def main(argv):
# if len(argv) > 1:
# raise app.UsageError('Too many command-line arguments.')
#
# runner_lib.load_gin_configs(FLAGS.gin_files, FLAGS.gin_bindings)
seed = 0
slate_size = 2
np.random.seed(seed)
env_config = {
'num_candidates': 5,
'slate_size': slate_size,
'resample_documents': True,
'seed': seed,
}
tmp_base_dir = 'C:/Users/Moham/PycharmProjects/ExpertDrivenRec/testing/'
runner = runner_lib.TrainRunner(
base_dir=tmp_base_dir,
create_agent_fn=create_agent,
env=interest_evolution.create_environment(env_config),
episode_log_file='',
max_training_steps=50,
num_iterations=10)
runner.run_experiment()
runner = runner_lib.EvalRunner(
base_dir=tmp_base_dir,
create_agent_fn=create_agent,
env=interest_evolution.create_environment(env_config),
max_eval_episodes=5,
test_mode=True)
runner.run_experiment()
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
runner_lib.load_gin_configs(FLAGS.gin_files, FLAGS.gin_bindings)
seed = 0
slate_size = 2
np.random.seed(seed)
env_config = {
'num_candidates': 5,
'slate_size': slate_size,
'resample_documents': True,
'seed': seed,
}
runner = runner_lib.TrainRunner(
base_dir=FLAGS.base_dir,
create_agent_fn=create_agent,
env=interest_evolution.create_environment(env_config),
episode_log_file=FLAGS.episode_log_file,
max_training_steps=50,
num_iterations=10)
runner.run_experiment()
runner = runner_lib.EvalRunner(
base_dir=FLAGS.base_dir,
create_agent_fn=create_agent,
env=interest_evolution.create_environment(env_config),
max_eval_episodes=5,
test_mode=True)
runner.run_experiment()
def test_multi_and_single_user_consistency(self, multiuser_env,
multiuser_agent,
should_succeed):
def create_agent(sess,
env,
summary_writer,
eval_mode,
multiuser_agent=True):
del sess, env, summary_writer, eval_mode # unused
action_space = gym.spaces.MultiDiscrete(
np.ones((self.slate_size, )))
if multiuser_agent:
action_space = gym.spaces.Tuple([action_space] *
self.num_users)
AgentClass = DummyMultiUserAgent # pylint:disable=invalid-name
else:
AgentClass = DummySingleUserAgent # pylint:disable=invalid-name
return AgentClass(action_space)
env_config = dict(multiuser_env=multiuser_env)
base_dir = '/tmp/Env%sAgent%s' % (multiuser_env, multiuser_agent)
create_agent_fn = functools.partial(create_agent,
multiuser_agent=multiuser_agent)
if not os.path.exists(base_dir):
os.makedirs(base_dir)
if should_succeed: # constructors should work
_ = runner_lib.TrainRunner(base_dir=base_dir,
create_agent_fn=create_agent_fn,
env=create_environment(env_config),
max_training_steps=1,
max_steps_per_episode=1,
num_iterations=1)
else: # agent constructor should raise error
with self.assertRaises(ValueError):
_ = runner_lib.TrainRunner(base_dir=base_dir,
create_agent_fn=create_agent_fn,
env=create_environment(env_config),
max_training_steps=1,
max_steps_per_episode=1,
num_iterations=1)