Silence input data warnings in tests (pytorch#2508)

Summary:
X-link: facebook/Ax#2739

Pull Request resolved: pytorch#2508

This logic broke since D61797434 updated the warning messages, leading to too many of these warnings in test outputs again.

Reviewed By: Balandat, esantorella

Differential Revision: D62200731

botorch/models/utils/assorted.py

@@ -166,8 +166,10 @@ def check_min_max_scaling(
         if torch.any(Xmin < -atol) or torch.any(Xmax > 1 + atol):
             msg = "contained"
         if msg is not None:
+            # NOTE: If you update this message, update the warning filters as well.
+            # See https://github.com/pytorch/botorch/pull/2508.
             msg = (
-                f"Data (input features) not {msg} to the unit cube. "
+                f"Data (input features) is not {msg} to the unit cube. "
                 "Please consider min-max scaling the input data."
             )
             if raise_on_fail:
@@ -196,9 +198,11 @@ def check_standardization(
         mean_not_zero = torch.abs(Ymean).max() > atol_mean
         if Y.shape[-2] <= 1:
             if mean_not_zero:
+                # NOTE: If you update this message, update the warning filters as well.
+                # See https://github.com/pytorch/botorch/pull/2508.
                 msg = (
-                    f"Data (outcome observations) not standardized (mean = {Ymean}). "
-                    "Please consider scaling the input to zero mean and unit variance."
+                    f"Data (outcome observations) is not standardized (mean = {Ymean})."
+                    " Please consider scaling the input to zero mean and unit variance."
                 )
                 if raise_on_fail:
                     raise InputDataError(msg)
@@ -207,8 +211,10 @@ def check_standardization(
             Ystd = torch.std(Y, dim=-2)
             std_not_one = torch.abs(Ystd - 1).max() > atol_std
             if mean_not_zero or std_not_one:
+                # NOTE: If you update this message, update the warning filters as well.
+                # See https://github.com/pytorch/botorch/pull/2508.
                 msg = (
-                    "Data (outcome observations) not standardized "
+                    "Data (outcome observations) is not standardized "
                     f"(std = {Ystd}, mean = {Ymean})."
                     "Please consider scaling the input to zero mean and unit variance."
                 )

botorch/utils/testing.py

@@ -62,12 +62,12 @@ def setUp(self, suppress_input_warnings: bool = True) -> None:
             )
             warnings.filterwarnings(
                 "ignore",
-                message="Data is not standardized.",
+                message=r"Data \(outcome observations\) is not standardized ",
                 category=InputDataWarning,
             )
             warnings.filterwarnings(
                 "ignore",
-                message="Input data is not contained to the unit cube.",
+                message=r"Data \(input features\) is not",
                 category=InputDataWarning,
             )
 

test/models/utils/test_assorted.py

@@ -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 \(outcome observations\) not standardized \(std ="
+        msg_more_than_1_obs = r"Data \(outcome observations\) is 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 \(outcome observations\) not standardized \(mean ="
+        msg_one_obs = r"Data \(outcome observations\) is not standardized \(mean ="
         y = torch.ones((3, 1, 2), dtype=torch.float32)
         with self.assertWarnsRegex(InputDataWarning, msg_one_obs):
             check_standardization(Y=y)

test/utils/test_testing.py

@@ -4,7 +4,11 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 
+import warnings
+
 import torch
+from botorch.exceptions.warnings import InputDataWarning
+from botorch.models.gp_regression import SingleTaskGP
 from botorch.utils.testing import BotorchTestCase, MockModel, MockPosterior
 
 
@@ -49,3 +53,15 @@ def test_basic(self) -> None:
         self.assertEqual(mm.num_outputs, 0)
         mm.state_dict()
         mm.load_state_dict()
+
+
+class TestMisc(BotorchTestCase):
+    def test_warning_filtering(self) -> None:
+        with warnings.catch_warnings(record=True) as ws:
+            # Model with unstandardized float data, which would typically raise
+            # multiple warnings.
+            SingleTaskGP(
+                train_X=torch.rand(5, 2, dtype=torch.float) * 10,
+                train_Y=torch.rand(5, 1, dtype=torch.float) * 10,
+            )
+        self.assertFalse(any(w.category == InputDataWarning for w in ws))