Ap, LinearSVM, OGD as estimators

src/Microsoft.ML.Core/Data/IEstimator.cs

@@ -237,7 +237,7 @@ public interface IEstimator<out TTransformer>
         /// <summary>
         /// Train and return a transformer.
         /// </summary>
-        TTransformer Fit(IDataView input);
+        TTransformer Fit(IDataView input, IDataView validationData = null, IPredictor initialPredictor = null);
 
         /// <summary>
         /// Schema propagation for estimators.

src/Microsoft.ML.Data/DataLoadSave/EstimatorChain.cs

@@ -42,7 +42,7 @@ public EstimatorChain()
             LastEstimator = null;
         }
 
-        public TransformerChain<TLastTransformer> Fit(IDataView input)
+        public TransformerChain<TLastTransformer> Fit(IDataView input, IDataView validationData = null, IPredictor initialPredictor = null)
         {
             // REVIEW: before fitting, run schema propagation.
             // Currently, it throws.

src/Microsoft.ML.Data/DataLoadSave/EstimatorExtensions.cs

@@ -90,7 +90,7 @@ public DelegateEstimator(IEstimator<TTransformer> estimator, Action<TTransformer
                 _onFit = onFit;
             }
 
-            public TTransformer Fit(IDataView input)
+            public TTransformer Fit(IDataView input, IDataView validationData = null, IPredictor initialPredictor = null)
             {
                 var trans = _est.Fit(input);
                 _onFit(trans);
@@ -102,9 +102,9 @@ public SchemaShape GetOutputSchema(SchemaShape inputSchema)
         }
 
         /// <summary>
-        /// Given an estimator, return a wrapping object that will call a delegate once <see cref="IEstimator{TTransformer}.Fit(IDataView)"/>
+        /// Given an estimator, return a wrapping object that will call a delegate once <see cref="IEstimator{TTransformer}.Fit(IDataView, IDataView, IPredictor)"/>
         /// is called. It is often important for an estimator to return information about what was fit, which is why the
-        /// <see cref="IEstimator{TTransformer}.Fit(IDataView)"/> method returns a specifically typed object, rather than just a general
+        /// <see cref="IEstimator{TTransformer}.Fit(IDataView, IDataView, IPredictor)"/> method returns a specifically typed object, rather than just a general
         /// <see cref="ITransformer"/>. However, at the same time, <see cref="IEstimator{TTransformer}"/> are often formed into pipelines
         /// with many objects, so we may need to build a chain of estimators via <see cref="EstimatorChain{TLastTransformer}"/> where the
         /// estimator for which we want to get the transformer is buried somewhere in this chain. For that scenario, we can through this
@@ -113,7 +113,7 @@ public SchemaShape GetOutputSchema(SchemaShape inputSchema)
         /// <typeparam name="TTransformer">The type of <see cref="ITransformer"/> returned by <paramref name="estimator"/></typeparam>
         /// <param name="estimator">The estimator to wrap</param>
         /// <param name="onFit">The delegate that is called with the resulting <typeparamref name="TTransformer"/> instances once
-        /// <see cref="IEstimator{TTransformer}.Fit(IDataView)"/> is called. Because <see cref="IEstimator{TTransformer}.Fit(IDataView)"/>
+        /// <see cref="IEstimator{TTransformer}.Fit(IDataView, IDataView, IPredictor)"/> is called. Because <see cref="IEstimator{TTransformer}.Fit(IDataView, IDataView, IPredictor)"/>
         /// may be called multiple times, this delegate may also be called multiple times.</param>
         /// <returns>A wrapping estimator that calls the indicated delegate whenever fit is called</returns>
         public static IEstimator<TTransformer> WithOnFitDelegate<TTransformer>(this IEstimator<TTransformer> estimator, Action<TTransformer> onFit)

src/Microsoft.ML.Data/DataLoadSave/TrivialEstimator.cs

@@ -8,7 +8,7 @@ namespace Microsoft.ML.Runtime.Data
 {
     /// <summary>
     /// The trivial implementation of <see cref="IEstimator{TTransformer}"/> that already has
-    /// the transformer and returns it on every call to <see cref="Fit(IDataView)"/>.
+    /// the transformer and returns it on every call to <see cref="Fit(IDataView, IDataView, IPredictor)"/>.
     ///
     /// Concrete implementations still have to provide the schema propagation mechanism, since
     /// there is no easy way to infer it from the transformer.
@@ -28,7 +28,7 @@ protected TrivialEstimator(IHost host, TTransformer transformer)
             Transformer = transformer;
         }
 
-        public TTransformer Fit(IDataView input) => Transformer;
+        public TTransformer Fit(IDataView input, IDataView validationData = null, IPredictor initialPredictor = null) => Transformer;
 
         public abstract SchemaShape GetOutputSchema(SchemaShape inputSchema);
     }

src/Microsoft.ML.Data/Training/TrainerEstimatorBase.cs

@@ -17,6 +17,12 @@ public abstract class TrainerEstimatorBase<TTransformer, TModel> : ITrainerEstim
         where TTransformer : IPredictionTransformer<TModel>
         where TModel : IPredictor
     {
+        /// <summary>
+        /// A standard string to use in errors or warnings by subclasses, to communicate the idea that no valid
+        /// instances were able to be found.
+        /// </summary>
+        protected const string NoTrainingInstancesMessage = "No valid training instances found, all instances have missing features.";
+
         /// <summary>
         /// The feature column that the trainer expects.
         /// </summary>
@@ -61,7 +67,7 @@ public TrainerEstimatorBase(IHost host, SchemaShape.Column feature, SchemaShape.
             WeightColumn = weight;
         }
 
-        public TTransformer Fit(IDataView input) => TrainTransformer(input);
+        public TTransformer Fit(IDataView input, IDataView validationData = null, IPredictor initialPredictor = null) => TrainTransformer(input, validationData, initialPredictor);
 
         public SchemaShape GetOutputSchema(SchemaShape inputSchema)
         {

src/Microsoft.ML.Data/Transforms/CopyColumnsTransform.cs

@@ -54,7 +54,7 @@ public CopyColumnsEstimator(IHostEnvironment env, params (string source, string
             _columns = columns;
         }
 
-        public CopyColumnsTransform Fit(IDataView input)
+        public CopyColumnsTransform Fit(IDataView input, IDataView validationData = null, IPredictor initialPredictor = null)
         {
             // Invoke schema validation.
             GetOutputSchema(SchemaShape.Create(input.Schema));

src/Microsoft.ML.Data/Transforms/Normalizer.cs

@@ -170,7 +170,7 @@ public Normalizer(IHostEnvironment env, params ColumnBase[] columns)
             _columns = columns.ToArray();
         }
 
-        public NormalizerTransformer Fit(IDataView input)
+        public NormalizerTransformer Fit(IDataView input, IDataView validationData = null, IPredictor initialPredictor = null)
         {
             _host.CheckValue(input, nameof(input));
             return NormalizerTransformer.Train(_host, input, _columns);

src/Microsoft.ML.Data/Transforms/TermEstimator.cs

@@ -23,7 +23,7 @@ public TermEstimator(IHostEnvironment env, params TermTransform.ColumnInfo[] col
             _columns = columns;
         }
 
-        public TermTransform Fit(IDataView input) => new TermTransform(_host, input, _columns);
+        public TermTransform Fit(IDataView input, IDataView validationData = null, IPredictor initialPredictor = null) => new TermTransform(_host, input, _columns);
 
         public SchemaShape GetOutputSchema(SchemaShape inputSchema)
         {

src/Microsoft.ML.StandardLearners/Standard/LinearClassificationTrainer.cs

@@ -1478,7 +1478,7 @@ protected override void CheckLabel(RoleMappedData examples, out int weightSetCou
         protected override BinaryPredictionTransformer<TScalarPredictor> MakeTransformer(TScalarPredictor model, ISchema trainSchema)
             => new BinaryPredictionTransformer<TScalarPredictor>(Host, model, trainSchema, FeatureColumn.Name);
 
-        public BinaryPredictionTransformer<TScalarPredictor> Train(IDataView trainData, IDataView validationData) => TrainTransformer(trainData, validationData);
+        public BinaryPredictionTransformer<TScalarPredictor> Train(IDataView trainData, IDataView validationData = null, IPredictor initialPredictor = null) => TrainTransformer(trainData, validationData, initialPredictor);
     }
 
     public sealed class StochasticGradientDescentClassificationTrainer :

src/Microsoft.ML.StandardLearners/Standard/LinearPredictor.cs

@@ -487,9 +487,7 @@ protected override void SaveCore(ModelSaveContext ctx)
             ctx.SetVersionInfo(GetVersionInfo());
         }
 
-        public override PredictionKind PredictionKind {
-            get { return PredictionKind.BinaryClassification; }
-        }
+        public override PredictionKind PredictionKind => PredictionKind.BinaryClassification;
 
         /// <summary>
         /// Combine a bunch of models into one by averaging parameters

src/Microsoft.ML.StandardLearners/Standard/Online/AveragedLinear.cs

@@ -11,6 +11,7 @@
 using Microsoft.ML.Runtime.Internal.Utilities;
 using Microsoft.ML.Runtime.Numeric;
 using Microsoft.ML.Runtime.Internal.Internallearn;
+using Microsoft.ML.Core.Data;
 
 // TODO: Check if it works properly if Averaged is set to false
 
@@ -52,12 +53,12 @@ public abstract class AveragedLinearArguments : OnlineLinearArguments
         public Float AveragedTolerance = (Float)1e-2;
     }
 
-    public abstract class AveragedLinearTrainer<TArguments, TPredictor> : OnlineLinearTrainer<TArguments, TPredictor>
-        where TArguments : AveragedLinearArguments
-        where TPredictor : IPredictorProducing<Float>
+    public abstract class AveragedLinearTrainer<TTransformer, TModel> : OnlineLinearTrainer<TTransformer, TModel>
+        where TTransformer : IPredictionTransformer<TModel>
+        where TModel : IPredictor
     {
+        protected readonly new AveragedLinearArguments Args;
         protected IScalarOutputLoss LossFunction;
-
         protected Float Gain;
 
         // For computing averaged weights and bias (if needed)
@@ -74,15 +75,18 @@ public abstract class AveragedLinearTrainer<TArguments, TPredictor> : OnlineLine
         // We'll keep a few things global to prevent garbage collection
         protected int NumNoUpdates;
 
-        protected AveragedLinearTrainer(TArguments args, IHostEnvironment env, string name)
-            : base(args, env, name)
+        protected AveragedLinearTrainer(AveragedLinearArguments args, IHostEnvironment env, string name, SchemaShape.Column label)
+            : base(args, env, name, label)
         {
             Contracts.CheckUserArg(args.LearningRate > 0, nameof(args.LearningRate), UserErrorPositive);
             Contracts.CheckUserArg(!args.ResetWeightsAfterXExamples.HasValue || args.ResetWeightsAfterXExamples > 0, nameof(args.ResetWeightsAfterXExamples), UserErrorPositive);
+
             // Weights are scaled down by 2 * L2 regularization on each update step, so 0.5 would scale all weights to 0, which is not sensible.
             Contracts.CheckUserArg(0 <= args.L2RegularizerWeight && args.L2RegularizerWeight < 0.5, nameof(args.L2RegularizerWeight), "must be in range [0, 0.5)");
             Contracts.CheckUserArg(args.RecencyGain >= 0, nameof(args.RecencyGain), UserErrorNonNegative);
             Contracts.CheckUserArg(args.AveragedTolerance >= 0, nameof(args.AveragedTolerance), UserErrorNonNegative);
+
+            Args = args;
         }
 
         protected override void InitCore(IChannel ch, int numFeatures, LinearPredictor predictor)

src/Microsoft.ML.StandardLearners/Standard/Online/AveragedPerceptron.cs

@@ -13,6 +13,7 @@
 using Microsoft.ML.Runtime.Learners;
 using Microsoft.ML.Runtime.Numeric;
 using Microsoft.ML.Runtime.Training;
+using Microsoft.ML.Core.Data;
 
 [assembly: LoadableClass(AveragedPerceptronTrainer.Summary, typeof(AveragedPerceptronTrainer), typeof(AveragedPerceptronTrainer.Arguments),
     new[] { typeof(SignatureBinaryClassifierTrainer), typeof(SignatureTrainer), typeof(SignatureFeatureScorerTrainer) },
@@ -29,14 +30,15 @@ namespace Microsoft.ML.Runtime.Learners
     //     - Feature normalization. By default, rescaling between min and max values for every feature
     //     - Prediction calibration to produce probabilities. Off by default, if on, uses exponential (aka Platt) calibration.
     /// <include file='doc.xml' path='doc/members/member[@name="AP"]/*' />
-    public sealed class AveragedPerceptronTrainer :
-        AveragedLinearTrainer<AveragedPerceptronTrainer.Arguments, LinearBinaryPredictor>
+    public sealed class AveragedPerceptronTrainer : AveragedLinearTrainer<BinaryPredictionTransformer<LinearBinaryPredictor> , LinearBinaryPredictor>
     {
         public const string LoadNameValue = "AveragedPerceptron";
         internal const string UserNameValue = "Averaged Perceptron";
         internal const string ShortName = "ap";
         internal const string Summary = "Averaged Perceptron Binary Classifier.";
 
+        internal new readonly Arguments Args;
+
         public class Arguments : AveragedLinearArguments
         {
             [Argument(ArgumentType.Multiple, HelpText = "Loss Function", ShortName = "loss", SortOrder = 50)]
@@ -49,22 +51,37 @@ public class Arguments : AveragedLinearArguments
             public int MaxCalibrationExamples = 1000000;
         }
 
-        protected override bool NeedCalibration => true;
-
         public AveragedPerceptronTrainer(IHostEnvironment env, Arguments args)
-            : base(args, env, UserNameValue)
+            : base(args, env, UserNameValue, MakeLabelColumn(args.LabelColumn))
         {
+            Args = args;
             LossFunction = Args.LossFunction.CreateComponent(env);
+
+            OutputColumns = new[]
+            {
+                new SchemaShape.Column(DefaultColumnNames.Score, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false),
+                new SchemaShape.Column(DefaultColumnNames.Probability, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false),
+                new SchemaShape.Column(DefaultColumnNames.PredictedLabel, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false)
+            };
         }
 
-        public override PredictionKind PredictionKind { get { return PredictionKind.BinaryClassification; } }
+        public override PredictionKind PredictionKind => PredictionKind.BinaryClassification;
+
+        protected override bool NeedCalibration => true;
+
+        protected override SchemaShape.Column[] OutputColumns { get; }
 
         protected override void CheckLabel(RoleMappedData data)
         {
             Contracts.AssertValue(data);
             data.CheckBinaryLabel();
         }
 
+        private static SchemaShape.Column MakeLabelColumn(string labelColumn)
+        {
+            return new SchemaShape.Column(labelColumn, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false);
+        }
+
         protected override LinearBinaryPredictor CreatePredictor()
         {
             Contracts.Assert(WeightsScale == 1);
@@ -87,6 +104,9 @@ protected override LinearBinaryPredictor CreatePredictor()
             return new LinearBinaryPredictor(Host, ref weights, bias);
         }
 
+        protected override BinaryPredictionTransformer<LinearBinaryPredictor> MakeTransformer(LinearBinaryPredictor model, ISchema trainSchema)
+        => new BinaryPredictionTransformer<LinearBinaryPredictor>(Host, model, trainSchema, FeatureColumn.Name);
+
         [TlcModule.EntryPoint(Name = "Trainers.AveragedPerceptronBinaryClassifier",
             Desc = Summary,
             UserName = UserNameValue,

src/Microsoft.ML.StandardLearners/Standard/Online/LinearSvm.cs

@@ -26,12 +26,13 @@
 
 namespace Microsoft.ML.Runtime.Learners
 {
+    using Microsoft.ML.Core.Data;
     using TPredictor = LinearBinaryPredictor;
 
     /// <summary>
     /// Linear SVM that implements PEGASOS for training. See: http://ttic.uchicago.edu/~shai/papers/ShalevSiSr07.pdf
     /// </summary>
-    public sealed class LinearSvm : OnlineLinearTrainer<LinearSvm.Arguments, TPredictor>
+    public sealed class LinearSvm : OnlineLinearTrainer<BinaryPredictionTransformer<LinearBinaryPredictor>, LinearBinaryPredictor>
     {
         public const string LoadNameValue = "LinearSVM";
         public const string ShortName = "svm";
@@ -41,6 +42,8 @@ public sealed class LinearSvm : OnlineLinearTrainer<LinearSvm.Arguments, TPredic
             + "and all the negative examples are on the other. After this mapping, quadratic programming is used to find the separating hyperplane that maximizes the "
             + "margin, i.e., the minimal distance between it and the instances.";
 
+        internal new readonly Arguments Args;
+
         public sealed class Arguments : OnlineLinearArguments
         {
             [Argument(ArgumentType.AtMostOnce, HelpText = "Regularizer constant", ShortName = "lambda", SortOrder = 50)]
@@ -83,13 +86,24 @@ public sealed class Arguments : OnlineLinearArguments
         protected override bool NeedCalibration => true;
 
         public LinearSvm(IHostEnvironment env, Arguments args)
-            : base(args, env, UserNameValue)
+            : base(args, env, UserNameValue, MakeLabelColumn(args.LabelColumn))
         {
             Contracts.CheckUserArg(args.Lambda > 0, nameof(args.Lambda), UserErrorPositive);
             Contracts.CheckUserArg(args.BatchSize > 0, nameof(args.BatchSize), UserErrorPositive);
+
+            Args = args;
+
+            OutputColumns = new[]
+            {
+                new SchemaShape.Column(DefaultColumnNames.Score, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false),
+                new SchemaShape.Column(DefaultColumnNames.Probability, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false),
+                new SchemaShape.Column(DefaultColumnNames.PredictedLabel, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false)
+            };
         }
 
-        public override PredictionKind PredictionKind { get { return PredictionKind.BinaryClassification; } }
+        public override PredictionKind PredictionKind => PredictionKind.BinaryClassification;
+
+        protected override SchemaShape.Column[] OutputColumns { get; }
 
         protected override void CheckLabel(RoleMappedData data)
         {
@@ -105,6 +119,11 @@ protected override Float Margin(ref VBuffer<Float> feat)
             return Bias + VectorUtils.DotProduct(ref feat, ref Weights) * WeightsScale;
         }
 
+        private static SchemaShape.Column MakeLabelColumn(string labelColumn)
+        {
+            return new SchemaShape.Column(labelColumn, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false);
+        }
+
         protected override void InitCore(IChannel ch, int numFeatures, LinearPredictor predictor)
         {
             base.InitCore(ch, numFeatures, predictor);
@@ -237,5 +256,8 @@ public static CommonOutputs.BinaryClassificationOutput TrainLinearSvm(IHostEnvir
                 () => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.LabelColumn),
                 calibrator: input.Calibrator, maxCalibrationExamples: input.MaxCalibrationExamples);
         }
+
+        protected override BinaryPredictionTransformer<LinearBinaryPredictor> MakeTransformer(LinearBinaryPredictor model, ISchema trainSchema)
+        => new BinaryPredictionTransformer<LinearBinaryPredictor>(Host, model, trainSchema, FeatureColumn.Name);
     }
 }