Scrubbing online learners

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

@@ -19,7 +19,7 @@ public static void Example()
             var trainTestData = mlContext.BinaryClassification.TrainTestSplit(data, testFraction: 0.1);
 
             // Create data training pipeline.
-            var pipeline = mlContext.BinaryClassification.Trainers.AveragedPerceptron(numIterations: 10);
+            var pipeline = mlContext.BinaryClassification.Trainers.AveragedPerceptron(numberOfIterations: 10);
 
             // Fit this pipeline to the training data.
             var model = pipeline.Fit(trainTestData.TrainSet);

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

@@ -25,7 +25,7 @@ public static void Example()
             {
                 LossFunction = new SmoothedHingeLoss.Options(),
                 LearningRate = 0.1f,
-                DoLazyUpdates = false,
+                LazyUpdate = false,
                 RecencyGain = 0.1f,
                 NumberOfIterations = 10
             };

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

@@ -57,16 +57,16 @@ public abstract class AveragedLinearOptions : OnlineLinearOptions
         /// <see langword="false" /> to update averaged weights on every example.
         /// Default is <see langword="true" />.
         /// </value>
-        [Argument(ArgumentType.AtMostOnce, HelpText = "Instead of updating averaged weights on every example, only update when loss is nonzero", ShortName = "lazy")]
-        public bool DoLazyUpdates = true;
+        [Argument(ArgumentType.AtMostOnce, HelpText = "Instead of updating averaged weights on every example, only update when loss is nonzero", ShortName = "lazy,DoLazyUpdates")]
+        public bool LazyUpdate = true;
 
         /// <summary>
         /// The L2 weight for <a href='tmpurl_regularization'>regularization</a>.
         /// </summary>
-        [Argument(ArgumentType.AtMostOnce, HelpText = "L2 Regularization Weight", ShortName = "reg", SortOrder = 50)]
+        [Argument(ArgumentType.AtMostOnce, HelpText = "L2 Regularization Weight", ShortName = "reg,L2RegularizerWeight", SortOrder = 50)]
         [TGUI(Label = "L2 Regularization Weight")]
         [TlcModule.SweepableFloatParam("L2RegularizerWeight", 0.0f, 0.4f)]
-        public float L2RegularizerWeight = AveragedDefault.L2RegularizerWeight;
+        public float L2Regularization = AveragedDefault.L2Regularization;
 
         /// <summary>
         /// Extra weight given to more recent updates.
@@ -85,8 +85,8 @@ public abstract class AveragedLinearOptions : OnlineLinearOptions
         /// <see langword="false" /> means <see cref="RecencyGain"/> is additive.
         /// Default is <see langword="false" />.
         /// </value>
-        [Argument(ArgumentType.AtMostOnce, HelpText = "Whether Recency Gain is multiplicative (vs. additive)", ShortName = "rgm")]
-        public bool RecencyGainMulti = false;
+        [Argument(ArgumentType.AtMostOnce, HelpText = "Whether Recency Gain is multiplicative (vs. additive)", ShortName = "rgm,RecencyGainMulti")]
+        public bool RecencyGainMultiplicative = false;
 
         /// <summary>
         /// Determines whether to do averaging or not.
@@ -109,7 +109,7 @@ internal class AveragedDefault : OnlineLinearOptions.OnlineDefault
         {
             public const float LearningRate = 1;
             public const bool DecreaseLearningRate = false;
-            public const float L2RegularizerWeight = 0;
+            public const float L2Regularization = 0;
         }
 
         internal abstract IComponentFactory<IScalarOutputLoss> LossFunctionFactory { get; }
@@ -186,7 +186,7 @@ public override void FinishIteration(IChannel ch)
                 // Finalize things
                 if (Averaged)
                 {
-                    if (_args.DoLazyUpdates && NumNoUpdates > 0)
+                    if (_args.LazyUpdate && NumNoUpdates > 0)
                     {
                         // Update the total weights to include the final loss=0 updates
                         VectorUtils.AddMult(in Weights, NumNoUpdates * WeightsScale, ref TotalWeights);
@@ -221,10 +221,10 @@ public override void ProcessDataInstance(IChannel ch, in VBuffer<float> feat, fl
                 // REVIEW: Should this be biasUpdate != 0?
                 // This loss does not incorporate L2 if present, but the chance of that addition to the loss
                 // exactly cancelling out loss is remote.
-                if (loss != 0 || _args.L2RegularizerWeight > 0)
+                if (loss != 0 || _args.L2Regularization > 0)
                 {
                     // If doing lazy weights, we need to update the totalWeights and totalBias before updating weights/bias
-                    if (_args.DoLazyUpdates && _args.Averaged && NumNoUpdates > 0 && TotalMultipliers * _args.AveragedTolerance <= PendingMultipliers)
+                    if (_args.LazyUpdate && _args.Averaged && NumNoUpdates > 0 && TotalMultipliers * _args.AveragedTolerance <= PendingMultipliers)
                     {
                         VectorUtils.AddMult(in Weights, NumNoUpdates * WeightsScale, ref TotalWeights);
                         TotalBias += Bias * NumNoUpdates * WeightsScale;
@@ -242,7 +242,7 @@ public override void ProcessDataInstance(IChannel ch, in VBuffer<float> feat, fl
 
                     // Perform the update to weights and bias.
                     VectorUtils.AddMult(in feat, biasUpdate / WeightsScale, ref Weights);
-                    WeightsScale *= 1 - 2 * _args.L2RegularizerWeight; // L2 regularization.
+                    WeightsScale *= 1 - 2 * _args.L2Regularization; // L2 regularization.
                     ScaleWeightsIfNeeded();
                     Bias += biasUpdate;
                     PendingMultipliers += Math.Abs(biasUpdate);
@@ -251,7 +251,7 @@ public override void ProcessDataInstance(IChannel ch, in VBuffer<float> feat, fl
                 // Add to averaged weights and increment the count.
                 if (Averaged)
                 {
-                    if (!_args.DoLazyUpdates)
+                    if (!_args.LazyUpdate)
                         IncrementAverageNonLazy();
                     else
                         NumNoUpdates++;
@@ -282,7 +282,7 @@ private void IncrementAverageNonLazy()
                 VectorUtils.AddMult(in Weights, Gain * WeightsScale, ref TotalWeights);
                 TotalBias += Gain * Bias;
                 NumWeightUpdates += Gain;
-                Gain = (_args.RecencyGainMulti ? Gain * _args.RecencyGain : Gain + _args.RecencyGain);
+                Gain = (_args.RecencyGainMultiplicative ? Gain * _args.RecencyGain : Gain + _args.RecencyGain);
 
                 // If gains got too big, rescale!
                 if (Gain > 1000)
@@ -303,11 +303,11 @@ private protected AveragedLinearTrainer(AveragedLinearOptions options, IHostEnvi
             Contracts.CheckUserArg(!options.ResetWeightsAfterXExamples.HasValue || options.ResetWeightsAfterXExamples > 0, nameof(options.ResetWeightsAfterXExamples), UserErrorPositive);
 
             // Weights are scaled down by 2 * L2 regularization on each update step, so 0.5 would scale all weights to 0, which is not sensible.
-            Contracts.CheckUserArg(0 <= options.L2RegularizerWeight && options.L2RegularizerWeight < 0.5, nameof(options.L2RegularizerWeight), "must be in range [0, 0.5)");
+            Contracts.CheckUserArg(0 <= options.L2Regularization && options.L2Regularization < 0.5, nameof(options.L2Regularization), "must be in range [0, 0.5)");
             Contracts.CheckUserArg(options.RecencyGain >= 0, nameof(options.RecencyGain), UserErrorNonNegative);
             Contracts.CheckUserArg(options.AveragedTolerance >= 0, nameof(options.AveragedTolerance), UserErrorNonNegative);
             // Verify user didn't specify parameters that conflict
-            Contracts.Check(!options.DoLazyUpdates || !options.RecencyGainMulti && options.RecencyGain == 0, "Cannot have both recency gain and lazy updates.");
+            Contracts.Check(!options.LazyUpdate || !options.RecencyGainMultiplicative && options.RecencyGain == 0, "Cannot have both recency gain and lazy updates.");
 
             AveragedLinearTrainerOptions = options;
         }

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

@@ -126,24 +126,24 @@ internal AveragedPerceptronTrainer(IHostEnvironment env, Options options)
         /// <param name="featureColumnName">The name of the feature column.</param>
         /// <param name="learningRate">The learning rate. </param>
         /// <param name="decreaseLearningRate">Whether to decrease learning rate as iterations progress.</param>
-        /// <param name="l2RegularizerWeight">L2 Regularization Weight.</param>
-        /// <param name="numIterations">The number of training iterations.</param>
+        /// <param name="l2Regularization">Weight of L2 regularization term.</param>
+        /// <param name="numberOfIterations">The number of training iterations.</param>
         internal AveragedPerceptronTrainer(IHostEnvironment env,
             string labelColumnName = DefaultColumnNames.Label,
             string featureColumnName = DefaultColumnNames.Features,
             IClassificationLoss lossFunction = null,
             float learningRate = Options.AveragedDefault.LearningRate,
             bool decreaseLearningRate = Options.AveragedDefault.DecreaseLearningRate,
-            float l2RegularizerWeight = Options.AveragedDefault.L2RegularizerWeight,
-            int numIterations = Options.AveragedDefault.NumIterations)
+            float l2Regularization = Options.AveragedDefault.L2Regularization,
+            int numberOfIterations = Options.AveragedDefault.NumberOfIterations)
             : this(env, new Options
             {
                 LabelColumnName = labelColumnName,
                 FeatureColumnName = featureColumnName,
                 LearningRate = learningRate,
                 DecreaseLearningRate = decreaseLearningRate,
-                L2RegularizerWeight = l2RegularizerWeight,
-                NumberOfIterations = numIterations,
+                L2Regularization = l2Regularization,
+                NumberOfIterations = numberOfIterations,
                 LossFunction = new TrivialFactory(lossFunction ?? new HingeLoss())
             })
         {

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

@@ -69,8 +69,8 @@ public sealed class Options : OnlineLinearOptions
             /// <summary>
             /// Column to use for example weight.
             /// </summary>
-            [Argument(ArgumentType.AtMostOnce, HelpText = "Column to use for example weight", ShortName = "weight", SortOrder = 4, Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)]
-            public string WeightColumn = null;
+            [Argument(ArgumentType.AtMostOnce, HelpText = "Column to use for example weight", ShortName = "weight,WeightColumn", SortOrder = 4, Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)]
+            public string ExampleWeightColumnName = null;
         }
 
         private sealed class TrainState : TrainStateBase
@@ -232,20 +232,20 @@ public override LinearBinaryModelParameters CreatePredictor()
         /// <param name="env">The environment to use.</param>
         /// <param name="labelColumn">The name of the label column. </param>
         /// <param name="featureColumn">The name of the feature column.</param>
-        /// <param name="weightColumn">The optional name of the weight column.</param>
-        /// <param name="numIterations">The number of training iteraitons.</param>
+        /// <param name="exampleWeightColumnName">The name of the example weight column (optional).</param>
+        /// <param name="numberOfIterations">The number of training iteraitons.</param>
         [BestFriend]
         internal LinearSvmTrainer(IHostEnvironment env,
             string labelColumn = DefaultColumnNames.Label,
             string featureColumn = DefaultColumnNames.Features,
-            string weightColumn = null,
-            int numIterations = Options.OnlineDefault.NumIterations)
+            string exampleWeightColumnName = null,
+            int numberOfIterations = Options.OnlineDefault.NumberOfIterations)
             : this(env, new Options
             {
                 LabelColumnName = labelColumn,
                 FeatureColumnName = featureColumn,
-                WeightColumn = weightColumn,
-                NumberOfIterations = numIterations,
+                ExampleWeightColumnName = exampleWeightColumnName,
+                NumberOfIterations = numberOfIterations,
             })
         {
         }

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

@@ -95,23 +95,23 @@ public override LinearRegressionModelParameters CreatePredictor()
         /// <param name="featureColumn">Name of the feature column.</param>
         /// <param name="learningRate">The learning Rate.</param>
         /// <param name="decreaseLearningRate">Decrease learning rate as iterations progress.</param>
-        /// <param name="l2RegularizerWeight">L2 Regularization Weight.</param>
-        /// <param name="numIterations">Number of training iterations through the data.</param>
+        /// <param name="l2Regularization">Weight of L2 regularization term.</param>
+        /// <param name="numberOfIterations">Number of training iterations through the data.</param>
         /// <param name="lossFunction">The custom loss functions. Defaults to <see cref="SquaredLoss"/> if not provided.</param>
         internal OnlineGradientDescentTrainer(IHostEnvironment env,
             string labelColumn = DefaultColumnNames.Label,
             string featureColumn = DefaultColumnNames.Features,
             float learningRate = Options.OgdDefaultArgs.LearningRate,
             bool decreaseLearningRate = Options.OgdDefaultArgs.DecreaseLearningRate,
-            float l2RegularizerWeight = Options.OgdDefaultArgs.L2RegularizerWeight,
-            int numIterations = Options.OgdDefaultArgs.NumIterations,
+            float l2Regularization = Options.OgdDefaultArgs.L2Regularization,
+            int numberOfIterations = Options.OgdDefaultArgs.NumberOfIterations,
             IRegressionLoss lossFunction = null)
             : this(env, new Options
             {
                 LearningRate = learningRate,
                 DecreaseLearningRate = decreaseLearningRate,
-                L2RegularizerWeight = l2RegularizerWeight,
-                NumberOfIterations = numIterations,
+                L2Regularization= l2Regularization,
+                NumberOfIterations = numberOfIterations,
                 LabelColumnName = labelColumn,
                 FeatureColumnName = featureColumn,
                 LossFunction = new TrivialFactory(lossFunction ?? new SquaredLoss())

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

@@ -27,7 +27,7 @@ public abstract class OnlineLinearOptions : TrainerInputBaseWithLabel
         [Argument(ArgumentType.AtMostOnce, HelpText = "Number of iterations", ShortName = "iter,numIterations", SortOrder = 50)]
         [TGUI(Label = "Number of Iterations", Description = "Number of training iterations through data", SuggestedSweeps = "1,10,100")]
         [TlcModule.SweepableLongParamAttribute("NumIterations", 1, 100, stepSize: 10, isLogScale: true)]
-        public int NumberOfIterations = OnlineDefault.NumIterations;
+        public int NumberOfIterations = OnlineDefault.NumberOfIterations;
 
         /// <summary>
         /// Initial weights and bias, comma-separated.
@@ -62,7 +62,7 @@ public abstract class OnlineLinearOptions : TrainerInputBaseWithLabel
         [BestFriend]
         internal class OnlineDefault
         {
-            public const int NumIterations = 1;
+            public const int NumberOfIterations = 1;
         }
     }