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fix: some small problem #46

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fix: some small problem #46

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3 changes: 1 addition & 2 deletions inference/predictor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -574,13 +574,11 @@ def _predict_reg(self, x_train:np.ndarray, y_train:np.ndarray, x_test:np.ndarray
"threshold", 1),
mixed_method=self.inference_config[id_pipe]["retrieval_config"].get(
"mixed_method", "max"),device=self.device)
outputs.append(output)
elif self.inference_with_DDP:
inference = InferenceResultWithRetrieval(model=self.model,
sample_selection_type="DDP")
output = inference.inference(x_[:len(y_train)].squeeze(1), y_, x_[len(y_train):].squeeze(1),
task_type="reg")
outputs.append(output)
else:
self.model.to(self.device)
with(torch.autocast(device_type=self.device.type if isinstance(self.device, torch.device) else self.device, enabled=self.mix_precision), torch.inference_mode()):
Expand All @@ -597,6 +595,7 @@ def _predict_reg(self, x_train:np.ndarray, y_train:np.ndarray, x_test:np.ndarray
output = output['reg_output']

output = output if isinstance(output, dict) else output.squeeze(0)
# FIX: append exactly once per pipeline; previously retrieval/DDP branches appended twice and biased the mean.
outputs.append(output)

output = torch.stack(outputs).squeeze(2).mean(dim=0)
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5 changes: 3 additions & 2 deletions utils/data_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -95,8 +95,9 @@ def __getitem__(self, idx: int) -> dict[str, list]:
return dict(
idx=int(idx),
X_train=self.X_train[idx], # Training features for this step (retrieved)
X_test=self.X_test[idx], # Training labels for this step (retrieved)
y_train=self.y_train[idx], # The test sample features
# FIX: keep the dict keys semantically correct (X_test is the test feature row; y_train is the retrieved labels)
X_test=self.X_test[idx], # Test sample features for this step
y_train=self.y_train[idx], # Retrieved training labels for this step
)
else:
# Return only the test data; training data is assumed to be fixed and
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14 changes: 10 additions & 4 deletions utils/inference_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -99,29 +99,35 @@ def generate_infenerce_config(args):
use_type=None,
)

# FIX: do not reuse the same retrieval_config dict across pipelines; later updates should not mutate others.
retrieval_config_1 = retrieval_config.copy()
retrieval_config_2 = retrieval_config.copy()
retrieval_config_3 = retrieval_config.copy()
retrieval_config_4 = retrieval_config.copy()

config_list = [
dict(RebalanceFeatureDistribution=dict(worker_tags=["quantile"], discrete_flag=False, original_flag=True,
svd_tag="svd"),
CategoricalFeatureEncoder=dict(encoding_strategy="ordinal_strict_feature_shuffled"),
FeatureShuffler=dict(mode="shuffle"),
retrieval_config=retrieval_config,
retrieval_config=retrieval_config_1,
),
dict(RebalanceFeatureDistribution=dict(worker_tags=["quantile"], discrete_flag=False, original_flag=True,
svd_tag="svd"),
CategoricalFeatureEncoder=dict(encoding_strategy="ordinal_strict_feature_shuffled"),
FeatureShuffler=dict(mode="shuffle"), retrieval_config=retrieval_config,
FeatureShuffler=dict(mode="shuffle"), retrieval_config=retrieval_config_2,
),
dict(RebalanceFeatureDistribution=dict(worker_tags=[None], discrete_flag=True, original_flag=False,
svd_tag=None),
CategoricalFeatureEncoder=dict(encoding_strategy="numeric"),
FeatureShuffler=dict(mode="shuffle"),
retrieval_config=retrieval_config,
retrieval_config=retrieval_config_3,
),
dict(RebalanceFeatureDistribution=dict(worker_tags=[None], discrete_flag=True, original_flag=False,
svd_tag=None),
CategoricalFeatureEncoder=dict(encoding_strategy="numeric"),
FeatureShuffler=dict(mode="shuffle"),
retrieval_config=retrieval_config)
retrieval_config=retrieval_config_4)
]

with open(args.inference_config_path, 'w') as f:
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