from batch import Batched
from coba.benchmarks import Benchmark
import re
def baseLearner():
from coba.learners import VowpalLearner
return VowpalLearner(seed=10, epsilon=0.1, flags='--coin')
learner_factories = [
Batched(delay=8, batchsize=1, learner=baseLearner),
Batched(delay=8, batchsize=2, learner=baseLearner),
Batched(delay=8, batchsize=4, learner=baseLearner),
Batched(delay=8, batchsize=8, learner=baseLearner),
]
processes = 4
maxtasksperchild = 1
json = "./exp.json"
log = re.sub('json$', 'log', json)
if __name__ == '__main__':
result = Benchmark.from_file(json).processes(processes).maxtasksperchild(
maxtasksperchild).evaluate(learner_factories, log)
result.standard_plot()
"""
This is an example script that creates a Benchmark that matches the bandit bakeoff paper.
This script requires that the matplotlib and vowpalwabbit packages be installed.
"""
from coba.learners import RandomLearner, EpsilonLearner, VowpalLearner, UcbTunedLearner, CorralLearner
from coba.benchmarks import Benchmark
if __name__ == '__main__':
benchmark = Benchmark.from_file("./examples/benchmark_short.json")
learners = [
RandomLearner(),
EpsilonLearner(epsilon=0.025),
UcbTunedLearner(),
VowpalLearner(bag=5, seed=10),
CorralLearner([VowpalLearner(bag=5, seed=10),
UcbTunedLearner()],
eta=.075,
T=40000,
seed=10),
]
benchmark.evaluate(learners, './examples/bakeoff.log',
seed=10).standard_plot()
