def decode_Alphabet(line):
if line.startswith('(None'):
return None
print(line)
alpha = Alphabet()
searchTxt = '_index_to_label= {'
(index_to_label_text, cnt) = findSubstring(line, searchTxt, '), }', 0, 0)
index_to_label_text = index_to_label_text[len(searchTxt):cnt]
# print('Alphabet: ')
# alpha._index_to_label = decode_dictionary(index_to_label_text)
alpha._index_to_label = decode_dictionary_v2(index_to_label_text)
# print( 'alpha._index_to_label = ' + str(alpha._index_to_label))
searchTxt = '_label_to_index= {'
(label_to_index_text, cnt) = findSubstring(line, searchTxt, '), }', 0, cnt)
label_to_index_text = label_to_index_text[len(searchTxt):cnt]
# alpha._label_to_index = decode_dictionary(label_to_index_text)
alpha._label_to_index = decode_dictionary_v2(label_to_index_text)
# print(alpha._label_to_index)
searchTxt = 'num_labels= '
(num_label_text, cnt) = findSubstring(line, searchTxt, ')', 0, cnt)
num_label_text = num_label_text[len(searchTxt):cnt]
alpha.num_labels = int(num_label_text)
# print(alpha.num_labels)
return alpha
def setup(self,action_type,instances,parser,feature_templates_file=None):
if feature_templates_file:
self._feats_templates_file = feature_templates_file
self.class_codebook = Alphabet.from_dict(dict((i,k) for i,(k,v) in enumerate(ACTION_TYPE_TABLE[action_type])),True)
self.feature_codebook = dict([(i,Alphabet()) for i in self.class_codebook._index_to_label.keys()])
self.read_templates()
#n_rel,n_tag = self._set_rel_tag_codebooks(instances,parser)
n_subclass = self._set_rel_tag_codebooks(instances,parser)
self._set_class_weight(self.class_codebook.size(),n_subclass)
self._set_statistics(instances)
self.output_feature_generator()
def __init__(self,elog=sys.stdout):
self.elog = elog
self.weight = None
self.aux_weight = None
self.avg_weight = None # for store the averaged weights
#self.n_class = n_class
#self.n_rel = n_rel
#self.n_tag = n_tag
self._feats_templates_file = _FEATURE_TEMPLATES_FILE
self._feature_templates_list = []
self._feats_gen_filename = None
self.feats_generator = None
self.token_to_concept_table = defaultdict(set)
self.pp_count_dict = defaultdict(int)
self.total_num_words = 0
self.token_label_set = defaultdict(set)
self.class_codebook = None
self.feature_codebook = None
self.rel_codebook = Alphabet()
self.tag_codebook = {
'Concept':Alphabet(),
'ETag':Alphabet(),
'ConstTag':Alphabet(),
'ABTTag':Alphabet()
}
self.abttag_count = defaultdict(int)
def setup(self,action_type,instances,feature_templates_file=None):
self._feats_templates_file = feature_templates_file
self.class_codebook = Alphabet.from_dict(dict((i,k) for i,(k,v) in enumerate(ACTION_TYPE_TABLE[action_type])),True)
self.feature_codebook = dict([(i,Alphabet()) for i in self.class_codebook._index_to_label.keys()])
self.read_templates()
n_rel,n_tag = self._set_rel_tag_codebooks(instances)
self._set_class_weight(self.class_codebook.size(),n_rel)
self._set_statistics(instances)
self.output_feature_generator()
def __init__(self,errlog=sys.stdout):
self.errlog = errlog
self.weight = None
self.aux_weight = None
self.avg_weight = None # for store the averaged weights
#self._feature_templates_list = []
#self._feats_gen_filename = None
self.feats_generator = None
self.class_codebook = Alphabet()
self.feature_codebook = Alphabet()
self.tagdict = {}
self.counts = None
def __init__(self,elog=sys.stdout):
self.elog = elog
self.weight = None
self.aux_weight = None
self.avg_weight = None # for store the averaged weights
#self.n_class = n_class
#self.n_rel = n_rel
#self.n_tag = n_tag
self._feats_templates_file = _FEATURE_TEMPLATES_FILE
self._feature_templates_list = []
self._feats_gen_filename = None
self.feats_generator = None
self.token_to_concept_table = defaultdict(set)
self.pp_count_dict = defaultdict(int)
self.total_num_words = 0
self.token_label_set = defaultdict(set)
self.class_codebook = None
self.feature_codebook = None
self.rel_codebook = Alphabet()
self.tag_codebook = {
'Concept':Alphabet(),
'ETag':Alphabet(),
'ConstTag':Alphabet(),
'ABTTag':Alphabet()
}
self.abttag_count = defaultdict(int)
class Model():
"""weights and templates"""
#weight = None
#n_class = None
indent = " "*4
#feature_codebook = None
#class_codebook = None
def __init__(self,errlog=sys.stdout):
self.errlog = errlog
self.weight = None
self.aux_weight = None
self.avg_weight = None # for store the averaged weights
#self._feature_templates_list = []
#self._feats_gen_filename = None
self.feats_generator = None
self.class_codebook = Alphabet()
self.feature_codebook = Alphabet()
self.tagdict = {}
self.counts = None
def setup(self,instances):
raise NotImplementedError("Must implement 'setup' method")
def _set_class_weight(self,n_class,init_feature_dim):
raise NotImplementedError("Must implement method")
def save_model(self,model_filename):
#pickle.dump(self,open(model_filename,'wb'),pickle.HIGHEST_PROTOCOL)
print >> self.errlog, 'Model info:'
print >> self.errlog,'class size: %s '%(self.class_codebook.size())
#print >> self.errlog,'feature codebook size: %s' % (self.feature_codebook.size())
#print 'weight shape: %s' % (self.avg_weight.shape)
#print >> self.errlog,'weight shape: %s' % (','.join(('%s:%s')%(i,w.shape) for i,w in enumerate(self.avg_weight)))
#print >> self.errlog,'word-pos table: %s' % (len(self.counts))
with contextlib.closing(bz2.BZ2File(model_filename, 'wb')) as f:
pickle.dump(self,f,pickle.HIGHEST_PROTOCOL)
#json.dump(self.toJSON(),open(model_filename,'wb'))
@staticmethod
def load_model(model_filename):
with contextlib.closing(bz2.BZ2File(model_filename, 'rb')) as f:
model = pickle.load(f)
return model
class Model():
"""weights and templates"""
#weight = None
#n_class = None
#n_rel = None
#n_tag = None
indent = " "*4
#feature_codebook = None
#class_codebook = None
#feats_generator = None
def __init__(self,elog=sys.stdout):
self.elog = elog
self.weight = None
self.aux_weight = None
self.avg_weight = None # for store the averaged weights
#self.n_class = n_class
#self.n_rel = n_rel
#self.n_tag = n_tag
self._feats_templates_file = None
self._feature_templates_list = []
self._feats_gen_filename = None
self.feats_generator = None
self.token_to_concept_table = defaultdict(set)
self.pp_count_dict = defaultdict(int)
self.total_num_words = 0
self.token_label_set = defaultdict(set)
self.class_codebook = None
self.feature_codebook = None
self.rel_codebook = Alphabet()
self.tag_codebook = {
'Concept':Alphabet(),
'ETag':Alphabet(),
'ConstTag':Alphabet()
}
def setup(self,action_type,instances,feature_templates_file=None):
self._feats_templates_file = feature_templates_file
self.class_codebook = Alphabet.from_dict(dict((i,k) for i,(k,v) in enumerate(ACTION_TYPE_TABLE[action_type])),True)
self.feature_codebook = dict([(i,Alphabet()) for i in self.class_codebook._index_to_label.keys()])
self.read_templates()
n_rel,n_tag = self._set_rel_tag_codebooks(instances)
self._set_class_weight(self.class_codebook.size(),n_rel)
self._set_statistics(instances)
self.output_feature_generator()
def _set_statistics(self,instances):
#pp_count_dict = defaultdict(int)
for inst in instances:
sent = inst.tokens
self.total_num_words += len(sent)
for token in sent:
if token['pos'] == 'IN' and token['rel'] == 'prep':
self.pp_count_dict[token['form'].lower()] += 1
def _set_rel_tag_codebooks(self,instances):
#TODO
self.rel_codebook.add(NULL_EDGE)
#self.tag_codebook['Concept'].add(NULL_TAG)
for inst in instances:
gold_graph = inst.gold_graph
gold_nodes = gold_graph.nodes
#gold_edges = gold_graph.edges
sent_tokens = inst.tokens
for g,d in gold_graph.tuples():
gnode = gold_nodes[g]
g_span_wds = [tok['lemma'] for tok in sent_tokens if tok['id'] in range(gnode.start,gnode.end)] if g!= 'x' else gnode.words
g_span_ne = sent_tokens[g]['ne']
g_entity_tag = gold_graph.get_node_tag(g)
#if len(g_span_wds) > 1:
#for gwd in g_span_wds:
# self.token_to_concept_table[gwd].add(g_entity_tag)
if g_span_ne not in ['O','NUMBER']:
self.token_to_concept_table[g_span_ne].add(g_entity_tag)
self.token_to_concept_table[','.join(g_span_wds)].add(g_entity_tag)
if isinstance(g_entity_tag,ETag):
self.tag_codebook['ETag'].add(g_entity_tag)
elif isinstance(g_entity_tag,ConstTag):
self.tag_codebook['ConstTag'].add(g_entity_tag)
else:
self.tag_codebook['Concept'].add(g_entity_tag)
dnode = gold_nodes[d]
d_span_wds = [tok['lemma'] for tok in sent_tokens if tok['id'] in range(dnode.start,dnode.end)] if d != 'x' else dnode.words
d_span_ne = sent_tokens[d]['ne']
d_entity_tag = gold_graph.get_node_tag(d)
#if len(d_span_wds) > 1:
# for dwd in d_span_wds:
# self.token_to_concept_table[dwd].add(d_entity_tag)
if d_span_ne not in ['O','NUMBER']:
self.token_to_concept_table[d_span_ne].add(d_entity_tag)
self.token_to_concept_table[','.join(d_span_wds)].add(d_entity_tag)
if isinstance(d_entity_tag,ETag):
self.tag_codebook['ETag'].add(d_entity_tag)
elif isinstance(d_entity_tag,ConstTag):
self.tag_codebook['ConstTag'].add(d_entity_tag)
else:
self.tag_codebook['Concept'].add(d_entity_tag)
#self.tag_codebook.add(d_entity_tag)
g_edge_label = gold_graph.get_edge_label(g,d)
if g_span_ne not in ['O','NUMBER']:
self.token_label_set[g_span_ne].add(g_edge_label)
self.token_label_set[','.join(g_span_wds)].add(g_edge_label)
self.rel_codebook.add(g_edge_label)
n_rel = [1]*self.class_codebook.size()
n_tag = [1]*self.class_codebook.size()
for k,v in self.class_codebook._index_to_label.items():
if v in ACTION_WITH_TAG:
n_tag[k] = reduce(lambda x,y: x+y, map(lambda z: self.tag_codebook[z].size(), self.tag_codebook.keys()))
if v in ACTION_WITH_EDGE:
n_rel[k] = self.rel_codebook.size()
return n_rel,n_tag
def _set_class_weight(self,n_class,n_rel=None,init_feature_dim = 5*10**5):
#if n_rel == None:
# n_rel = [1]*n_class
#assert len(n_rel) == n_class
#self.weight = [np.zeros(shape = (init_feature_dim,nt,nr),dtype=WEIGHT_DTYPE) for nr,nt in zip(n_rel,n_tag)]
#self.aux_weight = [np.zeros(shape = (init_feature_dim,nt,nr),dtype=WEIGHT_DTYPE) for nr,nt in zip(n_rel,n_tag)]
#self.avg_weight = [np.zeros(shape = (init_feature_dim,nt,nr),dtype=WEIGHT_DTYPE) for nr,nt in zip(n_rel,n_tag)]
self.weight = [np.zeros(shape = (init_feature_dim,nr),dtype=WEIGHT_DTYPE) for nr in n_rel]
self.aux_weight = [np.zeros(shape = (init_feature_dim,nr),dtype=WEIGHT_DTYPE) for nr in n_rel]
self.avg_weight = [np.zeros(shape = (init_feature_dim,nr),dtype=WEIGHT_DTYPE) for nr in n_rel]
def read_templates(self):
ff_name = self._feats_templates_file if self._feats_templates_file else _FEATURE_TEMPLATES_FILE
for line in open(ff_name,'r'):
line = line.strip()
if not line:
pass
elif line.startswith('#'):
pass
else:
elements = line.split()
#elements.extend(['tx'])
template = "'%s=%s' %% (%s)"%('&'.join(elements),'%s_'*len(elements),','.join(elements))
self._feature_templates_list.append((template,elements))
def output_feature_generator(self):
"""based on feature autoeval method in (Huang,2010)'s parser"""
import time
self._feats_gen_filename = 'feats_gen_'+self._feats_templates_file.split('/')[-1].split('.')[0] #str(int(time.time()))
output = open('./temp/'+self._feats_gen_filename+'.py','w')
output.write('#generated by model.py\n')
output.write('from constants import *\n')
output.write('def generate_features(state,action):\n')
output.write(Model.indent+'s0,b0,a0=state.get_feature_context_window(action)\n')
element_set = set([])
definition_str = Model.indent+'feats=[]\n'
append_feats_str = ''
definition_str += Model.indent+"act_idx = state.model.class_codebook.get_index(action['type'])\n"
definition_str += Model.indent+"tx = action['tag'] if 'tag' in action else EMPTY\n"
#definition_str += Model.indent+"lx = action['edge_label'] if 'edge_label' in action else EMPTY\n"
#definition_str += Model.indent+"print state.model.class_codebook._label_to_index\n"
#print self._feature_templates_list
for template,elements in self._feature_templates_list:
for e in elements: # definition
if e not in element_set:
sub_elements = e.split('_')
if len(sub_elements) == 2:
definition_str += "%s%s=%s['%s'] if %s else EMPTY\n" % (Model.indent,e,sub_elements[0],FEATS_ABBR[sub_elements[1]],sub_elements[0])
elif len(sub_elements) == 3:
definition_str += "%s%s=%s['%s']['%s'] if %s and %s['%s'] else EMPTY\n" % (Model.indent,e,sub_elements[0],FEATS_ABBR[sub_elements[1]],FEATS_ABBR[sub_elements[2]],sub_elements[0],sub_elements[0],FEATS_ABBR[sub_elements[1]])
else:
pass
element_set.add(e)
else:
pass
#append_feats_str += "%sif [%s] != %s*[None]:feats.append(%s)\n" % (Model.indent,','.join(elements),len(elements),template)
append_feats_str += "%sfeats.append(%s)\n" % (Model.indent,template)
#append_feats_str += "%sstate.model.feature_codebook[act_idx].add(%s)\n" % (Model.indent,template)
#definition_str += "%seqpfx=s0['lemma'][:4]==tx[:4] if tx is not EMPTY and len(s0['lemma'])>4 else EMPTY\n"%(Model.indent)
#append_feats_str += "%sfeats.append('s0_len&eqpfx=%%s_%%s_'%%(s0['len'],eqpfx))\n"%(Model.indent)
#definition_str += "%spath=[(state.sent[i]['pos'],state.sent[i]['rel']) for i in state.deptree.get_path(b0['id'],a0['id'])] if b0 and a0 else EMPTY\n"%(Model.indent)
definition_str += "%sdist1=abs(s0['id']-b0['id']) if b0 else EMPTY\n"%(Model.indent)
definition_str += "%sif dist1 > 10: dist1=10\n"%(Model.indent)
definition_str += "%sdist2=abs(a0['id']-b0['id']) if b0 and a0 else EMPTY\n"%(Model.indent)
definition_str += "%sif dist2 > 10: dist2=10\n"%(Model.indent)
output.write(definition_str)
output.write(append_feats_str)
output.write('%sreturn feats' % Model.indent)
output.close()
#sys.path.append('/temp/')
print "Importing feature generator!"
self.feats_generator = importlib.import_module('temp.'+self._feats_gen_filename).generate_features
def toJSON(self):
print 'Converting model to JSON'
print 'class size: %s \nrelation size: %s \ntag size: %s'%(self.class_codebook.size(),self.rel_codebook.size(),map(lambda x:'%s->%s '%(x,self.tag_codebook[x].size()),self.tag_codebook.keys()))
print 'feature codebook size: %s' % (','.join(('%s:%s')%(i,f.size()) for i,f in self.feature_codebook.items()))
print 'weight shape: %s' % (','.join(('%s:%s')%(i,w.shape) for i,w in enumerate(self.avg_weight)))
print 'token to concept table: %s' % (len(self.token_to_concept_table))
model_dict = {
'_feature_templates_list': self._feature_templates_list,
'_feats_gen_filename':self._feats_gen_filename,
#'weight':[w.tolist() for w in self.weight],
#'aux_weight':[axw.tolist() for axw in self.aux_weight],
'avg_weight':[agw.tolist() for agw in self.avg_weight],
'token_to_concept_table': dict([(k,list(v)) for k,v in self.token_to_concept_table.items()]),
'class_codebook':self.class_codebook.to_dict(),
'feature_codebook':self.feature_codebook.to_dict(),
'rel_codebook':self.rel_codebook.to_dict(),
'tag_codebook':dict([(k,self.tag_codebook[k].to_dict()) for k in self.tag_codebook])
}
return model_dict
def save_model(self,model_filename):
#pickle.dump(self,open(model_filename,'wb'),pickle.HIGHEST_PROTOCOL)
print >> self.elog, 'Model info:'
print >> self.elog,'class size: %s \nrelation size: %s \ntag size: %s'%(self.class_codebook.size(),self.rel_codebook.size(),map(lambda x:'%s->%s '%(x,self.tag_codebook[x].size()),self.tag_codebook.keys()))
print >> self.elog,'feature codebook size: %s' % (','.join(('%s:%s')%(i,f.size()) for i,f in self.feature_codebook.items()))
#print 'weight shape: %s' % (self.avg_weight.shape)
print >> self.elog,'weight shape: %s' % (','.join(('%s:%s')%(i,w.shape) for i,w in enumerate(self.avg_weight)))
print >> self.elog,'token to concept table: %s' % (len(self.token_to_concept_table))
with contextlib.closing(bz2.BZ2File(model_filename, 'wb')) as f:
pickle.dump(self,f,pickle.HIGHEST_PROTOCOL)
#json.dump(self.toJSON(),open(model_filename,'wb'))
@staticmethod
def load_model(model_filename):
with contextlib.closing(bz2.BZ2File(model_filename, 'rb')) as f:
model = pickle.load(f)
return model
#return pickle.load(open(model_filename,'rb'))
'''
def _pruning_abttag(self,threshold=8):
pruned_abttag_codebook = Alphabet()
for v in self.tag_codebook['ABTTag'].labels():
if self.abttag_count[v] >= 8:
pruned_abttag_codebook.add(v)
self.tag_codebook['ABTTag'] = pruned_abttag_codebook
class Model():
"""weights and templates"""
#weight = None
#n_class = None
#n_rel = None
#n_tag = None
indent = " "*4
#feature_codebook = None
#class_codebook = None
#feats_generator = None
def __init__(self,elog=sys.stdout):
self.elog = elog
self.weight = None
self.aux_weight = None
self.avg_weight = None # for store the averaged weights
#self.n_class = n_class
#self.n_rel = n_rel
#self.n_tag = n_tag
self._feats_templates_file = _FEATURE_TEMPLATES_FILE
self._feature_templates_list = []
self._feats_gen_filename = None
self.feats_generator = None
self.token_to_concept_table = defaultdict(set)
self.pp_count_dict = defaultdict(int)
self.total_num_words = 0
self.token_label_set = defaultdict(set)
self.class_codebook = None
self.feature_codebook = None
self.rel_codebook = Alphabet()
self.tag_codebook = {
'Concept':Alphabet(),
'ETag':Alphabet(),
'ConstTag':Alphabet(),
'ABTTag':Alphabet()
}
self.abttag_count = defaultdict(int)
def setup(self,action_type,instances,parser,feature_templates_file=None):
if feature_templates_file:
self._feats_templates_file = feature_templates_file
self.class_codebook = Alphabet.from_dict(dict((i,k) for i,(k,v) in enumerate(ACTION_TYPE_TABLE[action_type])),True)
self.feature_codebook = dict([(i,Alphabet()) for i in self.class_codebook._index_to_label.keys()])
self.read_templates()
#n_rel,n_tag = self._set_rel_tag_codebooks(instances,parser)
n_subclass = self._set_rel_tag_codebooks(instances,parser)
self._set_class_weight(self.class_codebook.size(),n_subclass)
self._set_statistics(instances)
self.output_feature_generator()
def _set_statistics(self,instances):
#pp_count_dict = defaultdict(int)
for inst in instances:
sent = inst.tokens
self.total_num_words += len(sent)
for token in sent:
if token['pos'] == 'IN' and token['rel'] == 'prep':
self.pp_count_dict[token['form'].lower()] += 1
def _set_rel_tag_codebooks(self,instances,parser):
#TODO
self.rel_codebook.add(NULL_EDGE)
self.rel_codebook.add(START_EDGE)
#self.tag_codebook['Concept'].add(NULL_TAG)
for inst in instances:
gold_graph = inst.gold_graph
gold_nodes = gold_graph.nodes
#gold_edges = gold_graph.edges
sent_tokens = inst.tokens
#state = parser.testOracleGuide(inst)
for g,d in gold_graph.tuples():
if isinstance(g,int):
gnode = gold_nodes[g]
g_span_wds = [tok['lemma'] for tok in sent_tokens if tok['id'] in range(gnode.start,gnode.end)]
g_span_ne = sent_tokens[g]['ne']
g_entity_tag = gold_graph.get_node_tag(g)
#if len(g_span_wds) > 1:
# for gwd in g_span_wds:
# self.token_to_concept_table[gwd].add(g_entity_tag)
if g_span_ne not in ['O','NUMBER']: # is name entity
self.token_to_concept_table[g_span_ne].add(g_entity_tag)
self.token_to_concept_table[','.join(g_span_wds)].add(g_entity_tag)
if isinstance(g_entity_tag,ETag):
self.tag_codebook['ETag'].add(g_entity_tag)
elif isinstance(g_entity_tag,ConstTag):
self.tag_codebook['ConstTag'].add(g_entity_tag)
else:
self.tag_codebook['Concept'].add(g_entity_tag)
else:
g_entity_tag = gold_graph.get_node_tag(g)
self.tag_codebook['ABTTag'].add(g_entity_tag)
self.abttag_count[g_entity_tag] += 1
'''
elif g in state.gold_graph.abt_node_table and isinstance(state.gold_graph.abt_node_table[g],int): # post aligned
gnode = state.A.nodes[state.gold_graph.abt_node_table[g]]
g_span_wds = [tok['lemma'] for tok in sent_tokens if tok['id'] in range(gnode.start,gnode.end)]
g_span_ne = sent_tokens[state.gold_graph.abt_node_table[g]]['ne']
g_entity_tag = gold_graph.get_node_tag(g)
if g_span_ne not in ['O','NUMBER']: # is name entity
self.token_to_concept_table[g_span_ne].add(g_entity_tag)
self.token_to_concept_table[','.join(g_span_wds)].add(g_entity_tag)
if isinstance(g_entity_tag,ETag):
self.tag_codebook['ETag'].add(g_entity_tag)
elif isinstance(g_entity_tag,ConstTag):
self.tag_codebook['ConstTag'].add(g_entity_tag)
else:
self.tag_codebook['Concept'].add(g_entity_tag)
'''
if isinstance(d,int):
dnode = gold_nodes[d]
d_span_wds = [tok['lemma'] for tok in sent_tokens if tok['id'] in range(dnode.start,dnode.end)]
d_span_ne = sent_tokens[d]['ne']
d_entity_tag = gold_graph.get_node_tag(d)
#if len(d_span_wds) > 1:
# for dwd in d_span_wds:
# self.token_to_concept_table[dwd].add(d_entity_tag)
if d_span_ne not in ['O','NUMBER']:
self.token_to_concept_table[d_span_ne].add(d_entity_tag)
self.token_to_concept_table[','.join(d_span_wds)].add(d_entity_tag)
if isinstance(d_entity_tag,ETag):
self.tag_codebook['ETag'].add(d_entity_tag)
elif isinstance(d_entity_tag,ConstTag):
self.tag_codebook['ConstTag'].add(d_entity_tag)
else:
self.tag_codebook['Concept'].add(d_entity_tag)
#self.tag_codebook.add(d_entity_tag)
else:
d_entity_tag = gold_graph.get_node_tag(d)
self.tag_codebook['ABTTag'].add(d_entity_tag)
self.abttag_count[d_entity_tag] += 1
'''
elif d in state.gold_graph.abt_node_table and isinstance(state.gold_graph.abt_node_table[d],int): # post aligned
dnode = state.A.nodes[state.gold_graph.abt_node_table[d]]
d_span_wds = [tok['lemma'] for tok in sent_tokens if tok['id'] in range(dnode.start,dnode.end)]
d_span_ne = sent_tokens[state.gold_graph.abt_node_table[d]]['ne']
d_entity_tag = gold_graph.get_node_tag(d)
if d_span_ne not in ['O','NUMBER']: # is name entity
self.token_to_concept_table[d_span_ne].add(d_entity_tag)
self.token_to_concept_table[','.join(d_span_wds)].add(d_entity_tag)
if isinstance(d_entity_tag,ETag):
self.tag_codebook['ETag'].add(d_entity_tag)
elif isinstance(d_entity_tag,ConstTag):
self.tag_codebook['ConstTag'].add(d_entity_tag)
else:
self.tag_codebook['Concept'].add(d_entity_tag)
'''
g_edge_label = gold_graph.get_edge_label(g,d)
#if g_span_ne not in ['O','NUMBER']:
# self.token_label_set[g_span_ne].add(g_edge_label)
#self.token_label_set[','.join(g_span_wds)].add(g_edge_label)
self.rel_codebook.add(g_edge_label)
# reset
# inst.gold_graph.abt_node_table = {}
#n_rel = [1]*self.class_codebook.size()
#n_tag = [1]*self.class_codebook.size()
n_subclass = [1]*self.class_codebook.size()
#self._pruning_abttag()
for k,v in self.class_codebook._index_to_label.items():
if v in ACTION_WITH_TAG:
#n_tag[k] = reduce(lambda x,y: x+y, map(lambda z: self.tag_codebook[z].size(), self.tag_codebook.keys()))
n_subclass[k] = self.tag_codebook['ABTTag'].size()
if v in ACTION_WITH_EDGE:
#n_rel[k] = self.rel_codebook.size()
n_subclass[k] = self.rel_codebook.size()
#return n_rel,n_tag
return n_subclass
def _pruning_abttag(self,threshold=8):
pruned_abttag_codebook = Alphabet()
for v in self.tag_codebook['ABTTag'].labels():
if self.abttag_count[v] >= 8:
pruned_abttag_codebook.add(v)
self.tag_codebook['ABTTag'] = pruned_abttag_codebook
def _set_class_weight(self,n_class,n_subclass=None,init_feature_dim = 5*10**5):
#if n_rel == None:
# n_rel = [1]*n_class
#assert len(n_rel) == n_class
#self.weight = [np.zeros(shape = (init_feature_dim,nt,nr),dtype=WEIGHT_DTYPE) for nr,nt in zip(n_rel,n_tag)]
#self.aux_weight = [np.zeros(shape = (init_feature_dim,nt,nr),dtype=WEIGHT_DTYPE) for nr,nt in zip(n_rel,n_tag)]
#self.avg_weight = [np.zeros(shape = (init_feature_dim,nt,nr),dtype=WEIGHT_DTYPE) for nr,nt in zip(n_rel,n_tag)]
self.weight = [np.zeros(shape = (init_feature_dim,ns),dtype=WEIGHT_DTYPE) for ns in n_subclass]
self.aux_weight = [np.zeros(shape = (init_feature_dim,ns),dtype=WEIGHT_DTYPE) for ns in n_subclass]
self.avg_weight = [np.zeros(shape = (init_feature_dim,ns),dtype=WEIGHT_DTYPE) for ns in n_subclass]
def read_templates(self):
ff_name = self._feats_templates_file
for line in open(ff_name,'r'):
line = line.strip()
if not line:
pass
elif line.startswith('#'):
pass
else:
elements = line.split()
#elements.extend(['tx'])
template = "'%s=%s' %% (%s)"%('&'.join(elements),'%s_'*len(elements),','.join(elements))
self._feature_templates_list.append((template,elements))
def output_feature_generator(self):
"""based on feature autoeval method in (Huang,2010)'s parser"""
import time
self._feats_gen_filename = 'feats_gen_'+self._feats_templates_file.split('/')[-1].split('.')[0]#str(int(time.time()))
output = open('./temp/'+self._feats_gen_filename+'.py','w')
output.write('#generated by model.py\n')
output.write('from constants import *\n')
output.write('def generate_features(state,action):\n')
output.write(Model.indent+'s0,b0,a0=state.get_feature_context_window(action)\n')
element_set = set([])
definition_str = Model.indent+'feats=[]\n'
append_feats_str = ''
definition_str += Model.indent+"act_idx = state.model.class_codebook.get_index(action['type'])\n"
definition_str += Model.indent+"tx = action['tag'] if 'tag' in action else EMPTY\n"
#definition_str += Model.indent+"txv = len(tx.split('-'))==2 if tx is not EMPTY else EMPTY\n"
#definition_str += Model.indent+"lx = action['edge_label'] if 'edge_label' in action else EMPTY\n"
#definition_str += Model.indent+"print state.model.class_codebook._label_to_index\n"
#print self._feature_templates_list
for template,elements in self._feature_templates_list:
for e in elements: # definition
if e not in element_set:
sub_elements = e.split('_')
if len(sub_elements) == 2:
definition_str += "%s%s=%s['%s'] if %s else EMPTY\n" % (Model.indent,e,sub_elements[0],FEATS_ABBR[sub_elements[1]],sub_elements[0])
elif len(sub_elements) == 3:
definition_str += "%s%s=%s['%s']['%s'] if %s and %s['%s'] else EMPTY\n" % (Model.indent,e,sub_elements[0],FEATS_ABBR[sub_elements[1]],FEATS_ABBR[sub_elements[2]],sub_elements[0],sub_elements[0],FEATS_ABBR[sub_elements[1]])
else:
pass
element_set.add(e)
else:
pass
append_feats_str += "%sif [%s] != %s*[None]:feats.append(%s)\n" % (Model.indent,','.join(elements),len(elements),template)
#append_feats_str += "%sfeats.append(%s)\n" % (Model.indent,template)
definition_str += "%sdist1=abs(s0['id']-b0['id']) if b0 and b0 is not ABT_TOKEN and s0 is not ABT_TOKEN else EMPTY\n"%(Model.indent)
definition_str += "%sif dist1 > 10: dist1=10\n"%(Model.indent)
definition_str += "%sdist2=abs(a0['id']-b0['id']) if b0 and a0 and b0 is not ABT_TOKEN and a0 is not ABT_TOKEN else EMPTY\n"%(Model.indent)
definition_str += "%sif dist2 > 10: dist2=10\n"%(Model.indent)
#for i in range(50):
# definition_str += "{}s_v{}=WORD_VECS[s0['form']][{}] if WORD_VECS[s0['form']] is not '' else EMPTY\n".format(Model.indent, i, i)
#for i in range(50):
# definition_str += "{}if s_v{} is not None: feats.append('s_v{}=s_v{}.format({}, {})')\n".format(
# Model.indent, i, i, i, i, i)
#definition_str += "%seqfrmset=s0['eqfrmset']\n"%(Model.indent)
output.write(definition_str)
output.write(append_feats_str)
output.write('%sreturn feats' % Model.indent)
output.close()
#sys.path.append('/temp/')
print "Importing feature generator!"
self.feats_generator = importlib.import_module('temp.'+self._feats_gen_filename).generate_features
def toJSON(self):
print 'Converting model to JSON'
print 'class size: %s \nrelation size: %s \ntag size: %s'%(self.class_codebook.size(),self.rel_codebook.size(),map(lambda x:'%s->%s '%(x,self.tag_codebook[x].size()),self.tag_codebook.keys()))
print 'feature codebook size: %s' % (','.join(('%s:%s')%(i,f.size()) for i,f in self.feature_codebook.items()))
print 'weight shape: %s' % (','.join(('%s:%s')%(i,w.shape) for i,w in enumerate(self.avg_weight)))
print 'token to concept table: %s' % (len(self.token_to_concept_table))
model_dict = {
'_feature_templates_list': self._feature_templates_list,
'_feats_gen_filename':self._feats_gen_filename,
#'weight':[w.tolist() for w in self.weight],
#'aux_weight':[axw.tolist() for axw in self.aux_weight],
'avg_weight':[agw.tolist() for agw in self.avg_weight],
'token_to_concept_table': dict([(k,list(v)) for k,v in self.token_to_concept_table.items()]),
'class_codebook':self.class_codebook.to_dict(),
'feature_codebook':self.feature_codebook.to_dict(),
'rel_codebook':self.rel_codebook.to_dict(),
'tag_codebook':dict([(k,self.tag_codebook[k].to_dict()) for k in self.tag_codebook])
}
return model_dict
def save_model(self,model_filename):
#pickle.dump(self,open(model_filename,'wb'),pickle.HIGHEST_PROTOCOL)
print >> self.elog, 'Model info:'
print >> self.elog,'class size: %s \nrelation size: %s \ntag size: %s'%(self.class_codebook.size(),self.rel_codebook.size(),map(lambda x:'%s->%s '%(x,self.tag_codebook[x].size()),self.tag_codebook.keys()))
print >> self.elog,'feature codebook size: %s' % (','.join(('%s:%s')%(i,f.size()) for i,f in self.feature_codebook.items()))
#print 'weight shape: %s' % (self.avg_weight.shape)
print >> self.elog,'weight shape: %s' % (','.join(('%s:%s')%(i,w.shape) for i,w in enumerate(self.avg_weight)))
print >> self.elog,'token to concept table: %s' % (len(self.token_to_concept_table))
#weight = self.weight
#aux_weight = self.aux_weight
#avg_weight = self.avg_weight
#self.weight = None
#self.aux_weight = None
#self.avg_weight = None
#try:
# np.save(open(model_filename+'.weight', 'wb'),avg_weight)
#except SystemError as e:
# print >> sys.stderr, 'Saving model error:', e
# pass
try:
with contextlib.closing(bz2.BZ2File(model_filename, 'wb')) as f:
pickle.dump(self,f,pickle.HIGHEST_PROTOCOL)
except:
print >> sys.stderr, 'Saving model error', sys.exc_info()[0]
pass
#self.weight = weight
#self.aux_weight = aux_weight
#self.avg_weight = avg_weight
@staticmethod
def load_model(model_filename):
with contextlib.closing(bz2.BZ2File(model_filename, 'rb')) as f:
model = pickle.load(f)
# deal with module name conflict
#tmp = sys.path.pop(0)
#model.avg_weight = np.load(open(model_filename+'.weight', 'rb'))
#sys.path.insert(0,tmp)
#return model
#return pickle.load(open(model_filename,'rb'))
'''
def _pruning_abttag(self,threshold=8):
pruned_abttag_codebook = Alphabet()
for v in self.tag_codebook['ABTTag'].labels():
if self.abttag_count[v] >= 8:
pruned_abttag_codebook.add(v)
self.tag_codebook['ABTTag'] = pruned_abttag_codebook
class Model():
"""weights and templates"""
#weight = None
#n_class = None
#n_rel = None
#n_tag = None
indent = " "*4
#feature_codebook = None
#class_codebook = None
#feats_generator = None
def __init__(self,elog=sys.stdout):
self.elog = elog
self.weight = None
self.aux_weight = None
self.avg_weight = None # for store the averaged weights
#self.n_class = n_class
#self.n_rel = n_rel
#self.n_tag = n_tag
self._feats_templates_file = _FEATURE_TEMPLATES_FILE
self._feature_templates_list = []
self._feats_gen_filename = None
self.feats_generator = None
self.token_to_concept_table = defaultdict(set)
self.pp_count_dict = defaultdict(int)
self.total_num_words = 0
self.token_label_set = defaultdict(set)
self.class_codebook = None
self.feature_codebook = None
self.rel_codebook = Alphabet()
self.tag_codebook = {
'Concept':Alphabet(),
'ETag':Alphabet(),
'ConstTag':Alphabet(),
'ABTTag':Alphabet()
}
self.abttag_count = defaultdict(int)
def setup(self,action_type,instances,parser,feature_templates_file=None):
if feature_templates_file:
self._feats_templates_file = feature_templates_file
self.class_codebook = Alphabet.from_dict(dict((i,k) for i,(k,v) in enumerate(ACTION_TYPE_TABLE[action_type])),True)
self.feature_codebook = dict([(i,Alphabet()) for i in self.class_codebook._index_to_label.keys()])
self.read_templates()
#n_rel,n_tag = self._set_rel_tag_codebooks(instances,parser)
#print dir(instances)
n_subclass = self._set_rel_tag_codebooks(instances,parser)
self._set_class_weight(self.class_codebook.size(),n_subclass)
self._set_statistics(instances)
self.output_feature_generator()
def _set_statistics(self,instances):
#pp_count_dict = defaultdict(int)
for inst in instances:
sent = inst.tokens
self.total_num_words += len(sent)
for token in sent:
if token['pos'] == 'IN' and token['rel'] == 'prep':
self.pp_count_dict[token['form'].lower()] += 1
def _set_rel_tag_codebooks(self,instances,parser):
#TODO
self.rel_codebook.add(NULL_EDGE)
self.rel_codebook.add(START_EDGE)
#self.tag_codebook['Concept'].add(NULL_TAG)
print len(instances)
#sys.exit()
for inst in instances:
gold_graph = inst.gold_graph
#print gold_graph.tuples()
gold_nodes = gold_graph.nodes
#print gold_nodes
#gold_edges = gold_graph.edges
sent_tokens = inst.tokens
#print sent_tokens
#state = parser.testOracleGuide(inst)
for g,d in gold_graph.tuples():
#print g
#print d
#print gold_graph.tuples()
if isinstance(g,int):
gnode = gold_nodes[g]
g_span_wds = [tok['lemma'] for tok in sent_tokens if tok['id'] in range(gnode.start,gnode.end)]
g_span_ne = sent_tokens[g]['ne']
g_entity_tag = gold_graph.get_node_tag(g)
#if len(g_span_wds) > 1:
# for gwd in g_span_wds:
# self.token_to_concept_table[gwd].add(g_entity_tag)
if g_span_ne not in ['O','NUMBER']: # is name entity
self.token_to_concept_table[g_span_ne].add(g_entity_tag)
self.token_to_concept_table[','.join(g_span_wds)].add(g_entity_tag)
if isinstance(g_entity_tag,ETag):
self.tag_codebook['ETag'].add(g_entity_tag)
elif isinstance(g_entity_tag,ConstTag):
self.tag_codebook['ConstTag'].add(g_entity_tag)
else:
self.tag_codebook['Concept'].add(g_entity_tag)
else:
g_entity_tag = gold_graph.get_node_tag(g)
self.tag_codebook['ABTTag'].add(g_entity_tag)
self.abttag_count[g_entity_tag] += 1
'''
elif g in state.gold_graph.abt_node_table and isinstance(state.gold_graph.abt_node_table[g],int): # post aligned
gnode = state.A.nodes[state.gold_graph.abt_node_table[g]]
g_span_wds = [tok['lemma'] for tok in sent_tokens if tok['id'] in range(gnode.start,gnode.end)]
g_span_ne = sent_tokens[state.gold_graph.abt_node_table[g]]['ne']
g_entity_tag = gold_graph.get_node_tag(g)
if g_span_ne not in ['O','NUMBER']: # is name entity
self.token_to_concept_table[g_span_ne].add(g_entity_tag)
self.token_to_concept_table[','.join(g_span_wds)].add(g_entity_tag)
if isinstance(g_entity_tag,ETag):
self.tag_codebook['ETag'].add(g_entity_tag)
elif isinstance(g_entity_tag,ConstTag):
self.tag_codebook['ConstTag'].add(g_entity_tag)
else:
self.tag_codebook['Concept'].add(g_entity_tag)
'''
if isinstance(d,int):
dnode = gold_nodes[d]
d_span_wds = [tok['lemma'] for tok in sent_tokens if tok['id'] in range(dnode.start,dnode.end)]
print sent_tokens
#print gold_nodes[d]
#print sent_tokens[d]
d_span_ne = sent_tokens[d]['ne']
d_entity_tag = gold_graph.get_node_tag(d)
#if len(d_span_wds) > 1:
# for dwd in d_span_wds:
# self.token_to_concept_table[dwd].add(d_entity_tag)
if d_span_ne not in ['O','NUMBER']:
self.token_to_concept_table[d_span_ne].add(d_entity_tag)
self.token_to_concept_table[','.join(d_span_wds)].add(d_entity_tag)
if isinstance(d_entity_tag,ETag):
self.tag_codebook['ETag'].add(d_entity_tag)
elif isinstance(d_entity_tag,ConstTag):
self.tag_codebook['ConstTag'].add(d_entity_tag)
else:
self.tag_codebook['Concept'].add(d_entity_tag)
#self.tag_codebook.add(d_entity_tag)
else:
d_entity_tag = gold_graph.get_node_tag(d)
self.tag_codebook['ABTTag'].add(d_entity_tag)
self.abttag_count[d_entity_tag] += 1
'''
elif d in state.gold_graph.abt_node_table and isinstance(state.gold_graph.abt_node_table[d],int): # post aligned
dnode = state.A.nodes[state.gold_graph.abt_node_table[d]]
d_span_wds = [tok['lemma'] for tok in sent_tokens if tok['id'] in range(dnode.start,dnode.end)]
d_span_ne = sent_tokens[state.gold_graph.abt_node_table[d]]['ne']
d_entity_tag = gold_graph.get_node_tag(d)
if d_span_ne not in ['O','NUMBER']: # is name entity
self.token_to_concept_table[d_span_ne].add(d_entity_tag)
self.token_to_concept_table[','.join(d_span_wds)].add(d_entity_tag)
if isinstance(d_entity_tag,ETag):
self.tag_codebook['ETag'].add(d_entity_tag)
elif isinstance(d_entity_tag,ConstTag):
self.tag_codebook['ConstTag'].add(d_entity_tag)
else:
self.tag_codebook['Concept'].add(d_entity_tag)
'''
g_edge_label = gold_graph.get_edge_label(g,d)
#if g_span_ne not in ['O','NUMBER']:
# self.token_label_set[g_span_ne].add(g_edge_label)
#self.token_label_set[','.join(g_span_wds)].add(g_edge_label)
self.rel_codebook.add(g_edge_label)
# reset
# inst.gold_graph.abt_node_table = {}
#n_rel = [1]*self.class_codebook.size()
#n_tag = [1]*self.class_codebook.size()
n_subclass = [1]*self.class_codebook.size()
#self._pruning_abttag()
for k,v in self.class_codebook._index_to_label.items():
if v in ACTION_WITH_TAG:
#n_tag[k] = reduce(lambda x,y: x+y, map(lambda z: self.tag_codebook[z].size(), self.tag_codebook.keys()))
n_subclass[k] = self.tag_codebook['ABTTag'].size()
if v in ACTION_WITH_EDGE:
#n_rel[k] = self.rel_codebook.size()
n_subclass[k] = self.rel_codebook.size()
#return n_rel,n_tag
return n_subclass
def _pruning_abttag(self,threshold=8):
pruned_abttag_codebook = Alphabet()
for v in self.tag_codebook['ABTTag'].labels():
if self.abttag_count[v] >= 8:
pruned_abttag_codebook.add(v)
self.tag_codebook['ABTTag'] = pruned_abttag_codebook
def _set_class_weight(self,n_class,n_subclass=None,init_feature_dim = 10**5):
#if n_rel == None:
# n_rel = [1]*n_class
#assert len(n_rel) == n_class
#self.weight = [np.zeros(shape = (init_feature_dim,nt,nr),dtype=WEIGHT_DTYPE) for nr,nt in zip(n_rel,n_tag)]
#self.aux_weight = [np.zeros(shape = (init_feature_dim,nt,nr),dtype=WEIGHT_DTYPE) for nr,nt in zip(n_rel,n_tag)]
#self.avg_weight = [np.zeros(shape = (init_feature_dim,nt,nr),dtype=WEIGHT_DTYPE) for nr,nt in zip(n_rel,n_tag)]
self.weight = [np.zeros(shape = (init_feature_dim,ns),dtype=WEIGHT_DTYPE) for ns in n_subclass]
self.aux_weight = [np.zeros(shape = (init_feature_dim,ns),dtype=WEIGHT_DTYPE) for ns in n_subclass]
self.avg_weight = [np.zeros(shape = (init_feature_dim,ns),dtype=WEIGHT_DTYPE) for ns in n_subclass]
def read_templates(self):
ff_name = self._feats_templates_file
for line in open(ff_name,'r'):
line = line.strip()
if not line:
pass
elif line.startswith('#'):
pass
else:
elements = line.split()
#elements.extend(['tx'])
template = "'%s=%s' %% (%s)"%('&'.join(elements),'%s_'*len(elements),','.join(elements))
self._feature_templates_list.append((template,elements))
def output_feature_generator(self):
"""based on feature autoeval method in (Huang,2010)'s parser"""
import time
self._feats_gen_filename = 'feats_gen_'+self._feats_templates_file.split('/')[-1].split('.')[0]#str(int(time.time()))
output = open('./temp/'+self._feats_gen_filename+'.py','w')
output.write('#generated by model.py\n')
output.write('from constants import *\n')
output.write('def generate_features(state,action):\n')
output.write(Model.indent+'s0,b0,a0=state.get_feature_context_window(action)\n')
element_set = set([])
definition_str = Model.indent+'feats=[]\n'
append_feats_str = ''
definition_str += Model.indent+"act_idx = state.model.class_codebook.get_index(action['type'])\n"
definition_str += Model.indent+"tx = action['tag'] if 'tag' in action else EMPTY\n"
#definition_str += Model.indent+"txv = len(tx.split('-'))==2 if tx is not EMPTY else EMPTY\n"
#definition_str += Model.indent+"lx = action['edge_label'] if 'edge_label' in action else EMPTY\n"
#definition_str += Model.indent+"print state.model.class_codebook._label_to_index\n"
#print self._feature_templates_list
for template,elements in self._feature_templates_list:
for e in elements: # definition
if e not in element_set:
sub_elements = e.split('_')
if len(sub_elements) == 2:
definition_str += "%s%s=%s['%s'] if %s else EMPTY\n" % (Model.indent,e,sub_elements[0],FEATS_ABBR[sub_elements[1]],sub_elements[0])
elif len(sub_elements) == 3:
definition_str += "%s%s=%s['%s']['%s'] if %s and %s['%s'] else EMPTY\n" % (Model.indent,e,sub_elements[0],FEATS_ABBR[sub_elements[1]],FEATS_ABBR[sub_elements[2]],sub_elements[0],sub_elements[0],FEATS_ABBR[sub_elements[1]])
else:
pass
element_set.add(e)
else:
pass
append_feats_str += "%sif [%s] != %s*[None]:feats.append(%s)\n" % (Model.indent,','.join(elements),len(elements),template)
#append_feats_str += "%sfeats.append(%s)\n" % (Model.indent,template)
definition_str += "%sdist1=abs(s0['id']-b0['id']) if b0 and b0 is not ABT_TOKEN and s0 is not ABT_TOKEN else EMPTY\n"%(Model.indent)
definition_str += "%sif dist1 > 10: dist1=10\n"%(Model.indent)
definition_str += "%sdist2=abs(a0['id']-b0['id']) if b0 and a0 and b0 is not ABT_TOKEN and a0 is not ABT_TOKEN else EMPTY\n"%(Model.indent)
definition_str += "%sif dist2 > 10: dist2=10\n"%(Model.indent)
#definition_str += "%seqfrmset=s0['eqfrmset']\n"%(Model.indent)
output.write(definition_str)
output.write(append_feats_str)
output.write('%sreturn feats' % Model.indent)
output.close()
#sys.path.append('/temp/')
print "Importing feature generator!"
self.feats_generator = importlib.import_module('temp.'+self._feats_gen_filename).generate_features
def toJSON(self):
print 'Converting model to JSON'
print 'class size: %s \nrelation size: %s \ntag size: %s'%(self.class_codebook.size(),self.rel_codebook.size(),map(lambda x:'%s->%s '%(x,self.tag_codebook[x].size()),self.tag_codebook.keys()))
print 'feature codebook size: %s' % (','.join(('%s:%s')%(i,f.size()) for i,f in self.feature_codebook.items()))
print 'weight shape: %s' % (','.join(('%s:%s')%(i,w.shape) for i,w in enumerate(self.avg_weight)))
print 'token to concept table: %s' % (len(self.token_to_concept_table))
model_dict = {
'_feature_templates_list': self._feature_templates_list,
'_feats_gen_filename':self._feats_gen_filename,
#'weight':[w.tolist() for w in self.weight],
#'aux_weight':[axw.tolist() for axw in self.aux_weight],
'avg_weight':[agw.tolist() for agw in self.avg_weight],
'token_to_concept_table': dict([(k,list(v)) for k,v in self.token_to_concept_table.items()]),
'class_codebook':self.class_codebook.to_dict(),
'feature_codebook':self.feature_codebook.to_dict(),
'rel_codebook':self.rel_codebook.to_dict(),
'tag_codebook':dict([(k,self.tag_codebook[k].to_dict()) for k in self.tag_codebook])
}
return model_dict
def save_model(self,model_filename):
#pickle.dump(self,open(model_filename,'wb'),pickle.HIGHEST_PROTOCOL)
print >> self.elog, 'Model info:'
print >> self.elog,'class size: %s \nrelation size: %s \ntag size: %s'%(self.class_codebook.size(),self.rel_codebook.size(),map(lambda x:'%s->%s '%(x,self.tag_codebook[x].size()),self.tag_codebook.keys()))
print >> self.elog,'feature codebook size: %s' % (','.join(('%s:%s')%(i,f.size()) for i,f in self.feature_codebook.items()))
#print 'weight shape: %s' % (self.avg_weight.shape)
print >> self.elog,'weight shape: %s' % (','.join(('%s:%s')%(i,w.shape) for i,w in enumerate(self.avg_weight)))
print >> self.elog,'token to concept table: %s' % (len(self.token_to_concept_table))
weight = self.weight
aux_weight = self.aux_weight
#avg_weight = self.avg_weight
self.weight = None
self.aux_weight = None
#self.avg_weight = None
#try:
# np.save(open(model_filename+'.weight', 'wb'),avg_weight)
#except SystemError as e:
# print >> sys.stderr, 'Saving model error:', e
# pass
try:
#with contextlib.closing(bz2.BZ2File(model_filename, 'wb')) as f:
with open(model_filename, 'wb') as f:
pickle.dump(self,f,pickle.HIGHEST_PROTOCOL)
except:
print >> sys.stderr, 'Saving model error', sys.exc_info()[0]
#raise
pass
self.weight = weight
self.aux_weight = aux_weight
#self.avg_weight = avg_weight
@staticmethod
def load_model(model_filename):
#with contextlib.closing(bz2.BZ2File(model_filename, 'rb')) as f:
with open(model_filename, 'rb') as f:
model = pickle.load(f)
# deal with module name conflict
#tmp = sys.path.pop(0)
#model.avg_weight = np.load(open(model_filename+'.weight', 'rb'))
#sys.path.insert(0,tmp)
return model
#return pickle.load(open(model_filename,'rb'))
'''
class Model():
"""weights and templates"""
#weight = None
#n_class = None
#n_rel = None
#n_tag = None
indent = " " * 4
#feature_codebook = None
#class_codebook = None
#feats_generator = None
def __init__(self, elog=sys.stdout):
self.elog = elog
self.weight = None
self.aux_weight = None
self.avg_weight = None # for store the averaged weights
#self.n_class = n_class
#self.n_rel = n_rel
#self.n_tag = n_tag
self._feats_templates_file = None
self._feature_templates_list = []
self._feats_gen_filename = None
self.feats_generator = None
self.token_to_concept_table = defaultdict(set)
self.pp_count_dict = defaultdict(int)
self.total_num_words = 0
self.token_label_set = defaultdict(set)
self.class_codebook = None
self.feature_codebook = None
self.rel_codebook = Alphabet()
self.tag_codebook = {
'Concept': Alphabet(),
'ETag': Alphabet(),
'ConstTag': Alphabet()
}
def setup(self, action_type, instances, feature_templates_file=None):
self._feats_templates_file = feature_templates_file
self.class_codebook = Alphabet.from_dict(
dict((i, k)
for i, (k, v) in enumerate(ACTION_TYPE_TABLE[action_type])),
True)
self.feature_codebook = dict([
(i, Alphabet())
for i in self.class_codebook._index_to_label.keys()
])
self.read_templates()
n_rel, n_tag = self._set_rel_tag_codebooks(instances)
self._set_class_weight(self.class_codebook.size(), n_rel)
self._set_statistics(instances)
self.output_feature_generator()
def _set_statistics(self, instances):
#pp_count_dict = defaultdict(int)
for inst in instances:
sent = inst.tokens
self.total_num_words += len(sent)
for token in sent:
if token['pos'] == 'IN' and token['rel'] == 'prep':
self.pp_count_dict[token['form'].lower()] += 1
def _set_rel_tag_codebooks(self, instances):
#TODO
self.rel_codebook.add(NULL_EDGE)
#self.tag_codebook['Concept'].add(NULL_TAG)
for inst in instances:
gold_graph = inst.gold_graph
gold_nodes = gold_graph.nodes
#gold_edges = gold_graph.edges
sent_tokens = inst.tokens
for g, d in gold_graph.tuples():
gnode = gold_nodes[g]
g_span_wds = [
tok['lemma'] for tok in sent_tokens
if tok['id'] in range(gnode.start, gnode.end)
] if g != 'x' else gnode.words
g_span_ne = sent_tokens[g]['ne']
g_entity_tag = gold_graph.get_node_tag(g)
#if len(g_span_wds) > 1:
#for gwd in g_span_wds:
# self.token_to_concept_table[gwd].add(g_entity_tag)
if g_span_ne not in ['O', 'NUMBER']:
self.token_to_concept_table[g_span_ne].add(g_entity_tag)
self.token_to_concept_table[','.join(g_span_wds)].add(
g_entity_tag)
if isinstance(g_entity_tag, ETag):
self.tag_codebook['ETag'].add(g_entity_tag)
elif isinstance(g_entity_tag, ConstTag):
self.tag_codebook['ConstTag'].add(g_entity_tag)
else:
self.tag_codebook['Concept'].add(g_entity_tag)
dnode = gold_nodes[d]
d_span_wds = [
tok['lemma'] for tok in sent_tokens
if tok['id'] in range(dnode.start, dnode.end)
] if d != 'x' else dnode.words
d_span_ne = sent_tokens[d]['ne']
d_entity_tag = gold_graph.get_node_tag(d)
#if len(d_span_wds) > 1:
# for dwd in d_span_wds:
# self.token_to_concept_table[dwd].add(d_entity_tag)
if d_span_ne not in ['O', 'NUMBER']:
self.token_to_concept_table[d_span_ne].add(d_entity_tag)
self.token_to_concept_table[','.join(d_span_wds)].add(
d_entity_tag)
if isinstance(d_entity_tag, ETag):
self.tag_codebook['ETag'].add(d_entity_tag)
elif isinstance(d_entity_tag, ConstTag):
self.tag_codebook['ConstTag'].add(d_entity_tag)
else:
self.tag_codebook['Concept'].add(d_entity_tag)
#self.tag_codebook.add(d_entity_tag)
g_edge_label = gold_graph.get_edge_label(g, d)
if g_span_ne not in ['O', 'NUMBER']:
self.token_label_set[g_span_ne].add(g_edge_label)
self.token_label_set[','.join(g_span_wds)].add(g_edge_label)
self.rel_codebook.add(g_edge_label)
n_rel = [1] * self.class_codebook.size()
n_tag = [1] * self.class_codebook.size()
for k, v in self.class_codebook._index_to_label.items():
if v in ACTION_WITH_TAG:
n_tag[k] = reduce(
lambda x, y: x + y,
map(lambda z: self.tag_codebook[z].size(),
self.tag_codebook.keys()))
if v in ACTION_WITH_EDGE:
n_rel[k] = self.rel_codebook.size()
return n_rel, n_tag
def _set_class_weight(self,
n_class,
n_rel=None,
init_feature_dim=5 * 10**5):
#if n_rel == None:
# n_rel = [1]*n_class
#assert len(n_rel) == n_class
#self.weight = [np.zeros(shape = (init_feature_dim,nt,nr),dtype=WEIGHT_DTYPE) for nr,nt in zip(n_rel,n_tag)]
#self.aux_weight = [np.zeros(shape = (init_feature_dim,nt,nr),dtype=WEIGHT_DTYPE) for nr,nt in zip(n_rel,n_tag)]
#self.avg_weight = [np.zeros(shape = (init_feature_dim,nt,nr),dtype=WEIGHT_DTYPE) for nr,nt in zip(n_rel,n_tag)]
self.weight = [
np.zeros(shape=(init_feature_dim, nr), dtype=WEIGHT_DTYPE)
for nr in n_rel
]
self.aux_weight = [
np.zeros(shape=(init_feature_dim, nr), dtype=WEIGHT_DTYPE)
for nr in n_rel
]
self.avg_weight = [
np.zeros(shape=(init_feature_dim, nr), dtype=WEIGHT_DTYPE)
for nr in n_rel
]
def read_templates(self):
ff_name = self._feats_templates_file if self._feats_templates_file else _FEATURE_TEMPLATES_FILE
for line in open(ff_name, 'r'):
line = line.strip()
if not line:
pass
elif line.startswith('#'):
pass
else:
elements = line.split()
#elements.extend(['tx'])
template = "'%s=%s' %% (%s)" % ('&'.join(elements),
'%s_' * len(elements),
','.join(elements))
self._feature_templates_list.append((template, elements))
def output_feature_generator(self):
"""based on feature autoeval method in (Huang,2010)'s parser"""
import time
self._feats_gen_filename = 'feats_gen_' + self._feats_templates_file.split(
'/')[-1].split('.')[0] #str(int(time.time()))
output = open('./temp/' + self._feats_gen_filename + '.py', 'w')
output.write('#generated by model.py\n')
output.write('from constants import *\n')
output.write('def generate_features(state,action):\n')
output.write(Model.indent +
's0,b0,a0=state.get_feature_context_window(action)\n')
element_set = set([])
definition_str = Model.indent + 'feats=[]\n'
append_feats_str = ''
definition_str += Model.indent + "act_idx = state.model.class_codebook.get_index(action['type'])\n"
definition_str += Model.indent + "tx = action['tag'] if 'tag' in action else EMPTY\n"
#definition_str += Model.indent+"lx = action['edge_label'] if 'edge_label' in action else EMPTY\n"
#definition_str += Model.indent+"print state.model.class_codebook._label_to_index\n"
#print self._feature_templates_list
for template, elements in self._feature_templates_list:
for e in elements: # definition
if e not in element_set:
sub_elements = e.split('_')
if len(sub_elements) == 2:
definition_str += "%s%s=%s['%s'] if %s else EMPTY\n" % (
Model.indent, e, sub_elements[0],
FEATS_ABBR[sub_elements[1]], sub_elements[0])
elif len(sub_elements) == 3:
definition_str += "%s%s=%s['%s']['%s'] if %s and %s['%s'] else EMPTY\n" % (
Model.indent, e, sub_elements[0],
FEATS_ABBR[sub_elements[1]],
FEATS_ABBR[sub_elements[2]], sub_elements[0],
sub_elements[0], FEATS_ABBR[sub_elements[1]])
else:
pass
element_set.add(e)
else:
pass
#append_feats_str += "%sif [%s] != %s*[None]:feats.append(%s)\n" % (Model.indent,','.join(elements),len(elements),template)
append_feats_str += "%sfeats.append(%s)\n" % (Model.indent,
template)
#append_feats_str += "%sstate.model.feature_codebook[act_idx].add(%s)\n" % (Model.indent,template)
#definition_str += "%seqpfx=s0['lemma'][:4]==tx[:4] if tx is not EMPTY and len(s0['lemma'])>4 else EMPTY\n"%(Model.indent)
#append_feats_str += "%sfeats.append('s0_len&eqpfx=%%s_%%s_'%%(s0['len'],eqpfx))\n"%(Model.indent)
#definition_str += "%spath=[(state.sent[i]['pos'],state.sent[i]['rel']) for i in state.deptree.get_path(b0['id'],a0['id'])] if b0 and a0 else EMPTY\n"%(Model.indent)
definition_str += "%sdist1=abs(s0['id']-b0['id']) if b0 else EMPTY\n" % (
Model.indent)
definition_str += "%sif dist1 > 10: dist1=10\n" % (Model.indent)
definition_str += "%sdist2=abs(a0['id']-b0['id']) if b0 and a0 else EMPTY\n" % (
Model.indent)
definition_str += "%sif dist2 > 10: dist2=10\n" % (Model.indent)
output.write(definition_str)
output.write(append_feats_str)
output.write('%sreturn feats' % Model.indent)
output.close()
#sys.path.append('/temp/')
print "Importing feature generator!"
self.feats_generator = importlib.import_module(
'temp.' + self._feats_gen_filename).generate_features
def toJSON(self):
print 'Converting model to JSON'
print 'class size: %s \nrelation size: %s \ntag size: %s' % (
self.class_codebook.size(), self.rel_codebook.size(),
map(lambda x: '%s->%s ' %
(x, self.tag_codebook[x].size()), self.tag_codebook.keys()))
print 'feature codebook size: %s' % (','.join(
('%s:%s') % (i, f.size())
for i, f in self.feature_codebook.items()))
print 'weight shape: %s' % (','.join(
('%s:%s') % (i, w.shape) for i, w in enumerate(self.avg_weight)))
print 'token to concept table: %s' % (len(self.token_to_concept_table))
model_dict = {
'_feature_templates_list':
self._feature_templates_list,
'_feats_gen_filename':
self._feats_gen_filename,
#'weight':[w.tolist() for w in self.weight],
#'aux_weight':[axw.tolist() for axw in self.aux_weight],
'avg_weight': [agw.tolist() for agw in self.avg_weight],
'token_to_concept_table':
dict([(k, list(v))
for k, v in self.token_to_concept_table.items()]),
'class_codebook':
self.class_codebook.to_dict(),
'feature_codebook':
self.feature_codebook.to_dict(),
'rel_codebook':
self.rel_codebook.to_dict(),
'tag_codebook':
dict([(k, self.tag_codebook[k].to_dict())
for k in self.tag_codebook])
}
return model_dict
def save_model(self, model_filename):
#pickle.dump(self,open(model_filename,'wb'),pickle.HIGHEST_PROTOCOL)
print >> self.elog, 'Model info:'
print >> self.elog, 'class size: %s \nrelation size: %s \ntag size: %s' % (
self.class_codebook.size(), self.rel_codebook.size(),
map(lambda x: '%s->%s ' %
(x, self.tag_codebook[x].size()), self.tag_codebook.keys()))
print >> self.elog, 'feature codebook size: %s' % (','.join(
('%s:%s') % (i, f.size())
for i, f in self.feature_codebook.items()))
#print 'weight shape: %s' % (self.avg_weight.shape)
print >> self.elog, 'weight shape: %s' % (','.join(
('%s:%s') % (i, w.shape) for i, w in enumerate(self.avg_weight)))
print >> self.elog, 'token to concept table: %s' % (len(
self.token_to_concept_table))
with contextlib.closing(bz2.BZ2File(model_filename, 'wb')) as f:
pickle.dump(self, f, pickle.HIGHEST_PROTOCOL)
#json.dump(self.toJSON(),open(model_filename,'wb'))
@staticmethod
def load_model(model_filename):
with contextlib.closing(bz2.BZ2File(model_filename, 'rb')) as f:
model = pickle.load(f)
return model
#return pickle.load(open(model_filename,'rb'))
'''
