def test_seq2slate_transformer_propensity_computation(
self, output_arch, temperature
):
"""
Test propensity computation of seq2slate net
"""
candidate_num = 4
candidate_dim = 2
hidden_size = 32
all_perm = torch.tensor(
list(permutations(torch.arange(candidate_num), candidate_num))
)
batch_size = len(all_perm)
device = torch.device("cpu")
seq2slate_net = create_seq2slate_net(
MODEL_TRANSFORMER,
candidate_num,
candidate_dim,
hidden_size,
output_arch,
temperature,
device,
)
batch = create_batch(
batch_size,
candidate_num,
candidate_dim,
device,
ON_POLICY,
diverse_input=False,
)
batch = rlt.PreprocessedRankingInput.from_input(
state=batch.state.float_features,
candidates=batch.src_seq.float_features,
device=device,
action=all_perm,
)
per_symbol_log_prob = seq2slate_net(
batch, mode=Seq2SlateMode.PER_SYMBOL_LOG_PROB_DIST_MODE
).log_probs
per_seq_log_prob = seq2slate_net(
batch, mode=Seq2SlateMode.PER_SEQ_LOG_PROB_MODE
).log_probs
per_seq_log_prob_computed = per_symbol_to_per_seq_log_probs(
per_symbol_log_prob, all_perm + 2
)
# probabilities of two modes should match
np.testing.assert_allclose(
per_seq_log_prob, per_seq_log_prob_computed, atol=0.00001
)
# probabilities of all possible permutations should sum up to 1
np.testing.assert_allclose(
torch.sum(torch.exp(per_seq_log_prob)), 1.0, atol=0.00001
)
def test_seq2slate_transformer_onplicy_basic_logic(self, output_arch,
temperature):
"""
Test basic logic of seq2slate on policy sampling
"""
device = torch.device("cpu")
candidate_num = 4
candidate_dim = 2
batch_size = 4096
hidden_size = 32
seq2slate_net = create_seq2slate_net(
MODEL_TRANSFORMER,
candidate_num,
candidate_dim,
hidden_size,
output_arch,
temperature,
device,
)
batch = create_batch(batch_size,
candidate_num,
candidate_dim,
device,
diverse_input=False)
action_to_propensity_map = {}
action_count = defaultdict(int)
total_count = 0
for i in range(50):
model_propensity, model_action = rank_on_policy(seq2slate_net,
batch,
candidate_num,
greedy=False)
for propensity, action in zip(model_propensity, model_action):
action_str = ",".join(map(str, action.numpy().tolist()))
# Same action always leads to same propensity
if action_to_propensity_map.get(action_str) is None:
action_to_propensity_map[action_str] = float(propensity)
else:
np.testing.assert_allclose(
action_to_propensity_map[action_str],
float(propensity),
atol=0.001,
rtol=0.0,
)
action_count[action_str] += 1
total_count += 1
logger.info(f"Finish {i} round, {total_count} data counts")
# Check action distribution
for action_str, count in action_count.items():
empirical_propensity = count / total_count
computed_propensity = action_to_propensity_map[action_str]
logger.info(
f"action={action_str}, empirical propensity={empirical_propensity}, "
f"computed propensity={computed_propensity}")
np.testing.assert_allclose(computed_propensity,
empirical_propensity,
atol=0.01,
rtol=0.0)