def accept_correction( meaning, correction, update_func='geometric', update_scale=10 ):
(lmk, lmk_prob, lmk_ent,
rel, rel_prob, rel_ent,
rel_exp_chain, rele_prob_chain, rele_ent_chain, rel_terminals, rel_landmarks,
lmk_exp_chain, lmke_prob_chain, lmke_ent_chain, lmk_terminals, lmk_landmarks,
rel_words, relw_prob, relw_ent,
lmk_words, lmkw_prob, lmkw_ent) = meaning.args
old_meaning_prob, old_meaning_entropy, lrpc, tps = get_sentence_meaning_likelihood( correction, lmk, rel )
update = update_funcs[update_func](lmk_prob * rel_prob, old_meaning_prob, lmk_ent + rel_ent, old_meaning_entropy) * update_scale
logger('Update functions is %s and update value is: %f' % (update_func, update))
# print 'lmk_prob, lmk_ent, rel_prob, rel_ent, old_meaning_prob, old_meaning_entropy, update', lmk_prob, lmk_ent, rel_prob, rel_ent, old_meaning_prob, old_meaning_entropy, update
# print lmk.object_class, type(rel)
dec_update = -update
for lhs,rhs,parent,_ in rel_exp_chain:
# print 'Decrementing production - lhs: %s, rhs: %s, parent: %s' % (lhs,rhs,parent)
update_expansion_counts( dec_update, lhs, rhs, parent, rel=rel )
for lhs,rhs,parent,lmk in lmk_exp_chain:
# print 'Decrementing production - lhs: %s, rhs: %s, parent: %s' % (lhs,rhs,parent)
update_expansion_counts( dec_update, lhs, rhs, parent, lmk_class=(lmk.object_class if lmk else None),
lmk_ori_rels=get_lmk_ori_rels_str(lmk),
lmk_color=(lmk.color if lmk else None) )
for term,word in zip(rel_terminals,rel_words):
# print 'Decrementing word - pos: %s, word: %s, rel: %s' % (term, word, rel)
update_word_counts( dec_update, term, word, rel=rel )
for term,word,lmk in zip(lmk_terminals,lmk_words,lmk_landmarks):
# print 'Decrementing word - pos: %s, word: %s, lmk_class: %s' % (term, word, lmk.object_class)
update_word_counts( dec_update, term, word, lmk_class=lmk.object_class,
lmk_ori_rels=get_lmk_ori_rels_str(lmk),
lmk_color=(lmk.color if lmk else None) )
# reward new words with old meaning
for lhs,rhs,parent,lmk,rel in lrpc:
# print 'Incrementing production - lhs: %s, rhs: %s, parent: %s' % (lhs,rhs,parent)
update_expansion_counts( update, lhs, rhs, parent, rel=rel,
lmk_class=(lmk.object_class if lmk else None),
lmk_ori_rels=get_lmk_ori_rels_str(lmk),
lmk_color=(lmk.color if lmk else None) )
for lhs,rhs,lmk,rel in tps:
# print 'Incrementing word - pos: %s, word: %s, lmk_class: %s' % (lhs, rhs, (lmk.object_class if lmk else None) )
update_word_counts( update, lhs, rhs, lmk_class=(lmk.object_class if lmk else None),
rel=rel,
lmk_ori_rels=get_lmk_ori_rels_str(lmk),
lmk_color=(lmk.color if lmk else None) )
def get_words(terminals, landmarks, rel=None):
words = []
probs = []
entropy = []
for n,lmk in zip(terminals, landmarks):
# if we could not get an expansion for the LHS, we just pass down the unexpanded nonterminal symbol
# it gets the probability of 1 and entropy of 0
if n in NONTERMINALS:
words.append(n)
probs.append(1.0)
entropy.append(0.0)
continue
lmk_class = (lmk.object_class if lmk else None)
lmk_color = (lmk.color if lmk else None)
rel_class = rel_type(rel)
dist_class = (rel.measurement.best_distance_class if hasattr(rel, 'measurement') else None)
deg_class = (rel.measurement.best_degree_class if hasattr(rel, 'measurement') else None)
cp_db = CWord.get_word_counts(pos=n,
lmk_class=lmk_class,
lmk_ori_rels=get_lmk_ori_rels_str(lmk),
lmk_color=lmk_color,
rel=rel_class,
rel_dist_class=dist_class,
rel_deg_class=deg_class)
if cp_db.count() <= 0:
logger( 'Could not expand %s (lmk_class: %s, lmk_color: %s, rel: %s, dist_class: %s, deg_class: %s)' % (n, lmk_class, lmk_color, rel_class, dist_class, deg_class) )
terminals.append( n )
continue
logger( 'Expanded %s (lmk_class: %s, lmk_color: %s, rel: %s, dist_class: %s, deg_class: %s)' % (n, lmk_class, lmk_color, rel_class, dist_class, deg_class) )
ckeys, ccounts = zip(*[(cword.word,cword.count) for cword in cp_db.all()])
ccounter = {}
for cword in cp_db.all():
if cword.word in ccounter: ccounter[cword.word] += cword.count
else: ccounter[cword.word] = cword.count
ckeys, ccounts = zip(*ccounter.items())
# print 'ckeys', ckeys
# print 'ccounts', ccounts
ccounts = np.array(ccounts, dtype=float)
ccounts /= ccounts.sum()
w, w_prob, w_entropy = categorical_sample(ckeys, ccounts)
words.append(w)
probs.append(w_prob)
entropy.append(w_entropy)
p, H = np.prod(probs), np.sum(entropy)
# print 'expanding %s to %s (p: %f, H: %f)' % (terminals, words, p, H)
return words, p, H
def accept_object_correction( meaning, sentence, update, eval_lmk=True, printing=True ):
lmk,rel = meaning
_,_, lrpc, tps = get_sentence_meaning_likelihood( sentence, lmk, rel, printing=printing)
# reward new words with old meaning
for lhs,rhs,parent,lmk,rel in lrpc:
# print 'Incrementing production - lhs: %s, rhs: %s, parent: %s' % (lhs,rhs,parent)
update_expansion_counts( update, lhs, rhs, parent, rel=rel,
lmk_class=(lmk.object_class if lmk else None),
lmk_ori_rels=get_lmk_ori_rels_str(lmk),
lmk_color=(lmk.color if lmk else None) )
for i in xrange(len(tps)):
lhs,rhs,lmk,rel = tps[i]
prev_word = tps[i-1][1] if i > 0 else None
# print 'Incrementing word - pos: %s, word: %s, lmk_class: %s' % (lhs, rhs, (lmk.object_class if lmk else None) )
update_word_counts( update, lhs, rhs, prev_word, lmk_class=(lmk.object_class if lmk else None),
rel=rel,
lmk_ori_rels=get_lmk_ori_rels_str(lmk),
lmk_color=(lmk.color if lmk else None) )
def train_rec( tree, parent=None, lmk=None, rel=None, prev_word='<no prev word>', update=1, printing=False):
lhs = tree.node
if isinstance(tree[0], ParentedTree): rhs = ' '.join(n.node for n in tree)
else: rhs = ' '.join(n for n in tree)
# check if this version of nltk uses a function for parent
if hasattr( tree.parent, '__call__' ):
parent = tree.parent().node if tree.parent() else None
else:
parent = tree.parent.node if tree.parent else None
if lhs == 'RELATION':
# everything under a RELATION node should ignore the landmark
lmk = None
if lhs == 'LANDMARK-PHRASE':
# everything under a LANDMARK-PHRASE node should ignore the relation
rel = None
if lhs == parent == 'LANDMARK-PHRASE':
# we need to move to the parent landmark
lmk = parent_landmark(lmk)
lmk_class = (lmk.object_class if lmk and lhs != 'LOCATION-PHRASE' else None)
lmk_ori_rels = get_lmk_ori_rels_str(lmk) if lhs != 'LOCATION-PHRASE' else None
lmk_color = (lmk.color if lmk and lhs != 'LOCATION-PHRASE' else None)
if lhs in NONTERMINALS:
update_expansion_counts(update=update,
lhs=lhs,
rhs=rhs,
parent=parent,
lmk_class=lmk_class,
lmk_ori_rels=lmk_ori_rels,
lmk_color=lmk_color,
rel=rel)
for subtree in tree:
prev_word = train_rec(tree=subtree, parent=parent, lmk=lmk, rel=rel, prev_word=prev_word, printing=printing)
else:
update_word_counts(update=update,
pos=lhs,
word=rhs,
prev_word=prev_word,
lmk_class=lmk_class,
lmk_ori_rels=lmk_ori_rels,
lmk_color=lmk_color,
rel=rel)
return rhs
def accept_new_words_meaning( lmk, rel, sentence, update_func='geometric', update_scale=10, num_meanings=10, printing=True ):
lmk, rel
old_meaning_prob, old_meaning_entropy, lrpc, tps = get_sentence_meaning_likelihood( sentence, lmk, rel, printing )
update = 10 * update_scale
# reward new words with old meaning
for lhs,rhs,parent,lmk,rel in lrpc:
# print 'Incrementing production - lhs: %s, rhs: %s, parent: %s' % (lhs,rhs,parent)
update_expansion_counts( update, lhs, rhs, parent, rel=rel,
lmk_class=(lmk.object_class if lmk else None),
lmk_ori_rels=get_lmk_ori_rels_str(lmk),
lmk_color=(lmk.color if lmk else None) )
for lhs,rhs,lmk,rel in tps:
# print 'Incrementing word - pos: %s, word: %s, lmk_class: %s' % (lhs, rhs, (lmk.object_class if lmk else None) )
update_word_counts( update, lhs, rhs, lmk_class=(lmk.object_class if lmk else None),
rel=rel,
lmk_ori_rels=get_lmk_ori_rels_str(lmk),
lmk_color=(lmk.color if lmk else None) )
print
def save_tree(tree, loc, rel, lmk, parent=None):
if len(tree.productions()) == 1:
# if this tree only has one production
# it means that its child is a terminal (word)
word = Word()
word.word = tree[0]
word.pos = tree.node
word.parent = parent
word.location = loc
else:
prod = Production()
prod.lhs = tree.node
prod.rhs = ' '.join(n.node for n in tree)
prod.parent = parent
prod.location = loc
# some productions are related to semantic representation
if prod.lhs == 'RELATION':
prod.relation = rel_type(rel)
if hasattr(rel, 'measurement'):
prod.relation_distance_class = rel.measurement.best_distance_class
prod.relation_degree_class = rel.measurement.best_degree_class
elif prod.lhs == 'LANDMARK-PHRASE':
prod.landmark = lmk_id(lmk)
prod.landmark_class = lmk.object_class
prod.landmark_orientation_relations = get_lmk_ori_rels_str(lmk)
prod.landmark_color = lmk.color
# next landmark phrase will need the parent landmark
lmk = parent_landmark(lmk)
elif prod.lhs == 'LANDMARK':
# LANDMARK has the same landmark as its parent LANDMARK-PHRASE
prod.landmark = parent.landmark
prod.landmark_class = parent.landmark_class
prod.landmark_orientation_relations = parent.landmark_orientation_relations
prod.landmark_color = parent.landmark_color
# save subtrees, keeping track of parent
for subtree in tree:
save_tree(subtree, loc, rel, lmk, prod)
def save_tree(tree, loc, rel, lmk, parent=None):
if len(tree.productions()) == 1:
# if this tree only has one production
# it means that its child is a terminal (word)
word = Word()
word.word = tree[0]
word.pos = tree.node
word.parent = parent
word.location = loc
else:
prod = Production()
prod.lhs = tree.node
prod.rhs = ' '.join(n.node for n in tree)
prod.parent = parent
prod.location = loc
# some productions are related to semantic representation
if prod.lhs == 'RELATION':
prod.relation = rel_type(rel)
if hasattr(rel, 'measurement'):
prod.relation_distance_class = rel.measurement.best_distance_class
prod.relation_degree_class = rel.measurement.best_degree_class
elif prod.lhs == 'LANDMARK-PHRASE':
prod.landmark = lmk_id(lmk)
prod.landmark_class = lmk.object_class
prod.landmark_orientation_relations = get_lmk_ori_rels_str(lmk)
prod.landmark_color = lmk.color
# next landmark phrase will need the parent landmark
lmk = parent_landmark(lmk)
elif prod.lhs == 'LANDMARK':
# LANDMARK has the same landmark as its parent LANDMARK-PHRASE
prod.landmark = parent.landmark
prod.landmark_class = parent.landmark_class
prod.landmark_orientation_relations = parent.landmark_orientation_relations
prod.landmark_color = parent.landmark_color
# save subtrees, keeping track of parent
for subtree in tree:
save_tree(subtree, loc, rel, lmk, prod)
def get_expansion(lhs, parent=None, lmk=None, rel=None):
lhs_rhs_parent_chain = []
prob_chain = []
entropy_chain = []
terminals = []
landmarks = []
for n in lhs.split():
if n in NONTERMINALS:
if n == parent == 'LANDMARK-PHRASE':
# we need to move to the parent landmark
lmk = parent_landmark(lmk)
lmk_class = (lmk.object_class if lmk else None)
lmk_ori_rels = get_lmk_ori_rels_str(lmk)
lmk_color = (lmk.color if lmk else None)
rel_class = rel_type(rel)
dist_class = (rel.measurement.best_distance_class if hasattr(rel, 'measurement') else None)
deg_class = (rel.measurement.best_degree_class if hasattr(rel, 'measurement') else None)
cp_db = CProduction.get_production_counts(lhs=n,
parent=parent,
lmk_class=lmk_class,
lmk_ori_rels=lmk_ori_rels,
lmk_color=lmk_color,
rel=rel_class,
dist_class=dist_class,
deg_class=deg_class)
if cp_db.count() <= 0:
logger('Could not expand %s (parent: %s, lmk_class: %s, lmk_ori_rels: %s, lmk_color: %s, rel: %s, dist_class: %s, deg_class: %s)' % (n, parent, lmk_class, lmk_ori_rels, lmk_color, rel_class, dist_class, deg_class))
terminals.append( n )
continue
ckeys, ccounts = zip(*[(cprod.rhs,cprod.count) for cprod in cp_db.all()])
ccounter = {}
for cprod in cp_db.all():
if cprod.rhs in ccounter: ccounter[cprod.rhs] += cprod.count
else: ccounter[cprod.rhs] = cprod.count
ckeys, ccounts = zip(*ccounter.items())
# print 'ckeys', ckeys
# print 'ccounts', ccounts
ccounts = np.array(ccounts, dtype=float)
ccounts /= ccounts.sum()
cprod, cprod_prob, cprod_entropy = categorical_sample(ckeys, ccounts)
# print cprod, cprod_prob, cprod_entropy
lhs_rhs_parent_chain.append( ( n,cprod,parent,lmk ) )
prob_chain.append( cprod_prob )
entropy_chain.append( cprod_entropy )
lrpc, pc, ec, t, ls = get_expansion( lhs=cprod, parent=n, lmk=lmk, rel=rel )
lhs_rhs_parent_chain.extend( lrpc )
prob_chain.extend( pc )
entropy_chain.extend( ec )
terminals.extend( t )
landmarks.extend( ls )
else:
terminals.append( n )
landmarks.append( lmk )
return lhs_rhs_parent_chain, prob_chain, entropy_chain, terminals, landmarks
def get_words(terminals, landmarks, rel=None, prevword=None):
words = []
probs = []
alphas = []
entropy = []
C = CWord.get_count
for n,lmk in zip(terminals, landmarks):
# if we could not get an expansion for the LHS, we just pass down the unexpanded nonterminal symbol
# it gets the probability of 1 and entropy of 0
if n in NONTERMINALS:
words.append(n)
probs.append(1.0)
entropy.append(0.0)
continue
lmk_class = (lmk.object_class if lmk else None)
lmk_color = (lmk.color if lmk else None)
rel_class = rel_type(rel)
dist_class = (rel.measurement.best_distance_class if hasattr(rel, 'measurement') else None)
deg_class = (rel.measurement.best_degree_class if hasattr(rel, 'measurement') else None)
meaning = dict(pos=n,
lmk_class=lmk_class,
lmk_ori_rels=get_lmk_ori_rels_str(lmk),
lmk_color=lmk_color,
rel=rel_class,
rel_dist_class=dist_class,
rel_deg_class=deg_class)
cp_db_uni = CWord.get_word_counts(**meaning)
ccounter = {}
for c in cp_db_uni:
ccounter[c.word] = ccounter.get(c.word, 0) + c.count
ckeys, ccounts_uni = zip(*ccounter.items())
ccounts_uni = np.array(ccounts_uni, dtype=float)
ccounts_uni /= ccounts_uni.sum()
prev_word = words[-1] if words else prevword
alpha = C(prev_word=prev_word, **meaning) / C(**meaning)
alphas.append(alpha)
if alpha:
cp_db_bi = CWord.get_word_counts(prev_word=prev_word, **meaning)
ccounter = {}
for c in cp_db_bi:
ccounter[c.word] = ccounter.get(c.word, 0) + c.count
ccounts_bi = np.array([ccounter.get(k,0) for k in ckeys], dtype=float)
ccounts_bi /= ccounts_bi.sum()
cprob = (alpha * ccounts_bi) + ((1-alpha) * ccounts_uni)
else:
cprob = ccounts_uni
# if cp_db.count() <= 0:
# logger( 'Could not expand %s (lmk_class: %s, lmk_color: %s, rel: %s, dist_class: %s, deg_class: %s)' % (n, lmk_class, lmk_color, rel_class, dist_class, deg_class) )
# terminals.append( n )
# continue
# ckeys, ccounts = zip(*[(cword.word,cword.count) for cword in cp_db.all()])
# ccounter = {}
# for cword in cp_db.all():
# if cword.word in ccounter: ccounter[cword.word] += cword.count
# else: ccounter[cword.word] = cword.count
# ckeys, ccounts = zip(*ccounter.items())
# print 'ckeys', ckeys
# print 'ccounts', ccounts
# ccounts = np.array(ccounts, dtype=float)
# ccounts /= ccounts.sum()
w, w_prob, w_entropy = categorical_sample(ckeys, cprob)
words.append(w)
probs.append(w_prob)
entropy.append(w_entropy)
p, H = np.prod(probs), np.sum(entropy)
# print 'expanding %s to %s (p: %f, H: %f)' % (terminals, words, p, H)
return words, p, H, alphas
def get_tree_probs(tree, lmk=None, rel=None):
lhs_rhs_parent_chain = []
prob_chain = []
entropy_chain = []
term_prods = []
lhs = tree.node
if isinstance(tree[0], ParentedTree): rhs = ' '.join(n.node for n in tree)
else: rhs = ' '.join(n for n in tree)
parent = tree.parent.node if tree.parent else None
if lhs == 'RELATION':
# everything under a RELATION node should ignore the landmark
lmk = None
if lhs == 'LANDMARK-PHRASE':
# everything under a LANDMARK-PHRASE node should ignore the relation
rel = None
if lhs == parent == 'LANDMARK-PHRASE':
# we need to move to the parent landmark
lmk = parent_landmark(lmk)
lmk_class = (lmk.object_class if lmk and lhs != 'LOCATION-PHRASE' else None)
lmk_ori_rels = get_lmk_ori_rels_str(lmk) if lhs != 'LOCATION-PHRASE' else None
lmk_color = (lmk.color if lmk and lhs != 'LOCATION-PHRASE' else None)
rel_class = rel_type(rel) if lhs != 'LOCATION-PHRASE' else None
dist_class = (rel.measurement.best_distance_class if hasattr(rel, 'measurement') and lhs != 'LOCATION-PHRASE' else None)
deg_class = (rel.measurement.best_degree_class if hasattr(rel, 'measurement') and lhs != 'LOCATION-PHRASE' else None)
if lhs in NONTERMINALS:
cp_db = CProduction.get_production_counts(lhs=lhs,
parent=parent,
lmk_class=lmk_class,
lmk_ori_rels=lmk_ori_rels,
lmk_color=lmk_color,
rel=rel_class,
dist_class=dist_class,
deg_class=deg_class)
if cp_db.count() <= 0:
logger('Could not expand %s (parent: %s, lmk_class: %s, lmk_ori_rels: %s, lmk_color: %s, rel: %s, dist_class: %s, deg_class: %s)' % (lhs, parent, lmk_class, lmk_ori_rels, lmk_color, rel_class, dist_class, deg_class))
else:
ckeys, ccounts = zip(*[(cprod.rhs,cprod.count) for cprod in cp_db.all()])
ccounter = {}
for cprod in cp_db.all():
if cprod.rhs in ccounter: ccounter[cprod.rhs] += cprod.count
else: ccounter[cprod.rhs] = cprod.count + 1
# we have never seen this RHS in this context before
if rhs not in ccounter: ccounter[rhs] = 1
ckeys, ccounts = zip(*ccounter.items())
# add 1 smoothing
ccounts = np.array(ccounts, dtype=float)
ccount_probs = ccounts / ccounts.sum()
cprod_entropy = -np.sum( (ccount_probs * np.log(ccount_probs)) )
cprod_prob = ccounter[rhs]/ccounts.sum()
# logger('ckeys: %s' % str(ckeys))
# logger('ccounts: %s' % str(ccounts))
# logger('rhs: %s, cprod_prob: %s, cprod_entropy: %s' % (rhs, cprod_prob, cprod_entropy))
prob_chain.append( cprod_prob )
entropy_chain.append( cprod_entropy )
lhs_rhs_parent_chain.append( ( lhs, rhs, parent, lmk, rel ) )
for subtree in tree:
pc, ec, lrpc, tps = get_tree_probs(subtree, lmk, rel)
prob_chain.extend( pc )
entropy_chain.extend( ec )
lhs_rhs_parent_chain.extend( lrpc )
term_prods.extend( tps )
else:
cw_db = CWord.get_word_counts(pos=lhs,
lmk_class=lmk_class,
lmk_ori_rels=lmk_ori_rels,
lmk_color=lmk_color,
rel=rel_class,
rel_dist_class=dist_class,
rel_deg_class=deg_class)
if cw_db.count() <= 0:
# we don't know the probability or entropy values for the context we have never seen before
# we just update the term_prods list
logger('Could not expand %s (lmk_class: %s, lmk_ori_rels: %s, lmk_color: %s, rel: %s, dist_class: %s, deg_class: %s)' % (lhs, lmk_class, lmk_ori_rels, lmk_color, rel_class, dist_class, deg_class))
else:
ckeys, ccounts = zip(*[(cword.word,cword.count) for cword in cw_db.all()])
ccounter = {}
for cword in cw_db.all():
if cword.word in ccounter: ccounter[cword.word] += cword.count
else: ccounter[cword.word] = cword.count + 1
# we have never seen this RHS in this context before
if rhs not in ccounter: ccounter[rhs] = 1
ckeys, ccounts = zip(*ccounter.items())
# logger('ckeys: %s' % str(ckeys))
# logger('ccounts: %s' % str(ccounts))
# add 1 smoothing
ccounts = np.array(ccounts, dtype=float)
ccount_probs = ccounts/ccounts.sum()
w_prob = ccounter[rhs]/ccounts.sum()
w_entropy = -np.sum( (ccount_probs * np.log(ccount_probs)) )
prob_chain.append(w_prob)
entropy_chain.append(w_entropy)
term_prods.append( (lhs, rhs, lmk, rel) )
return prob_chain, entropy_chain, lhs_rhs_parent_chain, term_prods
def accept_correction(meaning,
correction,
update_func='geometric',
update_scale=10):
(lmk, lmk_prob, lmk_ent, rel, rel_prob, rel_ent, rel_exp_chain,
rele_prob_chain, rele_ent_chain, rel_terminals, rel_landmarks,
lmk_exp_chain, lmke_prob_chain, lmke_ent_chain, lmk_terminals,
lmk_landmarks, rel_words, relw_prob, relw_ent, lmk_words, lmkw_prob,
lmkw_ent) = meaning.args
rel_a = meaning.rel_a
lmk_a = meaning.lmk_a
old_meaning_prob, old_meaning_entropy, lrpc, tps = get_sentence_meaning_likelihood(
correction, lmk, rel)
update = update_funcs[update_func](lmk_prob * rel_prob, old_meaning_prob,
lmk_ent + rel_ent,
old_meaning_entropy) * update_scale
logger('Update functions is %s and update value is: %f' %
(update_func, update))
# print 'lmk_prob, lmk_ent, rel_prob, rel_ent, old_meaning_prob, old_meaning_entropy, update', lmk_prob, lmk_ent, rel_prob, rel_ent, old_meaning_prob, old_meaning_entropy, update
# print lmk.object_class, type(rel)
dec_update = -update
for lhs, rhs, parent, _ in rel_exp_chain:
# print 'Decrementing production - lhs: %s, rhs: %s, parent: %s' % (lhs,rhs,parent)
update_expansion_counts(dec_update, lhs, rhs, parent, rel=rel)
for lhs, rhs, parent, lmk in lmk_exp_chain:
# print 'Decrementing production - lhs: %s, rhs: %s, parent: %s' % (lhs,rhs,parent)
update_expansion_counts(dec_update,
lhs,
rhs,
parent,
lmk_class=(lmk.object_class if lmk else None),
lmk_ori_rels=get_lmk_ori_rels_str(lmk),
lmk_color=(lmk.color if lmk else None))
data = zip(rel_terminals, rel_words)
for i in xrange(len(data)):
term, word = data[i]
prev_word = data[i - 1][1] if i > 0 else None
a = rel_a[i]
# print 'Decrementing word - pos: %s, word: %s, rel: %s' % (term, word, rel)
update_word_counts((1 - a) * dec_update, term, word, rel=rel)
update_word_counts(a * dec_update,
term,
word,
rel=rel,
prev_word=prev_word)
data = zip(lmk_terminals, lmk_words, lmk_landmarks)
for i in xrange(len(data)):
term, word, lmk = data[i]
prev_word = data[i - 1][1] if i > 0 else rel_words[-1]
a = lmk_a[i]
# print 'Decrementing word - pos: %s, word: %s, lmk_class: %s' % (term, word, lmk.object_class)
update_word_counts((1 - a) * dec_update,
term,
word,
lmk_class=lmk.object_class,
lmk_ori_rels=get_lmk_ori_rels_str(lmk),
lmk_color=(lmk.color if lmk else None))
update_word_counts(a * dec_update,
term,
word,
prev_word,
lmk_class=lmk.object_class,
lmk_ori_rels=get_lmk_ori_rels_str(lmk),
lmk_color=(lmk.color if lmk else None))
# reward new words with old meaning
for lhs, rhs, parent, lmk, rel in lrpc:
# print 'Incrementing production - lhs: %s, rhs: %s, parent: %s' % (lhs,rhs,parent)
update_expansion_counts(update,
lhs,
rhs,
parent,
rel=rel,
lmk_class=(lmk.object_class if lmk else None),
lmk_ori_rels=get_lmk_ori_rels_str(lmk),
lmk_color=(lmk.color if lmk else None))
for i in xrange(len(tps)):
lhs, rhs, lmk, rel = tps[i]
prev_word = tps[i - 1][1] if i > 0 else None
# print 'Incrementing word - pos: %s, word: %s, lmk_class: %s' % (lhs, rhs, (lmk.object_class if lmk else None) )
update_word_counts(update,
lhs,
rhs,
prev_word,
lmk_class=(lmk.object_class if lmk else None),
rel=rel,
lmk_ori_rels=get_lmk_ori_rels_str(lmk),
lmk_color=(lmk.color if lmk else None))
def get_expansion(lhs, parent=None, lmk=None, rel=None):
lhs_rhs_parent_chain = []
prob_chain = []
entropy_chain = []
terminals = []
landmarks = []
for n in lhs.split():
if n in NONTERMINALS:
if n == parent == 'LANDMARK-PHRASE':
# we need to move to the parent landmark
lmk = parent_landmark(lmk)
lmk_class = (lmk.object_class if lmk else None)
lmk_ori_rels = get_lmk_ori_rels_str(lmk)
lmk_color = (lmk.color if lmk else None)
rel_class = rel_type(rel)
dist_class = (rel.measurement.best_distance_class if hasattr(
rel, 'measurement') else None)
deg_class = (rel.measurement.best_degree_class if hasattr(
rel, 'measurement') else None)
cp_db = CProduction.get_production_counts(
lhs=n,
parent=parent,
lmk_class=lmk_class,
lmk_ori_rels=lmk_ori_rels,
lmk_color=lmk_color,
rel=rel_class,
dist_class=dist_class,
deg_class=deg_class)
if cp_db.count() <= 0:
logger(
'Could not expand %s (parent: %s, lmk_class: %s, lmk_ori_rels: %s, lmk_color: %s, rel: %s, dist_class: %s, deg_class: %s)'
% (n, parent, lmk_class, lmk_ori_rels, lmk_color,
rel_class, dist_class, deg_class))
terminals.append(n)
continue
ckeys, ccounts = zip(*[(cprod.rhs, cprod.count)
for cprod in cp_db.all()])
ccounter = {}
for cprod in cp_db.all():
if cprod.rhs in ccounter: ccounter[cprod.rhs] += cprod.count
else: ccounter[cprod.rhs] = cprod.count
ckeys, ccounts = zip(*ccounter.items())
# print 'ckeys', ckeys
# print 'ccounts', ccounts
ccounts = np.array(ccounts, dtype=float)
ccounts /= ccounts.sum()
cprod, cprod_prob, cprod_entropy = categorical_sample(
ckeys, ccounts)
# print cprod, cprod_prob, cprod_entropy
lhs_rhs_parent_chain.append((n, cprod, parent, lmk))
prob_chain.append(cprod_prob)
entropy_chain.append(cprod_entropy)
lrpc, pc, ec, t, ls = get_expansion(lhs=cprod,
parent=n,
lmk=lmk,
rel=rel)
lhs_rhs_parent_chain.extend(lrpc)
prob_chain.extend(pc)
entropy_chain.extend(ec)
terminals.extend(t)
landmarks.extend(ls)
else:
terminals.append(n)
landmarks.append(lmk)
return lhs_rhs_parent_chain, prob_chain, entropy_chain, terminals, landmarks
def get_words(terminals, landmarks, rel=None, prevword=None):
words = []
probs = []
alphas = []
entropy = []
C = CWord.get_count
for n, lmk in zip(terminals, landmarks):
# if we could not get an expansion for the LHS, we just pass down the unexpanded nonterminal symbol
# it gets the probability of 1 and entropy of 0
if n in NONTERMINALS:
words.append(n)
probs.append(1.0)
entropy.append(0.0)
continue
lmk_class = (lmk.object_class if lmk else None)
lmk_color = (lmk.color if lmk else None)
rel_class = rel_type(rel)
dist_class = (rel.measurement.best_distance_class if hasattr(
rel, 'measurement') else None)
deg_class = (rel.measurement.best_degree_class if hasattr(
rel, 'measurement') else None)
meaning = dict(pos=n,
lmk_class=lmk_class,
lmk_ori_rels=get_lmk_ori_rels_str(lmk),
lmk_color=lmk_color,
rel=rel_class,
rel_dist_class=dist_class,
rel_deg_class=deg_class)
cp_db_uni = CWord.get_word_counts(**meaning)
ccounter = {}
for c in cp_db_uni:
ccounter[c.word] = ccounter.get(c.word, 0) + c.count
ckeys, ccounts_uni = zip(*ccounter.items())
ccounts_uni = np.array(ccounts_uni, dtype=float)
ccounts_uni /= ccounts_uni.sum()
prev_word = words[-1] if words else prevword
alpha = C(prev_word=prev_word, **meaning) / C(**meaning)
alphas.append(alpha)
if alpha:
cp_db_bi = CWord.get_word_counts(prev_word=prev_word, **meaning)
ccounter = {}
for c in cp_db_bi:
ccounter[c.word] = ccounter.get(c.word, 0) + c.count
ccounts_bi = np.array([ccounter.get(k, 0) for k in ckeys],
dtype=float)
ccounts_bi /= ccounts_bi.sum()
cprob = (alpha * ccounts_bi) + ((1 - alpha) * ccounts_uni)
else:
cprob = ccounts_uni
# if cp_db.count() <= 0:
# logger( 'Could not expand %s (lmk_class: %s, lmk_color: %s, rel: %s, dist_class: %s, deg_class: %s)' % (n, lmk_class, lmk_color, rel_class, dist_class, deg_class) )
# terminals.append( n )
# continue
# ckeys, ccounts = zip(*[(cword.word,cword.count) for cword in cp_db.all()])
# ccounter = {}
# for cword in cp_db.all():
# if cword.word in ccounter: ccounter[cword.word] += cword.count
# else: ccounter[cword.word] = cword.count
# ckeys, ccounts = zip(*ccounter.items())
# print 'ckeys', ckeys
# print 'ccounts', ccounts
# ccounts = np.array(ccounts, dtype=float)
# ccounts /= ccounts.sum()
w, w_prob, w_entropy = categorical_sample(ckeys, cprob)
words.append(w)
probs.append(w_prob)
entropy.append(w_entropy)
p, H = np.prod(probs), np.sum(entropy)
# print 'expanding %s to %s (p: %f, H: %f)' % (terminals, words, p, H)
return words, p, H, alphas
def accept_correction( meaning, correction, update_func='geometric', update_scale=10, eval_lmk=True, multiply=False, printing=True ):
(lmk, lmk_prob, lmk_ent,
rel, rel_prob, rel_ent,
rel_exp_chain, rele_prob_chain, rele_ent_chain, rel_terminals, rel_landmarks,
lmk_exp_chain, lmke_prob_chain, lmke_ent_chain, lmk_terminals, lmk_landmarks,
rel_words, relw_prob, relw_ent,
lmk_words, lmkw_prob, lmkw_ent) = meaning.args
rel_a = meaning.rel_a
lmk_a = meaning.lmk_a
try:
if correction is None:
# If we recieved no correction, reward our generated sentence INSTEAD of decrementing
dec_update = (1 if multiply else update_scale)
else:
# otherwise we decrement it
old_meaning_prob, old_meaning_entropy, lrpc, tps = get_sentence_meaning_likelihood( correction, lmk, rel, printing=printing)
if eval_lmk:
update = update_funcs[update_func](lmk_prob * rel_prob, old_meaning_prob, lmk_ent + rel_ent, old_meaning_entropy) * (1 if multiply else update_scale)
else:
update = rel_prob * (0.1 if multiply else update_scale)
logger('Update functions is %s and update value is: %f (using multiply: %s)' % (update_func, update, multiply))
# print 'lmk_prob, lmk_ent, rel_prob, rel_ent, old_meaning_prob, old_meaning_entropy, update', lmk_prob, lmk_ent, rel_prob, rel_ent, old_meaning_prob, old_meaning_entropy, update
# print lmk.object_class, type(rel)
dec_update = -update
for lhs,rhs,parent,_ in rel_exp_chain:
# print 'Decrementing production - lhs: %s, rhs: %s, parent: %s' % (lhs,rhs,parent)
update_expansion_counts( dec_update, lhs, rhs, parent, rel=rel, multiply=multiply )
for lhs,rhs,parent,lmk in lmk_exp_chain:
# print 'Decrementing production - lhs: %s, rhs: %s, parent: %s' % (lhs,rhs,parent)
update_expansion_counts( dec_update, lhs, rhs, parent, lmk_class=(lmk.object_class if lmk else None),
lmk_ori_rels=get_lmk_ori_rels_str(lmk),
lmk_color=(lmk.color if lmk else None),
multiply=multiply )
data = zip(rel_terminals, rel_words)
for i in xrange(len(data)):
term,word = data[i]
prev_word = data[i-1][1] if i > 0 else None
a = rel_a[i]
# print 'Decrementing word - pos: %s, word: %s, rel: %s' % (term, word, rel)
update_word_counts( (1-a)*dec_update, term, word, rel=rel, multiply=multiply )
update_word_counts(a*dec_update, term, word, rel=rel, prev_word=prev_word, multiply=multiply )
data = zip(lmk_terminals, lmk_words, lmk_landmarks)
for i in xrange(len(data)):
term, word, lmk = data[i]
prev_word = data[i-1][1] if i > 0 else rel_words[-1]
a = lmk_a[i]
# print 'Decrementing word - pos: %s, word: %s, lmk_class: %s' % (term, word, lmk.object_class)
update_word_counts((1-a)*dec_update, term, word, lmk_class=(lmk.object_class if lmk else None),
lmk_ori_rels=get_lmk_ori_rels_str(lmk),
lmk_color=(lmk.color if lmk else None),
multiply=multiply )
update_word_counts( a*dec_update, term, word, prev_word, lmk_class=(lmk.object_class if lmk else None),
lmk_ori_rels=get_lmk_ori_rels_str(lmk),
lmk_color=(lmk.color if lmk else None),
multiply=multiply )
# Only enforce the correction if we got one
if correction is not None:
# reward new words with old meaning
for lhs,rhs,parent,lmk,rel in lrpc:
# print 'Incrementing production - lhs: %s, rhs: %s, parent: %s' % (lhs,rhs,parent)
update_expansion_counts( update, lhs, rhs, parent, rel=rel,
lmk_class=(lmk.object_class if lmk else None),
lmk_ori_rels=get_lmk_ori_rels_str(lmk),
lmk_color=(lmk.color if lmk else None),
multiply=multiply )
for i in xrange(len(tps)):
lhs,rhs,lmk,rel = tps[i]
prev_word = tps[i-1][1] if i > 0 else None
# print 'Incrementing word - pos: %s, word: %s, lmk_class: %s' % (lhs, rhs, (lmk.object_class if lmk else None) )
update_word_counts( update, lhs, rhs, prev_word, lmk_class=(lmk.object_class if lmk else None),
rel=rel,
lmk_ori_rels=get_lmk_ori_rels_str(lmk),
lmk_color=(lmk.color if lmk else None),
multiply=multiply )
except ParseError as pe:
logger( pe )
logger( 'No update performed' )
