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from dreamcoder.task import Task, EvaluationTimeout
import gc
from dreamcoder.utilities import *
from collections import Counter
import math
from dreamcoder.domains.regex.groundtruthRegexes import gt_dict
gt_dict = {"Data column no. "+str(num): r_str for num, r_str in gt_dict.items()}
class AllOrNothingLikelihoodModel:
def __init__(self, timeout=None):
self.timeout = timeout
def score(self, program, task):
logLikelihood = task.logLikelihood(program, self.timeout)
return valid(logLikelihood), logLikelihood
class EuclideanLikelihoodModel:
"""Likelihood is based on Euclidean distance between features"""
def __init__(self, featureExtractor, successCutoff=0.9):
self.extract = featureExtractor
self.successCutoff = successCutoff
def score(self, program, task):
taskFeat = self.extract.featuresOfTask(task)
progFeat = self.extract.featuresOfProgram(program, task.request)
assert len(taskFeat) == len(progFeat)
distance = sum((x1 - x2)**2 for x1, x2 in zip(taskFeat, progFeat))
logLikelihood = float(-distance) # FIXME: this is really naive
return exp(logLikelihood) > self.successCutoff, logLikelihood
def longest_common_substr(arr):
#array of examples
# Python 3 program to find the stem
# of given list of words
# function to find the stem (longest
# common substring) from the string array
# Determine size of the array
n = len(arr)
# Take first word from array
# as reference
s = arr[0]
l = len(s)
res = ""
for i in range(l) :
for j in range( i + 1, l + 1) :
# generating all possible substrings
# of our reference string arr[0] i.e s
stem = s[i:j]
k = 1
for k in range(1, n):
# Check if the generated stem is
# common to to all words
if stem not in arr[k]:
break
# If current substring is present in
# all strings and its length is greater
# than current result
if (k + 1 == n and len(res) < len(stem)): res = stem
return res
def add_string_constants(tasks):
for task in tasks:
task.str_const = longest_common_substr([example[1] for example in task.examples])
return tasks
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 add_cutoff_values(tasks, ll_cutoff):
from dreamcoder.domains.regex.makeRegexTasks import makeNewTasks
if ll_cutoff is None or ll_cutoff == "None":
for task in tasks:
task.ll_cutoff = None
return tasks
if ll_cutoff == "gt":
from dreamcoder.domains.regex.makeRegexTasks import regexHeldOutExamples
for task in tasks:
task.ll_cutoff = None
task.gt = get_gt_ll(task.name, [example[1] for example in task.examples])
task.gt_test = get_gt_ll(task.name,
[example[1] for example in regexHeldOutExamples(task) ])
return tasks
elif ll_cutoff == "plus":
for task in tasks:
task.ll_cutoff = regex_plus_bound([example[1] for example in task.examples])
return tasks
elif ll_cutoff == "bigram":
eprint("WARNING: using entire corpus to make bigram model")
#this means i do it twice, which is eh whatever
model = make_corpus_bigram(show_tasks(makeNewTasks()))
for task in tasks:
task.ll_cutoff = bigram_corpus_score([example[1] for example in task.examples], model)
return tasks
elif ll_cutoff =="unigram":
eprint("WARNING: using entire corpus to make unigram model")
#this means i do it twice, which is eh whatever
model = make_corpus_unigram(show_tasks(makeNewTasks()))
for task in tasks:
task.ll_cutoff = unigram_corpus_score([example[1] for example in task.examples], model)
return tasks
elif ll_cutoff =="mix":
eprint("WARNING: using entire corpus to make bigram model")
eprint("WARNING: using entire corpus to make unigram model")
#this means i do it twice, which is eh whatever
unigram = make_corpus_unigram(show_tasks(makeNewTasks()))
bigram = make_corpus_bigram(show_tasks(makeNewTasks()))
for task in tasks:
uniscore = unigram_corpus_score([example[1] for example in task.examples], unigram)
biscore = bigram_corpus_score([example[1] for example in task.examples], bigram)
task.ll_cutoff = math.log(0.75*math.exp(biscore) + 0.25*math.exp(uniscore))
return tasks
else:
eprint("not implemented")
eprint("cutoff val:")
eprint(ll_cutoff)
assert False
def show_tasks(dataset):
task_list = []
for task in dataset:
task_list.append([example[1] for example in task.examples])
return task_list
def regex_plus_bound(X):
from pregex import pregex
c = Counter(X)
regexes = [
pregex.create(".+"),
pregex.create("\d+"),
pregex.create("\w+"),
pregex.create("\s+"),
pregex.create("\\u+"),
pregex.create("\l+")]
regex_scores = []
for r in regexes:
regex_scores.append(sum(c[x] * r.match(x) for x in c)/float(sum([len(x) for x in X])) )
return max(regex_scores)
def make_corpus_unigram(C):
str_list = [example + '\n' for task in C for example in task]
c = Counter(char for example in str_list for char in example )
n = sum(c.values())
logp = {x:math.log(c[x]/n) for x in c}
return logp
def unigram_corpus_score(X, logp):
task_ll = 0
for x in X:
x = x + '\n'
task_ll += sum( logp.get(c, float('-inf')) for c in x)/len(x)
ll = task_ll/len(X)
return ll
def unigram_task_score(X):
"""
Given a list of strings, X, calculate the maximum log-likelihood per character for a unigram model over characters (including STOP symbol)
"""
c = Counter(x for s in X for x in s)
c.update("end" for s in X)
n = sum(c.values())
logp = {x:math.log(c[x]/n) for x in c}
return sum(c[x]*logp[x] for x in c)/n
def make_corpus_bigram(C):
#using newline as "end"
#C is a list of tasks
#make one big list of strings
str_list = [example + '\n' for task in C for example in task]
#make list of
head_count = Counter(element[0] for element in str_list)
head_n = sum(head_count.values())
head_logp = {x:math.log(head_count[x]/head_n) for x in head_count}
body_count = Counter(element[i:i+2] for element in str_list for i in range(len(element)-1))
body_bigram_n = sum(body_count.values())
#body_count/body_bigram_n gives the joint of a bigram
body_character_n = Counter(char for element in str_list for char in element)
body_unigram_n = sum(body_character_n.values())
body_logp = {x:math.log(body_count[x] / body_bigram_n / body_character_n[x[0]] * body_unigram_n) for x in body_count}
return {**head_logp, **body_logp}
def bigram_corpus_score(X, logp):
#assume you have a logp dict
task_ll = 0
for x in X:
bigram_list = [x[0]] + [x[i:i+2] for i in range(len(x)-1)] + [x[-1] + '\n']
bigram_list = [ ''.join(b) if isinstance(b,list) else b
for b in bigram_list ]
string_ll = sum(logp.get(bigram, float('-inf')) for bigram in bigram_list) #/(len(x) + 1)
task_ll += string_ll
ll = task_ll #/len(X)
return ll
class ProbabilisticLikelihoodModel:
def __init__(self, timeout):
self.timeout = timeout
# i need timeout
def score(self, program, task):
# need a try, catch here for problems, and for timeouts
# can copy task.py for the timeout structure
try:
def timeoutCallBack(_1, _2): raise EvaluationTimeout()
signal.signal(signal.SIGVTALRM, timeoutCallBack)
signal.setitimer(signal.ITIMER_VIRTUAL, self.timeout)
try:
string_pregex = program.evaluate([])
# if 'left_paren' in program.show(False):
#eprint("string_pregex:", string_pregex)
#eprint("string_pregex:", string_pregex)
preg = string_pregex # pregex.create(string_pregex)
except IndexError:
# free variable
return False, NEGATIVEINFINITY
except Exception as e:
eprint("Exception during evaluation:", e)
if "Attempt to evaluate fragment variable" in e:
eprint("program (bc fragment error)", program)
return False, NEGATIVEINFINITY
#tries and catches
# include prior somehow
# right now, just summing up log likelihoods. IDK if this is correct.
# also not using prior at all.
cum_ll = 0
example_list = [example[1] for example in task.examples]
c_example_list = Counter(example_list)
for c_example in c_example_list:
#might want a try, except around the following line:
try:
#eprint("about to match", program)
#print("preg:", preg)
ll = preg.match(c_example)
#eprint("completed match", ll, program)
except ValueError as e:
eprint("ValueError:", e)
ll = float('-inf')
#eprint("pregex:", string_pregex)
#eprint("example[1]", example[1])
if ll == float('-inf'):
return False, NEGATIVEINFINITY
else:
#ll_per_char = ll/float(len(example[1]))
#cum_ll_per_char += ll_per_char
cum_ll += c_example_list[c_example] * ll
#normalized_cum_ll_per_char = cum_ll_per_char/float(len(task.examples))
#avg_char_num = sum([len(example[1]) for example in task.examples])/float(len(task.examples))
#cutoff_ll = regex_plus_bound(example_list)
normalized_cum_ll = cum_ll/ float(sum([len(example) for example in example_list]))
#TODO: change the way normalized_cum_ll is calculated
#TODO: refactor to pass in bigram_model, and others
#TODO: refactor to do 95% certainty thing josh wants
success = normalized_cum_ll > task.ll_cutoff
#eprint("cutoff_ll:", cutoff_ll, ", norm_cum_ll:", normalized_cum_ll)
return success, normalized_cum_ll
except EvaluationTimeout:
eprint("Timed out while evaluating", program)
return False, NEGATIVEINFINITY
finally:
signal.signal(signal.SIGVTALRM, lambda *_: None)
signal.setitimer(signal.ITIMER_VIRTUAL, 0)
try:
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
from torch.autograd import Variable
class FeatureDiscriminatorLikelihoodModel(nn.Module):
def __init__(self, tasks, featureExtractor,
successCutoff=0.6, H=8, trainingSuccessRatio=0.5):
super(FeatureDiscriminatorLikelihoodModel, self).__init__()
self.extract = featureExtractor
self.successCutoff = successCutoff
self.trainingSuccessRatio = trainingSuccessRatio
self.W = nn.Linear(featureExtractor.outputDimensionality, H)
self.output = nn.Linear(H, 1)
# training on initialization
self.train(tasks)
def forward(self, examples):
"""
Examples is a list of feature sets corresponding to a particular example.
Output in [0,1] whether all examples correspond to the same program
"""
assert all(
len(x) == self.extract.outputDimensionality for x in examples)
examples = [F.tanh(self.W(ex)) for ex in examples]
maxed, _ = torch.max(torch.stack(examples), dim=0)
return F.sigmoid(self.output(maxed))
def train(self, tasks, steps=400):
# list of list of features for each example in each task
optimizer = torch.optim.Adam(self.parameters())
with timing("Trained discriminator"):
losses = []
for i in range(steps):
self.zero_grad()
if random.random() <= self.trainingSuccessRatio:
# success
t = random.choice(tasks)
features = [self.extract.featuresOfTask(
Task(t.name, t.request, [ex], t.features))
for ex in t.examples]
loss = (self(features) - 1.0)**2
else:
# fail
t1, t2 = random.sample(tasks, 2)
features1 = [self.extract.featuresOfTask(
Task(t1.name, t1.request, [ex], t1.features))
for ex in t1.examples[:len(t1.examples) / 2]]
features2 = [self.extract.featuresOfTask(
Task(t2.name, t2.request, [ex], t2.features))
for ex in t2.examples[len(t2.examples) / 2:]]
features = features1 + features2
loss = self(features)**2
loss.backward()
optimizer.step()
losses.append(loss.data[0])
if not i % 50:
eprint(
"Discriminator Epoch",
i,
"Loss",
sum(losses) /
len(losses))
gc.collect()
def score(self, program, task):
taskFeatures = self.extract.featuresOfTask(task)
progFeatures = self.extract.featuresOfProgram(
program, task.request)
likelihood = self([taskFeatures] + [progFeatures])
likelihood = float(likelihood)
return likelihood > self.successCutoff, log(likelihood)
except ImportError:
pass
if __name__=="__main__":
arr = ['MAM.OSBS.2014.06', 'MAM.OSBS.2013.07', 'MAM.OSBS.2013.09', 'MAM.OSBS.2014.05', 'MAM.OSBS.2014.11']
stems = longest_common_substr(arr)
print(stems)
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