def production_count(path, oldtable, tables, conf):
grammar = dict((row[0], tuple(row[1:])) for row in tables["infer_grammar"])
stats = dict()
prodnum = dict()
# print grammar
# print "\n"
for nonterm, P in grammar.iteritems():
for i, p in enumerate(P):
prodnum[(nonterm, p)] = i + 1
stats[(nonterm, i + 1)] = 0
if oldtable is not None:
for nonterm, p, count in oldtable:
stats[(nonterm, prodnum[(nonterm, p)])] = int(count)
# print prodnum
def callback(grammar, stats, node, depth):
if not node.children:
return
productions = grammar[node.label]
p = productions.index(":".join(kid.label for kid in node.children)) + 1
# print node.label + " => " + grammar[node.label][p-1]
stats[(node.label, p)] += 1
walktrees(conf["trees"], functools.partial(callback, grammar, stats))
table = [(key[0], grammar[key[0]][key[1] - 1], count) for key, count in stats.iteritems()]
table.sort(key=lambda x: (x[0], x[2]))
save(path, table)
return table
def symbol_counter(path, oldtable, trees, callback):
symbols = dict()
if oldtable is not None:
symbols.update(oldtable)
walktrees(trees, functools.partial(callback, symbols))
return save(path, tuple((name, count) for name, count in symbols.iteritems()))
def infer_grammar(path, oldtable, tables, conf):
productions = dict()
if oldtable is not None:
## TODO: clean this jankyness up! we shouldn't have to rejoin oldtable
## so we can parse it.
productions.update(
lib.parse_grammar('\n'.join(''.join(row) for row in oldtable))
)
def callback(productions, node, depth):
if not node.children: return
p = productions.get(node.label, set())
p.add(tuple(kid.label for kid in node.children))
productions[node.label] = p
walktrees(conf['trees'], functools.partial(callback, productions))
table = tuple(
tuple([nonterm] + [':'.join(p) for p in P])
for nonterm, P in productions.iteritems()
)
gramfile = '\n'.join(
' : '.join((
nonterm,
' '.join(prod)
))
for nonterm, prods in productions.iteritems()
for prod in prods
) + '\n'
with open(path, 'w') as f: f.write(gramfile)
return table
def production_coverage_global(path, oldtable, tables, conf):
grammar = dict(
(
nonterm,
tuple(sorted(':'.join(p) for p in prods))
)
for nonterm, prods in conf['grammar'].iteritems()
)
def initstats():
stats = dict()
total = 0
for nonterm, P in grammar.iteritems():
for i, p in enumerate(P):
stats[(nonterm, i+1)] = 0
total += 1
return stats, total
global_cov, total = initstats()
walktrees(conf['trees'], functools.partial(callback, grammar, global_cov))
print 'global', float(sum(val for val in global_cov.itervalues()))/total
def conditional_counts(path, oldtable, tables, conf):
grammar = dict((row[0], tuple(row[1:])) for row in tables["infer_grammar"])
counts = (
dict()
) # counts how many times a RULE is reached by a specific prevTuple (e.g. counts[NT => A:B:C][(NT1,NT2)] == 5)
nonterminalCounts = (
dict()
) # counts how many times a prevTuple reaches a specific NONTERMINAL (e.g. nonterminalCounts[(NT1,NT2)][NT] == 78)
stack = list()
lookBack = 2 # how many items in prevTuple?
def count_nonterms(nonterminalCounts, prevAsTuple, node):
if not nonterminalCounts.has_key(prevAsTuple):
nonterminalCounts[prevAsTuple] = {node.label: 1}
else:
if not nonterminalCounts.get(prevAsTuple).has_key(node.label):
nonterminalCounts[prevAsTuple][node.label] = 1
else:
nonterminalCounts[prevAsTuple][node.label] += 1
def increase_counts(counts, prevAsTuple, chosenRule):
if not counts.has_key(chosenRule):
counts[chosenRule] = {prevAsTuple: 1}
else:
if not counts.get(chosenRule).has_key(prevAsTuple):
counts[chosenRule][prevAsTuple] = 1
else:
counts[chosenRule][prevAsTuple] += 1
def callback(grammar, node, depth):
# if this is a new ast then we want to clear our stack
if node.label == "Start":
while stack:
stack.pop()
initStack = (tuple(None for x in range(lookBack)), False)
stack.append(initStack)
prev = stack[len(stack) - 1][0]
requirePop = stack[len(stack) - 1][1]
prevAsTuple = tuple(prev[x] for x in range(lookBack))
if not node.children:
if requirePop:
stack.pop()
return
if requirePop:
stack.pop()
productions = grammar[node.label]
p = productions.index(":".join(kid.label for kid in node.children)) + 1
chosenRule = node.label + " => " + grammar[node.label][p - 1]
increase_counts(counts, prevAsTuple, chosenRule)
count_nonterms(nonterminalCounts, prevAsTuple, node)
# append this new rule to the stack as our new "most previous"
if grammar[node.label][p - 1].count(":") > 1: # do we have more than 1 nonterminal in this rule?
stack.append((tuple(prev[x + 1] for x in range(lookBack - 1)) + (node.label,), False))
else: # if there is only one nonterminal in this rule then we want to log it as a previous but then pop it from the stack
# this way, rules that have >1 nonterminals will keep their "prev" relative to what it was originally.
# e.g. with NT:NT2:NT3, when we get to NT2, we dont want previous to include the previous from when we went down NT's productions
stack.append((tuple(prev[x + 1] for x in range(lookBack - 1)) + (node.label,), True))
walktrees(conf["trees"], functools.partial(callback, grammar))
retTables = dict()
retTables[0] = tuple(
(lookBack, rule) + tuple(nt for nt in prev) + (count,)
for rule, myCounts in counts.iteritems()
for prev, count in myCounts.iteritems()
)
# we don't save this guy as a csv but we need to log it so that conditional_probabilities() can work right
retTables[1] = tuple(
(lookBack,) + tuple(prev for prev in prevAsTuple) + (nonterm, count)
for prevAsTuple, myCounts in nonterminalCounts.iteritems()
for nonterm, count in myCounts.iteritems()
)
save(path, retTables[0])
return retTables
def conditional_probabilities(path, oldtable, tables, conf):
grammar = dict((row[0], tuple(row[1:])) for row in tables['infer_grammar'])
counts = dict() #counts how many times a RULE is reached by a specific prevTuple (e.g. counts[NT => A:B:C][(NT1,NT2)] == 5)
terminalCounts = dict() #counts how many times a prevTuple reaches a specific NONTERMINAL (e.g. terminalCounts[(NT1,NT2)][NT] == 78)
stack = list()
lookBack = 2 #how many items in prevTuple?
def callback(grammar, node, depth):
#if this is a new ast then we want to clear our stack
if node.label == "Start":
while stack:
stack.pop()
initStack = (tuple(None for x in range(lookBack)), False)
stack.append(initStack)
prev = stack[len(stack)-1][0]
requirePop = stack[len(stack)-1][1]
prevAsTuple = tuple(prev[x] for x in range(lookBack))
if not node.children:
if requirePop:
stack.pop()
return
if requirePop:
stack.pop()
productions = grammar[node.label]
p = productions.index(':'.join(kid.label for kid in node.children)) + 1
chosenRule = node.label + " => " + grammar[node.label][p-1]
if not counts.has_key(chosenRule):
counts[chosenRule] = {prevAsTuple : 1}
else:
if not counts.get(chosenRule).has_key(prevAsTuple):
counts[chosenRule][prevAsTuple] = 1
else:
counts[chosenRule][prevAsTuple] += 1
if not terminalCounts.has_key(prevAsTuple):
terminalCounts[prevAsTuple] = {node.label : 1}
else:
if not terminalCounts.get(prevAsTuple).has_key(node.label):
terminalCounts[prevAsTuple][node.label] = 1
else:
terminalCounts[prevAsTuple][node.label] += 1
#append this new rule to the stack as our new "most previous"
if grammar[node.label][p-1].count(":") > 1:
stack.append(
(
tuple(prev[x+1] for x in range(lookBack-1)) + (node.label,),
False
)
)
else: #if there is only one nonterminal in this rule then we want to log it as a previous but then pop it from the stack
#this way, rules that have >1 nonterminals will keep their "prev" relative to what it was originally.
#e.g. with NT:NT2:NT3, when we get to NT2, we dont want previous to include the previous from when we went down NT's productions
stack.append(
(
tuple(prev[x+1] for x in range(lookBack-1)) + (node.label,),
True
)
)
walktrees(conf['trees'], functools.partial(callback, grammar))
probabilities = dict(
(
rule,
dict(
(
prev,
float(num)/float(terminalCounts[prev][rule.split("=>")[0].strip()]) #P[rule | prev]
)
for prev, num in myCounts.iteritems()
)
)
for rule, myCounts in counts.iteritems()
)
table = tuple(
(lookBack, prod) + tuple(nt for nt in prev) + (probability,)
for prod, myCounts in probabilities.iteritems()
for prev, probability in myCounts.iteritems()
)
save(path, table)
return table
