def setUp(self):
super(AgentTest, self).setUp()
self.data_dir = os.path.join(
FLAGS.test_srcdir,
os.path.split(os.path.abspath(__file__))[0], 'test_data')
self.seeds = movie_lens_simulator.Seeds(user_model=0,
user_sampler=0,
train_eval_test=0)
self.user_config = movie_lens_simulator.UserConfig(accept_prob=0.5,
diversity_prob=0.5)
self.env_config = movie_lens_simulator.EnvConfig(
data_dir=self.data_dir,
embeddings_path=f'{self.data_dir}/embeddings.json',
genre_history_path=f'{self.data_dir}/genre_history.json',
seeds=self.seeds,
slate_size=5,
user_config=self.user_config)
self.df = generate_data.generate_data(self.env_config,
slate_type='test',
n_samples=50,
seed=0,
intercept=-5.,
rating_coef=1.,
div_seek_coef=3.,
diversity_coef=-0.5)
def setUp(self):
super(MovieLensTestNoShift, self).setUp()
self.data_dir = os.path.join(FLAGS.test_srcdir,
os.path.split(os.path.abspath(__file__))[0],
'test_data')
self.env_config = movie_lens.EnvConfig(
seeds=movie_lens.Seeds(0, 0),
data_dir=self.data_dir,
genre_history_path=os.path.join(self.data_dir, 'genre_history.json'),
embeddings_path=os.path.join(self.data_dir, 'embeddings.json'),
genre_shift=None,
bias_against_unseen=0.)
self._initialize_from_config(self.env_config)
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
seeds = movie_lens_simulator.Seeds(user_model=FLAGS.global_seed,
user_sampler=FLAGS.global_seed,
train_eval_test=FLAGS.global_seed)
user_config = movie_lens_simulator.UserConfig(
accept_prob=FLAGS.accept_prob, diversity_prob=FLAGS.diversity_prob)
# TODO(moberst): genre_shift should be passed in here, not calculated
# within the generate_data function
env_config = movie_lens_simulator.EnvConfig(
data_dir=FLAGS.data_directory,
embeddings_path=f'{FLAGS.data_directory}/{FLAGS.embedding_file}',
genre_history_path=f'{FLAGS.data_directory}/{FLAGS.genre_history_file}',
seeds=seeds,
user_config=user_config,
bias_against_unseen=0.)
if FLAGS.agent_file_str:
agent_file = f'{FLAGS.agent_directory}/{FLAGS.agent_file_str}.pkl'
recommender_agent = agent.load_agent(agent_file)
agent_str = FLAGS.agent_file_str
else:
# If None, this will be populated with a random agent in generate_data
recommender_agent = None
agent_str = 'random-agent'
df = generate_data(env_config=env_config,
slate_type=FLAGS.slate_type,
n_samples=FLAGS.n_samples,
seed=FLAGS.global_seed,
intercept=FLAGS.intercept,
rating_coef=FLAGS.rating_coef,
div_seek_coef=FLAGS.div_seek_coef,
diversity_coef=FLAGS.diversity_coef,
recommender_agent=recommender_agent,
shift=FLAGS.shift,
user_pool=FLAGS.user_pool)
write_csv_output(dataframe=df,
filename=(f'simulation_results'
f'_{FLAGS.n_samples}'
f'_{FLAGS.slate_type}'
f'_{FLAGS.accept_prob}'
f'_{FLAGS.user_pool}'
f'_{agent_str}'
f'{"_shift" if FLAGS.shift != 0. else ""}'
'.csv'),
directory=FLAGS.output_directory)
def setUp(self):
super(GenerateDataTest, self).setUp()
self.data_dir = os.path.join(FLAGS.test_srcdir,
os.path.split(os.path.abspath(__file__))[0],
'test_data')
self.seeds = movie_lens_simulator.Seeds(
user_model=0, user_sampler=0, train_eval_test=0)
self.user_config = movie_lens_simulator.UserConfig(
accept_prob=0.5, diversity_prob=0.5)
self.env_config = movie_lens_simulator.EnvConfig(
data_dir=self.data_dir,
embeddings_path=f'{self.data_dir}/embeddings.json',
genre_history_path=f'{self.data_dir}/genre_history.json',
seeds=self.seeds,
slate_size=5,
user_config=self.user_config)