static void Main(string[] args)
{
MLContext mlContext = new MLContext();
ClassGenerator classGenerator = new ClassGenerator("GeneratedIris", "CustomClass");
classGenerator.AddField("SepalLength", typeof(float), System.CodeDom.MemberAttributes.Public);
classGenerator.AddField("SepalWidth", typeof(float), System.CodeDom.MemberAttributes.Public);
classGenerator.AddField("PetalLength", typeof(float), System.CodeDom.MemberAttributes.Public);
classGenerator.AddField("PetalWidth", typeof(float), System.CodeDom.MemberAttributes.Public);
classGenerator.AddField("Label", typeof(string), System.CodeDom.MemberAttributes.Public);
classGenerator.Compile();
List <object> generatedDataSet = new List <object>();
dataset.ToList().ForEach((d) =>
{
generatedDataSet.Add(GetDynamicClass(d, classGenerator.GetInstance()));
});
var instance = classGenerator.GetInstance().GetType();
DataViewGenerator listGenerator = new DataViewGenerator("ListIris", "CustomGenerator", instance, classGenerator.NamespaceName);
var type = listGenerator.GeneratorType;
var methodInfo = type.GetMethod("GetDataView");
var dataView = methodInfo.Invoke(null, new object[] { generatedDataSet.ToList() });
IDataView trainingDataView = (IDataView)dataView;
trainingDataView.Schema.ToList().Add(new DataViewSchema.Column());
var pipeline = mlContext.Transforms.Conversion.MapValueToKey("Label")
.Append(mlContext.Transforms.Concatenate("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"))
.AppendCacheCheckpoint(mlContext)
.Append(mlContext.MulticlassClassification.Trainers.StochasticDualCoordinateAscent(labelColumnName: "Label", featureColumnName: "Features"))
.Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
TransformerChain <Microsoft.ML.Transforms.KeyToValueMappingTransformer> model = pipeline.Fit(trainingDataView);
var prediction = model.CreatePredictionEngine <IrisData, IrisPrediction>(mlContext).Predict(
new IrisData()
{
SepalLength = 5.9f,
SepalWidth = 3.0f,
PetalLength = 5.1f,
PetalWidth = 1.8f,
});
Console.WriteLine(prediction.PredictedLabels);
Console.ReadLine();
}
public static ClassGenerator GenerateLabelClass(string className, string classNamespace)
{
Dictionary <string, string> attributes = new Dictionary <string, string>();
attributes.Add("ColumnNameAttribute", "PredictedLabel");
ClassGenerator labelClassGenerator = new ClassGenerator(className, classNamespace);
labelClassGenerator.AddField("PredictedLabels", typeof(string), System.CodeDom.MemberAttributes.Public, attributes);
labelClassGenerator.Compile();
return(labelClassGenerator);
}
public static ClassGenerator GenerateDataSetClass(IEnumerable <MLFeature> features, string className, string classNamespace)
{
ClassGenerator classGenerator = new ClassGenerator(className, classNamespace);
foreach (var item in features)
{
classGenerator.AddField(item.Name, item.Type, System.CodeDom.MemberAttributes.Public);
}
classGenerator.Compile();
return(classGenerator);
}
public static ClassGenerator GenerateDataSetClass(Type classType, string className, string classNamespace)
{
if (classType.GetTypeInfo().IsClass)
{
ClassGenerator classGenerator = new ClassGenerator(className, classNamespace);
foreach (var item in classType.GetFields())
{
classGenerator.AddField(item.Name, item.FieldType, System.CodeDom.MemberAttributes.Public);
}
classGenerator.Compile();
return(classGenerator);
}
else
{
throw new Exception("Type not a class");
}
}
