Search Space¶
Overview¶
In NNI, tuner will sample parameters/architecture according to the search space, which is defined as a json file.
To define a search space, users should define the name of the variable, the type of sampling strategy and its parameters.
An example of a search space definition is as follow:
{
"dropout_rate": {"_type": "uniform", "_value": [0.1, 0.5]},
"conv_size": {"_type": "choice", "_value": [2, 3, 5, 7]},
"hidden_size": {"_type": "choice", "_value": [124, 512, 1024]},
"batch_size": {"_type": "choice", "_value": [50, 250, 500]},
"learning_rate": {"_type": "uniform", "_value": [0.0001, 0.1]}
}
Take the first line as an example. dropout_rate
is defined as a variable whose priori distribution is a uniform distribution with a range from 0.1
to 0.5
.
Note that the available sampling strategies within a search space depend on the tuner you want to use. We list the supported types for each builtin tuner below. For a customized tuner, you don’t have to follow our convention and you will have the flexibility to define any type you want.
Types¶
All types of sampling strategies and their parameter are listed here:
{"_type": "choice", "_value": options}
The variable’s value is one of the options. Here
options
should be a list of numbers or a list of strings. Using arbitrary objects as members of this list (like sublists, a mixture of numbers and strings, or null values) should work in most cases, but may trigger undefined behaviors.options
can also be a nested subsearchspace, this subsearchspace takes effect only when the corresponding element is chosen. The variables in this subsearchspace can be seen as conditional variables. Here is an simple example of nested search space definition. If an element in the options list is a dict, it is a subsearchspace, and for our builtin tuners you have to add a_name
key in this dict, which helps you to identify which element is chosen. Accordingly, here is a sample which users can get from nni with nested search space definition. See the table below for the tuners which support nested search spaces.
{"_type": "randint", "_value": [lower, upper]}
Choosing a random integer between
lower
(inclusive) andupper
(exclusive).Note: Different tuners may interpret
randint
differently. Some (e.g., TPE, GridSearch) treat integers from lower to upper as unordered ones, while others respect the ordering (e.g., SMAC). If you want all the tuners to respect the ordering, please usequniform
withq=1
.
{"_type": "uniform", "_value": [low, high]}
The variable value is uniformly sampled between low and high.
When optimizing, this variable is constrained to a twosided interval.
{"_type": "quniform", "_value": [low, high, q]}
The variable value is determined using
clip(round(uniform(low, high) / q) * q, low, high)
, where the clip operation is used to constrain the generated value within the bounds. For example, for_value
specified as [0, 10, 2.5], possible values are [0, 2.5, 5.0, 7.5, 10.0]; For_value
specified as [2, 10, 5], possible values are [2, 5, 10].Suitable for a discrete value with respect to which the objective is still somewhat “smooth”, but which should be bounded both above and below. If you want to uniformly choose an integer from a range [low, high], you can write
_value
like this:[low, high, 1]
.
{"_type": "loguniform", "_value": [low, high]}
The variable value is drawn from a range [low, high] according to a loguniform distribution like exp(uniform(log(low), log(high))), so that the logarithm of the return value is uniformly distributed.
When optimizing, this variable is constrained to be positive.
{"_type": "qloguniform", "_value": [low, high, q]}
The variable value is determined using
clip(round(loguniform(low, high) / q) * q, low, high)
, where the clip operation is used to constrain the generated value within the bounds.Suitable for a discrete variable with respect to which the objective is “smooth” and gets smoother with the size of the value, but which should be bounded both above and below.
{"_type": "normal", "_value": [mu, sigma]}
The variable value is a real value that’s normallydistributed with mean mu and standard deviation sigma. When optimizing, this is an unconstrained variable.
{"_type": "qnormal", "_value": [mu, sigma, q]}
The variable value is determined using
round(normal(mu, sigma) / q) * q
Suitable for a discrete variable that probably takes a value around mu, but is fundamentally unbounded.
{"_type": "lognormal", "_value": [mu, sigma]}
The variable value is drawn according to
exp(normal(mu, sigma))
so that the logarithm of the return value is normally distributed. When optimizing, this variable is constrained to be positive.
{"_type": "qlognormal", "_value": [mu, sigma, q]}
The variable value is determined using
round(exp(normal(mu, sigma)) / q) * q
Suitable for a discrete variable with respect to which the objective is smooth and gets smoother with the size of the variable, which is bounded from one side.
Search Space Types Supported by Each Tuner¶
choice 
choice(nested) 
randint 
uniform 
quniform 
loguniform 
qloguniform 
normal 
qnormal 
lognormal 
qlognormal 


TPE Tuner 
✓ 
✓ 
✓ 
✓ 
✓ 
✓ 
✓ 
✓ 
✓ 
✓ 
✓ 
Random Search Tuner 
✓ 
✓ 
✓ 
✓ 
✓ 
✓ 
✓ 
✓ 
✓ 
✓ 
✓ 
Anneal Tuner 
✓ 
✓ 
✓ 
✓ 
✓ 
✓ 
✓ 
✓ 
✓ 
✓ 
✓ 
Evolution Tuner 
✓ 
✓ 
✓ 
✓ 
✓ 
✓ 
✓ 
✓ 
✓ 
✓ 
✓ 
SMAC Tuner 
✓ 
✓ 
✓ 
✓ 
✓ 

Batch Tuner 
✓ 

Grid Search Tuner 
✓ 
✓ 
✓ 

Hyperband Advisor 
✓ 
✓ 
✓ 
✓ 
✓ 
✓ 
✓ 
✓ 
✓ 
✓ 

Metis Tuner 
✓ 
✓ 
✓ 
✓ 

GP Tuner 
✓ 
✓ 
✓ 
✓ 
✓ 
✓ 
Known Limitations:
GP Tuner and Metis Tuner support only numerical values in search space (
choice
type values can be nonumerical with other tuners, e.g. string values). Both GP Tuner and Metis Tuner use Gaussian Process Regressor(GPR). GPR make predictions based on a kernel function and the ‘distance’ between different points, it’s hard to get the true distance between nonumerical values.Note that for nested search space:
Only Random Search/TPE/Anneal/Evolution/Grid Search tuner supports nested search space