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lib_metric.py
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lib_metric.py
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import math, gensim
from config import *
from lib_process import findOneDBEntry
from list_term_object import List_Term_Object
def wordSim_tswe(term1,term2,cat,mongoObj):
conn=mongoObj.conn_tswe
item1=conn.find_one({'category':cat,'word':term1})
if item1 is None:
return 0.0
item2=conn.find_one({'category':cat,'word':term2})
if item2 is None:
return 0.0
v1=item1['vector']
v2=item2['vector']
sim=0.0
for i in range(30):
sim+=(v1[i]*v2[i])
print ('%s,%s,%f'%(term1,term2,sim))
if sim>=0.85:
return sim
else:
return 0.0
def wordSim(term1,term2,w2vmodel,bound):
if term1==term2:
return 1.0
if term1 not in w2vmodel or term2 not in w2vmodel:
return 0.0
sim=w2vmodel.similarity(term1,term2)
#print ('%s,%s,%f'%(term1,term2,sim))
'''
try:
sim=w2vmodel.similarity(term1,term2)
except:
sim=0.0
'''
if sim>=bound:
return sim
#return 1
else:
return 0.0
def get_dirichlet_prob(tf_t_d, len_d, tf_t_C, len_C, mu):
"""
Computes Dirichlet-smoothed probability
P(t|theta_d) = [tf(t, d) + mu P(t|C)] / [|d| + mu]
:param tf_t_d: tf(t,d)
:param len_d: |d|
:param tf_t_C: tf(t,C)
:param len_C: |C| = \sum_{d \in C} |d|
:param mu: \mu
:return:
"""
if mu == 0: # i.e. field does not have any content in the collection
return 0
else:
p_t_C = tf_t_C / len_C if len_C > 0.0 else 0.0
return (tf_t_d + mu * p_t_C) / (len_d + mu)
def mlm_sas2(queryObj,entityObj,structure,lucene_handler):
if len(entityObj.categories)==0:
return NEGATIVE_INFINITY
D=structure.cat_dag
lucene_cat=lucene_handler['category_corpus']
lucene_doc=lucene_handler['first_pass']
len_d_f=entityObj.lengths
max_score=NEGATIVE_INFINITY
len_C_f={}
mlm_weights={}
mu={}
for field in LIST_F:
len_C_f[field]=lucene_doc.get_coll_length(field)
mu[field]=lucene_doc.get_avg_len(field)
mlm_weights[field]=1.0/len(LIST_F)
if MODEL_NAME=='mlm-tc':
mlm_weights={'stemmed_names':0.2,'stemmed_catchall':0.8} if USED_QUERY_VERSION=='stemmed_raw_query' else {'names':0.2,'attributes':0.8}
def smooth_path(cat,path_len):
nonlocal D,cnt_path
nonlocal lucene_cat
nonlocal cf_g_f,len_c_g_f,n_c_g
visited.add(cat)
if cnt_path>TOP_PATH_NUM_PER_CAT:
return
# the following is end condition
if path_len==LIMIT_SAS_PATH_LENGTH or len(D[cat])==0:
return
# maintain useful temporary variables
d,docID=lucene_cat.findDoc(cat,'category',True)
cnt_doc_corpus=0
if d is not None:
# maintain
cnt_doc_corpus=int(d['num_articles'])
n_c_g+=cnt_doc_corpus
for f in LIST_F:
# get category corpus
term_freq=lucene_cat.get_term_freq(docID,f,True)
len_c=sum(term_freq.values())
len_c_g_f[f]+=len_c
for j in range(queryObj.contents_obj.length):
term=queryObj.contents_obj.term[j]
tf_c=term_freq.get(term,0.0)
cf_g_f[f][j]+=tf_c
cnt=0
for child in iter(D[cat]):
cnt+=1
if cnt>TOP_CATEGORY_NUM:
break
if child in visited:
continue
if child in D:
smooth_path(child,path_len+1)
# end of function smooth_path
visited=set()
for cat in entityObj.categories[:TOP_CATEGORY_NUM]:
if cat not in D:
continue
n_c_g=0
len_c_g_f={}
cf_g_f={} # collection frequency for each term in each field
for f in LIST_F:
len_c_g_f[f]=0
cf_g_f[f]=[0 for i in range(queryObj.contents_obj.length)]
cnt_path=0
visited.clear()
smooth_path(cat,0)
mu_g_f={}
for f in LIST_F:
mu_g_f[f]=len_c_g_f[f]/n_c_g if n_c_g>0 else 0.0
if n_c_g==0:
continue
#cof=0.003
cof=0.05
#cof=0
score=0.0
for j in range(queryObj.contents_obj.length):
term=queryObj.contents_obj.term[j]
ptd=0.0
for f in LIST_F:
tf_d_f=entityObj.term_freqs[f].get(term,0.0)
cf_f = lucene_doc.get_coll_termfreq(term, f)
ptc_doc=cf_f/len_C_f[f] if len_C_f[f]>0 else 0.0
ptc_g_f=cf_g_f[f][j]/len_c_g_f[f] if len_c_g_f[f]>0 else 0.0
if f in NO_SMOOTHING_LIST:
ptd_f=(tf_d_f+mu[f]*ptc_doc)/(len_d_f[f]+mu[f]) if (len_d_f[f]+mu[f])>0 else 0.0
else:
ptd_f=(tf_d_f+mu[f]*ptc_doc+cof*mu_g_f[f]*ptc_g_f)/(len_d_f[f]+mu[f]+cof*mu_g_f[f]) if (len_d_f[f]+mu[f]+cof*mu_g_f[f])>0 else 0.0
#ptd_f=(tf_d_f+mu[f]*ptc_doc)/(len_d_f[f]+mu[f]) if (len_d_f[f]+mu[f])>0 else 0.0
ptd+=mlm_weights[f]*ptd_f
if ptd>0:
score+=math.log(ptd)
max_score=max(score,max_score)
return max_score
def fsdm_sas2(queryObj,entityObj,structure,lucene_handler):
if len(entityObj.categories)==0:
return NEGATIVE_INFINITY
D=structure.cat_dag
lucene_cat=lucene_handler['category_corpus']
lucene_doc=lucene_handler['first_pass']
len_d_f=entityObj.lengths
max_score=NEGATIVE_INFINITY
len_C_f={}
mlm_weights={}
mu={}
for field in LIST_F:
len_C_f[field]=lucene_doc.get_coll_length(field)
mu[field]=lucene_doc.get_avg_len(field)
mlm_weights[field]=1.0/len(LIST_F)
if MODEL_NAME=='mlm-tc':
mlm_weights={'stemmed_names':0.2,'stemmed_catchall':0.8} if USED_QUERY_VERSION=='stemmed_raw_query' else {'names':0.2,'attributes':0.8}
w={} # (term,ordered)={field:field_weight}
for t in queryObj.contents_obj.term:
w[(t,True)]=get_mapping_prob(t,lucene_doc)
for bigram_pair in queryObj.bigrams:
bigram=bigram_pair[0]+' '+bigram_pair[1]
w[(bigram,True)]=get_mapping_prob(bigram,lucene_doc,ordered=True,slop=0)
for bigram_pair in queryObj.bigrams:
bigram=bigram_pair[0]+' '+bigram_pair[1]
w[(bigram,False)]=get_mapping_prob(bigram,lucene_doc,ordered=False,slop=6)
def smooth_path(cat,path_len):
nonlocal D,cnt_path
nonlocal lucene_cat
nonlocal cf_g_f,len_c_g_f,n_c_g
visited.add(cat)
if cnt_path>TOP_PATH_NUM_PER_CAT:
return
# the following is end condition
if path_len==LIMIT_SAS_PATH_LENGTH or len(D[cat])==0:
return
# maintain useful temporary variables
d,docID=lucene_cat.findDoc(cat,'category',True)
cnt_doc_corpus=0
if d is not None:
# maintain
cnt_doc_corpus=int(d['num_articles'])
n_c_g+=cnt_doc_corpus
for f in LIST_F:
# get category corpus
term_freq=lucene_cat.get_term_freq(docID,f,True)
len_c=sum(term_freq.values())
len_c_g_f[f]+=len_c
for j in range(queryObj.contents_obj.length):
term=queryObj.contents_obj.term[j]
tf_c=term_freq.get(term,0.0)
cf_g_f[f][j]+=tf_c
cnt=0
for child in iter(D[cat]):
cnt+=1
if cnt>TOP_CATEGORY_NUM:
break
if child in visited:
continue
if child in D:
smooth_path(child,path_len+1)
# end of function smooth_path
visited=set()
for cat in entityObj.categories[:TOP_CATEGORY_NUM]:
if cat not in D:
continue
n_c_g=0
len_c_g_f={}
cf_g_f={} # collection frequency for each term in each field
for f in LIST_F:
len_c_g_f[f]=0
cf_g_f[f]=[0 for i in range(queryObj.contents_obj.length)]
cnt_path=0
visited.clear()
smooth_path(cat,0)
mu_g_f={}
for f in LIST_F:
mu_g_f[f]=len_c_g_f[f]/n_c_g if n_c_g>0 else 0.0
if n_c_g==0:
continue
cof=0.05
ft_p=0.0
for j in range(queryObj.contents_obj.length):
term=queryObj.contents_obj.term[j]
ptd=0.0
for f in w[(term,True)]:
tf_d_f=entityObj.term_freqs[f].get(term,0.0)
cf_f = lucene_doc.get_coll_termfreq(term, f)
ptc_doc=cf_f/len_C_f[f] if len_C_f[f]>0 else 0.0
ptc_g_f=cf_g_f[f][j]/len_c_g_f[f] if len_c_g_f[f]>0 else 0.0
if f in NO_SMOOTHING_LIST:
ptd_f=(tf_d_f+mu[f]*ptc_doc)/(len_d_f[f]+mu[f]) if (len_d_f[f]+mu[f])>0 else 0.0
else:
ptd_f=(tf_d_f+mu[f]*ptc_doc+cof*mu_g_f[f]*ptc_g_f)/(len_d_f[f]+mu[f]+cof*mu_g_f[f]) if (len_d_f[f]+mu[f]+cof*mu_g_f[f])>0 else 0.0
ptd+=w[(term,True)][f]*ptd_f
if ptd>0:
ft_p+=math.log(ptd)
# for ordered bigrams
fo_p=0.0
if LAMBDA_O>0:
for j in range(len(queryObj.bigrams)):
bigram=queryObj.bigrams[j][0]+' '+queryObj.bigrams[j][1]
ptd=0.0
for f in w[(bigram,True)]:
tf_d_f,cf_f=lucene_doc.get_coll_bigram_freq(bigram,f,True,0,entityObj.title)
ptc_f=cf_f/len_C_f[f] if len_C_f[f]>0 else 0.0
Dt=mu[f]*ptc_f
Nt=mu[f]
ptd_f=(tf_d_f+Dt)/(len_d_f[f]+Nt) if len_d_f[f]+Nt>0 else 0.0
ptd+=w[(bigram,True)][f]*ptd_f
if ptd>0:
fo_p+=math.log(ptd)*w[(bigram,True)][f]
# for unordered bigrams
fu_p=0.0
if LAMBDA_U>0:
for j in range(len(queryObj.bigrams)):
bigram=queryObj.bigrams[j][0]+' '+queryObj.bigrams[j][1]
ptd=0.0
for f in w[(bigram,False)]:
tf_d_f,cf_f=lucene_doc.get_coll_bigram_freq(bigram,f,False,6,entityObj.title)
ptc_f=cf_f/len_C_f[f] if len_C_f[f]>0 else 0.0
Dt=mu[f]*ptc_f
Nt=mu[f]
ptd_f=(tf_d_f+Dt)/(len_d_f[f]+Nt) if len_d_f[f]+Nt>0 else 0.0
ptd+=w[(bigram,False)][f]*ptd_f
if ptd>0:
fu_p+=math.log(ptd)*w[(bigram,False)][f]
score_p=LAMBDA_T*ft_p+LAMBDA_O*fo_p+LAMBDA_U*fu_p
max_score=max(score_p,max_score)
return max_score
def elrSim(queryObj,entityObj,lucene_obj,mongoObj):
# only catchall field
field='catchall'
df_f=lucene_obj.get_doc_count(field)
score=NEGATIVE_INFINITY
for entity in queryObj.query_entities:
se=queryObj.query_entities[entity]
d,docID=lucene_obj.findDoc(entity,'title',True)
if d is None or docID is None:
continue
#print ('cur:'+entity)
title=entity.lower()
term_freq=lucene_obj.get_term_freq(docID,field,False)
#print (term_freq)
tf=1 if title in term_freq else 0
df_e_f=lucene_obj.get_doc_freq(title, field)
#print (df_e_f,df_f)
cur_score=NEGATIVE_INFINITY
sim=0.9*tf+0.1*(df_e_f/df_f)
#print (se)
#print (sim)
if sim>0:
cur_score=se*math.log(sim)
if cur_score>score:
score=cur_score
if score==NEGATIVE_INFINITY:
return 0.0
else:
#print ('score=%f'%(score))
return score
def bm25fSim(lt_obj1,entityObj,lucene_obj):
len_C_f={}
mu={}
for f in LIST_F:
len_C_f[f]=lucene_obj.get_coll_length(f)
mu[f]=lucene_obj.get_avg_len(f)
N=lucene_obj.get_doc_count('stemmed_catchall')
k1=2.44
b=0.297
boost=1
totalSim=0.0
for i in range(lt_obj1.length):
term=lt_obj1.term[i]
localSim=0.0
df_t=lucene_obj.get_doc_freq(term,'stemmed_catchall')
idf_t=math.log10((N-df_t+0.5)/(df_t+0.5))
weight_t_d=0.0
for f in LIST_F:
len_d_f = entityObj.lengths[f]
tf_t_d_f = entityObj.term_freqs[f].get(term,0)
tf_t_C_f = lucene_obj.get_coll_termfreq(term, f)
# compute f(p(t1|De),p(t2|De)...)
weight_t_d+=((tf_t_d_f*boost)/(1-b+b*(len_d_f/mu[f])))
totalSim+=(idf_t*(weight_t_d/(k1+weight_t_d)))
return totalSim
def lmSim(lt_obj1,entityObj,field,w2vmodel,lucene_obj,mongoObj=None):
# subquery x et[0..n-1]
totalSim=0.0
term_freq=entityObj.term_freq
len_C_f = lucene_obj.get_coll_length(field)
mu=lucene_obj.get_avg_len(field)
cnt=0
# iterate each t in term_freq and compare similarity
for i in range(lt_obj1.length):
qt=lt_obj1.term[i]
localSim=0.0
# compute p(t|De)
if WORD_EMBEDDING_TYPE=='NONE':
if qt in term_freq:
localSim=term_freq[qt]
else:
# use embedding
for et in term_freq:
localSim+=(wordSim(qt,et,w2vmodel,BOUND_SIM)*term_freq[et])
if localSim>0.0:
cnt+=1
len_d_f = entityObj.length
tf_t_d_f = localSim
tf_t_C_f = lucene_obj.get_coll_termfreq(qt, field)
p_t_d=get_dirichlet_prob(tf_t_d_f, float(len_d_f), float(tf_t_C_f), float(len_C_f), mu)
# compute f(p(t1|De),p(t2|De)...)
if p_t_d>0.0:
totalSim+=math.log(p_t_d)
return totalSim
def mlmSim(lt_obj1,entityObj,lucene_obj,mongoObj=None):
# need every field representation instead of single lt_obj for entity
# subquery x et[0..n-1]
len_C_f={}
mu={}
mlm_weights={}
for f in LIST_F:
len_C_f[f]=lucene_obj.get_coll_length(f)
mu[f]=lucene_obj.get_avg_len(f)
mlm_weights[f]=1.0/len(LIST_F)
if MODEL_NAME=='mlm-tc':
mlm_weights={'stemmed_names':0.2,'stemmed_catchall':0.8} if USED_QUERY_VERSION=='stemmed_raw_query' else {'names':0.2,'catchall':0.8}
totalSim=0.0
for i in range(lt_obj1.length):
qt=lt_obj1.term[i]
localSim=0.0
# compute p(t|Df)
for f in LIST_F:
len_d_f = entityObj.lengths[f]
tf_t_d_f = entityObj.term_freqs[f].get(qt,0)
tf_t_C_f = lucene_obj.get_coll_termfreq(qt, f)
tempSim=get_dirichlet_prob(tf_t_d_f, len_d_f, tf_t_C_f, len_C_f[f], mu[f])
# compute f(p(t1|De),p(t2|De)...)
localSim+=mlm_weights[f]*tempSim
if localSim>0.0:
totalSim+=math.log(localSim)
return totalSim
def prmsSim(lt_obj1,entityObj,lucene_obj):
# need every field representation instead of single lt_obj for entity
# subquery x et[0..n-1]
len_C_f={}
mu={}
for f in LIST_F:
len_C_f[f]=lucene_obj.get_coll_length(f)
mu[f]=lucene_obj.get_avg_len(f)
totalSim=0.0
for i in range(lt_obj1.length):
qt=lt_obj1.term[i]
localSim=0.0
# compute p(t|Df)
w=get_mapping_prob(qt,lucene_obj)
for f in w:
len_d_f = entityObj.lengths[f]
tf_t_d_f = entityObj.term_freqs[f].get(qt,0)
tf_t_C_f = lucene_obj.get_coll_termfreq(qt, f)
tempSim=get_dirichlet_prob(tf_t_d_f, len_d_f, tf_t_C_f, len_C_f[f], mu[f])
# compute f(p(t1|De),p(t2|De)...)
localSim+=w[f]*tempSim
if localSim>0.0:
totalSim+=math.log(localSim)
return totalSim
def sdmSim(queryObj,entityObj,field,lucene_obj):
ft=fo=fu=0.0
len_C_f = lucene_obj.get_coll_length(field)
mu=lucene_obj.get_avg_len(field)
ft=lmSim(queryObj.contents_obj,entityObj,field,None,lucene_obj)
if LAMBDA_O>0:
for bigram_pair in queryObj.bigrams:
bigram=bigram_pair[0]+' '+bigram_pair[1]
tf,cf=lucene_obj.get_coll_bigram_freq(bigram,field,True,0,entityObj.title)
ptd=get_dirichlet_prob(tf,entityObj.length,cf,len_C_f,mu)
if ptd>0:
fo+=math.log(ptd)
if LAMBDA_U>0:
for bigram_pair in queryObj.bigrams:
bigram=bigram_pair[0]+' '+bigram_pair[1]
tf,cf=lucene_obj.get_coll_bigram_freq(bigram,field,False,6,entityObj.title)
ptd=get_dirichlet_prob(tf,entityObj.length,cf,len_C_f,mu)
if ptd>0:
fu+=math.log(ptd)
score=LAMBDA_T*ft+LAMBDA_O*fo+LAMBDA_U*fu
return score
def fsdmSim(queryObj,entityObj,lucene_obj):
fields=LIST_F
len_C_f={}
mu={}
for f in LIST_F:
len_C_f[f]=lucene_obj.get_coll_length(f)
mu[f]=lucene_obj.get_avg_len(f)
ft=fo=fu=0.0
# w is a dict of weights for each field
# compute ft
for t in queryObj.contents_obj.term:
w=get_mapping_prob(t,lucene_obj)
ft_temp=0.0
for field in w:
tf=entityObj.term_freqs[field].get(t,0)
cf=lucene_obj.get_coll_termfreq(t, field)
ptd=get_dirichlet_prob(tf,entityObj.lengths[field],cf,len_C_f[field],mu[field])
if ptd>0:
ft_temp+=ptd*w[field]
if ft_temp>0:
ft+=math.log(ft_temp)
# compute fo
if LAMBDA_O>0:
for bigram_pair in queryObj.bigrams:
bigram=bigram_pair[0]+' '+bigram_pair[1]
w=get_mapping_prob(bigram,lucene_obj,ordered=True,slop=0)
fo_temp=0.0
for field in w:
tf,cf=lucene_obj.get_coll_bigram_freq(bigram,field,True,0,entityObj.title)
ptd=get_dirichlet_prob(tf,entityObj.lengths[field],cf,len_C_f[field],mu[field])
if ptd>0:
fo_temp+=ptd*w[field]
if fo_temp>0:
fo+=math.log(fo_temp)
# compute fu
if LAMBDA_U>0:
for bigram_pair in queryObj.bigrams:
bigram=bigram_pair[0]+' '+bigram_pair[1]
w=get_mapping_prob(bigram,lucene_obj,ordered=False,slop=6)
fu_temp=0.0
for field in w:
tf,cf=lucene_obj.get_coll_bigram_freq(bigram,field,False,6,entityObj.title)
ptd=get_dirichlet_prob(tf,entityObj.lengths[field],cf,len_C_f[field],mu[field])
if ptd>0:
fu_temp+=ptd*w[field]
if fu_temp>0:
fu+=math.log(fu_temp)
'''
if queryObj.contents_obj.length>1:
ft/=queryObj.contents_obj.length
fo/=(queryObj.contents_obj.length-1)
fu/=(queryObj.contents_obj.length-1)
'''
score=LAMBDA_T*ft+LAMBDA_O*fo+LAMBDA_U*fu
return score
def get_mapping_prob(t,lucene_obj,ordered=True,slop=0):
"""
Computes PRMS field mapping probability.
p(f|t) = P(t|f)P(f) / sum_f'(P(t|C_{f'_c})P(f'))
:param t: str
:param coll_termfreq_fields: {field: freq, ...}
:return Dictionary {field: prms_prob, ...}
"""
fields=LIST_F
if len(fields)==1:
# for sdm and lm
return {fields[0]:1.0}
is_bigram=True if t.find(' ')>-1 else False
#find cache
item=lucene_obj.get_mapping_prob_cached(t,ordered,slop)
if item is not None:
return item['weights']
coll_termfreq_fields={}
for f in fields:
if is_bigram==False:
coll_termfreq_fields[f]=lucene_obj.get_coll_termfreq(t, f)
else:
coll_termfreq_fields[f]=lucene_obj.get_coll_bigram_freq(t,f,ordered,slop,'NONE')[1]
total_field_freq=lucene_obj.get_total_field_freq(fields)
# calculates numerators for all fields: P(t|f)P(f)
numerators = {}
for f in fields:
p_t_f = coll_termfreq_fields[f] / lucene_obj.get_coll_length(f)
p_f = lucene_obj.get_doc_count(f) / total_field_freq
p_f_t = p_t_f * p_f
if p_f_t > 0:
numerators[f] = p_f_t
else:
numerators[f]=0
# calculates denominator: sum_f'(P(t|C_{f'_c})P(f'))
denominator = sum(numerators.values())
mapping_probs = {}
if denominator > 0: # if the term is present in the collection
for f in numerators:
mapping_probs[f] = numerators[f] / denominator
lucene_obj.insert_mapping_prob_cached(t,ordered,slop,mapping_probs)
return mapping_probs
def fsdm_sas(queryObj,entityObj,structure,lucene_handler):
if len(entityObj.categories)==0:
return NEGATIVE_INFINITY
D=structure.cat_dag
lucene_cat=lucene_handler['category_corpus']
lucene_doc=lucene_handler['first_pass']
len_d_f=entityObj.lengths
sum_score=0.0
max_score=NEGATIVE_INFINITY
len_C_f={}
sum_ptc={}
mu={}
# prepare field weights
w={} # (term,ordered)={field:field_weight}
for t in queryObj.contents_obj.term:
w[(t,True)]=get_mapping_prob(t,lucene_doc)
for bigram_pair in queryObj.bigrams:
bigram=bigram_pair[0]+' '+bigram_pair[1]
w[(bigram,True)]=get_mapping_prob(bigram,lucene_doc,ordered=True,slop=0)
for bigram_pair in queryObj.bigrams:
bigram=bigram_pair[0]+' '+bigram_pair[1]
w[(bigram,False)]=get_mapping_prob(bigram,lucene_doc,ordered=False,slop=6)
for field in LIST_F:
len_C_f[field]=lucene_doc.get_coll_length(field)
mu[field]=lucene_doc.get_avg_len(field)
sum_ptc[('T',field)]=[0.0 for i in range(queryObj.contents_obj.length)]
sum_ptc[('O',field)]=[0.0 for i in range(len(queryObj.bigrams))]
sum_ptc[('U',field)]=[0.0 for i in range(len(queryObj.bigrams))]
curPath=[]
def smooth_path(cat,path_len,alpha_t,sum_nominator):
nonlocal D,curPath,sum_ptc,cnt_path
nonlocal max_score_p_cat,max_score
nonlocal lucene_cat,lucene_doc
if cnt_path>TOP_PATH_NUM_PER_CAT:
return
# the following is end condition
if path_len==LIMIT_SAS_PATH_LENGTH or len(D[cat])==0:
# compute score
cnt_path+=1
if alpha_t==ALPHA_SAS:
return
# TAKE CARE OF COF !
#cof=(1-ALPHA_SAS)/(ALPHA_SAS-alpha_t)
cof=0.003
score_p=0.0
# for individual query terms
ft_p=0.0
for j in range(queryObj.contents_obj.length):
term=queryObj.contents_obj.term[j]
ptd=0.0
for f in w[(term,True)]:
tf_d_f=entityObj.term_freqs[f].get(term,0.0)
cf_f = lucene_doc.get_coll_termfreq(term, f)
ptc_f=cf_f/len_C_f[f] if len_C_f[f]>0 else 0.0
Dt=mu[f]*ptc_f
Nt=mu[f]
if f not in NO_SMOOTHING_LIST:
Dt+=cof*sum_ptc[('T',f)][j]
Nt+=cof*sum_nominator[f]
ptd_f=(tf_d_f+Dt)/(len_d_f[f]+Nt) if len_d_f[f]+Nt>0 else 0.0
ptd+=w[(term,True)][f]*ptd_f
if ptd>0:
ft_p+=math.log(ptd)
# for ordered bigrams
fo_p=0.0
if LAMBDA_O>0:
for j in range(len(queryObj.bigrams)):
bigram=queryObj.bigrams[j][0]+' '+queryObj.bigrams[j][1]
ptd=0.0
for f in w[(bigram,True)]:
tf_d_f,cf_f=lucene_doc.get_coll_bigram_freq(bigram,f,True,0,entityObj.title)
ptc_f=cf_f/len_C_f[f] if len_C_f[f]>0 else 0.0
Dt=mu[f]*ptc_f
Nt=mu[f]
if f not in NO_SMOOTHING_LIST:
Dt+=cof*sum_ptc[('O',f)][j]
Nt+=cof*sum_nominator[f]
ptd_f=(tf_d_f+Dt)/(len_d_f[f]+Nt) if len_d_f[f]+Nt>0 else 0.0
ptd+=w[(bigram,True)][f]*ptd_f
if ptd>0:
fo_p+=math.log(ptd)*w[(bigram,True)][f]
# for unordered bigrams
fu_p=0.0
if LAMBDA_U>0:
for j in range(len(queryObj.bigrams)):
bigram=queryObj.bigrams[j][0]+' '+queryObj.bigrams[j][1]
ptd=0.0
for f in w[(bigram,False)]:
tf_d_f,cf_f=lucene_doc.get_coll_bigram_freq(bigram,f,False,6,entityObj.title)
ptc_f=cf_f/len_C_f[f] if len_C_f[f]>0 else 0.0
Dt=mu[f]*ptc_f
Nt=mu[f]
if f not in NO_SMOOTHING_LIST:
Dt+=cof*sum_ptc[('U',f)][j]
Nt+=cof*sum_nominator[f]
ptd_f=(tf_d_f+Dt)/(len_d_f[f]+Nt) if len_d_f[f]+Nt>0 else 0.0
ptd+=w[(bigram,False)][f]*ptd_f
if ptd>0:
fu_p+=math.log(ptd)*w[(bigram,False)][f]
# end computing feature function
score_p=LAMBDA_T*ft_p+LAMBDA_O*fo_p+LAMBDA_U*fu_p
if score_p>max_score_p_cat:
max_score_p_cat=score_p
return
# maintain useful temporary variables
# current node is cat
cat_corpus,docID=lucene_cat.findDoc(cat,'category',True)
bak_sum_ptc=sum_ptc.copy()
if cat_corpus is not None:
# maintain
cnt_doc_corpus=int(cat_corpus['num_articles'])
for f in LIST_F:
if f in NO_SMOOTHING_LIST:
continue
# get category corpus
term_freq_c=lucene_cat.get_term_freq(docID,f,True)
len_c=sum(term_freq_c.values())
mu_c=len_c/cnt_doc_corpus if cnt_doc_corpus>0 else 0.0
sum_nominator[f]+=alpha_t*mu_c
# maintain individual query terms
for j in range(queryObj.contents_obj.length):
term=queryObj.contents_obj.term[j]
cf_c=term_freq_c.get(term,0.0)
ptc_f=cf_c/len_c if len_c>0 else -1
if ptc_f>-1:
sum_ptc[('T',f)][j]+=(alpha_t*ptc_f*mu_c)
# maintain ordered bigrams
if LAMBDA_O>0:
for j in range(len(queryObj.bigrams)):
bigram=queryObj.bigrams[j][0]+' '+queryObj.bigrams[j][1]
cf_c,cf_cc=lucene_cat.get_coll_bigram_freq(bigram,f,True,0,cat,field_cache='category')
ptc_f=cf_c/len_c if len_c>0 else -1
if ptc_f>-1:
sum_ptc[('O',f)][j]+=(alpha_t*ptc_f*mu_c)
# maintain unordered bigrams
if LAMBDA_U>0:
for j in range(len(queryObj.bigrams)):
bigram=queryObj.bigrams[j][0]+' '+queryObj.bigrams[j][1]
cf_c,cf_cc=lucene_cat.get_coll_bigram_freq(bigram,f,False,6,cat,field_cache='category')
ptc_f=cf_c/len_c if len_c>0 else -1
if ptc_f>-1:
sum_ptc[('U',f)][j]+=(alpha_t*ptc_f*mu_c)
cnt=0
for child in iter(D[cat]):
cnt+=1
if cnt>TOP_CATEGORY_NUM:
break
if child in D:
curPath.append(child)
smooth_path(child,path_len+1,alpha_t*ALPHA_SAS,sum_nominator)
curPath.pop()
sum_ptc=bak_sum_ptc.copy()
# end of function smooth_path
for cat in entityObj.categories[:TOP_CATEGORY_NUM]:
if cat not in D:
continue
max_score_p_cat=NEGATIVE_INFINITY
cnt_path=0
smooth_path(cat,1,ALPHA_SAS,{f:0.0 for f in LIST_F})
if max_score_p_cat>NEGATIVE_INFINITY:
sum_score+=max_score_p_cat
if max_score_p_cat>max_score:
max_score=max_score_p_cat
return max_score
# ============================
def prms_sas(queryObj,entityObj,structure,lucene_handler):
if len(entityObj.categories)==0:
return NEGATIVE_INFINITY
D=structure.cat_dag
lucene_cat=lucene_handler['category_corpus']
lucene_doc=lucene_handler['first_pass']
len_d_f=entityObj.lengths
sum_score=0.0
max_score=NEGATIVE_INFINITY
len_C_f={}
mlm_weights={}
sum_ptc={}
mu={}
for t in queryObj.contents_obj.term:
mlm_weights[t]=get_mapping_prob(t,lucene_doc)
for field in LIST_F:
len_C_f[field]=lucene_doc.get_coll_length(field)
mu[field]=lucene_doc.get_avg_len(field)
sum_ptc[field]=[0.0 for i in range(queryObj.contents_obj.length)]
curPath=[]
def smooth_path(cat,path_len,alpha_t,sum_nominator):
nonlocal D,curPath,sum_ptc,cnt_path
nonlocal max_score_p_cat,max_score
nonlocal lucene_cat,lucene_doc
if cnt_path>TOP_PATH_NUM_PER_CAT:
return
# the following is end condition
if path_len==LIMIT_SAS_PATH_LENGTH or len(D[cat])==0:
# compute score
cnt_path+=1
if alpha_t==ALPHA_SAS:
return
#cof=(1-ALPHA_SAS)/(ALPHA_SAS-alpha_t)
cof=0.003
#cof=1
score_p=0.0
for j in range(queryObj.contents_obj.length):
term=queryObj.contents_obj.term[j]
ptd=0.0
for f in mlm_weights[term]:
tf_d_f=entityObj.term_freqs[f].get(term,0.0)
cf_f = lucene_doc.get_coll_termfreq(term, f)
ptc_doc=cf_f/len_C_f[f] if len_C_f[f]>0 else 0.0
if f in NO_SMOOTHING_LIST:
ptd_f=(tf_d_f+mu[f]*ptc_doc)/(len_d_f[f]+mu[f]) if (len_d_f[f]+mu[f])>0 else 0.0
else:
ptd_f=(tf_d_f+mu[f]*ptc_doc+cof*sum_ptc[f][j])/(len_d_f[f]+mu[f]+cof*sum_nominator[f]) if (len_d_f[f]+mu[f]+cof*sum_nominator[f])>0 else 0.0
ptd+=mlm_weights[term][f]*ptd_f
if ptd>0:
score_p+=math.log(ptd)
if score_p>max_score_p_cat:
max_score_p_cat=score_p
return
# maintain useful temporary variables
d,docID=lucene_cat.findDoc(cat,'category',True)
bak_sum_ptc=sum_ptc.copy()
cnt_doc_corpus=0
if d is not None:
# maintain
cnt_doc_corpus=int(d['num_articles'])
for f in LIST_F:
if f in NO_SMOOTHING_LIST:
continue
# get category corpus
term_freq=lucene_cat.get_term_freq(docID,f,True)
len_c=sum(term_freq.values())
mu_c=len_c/cnt_doc_corpus if cnt_doc_corpus>0 else 0.0
sum_nominator[f]+=alpha_t*mu_c
for j in range(queryObj.contents_obj.length):
term=queryObj.contents_obj.term[j]
tf_c=term_freq.get(term,0.0)
ptc=tf_c/len_c if len_c>0 else -1
if ptc>-1:
sum_ptc[f][j]+=(alpha_t*mu_c*ptc)
cnt=0
for child in iter(D[cat]):
if child in D:
curPath.append(child)
smooth_path(child,path_len+1,alpha_t*ALPHA_SAS,sum_nominator)
curPath.pop()
sum_ptc=bak_sum_ptc.copy()
cnt+=1
if cnt>TOP_CATEGORY_NUM:
break
# end of function smooth_path
for cat in entityObj.categories[:TOP_CATEGORY_NUM]:
if cat not in D:
continue
max_score_p_cat=NEGATIVE_INFINITY
cnt_path=0
smooth_path(cat,1,ALPHA_SAS,{f:0.0 for f in LIST_F})
if max_score_p_cat>NEGATIVE_INFINITY:
sum_score+=max_score_p_cat
if max_score_p_cat>max_score:
max_score=max_score_p_cat
return max_score
def mlm_sas(queryObj,entityObj,structure,lucene_handler):
if len(entityObj.categories)==0:
return NEGATIVE_INFINITY
D=structure.cat_dag
lucene_cat=lucene_handler['category_corpus']
lucene_doc=lucene_handler['first_pass']
len_d_f=entityObj.lengths
sum_score=0.0
max_score=NEGATIVE_INFINITY
len_C_f={}
mlm_weights={}
sum_ptc={}
mu={}
for field in LIST_F:
len_C_f[field]=lucene_doc.get_coll_length(field)
mu[field]=lucene_doc.get_avg_len(field)
mlm_weights[field]=1.0/len(LIST_F)
sum_ptc[field]=[0.0 for i in range(queryObj.contents_obj.length)]
if MODEL_NAME=='mlm-tc':
mlm_weights={'stemmed_names':0.2,'stemmed_catchall':0.8} if USED_QUERY_VERSION=='stemmed_raw_query' else {'names':0.2,'attributes':0.8}
curPath=[]
def smooth_path(cat,path_len,alpha_t,sum_nominator):
nonlocal D,curPath,sum_ptc,cnt_path
nonlocal max_score_p_cat,max_score
nonlocal lucene_cat,lucene_doc
if cnt_path>TOP_PATH_NUM_PER_CAT:
return
# the following is end condition
if path_len==LIMIT_SAS_PATH_LENGTH or len(D[cat])==0:
# compute score
cnt_path+=1
if alpha_t==ALPHA_SAS:
return
cof=(1-ALPHA_SAS)/(ALPHA_SAS-alpha_t)
#cof=0.003
#cof=1
score_p=0.0
for j in range(queryObj.contents_obj.length):
term=queryObj.contents_obj.term[j]
ptd=0.0
for f in LIST_F:
tf_d_f=entityObj.term_freqs[f].get(term,0.0)
cf_f = lucene_doc.get_coll_termfreq(term, f)
ptc_doc=cf_f/len_C_f[f] if len_C_f[f]>0 else 0.0
if f in NO_SMOOTHING_LIST:
ptd_f=(tf_d_f+mu[f]*ptc_doc)/(len_d_f[f]+mu[f]) if (len_d_f[f]+mu[f])>0 else 0.0
else:
ptd_f=(tf_d_f+mu[f]*ptc_doc+cof*sum_ptc[f][j])/(len_d_f[f]+mu[f]+cof*sum_nominator[f]) if (len_d_f[f]+mu[f]+cof*sum_nominator[f])>0 else 0.0
#ptd_f=(tf_d_f+mu[f]*(ptc_doc+cof*sum_ptc[f][j]))/(len_d_f[f]+mu[f]) if len_d_f[f]+mu[f]>0 else 0.0
'''
if tf_d_f>0:
ptd_f=(tf_d_f+mu[f]*ptc_doc+cof*sum_ptc[f][j])/(len_d_f[f]+mu[f]+cof*sum_nominator[f]) if len_d_f[f]+mu[f]+cof*sum_nominator[f]>0 else 0.0
else:
ptd_f=(tf_d_f+mu[f]*ptc_doc)/(len_d_f[f]+mu[f]) if len_d_f[f]+mu[f]>0 else 0.0
'''
ptd+=mlm_weights[f]*ptd_f
if ptd>0:
score_p+=math.log(ptd)
if score_p>max_score_p_cat:
max_score_p_cat=score_p
return
# maintain useful temporary variables
d,docID=lucene_cat.findDoc(cat,'category',True)
bak_sum_ptc=sum_ptc.copy()
cnt_doc_corpus=0
if d is not None:
# maintain
cnt_doc_corpus=int(d['num_articles'])
for f in LIST_F:
if f in NO_SMOOTHING_LIST:
continue
# get category corpus
term_freq=lucene_cat.get_term_freq(docID,f,True)
len_c=sum(term_freq.values())
mu_c=len_c/cnt_doc_corpus if cnt_doc_corpus>0 else 0.0
sum_nominator[f]+=alpha_t*mu_c
#sum_nominator[f]+=alpha_t*mu_c/cnt_doc_corpus
#sum_nominator[f]+=mu_c/(path_len+1)
'''
# add this
mu_cc_f=lucene_cat.get_avg_len(field)
for j in range(queryObj.contents_obj.length):
term=queryObj.contents_obj.term[j]