def __init__(self, **kwargs):
super(JointSemanticModel, self).__init__(**kwargs)
self.hyper_para = HyperParameter(**kwargs)
self.external_resource = JointSemanticResource(**kwargs)
self.ranking_model = None
self.training_model = None
def __init__(self, **kwargs):
super(Spotter, self).__init__(**kwargs)
self.resource = JointSemanticResource(**kwargs) # can be initialized here or by set_resource
self.h_q_seg = dict()
if self.pre_defined_segments:
self.h_q_seg = json.load(open(self.pre_defined_segments))
logging.info('loaded predefined segments')
def __init__(self, **kwargs):
super(GroundCenter, self).__init__(**kwargs)
assert self.grounder_name in self.h_name_grounder
logging.info('use grounder [%s]', self.grounder_name)
self.grounder = self.h_name_grounder[self.grounder_name](**kwargs)
self.resource = JointSemanticResource(**kwargs)
self.grounder.set_resource(self.resource)
def __init__(self, **kwargs):
super(MatchCenter, self).__init__(**kwargs)
self.resource = JointSemanticResource(**kwargs)
self.h_q_info = dict()
self.h_d_info = dict()
self._load_qd_info()
self.l_feature_extractor = []
self._init_feature_extractors(**kwargs)
def __init__(self, **kwargs):
super(BoeLeToRFeatureExtractCenter, self).__init__(**kwargs)
self.resource = JointSemanticResource(**kwargs)
self.l_extractor = []
self.h_q_info = dict()
self.h_doc_info = dict()
self.h_qrel = dict()
self._load_data()
self._set_extractor(**kwargs)
def class_print_help(cls, inst=None):
super(MatchCenter, cls).class_print_help(inst)
JointSemanticResource.class_print_help(inst)
for name, extractor_class in cls.h_group_mapping.items():
print 'feature group:[%s]' % name
extractor_class.class_print_help(inst)
def class_print_help(cls, inst=None):
super(Spotter, cls).class_print_help()
JointSemanticResource.class_print_help(inst)
def class_print_help(cls, inst=None):
super(GroundCenter, cls).class_print_help(inst)
JointSemanticResource.class_print_help(inst)
for name, grounder_class in cls.h_name_grounder.items():
print "grounder [%s]" % name
grounder_class.class_print_help(inst)
l_sent = h_e_nlss.get(tail, [])
l_aligned.append([edge, tail, {'nlss_sent': l_sent}])
return l_aligned
def process(resource, out_name):
out = open(out_name, 'w')
for e, h_edge_info in resource.h_e_edge.items():
l_edge = h_edge_info.get('edges', [])
logging.info('aligning for [%s]', e)
l_nlss = resource.l_h_nlss[0].get(e, [])
h_aligned_info = align_per_entity(e, l_nlss, l_edge)
print >> out, json.dumps(h_aligned_info)
out.close()
logging.info('finished')
if __name__ == '__main__':
from knowledge4ir.utils import set_basic_log, load_py_config
set_basic_log()
if 3 != len(sys.argv):
print "2 para: resource config + output"
JointSemanticResource.class_print_help()
sys.exit(-1)
resource = JointSemanticResource(config=load_py_config(sys.argv[1]))
process(resource, sys.argv[2])
class AttBoeMainPara(Configurable):
spotted_info_in = Unicode(help='spotted query or doc info').tag(
config=True)
out_name = Unicode(help='out name').tag(config=True)
if __name__ == '__main__':
import sys
from knowledge4ir.utils import (
load_py_config,
set_basic_log,
)
set_basic_log(logging.INFO)
if 2 > len(sys.argv):
print "config + spot in (opt) + out name (opt):"
AttBoeMainPara.class_print_help()
AttentionBoe.class_print_help()
JointSemanticResource.class_print_help()
sys.exit(-1)
conf = load_py_config(sys.argv[1])
main_para = AttBoeMainPara(config=conf)
semantic_resource = JointSemanticResource(config=conf)
boe_constructor = AttentionBoe(config=conf)
boe_constructor.set_resource(semantic_resource)
if len(sys.argv) < 4:
boe_constructor.pipe_run(main_para.spotted_info_in, main_para.out_name)
else:
boe_constructor.pipe_run(sys.argv[2], sys.argv[3])
def __init__(self, **kwargs):
super(SpotSentAttention, self).__init__(**kwargs)
self.resource = JointSemanticResource(**kwargs)
def class_print_help(cls, inst=None):
super(AppendText, cls).class_print_help(inst)
JointSemanticResource.class_print_help(inst)
def __init__(self, **kwargs):
super(AppendText, self).__init__(**kwargs)
self.resource = JointSemanticResource(**kwargs)
self.h_qid_query = dict(load_qid_query(self.q_in))
self.h_e_text = self.resource.h_entity_fields