def main(args):
"""
Takes the return value of the `commandlineArguments()` function as input and
trains/tests the model on manipulating sequences of numbers.
"""
random.seed(args.pop("random_seed"))
dataset = args.pop("dataset")
tasks = {
"Lucas-old":
lambda: retrieveJSONTasks("data/list_tasks.json") + sortBootstrap(),
"bootstrap":
make_list_bootstrap_tasks,
"sorting":
sortBootstrap,
"Lucas-depth1":
lambda: retrieveJSONTasks("data/list_tasks2.json")[:105],
"Lucas-depth2":
lambda: retrieveJSONTasks("data/list_tasks2.json")[:4928],
"Lucas-depth3":
lambda: retrieveJSONTasks("data/list_tasks2.json"),
}[dataset]()
maxTasks = args.pop("maxTasks")
if maxTasks and len(tasks) > maxTasks:
necessaryTasks = [] # maxTasks will not consider these
if dataset.startswith("Lucas2.0") and dataset != "Lucas2.0-depth1":
necessaryTasks = tasks[:105]
eprint("Unwilling to handle {} tasks, truncating..".format(len(tasks)))
random.shuffle(tasks)
del tasks[maxTasks:]
tasks = necessaryTasks + tasks
if dataset.startswith("Lucas"):
# extra tasks for filter
tasks.extend([
Task("remove empty lists",
arrow(tlist(tlist(tbool)), tlist(tlist(tbool))),
[((ls, ), list(filter(lambda l: len(l) > 0, ls)))
for _ in range(15) for ls in [[[
random.random() < 0.5
for _ in range(random.randint(0, 3))
] for _ in range(4)]]]),
Task("keep squares", arrow(tlist(tint), tlist(tint)), [
((xs, ), list(filter(lambda x: int(math.sqrt(x))**2 == x, xs)))
for _ in range(15) for xs in [[
random.choice([0, 1, 4, 9, 16, 25])
if random.random() < 0.5 else random.randint(0, 9)
for _ in range(7)
]]
]),
Task("keep primes", arrow(tlist(tint), tlist(tint)), [
((xs, ),
list(
filter(
lambda x: x in
{2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37}, xs)))
for _ in range(15) for xs in [[
random.choice([2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37])
if random.random() < 0.5 else random.randint(0, 9)
for _ in range(7)
]]
]),
])
for i in range(4):
tasks.extend([
Task("keep eq %s" % i, arrow(tlist(tint), tlist(tint)),
[((xs, ), list(filter(lambda x: x == i, xs)))
for _ in range(15)
for xs in [[random.randint(0, 6) for _ in range(5)]]]),
Task("remove eq %s" % i, arrow(tlist(tint), tlist(tint)),
[((xs, ), list(filter(lambda x: x != i, xs)))
for _ in range(15)
for xs in [[random.randint(0, 6) for _ in range(5)]]]),
Task("keep gt %s" % i, arrow(tlist(tint), tlist(tint)),
[((xs, ), list(filter(lambda x: x > i, xs)))
for _ in range(15)
for xs in [[random.randint(0, 6) for _ in range(5)]]]),
Task("remove gt %s" % i, arrow(tlist(tint), tlist(tint)),
[((xs, ), list(filter(lambda x: not x > i, xs)))
for _ in range(15)
for xs in [[random.randint(0, 6) for _ in range(5)]]])
])
def isIdentityTask(t):
return all(len(xs) == 1 and xs[0] == y for xs, y in t.examples)
eprint("Removed", sum(isIdentityTask(t) for t in tasks),
"tasks that were just the identity function")
tasks = [t for t in tasks if not isIdentityTask(t)]
prims = {
"base": basePrimitives,
"McCarthy": McCarthyPrimitives,
"common": bootstrapTarget_extra,
"noLength": no_length,
"rich": primitives
}[args.pop("primitives")]()
haveLength = not args.pop("noLength")
haveMap = not args.pop("noMap")
haveUnfold = not args.pop("noUnfold")
eprint(f"Including map as a primitive? {haveMap}")
eprint(f"Including length as a primitive? {haveLength}")
eprint(f"Including unfold as a primitive? {haveUnfold}")
baseGrammar = Grammar.uniform([p
for p in prims
if (p.name != "map" or haveMap) and \
(p.name != "unfold" or haveUnfold) and \
(p.name != "length" or haveLength)])
extractor = {
"learned": LearnedFeatureExtractor,
}[args.pop("extractor")]
extractor.H = args.pop("hidden")
timestamp = datetime.datetime.now().isoformat()
outputDirectory = "experimentOutputs/list/%s" % timestamp
os.system("mkdir -p %s" % outputDirectory)
args.update({
"featureExtractor": extractor,
"outputPrefix": "%s/list" % outputDirectory,
"evaluationTimeout": 0.0005,
})
eprint("Got {} list tasks".format(len(tasks)))
split = args.pop("split")
if split:
train_some = defaultdict(list)
for t in tasks:
necessary = train_necessary(t)
if not necessary:
continue
if necessary == "some":
train_some[t.name.split()[0]].append(t)
else:
t.mustTrain = True
for k in sorted(train_some):
ts = train_some[k]
random.shuffle(ts)
ts.pop().mustTrain = True
test, train = testTrainSplit(tasks, split)
if True:
test = [t for t in test if t.name not in EASYLISTTASKS]
eprint("Alotted {} tasks for training and {} for testing".format(
len(train), len(test)))
else:
train = tasks
test = []
explorationCompression(baseGrammar, train, testingTasks=test, **args)
elif arguments.domain == "rational":
tasks = rational.makeTasks()
for t in tasks:
t.features = drawFunction(200, 10., t.f)
delattr(t, 'f')
test, _ = testTrainSplit(tasks, 100)
random.seed(42)
random.shuffle(test)
test = test[:100]
g = Grammar.uniform(
[real, real_division, real_addition, real_multiplication])
fe = FeatureExtractor([])
BATCHSIZE = 64
elif arguments.domain == "list":
BATCHSIZE = 16
tasks = retrieveJSONTasks("data/list_tasks.json") + sortBootstrap()
tasks.extend([
Task("remove empty lists",
arrow(tlist(tlist(tbool)), tlist(tlist(tbool))),
[((ls, ), list(filter(lambda l: len(l) > 0, ls)))
for _ in range(15) for ls in [[[
random.random() < 0.5
for _ in range(random.randint(0, 3))
] for _ in range(4)]]]),
Task("keep squares", arrow(tlist(tint), tlist(tint)), [
((xs, ), list(filter(lambda x: int(math.sqrt(x))**2 == x, xs)))
for _ in range(15) for xs in [[
random.choice([0, 1, 4, 9, 16, 25])
if random.random() < 0.5 else random.randint(0, 9)
for _ in range(7)
]]