static void Main(string[] args)
{
var mlContext = new MLContext();
// Creates a loader which will help format the data
var reader = mlContext.Data.CreateTextLoader(
columns: new TextLoader.Column[]
{
new TextLoader.Column(CocoaPercent, DataKind.Single, 1),
new TextLoader.Column("Label", DataKind.Single, 4) // Customer happiness is the label. The predicted features is called the Label.
},
hasHeader: true // First line of the data file is a header and not data row
);
IDataView trainingData = reader.Load(TrainDataPath);
PreviewUtil.Show(trainingData);
// Creates a pipeline - All operations like data operations, transformation, training are bundled as a pipeline
var pipeline =
mlContext.Transforms.Concatenate(outputColumnName: "Features", inputColumnNames: CocoaPercent)
.Append(mlContext.Regression.Trainers.LbfgsPoissonRegression());
// Train the model
var trainingModel = pipeline.Fit(trainingData);
// Using the training for one-time prediction
var predictionEngine = mlContext.Model.CreatePredictionEngine <ChocolateInput, ChocolateOutput>(trainingModel);
// Get the prediction
ChocolateOutput prediction = predictionEngine.Predict(new ChocolateInput()
{
CocoaPercent = 100
});
Console.WriteLine($"Predicted happiness: {prediction.CustomerHappiness}");
ChartGeneratorUtil.PlotRegressionChart(new PlotChartGeneratorModel()
{
Title = "Chocolate Consumer Happiness Prediction",
LabelX = "Cocoa Percent",
LabelY = "Customer Happiness",
ImageName = "CocoaPercentToHappiness.png",
PointsList = new List <PlotChartPointsList>
{
new PlotChartPointsList {
Points = ChartGeneratorUtil.GetChartPointsFromFile(TrainDataPath, 1, 4).ToList()
}
},
MaxLimitY = ChartGeneratorUtil.GetMaxColumnValueFromFile(TrainDataPath, 4) + 10
});
Console.ReadKey();
}
static void Main(string[] args)
{
var mlContext = new MLContext();
TextLoader reader = mlContext.Data.CreateTextLoader(
columns: new TextLoader.Column[]
{
new TextLoader.Column("Weight", DataKind.Single, 0),
new TextLoader.Column("CocoaPercent", DataKind.Single, 1),
new TextLoader.Column("Cost", DataKind.Single, 2),
new TextLoader.Column("Label", DataKind.Single, 3) // Label is customer happiness. The predicted feature is called the label
},
hasHeader: true);
IDataView trainingData = reader.Load(TrainingDataPath);
PreviewUtil.Show(trainingData);
var pipeline =
mlContext.Transforms.Concatenate("Features", "Weight")
.Append(mlContext.Regression.Trainers.LbfgsPoissonRegression());
var trainingModel = pipeline.Fit(trainingData);
/* Get the graph data */
/* Build the graph */
/* Calculate metrics */
/* Get the predictions */
/* Show Graph 3D */
// TODO: Add Graph, update instructions
Console.ReadKey();
}
