def main(args):
my_turk = turkdata.turkdata(args[0])
read_it = turkdata.init_reader(my_turk)
my_turk.read_all(read_it)
yes_no_set = my_turk.training_set()
if verbose:
pprint.pprint(yes_no_set)
## do this if you've pickled the yes_no sets
#fd = open("./mt_files/Batch_63422_result_training.pkl", 'rb')
#ob = pickle.load(fd)
#yes_no_set = [ob[0], ob[1]]
ptree_classifier = ptree.mult_sample_learn_ccc(yes_no_set[0], yes_no_set[1],
n_depth)
all_ngrams = n_grams.n_grams_depth_first(ptree_classifier, n_depth)
print len(all_ngrams), " ngrams created"
print '\nLOG LIKELIHOOD: ', n_grams.n_grams_prior(all_ngrams)
[n.print_ngram(ptree_classifier.words) for n in all_ngrams]
# def store(self):
cl_name = str(n_depth) + '_classifier_' + time.strftime("%M-%H-%j") + '.pkl'
out_name = re.sub('.csv', cl_name, my_turk.name)
try:
outfd = open(out_name, 'wb')
pickle.dump(ptree_classifier, outfd) # , pickle.HIGHEST_PROTOCOL)
print >> sys.stderr, 'Wrote ', out_name
outfd.close()
except pickle.PicklingError:
print >> sys.stderr, 'Failed to serialize ', out_name
def main(args):
my_turk = turkdata.turkdata(args[0])
read_it = turkdata.init_reader(my_turk)
my_turk.read_all(read_it)
yes_no_set = my_turk.training_set()
if verbose:
pprint.pprint(yes_no_set)
## do this if you've pickled the yes_no sets
#fd = open("./mt_files/Batch_63422_result_training.pkl", 'rb')
#ob = pickle.load(fd)
#yes_no_set = [ob[0], ob[1]]
ptree_classifier = ptree.mult_sample_learn_ccc(yes_no_set[0], yes_no_set[1],
n_depth)
all_ngrams = n_grams.n_grams_depth_first(ptree_classifier, n_depth)
print len(all_ngrams), " ngrams created"
print '\nLOG LIKELIHOOD: ', n_grams.n_grams_prior(all_ngrams)
[n.print_ngram(ptree_classifier.words) for n in all_ngrams]
# def store(self):
cl_name = str(n_depth) + '_classifier_' + time.strftime("%M-%H-%j") + '.pkl'
out_name = re.sub('.csv', cl_name, my_turk.name)
try:
outfd = open(out_name, 'wb')
pickle.dump(ptree_classifier, outfd) # , pickle.HIGHEST_PROTOCOL)
print >> sys.stderr, 'Wrote ', out_name
outfd.close()
except pickle.PicklingError:
print >> sys.stderr, 'Failed to serialize ', out_name
def build_class(yes_txt, no_txt):
## Be careful, this destroys its arguments.
yes_txt = [x for x in yes_txt] # copy sequence
no_txt = [x for x in no_txt] # copy sequence
ptree_classifier = ptree.mult_sample_learn_ccc(yes_txt, no_txt, n_depth)
if dbg:
ptree_classifier.print_tree()
return ptree_classifier
def main(args):
# Create the turkdata training set.
my_turk = turkdata.turkdata(args[0])
read_it = turkdata.init_reader(my_turk)
my_turk.read_all(read_it)
yes_no_set = my_turk.training_set()
if verbose:
pprint.pprint(yes_no_set)
## Create the cross validaton folds from the pos & neg examples
cv = cv_constructor.CrossValidationDataConstructor(yes_no_set[0], yes_no_set[1], numPartitions=n_folds)
cv_set = cv.getDataSets()
# pprint.pprint(cv_set.next)
## Run the plug-in classifier on each fold, computing fp & fn
for (training_set, test_set) in cv_set:
ptree_classifier = ptree.mult_sample_learn_ccc(yes_no_set[0], yes_no_set[1],
n_depth)
def main(args):
# Create the turkdata training set.
my_turk = turkdata.turkdata(args[0])
read_it = turkdata.init_reader(my_turk)
my_turk.read_all(read_it)
yes_no_set = my_turk.training_set()
if verbose:
pprint.pprint(yes_no_set)
## Create the cross validaton folds from the pos & neg examples
cv = cv_constructor.CrossValidationDataConstructor(yes_no_set[0],
yes_no_set[1],
numPartitions=n_folds)
cv_set = cv.getDataSets()
# pprint.pprint(cv_set.next)
## Run the plug-in classifier on each fold, computing fp & fn
for (training_set, test_set) in cv_set:
ptree_classifier = ptree.mult_sample_learn_ccc(yes_no_set[0],
yes_no_set[1], n_depth)
