Typing tweaks

mlos_bench/mlos_bench/optimizers/base_optimizer.py

@@ -209,7 +209,7 @@ def target(self) -> str:
         return self._opt_target
 
     @property
-    def direction(self) -> str:
+    def direction(self) -> Literal['min', 'max']:
         """
         The direction to optimize the target metric (e.g., min or max).
         """

mlos_bench/mlos_bench/storage/util.py

@@ -10,7 +10,7 @@
 
 import pandas
 
-from mlos_bench.tunables.tunable import TunableValue
+from mlos_bench.tunables.tunable import TunableValue, TunableValueTypeTuple
 from mlos_bench.util import try_parse_val
 
 
@@ -32,7 +32,7 @@ def kv_df_to_dict(dataframe: pandas.DataFrame) -> Dict[str, Optional[TunableValu
     data = {}
     for _, row in dataframe.astype('O').iterrows():
         assert isinstance(row['parameter'], str)
-        assert row['value'] is None or isinstance(row['value'], (str, int, float))
+        assert isinstance(row['value'], TunableValueTypeTuple)
         if row['parameter'] in data:
             raise ValueError(f"Duplicate parameter '{row['parameter']}' in dataframe")
         data[row['parameter']] = try_parse_val(row['value']) if isinstance(row['value'], str) else row['value']

mlos_bench/mlos_bench/tests/tunables/tunable_definition_test.py

@@ -9,7 +9,7 @@
 import json5 as json
 import pytest
 
-from mlos_bench.tunables.tunable import Tunable
+from mlos_bench.tunables.tunable import Tunable, TunableValueTypeName
 
 
 def test_tunable_name() -> None:
@@ -135,7 +135,7 @@ def test_categorical_tunable_disallow_repeats() -> None:
 
 
 @pytest.mark.parametrize("tunable_type", ["int", "float"])
-def test_numerical_tunable_disallow_null_default(tunable_type: str) -> None:
+def test_numerical_tunable_disallow_null_default(tunable_type: TunableValueTypeName) -> None:
     """
     Disallow null values as default for numerical tunables.
     """
@@ -148,7 +148,7 @@ def test_numerical_tunable_disallow_null_default(tunable_type: str) -> None:
 
 
 @pytest.mark.parametrize("tunable_type", ["int", "float"])
-def test_numerical_tunable_disallow_out_of_range(tunable_type: str) -> None:
+def test_numerical_tunable_disallow_out_of_range(tunable_type: TunableValueTypeName) -> None:
     """
     Disallow out of range values as default for numerical tunables.
     """
@@ -161,7 +161,7 @@ def test_numerical_tunable_disallow_out_of_range(tunable_type: str) -> None:
 
 
 @pytest.mark.parametrize("tunable_type", ["int", "float"])
-def test_numerical_tunable_wrong_params(tunable_type: str) -> None:
+def test_numerical_tunable_wrong_params(tunable_type: TunableValueTypeName) -> None:
     """
     Disallow values param for numerical tunables.
     """
@@ -175,7 +175,7 @@ def test_numerical_tunable_wrong_params(tunable_type: str) -> None:
 
 
 @pytest.mark.parametrize("tunable_type", ["int", "float"])
-def test_numerical_tunable_required_params(tunable_type: str) -> None:
+def test_numerical_tunable_required_params(tunable_type: TunableValueTypeName) -> None:
     """
     Disallow null values param for numerical tunables.
     """
@@ -192,7 +192,7 @@ def test_numerical_tunable_required_params(tunable_type: str) -> None:
 
 
 @pytest.mark.parametrize("tunable_type", ["int", "float"])
-def test_numerical_tunable_invalid_range(tunable_type: str) -> None:
+def test_numerical_tunable_invalid_range(tunable_type: TunableValueTypeName) -> None:
     """
     Disallow invalid range param for numerical tunables.
     """
@@ -209,7 +209,7 @@ def test_numerical_tunable_invalid_range(tunable_type: str) -> None:
 
 
 @pytest.mark.parametrize("tunable_type", ["int", "float"])
-def test_numerical_tunable_reversed_range(tunable_type: str) -> None:
+def test_numerical_tunable_reversed_range(tunable_type: TunableValueTypeName) -> None:
     """
     Disallow reverse range param for numerical tunables.
     """
@@ -226,7 +226,7 @@ def test_numerical_tunable_reversed_range(tunable_type: str) -> None:
 
 
 @pytest.mark.parametrize("tunable_type", ["int", "float"])
-def test_numerical_weights(tunable_type: str) -> None:
+def test_numerical_weights(tunable_type: TunableValueTypeName) -> None:
     """
     Instantiate a numerical tunable with weighted special values.
     """
@@ -248,7 +248,7 @@ def test_numerical_weights(tunable_type: str) -> None:
 
 
 @pytest.mark.parametrize("tunable_type", ["int", "float"])
-def test_numerical_quantization(tunable_type: str) -> None:
+def test_numerical_quantization(tunable_type: TunableValueTypeName) -> None:
     """
     Instantiate a numerical tunable with quantization.
     """
@@ -267,7 +267,7 @@ def test_numerical_quantization(tunable_type: str) -> None:
 
 
 @pytest.mark.parametrize("tunable_type", ["int", "float"])
-def test_numerical_log(tunable_type: str) -> None:
+def test_numerical_log(tunable_type: TunableValueTypeName) -> None:
     """
     Instantiate a numerical tunable with log scale.
     """
@@ -285,7 +285,7 @@ def test_numerical_log(tunable_type: str) -> None:
 
 
 @pytest.mark.parametrize("tunable_type", ["int", "float"])
-def test_numerical_weights_no_specials(tunable_type: str) -> None:
+def test_numerical_weights_no_specials(tunable_type: TunableValueTypeName) -> None:
     """
     Raise an error if special_weights are specified but no special values.
     """
@@ -303,7 +303,7 @@ def test_numerical_weights_no_specials(tunable_type: str) -> None:
 
 
 @pytest.mark.parametrize("tunable_type", ["int", "float"])
-def test_numerical_weights_non_normalized(tunable_type: str) -> None:
+def test_numerical_weights_non_normalized(tunable_type: TunableValueTypeName) -> None:
     """
     Instantiate a numerical tunable with non-normalized weights
     of the special values.
@@ -326,7 +326,7 @@ def test_numerical_weights_non_normalized(tunable_type: str) -> None:
 
 
 @pytest.mark.parametrize("tunable_type", ["int", "float"])
-def test_numerical_weights_wrong_count(tunable_type: str) -> None:
+def test_numerical_weights_wrong_count(tunable_type: TunableValueTypeName) -> None:
     """
     Try to instantiate a numerical tunable with incorrect number of weights.
     """
@@ -346,7 +346,7 @@ def test_numerical_weights_wrong_count(tunable_type: str) -> None:
 
 
 @pytest.mark.parametrize("tunable_type", ["int", "float"])
-def test_numerical_weights_no_range_weight(tunable_type: str) -> None:
+def test_numerical_weights_no_range_weight(tunable_type: TunableValueTypeName) -> None:
     """
     Try to instantiate a numerical tunable with weights but no range_weight.
     """
@@ -365,7 +365,7 @@ def test_numerical_weights_no_range_weight(tunable_type: str) -> None:
 
 
 @pytest.mark.parametrize("tunable_type", ["int", "float"])
-def test_numerical_range_weight_no_weights(tunable_type: str) -> None:
+def test_numerical_range_weight_no_weights(tunable_type: TunableValueTypeName) -> None:
     """
     Try to instantiate a numerical tunable with specials but no range_weight.
     """
@@ -384,7 +384,7 @@ def test_numerical_range_weight_no_weights(tunable_type: str) -> None:
 
 
 @pytest.mark.parametrize("tunable_type", ["int", "float"])
-def test_numerical_range_weight_no_specials(tunable_type: str) -> None:
+def test_numerical_range_weight_no_specials(tunable_type: TunableValueTypeName) -> None:
     """
     Try to instantiate a numerical tunable with specials but no range_weight.
     """
@@ -402,7 +402,7 @@ def test_numerical_range_weight_no_specials(tunable_type: str) -> None:
 
 
 @pytest.mark.parametrize("tunable_type", ["int", "float"])
-def test_numerical_weights_wrong_values(tunable_type: str) -> None:
+def test_numerical_weights_wrong_values(tunable_type: TunableValueTypeName) -> None:
     """
     Try to instantiate a numerical tunable with incorrect number of weights.
     """
@@ -422,7 +422,7 @@ def test_numerical_weights_wrong_values(tunable_type: str) -> None:
 
 
 @pytest.mark.parametrize("tunable_type", ["int", "float"])
-def test_numerical_quantization_wrong(tunable_type: str) -> None:
+def test_numerical_quantization_wrong(tunable_type: TunableValueTypeName) -> None:
     """
     Instantiate a numerical tunable with invalid number of quantization points.
     """

mlos_bench/mlos_bench/tests/tunables/tunable_distributions_test.py

@@ -9,7 +9,7 @@
 import json5 as json
 import pytest
 
-from mlos_bench.tunables.tunable import Tunable
+from mlos_bench.tunables.tunable import Tunable, TunableValueTypeName
 
 
 def test_categorical_distribution() -> None:
@@ -28,7 +28,7 @@ def test_categorical_distribution() -> None:
 
 
 @pytest.mark.parametrize("tunable_type", ["int", "float"])
-def test_numerical_distribution_uniform(tunable_type: str) -> None:
+def test_numerical_distribution_uniform(tunable_type: TunableValueTypeName) -> None:
     """
     Create a numeric Tunable with explicit uniform distribution.
     """
@@ -46,7 +46,7 @@ def test_numerical_distribution_uniform(tunable_type: str) -> None:
 
 
 @pytest.mark.parametrize("tunable_type", ["int", "float"])
-def test_numerical_distribution_normal(tunable_type: str) -> None:
+def test_numerical_distribution_normal(tunable_type: TunableValueTypeName) -> None:
     """
     Create a numeric Tunable with explicit Gaussian distribution specified.
     """
@@ -67,7 +67,7 @@ def test_numerical_distribution_normal(tunable_type: str) -> None:
 
 
 @pytest.mark.parametrize("tunable_type", ["int", "float"])
-def test_numerical_distribution_beta(tunable_type: str) -> None:
+def test_numerical_distribution_beta(tunable_type: TunableValueTypeName) -> None:
     """
     Create a numeric Tunable with explicit Beta distribution specified.
     """

mlos_bench/mlos_bench/tests/tunables/tunables_assign_test.py

@@ -162,19 +162,19 @@ def test_tunable_assign_null_to_int(tunable_int: Tunable) -> None:
     """
     Checks that we can't use null/None in integer tunables.
     """
-    with pytest.raises(TypeError):
+    with pytest.raises((TypeError, AssertionError)):
         tunable_int.value = None
-    with pytest.raises(TypeError):
+    with pytest.raises((TypeError, AssertionError)):
         tunable_int.numerical_value = None    # type: ignore[assignment]
 
 
 def test_tunable_assign_null_to_float(tunable_float: Tunable) -> None:
     """
     Checks that we can't use null/None in float tunables.
     """
-    with pytest.raises(TypeError):
+    with pytest.raises((TypeError, AssertionError)):
         tunable_float.value = None
-    with pytest.raises(TypeError):
+    with pytest.raises((TypeError, AssertionError)):
         tunable_float.numerical_value = None    # type: ignore[assignment]
 
 

mlos_bench/mlos_bench/tunables/tunable.py

@@ -17,6 +17,22 @@
 """A tunable parameter value type alias."""
 TunableValue = Union[int, float, Optional[str]]
 
+"""Tunable value type."""
+TunableValueType = Union[Type[int], Type[float], Type[str]]
+
+"""
+Tunable value type tuple.
+For checking with isinstance()
+"""
+TunableValueTypeTuple = (int, float, str, type(None))
+
+"""The string name of a tunable value type."""
+TunableValueTypeName = Literal["int", "float", "categorical"]
+
+"""Tunable values dictionary type"""
+TunableValuesDict = Dict[str, TunableValue]
+
+"""Tunable value distribution type"""
 DistributionName = Literal["uniform", "normal", "beta"]
 
 
@@ -38,7 +54,7 @@ class TunableDict(TypedDict, total=False):
     These are the types expected to be received from the json config.
     """
 
-    type: str
+    type: TunableValueTypeName
     description: Optional[str]
     default: TunableValue
     values: Optional[List[Optional[str]]]
@@ -59,7 +75,7 @@ class Tunable:  # pylint: disable=too-many-instance-attributes,too-many-public-m
     """
 
     # Maps tunable types to their corresponding Python types by name.
-    _DTYPE: Dict[str, Type] = {
+    _DTYPE: Dict[TunableValueTypeName, TunableValueType] = {
         "int": int,
         "float": float,
         "categorical": str,
@@ -79,7 +95,7 @@ def __init__(self, name: str, config: TunableDict):
         if not isinstance(name, str) or '!' in name:  # TODO: Use a regex here and in JSON schema
             raise ValueError(f"Invalid name of the tunable: {name}")
         self._name = name
-        self._type = config["type"]  # required
+        self._type: TunableValueTypeName = config["type"]  # required
         if self._type not in self._DTYPE:
             raise ValueError(f"Invalid parameter type: {self._type}")
         self._description = config.get("description")
@@ -302,6 +318,7 @@ def value(self, value: TunableValue) -> TunableValue:
             if self.is_categorical and value is None:
                 coerced_value = None
             else:
+                assert value is not None
                 coerced_value = self.dtype(value)
         except Exception:
             _LOG.error("Impossible conversion: %s %s <- %s %s",
@@ -482,7 +499,7 @@ def range_weight(self) -> Optional[float]:
         return self._range_weight
 
     @property
-    def type(self) -> str:
+    def type(self) -> TunableValueTypeName:
         """
         Get the data type of the tunable.
 
@@ -494,7 +511,7 @@ def type(self) -> str:
         return self._type
 
     @property
-    def dtype(self) -> Type:
+    def dtype(self) -> TunableValueType:
         """
         Get the actual Python data type of the tunable.