Add a sample for one class matrix factorization

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/OneClassMatrixFactorizationWithOptions.cs

@@ -0,0 +1,135 @@
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using Microsoft.ML;
+using Microsoft.ML.Data;
+using Microsoft.ML.Trainers;
+
+namespace Samples.Dynamic.Trainers.Recommendation
+{
+    public static class OneClassMatrixFactorizationWithOptions
+    {
+        // This example shows the use of ML.NET's one-class matrix factorization module which implements
+        // Algorithm 1 in a <a href="https://www.csie.ntu.edu.tw/~cjlin/papers/one-class-mf/biased-mf-sdm-with-supp.pdf">paper</a>.
+        // See page 28 in of <a href="https://www.csie.ntu.edu.tw/~cjlin/talks/facebook.pdf">slides</a> for a brief introduction to
+        // one-class matrix factorization.
+        // In this example we will create in-memory data and then use it to train a one-class matrix factorization model.
+        // Afterward, prediction values are reported.
+        // To run this example, it requires installation of additional nuget package
+        // <a href="https://www.nuget.org/packages/Microsoft.ML.Recommender/">Microsoft.ML.Recommender</a>.
+        public static void Example()
+        {
+            // Create a new context for ML.NET operations. It can be used for exception tracking and logging,
+            // as a catalog of available operations and as the source of randomness.
+            var mlContext = new MLContext(seed: 0);
+
+            // Get a small in-memory dataset.
+            GetOneClassMatrix(out List<MatrixElement> data, out List<MatrixElement> testData);
+
+            // Convert the in-memory matrix into an IDataView so that ML.NET components can consume it.
+            var dataView = mlContext.Data.LoadFromEnumerable(data);
+
+            // Create a matrix factorization trainer which may consume "Value" as the training label, "MatrixColumnIndex" as the
+            // matrix's column index, and "MatrixRowIndex" as the matrix's row index. Here nameof(...) is used to extract field
+            // names' in MatrixElement class.
+            var options = new MatrixFactorizationTrainer.Options
+            {
+                MatrixColumnIndexColumnName = nameof(MatrixElement.MatrixColumnIndex),
+                MatrixRowIndexColumnName = nameof(MatrixElement.MatrixRowIndex),
+                LabelColumnName = nameof(MatrixElement.Value),
+                NumberOfIterations = 20,
+                NumberOfThreads = 8,
+                ApproximationRank = 32,
+                Alpha = 1,
+                // The desired of unobserved values.
+                C = 0.15,
+                // To enable one-class matrix factorization, the following line is required.
+                LossFunction = MatrixFactorizationTrainer.LossFunctionType.SquareLossOneClass
+            };
+
+            var pipeline = mlContext.Recommendation().Trainers.MatrixFactorization(options);
+
+            // Train a matrix factorization model.
+            var model = pipeline.Fit(dataView);
+
+            // Apply the trained model to the test set. Notice that training is a partial 
+            var prediction = model.Transform(mlContext.Data.LoadFromEnumerable(testData));
+
+            var results = mlContext.Data.CreateEnumerable<MatrixElement>(prediction, false).ToList();
+            // Feed the test data into the model and then iterate through a few predictions.
+            foreach (var pred in results.Take(15))
+                Console.WriteLine($"Predicted value at row {pred.MatrixRowIndex - 1} and column {pred.MatrixColumnIndex - 1} is {pred.Score} and its expected value is {pred.Value}.");
+
+            // Expected output similar to:
+            // Predicted value at row 0 and column 0 is 0.9873335 and its expected value is 1.
+            // Predicted value at row 1 and column 0 is 0.1499522 and its expected value is 0.15.
+            // Predicted value at row 2 and column 0 is 0.1499791 and its expected value is 0.15.
+            // Predicted value at row 3 and column 0 is 0.1499254 and its expected value is 0.15.
+            // Predicted value at row 4 and column 0 is 0.1499074 and its expected value is 0.15.
+            // Predicted value at row 5 and column 0 is 0.1499968 and its expected value is 0.15.
+            // Predicted value at row 6 and column 0 is 0.1499791 and its expected value is 0.15.
+            // Predicted value at row 7 and column 0 is 0.1499805 and its expected value is 0.15.
+            // Predicted value at row 8 and column 0 is 0.1500055 and its expected value is 0.15.
+            // Predicted value at row 9 and column 0 is 0.1499199 and its expected value is 0.15.
+            // Predicted value at row 10 and column 0 is 0.9873335 and its expected value is 1.
+            // Predicted value at row 11 and column 0 is 0.1499522 and its expected value is 0.15.
+            // Predicted value at row 12 and column 0 is 0.1499791 and its expected value is 0.15.
+            // Predicted value at row 13 and column 0 is 0.1499254 and its expected value is 0.15.
+            // Predicted value at row 14 and column 0 is 0.1499074 and its expected value is 0.15.
+            //
+            // Note: use the advanced options constructor to set the number of threads to 1 for a deterministic behavior.
+
+            // Two columns with highest predicted score to the 2nd row (indexed by 1). If we view row index as user ID and column as game ID,
+            // the following list contains the games recommended by the trained model. Note that sometime, you may want to exclude training
+            // data from your predicted results because those games were already purchased.
+            var topColumns = results.Where(element => element.MatrixRowIndex == 1).OrderByDescending(element => element.Score).Take(2);
+        }
+
+        // The following variables defines the shape of a matrix. Its shape is _synthesizedMatrixRowCount-by-_synthesizedMatrixColumnCount.
+        // Because in ML.NET key type's minimal value is zero, the first row index is always zero in C# data structure (e.g., MatrixColumnIndex=0
+        // and MatrixRowIndex=0 in MatrixElement below specifies the value at the upper-left corner in the training matrix). If user's row index
+        // starts with 1, their row index 1 would be mapped to the 2nd row in matrix factorization module and their first row may contain no values.
+        // This behavior is also true to column index.
+        private const uint _synthesizedMatrixColumnCount = 60;
+        private const uint _synthesizedMatrixRowCount = 100;
+
+        // A data structure used to encode a single value in matrix
+        private class MatrixElement
+        {
+            // Matrix column index. Its allowed range is from 0 to _synthesizedMatrixColumnCount - 1.
+            [KeyType(_synthesizedMatrixColumnCount)]
+            public uint MatrixColumnIndex { get; set; }
+            // Matrix row index. Its allowed range is from 0 to _synthesizedMatrixRowCount - 1.
+            [KeyType(_synthesizedMatrixRowCount)]
+            public uint MatrixRowIndex { get; set; }
+            // The value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
+            public float Value { get; set; }
+            // The predicted value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
+            public float Score { get; set; }
+        }
+
+        // Create an in-memory matrix as a list of tuples (column index, row index, value). Notice that one-class matrix
+        // factorization handle scenerios where only positive signals (e.g., on Facebook, only likes are recorded and no dislike before)
+        // can be observed so that all values are set to 1.
+        private static void GetOneClassMatrix(out List<MatrixElement> observedMatrix, out List<MatrixElement> fullMatrix)
+        {
+            // The matrix factorization model will be trained only using observedMatrix but we will see it can learn all information 
+            // carried in fullMatrix.
+            observedMatrix = new List<MatrixElement>();
+            fullMatrix = new List<MatrixElement>();
+            for (uint i = 0; i < _synthesizedMatrixColumnCount; ++i)
+                for (uint j = 0; j < _synthesizedMatrixRowCount; ++j)
+                {
+                    if ((i + j) % 10 == 0)
+                    {
+                        // Set observed elements' values to 1 (means like).
+                        observedMatrix.Add(new MatrixElement() { MatrixColumnIndex = i, MatrixRowIndex = j, Value = 1, Score = 0 });
+                        fullMatrix.Add(new MatrixElement() { MatrixColumnIndex = i, MatrixRowIndex = j, Value = 1, Score = 0 });
+                    }
+                    else
+                        // Set unobserved elements' values to 0.15, a value smaller than observed values (means dislike).
+                        fullMatrix.Add(new MatrixElement() { MatrixColumnIndex = i, MatrixRowIndex = j, Value = 0.15f, Score = 0 });
+                }
+        }
+    }
+}

src/Microsoft.ML.Recommender/RecommenderCatalog.cs

@@ -87,6 +87,7 @@ public MatrixFactorizationTrainer MatrixFactorization(
             /// <format type="text/markdown">
             /// <![CDATA[
             ///  [!code-csharp[MatrixFactorization](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorizationWithOptions.cs)]
+            ///  [!code-csharp[MatrixFactorization](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/OneClassMatrixFactorizationWithOptions.cs)]
             /// ]]></format>
             /// </example>
             public MatrixFactorizationTrainer MatrixFactorization(

test/Microsoft.ML.Tests/TrainerEstimators/MatrixFactorizationTests.cs

@@ -603,5 +603,119 @@ public void OneClassMatrixFactorizationWithUnseenColumnAndRow()
             CompareNumbersWithTolerance(0.05511549, testResults[1].Score, digitsOfPrecision: 5);
             CompareNumbersWithTolerance(0.00316973357, testResults[2].Score, digitsOfPrecision: 5);
         }
+
+        [MatrixFactorizationFact]
+        public void OneClassMatrixFactorizationSample()
+        {
+            // Create a new context for ML.NET operations. It can be used for exception tracking and logging,
+            // as a catalog of available operations and as the source of randomness.
+            var mlContext = new MLContext(seed: 0);
+
+            // Get a small in-memory dataset.
+            GetOneClassMatrix(out List<OneClassMatrixElement> data, out List<OneClassMatrixElement> testData);
+
+            // Convert the in-memory matrix into an IDataView so that ML.NET components can consume it.
+            var dataView = mlContext.Data.LoadFromEnumerable(data);
+
+            // Create a matrix factorization trainer which may consume "Value" as the training label, "MatrixColumnIndex" as the
+            // matrix's column index, and "MatrixRowIndex" as the matrix's row index. Here nameof(...) is used to extract field
+            // names' in MatrixElement class.
+            var options = new MatrixFactorizationTrainer.Options
+            {
+                MatrixColumnIndexColumnName = nameof(OneClassMatrixElement.MatrixColumnIndex),
+                MatrixRowIndexColumnName = nameof(OneClassMatrixElement.MatrixRowIndex),
+                LabelColumnName = nameof(OneClassMatrixElement.Value),
+                NumberOfIterations = 20,
+                NumberOfThreads = 8,
+                ApproximationRank = 32,
+                Alpha = 1,
+                // The desired of unobserved values.
+                C = 0.15,
+                // To enable one-class matrix factorization, the following line is required.
+                LossFunction = MatrixFactorizationTrainer.LossFunctionType.SquareLossOneClass
+            };
+
+            var pipeline = mlContext.Recommendation().Trainers.MatrixFactorization(options);
+
+            // Train a matrix factorization model.
+            var model = pipeline.Fit(dataView);
+
+            // Apply the trained model to the test set. Notice that training is a partial 
+            var prediction = model.Transform(mlContext.Data.LoadFromEnumerable(testData));
+
+            var results = mlContext.Data.CreateEnumerable<OneClassMatrixElement>(prediction, false).ToList();
+
+            Assert.Equal(6000, results.Count);
+
+            var firstElement = results.First();
+            var lastElement = results.Last();
+
+            Assert.Equal(1u, firstElement.MatrixColumnIndex);
+            Assert.Equal(1u, firstElement.MatrixRowIndex);
+            Assert.Equal(0.987113833, firstElement.Score, 3);
+            Assert.Equal(1, firstElement.Value, 3);
+
+            Assert.Equal(60u, lastElement.MatrixColumnIndex);
+            Assert.Equal(100u, lastElement.MatrixRowIndex);
+            Assert.Equal(0.149993762, lastElement.Score, 3);
+            Assert.Equal(0.15, lastElement.Value, 3);
+
+            // Two columns with highest predicted score to the 2nd row (indexed by 1). If we view row index as user ID and column as game ID,
+            // the following list contains the games recommended by the trained model. Note that sometime, you may want to exclude training
+            // data from your predicted results because those games were already purchased.
+            var topColumns = results.Where(element => element.MatrixRowIndex == 1).OrderByDescending(element => element.Score).Take(2);
+
+            firstElement = topColumns.First();
+            lastElement = topColumns.Last();
+
+            Assert.Equal(1u, firstElement.MatrixColumnIndex);
+            Assert.Equal(1u, firstElement.MatrixRowIndex);
+            Assert.Equal(0.987113833, firstElement.Score, 3);
+            Assert.Equal(1, firstElement.Value, 3);
+
+            Assert.Equal(11u, lastElement.MatrixColumnIndex);
+            Assert.Equal(1u, lastElement.MatrixRowIndex);
+            Assert.Equal(0.987113833, lastElement.Score, 3);
+            Assert.Equal(1, lastElement.Value, 3);
+        }
+
+        // A data structure used to encode a single value in matrix
+        private class OneClassMatrixElement
+        {
+            // Matrix column index. Its allowed range is from 0 to _synthesizedMatrixColumnCount - 1.
+            [KeyType(_synthesizedMatrixColumnCount)]
+            public uint MatrixColumnIndex { get; set; }
+            // Matrix row index. Its allowed range is from 0 to _synthesizedMatrixRowCount - 1.
+            [KeyType(_synthesizedMatrixRowCount)]
+            public uint MatrixRowIndex { get; set; }
+            // The value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
+            public float Value { get; set; }
+            // The predicted value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
+            public float Score { get; set; }
+        }
+
+        // Create an in-memory matrix as a list of tuples (column index, row index, value). Notice that one-class matrix
+        // factorization handle scenerios where only positive signals (e.g., on Facebook, only likes are recorded and no dislike before)
+        // can be observed so that all values are set to 1.
+        private static void GetOneClassMatrix(out List<OneClassMatrixElement> observedMatrix, out List<OneClassMatrixElement> fullMatrix)
+        {
+            // The matrix factorization model will be trained only using observedMatrix but we will see it can learn all information 
+            // carried in fullMatrix.
+            observedMatrix = new List<OneClassMatrixElement>();
+            fullMatrix = new List<OneClassMatrixElement>();
+            for (uint i = 0; i < _synthesizedMatrixColumnCount; ++i)
+                for (uint j = 0; j < _synthesizedMatrixRowCount; ++j)
+                {
+                    if ((i + j) % 10 == 0)
+                    {
+                        // Set observed elements' values to 1 (means like).
+                        observedMatrix.Add(new OneClassMatrixElement() { MatrixColumnIndex = i, MatrixRowIndex = j, Value = 1, Score = 0 });
+                        fullMatrix.Add(new OneClassMatrixElement() { MatrixColumnIndex = i, MatrixRowIndex = j, Value = 1, Score = 0 });
+                    }
+                    else
+                        // Set unobserved elements' values to 0.15, a value smaller than observed values (means dislike).
+                        fullMatrix.Add(new OneClassMatrixElement() { MatrixColumnIndex = i, MatrixRowIndex = j, Value = 0.15f, Score = 0 });
+                }
+        }
     }
 }