private (Pipeline, ColumnInferenceResults) GetMockedOvaPipelineAndInference()
{
if (_mockedOvaPipeline == null)
{
MLContext context = new MLContext();
// same learners with different hyperparameters
var hyperparams1 = new Microsoft.ML.AutoML.ParameterSet(new List <Microsoft.ML.AutoML.IParameterValue>()
{
new LongParameterValue("NumLeaves", 2)
});
var trainer1 = new SuggestedTrainer(context, new FastForestOvaExtension(), new ColumnInformation(), hyperparams1);
var transforms1 = new List <SuggestedTransform>()
{
ColumnConcatenatingExtension.CreateSuggestedTransform(context, new[] { "In" }, "Out")
};
var inferredPipeline1 = new SuggestedPipeline(transforms1, new List <SuggestedTransform>(), trainer1, context, true);
this._mockedOvaPipeline = inferredPipeline1.ToPipeline();
var textLoaderArgs = new TextLoader.Options()
{
Columns = new[] {
new TextLoader.Column("Label", DataKind.Boolean, 0),
new TextLoader.Column("col1", DataKind.Single, 1),
new TextLoader.Column("col2", DataKind.Single, 0),
new TextLoader.Column("col3", DataKind.String, 0),
new TextLoader.Column("col4", DataKind.Int32, 0),
new TextLoader.Column("col5", DataKind.UInt32, 0),
},
AllowQuoting = true,
AllowSparse = true,
HasHeader = true,
Separators = new[] { ',' }
};
this._columnInference = new ColumnInferenceResults()
{
TextLoaderOptions = textLoaderArgs,
ColumnInformation = new ColumnInformation()
{
LabelColumnName = "Label"
}
};
}
return(_mockedOvaPipeline, _columnInference);
}
public static float[] ParameterSetAsFloatArray(IValueGenerator[] sweepParams, ParameterSet ps, bool expandCategoricals = true)
{
Runtime.Contracts.Assert(ps.Count == sweepParams.Length);
var result = new List <float>();
for (int i = 0; i < sweepParams.Length; i++)
{
// This allows us to query possible values of this parameter.
var sweepParam = sweepParams[i];
// This holds the actual value for this parameter, chosen in this parameter set.
var pset = ps[sweepParam.Name];
Runtime.Contracts.Assert(pset != null);
var parameterDiscrete = sweepParam as DiscreteValueGenerator;
if (parameterDiscrete != null)
{
int hotIndex = -1;
for (int j = 0; j < parameterDiscrete.Count; j++)
{
if (parameterDiscrete[j].Equals(pset))
{
hotIndex = j;
break;
}
}
Runtime.Contracts.Assert(hotIndex >= 0);
if (expandCategoricals)
{
for (int j = 0; j < parameterDiscrete.Count; j++)
{
result.Add(j == hotIndex ? 1 : 0);
}
}
else
{
result.Add(hotIndex);
}
}
else if (sweepParam is LongValueGenerator lvg)
{
var longValue = GetIfIParameterValueOfT <long>(pset) ?? long.Parse(pset.ValueText, CultureInfo.InvariantCulture);
// Normalizing all numeric parameters to [0,1] range.
result.Add(lvg.NormalizeValue(new LongParameterValue(pset.Name, longValue)));
}
else if (sweepParam is FloatValueGenerator fvg)
{
var floatValue = GetIfIParameterValueOfT <float>(pset) ?? float.Parse(pset.ValueText, CultureInfo.InvariantCulture);
// Normalizing all numeric parameters to [0,1] range.
result.Add(fvg.NormalizeValue(new FloatParameterValue(pset.Name, floatValue)));
}
else
{
throw new InvalidOperationException("Smart sweeper can only sweep over discrete and numeric parameters");
}
}
return(result.ToArray());
}
public RunResult(ParameterSet parameterSet, Double metricValue, bool isMetricMaximizing)
{
_parameterSet = parameterSet;
_metricValue = metricValue;
_isMetricMaximizing = isMetricMaximizing;
}
public void SetHyperparamValues(ParameterSet hyperParamSet)
{
HyperParamSet = hyperParamSet;
PropagateParamSetValues();
}
protected static bool AlreadyGenerated(ParameterSet paramSet, ISet <ParameterSet> previousRuns)
{
return(previousRuns.Contains(paramSet));
}