def query_expand(docs, unit_selector, query):
## get sentence set
sents = []
for doc in docs:
#if doc.doctype != 'NEWS STORY': continue
for sent in doc.sentences:
## skip short sentences
#if sent.length <= 5: continue
sents.append(sent)
## initialize sentences with query similarity
sent_values = prob_util.Counter()
for sent in sents:
try: sent_values[sent.original] = sent.sim_basic(query)
except: sent_values[sent.original] = 1
sent_values = sent_values.makeProbDist()
## get units in each sent
sent_units = {}
for sent in sents:
sent_units[sent.original] = prob_util.Counter()
units = unit_selector(sent.stemmed)
for unit in units:
if text.text_processor.is_just_stopwords(unit): continue
sent_units[sent.original][unit] += 1
## repeat until convergence
previous_entropy_sent = 0
previous_entropy_unit = 0
for iter in range(1, 51):
prev_sent_values = sent_values.copy()
## get new unit values from sent values
unit_values = prob_util.Counter()
for sent in sent_units:
for unit in sent_units[sent]:
unit_values[unit] += sent_values[sent]
unit_values = unit_values.makeProbDist()
## get sent values from unit values
sent_values = prob_util.Counter()
for sent in sent_units:
for unit in sent_units[sent]:
sent_values[sent] += unit_values[unit] #/ len(sent_units[sent])
sent_values = sent_values.makeProbDist()
#prob_util.Counter(unit_values).displaySorted(N=5)
#prob_util.Counter(sent_values).displaySorted(N=20)
## check for convergence
entropy_sent = prob_util.entropy(sent_values)
entropy_unit = prob_util.entropy(unit_values)
if entropy_sent >= previous_entropy_sent or entropy_unit >= previous_entropy_unit:
break
previous_entropy_sent = entropy_sent
previous_entropy_unit = entropy_unit
dist = prob_util.klDistance(prev_sent_values, sent_values)
sys.stderr.write('%d sent entropy [%1.4f] unit entropy [%1.4f] sent dist [%1.6f]\n' %(iter, entropy_sent, entropy_unit, dist))
#if iter == 2: break
if dist < 0.0001:
sys.stderr.write('----------------------------')
break
return prob_util.Counter(unit_values), prob_util.Counter(sent_values)
def query_expand(docs, unit_selector, query):
## get sentence set
sents = []
for doc in docs:
#if doc.doctype != 'NEWS STORY': continue
for sent in doc.sentences:
## skip short sentences
#if sent.length <= 5: continue
sents.append(sent)
## initialize sentences with query similarity
sent_values = prob_util.Counter()
for sent in sents:
try: sent_values[sent.original] = sent.sim_basic(query)
except: sent_values[sent.original] = 1
sent_values = sent_values.makeProbDist()
original_sent_values = sent_values.copy()
## get units in each sent
sent_units = {}
for sent in sents:
sent_units[sent.original] = prob_util.Counter()
units = unit_selector(sent.stemmed)
for unit in units:
if text.text_processor.is_just_stopwords(unit): continue
sent_units[sent.original][unit] += 1
## repeat until convergence
prev_unit_entropy = 0
prev_sent_entropy = 0
prev_unit_values = {}
prev_sent_values = {}
for iter in range(1, 51):
prev_sent_values = sent_values.copy()
## get new unit values from sent values
unit_values = prob_util.Counter()
for sent in sent_units:
for unit in sent_units[sent]:
unit_values[unit] += sent_values[sent]
unit_values = unit_values.makeProbDist()
## get sent values from unit values
sent_values = prob_util.Counter()
for sent in sent_units:
for unit in sent_units[sent]:
sent_values[sent] += unit_values[unit] #/ len(sent_units[sent])
sent_values = sent_values.makeProbDist()
## interpolate with original sent weights
sent_prior = 0.1
for sent in sent_values:
new_value = (sent_prior * original_sent_values[sent]) + ( (1-sent_prior) * sent_values[sent])
#sent_values[sent] = new_value
#prob_util.Counter(unit_values).displaySorted(N=100)
#prob_util.Counter(sent_values).displaySorted(N=20)
## check for convergence
entropy_sent = prob_util.entropy(sent_values)
entropy_unit = prob_util.entropy(unit_values)
dist = prob_util.klDistance(prev_sent_values, sent_values)
sys.stderr.write('%d sent entropy [%1.4f] unit entropy [%1.4f] sent dist [%1.6f]\n' %(iter, entropy_sent, entropy_unit, dist))
if iter == 2: break
if (entropy_unit >= prev_unit_entropy) and (entropy_sent >= prev_sent_entropy):
unit_values = prev_unit_values
sent_values = prev_sent_values
break
prev_unit_entropy = entropy_unit
prev_sent_entropy = entropy_sent
prev_unit_values = unit_values
prev_sent_values = sent_values
if dist < 0.0001: break
#prob_util.Counter(unit_values).displaySorted(N=10)
#prob_util.Counter(sent_values).displaySorted(N=20)
return prob_util.Counter(unit_values), prob_util.Counter(sent_values)
def map_iterative_sents(docs, unit_selector, query):
## get sentence set
sents = []
for doc in docs:
for sent in doc.sentences:
## skip short sentences
#if sent.length <= 5: continue
## skip sentences with no query overlap
if query: sim = sent.sim_basic(query)
else: sim = 1
if sim <= 0: continue
sents.append(sent)
## initialize uniform sentence priors
sent_values = prob_util.Counter()
for sent in sents:
sent_values[sent.original] = 1
sent_values = sent_values.makeProbDist()
## get units in each sent
sent_units = {}
for sent in sents:
sent_units[sent.original] = prob_util.Counter()
units = unit_selector(sent.stemmed)
for unit in units:
if text.text_processor.is_just_stopwords(unit): continue
sent_units[sent.original][unit] += 1
## repeat until convergence
for iter in range(1, 51):
prev_sent_values = sent_values.copy()
## get unit values from doc values
unit_values = prob_util.Counter()
for sent in sent_units:
for unit in sent_units[sent]:
unit_values[unit] += sent_values[sent]
unit_values = unit_values.makeProbDist()
## get sent values from unit values
sent_values = prob_util.Counter()
for sent in sent_units:
for unit in sent_units[sent]:
sent_values[sent] += unit_values[unit] #/ len(sent_units[sent])
sent_values = sent_values.makeProbDist()
#prob_util.Counter(unit_values).displaySorted(N=5)
#prob_util.Counter(sent_values).displaySorted(N=3)
## check for convergence
entropy_sent = prob_util.entropy(sent_values)
entropy_unit = prob_util.entropy(unit_values)
dist = prob_util.klDistance(prev_sent_values, sent_values)
#print '%d sent entropy [%1.4f] unit entropy [%1.4f] sent dist [%1.6f]' %(iter, entropy_sent, entropy_unit, dist)
if iter == 2: break
if dist < 0.0001:
#print '----------------------------'
break
return prob_util.Counter(unit_values), prob_util.Counter(sent_values)
def map_iterative_sents(docs, unit_selector, query):
## get sentence set
sents = []
for doc in docs:
for sent in doc.sentences:
## skip short sentences
#if sent.length <= 5: continue
## skip sentences with no query overlap
if query: sim = sent.sim_basic(query)
else: sim = 1
if sim <= 0: continue
sents.append(sent)
## initialize uniform sentence priors
sent_values = prob_util.Counter()
for sent in sents:
sent_values[sent.original] = 1
sent_values = sent_values.makeProbDist()
## get units in each sent
sent_units = {}
for sent in sents:
sent_units[sent.original] = prob_util.Counter()
units = unit_selector(sent.stemmed)
for unit in units:
if text.text_processor.is_just_stopwords(unit): continue
sent_units[sent.original][unit] += 1
## repeat until convergence
for iter in range(1, 51):
prev_sent_values = sent_values.copy()
## get unit values from doc values
unit_values = prob_util.Counter()
for sent in sent_units:
for unit in sent_units[sent]:
unit_values[unit] += sent_values[sent]
unit_values = unit_values.makeProbDist()
## get sent values from unit values
sent_values = prob_util.Counter()
for sent in sent_units:
for unit in sent_units[sent]:
sent_values[sent] += unit_values[unit] #/ len(sent_units[sent])
sent_values = sent_values.makeProbDist()
#prob_util.Counter(unit_values).displaySorted(N=5)
#prob_util.Counter(sent_values).displaySorted(N=3)
## check for convergence
entropy_sent = prob_util.entropy(sent_values)
entropy_unit = prob_util.entropy(unit_values)
dist = prob_util.klDistance(prev_sent_values, sent_values)
#print '%d sent entropy [%1.4f] unit entropy [%1.4f] sent dist [%1.6f]' %(iter, entropy_sent, entropy_unit, dist)
if iter == 2: break
if dist < 0.0001:
#print '----------------------------'
break
return prob_util.Counter(unit_values), prob_util.Counter(sent_values)
