def test_bundle_and_unbundle(self):
# Initialize agent
slate_size = 1
num_candidates = 3
action_space = spaces.MultiDiscrete(num_candidates * np.ones((slate_size,)))
user_model = ie.IEUserModel(
slate_size,
user_state_ctor=ie.IEUserState,
response_model_ctor=ie.IEResponse)
agent = greedy_pctr_agent.GreedyPCTRAgent(action_space,
user_model.avg_user_state)
# Create a set of documents
document_sampler = ie.IETopicDocumentSampler()
documents = {}
for i in range(num_candidates):
video = document_sampler.sample_document()
documents[i] = video.create_observation()
# Test that slate indices in correct range and length is correct
observation = dict(user=user_model.create_observation(), doc=documents)
agent.step(1, observation)
bundle_dict = agent.bundle_and_checkpoint('', 0)
self.assertTrue(agent.unbundle('', 0, bundle_dict))
self.assertEqual(bundle_dict, agent.bundle_and_checkpoint('', 0))
def test_bundle_and_unbundle_trivial(self):
action_space = spaces.MultiDiscrete(np.ones((1,)))
agent = greedy_pctr_agent.GreedyPCTRAgent(action_space, None)
self.assertFalse(agent.unbundle('', 0, {}))
self.assertEqual({
'episode_num': 0
}, agent.bundle_and_checkpoint('', 0))
def test_step(self):
# Create a simple user
slate_size = 2
num_candidates = 5
action_space = spaces.MultiDiscrete(num_candidates * np.ones((slate_size,)))
user_model = ie.IEUserModel(
slate_size,
user_state_ctor=ie.IEUserState,
response_model_ctor=ie.IEResponse)
# Create a set of documents
document_sampler = ie.IETopicDocumentSampler(seed=1)
ieenv = environment.Environment(
user_model,
document_sampler,
num_candidates,
slate_size,
resample_documents=True)
# Create agent
agent = greedy_pctr_agent.GreedyPCTRAgent(action_space,
user_model.avg_user_state)
# This agent doesn't use the previous user response
observation, documents = ieenv.reset()
slate = agent.step(1, dict(user=observation, doc=documents))
scores = [
user_model.avg_user_state.score_document(doc_obs)
for doc_obs in list(documents.values())
]
expected_slate = sorted(np.argsort(scores)[-2:])
self.assertAllEqual(sorted(slate), expected_slate)
def test_find_best_documents(self): action_space = spaces.MultiDiscrete(4 * np.ones((4,))) agent = greedy_pctr_agent.GreedyPCTRAgent(action_space, None) scores = [-1, -2, 4.32, 0, 15, -6, 4.32] indices = agent.findBestDocuments(scores) self.assertAllEqual(indices, [4, 2, 6, 3])
'slate_size': slate_size,
'resample_documents': True,
'seed': 0,
'reward_function': clicked_quality_reward
}
# User simulation environment: interest evolution model presented in the paper of SlateQ
recsim_gym_env = interest_evolution.create_environment(env_config1)
results_f = []
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
#agent = full_slate_q_agent.FullSlateQAgent(sess,
#recsim_gym_env.observation_space, recsim_gym_env.action_space)
agent = greedy_pctr_agent.GreedyPCTRAgent(
sess, recsim_gym_env.observation_space,
recsim_gym_env.action_space)
#agent = cluster_bandit_agent.ClusterBanditAgent(sess,recsim_gym_env.observation_space,recsim_gym_env.action_space)
for i in range(10):
steps_f, watch, time_f, q, q_vid, w_vid = evalRun_one_episode(
recsim_gym_env, agent, "fsq")
results_f += [[i, steps_f, watch, time_f, q, q_vid, w_vid]]
print("episode ", i)
sess.run(tf.global_variables_initializer())
episode_steps_f = []
episode_ratio_watch_f = []
episode_total_quality_f = []
episodes_qvf = []