Reverting the signature change on Fit()

Author: sfilipi

src/Microsoft.ML.Core/Data/IEstimator.cs

@@ -237,7 +237,7 @@ public interface IEstimator<out TTransformer>
         /// <summary>
         /// Train and return a transformer.
         /// </summary>
-        TTransformer Fit(IDataView input, IDataView validationData = null, IPredictor initialPredictor = null);
+        TTransformer Fit(IDataView input);
 
         /// <summary>
         /// Schema propagation for estimators.

src/Microsoft.ML.Data/DataLoadSave/EstimatorChain.cs

@@ -42,7 +42,7 @@ public EstimatorChain()
             LastEstimator = null;
         }
 
-        public TransformerChain<TLastTransformer> Fit(IDataView input, IDataView validationData = null, IPredictor initialPredictor = null)
+        public TransformerChain<TLastTransformer> Fit(IDataView input)
         {
             // REVIEW: before fitting, run schema propagation.
             // Currently, it throws.

src/Microsoft.ML.Data/DataLoadSave/EstimatorExtensions.cs

@@ -90,7 +90,7 @@ public DelegateEstimator(IEstimator<TTransformer> estimator, Action<TTransformer
                 _onFit = onFit;
             }
 
-            public TTransformer Fit(IDataView input, IDataView validationData = null, IPredictor initialPredictor = null)
+            public TTransformer Fit(IDataView input)
             {
                 var trans = _est.Fit(input);
                 _onFit(trans);
@@ -102,9 +102,9 @@ public SchemaShape GetOutputSchema(SchemaShape inputSchema)
         }
 
         /// <summary>
-        /// Given an estimator, return a wrapping object that will call a delegate once <see cref="IEstimator{TTransformer}.Fit(IDataView, IDataView, IPredictor)"/>
+        /// Given an estimator, return a wrapping object that will call a delegate once <see cref="IEstimator{TTransformer}.Fit(IDataView)"/>
         /// is called. It is often important for an estimator to return information about what was fit, which is why the
-        /// <see cref="IEstimator{TTransformer}.Fit(IDataView, IDataView, IPredictor)"/> method returns a specifically typed object, rather than just a general
+        /// <see cref="IEstimator{TTransformer}.Fit(IDataView)"/> method returns a specifically typed object, rather than just a general
         /// <see cref="ITransformer"/>. However, at the same time, <see cref="IEstimator{TTransformer}"/> are often formed into pipelines
         /// with many objects, so we may need to build a chain of estimators via <see cref="EstimatorChain{TLastTransformer}"/> where the
         /// estimator for which we want to get the transformer is buried somewhere in this chain. For that scenario, we can through this
@@ -113,7 +113,7 @@ public SchemaShape GetOutputSchema(SchemaShape inputSchema)
         /// <typeparam name="TTransformer">The type of <see cref="ITransformer"/> returned by <paramref name="estimator"/></typeparam>
         /// <param name="estimator">The estimator to wrap</param>
         /// <param name="onFit">The delegate that is called with the resulting <typeparamref name="TTransformer"/> instances once
-        /// <see cref="IEstimator{TTransformer}.Fit(IDataView, IDataView, IPredictor)"/> is called. Because <see cref="IEstimator{TTransformer}.Fit(IDataView, IDataView, IPredictor)"/>
+        /// <see cref="IEstimator{TTransformer}.Fit(IDataView)"/> is called. Because <see cref="IEstimator{TTransformer}.Fit(IDataView)"/>
         /// may be called multiple times, this delegate may also be called multiple times.</param>
         /// <returns>A wrapping estimator that calls the indicated delegate whenever fit is called</returns>
         public static IEstimator<TTransformer> WithOnFitDelegate<TTransformer>(this IEstimator<TTransformer> estimator, Action<TTransformer> onFit)

src/Microsoft.ML.Data/DataLoadSave/TrivialEstimator.cs

@@ -8,7 +8,7 @@ namespace Microsoft.ML.Runtime.Data
 {
     /// <summary>
     /// The trivial implementation of <see cref="IEstimator{TTransformer}"/> that already has
-    /// the transformer and returns it on every call to <see cref="Fit(IDataView, IDataView, IPredictor)"/>.
+    /// the transformer and returns it on every call to <see cref="Fit(IDataView)"/>.
     ///
     /// Concrete implementations still have to provide the schema propagation mechanism, since
     /// there is no easy way to infer it from the transformer.
@@ -28,7 +28,7 @@ protected TrivialEstimator(IHost host, TTransformer transformer)
             Transformer = transformer;
         }
 
-        public TTransformer Fit(IDataView input, IDataView validationData = null, IPredictor initialPredictor = null) => Transformer;
+        public TTransformer Fit(IDataView input) => Transformer;
 
         public abstract SchemaShape GetOutputSchema(SchemaShape inputSchema);
     }

src/Microsoft.ML.Data/Training/TrainerEstimatorBase.cs

@@ -67,7 +67,7 @@ public TrainerEstimatorBase(IHost host, SchemaShape.Column feature, SchemaShape.
             WeightColumn = weight;
         }
 
-        public TTransformer Fit(IDataView input, IDataView validationData = null, IPredictor initialPredictor = null) => TrainTransformer(input, validationData, initialPredictor);
+        public TTransformer Fit(IDataView input) => TrainTransformer(input);
 
         public SchemaShape GetOutputSchema(SchemaShape inputSchema)
         {

src/Microsoft.ML.Data/Transforms/CopyColumnsTransform.cs

@@ -54,7 +54,7 @@ public CopyColumnsEstimator(IHostEnvironment env, params (string source, string
             _columns = columns;
         }
 
-        public CopyColumnsTransform Fit(IDataView input, IDataView validationData = null, IPredictor initialPredictor = null)
+        public CopyColumnsTransform Fit(IDataView input)
         {
             // Invoke schema validation.
             GetOutputSchema(SchemaShape.Create(input.Schema));

src/Microsoft.ML.Data/Transforms/Normalizer.cs

@@ -170,7 +170,7 @@ public Normalizer(IHostEnvironment env, params ColumnBase[] columns)
             _columns = columns.ToArray();
         }
 
-        public NormalizerTransformer Fit(IDataView input, IDataView validationData = null, IPredictor initialPredictor = null)
+        public NormalizerTransformer Fit(IDataView input)
         {
             _host.CheckValue(input, nameof(input));
             return NormalizerTransformer.Train(_host, input, _columns);

src/Microsoft.ML.Data/Transforms/TermEstimator.cs

@@ -23,7 +23,7 @@ public TermEstimator(IHostEnvironment env, params TermTransform.ColumnInfo[] col
             _columns = columns;
         }
 
-        public TermTransform Fit(IDataView input, IDataView validationData = null, IPredictor initialPredictor = null) => new TermTransform(_host, input, _columns);
+        public TermTransform Fit(IDataView input) => new TermTransform(_host, input, _columns);
 
         public SchemaShape GetOutputSchema(SchemaShape inputSchema)
         {

test/Microsoft.ML.StaticPipelineTesting/StaticPipeFakes.cs

@@ -51,7 +51,7 @@ public override IEstimator<ITransformer> Reconcile(
 
         private sealed class FakeEstimator : IEstimator<ITransformer>
         {
-            public ITransformer Fit(IDataView input, IDataView validationData = null, IPredictor initialPredictor = null) => throw new NotImplementedException();
+            public ITransformer Fit(IDataView input) => throw new NotImplementedException();
             public SchemaShape GetOutputSchema(SchemaShape inputSchema) => throw new NotImplementedException();
         }
 

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

@@ -2,6 +2,7 @@
 // The .NET Foundation licenses this file to you under the MIT license.
 // See the LICENSE file in the project root for more information.
 
+using Microsoft.ML.Runtime;
 using Microsoft.ML.Runtime.Data;
 using Microsoft.ML.Runtime.Learners;
 using Xunit;
@@ -38,7 +39,9 @@ public void New_TrainWithInitialPredictor()
 
                 // Train the second predictor on the same data.
                 var secondTrainer = new AveragedPerceptronTrainer(env, new AveragedPerceptronTrainer.Arguments());
-                var finalModel = secondTrainer.Fit(trainData, initialPredictor: firstModel.Model);
+
+                var trainRoles = new RoleMappedData(trainData, label: "Label", feature: "Features");
+                var finalModel = secondTrainer.Train(new TrainContext(trainRoles, initialPredictor: firstModel.Model));
             }
         }
     }

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

@@ -245,7 +245,7 @@ protected TrainerBase(IHostEnvironment env, TrainerInfo trainerInfo, string feat
             Info = trainerInfo;
         }
 
-        public TTransformer Fit(IDataView input, IDataView validationData = null, IPredictor initialPredictor = null)
+        public TTransformer Fit(IDataView input)
         {
             return TrainTransformer(input);
         }
@@ -311,7 +311,7 @@ public MyTextTransform(IHostEnvironment env, TextTransform.Arguments args)
             _args = args;
         }
 
-        public TransformWrapper Fit(IDataView input, IDataView validationData = null, IPredictor initialPredictor = null)
+        public TransformWrapper Fit(IDataView input)
         {
             var xf = TextTransform.Create(_env, _args, input);
             var empty = new EmptyDataView(_env, input.Schema);
@@ -338,7 +338,7 @@ public MyConcatTransform(IHostEnvironment env, string name, params string[] sour
             _source = source;
         }
 
-        public TransformWrapper Fit(IDataView input, IDataView validationData = null, IPredictor initialPredictor = null)
+        public TransformWrapper Fit(IDataView input)
         {
             var xf = new ConcatTransform(_env, input, _name, _source);
             var empty = new EmptyDataView(_env, input.Schema);
@@ -365,7 +365,7 @@ public MyKeyToValueTransform(IHostEnvironment env, string name, string source =
             _source = source;
         }
 
-        public TransformWrapper Fit(IDataView input, IDataView validationData = null, IPredictor initialPredictor = null)
+        public TransformWrapper Fit(IDataView input)
         {
             var xf = new KeyToValueTransform(_env, input, _name, _source);
             var empty = new EmptyDataView(_env, input.Schema);
@@ -510,7 +510,7 @@ public MyLambdaTransform(IHostEnvironment env, Action<TSrc, TDst> action)
             _action = action;
         }
 
-        public TransformWrapper Fit(IDataView input, IDataView validationData = null, IPredictor initialPredictor = null)
+        public TransformWrapper Fit(IDataView input)
         {
             var xf = LambdaTransform.CreateMap(_env, input, _action);
             var empty = new EmptyDataView(_env, input.Schema);