autogluon.space¶

Search space of possible hyperparameter values to consider.

Example

Define a dummy training function with searchable spaces for hyperparameters lr and wd:

>>> import numpy as np
>>> import autogluon as ag
>>> @ag.args(
>>>     lr=ag.space.Real(1e-3, 1e-2, log=True),
...     wd=ag.space.Real(1e-3, 1e-2),
...     epochs=10)
>>> def train_fn(args, reporter):
...     print('lr: {}, wd: {}'.format(args.lr, args.wd))
...     for e in range(args.epochs):
...         dummy_accuracy = 1 - np.power(1.8, -np.random.uniform(e, 2*e))
...         reporter(epoch=e+1, accuracy=dummy_accuracy, lr=args.lr, wd=args.wd)

Create a scheduler to manage training jobs and begin hyperparameter tuning with the provided search space:

>>> scheduler = ag.scheduler.HyperbandScheduler(train_fn,
>>>                                             resource={'num_cpus': 2, 'num_gpus': 0},
>>>                                             num_trials=10,
>>>                                             reward_attr='accuracy',
>>>                                             time_attr='epoch',
>>>                                             grace_period=1)
>>> scheduler.run()
>>> scheduler.join_jobs()

Visualize the results:

>>> scheduler.get_training_curves(plot=True)

https://raw.githubusercontent.com/zhanghang1989/AutoGluonWebdata/master/doc/api/autogluon.1.png

Search Space¶

`Real`	Search space for numeric hyperparameter that takes continuous values.
`Int`	Search space for numeric hyperparameter that takes integer values.
`Bool`	Search space for hyperparameter that is either True or False.
`Categorical`	Nested search space for hyperparameters which are categorical.
`List`	Nested search space corresponding to an ordered list of hyperparameters.
`Dict`	Nested search space for dictionary containing multiple hyperparameters.
`AutoGluonObject`	Searchable objects, created by decorating a custom Python class or function using the `autogluon.obj()` or `autogluon.func()` decorators.

Real¶

class autogluon.space.Real(lower, upper, default=None, log=False)¶

Search space for numeric hyperparameter that takes continuous values.

Parameters

lowerfloat: the lower bound of the search space
upperfloat: the upper bound of the search space
defaultfloat (optional): default value
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 = ag.Real(0.01, 0.1, log=True)

Attributes

default: Return default value of hyperparameter corresponding to this search space.
hp: Return hyperparameter corresponding to this search space.
rand: Return randomly sampled (but valid) value from this search space.

Methods

get_hp(name)

Fetch particular hyperparameter based on its name.

get_hp(name)¶: Fetch particular hyperparameter based on its name.

Int¶

class autogluon.space.Int(lower, upper, default=None)¶

Search space for numeric hyperparameter that takes integer values.

Parameters

lowerint: The lower bound of the search space
upperint: The upper bound of the search space
defaultint (optional): Default value

Examples

>>> range = ag.space.Int(0, 100)

Attributes

default: Return default value of hyperparameter corresponding to this search space.
hp: Return hyperparameter corresponding to this search space.
rand: Return randomly sampled (but valid) value from this search space.

Methods

get_hp(name)

Fetch particular hyperparameter based on its name.

get_hp(name)¶: Fetch particular hyperparameter based on its name.

Bool¶

class autogluon.space.Bool¶

Search space for hyperparameter that is either True or False.: ag.Bool() serves as shorthand for: ag.space.Categorical(True, False)

Examples

pretrained = ag.space.Bool()

Attributes

default: Return default value of hyperparameter corresponding to this search space.
hp: Return hyperparameter corresponding to this search space.
rand: Return randomly sampled (but valid) value from this search space.

Methods

get_hp(name)

Fetch particular hyperparameter based on its name.

Categorical¶

class autogluon.space.Categorical(*data)¶

Nested search space for hyperparameters which are categorical. Such a hyperparameter takes one value out of the discrete set of provided options.

Parameters

dataSpace or python built-in objects: the choice candidates

Examples

a = ag.space.Categorical(‘a’, ‘b’, ‘c’, ‘d’) b = ag.space.Categorical(‘resnet50’, autogluon_obj())

Attributes

cs: ConfigSpace representation of this search space.
default: Return default value for hyperparameter corresponding to this search space.
kwspaces: OrderedDict representation of this search space.
rand: Randomly sample configuration from this nested search space.

Methods

sample(**config)

Sample a configuration from this search space.

property cs¶: ConfigSpace representation of this search space.

property kwspaces¶: OrderedDict representation of this search space.

sample(**config)¶: Sample a configuration from this search space.

List¶

class autogluon.space.List(*args)¶

Nested search space corresponding to an ordered list of hyperparameters.

Parameters

argslist: a list of search spaces.

Examples

>>> sequence = ag.List(
>>>     ag.space.Categorical('conv3x3', 'conv5x5', 'conv7x7'),
>>>     ag.space.Categorical('BatchNorm', 'InstanceNorm'),
>>>     ag.space.Categorical('relu', 'sigmoid'),
>>> )

Attributes

cs: ConfigSpace representation of this search space.
default: Return default value for hyperparameter corresponding to this search space.
kwspaces: OrderedDict representation of this search space.
rand: Randomly sample configuration from this nested search space.

Methods

sample(**config)

Sample a configuration from this search space.

property cs¶: ConfigSpace representation of this search space.

property kwspaces¶: OrderedDict representation of this search space.

sample(**config)¶: Sample a configuration from this search space.

Dict¶

class autogluon.space.Dict(**kwargs)¶

Nested search space for dictionary containing multiple hyperparameters.

Examples

>>> g = ag.space.Dict(
>>>         hyperparam1 = ag.space.Categorical('alpha', 'beta'),
>>>         hyperparam2 = ag.space.Int(0, 3)
>>>     )
>>> print(g)

Attributes

cs: ConfigSpace representation of this search space.
default: Return default value for hyperparameter corresponding to this search space.
kwspaces: OrderedDict representation of this search space.
rand: Randomly sample configuration from this nested search space.

Methods

sample(**config)

Sample a configuration from this search space.

property cs¶: ConfigSpace representation of this search space.

property kwspaces¶: OrderedDict representation of this search space.

sample(**config)¶: Sample a configuration from this search space.

AutoGluonObject¶

class autogluon.space.AutoGluonObject¶

Searchable objects, created by decorating a custom Python class or function using the autogluon.obj() or autogluon.func() decorators.

Attributes

cs: ConfigSpace representation of this search space.
default: Return default value for hyperparameter corresponding to this search space.
rand: Randomly sample configuration from this nested search space.

Methods

`__call__`(args, *kwargs)	Convenience method for interacting with AutoGluonObject.
`init`()	Instantiate an actual instance of this AutoGluonObject.
`sample`()	Sample a configuration from this search space.

property cs¶: ConfigSpace representation of this search space.

init()¶: Instantiate an actual instance of this AutoGluonObject. In order to interact with such an object, you must always first call: object.init().

sample()¶: Sample a configuration from this search space.