Conversion of Hogwild SGD to estimator

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

@@ -384,26 +384,51 @@ public static SchemaShape.Column MakeR4ScalarWeightColumn(string weightColumn)
             return new SchemaShape.Column(weightColumn, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false);
         }
 
+        private static void CheckArgColName(IHostEnvironment host, string defaultColName, string argValue)
+        {
+            if (argValue != defaultColName)
+                throw host.Except($"Don't supply a value for the {defaultColName} column in the arguments, as it will be ignored. Specify them in the loader, or constructor instead instead.");
+        }
+
         /// <summary>
         /// Check that the label, feature, weights, groupId column names are not supplied in the args of the constructor, through the advancedSettings parameter,
         /// for cases when the public constructor is called.
         /// The recommendation is to set the column names directly.
         /// </summary>
         public static void CheckArgsHaveDefaultColNames(IHostEnvironment host, LearnerInputBaseWithGroupId args)
         {
-            Action<string, string> checkArgColName = (defaultColName, argValue) =>
-            {
-                if (argValue != defaultColName)
-                    throw host.Except($"Don't supply a value for the {defaultColName} column in the arguments, as it will be ignored. Specify them in the loader, or constructor instead instead.");
-            };
-
             // check that the users didn't specify different label, group, feature, weights in the args, from what they supplied directly
-            checkArgColName(DefaultColumnNames.Label, args.LabelColumn);
-            checkArgColName(DefaultColumnNames.Features, args.FeatureColumn);
-            checkArgColName(DefaultColumnNames.Weight, args.WeightColumn);
+            CheckArgColName(host, DefaultColumnNames.Label, args.LabelColumn);
+            CheckArgColName(host, DefaultColumnNames.Features, args.FeatureColumn);
+            CheckArgColName(host, DefaultColumnNames.Weight, args.WeightColumn);
 
             if (args.GroupIdColumn != null)
-                checkArgColName(DefaultColumnNames.GroupId, args.GroupIdColumn);
+                CheckArgColName(host, DefaultColumnNames.GroupId, args.GroupIdColumn);
+        }
+
+        /// <summary>
+        /// Check that the label, feature, and weights column names are not supplied in the args of the constructor, through the advancedSettings parameter,
+        /// for cases when the public constructor is called.
+        /// The recommendation is to set the column names directly.
+        /// </summary>
+        public static void CheckArgsHaveDefaultColNames(IHostEnvironment host, LearnerInputBaseWithWeight args)
+        {
+            // check that the users didn't specify different label, group, feature, weights in the args, from what they supplied directly
+            CheckArgColName(host, DefaultColumnNames.Label, args.LabelColumn);
+            CheckArgColName(host, DefaultColumnNames.Features, args.FeatureColumn);
+            CheckArgColName(host, DefaultColumnNames.Weight, args.WeightColumn);
+        }
+
+        /// <summary>
+        /// Check that the label and feature column names are not supplied in the args of the constructor, through the advancedSettings parameter,
+        /// for cases when the public constructor is called.
+        /// The recommendation is to set the column names directly.
+        /// </summary>
+        public static void CheckArgsHaveDefaultColNames(IHostEnvironment host, LearnerInputBaseWithLabel args)
+        {
+            // check that the users didn't specify different label, group, feature, weights in the args, from what they supplied directly
+            CheckArgColName(host, DefaultColumnNames.Label, args.LabelColumn);
+            CheckArgColName(host, DefaultColumnNames.Features, args.FeatureColumn);
         }
 
         /// <summary>

src/Microsoft.ML.Legacy/CSharpApi.cs

@@ -10524,10 +10524,10 @@ public sealed partial class StochasticGradientDescentBinaryClassifier : Microsof
             public ClassificationLossFunction LossFunction { get; set; } = new LogLossClassificationLossFunction();
 
             /// <summary>
-            /// L2 regularizer constant
+            /// L2 Regularization constant
             /// </summary>
             [TlcModule.SweepableDiscreteParamAttribute("L2Const", new object[]{1E-07f, 5E-07f, 1E-06f, 5E-06f, 1E-05f})]
-            public float L2Const { get; set; } = 1E-06f;
+            public float L2Weight { get; set; } = 1E-06f;
 
             /// <summary>
             /// Degree of lock-free parallelism. Defaults to automatic depending on data sparseness. Determinism not guaranteed.

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

@@ -0,0 +1,65 @@
+// Licensed to the .NET Foundation under one or more agreements.
+// The .NET Foundation licenses this file to you under the MIT license.
+// See the LICENSE file in the project root for more information.
+
+using System;
+using Microsoft.ML.Runtime;
+using Microsoft.ML.Runtime.Data;
+using Microsoft.ML.Runtime.Internal.Calibration;
+using Microsoft.ML.Runtime.Internal.Internallearn;
+using Microsoft.ML.Runtime.Learners;
+using Microsoft.ML.StaticPipe.Runtime;
+
+namespace Microsoft.ML.StaticPipe
+{
+    using Arguments = StochasticGradientDescentClassificationTrainer.Arguments;
+
+    /// <summary>
+    /// Binary Classification trainer estimators.
+    /// </summary>
+    public static partial class BinaryClassificationTrainers
+    {
+        /// <summary>
+        ///  Predict a target using a linear binary classification model trained with the <see cref="Microsoft.ML.Runtime.Learners.StochasticGradientDescentClassificationTrainer"/> trainer.
+        /// </summary>
+        /// <param name="ctx">The binary classificaiton context trainer object.</param>
+        /// <param name="label">The name of the label column.</param>
+        /// <param name="features">The name of the feature column.</param>
+        /// <param name="weights">The name for the example weight column.</param>
+        /// <param name="maxIterations">The maximum number of iterations; set to 1 to simulate online learning.</param>
+        /// <param name="initLearningRate">The initial learning rate used by SGD.</param>
+        /// <param name="l2Weight">The L2 regularization constant.</param>
+        /// <param name="loss">The loss function to use.</param>
+        /// <param name="advancedSettings">A delegate to apply all the advanced arguments to the algorithm.</param>
+        /// <param name="onFit">A delegate that is called every time the
+        /// <see cref="Estimator{TTupleInShape, TTupleOutShape, TTransformer}.Fit(DataView{TTupleInShape})"/> method is called on the
+        /// <see cref="Estimator{TTupleInShape, TTupleOutShape, TTransformer}"/> instance created out of this. This delegate will receive
+        /// the linear model that was trained.  Note that this action cannot change the result in any way; it is only a way for the caller to
+        /// be informed about what was learnt.</param>
+        /// <returns>The predicted output.</returns>
+        public static (Scalar<float> score, Scalar<float> probability, Scalar<bool> predictedLabel) StochasticGradientDescentClassificationTrainer(this BinaryClassificationContext.BinaryClassificationTrainers ctx,
+            Scalar<bool> label,
+            Vector<float> features,
+            Scalar<float> weights = null,
+            int maxIterations = Arguments.Defaults.MaxIterations,
+            double initLearningRate = Arguments.Defaults.InitLearningRate,
+            float l2Weight = Arguments.Defaults.L2Weight,
+            ISupportClassificationLossFactory loss = null,
+            Action<Arguments> advancedSettings = null,
+            Action<IPredictorWithFeatureWeights<float>> onFit = null)
+        {
+            var rec = new TrainerEstimatorReconciler.BinaryClassifier(
+                (env, labelName, featuresName, weightsName) =>
+                {
+                    var trainer = new StochasticGradientDescentClassificationTrainer(env, featuresName, labelName, weightsName, maxIterations, initLearningRate, l2Weight, loss, advancedSettings);
+
+                    if (onFit != null)
+                        return trainer.WithOnFitDelegate(trans => onFit(trans.Model));
+                    return trainer;
+
+                }, label, features, weights);
+
+            return rec.Output;
+        }
+    }
+}