Created
March 15, 2021 20:12
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SkillDatingMatrixFactorization
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name | interest | rating | |
---|---|---|---|
Gino | .NET | 5 | |
Gino | C# | 5 | |
Gino | Docker | 5 | |
Gino | DevOps | 5 | |
Gino | Software Engineering | 5 | |
Gino | Java | 1 | |
Gino | Spring | 1 | |
Gino | Unity | 3 | |
Gino | Thymeleaf | 1 | |
Gino | Cloud | 5 | |
Imke | Webentwicklung | 5 | |
Imke | Apps | 5 | |
Imke | Datenbanken | 5 | |
Imke | Essen | 5 | |
Imke | SCRUM | 5 | |
Imke | Cloud | 1 | |
Petra | Java | 5 | |
Petra | Cloud | 5 | |
Petra | Spring | 5 | |
Petra | Thymeleaf | 5 | |
Petra | Computergrafik | 5 | |
Petra | Unity | 5 | |
Petra | SCRUM | 5 | |
Petra | Projektmanagement | 5 | |
Petra | Meerschweinchen | 5 | |
Petra | Datenbanken | 2 |
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using Microsoft.ML; | |
using Microsoft.ML.Data; | |
using Microsoft.ML.Trainers; | |
using System; | |
using System.IO; | |
// In machine learning, the columns that are used to make a prediction are called Features, | |
// and the column with the returned prediction is called the Label. | |
// We want to predict interest ratings => Label | |
// (Name, Interest) => Features ==> Predict the label | |
// Load | |
var mlContext = new MLContext(); | |
var trainingDataPath = Path.Combine(Environment.CurrentDirectory, "interests_train.csv"); | |
var trainingDataView = mlContext.Data.LoadFromTextFile<StudentInterest>(trainingDataPath, hasHeader: true, separatorChar: ','); | |
// Build and train model | |
var estimator = mlContext.Transforms.Conversion.MapValueToKey("nameEncoded", "Name") | |
.Append(mlContext.Transforms.Conversion.MapValueToKey("interestEncoded", "Interest")); | |
var options = new MatrixFactorizationTrainer.Options | |
{ | |
MatrixColumnIndexColumnName = "nameEncoded", | |
MatrixRowIndexColumnName = "interestEncoded", | |
LabelColumnName = "Label", | |
NumberOfIterations = 20, | |
ApproximationRank = 100 | |
}; | |
var trainerEstimator = estimator.Append(mlContext.Recommendation().Trainers.MatrixFactorization(options)); | |
var model = trainerEstimator.Fit(trainingDataView); | |
// Predict | |
var predictionEngine = mlContext.Model.CreatePredictionEngine<StudentInterest, StudentInterestPrediction>(model); | |
var testInput = new StudentInterest { Name = "Gino", Interest = "Datenbanken" }; | |
var prediction = predictionEngine.Predict(testInput); | |
Console.WriteLine(prediction.Score); // -> 2,2039702 just about right | |
Console.ReadLine(); | |
public class StudentInterest | |
{ | |
[LoadColumn(0)] | |
public string Name; | |
[LoadColumn(1)] | |
public string Interest; | |
[LoadColumn(2)] | |
public float Label; | |
} | |
public class StudentInterestPrediction | |
{ | |
public float Label; | |
public float Score; | |
} |
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