public string Execute(ImportMetadata metadata, string sampleNamespace, string modelsNamespace)
{
string autoMlCode = _autoMLCodeQuery.Execute(metadata);
autoMlCode = autoMlCode
.TrimEnd('\r', '\n')
.Replace("\n", "\n ");
return($@"using Microsoft.ML;
using Microsoft.ML.AutoML;
using Microsoft.ML.Data;
using {modelsNamespace};
namespace {sampleNamespace}
{{
public static class Program
{{
public static void Main(string[] args)
{{
string pathToCsv = ""[path-to-csv]"";
var mlContext = new MLContext();
IDataView trainingDataView = mlContext.Data.LoadFromTextFile<ModelInput>(
path: pathToCsv,
hasHeader: true,
separatorChar: ',',
allowQuoting: true,
allowSparse: false);
var model = AutoTrain(mlContext, trainingDataView, 5);
// Save training model to disk.
mlContext.Model.Save(model, trainingDataView.Schema, ""MLModel.zip"");
}}
{autoMlCode}
}}
}}
");
}
public string Execute(ImportMetadata metadata, string sampleNamespace, string modelsNamespace)
{
string autoMlCode = _autoMLCodeQuery.Execute(metadata);
autoMlCode = autoMlCode
.TrimEnd('\r', '\n')
.Replace("\n", "\n ");
return($@"using Microsoft.ML;
using Microsoft.ML.AutoML;
using Microsoft.ML.Data;
using System;
using {modelsNamespace}
namespace {sampleNamespace}
{{
public static class Program
{{
public static void Main(string[] args)
{{
var mlContext = new MLContext();
// Load model & create prediction engine
string modelPath = AppDomain.CurrentDomain.BaseDirectory + ""MLModel.zip"";
ITransformer mlModel = mlContext.Model.Load(modelPath, out var modelInputSchema);
var predEngine = mlContext.Model.CreatePredictionEngine<ModelInput, ModelOutput>(mlModel);
// TODO: Generate an input
ModelInput input = new ModelInput();
// Use model to make prediction on input data
ModelOutput result = predEngine.Predict(input);
Console.WriteLine(""Predicted value: "" + result.Prediction);
}}
}}
}}
");
}
