require output dim in MultiTaskGP (#383)

Summary:
Pull Request resolved: #383

- Require output dim in MTGP
- Validate training tensor dimensions
- Validate input scaling

Reviewed By: Balandat

Differential Revision: D20223856

fbshipit-source-id: 8dfd327ecc6a9bb141211dd43148baa34c22ed70

Author: sdaulton

botorch/models/multitask.py

@@ -27,6 +27,7 @@
 from torch import Tensor
 
 from .gpytorch import MultiTaskGPyTorchModel
+from .utils import validate_input_scaling
 
 
 class MultiTaskGP(ExactGP, MultiTaskGPyTorchModel):
@@ -74,10 +75,13 @@ def __init__(
             >>> train_Y = torch.cat(f1(X1), f2(X2))
             >>> model = MultiTaskGP(train_X, train_Y, task_feature=-1)
         """
-        # TODO: Validate input normalization/scaling
-        if train_X.ndimension() != 2:
+        self._validate_tensor_args(X=train_X, Y=train_Y)
+        validate_input_scaling(train_X=train_X, train_Y=train_Y)
+        if train_X.ndim != 2:
             # Currently, batch mode MTGPs are blocked upstream in GPyTorch
             raise ValueError(f"Unsupported shape {train_X.shape} for train_X.")
+        # squeeze output dim
+        train_Y = train_Y.squeeze(-1)
         d = train_X.shape[-1] - 1
         if not (-d <= task_feature <= d):
             raise ValueError(f"Must have that -{d} <= task_feature <= {d}")
@@ -199,6 +203,7 @@ def __init__(
             >>> train_Yvar = 0.1 + 0.1 * torch.rand_like(train_Y)
             >>> model = FixedNoiseMultiTaskGP(train_X, train_Y, train_Yvar, -1)
         """
+        self._validate_tensor_args(X=train_X, Y=train_Y, Yvar=train_Yvar)
         # We'll instatiate a MultiTaskGP and simply override the likelihood
         super().__init__(
             train_X=train_X,
@@ -207,5 +212,5 @@ def __init__(
             output_tasks=output_tasks,
             rank=rank,
         )
-        self.likelihood = FixedNoiseGaussianLikelihood(noise=train_Yvar)
+        self.likelihood = FixedNoiseGaussianLikelihood(noise=train_Yvar.squeeze(-1))
         self.to(train_X)

test/models/test_multitask.py

@@ -31,7 +31,7 @@ def _get_random_mt_data(**tkwargs):
     full_train_i = torch.cat([train_i_task1, train_i_task2])
     full_train_y = torch.cat([train_y1, train_y2])
     train_X = torch.stack([full_train_x, full_train_i.type_as(full_train_x)], dim=-1)
-    train_Y = full_train_y
+    train_Y = full_train_y.unsqueeze(-1)  # add output dim
     return train_X, train_Y
 
 
@@ -121,7 +121,7 @@ def test_MultiTaskGP(self):
 
             # test that unsupported batch shape MTGPs throw correct error
             with self.assertRaises(ValueError):
-                MultiTaskGP(torch.rand(2, 2, 2), torch.rand(2, 1), 0)
+                MultiTaskGP(torch.rand(2, 2, 2), torch.rand(2, 2, 1), 0)
 
             # test that bad feature index throws correct error
             train_X, train_Y = _get_random_mt_data(**tkwargs)
@@ -233,7 +233,7 @@ def test_FixedNoiseMultiTaskGP(self):
             # test that unsupported batch shape MTGPs throw correct error
             with self.assertRaises(ValueError):
                 FixedNoiseMultiTaskGP(
-                    torch.rand(2, 2, 2), torch.rand(2, 1), torch.rand(2, 1), 0
+                    torch.rand(2, 2, 2), torch.rand(2, 2, 1), torch.rand(2, 2, 1), 0
                 )
 
             # test that bad feature index throws correct error