public static PredictionEngine <TIn, TOut> SdcaLogisticRegression <TIn, TOut>(
IEnumerable <TIn> trainDataset,
string outputColumnName = "PredictedLabel",
string exampleWeightColumnName = null,
float?l1Regularization = null,
float?l2Regularization = null,
int?maximumNumberOfIterations = null,
Action <ITransformer> additionModelAction = null)
where TIn : class, new()
where TOut : class, new()
{
var context = new MLContext();
var type = typeof(TIn);
var labelColumnName = Preprocessing.LabelColumn(type.GetProperties()).Name;
var properties = Preprocessing.ExcludeColumns(type.GetProperties());
var preprocessor = context.OneHotEncoding(properties);
var trainDataframe = context.Data.LoadFromEnumerable(trainDataset);
var pipeline = context.Transforms.Concatenate("Features", preprocessor.CombinedFeatures.ToArray())
.Append(preprocessor.OneHotEncodingEstimator)
.AppendCacheCheckpoint(context)
.Append(context.BinaryClassification.Trainers.SdcaLogisticRegression(
labelColumnName: labelColumnName,
featureColumnName: "Features",
exampleWeightColumnName: exampleWeightColumnName,
l1Regularization: l1Regularization,
l2Regularization: l2Regularization,
maximumNumberOfIterations: maximumNumberOfIterations
));
var model = pipeline.Fit(trainDataframe);
var predictEngine = context.Model.CreatePredictionEngine <TIn, TOut>(model);
additionModelAction?.Invoke(model);
return(predictEngine);
}
/// <summary>
/// Create engine of Limited-Memory Broyden–Fletcher–Goldfarb–Shanno algorithm using training dataset and hyperparameters
/// </summary>
/// <typeparam name="TIn"></typeparam>
/// <typeparam name="TOut"></typeparam>
/// <param name="trainDataset">Enumerable of TIn type.</param>
/// <param name="labelColumnName">The name of the label column.</param>
/// <param name="outputColumnName">The name of the feature column.</param>
/// <param name="exampleWeightColumnName">The name of the example weight column.</param>
/// <param name="l1Regularization">Weight of L1 regularization term.</param>
/// <param name="l2Regularization">Weight of L2 regularization term.</param>
/// <param name="optimizationTolerance">Threshold for optimizer convergence.</param>
/// <param name="historySize">Memory size. Low=faster, less accurate.</param>
/// <param name="enforceNonNegativity">Enforce non-negative weights.</param>
/// <returns></returns>
public static PredictionEngine <TIn, TOut> LbfgsMaximumEntropy <TIn, TOut>(IEnumerable <TIn> trainDataset, string outputColumnName = "PredictedLabel", string exampleWeightColumnName = null, float l1Regularization = 1, float l2Regularization = 1, double optimizationTolerance = 1e-07, int historySize = 20, bool enforceNonNegativity = false, Action <ITransformer> additionModelAction = null)
where TIn : class, new()
where TOut : class, new()
{
var context = new MLContext();
var type = typeof(TIn);
var labelColumnName = Preprocessing.LabelColumn(type.GetProperties()).Name;
var properties = Preprocessing.ExcludeColumns(type.GetProperties());
var preprocessor = context.OneHotEncoding(properties);
var trainDataframe = context.Data.LoadFromEnumerable(trainDataset);
var pipeline = context.Transforms.Conversion.MapValueToKey(labelColumnName)
.Append(preprocessor.OneHotEncodingEstimator)
.Append(context.Transforms.Concatenate("Features", preprocessor.CombinedFeatures.ToArray()))
.Append(context.Transforms.ProjectToPrincipalComponents(outputColumnName: "PCAFeatures", inputColumnName: "Features", rank: 2))
.AppendCacheCheckpoint(context)
.Append(context.MulticlassClassification.Trainers.LbfgsMaximumEntropy(
labelColumnName: labelColumnName,
featureColumnName: "Features",
exampleWeightColumnName: exampleWeightColumnName,
l1Regularization: l1Regularization,
l2Regularization: l2Regularization,
optimizationTolerance: (float)optimizationTolerance,
historySize: historySize,
enforceNonNegativity: enforceNonNegativity
)
)
.Append(context.Transforms.Conversion.MapKeyToValue(outputColumnName));
var model = pipeline.Fit(trainDataframe);
var predictEngine = context.Model.CreatePredictionEngine <TIn, TOut>(model);
additionModelAction?.Invoke(model);
return(predictEngine);
}
public static PredictionEngine <TIn, TOut> FastForest <TIn, TOut>(
IEnumerable <TIn> trainDataset,
string exampleWeightColumnName = null,
int numberOfLeaves = 20,
int numberOfTrees = 100,
int minimumExampleCountPerLeaft = 10,
Action <ITransformer> additionModelAction = null)
where TIn : class, new()
where TOut : class, new()
{
var context = new MLContext();
var type = typeof(TIn);
var labelColumnName = Preprocessing.LabelColumn(type.GetProperties()).Name;
var properties = Preprocessing.ExcludeColumns(type.GetProperties());
var preprocessor = context.OneHotEncoding(properties);
var trainDataframe = context.Data.LoadFromEnumerable(trainDataset);
var pipeline = context.Transforms.Concatenate("Features", preprocessor.CombinedFeatures.ToArray())
.Append(preprocessor.OneHotEncodingEstimator)
.AppendCacheCheckpoint(context)
.Append(context.BinaryClassification.Trainers.FastForest(
labelColumnName: labelColumnName,
featureColumnName: "Features",
exampleWeightColumnName: exampleWeightColumnName,
numberOfLeaves: numberOfLeaves,
numberOfTrees: numberOfTrees,
minimumExampleCountPerLeaf: minimumExampleCountPerLeaft
));
var model = pipeline.Fit(trainDataframe);
var predictEngine = context.Model.CreatePredictionEngine <TIn, TOut>(model);
additionModelAction?.Invoke(model);
return(predictEngine);
}