[WIP] Change suggestion building in pytest example

Note that if you have a dataframe will all numeric types, df.iloc[0] will convert all columns to float64 because Series cannot be mixed type.

mlos_core/mlos_core/tests/optimizers/optimizer_test.py

@@ -1,5 +1,3 @@
-#
-# Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
 #
 """
@@ -301,14 +299,11 @@ def test_optimizer_type_defs(optimizer_class: Type[BaseOptimizer]) -> None:
 
 
 @pytest.mark.parametrize(('optimizer_type', 'kwargs'), [
+    # Default optimizer
+    (None, {}),
     # Enumerate all supported Optimizers
     *[(member, {}) for member in OptimizerType],
     # Optimizer with non-empty kwargs argument
-    (OptimizerType.SMAC, {
-        # Test with default config.
-        'use_default_config': True,
-        # 'n_random_init': 10,
-    }),
 ])
 def test_mixed_input_space_types(optimizer_type: OptimizerType, kwargs: Optional[dict]) -> None:
     """
@@ -328,11 +323,17 @@ def objective(point: pd.DataFrame) -> pd.Series:
     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))
 
-    optimizer: BaseOptimizer = OptimizerFactory.create(
-        parameter_space=input_space,
-        optimizer_type=optimizer_type,
-        optimizer_kwargs=kwargs,
-    )
+    if optimizer_type is None:
+        optimizer: BaseOptimizer = OptimizerFactory.create(
+            parameter_space=input_space,
+            optimizer_kwargs=kwargs,
+        )
+    else:
+        optimizer: BaseOptimizer = OptimizerFactory.create(
+            parameter_space=input_space,
+            optimizer_type=optimizer_type,
+            optimizer_kwargs=kwargs,
+        )
 
     with pytest.raises(ValueError, match="No observations"):
         optimizer.get_best_observation()
@@ -344,8 +345,12 @@ def objective(point: pd.DataFrame) -> pd.Series:
         suggestion = optimizer.suggest()
         assert isinstance(suggestion, pd.DataFrame)
         assert (suggestion.columns == ['x', 'y']).all()
+        # Build suggestion mapping to cooperate with Configuration, note that
+        # doing a .iloc[0].to_dict() will cause pandas convert all numeric types
+        # to float64
+        tmp_suggest = {'x': suggestion['x'].values[0], 'y': suggestion['y'].values[0]}
         # check that suggestion is in the space
-        configuration = CS.Configuration(optimizer.parameter_space, suggestion.iloc[0].to_dict())
+        configuration = CS.Configuration(optimizer.parameter_space, tmp_suggest)
         # Raises an error if outside of configuration space
         configuration.is_valid_configuration()
         observation = objective(suggestion)