Legacy API namespace rename

Author: Zruty0

src/Microsoft.ML.PipelineInference/Macros/PipelineSweeperMacro.cs

@@ -258,8 +258,8 @@ public static CommonOutputs.MacroOutput<Output> PipelineSweep(
             {
                 // Add a node to extract the sweep result.
                 var resultSubgraph = new Experiment(env);
-                var resultNode = new Microsoft.ML.Models.SweepResultExtractor() { State = amlsVarObj };
-                var resultOutput = new Models.SweepResultExtractor.Output() { State = outStateVar, Results = outDvVar };
+                var resultNode = new Microsoft.ML.Legacy.Models.SweepResultExtractor() { State = amlsVarObj };
+                var resultOutput = new Legacy.Models.SweepResultExtractor.Output() { State = outStateVar, Results = outDvVar };
                 resultSubgraph.Add(resultNode, resultOutput);
                 var resultSubgraphNodes = EntryPointNode.ValidateNodes(env, node.Context, resultSubgraph.GetNodes(), node.Catalog);
                 expNodes.AddRange(resultSubgraphNodes);
@@ -290,15 +290,15 @@ public static CommonOutputs.MacroOutput<Output> PipelineSweep(
 
             // Add recursive macro node
             var macroSubgraph = new Experiment(env);
-            var macroNode = new Models.PipelineSweeper()
+            var macroNode = new Legacy.Models.PipelineSweeper()
             {
                 BatchSize = input.BatchSize,
                 CandidateOutputs = new ArrayVar<IDataView>(pipelineIndicators.ToArray()),
                 TrainingData = training,
                 TestingData = testing,
                 State = amlsVarObj
             };
-            var output = new Models.PipelineSweeper.Output() { Results = outDvVar, State = outStateVar };
+            var output = new Legacy.Models.PipelineSweeper.Output() { Results = outDvVar, State = outStateVar };
             macroSubgraph.Add(macroNode, output);
 
             var subgraphNodes = EntryPointNode.ValidateNodes(env, node.Context, macroSubgraph.GetNodes(), node.Catalog);

src/Microsoft.ML.PipelineInference/PipelinePattern.cs

@@ -5,7 +5,7 @@
 using System;
 using System.Collections.Generic;
 using System.Linq;
-using Microsoft.ML;
+using Microsoft.ML.Legacy;
 using Microsoft.ML.Runtime.EntryPoints;
 using Microsoft.ML.Runtime.Data;
 
@@ -106,7 +106,7 @@ public AutoInference.EntryPointGraphDef ToEntryPointGraph(Experiment experiment
             // if transforms present.
             if (Transforms.Length > 0)
             {
-                var modelCombine = new ML.Transforms.ManyHeterogeneousModelCombiner
+                var modelCombine = new Legacy.Transforms.ManyHeterogeneousModelCombiner
                 {
                     TransformModels = new ArrayVar<ITransformModel>(transformsModels.ToArray()),
                     PredictorModel = returnedDataAndModel2?.Model
@@ -130,7 +130,7 @@ public AutoInference.EntryPointGraphDef ToEntryPointGraph(Experiment experiment
 
         // REVIEW: We may want to allow for sweeping with CV in the future, so we will need to add new methods like this, or refactor these in that case.
         public Experiment CreateTrainTestExperiment(IDataView trainData, IDataView testData, MacroUtils.TrainerKinds trainerKind,
-                bool includeTrainingMetrics, out Models.TrainTestEvaluator.Output resultsOutput)
+                bool includeTrainingMetrics, out Legacy.Models.TrainTestEvaluator.Output resultsOutput)
         {
             var graphDef = ToEntryPointGraph();
             var subGraph = graphDef.Graph;
@@ -142,7 +142,7 @@ public Experiment CreateTrainTestExperiment(IDataView trainData, IDataView testD
             var finalOutput = graphDef.ModelOutput;
 
             // TrainTestMacro
-            var trainTestInput = new Models.TrainTestEvaluator
+            var trainTestInput = new Legacy.Models.TrainTestEvaluator
             {
                 TransformModel = null,
                 Nodes = subGraph,
@@ -155,7 +155,7 @@ public Experiment CreateTrainTestExperiment(IDataView trainData, IDataView testD
                         PredictorModel = finalOutput
                     },
                 PipelineId = UniqueId.ToString("N"),
-                Kind = MacroUtils.TrainerKindApiValue<Models.MacroUtilsTrainerKinds>(trainerKind),
+                Kind = MacroUtils.TrainerKindApiValue<Legacy.Models.MacroUtilsTrainerKinds>(trainerKind),
                 IncludeTrainingMetrics = includeTrainingMetrics
             };
 
@@ -169,7 +169,7 @@ public Experiment CreateTrainTestExperiment(IDataView trainData, IDataView testD
             return experiment;
         }
 
-        public Models.TrainTestEvaluator.Output AddAsTrainTest(Var<IDataView> trainData, Var<IDataView> testData,
+        public Legacy.Models.TrainTestEvaluator.Output AddAsTrainTest(Var<IDataView> trainData, Var<IDataView> testData,
             MacroUtils.TrainerKinds trainerKind, Experiment experiment = null, bool includeTrainingMetrics = false)
         {
             experiment = experiment ?? _env.CreateExperiment();
@@ -179,7 +179,7 @@ public Models.TrainTestEvaluator.Output AddAsTrainTest(Var<IDataView> trainData,
             var finalOutput = graphDef.ModelOutput;
 
             // TrainTestMacro
-            var trainTestInput = new Models.TrainTestEvaluator
+            var trainTestInput = new Legacy.Models.TrainTestEvaluator
             {
                 Nodes = subGraph,
                 TransformModel = null,
@@ -193,7 +193,7 @@ public Models.TrainTestEvaluator.Output AddAsTrainTest(Var<IDataView> trainData,
                     },
                 TrainingData = trainData,
                 TestingData = testData,
-                Kind = MacroUtils.TrainerKindApiValue<Models.MacroUtilsTrainerKinds>(trainerKind),
+                Kind = MacroUtils.TrainerKindApiValue<Legacy.Models.MacroUtilsTrainerKinds>(trainerKind),
                 PipelineId = UniqueId.ToString("N"),
                 IncludeTrainingMetrics = includeTrainingMetrics
             };

src/Microsoft.ML.PipelineInference/RecipeInference.cs

@@ -99,7 +99,7 @@ public AutoInference.EntryPointGraphDef ToEntryPointGraph(IHostEnvironment env)
                     // if transforms present.
                     if (Transforms.Length > 0)
                     {
-                        var modelCombine = new ML.Transforms.ManyHeterogeneousModelCombiner
+                        var modelCombine = new ML.Legacy.Transforms.ManyHeterogeneousModelCombiner
                         {
                             TransformModels = new ArrayVar<ITransformModel>(transformsModels.ToArray()),
                             PredictorModel = learnerAddResult.Model
@@ -243,7 +243,7 @@ protected override IEnumerable<SuggestedRecipe> ApplyCore(Type predictorType,
                 {
                     learner.LoadableClassInfo = ComponentCatalog.GetLoadableClassInfo<SignatureTrainer>(Learners.AveragedPerceptronTrainer.LoadNameValue);
                     learner.Settings = "iter=10";
-                    var epInput = new Trainers.AveragedPerceptronBinaryClassifier
+                    var epInput = new Legacy.Trainers.AveragedPerceptronBinaryClassifier
                     {
                         NumIterations = 10
                     };
@@ -276,7 +276,7 @@ protected override IEnumerable<SuggestedRecipe> ApplyCore(Type predictorType,
                     learner.LoadableClassInfo =
                         ComponentCatalog.GetLoadableClassInfo<SignatureTrainer>(FastTreeBinaryClassificationTrainer.LoadNameValue);
                     learner.Settings = "";
-                    var epInput = new Trainers.FastTreeBinaryClassifier();
+                    var epInput = new Legacy.Trainers.FastTreeBinaryClassifier();
                     learner.PipelineNode = new TrainerPipelineNode(epInput);
                 }
 
@@ -304,7 +304,7 @@ protected override IEnumerable<SuggestedRecipe> ApplyCore(Type predictorType,
                 {
                     learner.LoadableClassInfo =
                         ComponentCatalog.GetLoadableClassInfo<SignatureTrainer>(Learners.LinearClassificationTrainer.LoadNameValue);
-                    var epInput = new Trainers.StochasticDualCoordinateAscentBinaryClassifier();
+                    var epInput = new Legacy.Trainers.StochasticDualCoordinateAscentBinaryClassifier();
                     learner.PipelineNode = new TrainerPipelineNode(epInput);
                 }
 
@@ -327,7 +327,7 @@ protected override IEnumerable<SuggestedRecipe> ApplyCore(Type predictorType,
                 learner.LoadableClassInfo =
                     ComponentCatalog.GetLoadableClassInfo<SignatureTrainer>(Learners.MultiClassNaiveBayesTrainer.LoadName);
                 learner.Settings = "";
-                var epInput = new Trainers.NaiveBayesClassifier();
+                var epInput = new Legacy.Trainers.NaiveBayesClassifier();
                 learner.PipelineNode = new TrainerPipelineNode(epInput);
                 yield return new SuggestedRecipe(ToString(), transforms, new[] { learner });
             }