Scrubbing SDCA learners

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

@@ -49,7 +49,7 @@ public static void Example()
             // the "Features" column produced by FeaturizeText as the features column.
             var pipeline = mlContext.Transforms.Text.FeaturizeText("SentimentText", "Features")
                     .AppendCacheCheckpoint(mlContext) // Add a data-cache step within a pipeline.
-                    .Append(mlContext.BinaryClassification.Trainers.StochasticDualCoordinateAscent(labelColumnName: "Sentiment", featureColumnName: "Features", l2Const: 0.001f));
+                    .Append(mlContext.BinaryClassification.Trainers.StochasticDualCoordinateAscent(labelColumnName: "Sentiment", featureColumnName: "Features", l2Regularization: 0.001f));
 
             // Step 3: Run Cross-Validation on this pipeline.
             var cvResults = mlContext.BinaryClassification.CrossValidate(data, pipeline, labelColumn: "Sentiment");
@@ -65,7 +65,7 @@ public static void Example()
                                         LabelColumnName = "Sentiment",
                                         FeatureColumnName = "Features",
                                         ConvergenceTolerance = 0.01f,  // The learning rate for adjusting bias from being regularized
-                                        NumThreads = 2, // Degree of lock-free parallelism 
+                                        NumberOfThreads = 2, // Degree of lock-free parallelism 
                                       }));
 
             // Run Cross-Validation on this second pipeline.

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

@@ -41,7 +41,7 @@ public static void Example()
             // Step 2: Create a binary classifier. This trainer may produce a logistic regression model.
             // We set the "Label" column as the label of the dataset, and the "Features" column as the features column.
             var pipeline = mlContext.BinaryClassification.Trainers.StochasticDualCoordinateAscentNonCalibrated(
-                labelColumnName: "Label", featureColumnName: "Features", loss: new HingeLoss(), l2Const: 0.001f);
+                labelColumnName: "Label", featureColumnName: "Features", loss: new HingeLoss(), l2Regularization: 0.001f);
 
             // Step 3: Train the pipeline created.
             var model = pipeline.Fit(data);

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

@@ -27,7 +27,7 @@ public static void Example()
                 // Make the convergence tolerance tighter.
                 ConvergenceTolerance = 0.05f,
                 // Increase the maximum number of passes over training data.
-                MaxIterations = 30,
+                NumberOfIterations = 30,
                 // Give the instances of the positive class slightly more weight.
                 PositiveInstanceWeight = 1.2f,
             };

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

@@ -28,9 +28,9 @@ public static void Example()
                 .Trainers.StochasticGradientDescentNonCalibrated(
                         new SgdNonCalibratedBinaryTrainer.Options
                         {
-                            InitLearningRate = 0.01,
-                            MaxIterations = 10,
-                            L2Weight = 1e-7f
+                            InitialLearningRate = 0.01,
+                            NumberOfIterations = 10,
+                            L2Regularization = 1e-7f
                         }
                        );
 

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

@@ -27,7 +27,7 @@ public static void Example()
                 // Make the convergence tolerance tighter.
                 ConvergenceTolerance = 5e-5,
                 // Increase the maximum number of passes over training data.
-                MaxIterations = 30,
+                NumberOfIterations = 30,
                 // Give the instances of the positive class slightly more weight.
                 PositiveInstanceWeight = 1.2f,
             };

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/StochasticDualCoordinateAscentWithOptions.cs

@@ -33,7 +33,7 @@ public static void Example()
                 // Make the convergence tolerance tighter.
                 ConvergenceTolerance = 0.05f,
                 // Increase the maximum number of passes over training data.
-                MaxIterations = 30,
+                NumberOfIterations = 30,
             };
 
             // Create a pipeline. 

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/StochasticDualCoordinateAscentWithOptions.cs

@@ -26,7 +26,7 @@ public static void Example()
                 // Make the convergence tolerance tighter.
                 ConvergenceTolerance = 0.02f,
                 // Increase the maximum number of passes over training data.
-                MaxIterations = 30,
+                NumberOfIterations = 30,
                 // Increase learning rate for bias
                 BiasLearningRate = 0.1f
             };

docs/samples/Microsoft.ML.Samples/Static/SDCABinaryClassification.cs

@@ -77,7 +77,7 @@ public static void SdcaBinaryClassification()
                             row.Label,
                             row.Features,
                             l1Threshold: 0.25f,
-                            maxIterations: 100)))
+                            numberOfIterations: 100)))
                 .Append(row => (
                     Label: row.Label,
                     Score: row.Score,

docs/samples/Microsoft.ML.Samples/Static/SDCARegression.cs

@@ -37,7 +37,7 @@ public static void SdcaRegression()
                                             r.label,
                                             r.features,
                                             l1Threshold: 0f,
-                                            maxIterations: 100,
+                                            numberOfIterations: 100,
                                         onFit: p => pred = p)
                                 )
                         );

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

@@ -402,6 +402,9 @@ ValueMapper<TSrc, VBuffer<float>> IFeatureContributionMapper.GetFeatureContribut
         }
     }
 
+    /// <summary>
+    /// The model parameters class for linear binary trainer estimators.
+    /// </summary>
     public sealed partial class LinearBinaryModelParameters : LinearModelParameters,
         ICanGetSummaryInKeyValuePairs,
         IParameterMixer<float>
@@ -577,6 +580,9 @@ private protected override void SaveAsIni(TextWriter writer, RoleMappedSchema sc
         }
     }
 
+    /// <summary>
+    /// The model parameters class for linear regression.
+    /// </summary>
     public sealed class LinearRegressionModelParameters : RegressionModelParameters,
         IParameterMixer<float>,
         ICanGetSummaryInKeyValuePairs