Modify get_data to error out on nan/inf

ax/modelbridge/transforms/tests/test_percentile_y_transform.py

@@ -13,6 +13,7 @@
 from ax.exceptions.core import DataRequiredError
 from ax.modelbridge.transforms.percentile_y import PercentileY
 from ax.utils.common.testutils import TestCase
+from ax.utils.testing.core_stubs import get_observations_with_invalid_value
 
 
 class PercentileYTransformTest(TestCase):
@@ -126,3 +127,13 @@ def test_TransformObservationsWithWinsorization(self) -> None:
             np.allclose(mean_results, expected),
             msg=f"Unexpected mean Results: {mean_results}. Expected: {expected}.",
         )
+
+    def test_non_finite_data_raises(self) -> None:
+        for invalid_value in [float("nan"), float("inf")]:
+            observations = get_observations_with_invalid_value(
+                invalid_value=invalid_value
+            )
+            with self.assertRaisesRegex(
+                ValueError, f"Non-finite data found for metric m1: {invalid_value}"
+            ):
+                PercentileY(observations=observations, config={"metrics": ["m1"]})

ax/modelbridge/transforms/tests/test_power_y_transform.py

@@ -26,6 +26,7 @@
 )
 from ax.modelbridge.transforms.utils import get_data, match_ci_width_truncated
 from ax.utils.common.testutils import TestCase
+from ax.utils.testing.core_stubs import get_observations_with_invalid_value
 from sklearn.preprocessing import PowerTransformer
 
 
@@ -328,3 +329,11 @@ def test_TransformOptimizationConfig(self) -> None:
             "that are part of a ScalarizedOutcomeConstraint.",
             str(cm.exception),
         )
+
+    def test_non_finite_data_raises(self) -> None:
+        for invalid_value in [float("nan"), float("inf")]:
+            observations = get_observations_with_invalid_value(invalid_value)
+            with self.assertRaisesRegex(
+                ValueError, f"Non-finite data found for metric m1: {invalid_value}"
+            ):
+                PowerTransformY(observations=observations, config={"metrics": ["m1"]})

ax/modelbridge/transforms/tests/test_standardize_y_transform.py

@@ -20,6 +20,7 @@
 from ax.exceptions.core import DataRequiredError
 from ax.modelbridge.transforms.standardize_y import StandardizeY
 from ax.utils.common.testutils import TestCase
+from ax.utils.testing.core_stubs import get_observations_with_invalid_value
 
 
 class StandardizeYTransformTest(TestCase):
@@ -153,6 +154,16 @@ def test_TransformOptimizationConfig(self) -> None:
         with self.assertRaises(ValueError):
             oc = self.t.transform_optimization_config(oc, None, None)
 
+    def test_non_finite_data_raises(self) -> None:
+        for invalid_value in [float("nan"), float("inf")]:
+            observations = get_observations_with_invalid_value(
+                invalid_value=invalid_value
+            )
+            with self.assertRaisesRegex(
+                ValueError, f"Non-finite data found for metric m1: {invalid_value}"
+            ):
+                StandardizeY(observations=observations, config={"metrics": ["m1"]})
+
 
 def osd_allclose(osd1: ObservationData, osd2: ObservationData) -> bool:
     if osd1.metric_names != osd2.metric_names:

ax/modelbridge/transforms/tests/test_winsorize_transform.py

@@ -8,7 +8,7 @@
 
 import warnings
 from copy import deepcopy
-from typing import Dict, Optional, Tuple
+from typing import Any, Dict, Optional, Tuple
 from unittest import mock
 
 import numpy as np
@@ -40,7 +40,10 @@
 )
 from ax.models.winsorization_config import WinsorizationConfig
 from ax.utils.common.testutils import TestCase
-from ax.utils.testing.core_stubs import get_optimization_config
+from ax.utils.testing.core_stubs import (
+    get_observations_with_invalid_value,
+    get_optimization_config,
+)
 from typing_extensions import SupportsIndex
 
 INF = float("inf")
@@ -642,6 +645,19 @@ def test_relative_constraints(
         )
         self.assertDictEqual(t.cutoffs, {"a": (-INF, 3.5), "b": (-INF, 12.0)})
 
+    def test_non_finite_data_raises(self) -> None:
+        for invalid_value in [float("nan"), float("inf")]:
+            observations = get_observations_with_invalid_value(
+                invalid_value=invalid_value
+            )
+            config: Dict[str, Any] = {
+                "winsorization_config": WinsorizationConfig(upper_quantile_margin=0.2)
+            }
+            with self.assertRaisesRegex(
+                ValueError, f"Non-finite data found for metric m1: {invalid_value}"
+            ):
+                Winsorize(search_space=None, observations=observations, config=config)
+
 
 # pyre-fixme[2]: Parameter must be annotated.
 def get_transform(observation_data, config=None, optimization_config=None) -> Winsorize:

ax/modelbridge/transforms/utils.py

@@ -66,14 +66,30 @@ def __getitem__(self, key: Number) -> Any:
 
 
 def get_data(
-    observation_data: List[ObservationData], metric_names: Union[List[str], None] = None
+    observation_data: List[ObservationData],
+    metric_names: Union[List[str], None] = None,
+    raise_on_non_finite_data: bool = True,
 ) -> Dict[str, List[float]]:
-    """Extract all metrics if `metric_names` is None."""
+    """Extract all metrics if `metric_names` is None.
+
+    Raises a value error if any data is non-finite.
+
+    Args:
+        observation_data: List of observation data.
+        metric_names: List of metric names.
+        raise_on_non_finite_data: If true, raises an exception on nan/inf.
+
+    Returns:
+        A dictionary mapping metric names to lists of metric values.
+    """
     Ys = defaultdict(list)
     for obsd in observation_data:
         for i, m in enumerate(obsd.metric_names):
             if metric_names is None or m in metric_names:
-                Ys[m].append(obsd.means[i])
+                val = obsd.means[i]
+                if raise_on_non_finite_data and (not np.isfinite(val)):
+                    raise ValueError(f"Non-finite data found for metric {m}: {val}")
+                Ys[m].append(val)
     return Ys
 
 

ax/utils/testing/core_stubs.py

@@ -41,7 +41,7 @@
 from ax.core.metric import Metric
 from ax.core.multi_type_experiment import MultiTypeExperiment
 from ax.core.objective import MultiObjective, Objective, ScalarizedObjective
-from ax.core.observation import ObservationFeatures
+from ax.core.observation import Observation, ObservationData, ObservationFeatures
 from ax.core.optimization_config import (
     MultiObjectiveOptimizationConfig,
     OptimizationConfig,
@@ -1987,6 +1987,18 @@ def get_map_data(trial_index: int = 0) -> MapData:
     )
 
 
+def get_observations_with_invalid_value(invalid_value: float) -> List[Observation]:
+    obsd_with_non_finite = ObservationData(
+        metric_names=["m1"] * 4,
+        means=np.array([-100, 4, invalid_value, 2]),
+        covariance=np.eye(4),
+    )
+    observations = [
+        Observation(features=ObservationFeatures({}), data=obsd_with_non_finite)
+    ]
+    return observations
+
+
 # pyre-fixme[24]: Generic type `MapKeyInfo` expects 1 type parameter.
 def get_map_key_info() -> MapKeyInfo:
     return MapKeyInfo(key="epoch", default_value=0.0)