UniformParameterRange
class automation.UniformParameterRange()
Uniform randomly sampled hyperparameter object.
Create a parameter to be sampled by the SearchStrategy
Parameters
name (str ) – The parameter name. Match the Task hyperparameter name.
min_value (float ) – The minimum sample to use for uniform random sampling.
max_value (float ) – The maximum sample to use for uniform random sampling.
step_size (float ) – If not
None
, set step size (quantization) for value sampling.include_max_value (bool ) – Range includes the
max_value
.The values are:
True
- The range includes themax_value
(Default)False
- Does not include.
UniformParameterRange.from_dict
classmethod from_dict(a_dict)
Construct Parameter object from a dict representation (deserialize from dict).
Return type
Parameter
Returns
The Parameter object.
Parameters
a_dict (Mapping [ str , str ] ) –
get_random_seed
static get_random_seed()
Get the global seed for all hyperparameter strategy random number sampling.
Return type
int
Returns
The random seed.
get_value
get_value()
Return uniformly sampled value based on object sampling definitions.
Return type
Mapping
[str
,Any
]Returns
{self.name: random value [self.min_value, self.max_value)}
set_random_seed
static set_random_seed(seed=1337)
Set global seed for all hyperparameter strategy random number sampling.
Parameters
seed (int ) – The random seed.
Return type
()
to_dict
to_dict()
Return a dict representation of the Parameter object. Used for serialization of the Parameter object.
Return type
Mapping
[str
,Union
[str
,Parameter
]]Returns
dict representation of the object (serialization).
to_list
to_list()
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 type
Sequence
[Mapping
[str
,float
]]Returns
list of dicts {name: float}