def insert_regex(in_str, choice_len, regex_constraint):
"""
insert sampling string for regex if needed
"""
insert_pos = set(range(choice_len))
for r in regex_constraint:
desired_counts = regex_constraint[r]
actual_counts = len(re.findall(ALL_REGEX[r], ''.join(in_str)))
num_of_inserts = max(0, desired_counts - actual_counts)
if len(insert_pos) < num_of_inserts:
raise IndexError
indices_of_inserts = set(random.sample(insert_pos, k=num_of_inserts))
for i in indices_of_inserts:
in_str[i] = pre.create(ALL_REGEX[r]).sample()
# print('Expected', desired_counts, r)
# print('Actual number is', actual_counts)
# print('After insert', ''.join(in_str))
insert_pos -= indices_of_inserts
str = ''.join(in_str)
return str[:choice_len] if len(str) > choice_len else str
def getValidNetworkOutputs(net, current_trace, examples, maxNetworkEvals=None):
if maxNetworkEvals is None: maxNetworkEvals = 10
lookup = {
concept: RegexWrapper(concept)
for concept in current_trace.baseConcepts
}
examples = tuple(sorted(examples))
isCached = examples in networkCache
if isCached:
o_generator = networkCache[examples]['valid']
else:
def get_more_outputs():
networkCache[examples] = {'valid': [], 'all': set()}
inputs = [[(example, ) for example in examples]] * 500
outputs_count = Counter(net.sample(inputs))
for o in sorted(outputs_count, key=outputs_count.get):
yield (o, outputs_count[o])
o_generator = (o for i in range(maxNetworkEvals)
for o in get_more_outputs())
group_idx = 0
for o, count in o_generator:
if not isCached:
if o in networkCache[examples]['all']:
continue
else:
networkCache[examples]['all'].add(o)
try:
if not isCached: networkCache[examples]['all'].add(o)
pre.create(o,
lookup=lookup) #throw error if o is not a valid regex
if not isCached: networkCache[examples]['valid'].append((o, count))
yield (o, count, group_idx)
group_idx += 1
except pre.ParseException:
pass
def __init__(self):
self.regexes = [
pre.create(".+"),
pre.create("\d+"),
pre.create("\w+"),
pre.create("\s+"),
pre.create("\\u+"),
pre.create("\l+")]
def getRelatedRegexConcepts(
o
): #Proposals that will be good only if getRegexConcept(o) is good
def extend(t, c): #add CRPs at the end
if any(x in modes for x in ("regex-crp", "regex-crp-crp")):
t, c = t.addPY(c)
if "regex-crp" in modes: yield (t, c)
if "regex-crp-crp" in modes:
t, c = t.addPY(c)
yield (t, c)
for (t, c) in extend(*getRegexConcept(o)):
yield (t, c)
for i in range(len(o)):
if o[i] in current_trace.baseConcepts:
if fuzzConcepts:
#Try replacing one concept in regex with parent or child
for o_alt in similarConcepts.get(o[i], []):
r = pre.create(o[:i] + (o_alt, ) + o[i + 1:],
lookup=lookup)
t, c = current_trace.addregex(r)
yield (t, c)
for (t, c) in extend(t, c):
yield (t, c)
if "crp-regex" in modes:
#Try replacing one concept with a new PYConcept
t, c = current_trace.addPY(o[i])
r = pre.create(o[:i] + (c, ) + o[i + 1:],
lookup={
**lookup, c: RegexWrapper(c)
})
t, c = t.addregex(r)
yield (t, c)
for (t, c) in extend(t, c):
yield (t, c)
def evaluate_datum(i, datum, model, dcModel, nRepeats, mdl, max_to_check):
t = time.time()
samples = {(PregHole, )} # make more general # TODO, i don't think
n_checked, n_hit = 0, 0
if model:
if args.beam:
samples, _scores = model.beam_decode([datum.IO[:nExamples]],
beam_size=nRepeats)
else:
samples, _scores, _ = model.sampleAndScore([datum.IO[:nExamples]],
nRepeats=nRepeats)
# only loop over unique samples:
samples = {tuple(sample) for sample in samples} # only
if (not holeSpecificDcModel) or (not dcModel):
g = basegrammar if not dcModel else dcModel.infer_grammar(
datum.IO[:nExamples]) # TODO pp
g = untorch(g)
sketchtups = []
for sample in samples:
try:
sk = pre_to_prog(pre.create(sample, lookup=lookup_d))
if holeSpecificDcModel:
g = untorch(
dcModel.infer_grammar(
(datum.IO[:nExamples],
sample))) #TODO: make sure this line is correct ..
sketchtups.append(SketchTup(sk, g))
except ParseException:
n_checked += 1
yield (RegexResult(sample, None, float('-inf'), n_checked,
time.time() - t), float('-inf'))
continue
# only loop over unique sketches:
sketchtups = {sk for sk in sketchtups} #fine
#alternate which sketch to enumerate from each time
results, n_checked, n_hit = pypy_enumerate(datum.tp, datum.IO, mdl,
sketchtups, n_checked, n_hit, t,
max_to_check)
yield from (result for result in results)
######TODO: want search time and total time to hit task ######
print(f"task {i}:")
print(f"evaluation for task {i} took {time.time()-t} seconds")
print(f"For task {i}, tried {n_checked} sketches, found {n_hit} hits")
def get_gt_ll(name, examples):
#gets groundtruth from dict
import pregex as pre
r_str = gt_dict[name]
preg = pre.create(r_str)
if type(examples[0]) == list:
examples = ["".join(example) for example in examples]
s = sum(preg.match(example) for example in examples)
if s == float("-inf"):
print("bad for ", name)
print('preg:', preg)
print('preg sample:', [preg.sample() for i in range(3)])
print("exs", examples)
#assert False
return s
def regex_bound(X):
c = Counter(X)
regexes = [
pre.create(".+"),
pre.create("\d+"),
pre.create("\w+"),
pre.create("\s"),
pre.create("\\u+"),
pre.create("\l+")
]
regex_scores = []
for r in regexes:
regex_scores.append(
sum(c[x] * r.match(x) for x in X) / sum(c.values()))
return max(regex_scores)
def generate():
l = random.choice(list(range(1, MAX_CONST_LEN)))
c = pre.create("."*l).sample()
return Constant(c)
import random
import pregex as pre
import math
assert (pre.create("f(a|o)*") == pre.Concat([
pre.String("f"),
pre.KleeneStar(pre.Alt([pre.String("a"), pre.String("o")]))
]))
assert (pre.create("fa|o*") == pre.Concat([
pre.String("f"),
pre.Alt([pre.String("a"), pre.KleeneStar(pre.String("o"))])
]))
assert (pre.create("(f.*)+") == pre.Plus(
pre.Concat([pre.String("f"), pre.KleeneStar(pre.dot)])))
test_cases = [("foo", "fo", False), ("foo", "foo", True),
("foo", "fooo", False), ("foo", "fo*", True),
("foo", "fo+", True), ("foo", "f(oo)*", True),
("foo", "f(a|b)*", False), ("foo", "f(a|o)*", True),
("foo", "fa|o*", True), ("foo", "fo|a*", False),
("foo", "f|ao|ao|a", True),
("f" + "o" * 50, "f" + "o*" * 10, True), ("foo", "fo?+", True),
("foo", "fo**", True), ("(foo)", "\\(foo\\)", True),
("foo foo. foo foo foo.", "foo(\\.? foo)*\\.", True),
("123abcABC ", ".+", True), ("123abcABC ", '\\w+', False),
("123abcABC ", "\\w+\\s", True),
("123abcABC ", "\\d+\\l+\\u+\\s", True)]
for (string, regex, matches) in test_cases:
print("Parsing", regex)
r = pre.create(regex)
print("Matching", string, r)
while nextInput:
s = input()
if s == "":
nextInput = False
else:
examples.append(s)
print("calculating... ")
samples, scores = model.sampleAndScore([examples], nRepeats=2)
print(samples)
print(scores)
print(len(scores), len(samples))
index = scores.index(max(scores))
#print(samples[index])
try:
sample = pre.create(list(samples[index]))
except:
sample = samples[index]
#sample = samples[index]
print("best example by nn score:", sample, ", nn score:", max(scores))
pregexes = []
pscores = []
for samp in samples:
try:
reg = pre.create(list(samp))
pregexes.append(reg)
pscores.append(sum(reg.match(ex) for ex in examples))
except:
pregexes.append(samp)
pscores.append(float('-inf'))
if __name__ == '__main__':
import time
import pregex as pre
g = basegrammar
d = sample_datum(g=g,
N=4,
compute_sketches=True,
top_k_sketches=100,
inv_temp=1.0,
reward_fn=None,
sample_fn=None,
dc_model=None)
print(d.p)
print(d.p.evaluate([]))
print(d.sketch)
#print(d.sketch.evaluate([])(pre.String("")))
print(d.sketch.evaluate([]))
print(d.sketchseq)
for o in d.IO:
print("example")
print(o)
from util.regex_util import PregHole, pre_to_prog
preg = pre.create(d.sketchseq, lookup={PregHole: PregHole()})
print(preg)
print(pre_to_prog(preg))
print(task.name)
totalTasks += 1
print("\tTRAIN\t", ["".join(example[1]) for example in task.examples])
testingExamples = regexHeldOutExamples(task)
print("\tTEST\t", [example[1] for example in testingExamples])
gt_preg = gt_dict[int(task.name.split(" ")[-1])]
print("\tHuman written regex:", gt_preg)
eprint(verbatimTable(["".join(example[1]) for example in task.examples] + [None] + \
[gt_preg,None] + \
[example[1] for example in testingExamples]))
eprint("&")
gt_preg = pre.create(gt_preg)
def examineProgram(entry):
global preg
global diff_lookup
program = entry.program
ll = entry.logLikelihood
program = program.visit(ConstantVisitor(task.str_const))
print(program)
preg = program.evaluate([])(pre.String(""))
Pstring = prettyRegex(preg)
if autodiff:
params, diff_lookup = create_params() #TODO
opt = optim.Adam(params, lr=0.1)
def getRegexConcept(o):
r = pre.create(o, lookup=lookup)
t, c = current_trace.addregex(r)
return (t, c)
if type(state.regex) is pre.Alt:
for x in state.regex.values:
if type(x) is RegexWrapper:
addParent(c, x.concept)
#return {c: descendants[c] + ancestors[c] for c in self.baseConcepts}
return {c: parents[c] + children[c] for c in self.baseConcepts}
# ------------ Unit tests ------------
if __name__=="__main__":
import pickle
import os
trace = Trace()
trace, firstName = trace.addPYregex(pre.create("\\w+"))
trace, lastName = trace.addPYregex(pre.create("\\w+"))
regex = pre.create("f l", {"f":RegexWrapper(firstName), "l":RegexWrapper(lastName)})
trace, fullName = trace.addPYregex(regex)
trace, observation1 = trace.observe(fullName, "Luke Hewitt")
trace, observation2 = trace.observe(fullName, "Kevin Ellis")
trace, observation3 = trace.observe(fullName, "Max Nye")
trace, observation4 = trace.observe(fullName, "Max Siegel")
trace, observation5 = trace.observe(fullName, "Max KW")
with open('trace_Test.p', 'wb') as file:
pickle.dump(trace, file)
with open('trace_Test.p', 'rb') as file:
def loadData(file, n_examples, n_tasks, max_length):
if file[-9:] == "csv_900.p":
print("Loading csv_900.p, ignoring data params.")
with open(file, 'rb') as f:
return pickle.load(f)
rand = np.random.RandomState()
rand.seed(0)
all_tasks = []
for x in pickle.load(open(file, 'rb')):
elems_filtered = [elem for elem in x['data'] if len(elem) < max_length]
if len(elems_filtered) == 0: continue
task = rand.choice(elems_filtered,
size=min(len(elems_filtered), n_examples),
replace=False).tolist()
all_tasks.append(task)
data = []
def lenEntropy(examples):
return (max(len(x) for x in examples), -util.entropy(examples))
all_tasks = sorted(all_tasks, key=lenEntropy)
tasks_unique = []
for task in all_tasks:
unique = set(task)
if not any(
len(unique ^ x) / len(unique) < 0.7 for x in tasks_unique
): #No two tasks should have mostly the same unique elements
data.append(task)
tasks_unique.append(unique)
data = [X for X in data if not all(x == X[0] for x in X)]
grouped_data = [[
examples for examples in data if max(len(x)
for x in examples[:100]) == i
] for i in range(max_length)]
grouped_data = [X for X in grouped_data if len(X) > 0]
#pos_int_regex = pre.create("0|((1|2|3|4|5|6|7|8|9)\d*)")
#float_regex = pre.Concat([pos_int_regex, pre.create("\.\d+")])
num_regex = pre.create("-?0|((1|2|3|4|5|6|7|8|9)\d*)(\.\d+)?")
test_data = []
for i in range(len(grouped_data)):
#rand.shuffle(grouped_data[i])
for fil in [num_regex]:
fil_idxs = [
j for j, xs in enumerate(grouped_data[i]) if all(
fil.match(x) > float("-inf") for x in Counter(xs))
] #Indexes that match filter
grouped_data[i] = [
grouped_data[i][j] for j in range(len(grouped_data[i]))
if j not in fil_idxs[math.ceil(0.25 * len(grouped_data[i])):]
] #Keep at most 20%
grouped_data[i].sort(key=len, reverse=True)
test_data.extend(
[X for X in grouped_data[i][n_tasks:] if len(set(X)) >= 5])
grouped_data[i] = grouped_data[i][:n_tasks]
grouped_data[i].sort(key=lenEntropy)
data = [x for examples in grouped_data for x in examples]
#group_idxs = list(np.cumsum([len(X) for X in grouped_data]))
# rand.shuffle(data)
# if args.n_tasks is not None:
# data = data[args.skip_tasks:args.n_tasks + args.skip_tasks]
# data = sorted(data, key=lambda examples: (max(len(x) for x in examples), -len(set(examples))))
test_data = test_data[:-(len(test_data) % 10)]
data = data[:-((len(data) + len(test_data)) % 100)]
data.sort(key=lenEntropy)
test_data.sort(key=lenEntropy)
unique_lengths = sorted(list(set([max(len(x) for x in X) for X in data])))
group_idxs = np.cumsum([
len([X for X in data if max(len(x) for x in X) == l])
for l in unique_lengths
])
return data, group_idxs, test_data