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Merged
manujosephv merged 1 commit into from
Dec 26, 2023
Merged

Bug Fix in bagging_pred and TTA #356

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27 changes: 11 additions & 16 deletions src/pytorch_tabular/tabular_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1424,9 +1424,8 @@ def predict(
for regression. For classification, the previous options are applied to the confidence
scores (soft voting) and then converted to final prediction. An additional option
"hard_voting" is available for classification.
If callable, should be a function that takes in a list of 2D arrays (num_samples, num_targets)
and returns a 2D array (num_samples, num_targets). Defaults to "mean".

If callable, should be a function that takes in a list of 3D arrays (num_samples, num_cv, num_targets)
and returns a 2D array of final probabilities (num_samples, num_targets). Defaults to "mean".

Returns:
DataFrame: Returns a dataframe with predictions and features (if `include_input_features=True`).
Expand Down Expand Up @@ -1454,7 +1453,6 @@ def add_noise(module, input, output):

# Register the hook to the embedding_layer
handle = self.model.embedding_layer.register_forward_hook(add_noise)
pred_l = []
pred_prob_l = []
for _ in range(num_tta):
pred_df = self._predict(
Expand All @@ -1468,11 +1466,10 @@ def add_noise(module, input, output):
)
pred_idx = pred_df.index
if self.config.task == "classification":
pred_l.append(pred_df.values[:, -len(self.config.target) :].astype(int))
pred_prob_l.append(pred_df.values[:, : -len(self.config.target)])
elif self.config.task == "regression":
pred_prob_l.append(pred_df.values)
pred_df = self._combine_predictions(pred_l, pred_prob_l, pred_idx, aggregate_tta, None)
pred_df = self._combine_predictions(pred_prob_l, pred_idx, aggregate_tta, None)
# Remove the hook
handle.remove()
else:
Expand Down Expand Up @@ -1993,7 +1990,6 @@ def cross_validate(

def _combine_predictions(
self,
pred_l: List[DataFrame],
pred_prob_l: List[DataFrame],
pred_idx: Union[pd.Index, List],
aggregate: Union[str, Callable],
Expand All @@ -2008,15 +2004,16 @@ def _combine_predictions(
elif aggregate == "max":
bagged_pred = np.max(pred_prob_l, axis=0)
elif aggregate == "hard_voting" and self.config.task == "classification":
pred_l = [np.argmax(p, axis=1) for p in pred_prob_l]
final_pred = np.apply_along_axis(
lambda x: np.argmax(np.bincount(x)),
axis=0,
arr=[p[:, -1].astype(int) for p in pred_l],
arr=pred_l,
)
elif callable(aggregate):
final_pred = bagged_pred = aggregate(pred_prob_l)
bagged_pred = aggregate(pred_prob_l)
if self.config.task == "classification":
if aggregate == "hard_voting" or callable(aggregate):
if aggregate == "hard_voting":
pred_df = pd.DataFrame(
np.concatenate(pred_prob_l, axis=1),
columns=[
Expand Down Expand Up @@ -2094,8 +2091,8 @@ def bagging_predict(
for regression. For classification, the previous options are applied to the confidence
scores (soft voting) and then converted to final prediction. An additional option
"hard_voting" is available for classification.
If callable, should be a function that takes in a list of 2D arrays (num_samples, num_targets)
and returns a 2D array (num_samples, num_targets). Defaults to "mean".
If callable, should be a function that takes in a list of 3D arrays (num_samples, num_cv, num_targets)
and returns a 2D array of final probabilities (num_samples, num_targets). Defaults to "mean".

weights (Optional[List[float]], optional): The weights to be used for aggregating the predictions
from each fold. If None, will use equal weights. This is only used when `aggregate` is "mean".
Expand All @@ -2122,7 +2119,6 @@ def bagging_predict(
assert aggregate != "hard_voting", "hard_voting is only available for classification"
cv = self._check_cv(cv)
prep_dl_kwargs, prep_model_kwargs, train_kwargs = self._split_kwargs(kwargs)
pred_l = []
pred_prob_l = []
datamodule = None
model = None
Expand All @@ -2149,15 +2145,14 @@ def bagging_predict(
fold_preds = self.predict(test, include_input_features=False)
pred_idx = fold_preds.index
if self.config.task == "classification":
pred_l.append(fold_preds.values[:, -len(self.config.target) :].astype(int))
pred_prob_l.append(fold_preds.values[:, : -len(self.config.target)])
elif self.config.task == "regression":
pred_prob_l.append(fold_preds.values)
if verbose:
logger.info(f"Fold {fold+1}/{cv.get_n_splits()} prediction done")
self.model.reset_weights()
pred_df = self._combine_predictions(pred_l, pred_prob_l, pred_idx, aggregate, weights)
pred_df = self._combine_predictions(pred_prob_l, pred_idx, aggregate, weights)
if return_raw_predictions:
return pred_df, pred_l, pred_prob_l
return pred_df, pred_prob_l
else:
return pred_df
4 changes: 2 additions & 2 deletions tests/test_common.py
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Original file line number Diff line number Diff line change
Expand Up @@ -916,7 +916,7 @@ def _run_bagging(
@pytest.mark.parametrize("cv", [2])
@pytest.mark.parametrize(
"aggregate",
["mean", "median", "min", "max", "hard_voting", lambda x: np.argmax(np.median(x, axis=0), axis=1)],
["mean", "median", "min", "max", "hard_voting", lambda x: np.median(x, axis=0)],
)
def test_bagging_classification(
classification_data,
Expand Down Expand Up @@ -1040,7 +1040,7 @@ def _run_tta(
@pytest.mark.parametrize("categorical_cols", [["feature_0_cat"]])
@pytest.mark.parametrize(
"aggregate",
["mean", "median", "min", "max", "hard_voting", lambda x: np.argmax(np.median(x, axis=0), axis=1)],
["mean", "median", "min", "max", "hard_voting", lambda x: np.median(x, axis=0)],
)
def test_tta_classification(
classification_data,
Expand Down