/// <summary>
/// Get the calibration summary in INI format
/// </summary>
private static string AddCalibrationToIni(IHost host, string ini, ICalibrator calibrator)
{
host.AssertValue(ini);
host.AssertValueOrNull(calibrator);
if (calibrator == null)
{
return(ini);
}
if (calibrator is PlattCalibrator)
{
string calibratorEvaluatorIni = IniFileUtils.GetCalibratorEvaluatorIni(ini, calibrator as PlattCalibrator);
return(IniFileUtils.AddEvaluator(ini, calibratorEvaluatorIni));
}
else
{
StringBuilder newSection = new StringBuilder();
newSection.AppendLine();
newSection.AppendLine();
newSection.AppendLine("[TLCCalibration]");
newSection.AppendLine("Type=" + calibrator.GetType().Name);
return(ini + newSection);
}
}
/// <summary>
/// Build a Bing TreeEnsemble .ini representation of the given predictor
/// </summary>
public static string LinearModelAsIni(ref VBuffer <Float> weights, Float bias, IPredictor predictor = null,
RoleMappedSchema schema = null, PlattCalibrator calibrator = null)
{
// TODO: Might need to consider a max line length for the Weights list, requiring us to split it up into
// multiple evaluators
StringBuilder inputBuilder = new StringBuilder();
StringBuilder aggregatedNodesBuilder = new StringBuilder("Nodes=");
StringBuilder weightsBuilder = new StringBuilder("Weights=");
var featureNames = default(VBuffer <ReadOnlyMemory <char> >);
MetadataUtils.GetSlotNames(schema, RoleMappedSchema.ColumnRole.Feature, weights.Length, ref featureNames);
int numNonZeroWeights = 0;
const string weightsSep = "\t";
VBufferUtils.ForEachDefined(ref weights,
(idx, value) =>
{
if (Math.Abs(value - 0) >= Epsilon)
{
numNonZeroWeights++;
var name = featureNames.GetItemOrDefault(idx);
inputBuilder.AppendLine("[Input:" + numNonZeroWeights + "]");
inputBuilder.AppendLine("Name=" + (featureNames.Count == 0 ? "Feature_" + idx : name.IsEmpty ? $"f{idx}" : name.ToString()));
inputBuilder.AppendLine("Transform=linear");
inputBuilder.AppendLine("Slope=1");
inputBuilder.AppendLine("Intercept=0");
inputBuilder.AppendLine();
aggregatedNodesBuilder.Append("I:" + numNonZeroWeights + weightsSep);
weightsBuilder.Append(value + weightsSep);
}
});
StringBuilder builder = new StringBuilder();
builder.AppendLine("[TreeEnsemble]");
builder.AppendLine("Inputs=" + numNonZeroWeights);
builder.AppendLine("Evaluators=1");
builder.AppendLine();
builder.AppendLine(inputBuilder.ToString());
builder.AppendLine("[Evaluator:1]");
builder.AppendLine("EvaluatorType=Aggregator");
builder.AppendLine("Type=Linear");
builder.AppendLine("Bias=" + bias);
builder.AppendLine("NumNodes=" + numNonZeroWeights);
builder.AppendLine(aggregatedNodesBuilder.ToString().Trim());
builder.AppendLine(weightsBuilder.ToString().Trim());
#if false // REVIEW: This should be done by the caller using the actual training args!
builder.AppendLine();
builder.AppendLine("[Comments]");
builder.Append("Trained by TLC");
if (predictor != null)
{
builder.Append(" as /cl " + predictor.GetType().Name);
if (predictor is IInitializable)
{
string settings = string.Join(";", (predictor as IInitializable).GetSettings());
if (!string.IsNullOrEmpty(settings))
{
builder.Append(" /cls " + settings);
}
}
}
#endif
string ini = builder.ToString();
// Add the calibration if the model was trained with calibration
if (calibrator != null)
{
string calibratorEvaluatorIni = IniFileUtils.GetCalibratorEvaluatorIni(ini, calibrator);
ini = IniFileUtils.AddEvaluator(ini, calibratorEvaluatorIni);
}
return(ini);
}
