public static void DoTrainingLearning(string path)
{
Data.TransformPipeline.Append
(
ml_dotnet_context.Regression.Trainers
.FastForest()
//.FastTree()
//.FastTreeTweedie()
//.Gam()
//.LbfgsPoissonRegression()
//.OnlineGradientDescent()
//.Sdca()
);
Data.TransformPipeline.Fit(Data.DataViewTraining);
Microsoft.ML.Transforms.ColumnCopyingTransformer model_endomorphic = null;
using
(
FileStream file_stream = new FileStream
(
Data.ModelPathEndomorphic,
FileMode.Open,
FileAccess.Read,
FileShare.Read
)
)
{
ml_dotnet_context.Model.Save //<SomatotypeInputData>
(
model_endomorphic,
Data.DataViewSchema,
file_stream
);
}
//DumpData(data_view);
return;
}
public static ITransformer TrainLearnMLdotnet(MLContext mlContext, string path)
{
IDataView dataView = null;
dataView = mlContext
.Data
.LoadFromTextFile <SomatotypeInputData>
(
path,
hasHeader: true,
separatorChar: ','
);
DataOperationsCatalog.TrainTestData dataSplit;
dataSplit = mlContext.Data.TrainTestSplit(dataView, testFraction: 0.25);
trainData = dataSplit.TrainSet;
testData = dataSplit.TestSet;
IEnumerable <SomatotypeInputData> list = null;
list = mlContext
.Data
.CreateEnumerable <SomatotypeInputData>(dataView, reuseRowObject: false)
.ToList();
for (int i = 0; i < list.Count(); i++)
{
Console.WriteLine($" Id = {list.ElementAt(i).Id}");
Console.WriteLine($" EndomorphicComponent = {list.ElementAt(i).EndomorphicComponent}");
Console.WriteLine($" MesomorphicComponent = {list.ElementAt(i).MesomorphicComponent}");
Console.WriteLine($" EctomorphicComponent = {list.ElementAt(i).EctomorphicComponent}");
}
Microsoft.ML.Transforms.ColumnCopyingEstimator pipeline = null;
pipeline = mlContext.Transforms.CopyColumns
(
outputColumnName: "Label",
inputColumnName: "EndomorphicComponent"
);
pipeline.Append
(
mlContext.Transforms.Concatenate
(
"Features",
"Height",
"Mass",
"BreadthHumerus",
"BreadthFemur",
"GirthArmUpper",
"GirthCalfStanding",
"SkinfoldSubscapular",
"SkinfoldTriceps",
"SkinfoldSupraspinale",
"SkinfoldMedialCalf"
)
);
pipeline.Append(mlContext.Regression.Trainers.FastTree());
Microsoft.ML.Transforms.ColumnCopyingTransformer model_1 = pipeline.Fit(trainData);
return(model = model_1);
}
