def get_feature_avg(svm_in):
l_svm_data = load_svm_feature(svm_in)
h_rel_feature = {}
h_irrel_feature = {}
rel_cnt = 0
irrel_cnt = 0
for data in l_svm_data:
label = data['score']
h_feature = data['feature']
for key, score in h_feature.items():
if score < -20:
score = 0
else:
score = math.exp(score)
h_feature[key] = score
if label > 0:
h_rel_feature = add_svm_feature(h_rel_feature, h_feature)
rel_cnt += 1
else:
h_irrel_feature = add_svm_feature(h_irrel_feature, h_feature)
irrel_cnt += 1
rel_cnt = float(rel_cnt)
irrel_cnt = float(irrel_cnt)
for key in h_rel_feature:
h_rel_feature[key] /= rel_cnt
for key in h_irrel_feature:
h_irrel_feature[key] /= irrel_cnt
return h_rel_feature, h_irrel_feature
def _load_svm_ltr_feature(self):
logging.info('loading svm ltr feature [%s]', self.ltr_f_in)
l_svm_data = load_svm_feature(self.ltr_f_in)
for svm_data in l_svm_data:
qid, docno, h_feature = svm_data['qid'], svm_data[
'comment'], svm_data['feature']
self.h_qid_docno_ltr_feature[qid + '\t' + docno] = h_feature
logging.info('loaded [%d] pairs of pre extracted ltr feature',
len(self.h_qid_docno_ltr_feature))
def load_multiple_svm_and_feature(svm_files_in):
l_name_fields = open(svm_files_in).read().splitlines()
l_names = [line.split('\t')[0] for line in l_name_fields]
# l_fields = [line.split('\t')[1] for line in l_name_fields]
ll_svm_data = [load_svm_feature(name) for name in l_names]
l_h_feature_name = [
json.load(open(name + '_name.json')) for name in l_names
]
l_new_h_feature_name = []
for h_feature_name in l_h_feature_name:
l_new_h_feature_name.append(h_feature_name)
return ll_svm_data, l_new_h_feature_name
import sys
if 4 != len(sys.argv):
print "convert svm feature to csv format"
print "3 para: svm in + feature name in + out"
sys.exit(-1)
h_feature_name = json.load(open(sys.argv[2]))
l_feature_name = h_feature_name.items()
l_feature_name.sort(key=lambda item: item[1])
l_feature_name = [item[0] for item in l_feature_name]
head_str = 'qid,docno,label,' + ','.join(l_feature_name)
out = open(sys.argv[3], 'w')
print >> out, head_str
l_svm_data = load_svm_feature(sys.argv[1])
for svm_data in l_svm_data:
line = svm_data['qid'] + ',' + svm_data[
'comment'] + ',%d,' % svm_data['score']
h_feature = svm_data['feature']
l_feature = h_feature.items()
l_feature.sort(key=lambda item: item[0])
l_feature_value = ['%.4f' % item[1] for item in l_feature]
line += ','.join(l_feature_value)
print >> out, line
out.close()
print "finished"
from knowledge4ir.utils import load_svm_feature, dump_svm_feature
import sys
if 3 != len(sys.argv):
print "svm in + out"
sys.exit()
l = load_svm_feature(sys.argv[1])
dump_svm_feature(l, sys.argv[2])
