mlos_bench.tunables.tunable module¶

Tunable parameter definition.

class mlos_bench.tunables.tunable.DistributionDict¶

Bases: TypedDict

A typed dict for tunable parameters’ distributions.

Methods

`clear`()
`copy`()
`fromkeys`(iterable[, value])	Create a new dictionary with keys from iterable and values set to value.
`get`(key[, default])	Return the value for key if key is in the dictionary, else default.
`items`()
`keys`()
`pop`(key[, default])	If the key is not found, return the default if given; otherwise, raise a KeyError.
`popitem`(/)	Remove and return a (key, value) pair as a 2-tuple.
`setdefault`(key[, default])	Insert key with a value of default if key is not in the dictionary.
`update`([E, ]**F)	If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
`values`()

params: Dict[str, float] | None¶

type: Literal['uniform', 'normal', 'beta']¶

class mlos_bench.tunables.tunable.Tunable(name: str, config: TunableDict)¶

Bases: object

A tunable parameter definition and its current value.

Attributes:

cardinality: Gets the cardinality of elements in this tunable, or else None.
categories: Get the list of all possible values of a categorical tunable.
category: Get the current value of the tunable as a number.
default: Get the default value of the tunable.
distribution: Get the name of the distribution (uniform, normal, or beta) if specified.
distribution_params: Get the parameters of the distribution, if specified.
dtype: Get the actual Python data type of the tunable.
is_categorical: Check if the tunable is categorical.
is_log: Check if numeric tunable is log scale.
is_numerical: Check if the tunable is an integer or float.
is_special: Check if the current value of the tunable is special.
meta: Get the tunable’s metadata.
name: Get the name / string ID of the tunable.
numerical_value: Get the current value of the tunable as a number.
quantization_bins: Get the number of quantization bins, if specified.
quantized_values: Get a sequence of quanitized values for this tunable.
range: Get the range of the tunable if it is numerical, None otherwise.
range_weight: Get weight of the range of the numeric tunable.
span: Gets the span of the range.
special: Get the special values of the tunable.
type: Get the data type of the tunable.
value: Get the current value of the tunable.
values: Gets the categories or quantized values for this tunable.
weights: Get the weights of the categories or special values of the tunable.

Methods

`copy`()	Deep copy of the Tunable object.
`in_range`(value)	Check if the value is within the range of the tunable.
`is_default`()	Checks whether the currently assigned value of the tunable is at its default.
`is_valid`(value)	Check if the value can be assigned to the tunable.
`update`(value)	Assign the value to the tunable.

property cardinality: int | None¶

Gets the cardinality of elements in this tunable, or else None. (i.e., when the tunable is continuous float and not quantized).

If the tunable has quantization set, this

Returns:

cardinalityint: Either the number of points in the tunable or else None.

property categories: List[str | None]¶

Get the list of all possible values of a categorical tunable. Return None if the tunable is not categorical.

Returns:

valuesList[str]: List of all possible values of a categorical tunable.

property category: str | None¶: Get the current value of the tunable as a number.

copy() → Tunable¶

Deep copy of the Tunable object.

Returns:

tunableTunable: A new Tunable object that is a deep copy of the original one.

property default: int | float | str | None¶: Get the default value of the tunable.

property distribution: Literal['uniform', 'normal', 'beta'] | None¶

Get the name of the distribution (uniform, normal, or beta) if specified.

Returns:

distributionstr: Name of the distribution (uniform, normal, or beta) or None.

property distribution_params: Dict[str, float]¶

Get the parameters of the distribution, if specified.

Returns:

distribution_paramsDict[str, float]: Parameters of the distribution or None.

property dtype: Type[int] | Type[float] | Type[str]¶

Get the actual Python data type of the tunable.

This is useful for bulk conversions of the input data.

Returns:

dtypetype: Data type of the tunable - one of {int, float, str}.

in_range(value: int | float | str | None) → bool¶

Check if the value is within the range of the tunable.

Do NOT check for special values. Return False if the tunable or value is categorical or None.

property is_categorical: bool¶

Check if the tunable is categorical.

Returns:

is_categoricalbool: True if the tunable is categorical, False otherwise.

is_default() → int | float | str | None¶: Checks whether the currently assigned value of the tunable is at its default.

property is_log: bool | None¶

Check if numeric tunable is log scale.

Returns:

logbool: True if numeric tunable is log scale, False if linear.

property is_numerical: bool¶

Check if the tunable is an integer or float.

Returns:

is_intbool: True if the tunable is an integer or float, False otherwise.

property is_special: bool¶

Check if the current value of the tunable is special.

Returns:

is_specialbool: True if the current value of the tunable is special, False otherwise.

is_valid(value: int | float | str | None) → bool¶

Check if the value can be assigned to the tunable.

Parameters:

valueUnion[int, float, str]: Value to validate.

Returns:

is_validbool: True if the value is valid, False otherwise.

property meta: Dict[str, Any]¶

Get the tunable’s metadata.

This is a free-form dictionary that can be used to store any additional information about the tunable (e.g., the unit information).

property name: str¶: Get the name / string ID of the tunable.

property numerical_value: int | float¶: Get the current value of the tunable as a number.

property quantization_bins: int | None¶

Get the number of quantization bins, if specified.

Returns:

quantization_binsint | None: Number of quantization bins, or None.

property quantized_values: Iterable[int] | Iterable[float] | None¶

Get a sequence of quanitized values for this tunable.

Returns:

Optional[Union[Iterable[int], Iterable[float]]]: If the Tunable is quantizable, returns a sequence of those elements, else None (e.g., for unquantized float type tunables).

property range: Tuple[int, int] | Tuple[float, float]¶

Get the range of the tunable if it is numerical, None otherwise.

Returns:

range(number, number): A 2-tuple of numbers that represents the range of the tunable. Numbers can be int or float, depending on the type of the tunable.

property range_weight: float | None¶

Get weight of the range of the numeric tunable. Return None if there are no weights or a tunable is categorical.

Returns:

weightfloat: Weight of the range or None.

property span: int | float¶

Gets the span of the range.

Note: this does not take quantization into account.

Returns:

Union[int, float]: (max - min) for numerical tunables.

property special: List[int] | List[float]¶

Get the special values of the tunable. Return an empty list if there are none.

Returns:

special[int] | [float]: A list of special values of the tunable. Can be empty.

property type: Literal['int', 'float', 'categorical']¶

Get the data type of the tunable.

Returns:

typestr: Data type of the tunable - one of {‘int’, ‘float’, ‘categorical’}.

update(value: int | float | str | None) → bool¶

Assign the value to the tunable. Return True if it is a new value, False otherwise.

Parameters:

valueUnion[int, float, str]: Value to assign.

Returns:

is_updatedbool: True if the new value is different from the previous one, False otherwise.

property value: int | float | str | None¶: Get the current value of the tunable.

property values: Iterable[str | None] | Iterable[int] | Iterable[float] | None¶

Gets the categories or quantized values for this tunable.

Returns:

Optional[Union[Iterable[Optional[str]], Iterable[int], Iterable[float]]]: Categories or quantized values.

property weights: List[float] | None¶

Get the weights of the categories or special values of the tunable. Return None if there are none.

Returns:

weights[float]: A list of weights or None.

class mlos_bench.tunables.tunable.TunableDict¶

Bases: TypedDict

A typed dict for tunable parameters.

Mostly used for mypy type checking.

These are the types expected to be received from the json config.

Methods

`clear`()
`copy`()
`fromkeys`(iterable[, value])	Create a new dictionary with keys from iterable and values set to value.
`get`(key[, default])	Return the value for key if key is in the dictionary, else default.
`items`()
`keys`()
`pop`(key[, default])	If the key is not found, return the default if given; otherwise, raise a KeyError.
`popitem`(/)	Remove and return a (key, value) pair as a 2-tuple.
`setdefault`(key[, default])	Insert key with a value of default if key is not in the dictionary.
`update`([E, ]**F)	If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
`values`()

default: int | float | str | None¶

description: str | None¶

distribution: DistributionDict | None¶

log: bool | None¶

meta: Dict[str, Any]¶

quantization_bins: int | None¶

range: Sequence[int] | Sequence[float] | None¶

range_weight: float | None¶

special: List[int] | List[float] | None¶

special_weights: List[float] | None¶

type: Literal['int', 'float', 'categorical']¶

values_weights: List[float] | None¶

mlos_bench.tunables.tunable.TunableValue¶

Tunable value type.

alias of int | float | str | None

mlos_bench.tunables.tunable.TunableValueType¶

Tunable value type tuple.

For checking with isinstance()

alias of Type[int] | Type[float] | Type[str]

mlos_bench.tunables.tunable.TunableValueTypeName¶

Tunable values dictionary type.

alias of Literal[‘int’, ‘float’, ‘categorical’]

mlos_bench.tunables.tunable.TunableValueTypeTuple = (<class 'int'>, <class 'float'>, <class 'str'>, <class 'NoneType'>)¶: The string name of a tunable value type.

mlos_bench.tunables.tunable.TunableValuesDict¶

Tunable value distribution type.

alias of Dict[str, int | float | str | None]