static void Main(string[] args)
{
var assetsPath = ModelHelpers.GetAssetsPath(@"..\..\..\assets");
var transactionsCsv = Path.Combine(assetsPath, "inputs", "transactions.csv");
var offersCsv = Path.Combine(assetsPath, "inputs", "offers.csv");
var pivotCsv = Path.Combine(assetsPath, "inputs", "pivot.csv");
var modelZip = Path.Combine(assetsPath, "outputs", "retailClustering.zip");
try
{
//DataHelpers.PreProcessAndSave(offersCsv, transactionsCsv, pivotCsv);
//var modelBuilder = new ModelBuilder(pivotCsv, modelZip, kValuesSvg);
//modelBuilder.BuildAndTrain();
//STEP 0: Special data pre-process in this sample creating the PivotTable csv file
DataHelpers.PreProcessAndSave(offersCsv, transactionsCsv, pivotCsv);
//Create the MLContext to share across components for deterministic results
MLContext mlContext = new MLContext(seed: 1); //Seed set to any number so you have a deterministic environment
//STEP 1: Common data loading
DataLoader dataLoader = new DataLoader(mlContext);
var pivotDataView = dataLoader.GetDataView(pivotCsv);
//STEP 2: Process data transformations in pipeline
var dataProcessor = new DataProcessor(mlContext, 2);
var dataProcessPipeline = dataProcessor.DataProcessPipeline;
// (Optional) Peek data in training DataView after applying the ProcessPipeline's transformations
Common.ConsoleHelper.PeekDataViewInConsole <PivotObservation>(mlContext, pivotDataView, dataProcessPipeline, 10);
Common.ConsoleHelper.PeekVectorColumnDataInConsole(mlContext, "Features", pivotDataView, dataProcessPipeline, 10);
// STEP 3: Create and train the model
var trainer = mlContext.Clustering.Trainers.KMeans("Features", clustersCount: 3);
var modelBuilder = new Common.ModelBuilder <PivotObservation, ClusteringPrediction>(mlContext, dataProcessPipeline);
modelBuilder.AddTrainer(trainer);
var trainedModel = modelBuilder.Train(pivotDataView);
// STEP4: Evaluate accuracy of the model
var metrics = modelBuilder.EvaluateClusteringModel(pivotDataView);
Common.ConsoleHelper.PrintClusteringMetrics("KMeans", metrics);
// STEP5: Save/persist the model as a .ZIP file
modelBuilder.SaveModelAsFile(modelZip);
} catch (Exception ex)
{
Common.ConsoleHelper.ConsoleWriteException(ex.Message);
}
Common.ConsoleHelper.ConsolePressAnyKey();
}
static void Main(string[] args)
{
var assetsPath = ModelHelpers.GetAssetsPath(@"..\..\..\assets");
var transactionsCsv = Path.Combine(assetsPath, "inputs", "transactions.csv");
var offersCsv = Path.Combine(assetsPath, "inputs", "offers.csv");
var pivotCsv = Path.Combine(assetsPath, "inputs", "pivot.csv");
var modelZip = Path.Combine(assetsPath, "outputs", "retailClustering.zip");
try
{
//STEP 0: Special data pre-process in this sample creating the PivotTable csv file
DataHelpers.PreProcessAndSave(offersCsv, transactionsCsv, pivotCsv);
//Create the MLContext to share across components for deterministic results
MLContext mlContext = new MLContext(seed: 1); //Seed set to any number so you have a deterministic environment
// STEP 1: Common data loading configuration
var textLoader = CustomerSegmentationTextLoaderFactory.CreateTextLoader(mlContext);
var pivotDataView = textLoader.Read(pivotCsv);
//STEP 2: Configure data transformations in pipeline
var dataProcessPipeline = new PrincipalComponentAnalysisEstimator(mlContext, "Features", "PCAFeatures", rank: 2)
.Append(new OneHotEncodingEstimator(mlContext, new[] { new OneHotEncodingEstimator.ColumnInfo("LastName",
"LastNameKey",
CategoricalTransform.OutputKind.Ind) }));
// (Optional) Peek data in training DataView after applying the ProcessPipeline's transformations
Common.ConsoleHelper.PeekDataViewInConsole <PivotObservation>(mlContext, pivotDataView, dataProcessPipeline, 10);
Common.ConsoleHelper.PeekVectorColumnDataInConsole(mlContext, "Features", pivotDataView, dataProcessPipeline, 10);
// STEP 3: Create and train the model
var trainer = mlContext.Clustering.Trainers.KMeans("Features", clustersCount: 3);
var modelBuilder = new Common.ModelBuilder <PivotObservation, ClusteringPrediction>(mlContext, dataProcessPipeline);
modelBuilder.AddTrainer(trainer);
var trainedModel = modelBuilder.Train(pivotDataView);
// STEP4: Evaluate accuracy of the model
var metrics = modelBuilder.EvaluateClusteringModel(pivotDataView);
Common.ConsoleHelper.PrintClusteringMetrics(trainer.ToString(), metrics);
// STEP5: Save/persist the model as a .ZIP file
modelBuilder.SaveModelAsFile(modelZip);
} catch (Exception ex)
{
Common.ConsoleHelper.ConsoleWriteException(ex.Message);
}
Common.ConsoleHelper.ConsolePressAnyKey();
}