public void OptionBuilder_should_work_with_random_sweeper()
{
var context = new MLContext();
var builder = new TestOptionBuilderWithSweepableAttributeOnly();
var maximum = 10;
var sweeperOption = new UniformRandomSweeper.Option();
var randomSweeper = new UniformRandomSweeper(sweeperOption);
foreach (var sweeperOutput in randomSweeper.ProposeSweeps(builder, maximum))
{
maximum -= 1;
var option = builder.BuildFromParameters(sweeperOutput);
option.LongOption
.Should()
.BeLessOrEqualTo(100)
.And
.BeGreaterOrEqualTo(0);
option.FloatOption
.Should()
.BeLessOrEqualTo(100f)
.And
.BeGreaterOrEqualTo(0f);
option.StringOption
.Should()
.BeOneOf(new string[] { "str1", "str2", "str3", "str4" });
maximum.Should().BeGreaterThan(-2);
}
}
public UniformRandomSweeper CreateUniformRandomSweeper(int retry = 10)
{
var option = new UniformRandomSweeper.Option()
{
Retry = retry,
};
return(new UniformRandomSweeper(option));
}
public GaussProcessSweeper(Option option)
{
this._option = option;
this._generated = new HashSet <IDictionary <string, string> >();
this._runHistory = new List <IRunResult>();
var randomSweeperOption = new UniformRandomSweeper.Option()
{
Retry = option.Retry,
};
this._randomSweeper = new UniformRandomSweeper(randomSweeperOption);
var gpOption = new GaussProcessRegressor.Options()
{
l = option.L,
sigma = option.Sigma,
noise = option.Noise,
};
this._regressor = new GaussProcessRegressor(gpOption);
}