def update_expansion_counts(update, lhs, rhs, parent=None, lmk_class=None, lmk_ori_rels=None, lmk_color=None, rel=None):
CProduction.update_production_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_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))
def print_tree_entropy(tree,
cptotalentropy=None,
cpcolumns=None,
cwtotalentropy=None,
cwcolumns=None,
printlength=None):
if cptotalentropy is None:
cptotalentropy,cpcolumns,cwtotalentropy,cwcolumns,printlength = get_total_entropy()
lhs = tree.node
if isinstance(tree[0], ParentedTree): rhs = ' '.join(n.node for n in tree)
else: rhs = ' '.join(n for n in tree)
print tree
print '+++',lhs,'-->',rhs,'+++'
if lhs in NONTERMINALS:
cp_db = CProduction.get_production_counts(lhs=lhs,rhs=rhs)
totalss = get_query_totalss(cp_db,cpcolumns)
print_totalss_entropy(totalss,cptotalentropy,cpcolumns,printlength)
for subtree in tree:
print_tree_entropy(subtree,
cptotalentropy,
cpcolumns,
cwtotalentropy,
cwcolumns,
printlength)
else:
cw_db = CWord.get_word_counts(pos=lhs,word=rhs)
totalss = get_query_totalss(cw_db,cwcolumns)
print_totalss_entropy(totalss,cwtotalentropy,cwcolumns,printlength)
def build_meaning(tree,
parent=None,
parts=[],
cptotalentropy=None,
cpcolumns=None,
cwtotalentropy=None,
cwcolumns=None,
threshold=0.75):
cptotalentropy,cpcolumns,cwtotalentropy,cwcolumns,printlength = get_total_entropy()
lhs = tree.node
if isinstance(tree[0], ParentedTree): rhs = ' '.join(n.node for n in tree)
else: rhs = ' '.join(n for n in tree)
print '+++',lhs,'-->',rhs,'+++'
if lhs in NONTERMINALS:
if not lhs == 'LOCATION-PHRASE':
if lhs == 'RELATION':
parts.append( ('relation',Counter()) )
elif lhs == parent == 'LANDMARK-PHRASE':
parts.append( ('parent-landmark',Counter()) )
elif lhs == 'LANDMARK-PHRASE':
parts.append( ('landmark',Counter()) )
cp_db = CProduction.get_production_counts(lhs=lhs,rhs=rhs)
totalss = get_query_totalss(cp_db,cpcolumns)
for name,totals in zip(cpcolumns[:-1],totalss):
ent = entropy_of_counts( totals.values() )
totent = cptotalentropy[name.name]
if ent < threshold*totent:
parts[-1][1][ "%s = %s" % (name.name, max(zip(*reversed(zip(*totals.items()))))[1]) ]+=1
for subtree in tree:
parts = build_meaning(subtree,
lhs,
parts,
cptotalentropy,
cpcolumns,
cwtotalentropy,
cwcolumns,
threshold)
else:
cw_db = CWord.get_word_counts(pos=lhs,word=rhs)
totalss = get_query_totalss(cw_db,cwcolumns)
for name,totals in zip(cwcolumns[:-1],totalss):
ent = entropy_of_counts( totals.values() )
totent = cwtotalentropy[name.name]
if ent < threshold*totent:
parts[-1][1][ "%s = %s" % (name.name, max(zip(*reversed(zip(*totals.items()))))[1]) ]+=1
return parts
def update_expansion_counts(update,
lhs,
rhs,
parent=None,
lmk_class=None,
lmk_ori_rels=None,
lmk_color=None,
rel=None):
CProduction.update_production_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_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))
def db_mass():
total = CProduction.get_production_sum(None)
total += CWord.get_word_sum(None)
return total
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_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 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
parent = aliased(Production)
qry = session.query(Production.lhs, Production.rhs,
Production.landmark, Production.landmark_class, Production.landmark_orientation_relations, Production.landmark_color,
Production.relation, Production.relation_distance_class,
Production.relation_degree_class, func.count(Production.id)).\
filter_by(parent=None).\
group_by(Production.lhs, Production.rhs,
Production.landmark, Production.landmark_class, Production.landmark_orientation_relations,
Production.relation, Production.relation_distance_class,
Production.relation_degree_class)
for row in qry:
cp = CProduction(lhs=row[0],
rhs=row[1],
landmark=row[2],
landmark_class=row[3],
landmark_orientation_relations=row[4],
landmark_color=row[5],
relation=row[6],
relation_distance_class=row[7],
relation_degree_class=row[8],
count=row[9])
# count productions with parent
parent = aliased(Production)
qry = session.query(Production.lhs, Production.rhs,
parent.lhs, Production.landmark, Production.landmark_class, Production.landmark_orientation_relations, Production.landmark_color,
Production.relation, Production.relation_distance_class,
Production.relation_degree_class, func.count(Production.id)).\
join(parent, Production.parent).\
group_by(Production.lhs, Production.rhs,
parent.lhs, Production.landmark, Production.landmark_class, Production.landmark_orientation_relations,
Production.relation, Production.relation_distance_class,
