Regression Example with ML.NET in C#

   ML.NET is a machine learning library for .NET developers. In my previous post, we learned how to build a classification model and predict test data. In this post, we learn how to build a regression model and predict test data with ML.NET in C#.
   This tutorial includes the following steps:

1. Loading the required libraries.
2. Creating classes for data and prediction.
3. Generating a sample dataset.
4. Building a regression model with a generalized additive regressor.
5. Evaluating accuracy.
6. Predicting a new set of data with a fitted model.

Required libraries

ML.NET package can be installed with a NuGet Package Manager. For installation, please refer to the previous post. We load below libraries.

using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Models;
using Microsoft.ML.Runtime.Api;
using Microsoft.ML.Trainers;
using Microsoft.ML.Transforms;
using System;
using System.Collections.Generic;
using System.Linq;
using C = System.Console;

Data container class

A train and test data should be collected into the class object. We create a UnitData class for data collection and UnitPrediction class for predicted data.

public class UnitData
{
    [Column("0")]
    public float UnitA;

    [Column("1")]
    public float UnitS;

    [Column("2")]
    public float Volume;

    [Column("3")]
    public string Status;

    [Column("4")]
    public float Level;
}

public class UnitPrediction
{
    [ColumnName("Score")]
    public float Level;
}

Creating Sample Dataset

We create sample data with a GenerateUData method below. You can use your dataset too. In this case, you should create the class properties according to your data content.

internal static List<UnitData> GenerateUData(int count)
{
    var pdlist = new List<UnitData>();
    var random = new Random();
    for (int i = 0; i < count; i++)
    {
        var pd = new UnitData
        {
            UnitA = random.Next(0, 20),
            UnitS = random.Next(0, 10),
            Volume = random.Next(4, 30)
        };

        if ((pd.UnitA > 16 || pd.UnitS > 7) && pd.Volume > 23)
            pd.Status = "Warning";
        else if ((pd.UnitA < 5 || pd.UnitS < 3) && pd.Volume < 15)
            pd.Status = "Alert";
        else
            pd.Status = "Normal";

        pd.Level = (pd.UnitA + pd.UnitS + pd.Volume) - random.Next(1, 3);

        pdlist.Add(pd);
    }
    return pdlist;
}

Next, we create a train and test data. Train contains 2000 records and test contains 20 records.

var trainData = GenerateUData(2000);
var testData = GenerateUData(20);
C.WriteLine("Dataset content:");
C.WriteLine("id,UnitA,UnitS,Volume,Status,Level");
foreach (var item in trainData.Take(20))
    C.WriteLine(string.Join(",",item.UnitA, item.UnitS, item.Volume, 

                                item.Status, item.Level));

The train data should be converted into the collection data source, and it can be done with CollectionDataSource class.

var trainCollection = CollectionDataSource.Create(trainData);

Building a model

Next, we create a learning pipe object and add all the components of a model. There are many regression algorithms ML.NET that can be used in regression analysis, here, I use GeneralizedAdditiveModelRegressor method.

var learningPipe = new LearningPipeline();
// add components
learningPipe.Add(trainCollection);
learningPipe.Add(new ColumnCopier(("Level", "Label")));
learningPipe.Add(new CategoricalOneHotVectorizer("Status"));
learningPipe.Add(new ColumnConcatenator("Features", 

                        "UnitA", "UnitS", "Volume", "Status")); 

// add regression algorithm
learningPipe.Add(new GeneralizedAdditiveModelRegressor());

Above components in a pipe are trained with Train() method.

C.WriteLine("\nTraining a model:");
var model = learningPipe.Train<UnitData, UnitPrediction>();

Accuracy check

RegressionEvaluator() class helps us to evaluate regression model. We check the RMS and root squared values of a model.

var evaluator = new RegressionEvaluator();
var metrics = evaluator.Evaluate(model, trainCollection);
C.WriteLine("\nModel Evaluation:");
C.WriteLine("RMS: " + metrics.Rms);
C.WriteLine("Root Squared: " + metrics.RSquared);

Test data prediction

Finally, we predict the test data. To check the original and predicted data, we print them both together.

var predicted = model.Predict(testData);

// print testdata and predicted output
var results = testData.Zip(predicted, (t, p) =>
    Tuple.Create(t.UnitA, t.UnitS, t.Volume, t.Status, t.Level, p.Level));

int i = 1;
C.WriteLine("\nPredicted result:");
C.WriteLine("id,UnitA,UnitS,Volume,Status,LevelOriginal,LevelPredicted");
foreach (var item in results)
{
    C.WriteLine(string.Join(",", i, item.Item1,
        item.Item2, item.Item3, item.Item4, item.Item5, item.Item6));
    i++;
}   

We have briefly learned how to use regression model with ML.NET in C#. Thank you for reading!

Classification Example with ML.NET in C#

Sentiment Analysis Example with ML.NET in C#

The full source code is listed below.

using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Models;
using Microsoft.ML.Runtime.Api;
using Microsoft.ML.Trainers;
using Microsoft.ML.Transforms;
using System;
using System.Collections.Generic;
using System.Linq;
using C = System.Console;

namespace mltest
{
    public class UnitData
    {
        [Column("0")]
        public float UnitA;

        [Column("1")]
        public float UnitS;

        [Column("2")]
        public float Volume;

        [Column("3")]
        public string Status;

        [Column("4")]
        public float Level;
    }

    public class UnitPrediction
    {
        [ColumnName("Score")]
        public float Level;
    }

    class Program
    {
        static void Main(string[] args)
        {
            // generate train and test data
            var trainData = GenerateUData(2000);
            var testData = GenerateUData(20);
            C.WriteLine("Dataset content:");
            C.WriteLine("id,UnitA,UnitS,Volume,Status,Level");
            foreach (var item in trainData.Take(20))
                C.WriteLine(string.Join(",",item.UnitA, item.UnitS, 
                                 item.Volume, item.Status, item.Level));
                        
            // convert list to collectiong data source
            var trainCollection = CollectionDataSource.Create(trainData);

            // create object for machine learning task 
            var learningPipe = new LearningPipeline();
            // add components
            learningPipe.Add(trainCollection);
            learningPipe.Add(new ColumnCopier(("Level", "Label")));
            learningPipe.Add(new CategoricalOneHotVectorizer("Status"));
            learningPipe.Add(new ColumnConcatenator("Features", "UnitA", 
               "UnitS", "Volume", "Status"));                        
            // add regression algorithm
            learningPipe.Add(new GeneralizedAdditiveModelRegressor());
            
            // train the model
            C.WriteLine("\nTraining a model:");
            var model = learningPipe.Train<UnitData, UnitPrediction>();
            
            // model evaluation and accuracy check
            var evaluator = new RegressionEvaluator();
            var metrics = evaluator.Evaluate(model, trainCollection);
            C.WriteLine("\nModel Evaluation:");
            C.WriteLine("RMS: " + metrics.Rms);
            C.WriteLine("Root Squared: " + metrics.RSquared);

            // predict test data
            var predicted = model.Predict(testData);

            // print testdata and predicted output
            var results = testData.Zip(predicted, (t, p) =>
                Tuple.Create(t.UnitA, t.UnitS, t.Volume, t.Status, t.Level, p.Level));

            int i = 1;
            C.WriteLine("\nPredicted result:");
            C.WriteLine("id,UnitA,UnitS,Volume,Status,LevelOriginal,LevelPredicted");
            foreach (var item in results)
            {
                C.WriteLine(string.Join(",", i, item.Item1,
                    item.Item2, item.Item3, item.Item4, item.Item5, item.Item6));
                i++;
            }   

            C.ReadLine();
        }

        internal static List<UnitData> GenerateUData(int count)
        {
            var pdlist = new List<UnitData>();
            var random = new Random();
            for (int i = 0; i < count; i++)
            {
                var pd = new UnitData
                {
                    UnitA = random.Next(0, 20),
                    UnitS = random.Next(0, 10),
                    Volume = random.Next(4, 30)
                };

                if ((pd.UnitA > 16 || pd.UnitS > 7) && pd.Volume > 23)
                    pd.Status = "Warning";
                else if ((pd.UnitA < 5 || pd.UnitS < 3) && pd.Volume < 15)
                    pd.Status = "Alert";
                else
                    pd.Status = "Normal";

                pd.Level = (pd.UnitA + pd.UnitS + pd.Volume) - random.Next(1, 3);

                pdlist.Add(pd);
            }
            return pdlist;
        }
    }
}