Make accessor of linear coefficients unique to the public

docs/samples/Microsoft.ML.Samples/Dynamic/FeatureContributionCalculationTransform.cs

@@ -44,8 +44,7 @@ public static void Example()
  var outData = featureContributionCalculator.Fit(scoredData).Transform(scoredData);
 
  // Let's extract the weights from the linear model to use as a comparison
- var weights = new VBuffer<float>();
- model.Model.GetFeatureWeights(ref weights);
+ var weights = model.Model.Weights;
 
  // Let's now walk through the first ten records and see which feature drove the values the most
  // Get prediction scores and contributions
@@ -63,7 +62,7 @@ public static void Example()
  var value = row.Features[featureOfInterest];
  var contribution = row.FeatureContributions[featureOfInterest];
  var name = data.Schema[featureOfInterest + 1].Name;
- var weight = weights.GetValues()[featureOfInterest];
+ var weight = weights[featureOfInterest];
 
  Console.WriteLine("{0:0.00}\t{1:0.00}\t{2}\t{3:0.00}\t{4:0.00}\t{5:0.00}",
  row.MedianHomeValue,

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

@@ -46,12 +46,10 @@ public static void SdcaRegression()
  var model = learningPipeline.Fit(trainData);
 
  // Check the weights that the model learned
- VBuffer<float> weights = default;
- pred.GetFeatureWeights(ref weights);
+ var weights = pred.Weights;
 
- var weightsValues = weights.GetValues();
- Console.WriteLine($"weight 0 - {weightsValues[0]}");
- Console.WriteLine($"weight 1 - {weightsValues[1]}");
+ Console.WriteLine($"weight 0 - {weights[0]}");
+ Console.WriteLine($"weight 1 - {weights[1]}");
 
  // Evaluate how the model is doing on the test data
  var dataWithPredictions = model.Transform(testData);

src/Microsoft.ML.Data/Dirty/PredictorInterfaces.cs

@@ -146,7 +146,8 @@ internal interface ICanSaveInSourceCode
  /// <summary>
  /// Interface implemented by components that can assign weights to features.
  /// </summary>
- public interface IHaveFeatureWeights
+ [BestFriend]
+ internal interface IHaveFeatureWeights
  {
  /// <summary>
  /// Returns the weights for the features.

src/Microsoft.ML.HalLearners/SymSgdClassificationTrainer.cs

@@ -671,7 +671,7 @@ private TPredictor TrainCore(IChannel ch, RoleMappedData data, LinearModelParame
  float bias = 0.0f;
  if (predictor != null)
  {
- predictor.GetFeatureWeights(ref weights);
+((IHaveFeatureWeights)predictor).GetFeatureWeights(ref weights);
  VBufferUtils.Densify(ref weights);
  bias = predictor.Bias;
  }

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

@@ -11,7 +11,6 @@
 using Microsoft.ML;
 using Microsoft.ML.Calibrators;
 using Microsoft.ML.Data;
-using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.Internal.Utilities;
 using Microsoft.ML.Model;
 using Microsoft.ML.Model.OnnxConverter;
@@ -384,7 +383,7 @@ private protected virtual DataViewRow GetSummaryIRowOrNull(RoleMappedSchema sche
 
  void ICanSaveInIniFormat.SaveAsIni(TextWriter writer, RoleMappedSchema schema, ICalibrator calibrator) => SaveAsIni(writer, schema, calibrator);
 
-public void GetFeatureWeights(ref VBuffer<float> weights)
+ void IHaveFeatureWeights.GetFeatureWeights(ref VBuffer<float> weights)
  {
  Weight.CopyTo(ref weights);
  }

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

@@ -438,7 +438,7 @@ internal DataViewSchema.Annotations MakeStatisticsMetadata(LinearBinaryModelPara
  builder.AddPrimitiveValue("BiasPValue", NumberDataViewType.Single, biasPValue);
 
  var weights = default(VBuffer<float>);
- parent.GetFeatureWeights(ref weights);
+((IHaveFeatureWeights)parent).GetFeatureWeights(ref weights);
  var estimate = default(VBuffer<float>);
  var stdErr = default(VBuffer<float>);
  var zScore = default(VBuffer<float>);

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

@@ -10,6 +10,7 @@
 using Microsoft.ML.EntryPoints;
 using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.Internal.Utilities;
+using Microsoft.ML.Model;
 using Microsoft.ML.Numeric;
 
 namespace Microsoft.ML.Trainers
@@ -130,7 +131,7 @@ protected TrainStateBase(IChannel ch, int numFeatures, LinearModelParameters pre
  // unless we have a lot of features.
  if (predictor != null)
  {
- predictor.GetFeatureWeights(ref Weights);
+((IHaveFeatureWeights)predictor).GetFeatureWeights(ref Weights);
  VBufferUtils.Densify(ref Weights);
  Bias = predictor.Bias;
  }

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

@@ -1937,7 +1937,7 @@ private protected override TModel TrainCore(IChannel ch, RoleMappedData data, Li
  float bias = 0.0f;
  if (predictor != null)
  {
- predictor.GetFeatureWeights(ref weights);
+((IHaveFeatureWeights)predictor).GetFeatureWeights(ref weights);
  VBufferUtils.Densify(ref weights);
  bias = predictor.Bias;
  }

test/Microsoft.ML.StaticPipelineTesting/Training.cs

@@ -627,9 +627,7 @@ public void PoissonRegression()
  var model = pipe.Fit(dataSource);
  Assert.NotNull(pred);
  // 11 input features, so we ought to have 11 weights.
- VBuffer<float> weights = new VBuffer<float>();
- pred.GetFeatureWeights(ref weights);
- Assert.Equal(11, weights.Length);
+ Assert.Equal(11, pred.Weights.Count);
 
  var data = model.Load(dataSource);
 
@@ -751,9 +749,7 @@ public void OnlineGradientDescent()
  var model = pipe.Fit(dataSource);
  Assert.NotNull(pred);
  // 11 input features, so we ought to have 11 weights.
- VBuffer<float> weights = new VBuffer<float>();
- pred.GetFeatureWeights(ref weights);
- Assert.Equal(11, weights.Length);
+ Assert.Equal(11, pred.Weights.Count);
 
  var data = model.Load(dataSource);
 

test/Microsoft.ML.Tests/Scenarios/Api/Estimators/IntrospectiveTraining.cs

@@ -42,8 +42,7 @@ public void IntrospectiveTraining()
  var model = pipeline.Fit(data);
 
  // Get feature weights.
- VBuffer<float> weights = default;
- model.LastTransformer.Model.GetFeatureWeights(ref weights);
+ var weights = model.LastTransformer.Model.Weights;
  }
 
  [Fact]

docs/samples/Microsoft.ML.Samples/Dynamic/FeatureContributionCalculationTransform.cs

@@ -44,8 +44,7 @@ public static void Example()
  var outData = featureContributionCalculator.Fit(scoredData).Transform(scoredData);
 
  // Let's extract the weights from the linear model to use as a comparison
- var weights = new VBuffer<float>();
- model.Model.GetFeatureWeights(ref weights);
+ var weights = model.Model.Weights;
 
  // Let's now walk through the first ten records and see which feature drove the values the most
  // Get prediction scores and contributions
@@ -63,7 +62,7 @@ public static void Example()
  var value = row.Features[featureOfInterest];
  var contribution = row.FeatureContributions[featureOfInterest];
  var name = data.Schema[featureOfInterest + 1].Name;
- var weight = weights.GetValues()[featureOfInterest];
+ var weight = weights[featureOfInterest];
 
  Console.WriteLine("{0:0.00}\t{1:0.00}\t{2}\t{3:0.00}\t{4:0.00}\t{5:0.00}",
  row.MedianHomeValue,

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

@@ -46,12 +46,10 @@ public static void SdcaRegression()
  var model = learningPipeline.Fit(trainData);
 
  // Check the weights that the model learned
- VBuffer<float> weights = default;
- pred.GetFeatureWeights(ref weights);
+ var weights = pred.Weights;
 
- var weightsValues = weights.GetValues();
- Console.WriteLine($"weight 0 - {weightsValues[0]}");
- Console.WriteLine($"weight 1 - {weightsValues[1]}");
+ Console.WriteLine($"weight 0 - {weights[0]}");
+ Console.WriteLine($"weight 1 - {weights[1]}");
 
  // Evaluate how the model is doing on the test data
  var dataWithPredictions = model.Transform(testData);

src/Microsoft.ML.Data/Dirty/PredictorInterfaces.cs

@@ -146,7 +146,8 @@ internal interface ICanSaveInSourceCode
  /// <summary>
  /// Interface implemented by components that can assign weights to features.
  /// </summary>
- public interface IHaveFeatureWeights
+ [BestFriend]
+ internal interface IHaveFeatureWeights
  {
  /// <summary>
  /// Returns the weights for the features.

src/Microsoft.ML.HalLearners/SymSgdClassificationTrainer.cs

@@ -671,7 +671,7 @@ private TPredictor TrainCore(IChannel ch, RoleMappedData data, LinearModelParame
  float bias = 0.0f;
  if (predictor != null)
  {
- predictor.GetFeatureWeights(ref weights);
+((IHaveFeatureWeights)predictor).GetFeatureWeights(ref weights);
  VBufferUtils.Densify(ref weights);
  bias = predictor.Bias;
  }

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

@@ -11,7 +11,6 @@
 using Microsoft.ML;
 using Microsoft.ML.Calibrators;
 using Microsoft.ML.Data;
-using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.Internal.Utilities;
 using Microsoft.ML.Model;
 using Microsoft.ML.Model.OnnxConverter;
@@ -384,7 +383,7 @@ private protected virtual DataViewRow GetSummaryIRowOrNull(RoleMappedSchema sche
 
  void ICanSaveInIniFormat.SaveAsIni(TextWriter writer, RoleMappedSchema schema, ICalibrator calibrator) => SaveAsIni(writer, schema, calibrator);
 
-public void GetFeatureWeights(ref VBuffer<float> weights)
+ void IHaveFeatureWeights.GetFeatureWeights(ref VBuffer<float> weights)
  {
  Weight.CopyTo(ref weights);
  }

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

@@ -438,7 +438,7 @@ internal DataViewSchema.Annotations MakeStatisticsMetadata(LinearBinaryModelPara
  builder.AddPrimitiveValue("BiasPValue", NumberDataViewType.Single, biasPValue);
 
  var weights = default(VBuffer<float>);
- parent.GetFeatureWeights(ref weights);
+((IHaveFeatureWeights)parent).GetFeatureWeights(ref weights);
  var estimate = default(VBuffer<float>);
  var stdErr = default(VBuffer<float>);
  var zScore = default(VBuffer<float>);

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

@@ -10,6 +10,7 @@
 using Microsoft.ML.EntryPoints;
 using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.Internal.Utilities;
+using Microsoft.ML.Model;
 using Microsoft.ML.Numeric;
 
 namespace Microsoft.ML.Trainers
@@ -130,7 +131,7 @@ protected TrainStateBase(IChannel ch, int numFeatures, LinearModelParameters pre
  // unless we have a lot of features.
  if (predictor != null)
  {
- predictor.GetFeatureWeights(ref Weights);
+((IHaveFeatureWeights)predictor).GetFeatureWeights(ref Weights);
  VBufferUtils.Densify(ref Weights);
  Bias = predictor.Bias;
  }

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

@@ -1937,7 +1937,7 @@ private protected override TModel TrainCore(IChannel ch, RoleMappedData data, Li
  float bias = 0.0f;
  if (predictor != null)
  {
- predictor.GetFeatureWeights(ref weights);
+((IHaveFeatureWeights)predictor).GetFeatureWeights(ref weights);
  VBufferUtils.Densify(ref weights);
  bias = predictor.Bias;
  }

test/Microsoft.ML.StaticPipelineTesting/Training.cs

@@ -627,9 +627,7 @@ public void PoissonRegression()
  var model = pipe.Fit(dataSource);
  Assert.NotNull(pred);
  // 11 input features, so we ought to have 11 weights.
- VBuffer<float> weights = new VBuffer<float>();
- pred.GetFeatureWeights(ref weights);
- Assert.Equal(11, weights.Length);
+ Assert.Equal(11, pred.Weights.Count);
 
  var data = model.Load(dataSource);
 
@@ -751,9 +749,7 @@ public void OnlineGradientDescent()
  var model = pipe.Fit(dataSource);
  Assert.NotNull(pred);
  // 11 input features, so we ought to have 11 weights.
- VBuffer<float> weights = new VBuffer<float>();
- pred.GetFeatureWeights(ref weights);
- Assert.Equal(11, weights.Length);
+ Assert.Equal(11, pred.Weights.Count);
 
  var data = model.Load(dataSource);
 

test/Microsoft.ML.Tests/Scenarios/Api/Estimators/IntrospectiveTraining.cs

@@ -42,8 +42,7 @@ public void IntrospectiveTraining()
  var model = pipeline.Fit(data);
 
  // Get feature weights.
- VBuffer<float> weights = default;
- model.LastTransformer.Model.GetFeatureWeights(ref weights);
+ var weights = model.LastTransformer.Model.Weights;
  }
 
  [Fact]

docs/samples/Microsoft.ML.Samples/Dynamic/FeatureContributionCalculationTransform.cs

@@ -44,8 +44,7 @@ public static void Example()
  var outData = featureContributionCalculator.Fit(scoredData).Transform(scoredData);
 
  // Let's extract the weights from the linear model to use as a comparison
- var weights = new VBuffer<float>();
- model.Model.GetFeatureWeights(ref weights);
+ var weights = model.Model.Weights;
 
  // Let's now walk through the first ten records and see which feature drove the values the most
  // Get prediction scores and contributions
@@ -63,7 +62,7 @@ public static void Example()
  var value = row.Features[featureOfInterest];
  var contribution = row.FeatureContributions[featureOfInterest];
  var name = data.Schema[featureOfInterest + 1].Name;
- var weight = weights.GetValues()[featureOfInterest];
+ var weight = weights[featureOfInterest];
 
  Console.WriteLine("{0:0.00}\t{1:0.00}\t{2}\t{3:0.00}\t{4:0.00}\t{5:0.00}",
  row.MedianHomeValue,

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

@@ -46,12 +46,10 @@ public static void SdcaRegression()
  var model = learningPipeline.Fit(trainData);
 
  // Check the weights that the model learned
- VBuffer<float> weights = default;
- pred.GetFeatureWeights(ref weights);
+ var weights = pred.Weights;
 
- var weightsValues = weights.GetValues();
- Console.WriteLine($"weight 0 - {weightsValues[0]}");
- Console.WriteLine($"weight 1 - {weightsValues[1]}");
+ Console.WriteLine($"weight 0 - {weights[0]}");
+ Console.WriteLine($"weight 1 - {weights[1]}");
 
  // Evaluate how the model is doing on the test data
  var dataWithPredictions = model.Transform(testData);

src/Microsoft.ML.Data/Dirty/PredictorInterfaces.cs

@@ -146,7 +146,8 @@ internal interface ICanSaveInSourceCode
  /// <summary>
  /// Interface implemented by components that can assign weights to features.
  /// </summary>
- public interface IHaveFeatureWeights
+ [BestFriend]
+ internal interface IHaveFeatureWeights
  {
  /// <summary>
  /// Returns the weights for the features.

src/Microsoft.ML.HalLearners/SymSgdClassificationTrainer.cs

@@ -671,7 +671,7 @@ private TPredictor TrainCore(IChannel ch, RoleMappedData data, LinearModelParame
  float bias = 0.0f;
  if (predictor != null)
  {
- predictor.GetFeatureWeights(ref weights);
+((IHaveFeatureWeights)predictor).GetFeatureWeights(ref weights);
  VBufferUtils.Densify(ref weights);
  bias = predictor.Bias;
  }

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

@@ -11,7 +11,6 @@
 using Microsoft.ML;
 using Microsoft.ML.Calibrators;
 using Microsoft.ML.Data;
-using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.Internal.Utilities;
 using Microsoft.ML.Model;
 using Microsoft.ML.Model.OnnxConverter;
@@ -384,7 +383,7 @@ private protected virtual DataViewRow GetSummaryIRowOrNull(RoleMappedSchema sche
 
  void ICanSaveInIniFormat.SaveAsIni(TextWriter writer, RoleMappedSchema schema, ICalibrator calibrator) => SaveAsIni(writer, schema, calibrator);
 
-public void GetFeatureWeights(ref VBuffer<float> weights)
+ void IHaveFeatureWeights.GetFeatureWeights(ref VBuffer<float> weights)
  {
  Weight.CopyTo(ref weights);
  }

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

@@ -438,7 +438,7 @@ internal DataViewSchema.Annotations MakeStatisticsMetadata(LinearBinaryModelPara
  builder.AddPrimitiveValue("BiasPValue", NumberDataViewType.Single, biasPValue);
 
  var weights = default(VBuffer<float>);
- parent.GetFeatureWeights(ref weights);
+((IHaveFeatureWeights)parent).GetFeatureWeights(ref weights);
  var estimate = default(VBuffer<float>);
  var stdErr = default(VBuffer<float>);
  var zScore = default(VBuffer<float>);

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

@@ -10,6 +10,7 @@
 using Microsoft.ML.EntryPoints;
 using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.Internal.Utilities;
+using Microsoft.ML.Model;
 using Microsoft.ML.Numeric;
 
 namespace Microsoft.ML.Trainers
@@ -130,7 +131,7 @@ protected TrainStateBase(IChannel ch, int numFeatures, LinearModelParameters pre
  // unless we have a lot of features.
  if (predictor != null)
  {
- predictor.GetFeatureWeights(ref Weights);
+((IHaveFeatureWeights)predictor).GetFeatureWeights(ref Weights);
  VBufferUtils.Densify(ref Weights);
  Bias = predictor.Bias;
  }

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

@@ -1937,7 +1937,7 @@ private protected override TModel TrainCore(IChannel ch, RoleMappedData data, Li
  float bias = 0.0f;
  if (predictor != null)
  {
- predictor.GetFeatureWeights(ref weights);
+((IHaveFeatureWeights)predictor).GetFeatureWeights(ref weights);
  VBufferUtils.Densify(ref weights);
  bias = predictor.Bias;
  }

test/Microsoft.ML.StaticPipelineTesting/Training.cs

@@ -627,9 +627,7 @@ public void PoissonRegression()
  var model = pipe.Fit(dataSource);
  Assert.NotNull(pred);
  // 11 input features, so we ought to have 11 weights.
- VBuffer<float> weights = new VBuffer<float>();
- pred.GetFeatureWeights(ref weights);
- Assert.Equal(11, weights.Length);
+ Assert.Equal(11, pred.Weights.Count);
 
  var data = model.Load(dataSource);
 
@@ -751,9 +749,7 @@ public void OnlineGradientDescent()
  var model = pipe.Fit(dataSource);
  Assert.NotNull(pred);
  // 11 input features, so we ought to have 11 weights.
- VBuffer<float> weights = new VBuffer<float>();
- pred.GetFeatureWeights(ref weights);
- Assert.Equal(11, weights.Length);
+ Assert.Equal(11, pred.Weights.Count);
 
  var data = model.Load(dataSource);
 

test/Microsoft.ML.Tests/Scenarios/Api/Estimators/IntrospectiveTraining.cs

@@ -42,8 +42,7 @@ public void IntrospectiveTraining()
  var model = pipeline.Fit(data);
 
  // Get feature weights.
- VBuffer<float> weights = default;
- model.LastTransformer.Model.GetFeatureWeights(ref weights);
+ var weights = model.LastTransformer.Model.Weights;
  }
 
  [Fact]

docs/samples/Microsoft.ML.Samples/Dynamic/FeatureContributionCalculationTransform.cs

@@ -44,8 +44,7 @@ public static void Example()
  var outData = featureContributionCalculator.Fit(scoredData).Transform(scoredData);
 
  // Let's extract the weights from the linear model to use as a comparison
- var weights = new VBuffer<float>();
- model.Model.GetFeatureWeights(ref weights);
+ var weights = model.Model.Weights;
 
  // Let's now walk through the first ten records and see which feature drove the values the most
  // Get prediction scores and contributions
@@ -63,7 +62,7 @@ public static void Example()
  var value = row.Features[featureOfInterest];
  var contribution = row.FeatureContributions[featureOfInterest];
  var name = data.Schema[featureOfInterest + 1].Name;
- var weight = weights.GetValues()[featureOfInterest];
+ var weight = weights[featureOfInterest];
 
  Console.WriteLine("{0:0.00}\t{1:0.00}\t{2}\t{3:0.00}\t{4:0.00}\t{5:0.00}",
  row.MedianHomeValue,

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

@@ -46,12 +46,10 @@ public static void SdcaRegression()
  var model = learningPipeline.Fit(trainData);
 
  // Check the weights that the model learned
- VBuffer<float> weights = default;
- pred.GetFeatureWeights(ref weights);
+ var weights = pred.Weights;
 
- var weightsValues = weights.GetValues();
- Console.WriteLine($"weight 0 - {weightsValues[0]}");
- Console.WriteLine($"weight 1 - {weightsValues[1]}");
+ Console.WriteLine($"weight 0 - {weights[0]}");
+ Console.WriteLine($"weight 1 - {weights[1]}");
 
  // Evaluate how the model is doing on the test data
  var dataWithPredictions = model.Transform(testData);

src/Microsoft.ML.Data/Dirty/PredictorInterfaces.cs

@@ -146,7 +146,8 @@ internal interface ICanSaveInSourceCode
  /// <summary>
  /// Interface implemented by components that can assign weights to features.
  /// </summary>
- public interface IHaveFeatureWeights
+ [BestFriend]
+ internal interface IHaveFeatureWeights
  {
  /// <summary>
  /// Returns the weights for the features.

src/Microsoft.ML.HalLearners/SymSgdClassificationTrainer.cs

@@ -671,7 +671,7 @@ private TPredictor TrainCore(IChannel ch, RoleMappedData data, LinearModelParame
  float bias = 0.0f;
  if (predictor != null)
  {
- predictor.GetFeatureWeights(ref weights);
+((IHaveFeatureWeights)predictor).GetFeatureWeights(ref weights);
  VBufferUtils.Densify(ref weights);
  bias = predictor.Bias;
  }

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

@@ -11,7 +11,6 @@
 using Microsoft.ML;
 using Microsoft.ML.Calibrators;
 using Microsoft.ML.Data;
-using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.Internal.Utilities;
 using Microsoft.ML.Model;
 using Microsoft.ML.Model.OnnxConverter;
@@ -384,7 +383,7 @@ private protected virtual DataViewRow GetSummaryIRowOrNull(RoleMappedSchema sche
 
  void ICanSaveInIniFormat.SaveAsIni(TextWriter writer, RoleMappedSchema schema, ICalibrator calibrator) => SaveAsIni(writer, schema, calibrator);
 
-public void GetFeatureWeights(ref VBuffer<float> weights)
+ void IHaveFeatureWeights.GetFeatureWeights(ref VBuffer<float> weights)
  {
  Weight.CopyTo(ref weights);
  }

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

@@ -438,7 +438,7 @@ internal DataViewSchema.Annotations MakeStatisticsMetadata(LinearBinaryModelPara
  builder.AddPrimitiveValue("BiasPValue", NumberDataViewType.Single, biasPValue);
 
  var weights = default(VBuffer<float>);
- parent.GetFeatureWeights(ref weights);
+((IHaveFeatureWeights)parent).GetFeatureWeights(ref weights);
  var estimate = default(VBuffer<float>);
  var stdErr = default(VBuffer<float>);
  var zScore = default(VBuffer<float>);

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

@@ -10,6 +10,7 @@
 using Microsoft.ML.EntryPoints;
 using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.Internal.Utilities;
+using Microsoft.ML.Model;
 using Microsoft.ML.Numeric;
 
 namespace Microsoft.ML.Trainers
@@ -130,7 +131,7 @@ protected TrainStateBase(IChannel ch, int numFeatures, LinearModelParameters pre
  // unless we have a lot of features.
  if (predictor != null)
  {
- predictor.GetFeatureWeights(ref Weights);
+((IHaveFeatureWeights)predictor).GetFeatureWeights(ref Weights);
  VBufferUtils.Densify(ref Weights);
  Bias = predictor.Bias;
  }

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

@@ -1937,7 +1937,7 @@ private protected override TModel TrainCore(IChannel ch, RoleMappedData data, Li
  float bias = 0.0f;
  if (predictor != null)
  {
- predictor.GetFeatureWeights(ref weights);
+((IHaveFeatureWeights)predictor).GetFeatureWeights(ref weights);
  VBufferUtils.Densify(ref weights);
  bias = predictor.Bias;
  }

test/Microsoft.ML.StaticPipelineTesting/Training.cs

@@ -627,9 +627,7 @@ public void PoissonRegression()
  var model = pipe.Fit(dataSource);
  Assert.NotNull(pred);
  // 11 input features, so we ought to have 11 weights.
- VBuffer<float> weights = new VBuffer<float>();
- pred.GetFeatureWeights(ref weights);
- Assert.Equal(11, weights.Length);
+ Assert.Equal(11, pred.Weights.Count);
 
  var data = model.Load(dataSource);
 
@@ -751,9 +749,7 @@ public void OnlineGradientDescent()
  var model = pipe.Fit(dataSource);
  Assert.NotNull(pred);
  // 11 input features, so we ought to have 11 weights.
- VBuffer<float> weights = new VBuffer<float>();
- pred.GetFeatureWeights(ref weights);
- Assert.Equal(11, weights.Length);
+ Assert.Equal(11, pred.Weights.Count);
 
  var data = model.Load(dataSource);
 

test/Microsoft.ML.Tests/Scenarios/Api/Estimators/IntrospectiveTraining.cs

@@ -42,8 +42,7 @@ public void IntrospectiveTraining()
  var model = pipeline.Fit(data);
 
  // Get feature weights.
- VBuffer<float> weights = default;
- model.LastTransformer.Model.GetFeatureWeights(ref weights);
+ var weights = model.LastTransformer.Model.Weights;
  }
 
  [Fact]

docs/samples/Microsoft.ML.Samples/Dynamic/FeatureContributionCalculationTransform.cs

@@ -44,8 +44,7 @@ public static void Example()
  var outData = featureContributionCalculator.Fit(scoredData).Transform(scoredData);
 
  // Let's extract the weights from the linear model to use as a comparison
- var weights = new VBuffer<float>();
- model.Model.GetFeatureWeights(ref weights);
+ var weights = model.Model.Weights;
 
  // Let's now walk through the first ten records and see which feature drove the values the most
  // Get prediction scores and contributions
@@ -63,7 +62,7 @@ public static void Example()
  var value = row.Features[featureOfInterest];
  var contribution = row.FeatureContributions[featureOfInterest];
  var name = data.Schema[featureOfInterest + 1].Name;
- var weight = weights.GetValues()[featureOfInterest];
+ var weight = weights[featureOfInterest];
 
  Console.WriteLine("{0:0.00}\t{1:0.00}\t{2}\t{3:0.00}\t{4:0.00}\t{5:0.00}",
  row.MedianHomeValue,

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

@@ -46,12 +46,10 @@ public static void SdcaRegression()
  var model = learningPipeline.Fit(trainData);
 
  // Check the weights that the model learned
- VBuffer<float> weights = default;
- pred.GetFeatureWeights(ref weights);
+ var weights = pred.Weights;
 
- var weightsValues = weights.GetValues();
- Console.WriteLine($"weight 0 - {weightsValues[0]}");
- Console.WriteLine($"weight 1 - {weightsValues[1]}");
+ Console.WriteLine($"weight 0 - {weights[0]}");
+ Console.WriteLine($"weight 1 - {weights[1]}");
 
  // Evaluate how the model is doing on the test data
  var dataWithPredictions = model.Transform(testData);

src/Microsoft.ML.Data/Dirty/PredictorInterfaces.cs

@@ -146,7 +146,8 @@ internal interface ICanSaveInSourceCode
  /// <summary>
  /// Interface implemented by components that can assign weights to features.
  /// </summary>
- public interface IHaveFeatureWeights
+ [BestFriend]
+ internal interface IHaveFeatureWeights
  {
  /// <summary>
  /// Returns the weights for the features.

src/Microsoft.ML.HalLearners/SymSgdClassificationTrainer.cs

@@ -671,7 +671,7 @@ private TPredictor TrainCore(IChannel ch, RoleMappedData data, LinearModelParame
  float bias = 0.0f;
  if (predictor != null)
  {
- predictor.GetFeatureWeights(ref weights);
+((IHaveFeatureWeights)predictor).GetFeatureWeights(ref weights);
  VBufferUtils.Densify(ref weights);
  bias = predictor.Bias;
  }

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

@@ -11,7 +11,6 @@
 using Microsoft.ML;
 using Microsoft.ML.Calibrators;
 using Microsoft.ML.Data;
-using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.Internal.Utilities;
 using Microsoft.ML.Model;
 using Microsoft.ML.Model.OnnxConverter;
@@ -384,7 +383,7 @@ private protected virtual DataViewRow GetSummaryIRowOrNull(RoleMappedSchema sche
 
  void ICanSaveInIniFormat.SaveAsIni(TextWriter writer, RoleMappedSchema schema, ICalibrator calibrator) => SaveAsIni(writer, schema, calibrator);
 
-public void GetFeatureWeights(ref VBuffer<float> weights)
+ void IHaveFeatureWeights.GetFeatureWeights(ref VBuffer<float> weights)
  {
  Weight.CopyTo(ref weights);
  }

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

@@ -438,7 +438,7 @@ internal DataViewSchema.Annotations MakeStatisticsMetadata(LinearBinaryModelPara
  builder.AddPrimitiveValue("BiasPValue", NumberDataViewType.Single, biasPValue);
 
  var weights = default(VBuffer<float>);
- parent.GetFeatureWeights(ref weights);
+((IHaveFeatureWeights)parent).GetFeatureWeights(ref weights);
  var estimate = default(VBuffer<float>);
  var stdErr = default(VBuffer<float>);
  var zScore = default(VBuffer<float>);

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

@@ -10,6 +10,7 @@
 using Microsoft.ML.EntryPoints;
 using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.Internal.Utilities;
+using Microsoft.ML.Model;
 using Microsoft.ML.Numeric;
 
 namespace Microsoft.ML.Trainers
@@ -130,7 +131,7 @@ protected TrainStateBase(IChannel ch, int numFeatures, LinearModelParameters pre
  // unless we have a lot of features.
  if (predictor != null)
  {
- predictor.GetFeatureWeights(ref Weights);
+((IHaveFeatureWeights)predictor).GetFeatureWeights(ref Weights);
  VBufferUtils.Densify(ref Weights);
  Bias = predictor.Bias;
  }

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

@@ -1937,7 +1937,7 @@ private protected override TModel TrainCore(IChannel ch, RoleMappedData data, Li
  float bias = 0.0f;
  if (predictor != null)
  {
- predictor.GetFeatureWeights(ref weights);
+((IHaveFeatureWeights)predictor).GetFeatureWeights(ref weights);
  VBufferUtils.Densify(ref weights);
  bias = predictor.Bias;
  }

test/Microsoft.ML.StaticPipelineTesting/Training.cs

@@ -627,9 +627,7 @@ public void PoissonRegression()
  var model = pipe.Fit(dataSource);
  Assert.NotNull(pred);
  // 11 input features, so we ought to have 11 weights.
- VBuffer<float> weights = new VBuffer<float>();
- pred.GetFeatureWeights(ref weights);
- Assert.Equal(11, weights.Length);
+ Assert.Equal(11, pred.Weights.Count);
 
  var data = model.Load(dataSource);
 
@@ -751,9 +749,7 @@ public void OnlineGradientDescent()
  var model = pipe.Fit(dataSource);
  Assert.NotNull(pred);
  // 11 input features, so we ought to have 11 weights.
- VBuffer<float> weights = new VBuffer<float>();
- pred.GetFeatureWeights(ref weights);
- Assert.Equal(11, weights.Length);
+ Assert.Equal(11, pred.Weights.Count);
 
  var data = model.Load(dataSource);
 

test/Microsoft.ML.Tests/Scenarios/Api/Estimators/IntrospectiveTraining.cs

@@ -42,8 +42,7 @@ public void IntrospectiveTraining()
  var model = pipeline.Fit(data);
 
  // Get feature weights.
- VBuffer<float> weights = default;
- model.LastTransformer.Model.GetFeatureWeights(ref weights);
+ var weights = model.LastTransformer.Model.Weights;
  }
 
  [Fact]

docs/samples/Microsoft.ML.Samples/Dynamic/FeatureContributionCalculationTransform.cs

@@ -44,8 +44,7 @@ public static void Example()
  var outData = featureContributionCalculator.Fit(scoredData).Transform(scoredData);
 
  // Let's extract the weights from the linear model to use as a comparison
- var weights = new VBuffer<float>();
- model.Model.GetFeatureWeights(ref weights);
+ var weights = model.Model.Weights;
 
  // Let's now walk through the first ten records and see which feature drove the values the most
  // Get prediction scores and contributions
@@ -63,7 +62,7 @@ public static void Example()
  var value = row.Features[featureOfInterest];
  var contribution = row.FeatureContributions[featureOfInterest];
  var name = data.Schema[featureOfInterest + 1].Name;
- var weight = weights.GetValues()[featureOfInterest];
+ var weight = weights[featureOfInterest];
 
  Console.WriteLine("{0:0.00}\t{1:0.00}\t{2}\t{3:0.00}\t{4:0.00}\t{5:0.00}",
  row.MedianHomeValue,

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

@@ -46,12 +46,10 @@ public static void SdcaRegression()
  var model = learningPipeline.Fit(trainData);
 
  // Check the weights that the model learned
- VBuffer<float> weights = default;
- pred.GetFeatureWeights(ref weights);
+ var weights = pred.Weights;
 
- var weightsValues = weights.GetValues();
- Console.WriteLine($"weight 0 - {weightsValues[0]}");
- Console.WriteLine($"weight 1 - {weightsValues[1]}");
+ Console.WriteLine($"weight 0 - {weights[0]}");
+ Console.WriteLine($"weight 1 - {weights[1]}");
 
  // Evaluate how the model is doing on the test data
  var dataWithPredictions = model.Transform(testData);

src/Microsoft.ML.Data/Dirty/PredictorInterfaces.cs

@@ -146,7 +146,8 @@ internal interface ICanSaveInSourceCode
  /// <summary>
  /// Interface implemented by components that can assign weights to features.
  /// </summary>
- public interface IHaveFeatureWeights
+ [BestFriend]
+ internal interface IHaveFeatureWeights
  {
  /// <summary>
  /// Returns the weights for the features.

src/Microsoft.ML.HalLearners/SymSgdClassificationTrainer.cs

@@ -671,7 +671,7 @@ private TPredictor TrainCore(IChannel ch, RoleMappedData data, LinearModelParame
  float bias = 0.0f;
  if (predictor != null)
  {
- predictor.GetFeatureWeights(ref weights);
+((IHaveFeatureWeights)predictor).GetFeatureWeights(ref weights);
  VBufferUtils.Densify(ref weights);
  bias = predictor.Bias;
  }

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

@@ -11,7 +11,6 @@
 using Microsoft.ML;
 using Microsoft.ML.Calibrators;
 using Microsoft.ML.Data;
-using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.Internal.Utilities;
 using Microsoft.ML.Model;
 using Microsoft.ML.Model.OnnxConverter;
@@ -384,7 +383,7 @@ private protected virtual DataViewRow GetSummaryIRowOrNull(RoleMappedSchema sche
 
  void ICanSaveInIniFormat.SaveAsIni(TextWriter writer, RoleMappedSchema schema, ICalibrator calibrator) => SaveAsIni(writer, schema, calibrator);
 
-public void GetFeatureWeights(ref VBuffer<float> weights)
+ void IHaveFeatureWeights.GetFeatureWeights(ref VBuffer<float> weights)
  {
  Weight.CopyTo(ref weights);
  }

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

@@ -438,7 +438,7 @@ internal DataViewSchema.Annotations MakeStatisticsMetadata(LinearBinaryModelPara
  builder.AddPrimitiveValue("BiasPValue", NumberDataViewType.Single, biasPValue);
 
  var weights = default(VBuffer<float>);
- parent.GetFeatureWeights(ref weights);
+((IHaveFeatureWeights)parent).GetFeatureWeights(ref weights);
  var estimate = default(VBuffer<float>);
  var stdErr = default(VBuffer<float>);
  var zScore = default(VBuffer<float>);

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

@@ -10,6 +10,7 @@
 using Microsoft.ML.EntryPoints;
 using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.Internal.Utilities;
+using Microsoft.ML.Model;
 using Microsoft.ML.Numeric;
 
 namespace Microsoft.ML.Trainers
@@ -130,7 +131,7 @@ protected TrainStateBase(IChannel ch, int numFeatures, LinearModelParameters pre
  // unless we have a lot of features.
  if (predictor != null)
  {
- predictor.GetFeatureWeights(ref Weights);
+((IHaveFeatureWeights)predictor).GetFeatureWeights(ref Weights);
  VBufferUtils.Densify(ref Weights);
  Bias = predictor.Bias;
  }

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

@@ -1937,7 +1937,7 @@ private protected override TModel TrainCore(IChannel ch, RoleMappedData data, Li
  float bias = 0.0f;
  if (predictor != null)
  {
- predictor.GetFeatureWeights(ref weights);
+((IHaveFeatureWeights)predictor).GetFeatureWeights(ref weights);
  VBufferUtils.Densify(ref weights);
  bias = predictor.Bias;
  }

test/Microsoft.ML.StaticPipelineTesting/Training.cs

@@ -627,9 +627,7 @@ public void PoissonRegression()
  var model = pipe.Fit(dataSource);
  Assert.NotNull(pred);
  // 11 input features, so we ought to have 11 weights.
- VBuffer<float> weights = new VBuffer<float>();
- pred.GetFeatureWeights(ref weights);
- Assert.Equal(11, weights.Length);
+ Assert.Equal(11, pred.Weights.Count);
 
  var data = model.Load(dataSource);
 
@@ -751,9 +749,7 @@ public void OnlineGradientDescent()
  var model = pipe.Fit(dataSource);
  Assert.NotNull(pred);
  // 11 input features, so we ought to have 11 weights.
- VBuffer<float> weights = new VBuffer<float>();
- pred.GetFeatureWeights(ref weights);
- Assert.Equal(11, weights.Length);
+ Assert.Equal(11, pred.Weights.Count);
 
  var data = model.Load(dataSource);
 

test/Microsoft.ML.Tests/Scenarios/Api/Estimators/IntrospectiveTraining.cs

@@ -42,8 +42,7 @@ public void IntrospectiveTraining()
  var model = pipeline.Fit(data);
 
  // Get feature weights.
- VBuffer<float> weights = default;
- model.LastTransformer.Model.GetFeatureWeights(ref weights);
+ var weights = model.LastTransformer.Model.Weights;
  }
 
  [Fact]

docs/samples/Microsoft.ML.Samples/Dynamic/FeatureContributionCalculationTransform.cs

@@ -44,8 +44,7 @@ public static void Example()
  var outData = featureContributionCalculator.Fit(scoredData).Transform(scoredData);
 
  // Let's extract the weights from the linear model to use as a comparison
- var weights = new VBuffer<float>();
- model.Model.GetFeatureWeights(ref weights);
+ var weights = model.Model.Weights;
 
  // Let's now walk through the first ten records and see which feature drove the values the most
  // Get prediction scores and contributions
@@ -63,7 +62,7 @@ public static void Example()
  var value = row.Features[featureOfInterest];
  var contribution = row.FeatureContributions[featureOfInterest];
  var name = data.Schema[featureOfInterest + 1].Name;
- var weight = weights.GetValues()[featureOfInterest];
+ var weight = weights[featureOfInterest];
 
  Console.WriteLine("{0:0.00}\t{1:0.00}\t{2}\t{3:0.00}\t{4:0.00}\t{5:0.00}",
  row.MedianHomeValue,

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

@@ -46,12 +46,10 @@ public static void SdcaRegression()
  var model = learningPipeline.Fit(trainData);
 
  // Check the weights that the model learned
- VBuffer<float> weights = default;
- pred.GetFeatureWeights(ref weights);
+ var weights = pred.Weights;
 
- var weightsValues = weights.GetValues();
- Console.WriteLine($"weight 0 - {weightsValues[0]}");
- Console.WriteLine($"weight 1 - {weightsValues[1]}");
+ Console.WriteLine($"weight 0 - {weights[0]}");
+ Console.WriteLine($"weight 1 - {weights[1]}");
 
  // Evaluate how the model is doing on the test data
  var dataWithPredictions = model.Transform(testData);

src/Microsoft.ML.Data/Dirty/PredictorInterfaces.cs

@@ -146,7 +146,8 @@ internal interface ICanSaveInSourceCode
  /// <summary>
  /// Interface implemented by components that can assign weights to features.
  /// </summary>
- public interface IHaveFeatureWeights
+ [BestFriend]
+ internal interface IHaveFeatureWeights
  {
  /// <summary>
  /// Returns the weights for the features.

src/Microsoft.ML.HalLearners/SymSgdClassificationTrainer.cs

@@ -671,7 +671,7 @@ private TPredictor TrainCore(IChannel ch, RoleMappedData data, LinearModelParame
  float bias = 0.0f;
  if (predictor != null)
  {
- predictor.GetFeatureWeights(ref weights);
+((IHaveFeatureWeights)predictor).GetFeatureWeights(ref weights);
  VBufferUtils.Densify(ref weights);
  bias = predictor.Bias;
  }

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

@@ -11,7 +11,6 @@
 using Microsoft.ML;
 using Microsoft.ML.Calibrators;
 using Microsoft.ML.Data;
-using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.Internal.Utilities;
 using Microsoft.ML.Model;
 using Microsoft.ML.Model.OnnxConverter;
@@ -384,7 +383,7 @@ private protected virtual DataViewRow GetSummaryIRowOrNull(RoleMappedSchema sche
 
  void ICanSaveInIniFormat.SaveAsIni(TextWriter writer, RoleMappedSchema schema, ICalibrator calibrator) => SaveAsIni(writer, schema, calibrator);
 
-public void GetFeatureWeights(ref VBuffer<float> weights)
+ void IHaveFeatureWeights.GetFeatureWeights(ref VBuffer<float> weights)
  {
  Weight.CopyTo(ref weights);
  }

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

@@ -438,7 +438,7 @@ internal DataViewSchema.Annotations MakeStatisticsMetadata(LinearBinaryModelPara
  builder.AddPrimitiveValue("BiasPValue", NumberDataViewType.Single, biasPValue);
 
  var weights = default(VBuffer<float>);
- parent.GetFeatureWeights(ref weights);
+((IHaveFeatureWeights)parent).GetFeatureWeights(ref weights);
  var estimate = default(VBuffer<float>);
  var stdErr = default(VBuffer<float>);
  var zScore = default(VBuffer<float>);

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

@@ -10,6 +10,7 @@
 using Microsoft.ML.EntryPoints;
 using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.Internal.Utilities;
+using Microsoft.ML.Model;
 using Microsoft.ML.Numeric;
 
 namespace Microsoft.ML.Trainers
@@ -130,7 +131,7 @@ protected TrainStateBase(IChannel ch, int numFeatures, LinearModelParameters pre
  // unless we have a lot of features.
  if (predictor != null)
  {
- predictor.GetFeatureWeights(ref Weights);
+((IHaveFeatureWeights)predictor).GetFeatureWeights(ref Weights);
  VBufferUtils.Densify(ref Weights);
  Bias = predictor.Bias;
  }

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

@@ -1937,7 +1937,7 @@ private protected override TModel TrainCore(IChannel ch, RoleMappedData data, Li
  float bias = 0.0f;
  if (predictor != null)
  {
- predictor.GetFeatureWeights(ref weights);
+((IHaveFeatureWeights)predictor).GetFeatureWeights(ref weights);
  VBufferUtils.Densify(ref weights);
  bias = predictor.Bias;
  }

test/Microsoft.ML.StaticPipelineTesting/Training.cs

@@ -627,9 +627,7 @@ public void PoissonRegression()
  var model = pipe.Fit(dataSource);
  Assert.NotNull(pred);
  // 11 input features, so we ought to have 11 weights.
- VBuffer<float> weights = new VBuffer<float>();
- pred.GetFeatureWeights(ref weights);
- Assert.Equal(11, weights.Length);
+ Assert.Equal(11, pred.Weights.Count);
 
  var data = model.Load(dataSource);
 
@@ -751,9 +749,7 @@ public void OnlineGradientDescent()
  var model = pipe.Fit(dataSource);
  Assert.NotNull(pred);
  // 11 input features, so we ought to have 11 weights.
- VBuffer<float> weights = new VBuffer<float>();
- pred.GetFeatureWeights(ref weights);
- Assert.Equal(11, weights.Length);
+ Assert.Equal(11, pred.Weights.Count);
 
  var data = model.Load(dataSource);
 

test/Microsoft.ML.Tests/Scenarios/Api/Estimators/IntrospectiveTraining.cs

@@ -42,8 +42,7 @@ public void IntrospectiveTraining()
  var model = pipeline.Fit(data);
 
  // Get feature weights.
- VBuffer<float> weights = default;
- model.LastTransformer.Model.GetFeatureWeights(ref weights);
+ var weights = model.LastTransformer.Model.Weights;
  }
 
  [Fact]

docs/samples/Microsoft.ML.Samples/Dynamic/FeatureContributionCalculationTransform.cs

@@ -44,8 +44,7 @@ public static void Example()
  var outData = featureContributionCalculator.Fit(scoredData).Transform(scoredData);
 
  // Let's extract the weights from the linear model to use as a comparison
- var weights = new VBuffer<float>();
- model.Model.GetFeatureWeights(ref weights);
+ var weights = model.Model.Weights;
 
  // Let's now walk through the first ten records and see which feature drove the values the most
  // Get prediction scores and contributions
@@ -63,7 +62,7 @@ public static void Example()
  var value = row.Features[featureOfInterest];
  var contribution = row.FeatureContributions[featureOfInterest];
  var name = data.Schema[featureOfInterest + 1].Name;
- var weight = weights.GetValues()[featureOfInterest];
+ var weight = weights[featureOfInterest];
 
  Console.WriteLine("{0:0.00}\t{1:0.00}\t{2}\t{3:0.00}\t{4:0.00}\t{5:0.00}",
  row.MedianHomeValue,

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

@@ -46,12 +46,10 @@ public static void SdcaRegression()
  var model = learningPipeline.Fit(trainData);
 
  // Check the weights that the model learned
- VBuffer<float> weights = default;
- pred.GetFeatureWeights(ref weights);
+ var weights = pred.Weights;
 
- var weightsValues = weights.GetValues();
- Console.WriteLine($"weight 0 - {weightsValues[0]}");
- Console.WriteLine($"weight 1 - {weightsValues[1]}");
+ Console.WriteLine($"weight 0 - {weights[0]}");
+ Console.WriteLine($"weight 1 - {weights[1]}");
 
  // Evaluate how the model is doing on the test data
  var dataWithPredictions = model.Transform(testData);

src/Microsoft.ML.Data/Dirty/PredictorInterfaces.cs

@@ -146,7 +146,8 @@ internal interface ICanSaveInSourceCode
  /// <summary>
  /// Interface implemented by components that can assign weights to features.
  /// </summary>
- public interface IHaveFeatureWeights
+ [BestFriend]
+ internal interface IHaveFeatureWeights
  {
  /// <summary>
  /// Returns the weights for the features.

src/Microsoft.ML.HalLearners/SymSgdClassificationTrainer.cs

@@ -671,7 +671,7 @@ private TPredictor TrainCore(IChannel ch, RoleMappedData data, LinearModelParame
  float bias = 0.0f;
  if (predictor != null)
  {
- predictor.GetFeatureWeights(ref weights);
+((IHaveFeatureWeights)predictor).GetFeatureWeights(ref weights);
  VBufferUtils.Densify(ref weights);
  bias = predictor.Bias;
  }

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

@@ -11,7 +11,6 @@
 using Microsoft.ML;
 using Microsoft.ML.Calibrators;
 using Microsoft.ML.Data;
-using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.Internal.Utilities;
 using Microsoft.ML.Model;
 using Microsoft.ML.Model.OnnxConverter;
@@ -384,7 +383,7 @@ private protected virtual DataViewRow GetSummaryIRowOrNull(RoleMappedSchema sche
 
  void ICanSaveInIniFormat.SaveAsIni(TextWriter writer, RoleMappedSchema schema, ICalibrator calibrator) => SaveAsIni(writer, schema, calibrator);
 
-public void GetFeatureWeights(ref VBuffer<float> weights)
+ void IHaveFeatureWeights.GetFeatureWeights(ref VBuffer<float> weights)
  {
  Weight.CopyTo(ref weights);
  }

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

@@ -438,7 +438,7 @@ internal DataViewSchema.Annotations MakeStatisticsMetadata(LinearBinaryModelPara
  builder.AddPrimitiveValue("BiasPValue", NumberDataViewType.Single, biasPValue);
 
  var weights = default(VBuffer<float>);
- parent.GetFeatureWeights(ref weights);
+((IHaveFeatureWeights)parent).GetFeatureWeights(ref weights);
  var estimate = default(VBuffer<float>);
  var stdErr = default(VBuffer<float>);
  var zScore = default(VBuffer<float>);

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

@@ -10,6 +10,7 @@
 using Microsoft.ML.EntryPoints;
 using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.Internal.Utilities;
+using Microsoft.ML.Model;
 using Microsoft.ML.Numeric;
 
 namespace Microsoft.ML.Trainers
@@ -130,7 +131,7 @@ protected TrainStateBase(IChannel ch, int numFeatures, LinearModelParameters pre
  // unless we have a lot of features.
  if (predictor != null)
  {
- predictor.GetFeatureWeights(ref Weights);
+((IHaveFeatureWeights)predictor).GetFeatureWeights(ref Weights);
  VBufferUtils.Densify(ref Weights);
  Bias = predictor.Bias;
  }

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

@@ -1937,7 +1937,7 @@ private protected override TModel TrainCore(IChannel ch, RoleMappedData data, Li
  float bias = 0.0f;
  if (predictor != null)
  {
- predictor.GetFeatureWeights(ref weights);
+((IHaveFeatureWeights)predictor).GetFeatureWeights(ref weights);
  VBufferUtils.Densify(ref weights);
  bias = predictor.Bias;
  }

test/Microsoft.ML.StaticPipelineTesting/Training.cs

@@ -627,9 +627,7 @@ public void PoissonRegression()
  var model = pipe.Fit(dataSource);
  Assert.NotNull(pred);
  // 11 input features, so we ought to have 11 weights.
- VBuffer<float> weights = new VBuffer<float>();
- pred.GetFeatureWeights(ref weights);
- Assert.Equal(11, weights.Length);
+ Assert.Equal(11, pred.Weights.Count);
 
  var data = model.Load(dataSource);
 
@@ -751,9 +749,7 @@ public void OnlineGradientDescent()
  var model = pipe.Fit(dataSource);
  Assert.NotNull(pred);
  // 11 input features, so we ought to have 11 weights.
- VBuffer<float> weights = new VBuffer<float>();
- pred.GetFeatureWeights(ref weights);
- Assert.Equal(11, weights.Length);
+ Assert.Equal(11, pred.Weights.Count);
 
  var data = model.Load(dataSource);
 

test/Microsoft.ML.Tests/Scenarios/Api/Estimators/IntrospectiveTraining.cs

@@ -42,8 +42,7 @@ public void IntrospectiveTraining()
  var model = pipeline.Fit(data);
 
  // Get feature weights.
- VBuffer<float> weights = default;
- model.LastTransformer.Model.GetFeatureWeights(ref weights);
+ var weights = model.LastTransformer.Model.Weights;
  }
 
  [Fact]