- Updating from feedback

Author: singlis

src/Microsoft.ML.StandardLearners/Standard/MultiClass/OneVersusAllTrainer.cs

@@ -49,7 +49,7 @@ public sealed class OneVersusAllTrainer : MetaMulticlassTrainer<MulticlassPredic
         private readonly Options _options;
 
         /// <summary>
-        /// Options passed to OneVsAll.
+        /// Options passed to <see cref="OneVersusAllTrainer"/>
         /// </summary>
         internal sealed class Options : OptionsBase
         {
@@ -78,26 +78,26 @@ internal OneVersusAllTrainer(IHostEnvironment env, Options options)
         /// <param name="env">The <see cref="IHostEnvironment"/> instance.</param>
         /// <param name="binaryEstimator">An instance of a binary <see cref="ITrainerEstimator{TTransformer, TPredictor}"/> used as the base trainer.</param>
         /// <param name="calibrator">The calibrator. If a calibrator is not provided, it will default to <see cref="PlattCalibratorTrainer"/></param>
-        /// <param name="labelColumn">The name of the label colum.</param>
+        /// <param name="labelColumnName">The name of the label colum.</param>
         /// <param name="imputeMissingLabelsAsNegative">If true will treat missing labels as negative labels.</param>
-        /// <param name="maxCalibrationExamples">Number of instances to train the calibrator.</param>
+        /// <param name="maximumCalibrationExampleCount">Number of instances to train the calibrator.</param>
         /// <param name="useProbabilities">Use probabilities (vs. raw outputs) to identify top-score category.</param>
         internal OneVersusAllTrainer(IHostEnvironment env,
             TScalarTrainer binaryEstimator,
-            string labelColumn = DefaultColumnNames.Label,
+            string labelColumnName = DefaultColumnNames.Label,
             bool imputeMissingLabelsAsNegative = false,
             ICalibratorTrainer calibrator = null,
-            int maxCalibrationExamples = 1000000000,
+            int maximumCalibrationExampleCount = 1000000000,
             bool useProbabilities = true)
          : base(env,
                new Options
                {
                    ImputeMissingLabelsAsNegative = imputeMissingLabelsAsNegative,
-                   MaxCalibrationExamples = maxCalibrationExamples,
+                   MaxCalibrationExamples = maximumCalibrationExampleCount,
                },
-               LoadNameValue, labelColumn, binaryEstimator, calibrator)
+               LoadNameValue, labelColumnName, binaryEstimator, calibrator)
         {
-            Host.CheckValue(labelColumn, nameof(labelColumn), "Label column should not be null.");
+            Host.CheckValue(labelColumnName, nameof(labelColumnName), "Label column should not be null.");
             _options = (Options)Args;
             _options.UseProbabilities = useProbabilities;
         }
@@ -165,12 +165,12 @@ private IDataView MapLabels(RoleMappedData data, int cls)
                 return MapLabelsCore(NumberDataViewType.Double, (in double val) => key == val, data);
             }
 
-            throw Host.ExceptNotSupp($"Label column type is not supported by OneVsAllTrainer: {lab.Type}");
+            throw Host.ExceptNotSupp($"Label column type is not supported by OneVersusAllTrainer: {lab.Type.RawType}");
         }
 
-        /// <summary> Trains a <see cref="MulticlassPredictionTransformer{OneVsAllModelParameters}"/> model.</summary>
+        /// <summary> Trains a <see cref="MulticlassPredictionTransformer{OneVersusAllModelParameters}"/> model.</summary>
         /// <param name="input">The input data.</param>
-        /// <returns>A <see cref="MulticlassPredictionTransformer{OneVsAllModelParameters}"/> model./></returns>
+        /// <returns>A <see cref="MulticlassPredictionTransformer{OneVersusAllModelParameters}"/> model./></returns>
         public override MulticlassPredictionTransformer<OneVersusAllModelParameters> Fit(IDataView input)
         {
             var roles = new KeyValuePair<CR, string>[1];
@@ -203,7 +203,7 @@ public override MulticlassPredictionTransformer<OneVersusAllModelParameters> Fit
     }
 
     /// <summary>
-    /// Contains the model parameters and prediction functions for OneVsAll.
+    /// Contains the model parameters and prediction functions for <see cref="OneVersusAllTrainer"/>.
     /// </summary>
     public sealed class OneVersusAllModelParameters :
         ModelParametersBase<VBuffer<float>>,
@@ -305,7 +305,7 @@ internal static OneVersusAllModelParameters Create(IHost host, bool useProbabili
         }
 
         /// <summary>
-        /// Create a OneVsAll predictor from an array of predictors.
+        /// Create a <see cref="OneVersusAllModelParameters"/> from an array of predictors.
         /// </summary>
         [BestFriend]
         internal static OneVersusAllModelParameters Create(IHost host, TScalarPredictor[] predictors)

src/Microsoft.ML.StandardLearners/Standard/MultiClass/Pkpd.cs

@@ -50,7 +50,7 @@ namespace Microsoft.ML.Trainers
     /// logistic regression is a more principled way to solve a multiclass problem, it
     /// requires that the learner store a lot more intermediate state in the form of
     /// L-BFGS history for all classes *simultaneously*, rather than just one-by-one
-    /// as would be needed for OneVersusAll.
+    /// as would be needed for a one-versus-all classification model.
     /// </summary>
     public sealed class Pkpd : MetaMulticlassTrainer<MulticlassPredictionTransformer<PkpdModelParameters>, PkpdModelParameters>
     {
@@ -82,9 +82,9 @@ internal Pkpd(IHostEnvironment env, Options options)
         /// </summary>
         /// <param name="env">The <see cref="IHostEnvironment"/> instance.</param>
         /// <param name="binaryEstimator">An instance of a binary <see cref="ITrainerEstimator{TTransformer, TPredictor}"/> used as the base trainer.</param>
-        /// <param name="calibrator">The calibrator. If a calibrator is not explicitely provided, it will default to <see cref="PlattCalibratorTrainer"/></param>
         /// <param name="labelColumn">The name of the label colum.</param>
         /// <param name="imputeMissingLabelsAsNegative">Whether to treat missing labels as having negative labels, instead of keeping them missing.</param>
+        /// <param name="calibrator">The calibrator to use for each model instance. If a calibrator is not explicitely provided, it will default to <see cref="PlattCalibratorTrainer"/></param>
         /// <param name="maxCalibrationExamples">Number of instances to train the calibrator.</param>
         internal Pkpd(IHostEnvironment env,
             TScalarTrainer binaryEstimator,