullGrammarHypothesis import FullGrammarHypothesis

from LOTlib.Inference.Samplers.MetropolisHastings import MHSampler

h0 = SimpleGrammarHypothesis(counts, L, GroupLength, prior_offset, NYes,
                             NTrials, Output)
# h0 = FullGrammarHypothesis(counts, L, GroupLength, prior_offset, NYes, NTrials, Output)

writ = []
mhs = MHSampler(h0, [], 100, skip=500)
for s, h in break_ctrlc(enumerate(mhs)):

    if isinstance(h, SimpleGrammarHypothesis):
        a = str(mhs.acceptance_ratio()) + ',' + str(h.prior) + ',' + str(h.likelihood) +  ',BOOLS,' +\
            ','.join([str(x) for x in h.value['BOOL'].value ]) + ',PREDS,' + ','.join([str(x) for x in h.value['PREDICATE'].value ])
    else:
        assert isinstance(h, FullGrammarHypothesis)
        a = str(mhs.acceptance_ratio()) + ',' + str(h.prior) + ',' + str(h.likelihood) +  ',' + \
        str(h.value['alpha'].value[0]) + ',' + str(h.value['beta'].value[0]) + ',' + \
        str(h.value['prior_temperature']) + ',' + str(h.value['likelihood_temperature'])  + ',RULES,' +\
            ','.join([str(x) for x in h.value['rulep']['PREDICATE'].value ])
    print a
    writ.append(a)

with open('Viz/' + MODEL + '.csv', 'w') as f:
    f.writelines('\n'.join(writ))