def cm_ebmcat(self):
path = os.path.join(config.getpath('corpora','alta2012-ebm'),'GS', 'gs1.txt')
cm = {}
with open(path) as f:
reader = csv.reader(f, delimiter='\t')
for row in reader:
key = '{0}-{1}'.format(row[0], row[1])
value = row[2].split(',')
cm[key] = value
return cm
def _parse_data(self):
if self._data is None:
ts = {}
with open(os.path.join(config.getpath('corpora','alta2012-ebm'),'test.csv')) as f:
reader = csv.DictReader(f)
for row in reader:
docid = row['Document'] + '-' + row['Sentence']
ts[docid] = row['Text']
self._data = {}
self._data['ts'] = dict(ts)
def _parse_data(self):
if self._data is None:
cm = defaultdict(list)
ts = {}
with open(os.path.join(config.getpath('corpora','alta2012-ebm'),'train.csv')) as f:
reader = csv.DictReader(f)
for row in reader:
docid = row['Document'] + '-' + row['Sentence']
if row['Prediction'] == '1':
cm[docid].append(row['Label'])
ts[docid] = row['Text']
self._data = {}
self._data['cm'] = dict(cm)
self._data['ts'] = dict(ts)
def sp_crossvalidation(self):
sq_index = dict( (s[0].split('-')[0],s) for s in self.sequence('abstract'))
sp = {}
with open(os.path.join(config.getpath('corpora','alta2012-ebm'),'data.testset')) as f:
for i,row in enumerate(f):
key = 'fold{0}'.format(i)
value = []
for abs_id in row.split('\t',1)[1].split(':'):
if abs_id.strip():
try:
value.extend(sq_index[abs_id])
except KeyError:
# No sequence, single-sentence abstract
value.append('{0}-1'.format(abs_id))
sp[key] = value
return sp
def __unpack(self):
if self.model is None:
with Timer() as t:
if self.path is None:
logger.info("unpacking default model")
model = PIBOSOModel(*load_default_model())
else:
logger.info("unpacking model from: {}".format(self.path))
model = PIBOSOModel(*load_model(self.path))
logger.info("unpacking took {0:.2f}s".format(t.elapsed))
# hydrat hardcodes the paths for the classifier, which need to be updated
# if they are installed at a different location
classifier = config.getpath("tools", "liblinearclassifier")
# TODO: Check that the tool exists
if model.L1_cl.classifier != classifier:
logger.debug("updating classifier path from {0} to {1}".format(model.L1_cl.classifier, classifier))
model.L1_cl.classifier = classifier
for c in model.L0_cl:
c.classifier = classifier
self.model = model
else:
logger.debug("already unpacked!")
