public static object Predict(object predictionEngine, ClassGenerator classGenerator, object example)
{
var methodInfo = predictionEngine.GetType().GetMethod("Predict", new[] { classGenerator.ClassType });
var prediction = methodInfo.Invoke(predictionEngine, new object[] { example });
return(prediction);
}
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);
}
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 MLTypesGenerator(string className, string namespaceName, ClassGenerator datSetClass, ClassGenerator labelClass)
{
// Add an assembly reference.
compilerParameters.ReferencedAssemblies.AddRange(new []
{
"System.dll",
"System.Collections.dll",
"System.Collections.Concurrent.dll",
"System.Core.dll",
"netstandard.dll",
"System.Linq.dll",
"System.Xml.dll",
"System.Xml.Linq.dll",
"System.Linq.Expressions.dll",
"System.Linq.Parallel.dll",
"System.Linq.Queryable.dll",
"Microsoft.ML.Data.dll",
"Microsoft.ML.Core.dll",
"Microsoft.Data.DataView.dll",
$"{datSetClass.namespaceName}.dll",
$"{labelClass.namespaceName}.dll"
});
compilerParameters.GenerateExecutable = false;
compilerParameters.GenerateInMemory = false;
this.OutputDll = $"{namespaceName}.dll";
compilerParameters.OutputAssembly = this.OutputDll;
this.namespaceName = namespaceName;
namespaces = new CodeNamespace(namespaceName);
namespaces.Imports.Add(new CodeNamespaceImport("System"));
namespaces.Imports.Add(new CodeNamespaceImport("System.Collections"));
namespaces.Imports.Add(new CodeNamespaceImport("System.Collections.Generic"));
namespaces.Imports.Add(new CodeNamespaceImport("System.Linq"));
namespaces.Imports.Add(new CodeNamespaceImport("Microsoft.ML"));
namespaces.Imports.Add(new CodeNamespaceImport("Microsoft.ML.Data"));
namespaces.Imports.Add(new CodeNamespaceImport("Microsoft.Data.DataView"));
namespaces.Imports.Add(new CodeNamespaceImport($"{datSetClass.namespaceName}"));
namespaces.Imports.Add(new CodeNamespaceImport($"{labelClass.namespaceName}"));
compileUnit.Namespaces.Add(namespaces);
this.className = className;
customClass = new CodeTypeDeclaration(className);
customClass.IsClass = true;
customClass.TypeAttributes = System.Reflection.TypeAttributes.Public;
this.datSetClass = datSetClass;
this.labelClass = labelClass;
this.Compile();
}
public static ClassGenerator GenerateDataSetClass(Type classType, string className, string classNamespace)
{
if (classType.GetTypeInfo().IsClass)
{
ClassGenerator classGenerator = new ClassGenerator(className, classNamespace);
foreach (var item in classType.GetFields())
{
classGenerator.AddField(item.Name, item.FieldType, System.CodeDom.MemberAttributes.Public);
}
classGenerator.Compile();
return(classGenerator);
}
else
{
throw new Exception("Type not a class");
}
}
public static MLTypesGenerator CreateTypesGenarator(ClassGenerator classGenerator, ClassGenerator labelClassGenerator)
{
MLTypesGenerator typesGenerator = new MLTypesGenerator($"{classGenerator.className}TypesGnerator", $"{classGenerator.className}TypesGneratorNamespace", classGenerator, labelClassGenerator);
return(typesGenerator);
}
