Microsoft.ML.EntryPoints.JsonUtils -> Microsoft.ML.EntryPoints

and
Microsoft.ML.FactorizationMachine -> Microsoft.ML.Trainers.FactorizationMachine

Author: sfilipi

src/Microsoft.ML.Data/EntryPoints/EntryPointNode.cs

@@ -8,8 +8,6 @@
 using System.Linq;
 using System.Text.RegularExpressions;
 using Microsoft.Data.DataView;
-using Microsoft.ML.Data;
-using Microsoft.ML.EntryPoints.JsonUtils;
 using Microsoft.ML.Internal.Utilities;
 using Newtonsoft.Json;
 using Newtonsoft.Json.Linq;

src/Microsoft.ML.Data/EntryPoints/InputBuilder.cs

@@ -11,7 +11,7 @@
 using Microsoft.ML.Internal.Utilities;
 using Newtonsoft.Json.Linq;
 
-namespace Microsoft.ML.EntryPoints.JsonUtils
+namespace Microsoft.ML.EntryPoints
 {
     /// <summary>
     /// The class that creates and wraps around an instance of an input object and gradually populates all fields, keeping track of missing

src/Microsoft.ML.EntryPoints/JsonUtils/ExecuteGraphCommand.cs

@@ -10,15 +10,15 @@
 using Microsoft.ML.CommandLine;
 using Microsoft.ML.Data;
 using Microsoft.ML.Data.IO;
-using Microsoft.ML.EntryPoints.JsonUtils;
+using Microsoft.ML.EntryPoints;
 using Microsoft.ML.Internal.Utilities;
 using Newtonsoft.Json;
 using Newtonsoft.Json.Linq;
 
 [assembly: LoadableClass(typeof(ExecuteGraphCommand), typeof(ExecuteGraphCommand.Arguments), typeof(SignatureCommand),
     "", "ExecGraph")]
 
-namespace Microsoft.ML.EntryPoints.JsonUtils
+namespace Microsoft.ML.EntryPoints
 {
     internal sealed class ExecuteGraphCommand : ICommand
     {

src/Microsoft.ML.EntryPoints/JsonUtils/GraphRunner.cs

@@ -5,7 +5,7 @@
 using System.Linq;
 using Newtonsoft.Json.Linq;
 
-namespace Microsoft.ML.EntryPoints.JsonUtils
+namespace Microsoft.ML.EntryPoints
 {
     /// <summary>
     /// This class runs a graph of entry points with the specified inputs, and produces the specified outputs.

src/Microsoft.ML.EntryPoints/JsonUtils/JsonManifestUtils.cs

@@ -10,7 +10,7 @@
 using Microsoft.ML.Internal.Utilities;
 using Newtonsoft.Json.Linq;
 
-namespace Microsoft.ML.EntryPoints.JsonUtils
+namespace Microsoft.ML.EntryPoints
 {
     /// <summary>
     /// Utilities to generate JSON manifests for entry points and other components.

src/Microsoft.ML.StandardLearners/FactorizationMachine/FactorizationMachineCatalog.cs

@@ -2,9 +2,8 @@
 // The .NET Foundation licenses this file to you under the MIT license.
 // See the LICENSE file in the project root for more information.
 
-using System;
 using Microsoft.ML.Data;
-using Microsoft.ML.FactorizationMachine;
+using Microsoft.ML.Trainers.FactorizationMachine;
 
 namespace Microsoft.ML
 {

src/Microsoft.ML.StandardLearners/FactorizationMachine/FactorizationMachineInterface.cs

@@ -6,7 +6,7 @@
 using System.Security;
 using Microsoft.ML.Internal.CpuMath;
 
-namespace Microsoft.ML.FactorizationMachine
+namespace Microsoft.ML.Trainers.FactorizationMachine
 {
     internal static unsafe class FieldAwareFactorizationMachineInterface
     {

src/Microsoft.ML.StandardLearners/FactorizationMachine/FactorizationMachineTrainer.cs

@@ -10,9 +10,9 @@
 using Microsoft.ML.CommandLine;
 using Microsoft.ML.Data;
 using Microsoft.ML.EntryPoints;
-using Microsoft.ML.FactorizationMachine;
 using Microsoft.ML.Internal.CpuMath;
 using Microsoft.ML.Internal.Utilities;
+using Microsoft.ML.Trainers.FactorizationMachine;
 using Microsoft.ML.Training;
 
 [assembly: LoadableClass(FieldAwareFactorizationMachineTrainer.Summary, typeof(FieldAwareFactorizationMachineTrainer),
@@ -22,7 +22,7 @@
 
 [assembly: LoadableClass(typeof(void), typeof(FieldAwareFactorizationMachineTrainer), null, typeof(SignatureEntryPointModule), FieldAwareFactorizationMachineTrainer.LoadName)]
 
-namespace Microsoft.ML.FactorizationMachine
+namespace Microsoft.ML.Trainers.FactorizationMachine
 {
     /*
      Train a field-aware factorization machine using ADAGRAD (an advanced stochastic gradient method). See references below

src/Microsoft.ML.StandardLearners/FactorizationMachine/FieldAwareFactorizationMachineModelParameters.cs

@@ -8,18 +8,18 @@
 using Microsoft.ML;
 using Microsoft.ML.Data;
 using Microsoft.ML.Data.IO;
-using Microsoft.ML.FactorizationMachine;
 using Microsoft.ML.Internal.CpuMath;
 using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.Internal.Utilities;
 using Microsoft.ML.Model;
+using Microsoft.ML.Trainers.FactorizationMachine;
 
 [assembly: LoadableClass(typeof(FieldAwareFactorizationMachineModelParameters), null, typeof(SignatureLoadModel), "Field Aware Factorization Machine", FieldAwareFactorizationMachineModelParameters.LoaderSignature)]
 
 [assembly: LoadableClass(typeof(FieldAwareFactorizationMachinePredictionTransformer), typeof(FieldAwareFactorizationMachinePredictionTransformer), null, typeof(SignatureLoadModel),
     "", FieldAwareFactorizationMachinePredictionTransformer.LoaderSignature)]
 
-namespace Microsoft.ML.FactorizationMachine
+namespace Microsoft.ML.Trainers.FactorizationMachine
 {
     public sealed class FieldAwareFactorizationMachineModelParameters : ModelParametersBase<float>, ISchemaBindableMapper
     {

src/Microsoft.ML.StandardLearners/FactorizationMachine/FieldAwareFactorizationMachineUtils.cs

@@ -10,7 +10,7 @@
 using Microsoft.ML.Internal.CpuMath;
 using Microsoft.ML.Internal.Utilities;
 
-namespace Microsoft.ML.FactorizationMachine
+namespace Microsoft.ML.Trainers.FactorizationMachine
 {
     internal sealed class FieldAwareFactorizationMachineUtils
     {

src/Microsoft.ML.StaticPipe/FactorizationMachineStatic.cs

@@ -6,9 +6,9 @@
 using System.Collections.Generic;
 using System.Linq;
 using Microsoft.ML.Data;
-using Microsoft.ML.FactorizationMachine;
 using Microsoft.ML.Internal.Utilities;
 using Microsoft.ML.StaticPipe.Runtime;
+using Microsoft.ML.Trainers.FactorizationMachine;
 
 namespace Microsoft.ML.StaticPipe
 {

test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs

@@ -12,7 +12,6 @@
 using Microsoft.ML.Data;
 using Microsoft.ML.Data.IO;
 using Microsoft.ML.EntryPoints;
-using Microsoft.ML.EntryPoints.JsonUtils;
 using Microsoft.ML.ImageAnalytics;
 using Microsoft.ML.Internal.Calibration;
 using Microsoft.ML.Internal.Internallearn;

test/Microsoft.ML.StaticPipelineTesting/Training.cs

@@ -7,7 +7,6 @@
 using Microsoft.Data.DataView;
 using Microsoft.ML;
 using Microsoft.ML.Data;
-using Microsoft.ML.FactorizationMachine;
 using Microsoft.ML.Internal.Calibration;
 using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.LightGBM;
@@ -16,6 +15,7 @@
 using Microsoft.ML.StaticPipe;
 using Microsoft.ML.TestFramework.Attributes;
 using Microsoft.ML.Trainers;
+using Microsoft.ML.Trainers.FactorizationMachine;
 using Microsoft.ML.Trainers.FastTree;
 using Microsoft.ML.Trainers.KMeans;
 using Microsoft.ML.Trainers.Recommender;

test/Microsoft.ML.Tests/TextLoaderTests.cs

@@ -7,7 +7,6 @@
 using System.IO;
 using Microsoft.Data.DataView;
 using Microsoft.ML.Data;
-using Microsoft.ML.EntryPoints.JsonUtils;
 using Microsoft.ML.Model;
 using Microsoft.ML.RunTests;
 using Microsoft.ML.TestFramework;

test/Microsoft.ML.Tests/TrainerEstimators/FAFMEstimator.cs

@@ -3,9 +3,9 @@
 // See the LICENSE file in the project root for more information.
 
 using Microsoft.ML.Data;
-using Microsoft.ML.FactorizationMachine;
 using Microsoft.ML.RunTests;
 using Microsoft.ML.SamplesUtils;
+using Microsoft.ML.Trainers.FactorizationMachine;
 using Xunit;
 
 namespace Microsoft.ML.Tests.TrainerEstimators