def run(*args):
#print "# Running data"
global hypotheses
data_size = args[0]
p_representation = defaultdict(int) # how often do you get the right representation
p_response = defaultdict(int) # how often do you get the right response?
p_representation_literal = defaultdict(int) # how often do you get the right representation
p_response_literal = defaultdict(int) # how often do you get the right response?
p_representation_presup = defaultdict(int) # how often do you get the right representation
p_response_presup = defaultdict(int) # how often do you get the right response?
#print "# Generating data"
data = generate_data(data_size)
# recompute these
#print "# Computing posterior"
#[ x.unclear_functions() for x in hypotheses ]
[ x.compute_posterior(data) for x in hypotheses ]
# normalize the posterior in fs
#print "# Computing normalizer"
Z = logsumexp([x.posterior_score for x in hypotheses])
# and output the top hypotheses
qq = FiniteBestSet(max=True, N=25)
for h in hypotheses: qq.push(h, h.posterior_score) # get the tops
for i, h in enumerate(qq.get_all(sorted=True)):
for w in h.all_words():
fprintn(8, data_size, i, w, h.posterior_score, q(h.value[w]), f=options.OUT_PATH+"-hypotheses."+str(get_rank())+".txt")
# and compute the probability of being correct
#print "# Computing correct probability"
for h in hypotheses:
hstr = str(h)
#print data_size, len(data), exp(h.posterior_score), correct[ str(h)+":"+w ]
for w in words:
p = exp(h.posterior_score - Z)
key = w + ":" + hstr
p_representation[w] += p * (agree_pct[key] == 1.)
p_representation_presup[w] += p * (agree_pct_presup[key] == 1.) # if we always agree with the target, then we count as the right rep.
p_representation_literal[w] += p * (agree_pct_literal[key] == 1.)
# and just how often does the hypothesis agree?
p_response[w] += p * agree_pct[key]
p_response_presup[w] += p * agree_pct_presup[key]
p_response_literal[w] += p * agree_pct_literal[key]
#print "# Outputting"
for w in words:
fprintn(10, str(get_rank()), q(w), data_size, p_representation[w], p_representation_presup[w], p_representation_literal[w], p_response[w], p_response_presup[w], p_response_literal[w], f=options.OUT_PATH+"-stats."+str(get_rank())+".txt")
return 0
def run(*args):
#print "# Running data"
global hypotheses
data_size = args[0]
p_representation = defaultdict(
int) # how often do you get the right representation
p_response = defaultdict(int) # how often do you get the right response?
p_representation_literal = defaultdict(
int) # how often do you get the right representation
p_response_literal = defaultdict(
int) # how often do you get the right response?
p_representation_presup = defaultdict(
int) # how often do you get the right representation
p_response_presup = defaultdict(
int) # how often do you get the right response?
#print "# Generating data"
data = generate_data(data_size)
# recompute these
#print "# Computing posterior"
#[ x.unclear_functions() for x in hypotheses ]
[x.compute_posterior(data) for x in hypotheses]
# normalize the posterior in fs
#print "# Computing normalizer"
Z = logsumexp([x.posterior_score for x in hypotheses])
# and output the top hypotheses
qq = FiniteBestSet(max=True, N=25)
for h in hypotheses:
qq.push(h, h.posterior_score) # get the tops
for i, h in enumerate(qq.get_all(sorted=True)):
for w in h.all_words():
fprintn(8,
data_size,
i,
w,
h.posterior_score,
q(h.value[w]),
f=options.OUT_PATH + "-hypotheses." + str(get_rank()) +
".txt")
# and compute the probability of being correct
#print "# Computing correct probability"
for h in hypotheses:
hstr = str(h)
#print data_size, len(data), exp(h.posterior_score), correct[ str(h)+":"+w ]
for w in words:
p = exp(h.posterior_score - Z)
key = w + ":" + hstr
p_representation[w] += p * (agree_pct[key] == 1.)
p_representation_presup[w] += p * (
agree_pct_presup[key] == 1.
) # if we always agree with the target, then we count as the right rep.
p_representation_literal[w] += p * (agree_pct_literal[key] == 1.)
# and just how often does the hypothesis agree?
p_response[w] += p * agree_pct[key]
p_response_presup[w] += p * agree_pct_presup[key]
p_response_literal[w] += p * agree_pct_literal[key]
#print "# Outputting"
for w in words:
fprintn(10,
str(get_rank()),
q(w),
data_size,
p_representation[w],
p_representation_presup[w],
p_representation_literal[w],
p_response[w],
p_response_presup[w],
p_response_literal[w],
f=options.OUT_PATH + "-stats." + str(get_rank()) + ".txt")
return 0
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# MPI interface
if True: #not is_master_process(): # only load if you aren't zero (else we must wait for zero to load!!
from LOTlib.Serialization import *
fs = file2object(options.LOAD_HYPOTHESES_PATH)
## The finite set of samples
#inh = open(options.LOAD_HYPOTHESES_PATH)
#fs = pickle.load(inh)
hypotheses = fs.get_all()
print "#", get_rank(), ": Loaded pickle. ", len(hypotheses), " hypotheses."
# get all the words
words = hypotheses[0].all_words(
) # just get the words from the first hypothesis
# now figure out how often each meaning is right for each word
agree_pct = dict(
) # how often does each part of meaning agree with each word?
agree_pct_presup = dict()
agree_pct_literal = dict()
for h in hypotheses:
for w in words:
tresp = [target.value[w](t) for t in TESTING_SET]
hresp = [h.value[w](t) for t in TESTING_SET]
return 0
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# MPI interface
if True: #not is_master_process(): # only load if you aren't zero (else we must wait for zero to load!!
from LOTlib.Serialization import *
fs = file2object(options.LOAD_HYPOTHESES_PATH)
## The finite set of samples
#inh = open(options.LOAD_HYPOTHESES_PATH)
#fs = pickle.load(inh)
hypotheses = fs.get_all()
print "#", get_rank(), ": Loaded pickle. ", len(hypotheses), " hypotheses."
# get all the words
words = hypotheses[0].all_words() # just get the words from the first hypothesis
# now figure out how often each meaning is right for each word
agree_pct = dict() # how often does each part of meaning agree with each word?
agree_pct_presup = dict()
agree_pct_literal = dict()
for h in hypotheses:
for w in words:
tresp = [ target.value[w](t) for t in TESTING_SET]
hresp = [ h.value[w](t) for t in TESTING_SET]
key = w+":"+str(h)
agree_pct[key] = np.mean( collapse_undefs(tresp) == collapse_undefs(hresp) )
