Scrubbing FieldAwareFactorizationMachine learner.

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FieldAwareFactorizationMachine.cs

@@ -48,7 +48,7 @@ public static void Example()
             Console.WriteLine("The linear weights of some of the features are: " + 
                 string.Concat(Enumerable.Range(1, 10).Select(i => $"{linearWeights[i]:F4} ")));
             Console.WriteLine("The weights of some of the latent features are: " + 
-                 string.Concat(Enumerable.Range(1, 10).Select(i => $"{latentWeights[i]:F4} ")));
+                string.Concat(Enumerable.Range(1, 10).Select(i => $"{latentWeights[i]:F4} ")));
 
             //  The feature count is: 9374
             //  The number of fields is: 1

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FieldAwareFactorizationMachinewWithOptions.cs

@@ -30,12 +30,12 @@ public static void Example()
             var pipeline = new EstimatorChain<ITransformer>().AppendCacheCheckpoint(mlContext)
                 .Append(mlContext.BinaryClassification.Trainers.
                     FieldAwareFactorizationMachine(
-                        new FieldAwareFactorizationMachineTrainer.Options
+                        new FieldAwareFactorizationMachineBinaryClassificationTrainer.Options
                         {
                             FeatureColumn = "Features",
                             LabelColumn = "Sentiment",
                             LearningRate = 0.1f,
-                            Iterations = 10
+                            NumberOfIterations = 10
                         }));
 
             // Fit the model.
@@ -57,7 +57,7 @@ public static void Example()
             Console.WriteLine("The linear weights of some of the features are: " +
                 string.Concat(Enumerable.Range(1, 10).Select(i => $"{linearWeights[i]:F4} ")));
             Console.WriteLine("The weights of some of the latent features are: " +
-                 string.Concat(Enumerable.Range(1, 10).Select(i => $"{latentWeights[i]:F4} ")));
+                string.Concat(Enumerable.Range(1, 10).Select(i => $"{latentWeights[i]:F4} ")));
 
             //  The feature count is: 9374
             //  The number of fields is: 1

src/Microsoft.ML.StandardLearners/FactorizationMachine/FactorizationMachineCatalog.cs

@@ -9,7 +9,7 @@
 namespace Microsoft.ML
 {
     /// <summary>
-    /// Extension method to create <see cref="FieldAwareFactorizationMachineTrainer"/>
+    /// Extension method to create <see cref="FieldAwareFactorizationMachineBinaryClassificationTrainer"/>
     /// </summary>
     public static class FactorizationMachineExtensions
     {
@@ -26,14 +26,14 @@ public static class FactorizationMachineExtensions
         ///  [!code-csharp[FieldAwareFactorizationMachine](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FieldAwareFactorizationMachine.cs)]
         /// ]]></format>
         /// </example>
-        public static FieldAwareFactorizationMachineTrainer FieldAwareFactorizationMachine(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
+        public static FieldAwareFactorizationMachineBinaryClassificationTrainer FieldAwareFactorizationMachine(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
             string[] featureColumnNames,
             string labelColumnName = DefaultColumnNames.Label,
             string exampleWeightColumnName = null)
         {
             Contracts.CheckValue(catalog, nameof(catalog));
             var env = CatalogUtils.GetEnvironment(catalog);
-            return new FieldAwareFactorizationMachineTrainer(env, featureColumnNames, labelColumnName, exampleWeightColumnName);
+            return new FieldAwareFactorizationMachineBinaryClassificationTrainer(env, featureColumnNames, labelColumnName, exampleWeightColumnName);
         }
 
         /// <summary>
@@ -47,12 +47,12 @@ public static FieldAwareFactorizationMachineTrainer FieldAwareFactorizationMachi
         ///  [!code-csharp[FieldAwareFactorizationMachine](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FieldAwareFactorizationMachineWithOptions.cs)]
         /// ]]></format>
         /// </example>
-        public static FieldAwareFactorizationMachineTrainer FieldAwareFactorizationMachine(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
-            FieldAwareFactorizationMachineTrainer.Options options)
+        public static FieldAwareFactorizationMachineBinaryClassificationTrainer FieldAwareFactorizationMachine(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
+            FieldAwareFactorizationMachineBinaryClassificationTrainer.Options options)
         {
             Contracts.CheckValue(catalog, nameof(catalog));
             var env = CatalogUtils.GetEnvironment(catalog);
-            return new FieldAwareFactorizationMachineTrainer(env, options);
+            return new FieldAwareFactorizationMachineBinaryClassificationTrainer(env, options);
         }
     }
 }

src/Microsoft.ML.StandardLearners/FactorizationMachine/FactorizationMachineTrainer.cs

@@ -15,12 +15,12 @@
 using Microsoft.ML.Internal.Utilities;
 using Microsoft.ML.Trainers;
 
-[assembly: LoadableClass(FieldAwareFactorizationMachineTrainer.Summary, typeof(FieldAwareFactorizationMachineTrainer),
-    typeof(FieldAwareFactorizationMachineTrainer.Options), new[] { typeof(SignatureBinaryClassifierTrainer), typeof(SignatureTrainer) }
-    , FieldAwareFactorizationMachineTrainer.UserName, FieldAwareFactorizationMachineTrainer.LoadName,
-    FieldAwareFactorizationMachineTrainer.ShortName, DocName = "trainer/FactorizationMachine.md")]
+[assembly: LoadableClass(FieldAwareFactorizationMachineBinaryClassificationTrainer.Summary, typeof(FieldAwareFactorizationMachineBinaryClassificationTrainer),
+    typeof(FieldAwareFactorizationMachineBinaryClassificationTrainer.Options), new[] { typeof(SignatureBinaryClassifierTrainer), typeof(SignatureTrainer) }
+    , FieldAwareFactorizationMachineBinaryClassificationTrainer.UserName, FieldAwareFactorizationMachineBinaryClassificationTrainer.LoadName,
+    FieldAwareFactorizationMachineBinaryClassificationTrainer.ShortName, DocName = "trainer/FactorizationMachine.md")]
 
-[assembly: LoadableClass(typeof(void), typeof(FieldAwareFactorizationMachineTrainer), null, typeof(SignatureEntryPointModule), FieldAwareFactorizationMachineTrainer.LoadName)]
+[assembly: LoadableClass(typeof(void), typeof(FieldAwareFactorizationMachineBinaryClassificationTrainer), null, typeof(SignatureEntryPointModule), FieldAwareFactorizationMachineBinaryClassificationTrainer.LoadName)]
 
 namespace Microsoft.ML.FactorizationMachine
 {
@@ -32,7 +32,7 @@ namespace Microsoft.ML.FactorizationMachine
      [3] https://github.com/wschin/fast-ffm/blob/master/fast-ffm.pdf
     */
     /// <include file='doc.xml' path='doc/members/member[@name="FieldAwareFactorizationMachineBinaryClassifier"]/*' />
-    public sealed class FieldAwareFactorizationMachineTrainer : ITrainer<FieldAwareFactorizationMachineModelParameters>,
+    public sealed class FieldAwareFactorizationMachineBinaryClassificationTrainer : ITrainer<FieldAwareFactorizationMachineModelParameters>,
         IEstimator<FieldAwareFactorizationMachinePredictionTransformer>
     {
         internal const string Summary = "Train a field-aware factorization machine for binary classification";
@@ -52,9 +52,9 @@ public sealed class Options : LearnerInputBaseWithWeight
             /// <summary>
             /// Number of training iterations.
             /// </summary>
-            [Argument(ArgumentType.AtMostOnce, HelpText = "Number of training iterations", ShortName = "iters", SortOrder = 2)]
+            [Argument(ArgumentType.AtMostOnce, HelpText = "Number of training iterations", ShortName = "iters,iter", SortOrder = 2)]
             [TlcModule.SweepableLongParam(1, 100)]
-            public int Iterations = 5;
+            public int NumberOfIterations = 5;
 
             /// <summary>
             /// Latent space dimension.
@@ -151,12 +151,12 @@ public sealed class Options : LearnerInputBaseWithWeight
         private float _radius;
 
         /// <summary>
-        /// Initializes a new instance of <see cref="FieldAwareFactorizationMachineTrainer"/> through the <see cref="Options"/> class.
+        /// Initializes a new instance of <see cref="FieldAwareFactorizationMachineBinaryClassificationTrainer"/> through the <see cref="Options"/> class.
         /// </summary>
         /// <param name="env">The private instance of <see cref="IHostEnvironment"/>.</param>
         /// <param name="options">An instance of the legacy <see cref="Options"/> to apply advanced parameters to the algorithm.</param>
         [BestFriend]
-        internal FieldAwareFactorizationMachineTrainer(IHostEnvironment env, Options options)
+        internal FieldAwareFactorizationMachineBinaryClassificationTrainer(IHostEnvironment env, Options options)
         {
             Contracts.CheckValue(env, nameof(env));
             _host = env.Register(LoadName);
@@ -178,14 +178,14 @@ internal FieldAwareFactorizationMachineTrainer(IHostEnvironment env, Options opt
         }
 
         /// <summary>
-        /// Initializes a new instance of <see cref="FieldAwareFactorizationMachineTrainer"/>.
+        /// Initializes a new instance of <see cref="FieldAwareFactorizationMachineBinaryClassificationTrainer"/>.
         /// </summary>
         /// <param name="env">The private instance of <see cref="IHostEnvironment"/>.</param>
         /// <param name="featureColumnNames">The name of column hosting the features. The i-th element stores feature column of the i-th field.</param>
         /// <param name="labelColumnName">The name of the label column.</param>
         /// <param name="weightColumnName">The name of the weight column (optional).</param>
         [BestFriend]
-        internal FieldAwareFactorizationMachineTrainer(IHostEnvironment env,
+        internal FieldAwareFactorizationMachineBinaryClassificationTrainer(IHostEnvironment env,
             string[] featureColumnNames,
             string labelColumnName = DefaultColumnNames.Label,
             string weightColumnName = null)
@@ -218,13 +218,13 @@ private void Initialize(IHostEnvironment env, Options options)
             _host.CheckUserArg(options.LambdaLinear >= 0, nameof(options.LambdaLinear), "Must be non-negative");
             _host.CheckUserArg(options.LambdaLatent >= 0, nameof(options.LambdaLatent), "Must be non-negative");
             _host.CheckUserArg(options.LearningRate > 0, nameof(options.LearningRate), "Must be positive");
-            _host.CheckUserArg(options.Iterations >= 0, nameof(options.Iterations), "Must be non-negative");
+            _host.CheckUserArg(options.NumberOfIterations >= 0, nameof(options.NumberOfIterations), "Must be non-negative");
             _latentDim = options.LatentDimension;
             _latentDimAligned = FieldAwareFactorizationMachineUtils.GetAlignedVectorLength(_latentDim);
             _lambdaLinear = options.LambdaLinear;
             _lambdaLatent = options.LambdaLatent;
             _learningRate = options.LearningRate;
-            _numIterations = options.Iterations;
+            _numIterations = options.NumberOfIterations;
             _norm = options.Normalize;
             _shuffle = options.Shuffle;
             _verbose = options.Verbose;
@@ -514,12 +514,12 @@ internal static CommonOutputs.BinaryClassificationOutput TrainBinary(IHostEnviro
             var host = env.Register("Train a field-aware factorization machine");
             host.CheckValue(input, nameof(input));
             EntryPointUtils.CheckInputArgs(host, input);
-            return LearnerEntryPointsUtils.Train<Options, CommonOutputs.BinaryClassificationOutput>(host, input, () => new FieldAwareFactorizationMachineTrainer(host, input),
+            return LearnerEntryPointsUtils.Train<Options, CommonOutputs.BinaryClassificationOutput>(host, input, () => new FieldAwareFactorizationMachineBinaryClassificationTrainer(host, input),
                 () => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.LabelColumn));
         }
 
         /// <summary>
-        /// Continues the training of a <see cref="FieldAwareFactorizationMachineTrainer"/> using an already trained <paramref name="modelParameters"/> and/or validation data,
+        /// Continues the training of a <see cref="FieldAwareFactorizationMachineBinaryClassificationTrainer"/> using an already trained <paramref name="modelParameters"/> and/or validation data,
         /// and returns a <see cref="FieldAwareFactorizationMachinePredictionTransformer"/>.
         /// </summary>
         public FieldAwareFactorizationMachinePredictionTransformer Fit(IDataView trainData,

src/Microsoft.ML.StandardLearners/FactorizationMachine/FieldAwareFactorizationMachineModelParameters.cs

@@ -253,11 +253,9 @@ internal void CopyLatentWeightsTo(AlignedArray latentWeights)
         /// <summary>
         /// The linear coefficients of the features. It's the symbol `w` in the doc: https://github.com/wschin/fast-ffm/blob/master/fast-ffm.pdf
         /// </summary>
-        public float[] GetLinearWeights()
+        public IReadOnlyList<float> GetLinearWeights()
         {
-            var linearWeights = new float[_linearWeights.Length];
-            CopyLinearWeightsTo(linearWeights);
-            return linearWeights;
+            return _linearWeights;
         }
 
         /// <summary>
@@ -267,7 +265,7 @@ public float[] GetLinearWeights()
         /// The k-th element in v_{j, f} is latentWeights[j * fieldCount * latentDim + f * latentDim + k].
         /// The size of the returned value is featureCount x fieldCount x latentDim.
         /// </summary>
-        public float[] GetLatentWeights()
+        public IReadOnlyList<float> GetLatentWeights()
         {
             var latentWeights = new float[FeatureCount * FieldCount * LatentDimension];
             for (int j = 0; j < FeatureCount; j++)

src/Microsoft.ML.StaticPipe/FactorizationMachineStatic.cs

@@ -40,7 +40,7 @@ public static (Scalar<float> score, Scalar<bool> predictedLabel) FieldAwareFacto
 
             var rec = new CustomReconciler((env, labelCol, featureCols) =>
             {
-                var trainer = new FieldAwareFactorizationMachineTrainer(env, featureCols, labelCol);
+                var trainer = new FieldAwareFactorizationMachineBinaryClassificationTrainer(env, featureCols, labelCol);
 
                 if (onFit != null)
                     return trainer.WithOnFitDelegate(trans => onFit(trans.Model));
@@ -66,7 +66,7 @@ public static (Scalar<float> score, Scalar<bool> predictedLabel) FieldAwareFacto
         /// <returns>The predicted output.</returns>
         public static (Scalar<float> score, Scalar<bool> predictedLabel) FieldAwareFactorizationMachine(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
             Scalar<bool> label, Vector<float>[] features,
-            FieldAwareFactorizationMachineTrainer.Options options,
+            FieldAwareFactorizationMachineBinaryClassificationTrainer.Options options,
             Action<FieldAwareFactorizationMachineModelParameters> onFit = null)
         {
             Contracts.CheckValue(label, nameof(label));
@@ -77,7 +77,7 @@ public static (Scalar<float> score, Scalar<bool> predictedLabel) FieldAwareFacto
 
             var rec = new CustomReconciler((env, labelCol, featureCols) =>
             {
-                var trainer = new FieldAwareFactorizationMachineTrainer(env, options);
+                var trainer = new FieldAwareFactorizationMachineBinaryClassificationTrainer(env, options);
                 if (onFit != null)
                     return trainer.WithOnFitDelegate(trans => onFit(trans.Model));
                 else

test/BaselineOutput/Common/EntryPoints/core_ep-list.tsv

@@ -49,7 +49,7 @@ Trainers.FastTreeBinaryClassifier	Uses a logit-boost boosted tree learner to per
 Trainers.FastTreeRanker	Trains gradient boosted decision trees to the LambdaRank quasi-gradient.	Microsoft.ML.Trainers.FastTree.FastTree	TrainRanking	Microsoft.ML.Trainers.FastTree.FastTreeRankingTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+RankingOutput
 Trainers.FastTreeRegressor	Trains gradient boosted decision trees to fit target values using least-squares.	Microsoft.ML.Trainers.FastTree.FastTree	TrainRegression	Microsoft.ML.Trainers.FastTree.FastTreeRegressionTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+RegressionOutput
 Trainers.FastTreeTweedieRegressor	Trains gradient boosted decision trees to fit target values using a Tweedie loss function. This learner is a generalization of Poisson, compound Poisson, and gamma regression.	Microsoft.ML.Trainers.FastTree.FastTree	TrainTweedieRegression	Microsoft.ML.Trainers.FastTree.FastTreeTweedieTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+RegressionOutput
-Trainers.FieldAwareFactorizationMachineBinaryClassifier	Train a field-aware factorization machine for binary classification	Microsoft.ML.FactorizationMachine.FieldAwareFactorizationMachineTrainer	TrainBinary	Microsoft.ML.FactorizationMachine.FieldAwareFactorizationMachineTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+BinaryClassificationOutput
+Trainers.FieldAwareFactorizationMachineBinaryClassifier	Train a field-aware factorization machine for binary classification	Microsoft.ML.FactorizationMachine.FieldAwareFactorizationMachineBinaryClassificationTrainer	TrainBinary	Microsoft.ML.FactorizationMachine.FieldAwareFactorizationMachineBinaryClassificationTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+BinaryClassificationOutput
 Trainers.GeneralizedAdditiveModelBinaryClassifier	Trains a gradient boosted stump per feature, on all features simultaneously, to fit target values using least-squares. It mantains no interactions between features.	Microsoft.ML.Trainers.FastTree.Gam	TrainBinary	Microsoft.ML.Trainers.FastTree.BinaryClassificationGamTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+BinaryClassificationOutput
 Trainers.GeneralizedAdditiveModelRegressor	Trains a gradient boosted stump per feature, on all features simultaneously, to fit target values using least-squares. It mantains no interactions between features.	Microsoft.ML.Trainers.FastTree.Gam	TrainRegression	Microsoft.ML.Trainers.FastTree.RegressionGamTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+RegressionOutput
 Trainers.KMeansPlusPlusClusterer	K-means is a popular clustering algorithm. With K-means, the data is clustered into a specified number of clusters in order to minimize the within-cluster sum of squares. K-means++ improves upon K-means by using a better method for choosing the initial cluster centers.	Microsoft.ML.Trainers.KMeans.KMeansPlusPlusTrainer	TrainKMeans	Microsoft.ML.Trainers.KMeans.KMeansPlusPlusTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+ClusteringOutput

test/BaselineOutput/Common/EntryPoints/core_manifest.json

@@ -10119,11 +10119,12 @@
           "IsNullable": false
         },
         {
-          "Name": "Iterations",
+          "Name": "NumberOfIterations",
           "Type": "Int",
           "Desc": "Number of training iterations",
           "Aliases": [
-            "iters"
+            "iters",
+            "iter"
           ],
           "Required": false,
           "SortOrder": 2.0,

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

@@ -19,7 +19,7 @@ public void FfmBinaryClassificationWithAdvancedArguments()
             var data = DatasetUtils.GenerateFfmSamples(500);
             var dataView = mlContext.Data.ReadFromEnumerable(data);
 
-            var ffmArgs = new FieldAwareFactorizationMachineTrainer.Options();
+            var ffmArgs = new FieldAwareFactorizationMachineBinaryClassificationTrainer.Options();
 
             // Customized the field names.
             ffmArgs.FeatureColumn = nameof(DatasetUtils.FfmExample.Field0); // First field.
@@ -44,11 +44,11 @@ public void FieldAwareFactorizationMachine_Estimator()
             var data = new TextLoader(Env, GetFafmBCLoaderArgs())
                     .Read(GetDataPath(TestDatasets.breastCancer.trainFilename));
 
-            var ffmArgs = new FieldAwareFactorizationMachineTrainer.Options {
+            var ffmArgs = new FieldAwareFactorizationMachineBinaryClassificationTrainer.Options {
                 FeatureColumn = "Feature1", // Features from the 1st field.
                 ExtraFeatureColumns = new[] { "Feature2", "Feature3",  "Feature4" }, // 2nd field's feature column, 3rd field's feature column, 4th field's feature column.
                 Shuffle = false,
-                Iterations = 3,
+                NumberOfIterations = 3,
                 LatentDimension = 7,
             };