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Provide more informative warning messages in InputDataWarning #2489

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Provide more informative warning messages in InputDataWarning #2489

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7 changes: 4 additions & 3 deletions botorch/models/utils/assorted.py
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Original file line number Diff line number Diff line change
Expand Up @@ -167,7 +167,7 @@ def check_min_max_scaling(
msg = "contained"
if msg is not None:
msg = (
f"Input data is not {msg} to the unit cube. "
f"Data (input features) not {msg} to the unit cube. "
"Please consider min-max scaling the input data."
)
if raise_on_fail:
Expand Down Expand Up @@ -197,7 +197,7 @@ def check_standardization(
if Y.shape[-2] <= 1:
if mean_not_zero:
msg = (
f"Data is not standardized (mean = {Ymean}). "
f"Data (outcome observations) not standardized (mean = {Ymean}). "
"Please consider scaling the input to zero mean and unit variance."
)
if raise_on_fail:
Expand All @@ -208,7 +208,8 @@ def check_standardization(
std_not_one = torch.abs(Ystd - 1).max() > atol_std
if mean_not_zero or std_not_one:
msg = (
f"Data is not standardized (std = {Ystd}, mean = {Ymean}). "
"Data (outcome observations) not standardized "
f"(std = {Ystd}, mean = {Ymean})."
"Please consider scaling the input to zero mean and unit variance."
)
if raise_on_fail:
Expand Down
4 changes: 2 additions & 2 deletions test/models/utils/test_assorted.py
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Original file line number Diff line number Diff line change
Expand Up @@ -158,14 +158,14 @@ def test_check_standardization(self):
check_standardization(Y=y, raise_on_fail=True)

# check nonzero mean for case where >= 2 observations per batch
msg_more_than_1_obs = r"Data is not standardized \(std ="
msg_more_than_1_obs = r"Data \(outcome observations\) not standardized \(std ="
with self.assertWarnsRegex(InputDataWarning, msg_more_than_1_obs):
check_standardization(Y=Yst + 1)
with self.assertRaisesRegex(InputDataError, msg_more_than_1_obs):
check_standardization(Y=Yst + 1, raise_on_fail=True)

# check nonzero mean for case where < 2 observations per batch
msg_one_obs = r"Data is not standardized \(mean ="
msg_one_obs = r"Data \(outcome observations\) not standardized \(mean ="
y = torch.ones((3, 1, 2), dtype=torch.float32)
with self.assertWarnsRegex(InputDataWarning, msg_one_obs):
check_standardization(Y=y)
Expand Down