import sys
from itertools import product
from random import Random as BaseRandom
from typing import Mapping, Any, Sequence, Optional, Union
class RandomSeed(object):
"""
The base class controlling random sampling for every optimization strategy.
"""
_random = BaseRandom(1337)
_seed = 1337
@staticmethod
def set_random_seed(seed=1337):
# type: (int) -> ()
"""
Set global seed for all hyper-parameter strategy random number sampling.
:param int seed: The random seed.
"""
RandomSeed._seed = seed
RandomSeed._random = BaseRandom(seed)
@staticmethod
def get_random_seed():
# type: () -> int
"""
Get the global seed for all hyper-parameter strategy random number sampling.
:return: The random seed.
"""
return RandomSeed._seed
class Parameter(RandomSeed):
"""
The base hyper-parameter optimization object.
"""
_class_type_serialize_name = 'type'
def __init__(self, name):
# type: (Optional[str]) -> ()
"""
Create a new Parameter for hyper-parameter optimization
:param str name: The new Parameter name. This is the parameter name that will be passed to a Task.
"""
self.name = name
def get_value(self):
# type: () -> Mapping[str, Any]
"""
Return a dict with the Parameter name and a sampled value for the Parameter.
:return:
For example:
.. code-block:: py
{'answer': 0.42}
"""
pass
def to_list(self):
# type: () -> Sequence[Mapping[str, Any]]
"""
Return a list of all the valid values of the Parameter.
:return: List of dicts {name: value}
"""
pass
def to_dict(self):
# type: () -> Mapping[str, Union[str, Parameter]]
"""
Return a dict representation of the Parameter object. Used for serialization of the Parameter object.
:return: dict representation of the object (serialization).
"""
serialize = {self._class_type_serialize_name: str(self.__class__).split('.')[-1][:-2]}
# noinspection PyCallingNonCallable
serialize.update(dict(((k, v.to_dict() if hasattr(v, 'to_dict') else v) for k, v in self.__dict__.items())))
return serialize
@classmethod
def from_dict(cls, a_dict):
# type: (Mapping[str, str]) -> Parameter
"""
Construct Parameter object from a dict representation (deserialize from dict).
:return: The Parameter object.
"""
a_dict = a_dict.copy()
a_cls = a_dict.pop(cls._class_type_serialize_name, None)
if not a_cls:
return None
try:
a_cls = getattr(sys.modules[__name__], a_cls)
except AttributeError:
return None
instance = a_cls.__new__(a_cls)
instance.__dict__ = dict(
(k, cls.from_dict(v) if isinstance(v, dict) and cls._class_type_serialize_name in v else v)
for k, v in a_dict.items())
return instance
class UniformParameterRange(Parameter):
"""
Uniform randomly sampled hyper-parameter object.
"""
def __init__(
self,
name, # type: str
min_value, # type: float
max_value, # type: float
step_size=None, # type: Optional[float]
include_max_value=True # type: bool
):
# type: (...) -> ()
"""
Create a parameter to be sampled by the SearchStrategy
:param str name: The parameter name. Match the Task hyper-parameter name.
:param float min_value: The minimum sample to use for uniform random sampling.
:param float max_value: The maximum sample to use for uniform random sampling.
:param float step_size: If not ``None``, set step size (quantization) for value sampling.
:param bool include_max_value: Range includes the ``max_value``
The values are:
- ``True`` - The range includes the ``max_value`` (Default)
- ``False`` - Does not include.
"""
super(UniformParameterRange, self).__init__(name=name)
self.min_value = float(min_value)
self.max_value = float(max_value)
self.step_size = float(step_size) if step_size is not None else None
self.include_max = include_max_value
def get_value(self):
# type: () -> Mapping[str, Any]
"""
Return uniformly sampled value based on object sampling definitions.
:return: {self.name: random value [self.min_value, self.max_value)}
"""
if not self.step_size:
return {self.name: self._random.uniform(self.min_value, self.max_value)}
steps = (self.max_value - self.min_value) / self.step_size
return {self.name: self.min_value + (self._random.randrange(start=0, stop=round(steps)) * self.step_size)}
def to_list(self):
# type: () -> Sequence[Mapping[str, float]]
"""
Return a list of all the valid values of the Parameter. If ``self.step_size`` is not defined, return 100 points
between min/max values.
:return: list of dicts {name: float}
"""
step_size = self.step_size or (self.max_value - self.min_value) / 100.
steps = (self.max_value - self.min_value) / self.step_size
values = [v*step_size for v in range(0, int(steps))]
if self.include_max and (not values or values[-1] < self.max_value):
values.append(self.max_value)
return [{self.name: v} for v in values]
class UniformIntegerParameterRange(Parameter):
"""
Uniform randomly sampled integer Hyper-Parameter object.
"""
def __init__(self, name, min_value, max_value, step_size=1, include_max_value=True):
# type: (str, int, int, int, bool) -> ()
"""
Create a parameter to be sampled by the SearchStrategy.
:param str name: The parameter name. Match the task hyper-parameter name.
:param int min_value: The minimum sample to use for uniform random sampling.
:param int max_value: The maximum sample to use for uniform random sampling.
:param int step_size: The default step size is ``1``.
:param bool include_max_value: Range includes the ``max_value``
The values are:
- ``True`` - Includes the ``max_value`` (Default)
- ``False`` - Does not include.
"""
super(UniformIntegerParameterRange, self).__init__(name=name)
self.min_value = int(min_value)
self.max_value = int(max_value)
self.step_size = int(step_size) if step_size is not None else None
self.include_max = include_max_value
def get_value(self):
# type: () -> Mapping[str, Any]
"""
Return uniformly sampled value based on object sampling definitions.
:return: {self.name: random value [self.min_value, self.max_value)}
"""
return {self.name: self._random.randrange(
start=self.min_value, step=self.step_size,
stop=self.max_value + (0 if not self.include_max else self.step_size))}
def to_list(self):
# type: () -> Sequence[Mapping[str, int]]
"""
Return a list of all the valid values of the Parameter. If ``self.step_size`` is not defined, return 100 points
between minmax values.
:return: list of dicts {name: int}
"""
values = list(range(self.min_value, self.max_value, self.step_size))
if self.include_max and (not values or values[-1] < self.max_value):
values.append(self.max_value)
return [{self.name: v} for v in values]
class DiscreteParameterRange(Parameter):
"""
Discrete randomly sampled hyper-parameter object.
"""
def __init__(self, name, values=()):
# type: (str, Sequence[Any]) -> ()
"""
Uniformly sample values form a list of discrete options.
:param str name: The parameter name. Match the task hyper-parameter name.
:param list values: The list/tuple of valid parameter values to sample from.
"""
super(DiscreteParameterRange, self).__init__(name=name)
self.values = values
def get_value(self):
# type: () -> Mapping[str, Any]
"""
Return uniformly sampled value from the valid list of values.
:return: {self.name: random entry from self.value}
"""
return {self.name: self._random.choice(self.values)}
def to_list(self):
# type: () -> Sequence[Mapping[str, Any]]
"""
Return a list of all the valid values of the Parameter.
:return: list of dicts {name: value}
"""
return [{self.name: v} for v in self.values]
class ParameterSet(Parameter):
"""
Discrete randomly sampled Hyper-Parameter object.
"""
def __init__(self, parameter_combinations=()):
# type: (Sequence[Mapping[str, Union[float, int, str, Parameter]]]) -> ()
"""
Uniformly sample values form a list of discrete options (combinations) of parameters.
:param list parameter_combinations: The list/tuple of valid parameter combinations.
For example, two combinations with three specific parameters per combination:
.. code-block:: javascript
[ {'opt1': 10, 'arg2': 20, 'arg2': 30},
{'opt2': 11, 'arg2': 22, 'arg2': 33}, ]
Two complex combination each one sampled from a different range:
.. code-block:: javascript
[ {'opt1': UniformParameterRange('arg1',0,1) , 'arg2': 20},
{'opt2': UniformParameterRange('arg1',11,12), 'arg2': 22},]
"""
super(ParameterSet, self).__init__(name=None)
self.values = parameter_combinations
def get_value(self):
# type: () -> Mapping[str, Any]
"""
Return uniformly sampled value from the valid list of values.
:return: {self.name: random entry from self.value}
"""
return self._get_value(self._random.choice(self.values))
def to_list(self):
# type: () -> Sequence[Mapping[str, Any]]
"""
Return a list of all the valid values of the Parameter.
:return: list of dicts {name: value}
"""
combinations = []
for combination in self.values:
single_option = {}
for k, v in combination.items():
if isinstance(v, Parameter):
single_option[k] = v.to_list()
else:
single_option[k] = [{k: v}, ]
for state in product(*single_option.values()):
combinations.append(dict(kv for d in state for kv in d.items()))
return combinations
@staticmethod
def _get_value(combination):
# type: (dict) -> dict
value_dict = {}
for k, v in combination.items():
if isinstance(v, Parameter):
value_dict.update(v.get_value())
else:
value_dict[k] = v
return value_dict