small updates

Author: Ivan Matantsev

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

@@ -24,7 +24,7 @@ public static void Example()
             // Get DefaultBinaryPipine for adult dataset and append SymSGD to it.
             var pipeline = SamplesUtils.DatasetUtils.DefaultBinaryPipeline(mlContext)
                 .Append(mlContext.BinaryClassification.Trainers.SymbolicStochasticGradientDescent(
-                    new Trainers.SymSgd.SymSgdClassificationTrainer.Options()
+                    new Trainers.HalLearners.SymSgdClassificationTrainer.Options()
                     {
                         LabelColumn = "IsOver50K",
                         LearningRate = 0.2f,

src/Microsoft.ML.HalLearners/HalLearnersCatalog.cs

@@ -20,11 +20,11 @@ public static class HalLearnersCatalog
         /// <param name="catalog">The <see cref="RegressionCatalog"/>.</param>
         /// <param name="labelColumn">The name of label column.</param>
         /// <param name="featureColumn">The name of feature column.</param>
-        /// <param name="weightColumn">The name of weight column.</param>
+        /// <param name="weightColumn">The name of optional weight column.</param>
         /// <example>
         /// <format type="text/markdown">
         /// <![CDATA[
-        /// [!code-csharp[ScoreTensorFlowModel](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/OrdinaryLeastSquares.cs)]
+        /// [!code-csharp[OrdinaryLeastSquares](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/OrdinaryLeastSquares.cs)]
         /// ]]>
         /// </format>
         /// </example>
@@ -53,7 +53,7 @@ public static OlsLinearRegressionTrainer OrdinaryLeastSquares(this RegressionCat
         /// <example>
         /// <format type="text/markdown">
         /// <![CDATA[
-        /// [!code-csharp[ScoreTensorFlowModel](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/OrdinaryLeastSquaresWithOptions.cs)]
+        /// [!code-csharp[OrdinaryLeastSquares](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/OrdinaryLeastSquaresWithOptions.cs)]
         /// ]]>
         /// </format>
         /// </example>
@@ -78,7 +78,7 @@ public static OlsLinearRegressionTrainer OrdinaryLeastSquares(
         /// <example>
         /// <format type="text/markdown">
         /// <![CDATA[
-        /// [!code-csharp[ScoreTensorFlowModel](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/SymbolicStochasticGradientDescent.cs)]
+        /// [!code-csharp[SymbolicStochasticGradientDescent](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/SymbolicStochasticGradientDescent.cs)]
         /// ]]>
         /// </format>
         /// </example>
@@ -107,7 +107,7 @@ public static SymSgdClassificationTrainer SymbolicStochasticGradientDescent(this
         /// <example>
         /// <format type="text/markdown">
         /// <![CDATA[
-        /// [!code-csharp[ScoreTensorFlowModel](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/SymbolicStochasticGradientDescentWithOptions.cs)]
+        /// [!code-csharp[SymbolicStochasticGradientDescent](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/SymbolicStochasticGradientDescentWithOptions.cs)]
         /// ]]>
         /// </format>
         /// </example>

src/Microsoft.ML.HalLearners/OlsLinearRegression.cs

@@ -751,29 +751,6 @@ private protected override void SaveSummary(TextWriter writer, RoleMappedSchema
                 }
             }
         }
-        /// <summary>
-        /// Returns model coefficents.
-        /// </summary>
-        /// <param name="coefficents"></param>
-        public void GetCoefficents(ref VBuffer<float> coefficents)
-        {
-            if (PValues == null)
-            {
-                base.GetFeatureWeights(ref coefficents);
-                return;
-            }
-
-            var size = PValues.Count - 1;
-            var editor = VBufferEditor.Create(ref coefficents, size);
-            for (int i = 0; i < size; i++)
-            {
-                var score = -(float)Math.Log(PValues[i + 1]);
-                if (score > float.MaxValue)
-                    score = float.MaxValue;
-                editor.Values[i] = score;
-            }
-            coefficents = editor.Commit();
-        }
     }
 }
 

src/Microsoft.ML.Recommender/MatrixFactorizationTrainer.cs

@@ -226,7 +226,7 @@ public sealed class Options
             /// <summary>
             /// Number of threads will be used during training. If unspecified all aviable threads will be use.
             /// </summary>
-            [Argument(ArgumentType.AtMostOnce, HelpText = "Number of threads can be used in the training procedure.", ShortName = "t")]
+            [Argument(ArgumentType.AtMostOnce, HelpText = "Number of threads can be used in the training procedure.", ShortName = "t,numthreads")]
             public int? NumberOfThreads;
 
             /// <summary>