Fix mixed numeric datatypes for optimizers

mlos_bench/mlos_bench/storage/util.py

@@ -30,7 +30,7 @@ def kv_df_to_dict(dataframe: pandas.DataFrame) -> Dict[str, Optional[TunableValu
         dataframe.rename(columns={'metric': 'parameter'}, inplace=True)
     assert dataframe.columns.tolist() == ['parameter', 'value']
     data = {}
-    for _, row in dataframe.iterrows():
+    for _, row in dataframe.astype('O').iterrows():
         assert isinstance(row['parameter'], str)
         assert row['value'] is None or isinstance(row['value'], (str, int, float))
         if row['parameter'] in data:

mlos_core/mlos_core/optimizers/bayesian_optimizers/smac_optimizer.py

@@ -336,5 +336,5 @@ def _to_configspace_configs(self, configurations: pd.DataFrame) -> List[ConfigSp
         """
         return [
             ConfigSpace.Configuration(self.optimizer_parameter_space, values=config.to_dict())
-            for (_, config) in configurations.iterrows()
+            for (_, config) in configurations.astype('O').iterrows()
         ]

mlos_core/mlos_core/optimizers/flaml_optimizer.py

@@ -86,7 +86,7 @@ def _register(self, configurations: pd.DataFrame, scores: pd.Series,
         """
         if context is not None:
             raise NotImplementedError()
-        for (_, config), score in zip(configurations.iterrows(), scores):
+        for (_, config), score in zip(configurations.astype('O').iterrows(), scores):
             cs_config: ConfigSpace.Configuration = ConfigSpace.Configuration(
                 self.optimizer_parameter_space, values=config.to_dict())
             if cs_config in self.evaluated_samples:

mlos_core/mlos_core/spaces/adapters/llamatune.py

@@ -89,8 +89,9 @@ def target_parameter_space(self) -> ConfigSpace.ConfigurationSpace:
 
     def inverse_transform(self, configurations: pd.DataFrame) -> pd.DataFrame:
         target_configurations = []
-        for (_, config) in configurations.iterrows():
-            configuration = ConfigSpace.Configuration(self.orig_parameter_space, values=config.to_dict())
+        for (_, config) in configurations.astype('O').iterrows():
+            configuration = ConfigSpace.Configuration(
+                self.orig_parameter_space, values=config.to_dict())
 
             target_config = self._suggested_configs.get(configuration, None)
             # NOTE: HeSBO is a non-linear projection method, and does not inherently support inverse projection

mlos_core/mlos_core/tests/__init__.py

@@ -12,6 +12,9 @@
 from pkgutil import walk_packages
 from typing import List, Optional, Set, Type, TypeVar
 
+# A common seed to use to avoid tracking down race conditions and intermingling
+# issues of seeds across tests that run in non-deterministic parallel orders.
+SEED = 42
 
 if sys.version_info >= (3, 10):
     from typing import TypeAlias

mlos_core/mlos_core/tests/optimizers/optimizer_test.py

@@ -23,7 +23,7 @@
 from mlos_core.optimizers.bayesian_optimizers import BaseBayesianOptimizer, SmacOptimizer
 from mlos_core.spaces.adapters import SpaceAdapterType
 
-from mlos_core.tests import get_all_concrete_subclasses
+from mlos_core.tests import get_all_concrete_subclasses, SEED
 
 
 _LOG = logging.getLogger(__name__)
@@ -76,7 +76,7 @@ def objective(x: pd.Series) -> npt.ArrayLike:   # pylint: disable=invalid-name
         ret: npt.ArrayLike = (6 * x - 2)**2 * np.sin(12 * x - 4)
         return ret
     # Emukit doesn't allow specifying a random state, so we set the global seed.
-    np.random.seed(42)
+    np.random.seed(SEED)
     optimizer = optimizer_class(parameter_space=configuration_space, **kwargs)
 
     with pytest.raises(ValueError, match="No observations"):
@@ -298,3 +298,69 @@ def test_optimizer_type_defs(optimizer_class: Type[BaseOptimizer]) -> None:
     """
     optimizer_type_classes = {member.value for member in OptimizerType}
     assert optimizer_class in optimizer_type_classes
+
+
+@pytest.mark.parametrize(('optimizer_type', 'kwargs'), [
+    # Default optimizer
+    (None, {}),
+    # Enumerate all supported Optimizers
+    *[(member, {}) for member in OptimizerType],
+    # Optimizer with non-empty kwargs argument
+])
+def test_mixed_numeric_type_input_space_types(optimizer_type: Optional[OptimizerType], kwargs: Optional[dict]) -> None:
+    """
+    Toy problem to test the optimizers with mixed numeric types to ensure that original dtypes are retained.
+    """
+    max_iterations = 10
+    if kwargs is None:
+        kwargs = {}
+
+    def objective(point: pd.DataFrame) -> pd.Series:
+        # mix of hyperparameters, optimal is to select the highest possible
+        ret: pd.Series = point["x"] + point["y"]
+        return ret
+
+    input_space = CS.ConfigurationSpace(seed=SEED)
+    # add a mix of numeric datatypes
+    input_space.add_hyperparameter(CS.UniformIntegerHyperparameter(name='x', lower=0, upper=5))
+    input_space.add_hyperparameter(CS.UniformFloatHyperparameter(name='y', lower=0.0, upper=5.0))
+
+    if optimizer_type is None:
+        optimizer = OptimizerFactory.create(
+            parameter_space=input_space,
+            optimizer_kwargs=kwargs,
+        )
+    else:
+        optimizer = OptimizerFactory.create(
+            parameter_space=input_space,
+            optimizer_type=optimizer_type,
+            optimizer_kwargs=kwargs,
+        )
+
+    with pytest.raises(ValueError, match="No observations"):
+        optimizer.get_best_observation()
+
+    with pytest.raises(ValueError, match="No observations"):
+        optimizer.get_observations()
+
+    for _ in range(max_iterations):
+        suggestion = optimizer.suggest()
+        assert isinstance(suggestion, pd.DataFrame)
+        assert (suggestion.columns == ['x', 'y']).all()
+        # Check suggestion values are the expected dtype
+        assert isinstance(suggestion['x'].iloc[0], np.integer)
+        assert isinstance(suggestion['y'].iloc[0], np.floating)
+        # Check that suggestion is in the space
+        test_configuration = CS.Configuration(optimizer.parameter_space, suggestion.astype('O').iloc[0].to_dict())
+        # Raises an error if outside of configuration space
+        test_configuration.is_valid_configuration()
+        # Test registering the suggested configuration with a score.
+        observation = objective(suggestion)
+        assert isinstance(observation, pd.Series)
+        optimizer.register(suggestion, observation)
+
+    best_observation = optimizer.get_best_observation()
+    assert isinstance(best_observation, pd.DataFrame)
+
+    all_observations = optimizer.get_observations()
+    assert isinstance(all_observations, pd.DataFrame)