minor changes + make it pass tests.

Author: Ivan Matantsev

src/Microsoft.ML.Recommender/MatrixFactorizationPredictor.cs

@@ -57,15 +57,15 @@ private static VersionInfo GetVersionInfo()
         public readonly int NumberOfColumns;
         ///<summary> The rank of the factor matrices.</summary>
         public readonly int ApproximationRank;
-        // Packed _numberOfRows by _approximationRank matrix.
+        // Packed NumberOfRows by ApproximationRank matrix.
         private readonly float[] _leftFactorMatrix;
-        // Packed _approximationRank by _numberofColumns matrix.
+        // Packed ApproximationRank by NumberOfColumns matrix.
         private readonly float[] _rightFactorMatrix;
 
         /// <summary>
         /// Returns left approximation matrix.
         /// </summary>
-        /// <returns>Flattened into one dimension array with size equal to <see cref="NumberOfRows"/> * <see cref="ApproximationRank"/></returns>
+        /// <returns>Flattened into one-dimensional array with size equal to <see cref="NumberOfRows"/> * <see cref="ApproximationRank"/></returns>
         public float[] GetLeftFactorMatrix()
         {
             float[] result = new float[NumberOfRows * ApproximationRank];
@@ -75,7 +75,7 @@ public float[] GetLeftFactorMatrix()
         /// <summary>
         /// Returns right approximation matrix.
         /// </summary>
-        /// <returns>Flattened into one dimension array with size equal to <see cref="ApproximationRank"/> * <see cref="NumberOfColumns"/></returns>
+        /// <returns>Flattened into one-dimensional array with size equal to <see cref="ApproximationRank"/> * <see cref="NumberOfColumns"/></returns>
         public float[] GetRightFactorMatrix()
         {
             float[] result = new float[ApproximationRank* NumberOfColumns];
@@ -145,8 +145,8 @@ private MatrixFactorizationModelParameters(IHostEnvironment env, ModelLoadContex
             _leftFactorMatrix = Utils.ReadSingleArray(ctx.Reader, checked(NumberOfRows * ApproximationRank));
             _rightFactorMatrix = Utils.ReadSingleArray(ctx.Reader, checked(NumberOfColumns * ApproximationRank));
 
-            MatrixColumnIndexType = new KeyType(typeof(uint), NumberOfRows);
-            MatrixRowIndexType = new KeyType(typeof(uint), NumberOfColumns);
+            MatrixColumnIndexType = new KeyType(typeof(uint), NumberOfColumns);
+            MatrixRowIndexType = new KeyType(typeof(uint), NumberOfRows);
         }
 
         /// <summary>
@@ -183,8 +183,8 @@ public void Save(ModelSaveContext ctx)
             ctx.Writer.Write(ApproximationRank);
             _host.Check(Utils.Size(_leftFactorMatrix) == NumberOfRows * ApproximationRank, "Unexpected matrix size of a factor matrix (matrix P in LIBMF paper)");
             _host.Check(Utils.Size(_rightFactorMatrix) == NumberOfColumns * ApproximationRank, "Unexpected matrix size of a factor matrix (matrix Q in LIBMF paper)");
-            Utils.WriteSinglesNoCount(ctx.Writer, _leftFactorMatrix.AsSpan(0, NumberOfRows * ApproximationRank));
-            Utils.WriteSinglesNoCount(ctx.Writer, _rightFactorMatrix.AsSpan(0, NumberOfColumns * ApproximationRank));
+            Utils.WriteSinglesNoCount(ctx.Writer, _leftFactorMatrix.AsSpan(0, _leftFactorMatrix.Length));
+            Utils.WriteSinglesNoCount(ctx.Writer, _rightFactorMatrix.AsSpan(0, _rightFactorMatrix.Length));
         }
 
         /// <summary>

src/Microsoft.ML.Recommender/MatrixFactorizationTrainer.cs

@@ -149,7 +149,7 @@ public sealed class Options
             public double Lambda = Defaults.Lambda;
 
             /// <summary>
-            /// Approximation rank.
+            /// Rank of approximation matrixes.
             /// </summary>
             /// <remarks>
             /// If input data has size of m by n we would build two approximation matrixes m by k and k by n where k is approxiation rank.
@@ -338,7 +338,7 @@ internal MatrixFactorizationTrainer(IHostEnvironment env, Options options) : bas
         /// <param name="matrixColumnIndexColumnName">The name of the column hosting the matrix's column IDs.</param>
         /// <param name="matrixRowIndexColumnName">The name of the column hosting the matrix's row IDs.</param>
         /// <param name="labelColumn">The name of the label column.</param>
-        /// <param name="approximationRank">Approximation rank.</param>
+        /// <param name="approximationRank">Rank of approximation matrixes.</param>
         /// <param name="learningRate">Initial learning rate. It specifies the speed of the training algorithm.</param>
         /// <param name="numIterations">Number of training iterations.</param>
         [BestFriend]

src/Microsoft.ML.Recommender/RecommenderCatalog.cs

@@ -55,7 +55,7 @@ internal RecommendationTrainers(RecommendationCatalog catalog)
             /// <param name="matrixColumnIndexColumnName">The name of the column hosting the matrix's column IDs.</param>
             /// <param name="matrixRowIndexColumnName">The name of the column hosting the matrix's row IDs.</param>
             /// <param name="labelColumn">The name of the label column.</param>
-            /// <param name="approximationRank">Approximation rank.</param>
+            /// <param name="approximationRank">Rank of approximation matrixes.</param>
             /// <param name="learningRate">Initial learning rate. It specifies the speed of the training algorithm.</param>
             /// <param name="numIterations">Number of training iterations.</param>
             /// <example>

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

@@ -18,7 +18,7 @@ namespace Microsoft.ML.Tests.TrainerEstimators
     public partial class TrainerEstimators : TestDataPipeBase
     {
         [ConditionalFact(typeof(Environment), nameof(Environment.Is64BitProcess))] // This test is being fixed as part of issue #1441.
-        public void MatrixFactorization_Estimator()
+        public void MatrixFactorizationEstimator()
         {
             string labelColumnName = "Label";
             string matrixColumnIndexColumnName = "Col";
@@ -39,11 +39,11 @@ public void MatrixFactorization_Estimator()
                 MatrixRowIndexColumnName = matrixRowIndexColumnName,
                 LabelColumnName = labelColumnName,
                 NumIterations = 3,
-                NumThreads = 1, 
-                K = 4,
+                NumThreads = 1,
+                ApproximationRank = 4,
             };
 
-            var est = new MatrixFactorizationTrainer(Env, options);
+            var est = ML.Recommendation().Trainers.MatrixFactorization(options);
 
             TestEstimatorCore(est, data, invalidInput: invalidData);
 
@@ -68,20 +68,28 @@ public void MatrixFactorizationSimpleTrainAndPredict()
             var data = reader.Read(new MultiFileSource(GetDataPath(TestDatasets.trivialMatrixFactorization.trainFilename)));
 
             // Create a pipeline with a single operator.
-            var options = new MatrixFactorizationTrainer.Options {
+            var options = new MatrixFactorizationTrainer.Options
+            {
                 MatrixColumnIndexColumnName = userColumnName,
                 MatrixRowIndexColumnName = itemColumnName,
                 LabelColumnName = labelColumnName,
                 NumIterations = 3,
                 NumThreads = 1, // To eliminate randomness, # of threads must be 1.
-                K = 7,
+                ApproximationRank = 7,
             };
 
             var pipeline = mlContext.Recommendation().Trainers.MatrixFactorization(options);
 
             // Train a matrix factorization model.
             var model = pipeline.Fit(data);
 
+            // Les's validate content of the model.
+            Assert.Equal(model.Model.ApproximationRank, options.ApproximationRank);
+            var leftMatrix = model.Model.GetLeftFactorMatrix();
+            var rightMatrix = model.Model.GetRightFactorMatrix();
+            Assert.Equal(leftMatrix.Length, model.Model.NumberOfRows * model.Model.ApproximationRank);
+            Assert.Equal(rightMatrix.Length, model.Model.NumberOfColumns * model.Model.ApproximationRank);
+
             // Read the test data set as an IDataView
             var testData = reader.Read(new MultiFileSource(GetDataPath(TestDatasets.trivialMatrixFactorization.testFilename)));
 
@@ -197,7 +205,7 @@ public void MatrixFactorizationInMemoryData()
                 LabelColumnName = nameof(MatrixElement.Value),
                 NumIterations = 10,
                 NumThreads = 1, // To eliminate randomness, # of threads must be 1.
-                K = 32,
+                ApproximationRank = 32,
             };
 
             var pipeline = mlContext.Recommendation().Trainers.MatrixFactorization(options);
@@ -287,7 +295,7 @@ public void MatrixFactorizationInMemoryDataZeroBaseIndex()
                 LabelColumnName = nameof(MatrixElement.Value),
                 NumIterations = 100,
                 NumThreads = 1, // To eliminate randomness, # of threads must be 1.
-                K = 32,
+                ApproximationRank = 32,
                 LearningRate = 0.5,
             };
 
@@ -409,7 +417,7 @@ public void OneClassMatrixFactorizationInMemoryDataZeroBaseIndex()
                 NumIterations = 100,
                 NumThreads = 1, // To eliminate randomness, # of threads must be 1.
                 Lambda = 0.025, // Let's test non-default regularization coefficient.
-                K = 16,
+                ApproximationRank = 16,
                 Alpha = 0.01, // Importance coefficient of loss function over matrix elements not specified in the input matrix.
                 C = 0.15, // Desired value for matrix elements not specified in the input matrix.
             };