Rename quantization -> quantization_bins (#844)

Merge after (or instead of) #835 

diff from #835 :: https://github.com/motus/MLOS/pull/15/files

Closes #803

---------

Co-authored-by: Brian Kroth <bpkroth@users.noreply.github.com>
Co-authored-by: Brian Kroth <bpkroth@microsoft.com>

mlos_bench/mlos_bench/config/schemas/tunables/tunable-params-schema.json

@@ -123,7 +123,7 @@
             "maxItems": 2,
             "uniqueItems": true
         },
-        "quantization": {
+        "quantization_bins": {
             "description": "The number of buckets to quantize the range into.",
             "type": "integer",
             "exclusiveMinimum": 1
@@ -187,7 +187,7 @@
             },
             "required": ["type", "default", "values"],
             "not": {
-                "required": ["range", "special", "special_weights", "range_weight", "log", "quantization", "distribution"]
+                "required": ["range", "special", "special_weights", "range_weight", "log", "quantization_bins", "distribution"]
             },
             "$comment": "TODO: add check that default is in values",
             "unevaluatedProperties": false
@@ -217,8 +217,8 @@
                 "distribution": {
                     "$ref": "#/$defs/tunable_param_distribution"
                 },
-                "quantization": {
-                    "$ref": "#/$defs/quantization"
+                "quantization_bins": {
+                    "$ref": "#/$defs/quantization_bins"
                 },
                 "log": {
                     "$ref": "#/$defs/log_scale"
@@ -265,8 +265,8 @@
                 "distribution": {
                     "$ref": "#/$defs/tunable_param_distribution"
                 },
-                "quantization": {
-                    "$ref": "#/$defs/quantization"
+                "quantization_bins": {
+                    "$ref": "#/$defs/quantization_bins"
                 },
                 "log": {
                     "$ref": "#/$defs/log_scale"

mlos_bench/mlos_bench/optimizers/convert_configspace.py

@@ -168,10 +168,10 @@ def _tunable_to_configspace(
     else:
         raise TypeError(f"Invalid Parameter Type: {tunable.type}")
 
-    if tunable.quantization:
+    if tunable.quantization_bins:
         # Temporary workaround to dropped quantization support in ConfigSpace 1.0
         # See Also: https://github.com/automl/ConfigSpace/issues/390
-        _monkey_patch_quantization(range_hp, tunable.quantization)
+        _monkey_patch_quantization(range_hp, tunable.quantization_bins)
 
     if not tunable.special:
         return ConfigurationSpace({tunable.name: range_hp})

mlos_bench/mlos_bench/tests/config/schemas/tunable-params/test-cases/bad/invalid/tunable-params-float-bad-quantization-type.jsonc

@@ -6,7 +6,7 @@
                 "type": "float",
                 "default": 10,
                 "range": [0, 10],
-                "quantization": true  // <-- this is invalid
+                "quantization_bins": true  // <-- this is invalid
             }
         }
     }

mlos_bench/mlos_bench/tests/config/schemas/tunable-params/test-cases/bad/invalid/tunable-params-int-bad-float-quantization.jsonc

@@ -6,7 +6,7 @@
                 "type": "float",
                 "default": 10,
                 "range": [1, 500],
-                "quantization": 1   // <-- should be greater than 1
+                "quantization_bins": 1   // <-- should be greater than 1
             }
         }
     }

mlos_bench/mlos_bench/tests/config/schemas/tunable-params/test-cases/bad/invalid/tunable-params-int-bad-int-quantization.jsonc

@@ -6,7 +6,7 @@
                 "type": "int",
                 "default": 10,
                 "range": [1, 500],
-                "quantization": 1   // <-- should be greater than 1
+                "quantization_bins": 1   // <-- should be greater than 1
             }
         }
     }

mlos_bench/mlos_bench/tests/config/schemas/tunable-params/test-cases/bad/invalid/tunable-params-int-wrong-quantization-type.jsonc

@@ -6,7 +6,7 @@
                 "type": "int",
                 "default": 10,
                 "range": [1, 500],
-                "quantization": "yes"  // <-- this is invalid
+                "quantization_bins": "yes"  // <-- this is invalid
             }
         }
     }

mlos_bench/mlos_bench/tests/config/schemas/tunable-params/test-cases/good/full/full-tunable-params-test.jsonc

@@ -12,7 +12,7 @@
                 "special": [-1],
                 "special_weights": [0.1],
                 "range_weight": 0.9,
-                "quantization": 50,
+                "quantization_bins": 50,
                 "distribution": {
                     "type": "beta",
                     "params": {
@@ -31,7 +31,7 @@
                 "special": [-1],
                 "special_weights": [0.1],
                 "range_weight": 0.9,
-                "quantization": 50,
+                "quantization_bins": 50,
                 "distribution": {
                     "type": "normal",
                     "params": {
@@ -48,7 +48,7 @@
                 "meta": {"scale": 1000, "prefix": "/proc/var/random/", "base": 2.71828},
                 "range": [1.1, 111.1],
                 "special": [-1.1],
-                "quantization": 11,
+                "quantization_bins": 11,
                 "distribution": {
                     "type": "uniform"
                 },

mlos_bench/mlos_bench/tests/optimizers/grid_search_optimizer_test.py

@@ -41,7 +41,7 @@ def grid_search_tunables_config() -> dict:
                     "type": "float",
                     "range": [0, 1],
                     "default": 0.5,
-                    "quantization": 5,
+                    "quantization_bins": 5,
                 },
             },
         },

mlos_bench/mlos_bench/tests/tunable_groups_fixtures.py

@@ -62,7 +62,7 @@
                 "type": "int",
                 "default": 2000000,
                 "range": [0, 1000000000],
-                "quantization": 11,
+                "quantization_bins": 11,
                 "log": false
             }
         }

mlos_bench/mlos_bench/tests/tunables/test_tunables_size_props.py

@@ -71,13 +71,13 @@ def test_tunable_quantized_int_size_props() -> None:
             "type": "int",
             "range": [100, 1000],
             "default": 100,
-            "quantization": 10,
+            "quantization_bins": 10,
         },
     )
     expected = [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000]
     assert tunable.span == 900
     assert tunable.cardinality == len(expected)
-    assert tunable.quantization == len(expected)
+    assert tunable.quantization_bins == len(expected)
     assert list(tunable.quantized_values or []) == expected
     assert list(tunable.values or []) == expected
 
@@ -90,12 +90,12 @@ def test_tunable_quantized_float_size_props() -> None:
             "type": "float",
             "range": [0, 1],
             "default": 0,
-            "quantization": 11,
+            "quantization_bins": 11,
         },
     )
     expected = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
     assert tunable.span == 1
     assert tunable.cardinality == len(expected)
-    assert tunable.quantization == len(expected)
+    assert tunable.quantization_bins == len(expected)
     assert pytest.approx(list(tunable.quantized_values or []), 0.0001) == expected
     assert pytest.approx(list(tunable.values or []), 0.0001) == expected

mlos_bench/mlos_bench/tests/tunables/tunable_definition_test.py

@@ -234,14 +234,14 @@ def test_numerical_quantization(tunable_type: TunableValueTypeName) -> None:
     {{
         "type": "{tunable_type}",
         "range": [0, 100],
-        "quantization": 11,
+        "quantization_bins": 11,
         "default": 0
     }}
     """
     config = json.loads(json_config)
     tunable = Tunable(name="test", config=config)
     expected = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
-    assert tunable.quantization == len(expected)
+    assert tunable.quantization_bins == len(expected)
     assert pytest.approx(list(tunable.quantized_values or []), 1e-8) == expected
     assert not tunable.is_log
 
@@ -393,7 +393,7 @@ def test_numerical_quantization_wrong(tunable_type: TunableValueTypeName) -> Non
     {{
         "type": "{tunable_type}",
         "range": [0, 100],
-        "quantization": 0,
+        "quantization_bins": 0,
         "default": 0
     }}
     """

mlos_bench/mlos_bench/tunables/tunable.py

@@ -64,7 +64,7 @@ class TunableDict(TypedDict, total=False):
     default: TunableValue
     values: Optional[List[Optional[str]]]
     range: Optional[Union[Sequence[int], Sequence[float]]]
-    quantization: Optional[int]
+    quantization_bins: Optional[int]
     log: Optional[bool]
     distribution: Optional[DistributionDict]
     special: Optional[Union[List[int], List[float]]]
@@ -109,7 +109,7 @@ def __init__(self, name: str, config: TunableDict):
             self._values = [str(v) if v is not None else v for v in self._values]
         self._meta: Dict[str, Any] = config.get("meta", {})
         self._range: Optional[Union[Tuple[int, int], Tuple[float, float]]] = None
-        self._quantization: Optional[int] = config.get("quantization")
+        self._quantization_bins: Optional[int] = config.get("quantization_bins")
         self._log: Optional[bool] = config.get("log")
         self._distribution: Optional[DistributionName] = None
         self._distribution_params: Dict[str, float] = {}
@@ -162,7 +162,7 @@ def _sanity_check_categorical(self) -> None:
             raise ValueError(f"Categorical tunable cannot have range_weight: {self}")
         if self._log is not None:
             raise ValueError(f"Categorical tunable cannot have log parameter: {self}")
-        if self._quantization is not None:
+        if self._quantization_bins is not None:
             raise ValueError(f"Categorical tunable cannot have quantization parameter: {self}")
         if self._distribution is not None:
             raise ValueError(f"Categorical parameters do not support `distribution`: {self}")
@@ -182,7 +182,7 @@ def _sanity_check_numerical(self) -> None:
             raise ValueError(f"Values must be None for the numerical type tunable {self}")
         if not self._range or len(self._range) != 2 or self._range[0] >= self._range[1]:
             raise ValueError(f"Invalid range for tunable {self}: {self._range}")
-        if self._quantization is not None and self._quantization <= 1:
+        if self._quantization_bins is not None and self._quantization_bins <= 1:
             raise ValueError(f"Number of quantization bins is <= 1: {self}")
         if self._distribution is not None and self._distribution not in {
             "uniform",
@@ -580,18 +580,18 @@ def span(self) -> Union[int, float]:
         return num_range[1] - num_range[0]
 
     @property
-    def quantization(self) -> Optional[int]:
+    def quantization_bins(self) -> Optional[int]:
         """
         Get the number of quantization bins, if specified.
 
         Returns
         -------
-        quantization : int | None
+        quantization_bins : int | None
             Number of quantization bins, or None.
         """
         if self.is_categorical:
             return None
-        return self._quantization
+        return self._quantization_bins
 
     @property
     def quantized_values(self) -> Optional[Union[Iterable[int], Iterable[float]]]:
@@ -606,23 +606,23 @@ def quantized_values(self) -> Optional[Union[Iterable[int], Iterable[float]]]:
         """
         num_range = self.range
         if self.type == "float":
-            if not self.quantization:
+            if not self.quantization_bins:
                 return None
             # Be sure to return python types instead of numpy types.
             return (
                 float(x)
                 for x in np.linspace(
                     start=num_range[0],
                     stop=num_range[1],
-                    num=self.quantization,
+                    num=self.quantization_bins,
                     endpoint=True,
                 )
             )
         assert self.type == "int", f"Unhandled tunable type: {self}"
         return range(
             int(num_range[0]),
             int(num_range[1]) + 1,
-            int(self.span / (self.quantization - 1)) if self.quantization else 1,
+            int(self.span / (self.quantization_bins - 1)) if self.quantization_bins else 1,
         )
 
     @property
@@ -640,8 +640,8 @@ def cardinality(self) -> Optional[int]:
         """
         if self.is_categorical:
             return len(self.categories)
-        if self.quantization:
-            return self.quantization
+        if self.quantization_bins:
+            return self.quantization_bins
         if self.type == "int":
             return int(self.span) + 1
         return None