def test_seq2slate_eval_data_page(self):
"""
Create 3 slate ranking logs and evaluate using Direct Method, Inverse
Propensity Scores, and Doubly Robust.
The logs are as follows:
state: [1, 0, 0], [0, 1, 0], [0, 0, 1]
indices in logged slates: [3, 2], [3, 2], [3, 2]
model output indices: [2, 3], [3, 2], [2, 3]
logged reward: 4, 5, 7
logged propensities: 0.2, 0.5, 0.4
predicted rewards on logged slates: 2, 4, 6
predicted rewards on model outputted slates: 1, 4, 5
predicted propensities: 0.4, 0.3, 0.7
When eval_greedy=True:
Direct Method uses the predicted rewards on model outputted slates.
Thus the result is expected to be (1 + 4 + 5) / 3
Inverse Propensity Scores would scale the reward by 1.0 / logged propensities
whenever the model output slate matches with the logged slate.
Since only the second log matches with the model output, the IPS result
is expected to be 5 / 0.5 / 3
Doubly Robust is the sum of the direct method result and propensity-scaled
reward difference; the latter is defined as:
1.0 / logged_propensities * (logged reward - predicted reward on logged slate)
* Indicator(model slate == logged slate)
Since only the second logged slate matches with the model outputted slate,
the DR result is expected to be (1 + 4 + 5) / 3 + 1.0 / 0.5 * (5 - 4) / 3
When eval_greedy=False:
Only Inverse Propensity Scores would be accurate. Because it would be too
expensive to compute all possible slates' propensities and predicted rewards
for Direct Method.
The expected IPS = (0.4 / 0.2 * 4 + 0.3 / 0.5 * 5 + 0.7 / 0.4 * 7) / 3
"""
batch_size = 3
state_dim = 3
src_seq_len = 2
tgt_seq_len = 2
candidate_dim = 2
reward_net = FakeSeq2SlateRewardNetwork()
seq2slate_net = FakeSeq2SlateTransformerNet()
src_seq = torch.eye(candidate_dim).repeat(batch_size, 1, 1)
tgt_out_idx = torch.LongTensor([[3, 2], [3, 2], [3, 2]])
tgt_out_seq = src_seq[torch.arange(batch_size).
repeat_interleave(tgt_seq_len), # type: ignore
tgt_out_idx.flatten() - 2, ].reshape(
batch_size, tgt_seq_len, candidate_dim)
ptb = rlt.PreprocessedTrainingBatch(
training_input=rlt.PreprocessedRankingInput(
state=rlt.PreprocessedFeatureVector(
float_features=torch.eye(state_dim)),
src_seq=rlt.PreprocessedFeatureVector(float_features=src_seq),
tgt_out_seq=rlt.PreprocessedFeatureVector(
float_features=tgt_out_seq),
src_src_mask=torch.ones(batch_size, src_seq_len, src_seq_len),
tgt_out_idx=tgt_out_idx,
tgt_out_probs=torch.tensor([0.2, 0.5, 0.4]),
slate_reward=torch.tensor([4.0, 5.0, 7.0]),
),
extras=rlt.ExtraData(
sequence_number=torch.tensor([0, 0, 0]),
mdp_id=np.array(["0", "1", "2"]),
),
)
edp = EvaluationDataPage.create_from_tensors_seq2slate(
seq2slate_net, reward_net, ptb.training_input, eval_greedy=True)
logger.info(
"---------- Start evaluating eval_greedy=True -----------------")
doubly_robust_estimator = DoublyRobustEstimator()
direct_method, inverse_propensity, doubly_robust = doubly_robust_estimator.estimate(
edp)
logger.info(f"{direct_method}, {inverse_propensity}, {doubly_robust}")
avg_logged_reward = (4 + 5 + 7) / 3
self.assertAlmostEqual(direct_method.raw, (1 + 4 + 5) / 3, delta=1e-6)
self.assertAlmostEqual(direct_method.normalized,
direct_method.raw / avg_logged_reward,
delta=1e-6)
self.assertAlmostEqual(inverse_propensity.raw, 5 / 0.5 / 3, delta=1e-6)
self.assertAlmostEqual(
inverse_propensity.normalized,
inverse_propensity.raw / avg_logged_reward,
delta=1e-6,
)
self.assertAlmostEqual(doubly_robust.raw,
direct_method.raw + 1 / 0.5 * (5 - 4) / 3,
delta=1e-6)
self.assertAlmostEqual(doubly_robust.normalized,
doubly_robust.raw / avg_logged_reward,
delta=1e-6)
logger.info(
"---------- Finish evaluating eval_greedy=True -----------------")
logger.info(
"---------- Start evaluating eval_greedy=False -----------------")
edp = EvaluationDataPage.create_from_tensors_seq2slate(
seq2slate_net, reward_net, ptb.training_input, eval_greedy=False)
doubly_robust_estimator = DoublyRobustEstimator()
_, inverse_propensity, _ = doubly_robust_estimator.estimate(edp)
self.assertAlmostEqual(
inverse_propensity.raw,
(0.4 / 0.2 * 4 + 0.3 / 0.5 * 5 + 0.7 / 0.4 * 7) / 3,
delta=1e-6,
)
self.assertAlmostEqual(
inverse_propensity.normalized,
inverse_propensity.raw / avg_logged_reward,
delta=1e-6,
)
logger.info(
"---------- Finish evaluating eval_greedy=False -----------------")
def evaluate(self, eval_tdp: PreprocessedTrainingBatch):
seq2slate_net = self.trainer.seq2slate_net
baseline_net = self.trainer.baseline_net
seq2slate_net_prev_mode = seq2slate_net.training
baseline_net_prev_mode = baseline_net.training
seq2slate_net.eval()
baseline_net.eval()
logged_slate_log_prob = (seq2slate_net(
eval_tdp.training_input, mode=Seq2SlateMode.PER_SEQ_LOG_PROB_MODE).
log_probs.detach().flatten().cpu().numpy())
b = baseline_net(eval_tdp.training_input).squeeze().detach()
advantage = (eval_tdp.training_input.slate_reward -
b).flatten().cpu().numpy()
self.baseline_loss.append(
F.mse_loss(b, eval_tdp.training_input.slate_reward).item())
self.advantages.append(advantage)
self.logged_slate_log_probs.append(logged_slate_log_prob)
ranked_slate_output = seq2slate_net(eval_tdp.training_input,
Seq2SlateMode.RANK_MODE,
greedy=True)
ranked_slate_prob = (torch.prod(
torch.gather(
ranked_slate_output.ranked_tgt_out_probs,
2,
ranked_slate_output.ranked_tgt_out_idx.unsqueeze(-1),
).squeeze(),
-1,
).cpu().numpy())
self.ranked_slate_probs.append(ranked_slate_prob)
seq2slate_net.train(seq2slate_net_prev_mode)
baseline_net.train(baseline_net_prev_mode)
if not self.calc_cpe:
return
edp_g = EvaluationDataPage.create_from_tensors_seq2slate(
seq2slate_net,
self.reward_network,
eval_tdp.training_input,
eval_greedy=True,
)
if self.eval_data_pages_g is None:
self.eval_data_pages_g = edp_g
else:
self.eval_data_pages_g = self.eval_data_pages_g.append(edp_g)
edp_ng = EvaluationDataPage.create_from_tensors_seq2slate(
seq2slate_net,
self.reward_network,
eval_tdp.training_input,
eval_greedy=False,
)
if self.eval_data_pages_ng is None:
self.eval_data_pages_ng = edp_ng
else:
self.eval_data_pages_ng = self.eval_data_pages_ng.append(edp_ng)
