def runTest(self):
NSAMPLES = 10000
from LOTlib.DefaultGrammars import finiteTestGrammar as grammar
from LOTlib.Hypotheses.LOTHypothesis import LOTHypothesis
class MyH(LOTHypothesis):
@attrmem('likelihood')
def compute_likelihood(self, *args, **kwargs):
return 0.0
@attrmem('prior')
def compute_prior(self):
return grammar.log_probability(self.value)
print "# Taking MHSampler for a test run"
cnt = Counter()
h0 = MyH(grammar=grammar)
for h in break_ctrlc(
MHSampler(h0, [], steps=NSAMPLES,
skip=10)): # huh the skip here seems to be important
cnt[h] += 1
trees = list(cnt.keys())
print "# Done taking MHSampler for a test run"
## TODO: When the MCMC methods get cleaned up for how many samples they return, we will assert that we got the right number here
# assert sum(cnt.values()) == NSAMPLES # Just make sure we aren't using a sampler that returns fewer samples! I'm looking at you, ParallelTempering
Z = logsumexp([grammar.log_probability(t.value) for t in trees
]) # renormalize to the trees in self.trees
obsc = [cnt[t] for t in trees]
expc = [
exp(grammar.log_probability(t.value)) * sum(obsc) for t in trees
]
# And plot here
expc, obsc, trees = zip(*sorted(zip(expc, obsc, trees), reverse=True))
import matplotlib.pyplot as plt
plt.subplot(111)
# Log here spaces things out at the high end, where we can see it!
plt.scatter(log(range(len(trees))), expc, color="red", alpha=1.)
plt.scatter(log(range(len(trees))),
obsc,
color="blue",
marker="x",
alpha=1.)
plt.savefig('finite-sampler-test.pdf')
plt.clf()
# Do chi squared test
csq, pv = chisquare(obsc, expc)
self.assertAlmostEqual(sum(obsc), sum(expc))
# And examine
for t, c, s in zip(trees, obsc, expc):
print c, s, t
print(csq, pv), sum(obsc)
self.assertGreater(pv, 0.01, msg="Sampler failed chi squared!")
def runTest(self):
NSAMPLES = 10000
from LOTlib.DefaultGrammars import finiteTestGrammar as grammar
from LOTlib.Hypotheses.LOTHypothesis import LOTHypothesis
class MyH(LOTHypothesis):
@attrmem('likelihood')
def compute_likelihood(self, *args, **kwargs):
return 0.0
@attrmem('prior')
def compute_prior(self):
return grammar.log_probability(self.value)
print "# Taking MHSampler for a test run"
cnt = Counter()
h0 = MyH(grammar=grammar)
for h in break_ctrlc(MHSampler(h0, [], steps=NSAMPLES, skip=10)): # huh the skip here seems to be important
cnt[h] += 1
trees = list(cnt.keys())
print "# Done taking MHSampler for a test run"
## TODO: When the MCMC methods get cleaned up for how many samples they return, we will assert that we got the right number here
# assert sum(cnt.values()) == NSAMPLES # Just make sure we aren't using a sampler that returns fewer samples! I'm looking at you, ParallelTempering
Z = logsumexp([grammar.log_probability(t.value) for t in trees]) # renormalize to the trees in self.trees
obsc = [cnt[t] for t in trees]
expc = [exp( grammar.log_probability(t.value))*sum(obsc) for t in trees]
# And plot here
expc, obsc, trees = zip(*sorted(zip(expc, obsc, trees), reverse=True))
import matplotlib.pyplot as plt
plt.subplot(111)
# Log here spaces things out at the high end, where we can see it!
plt.scatter(log(range(len(trees))), expc, color="red", alpha=1.)
plt.scatter(log(range(len(trees))), obsc, color="blue", marker="x", alpha=1.)
plt.savefig('finite-sampler-test.pdf')
plt.clf()
# Do chi squared test
csq, pv = chisquare(obsc, expc)
self.assertAlmostEqual(sum(obsc), sum(expc))
# And examine
for t, c, s in zip(trees, obsc, expc):
print c, s, t
print (csq, pv), sum(obsc)
self.assertGreater(pv, 0.01, msg="Sampler failed chi squared!")
def compute_prior(self):
return grammar.log_probability(self.value)
def compute_prior(self):
return grammar.log_probability(self.value)
