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Alternative Experiment Types
A random search experiment enables you to randomly search over your parameter space with the SigOpt Platform. To create a random search Experiment, simply set the Experiment
type to "random".By default, SigOpt random suggests uniformly from the parameter space. Random search can be combined with Prior Beliefs to modify the search distribution.
Python
Java
experiment = conn.experiments().create(
name="Random search",
type="random",
parameters=[
dict(
name="omp_thread",
type="int",
bounds=dict(
min=1,
max=16,
)
),
dict(
name="omp_places",
type="categorical",
categorical_values=[
"threads",
"core",
"sockets",
]
)
],
metrics=[
dict(
name="throughput",
objective="maximize"
)
],
observation_budget=30
)
import com.sigopt.SigOpt;
import com.sigopt.exception.SigoptException;
import com.sigopt.model.*;
import java.util.Arrays;
public class YourSigoptExperiment {
public static Experiment createExperiment() throws SigoptException {
Experiment experiment = Experiment.create()
.data(
new Experiment.Builder()
.name("Random search")
.parameters(java.util.Arrays.asList(
new Parameter.Builder()
.name("omp_places")
.categoricalValues(java.util.Arrays.asList(
new CategoricalValue(
"threads"
1
),
new CategoricalValue(
"core"
2
),
new CategoricalValue(
"sockets"
3
)
))
.type("categorical")
.build(),
new Parameter.Builder()
.name("omp_thread")
.bounds(new Bounds.Builder()
.min(1)
.max(16)
.build())
.type("int")
.build()
))
.metrics(java.util.Arrays.asList(
new Metric.Builder()
.name("throughput")
.objective("maximize")
.strategy("optimize")
.build()
))
.observationBudget(30)
.type("random")
.build()
)
.call();
return experiment;
}
Python
YAML
sigopt.create_experiment(
name="Random search",
type="random",
parameters=[
dict(
name="learning_rate",
type="double",
bounds=dict(
min=1e-5,
max=1e-2
),
transformation="log"
),
dict(
name="activation_function",
type="categorical",
categorical_values=[
"relu",
"tanh"
]
)
],
metrics=[
dict(
name="holdout_accuracy",
objective="maximize"
)
],
budget=30
)
name: Random search
type: random
parameters:
- name: learning_rate
type: double
bounds:
min: 1e-5
max: 1e-2
transformation: log
- name: activation_function
type: categorical
categorical_values:
- relu
- tanh
metrics:
- name: holdout_accuracy
objective: maximize
budget: 30
A grid search experiment enables users to execute an exhaustive search on a user-defined grid with the SigOpt Platform. This means that every parameter needs to have discrete values; meaning either
categorical type or parameters with assigned grid values. To create a grid search Experiment, simply set the Experiment type to "grid".Grid Search Experiments must have budget that match up with the total number of possible combination of parameter values.
Python
Java
experiment = conn.experiments().create(
name="Grid search",
type="grid",
parameters=[
dict(
name="omp_thread",
type="int",
grid=[1, 2, 3, 4, 5, 6, 7, 8]
),
dict(
name="omp_places",
type="categorical",
categorical_values=[
"threads",
"core",
"sockets",
]
)
],
metrics=[
dict(
name="throughput",
objective="maximize"
)
],
observation_budget=24
)
import com.sigopt.SigOpt;
import com.sigopt.exception.SigoptException;
import com.sigopt.model.*;
import java.util.Arrays;
public class YourSigoptExperiment {
public static Experiment createExperiment() throws SigoptException {
Experiment experiment = Experiment.create()
.data(
new Experiment.Builder()
.name("Grid search")
.parameters(java.util.Arrays.asList(
new Parameter.Builder()
.name("omp_places")
.categoricalValues(java.util.Arrays.asList(
new CategoricalValue(
"threads"
1
),
new CategoricalValue(
"core"
2
),
new CategoricalValue(
"sockets"
3
)
))
.type("categorical")
.build(),
new Parameter.Builder()
.name("omp_thread")
.grid(java.util.Arrays.asList(
1,
2,
3,
4,
5,
6,
7,
8
))
.type("int")
.build()
))
.metrics(java.util.Arrays.asList(
new Metric.Builder()
.name("throughput")
.objective("maximize")
.strategy("optimize")
.build()
))
.observationBudget(24)
.type("grid")
.build()
)
.call();
return experiment;
}
Python
YAML
sigopt.create_experiment(
name="Grid search",
type="grid",
parameters=[
dict(
name="learning_rate",
type="double",
grid=[1e-5, 1e-4, 1e-3, 1e-2],
transformation="log"
),
dict(
name="kernel_size",
type="int",
grid=[2, 4, 6, 12]
),
dict(
name="activation_function",
type="categorical",
categorical_values=[
"relu",
"tanh"
]
)
],
metrics=[
dict(
name="holdout_accuracy",
objective="maximize"
)
],
budget=32
)
name: Grid search
type: grid
parameters:
- name: learning_rate
type: double
grid:
- 1.0e-05
- 0.0001
- 0.001
- 0.01
transformation: log
- name: kernel_size
type: int
grid:
- 2
- 4
- 6
- 12
- name: activation_function
type: categorical
categorical_values:
- relu
- tanh
metrics:
- name: holdout_accuracy
objective: maximize
budget: 32
Last modified 10mo ago