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remove input transform checks #1568

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ea7b1ea
remove input transform checks
jduerholt Dec 15, 2022
ca0ed76
fix flake8
jduerholt Dec 15, 2022
2cd11f2
Merge branch 'main' into mixed_gp_input_transform
Balandat Dec 23, 2022
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22 changes: 5 additions & 17 deletions botorch/models/gp_regression_mixed.py
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Original file line number Diff line number Diff line change
Expand Up @@ -89,29 +89,17 @@ def __init__(
`.posterior` on the model will be on the original scale).
input_transform: An input transform that is applied in the model's
forward pass. Only input transforms are allowed which do not
transform the categorical dimensions. This can be achieved
by using the `indices` argument when constructing the transform.
transform the categorical dimensions. If you want to use it
for example in combination with a `OneHotToNumeric` input transform
one has to instantiate the transform with `transform_on_train` == False
and pass in the already transformed input.
"""
if input_transform is not None:
if not hasattr(input_transform, "indices"):
raise ValueError(
"Only continuous inputs can be transformed. "
"Please use `indices` in the `input_transform`."
)
# check that no cat dim is in indices
elif any(idx in input_transform.indices for idx in cat_dims):
raise ValueError(
"Only continuous inputs can be transformed. "
"Categorical index found in `indices` of the `input_transform`."
)
if len(cat_dims) == 0:
raise ValueError(
"Must specify categorical dimensions for MixedSingleTaskGP"
)
self._ignore_X_dims_scaling_check = cat_dims
input_batch_shape, aug_batch_shape = self.get_batch_dimensions(
train_X=train_X, train_Y=train_Y
)
_, aug_batch_shape = self.get_batch_dimensions(train_X=train_X, train_Y=train_Y)

if cont_kernel_factory is None:

Expand Down
34 changes: 0 additions & 34 deletions test/models/test_gp_regression_mixed.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,6 @@
# LICENSE file in the root directory of this source tree.

import itertools
import random
import warnings

import torch
Expand Down Expand Up @@ -44,15 +43,6 @@ def test_gp(self):
)
cat_dims = list(range(ncat))
ord_dims = sorted(set(range(d)) - set(cat_dims))
with self.assertRaises(ValueError):
MixedSingleTaskGP(
train_X,
train_Y,
cat_dims=cat_dims,
input_transform=Normalize(
d=d, bounds=bounds.to(**tkwargs), transform_on_train=True
),
)
# test correct indices
if (ncat < 3) and (ncat > 0):
MixedSingleTaskGP(
Expand All @@ -66,30 +56,6 @@ def test_gp(self):
indices=ord_dims,
),
)
with self.assertRaises(ValueError):
MixedSingleTaskGP(
train_X,
train_Y,
cat_dims=cat_dims,
input_transform=Normalize(
d=d,
bounds=bounds.to(**tkwargs),
transform_on_train=True,
indices=cat_dims,
),
)
with self.assertRaises(ValueError):
MixedSingleTaskGP(
train_X,
train_Y,
cat_dims=cat_dims,
input_transform=Normalize(
d=d,
bounds=bounds.to(**tkwargs),
transform_on_train=True,
indices=ord_dims + [random.choice(cat_dims)],
),
)

if len(cat_dims) == 0:
with self.assertRaises(ValueError):
Expand Down