autogluon.common.space¶
Search Space¶
You can use AutoGluon search space to perform HPO. For a high-level overview, see this example:
from autogluon.common import space
categorical_space = space.Categorical('a', 'b', 'c', 'd') # Nested search space for hyperparameters which are categorical.
real_space = space.Real(0.01, 0.1) # Search space for numeric hyperparameter that takes continuous values
int_space = space.Int(0, 100) # Search space for numeric hyperparameter that takes integer values
bool_space = space.Bool() # Search space for hyperparameter that is either True or False.
For how to use the search space to perform HPO, check out Tabular Indepth Tutorial
Categorical¶
- class autogluon.common.space.Categorical(*data)[source]¶
- Nested search space for hyperparameters which are categorical. Such a hyperparameter takes one value out of the discrete set of provided options.
The first value in the list of options will be the default value that gets tried first during HPO.
- Parameters:
data (Space or python built-in objects) – the choice candidates
Examples
>>> a = Categorical('a', 'b', 'c', 'd') # 'a' will be default value tried first during HPO
Real¶
- class autogluon.common.space.Real(lower, upper, default=None, log=False)[source]¶
Search space for numeric hyperparameter that takes continuous values.
- Parameters:
lower (float) – The lower bound of the search space (minimum possible value of hyperparameter)
upper (float) – The upper bound of the search space (maximum possible value of hyperparameter)
default (float (optional)) – Default value tried first during hyperparameter optimization
log ((True/False)) – Whether to search the values on a logarithmic rather than linear scale. This is useful for numeric hyperparameters (such as learning rates) whose search space spans many orders of magnitude.
Examples
>>> learning_rate = Real(0.01, 0.1, log=True)
Int¶
- class autogluon.common.space.Int(lower, upper, default=None)[source]¶
Search space for numeric hyperparameter that takes integer values.
- Parameters:
lower (int) – The lower bound of the search space (minimum possible value of hyperparameter)
upper (int) – The upper bound of the search space (maximum possible value of hyperparameter)
default (int (optional)) – Default value tried first during hyperparameter optimization
Examples
>>> range = Int(0, 100)