diff --git a/test/Microsoft.ML.Tests/CollectionDataSourceTests.cs b/test/Microsoft.ML.Tests/CollectionsDataViewTest.cs
similarity index 70%
rename from test/Microsoft.ML.Tests/CollectionDataSourceTests.cs
rename to test/Microsoft.ML.Tests/CollectionsDataViewTest.cs
index 1003620157..f599a8c850 100644
--- a/test/Microsoft.ML.Tests/CollectionDataSourceTests.cs
+++ b/test/Microsoft.ML.Tests/CollectionsDataViewTest.cs
@@ -7,9 +7,6 @@
 using System.Linq;
 using System.Reflection;
 using Microsoft.ML.Data;
-using Microsoft.ML.Legacy.Data;
-using Microsoft.ML.Legacy.Trainers;
-using Microsoft.ML.Legacy.Transforms;
 using Microsoft.ML.TestFramework;
 using Xunit;
 using Xunit.Abstractions;
@@ -17,241 +14,13 @@
 namespace Microsoft.ML.EntryPoints.Tests
 {
 #pragma warning disable 612
-    public class CollectionDataSourceTests : BaseTestClass
+    public class CollectionsDataViewTest : BaseTestClass
     {
-        public CollectionDataSourceTests(ITestOutputHelper output)
+        public CollectionsDataViewTest(ITestOutputHelper output)
             : base(output)
         {
         }
 
-        [Fact]
-        public void CheckConstructor()
-        {
-            Assert.NotNull(CollectionDataSource.Create(new List<Input>() { new Input { Number1 = 1, String1 = "1" } }));
-            Assert.NotNull(CollectionDataSource.Create(new Input[1] { new Input { Number1 = 1, String1 = "1" } }));
-            Assert.NotNull(CollectionDataSource.Create(new Input[1] { new Input { Number1 = 1, String1 = "1" } }.AsEnumerable()));
-
-            bool thrown = false;
-            try
-            {
-                CollectionDataSource.Create(new List<Input>());
-            }
-            catch
-            {
-                thrown = true;
-            }
-            Assert.True(thrown);
-
-            thrown = false;
-            try
-            {
-                CollectionDataSource.Create(new Input[0]);
-            }
-            catch
-            {
-                thrown = true;
-            }
-            Assert.True(thrown);
-        }
-
-        [Fact]
-        public void CanSuccessfullyApplyATransform()
-        {
-            var collection = CollectionDataSource.Create(new List<Input>() { new Input { Number1 = 1, String1 = "1" } });
-            var environment = new MLContext();
-                Experiment experiment = environment.CreateExperiment();
-                Legacy.ILearningPipelineDataStep output = (Legacy.ILearningPipelineDataStep)collection.ApplyStep(null, experiment);
-
-                Assert.NotNull(output.Data);
-                Assert.NotNull(output.Data.VarName);
-                Assert.Null(output.Model);
-        }
-
-        [Fact]
-        public void CanSuccessfullyEnumerated()
-        {
-            var collection = CollectionDataSource.Create(new List<Input>() {
-                new Input { Number1 = 1, String1 = "1" },
-                new Input { Number1 = 2, String1 = "2" },
-                new Input { Number1 = 3, String1 = "3" }
-            });
-
-            var environment = new MLContext();
-            Experiment experiment = environment.CreateExperiment();
-                Legacy.ILearningPipelineDataStep output = collection.ApplyStep(null, experiment) as Legacy.ILearningPipelineDataStep;
-
-                experiment.Compile();
-                collection.SetInput(environment, experiment);
-                experiment.Run();
-
-                IDataView data = experiment.GetOutput(output.Data);
-                Assert.NotNull(data);
-
-                using (var cursor = data.GetRowCursor((a => true)))
-                {
-                    var IDGetter = cursor.GetGetter<float>(0);
-                    var TextGetter = cursor.GetGetter<ReadOnlyMemory<char>>(1);
-
-                    Assert.True(cursor.MoveNext());
-
-                    float ID = 0;
-                    IDGetter(ref ID);
-                    Assert.Equal(1, ID);
-
-                    ReadOnlyMemory<char> Text = new ReadOnlyMemory<char>();
-                    TextGetter(ref Text);
-                    Assert.Equal("1", Text.ToString());
-
-                    Assert.True(cursor.MoveNext());
-
-                    ID = 0;
-                    IDGetter(ref ID);
-                    Assert.Equal(2, ID);
-
-                    Text = new ReadOnlyMemory<char>();
-                    TextGetter(ref Text);
-                    Assert.Equal("2", Text.ToString());
-
-                    Assert.True(cursor.MoveNext());
-
-                    ID = 0;
-                    IDGetter(ref ID);
-                    Assert.Equal(3, ID);
-
-                    Text = new ReadOnlyMemory<char>();
-                    TextGetter(ref Text);
-                    Assert.Equal("3", Text.ToString());
-
-                    Assert.False(cursor.MoveNext());
-                }
-        }
-
-        [Fact]
-        public void CanTrain()
-        {
-            var pipeline = new Legacy.LearningPipeline();
-            var data = new List<IrisData>() {
-                new IrisData { SepalLength = 1f, SepalWidth = 1f, PetalLength=0.3f, PetalWidth=5.1f, Label=1},
-                new IrisData { SepalLength = 1f, SepalWidth = 1f, PetalLength=0.3f, PetalWidth=5.1f, Label=1},
-                new IrisData { SepalLength = 1.2f, SepalWidth = 0.5f, PetalLength=0.3f, PetalWidth=5.1f, Label=0}
-            };
-            var collection = CollectionDataSource.Create(data);
-
-            pipeline.Add(collection);
-            pipeline.Add(new ColumnConcatenator(outputColumn: "Features",
-                "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"));
-            pipeline.Add(new StochasticDualCoordinateAscentClassifier());
-            var model = pipeline.Train<IrisData, IrisPrediction>();
-
-            IrisPrediction prediction = model.Predict(new IrisData()
-            {
-                SepalLength = 3.3f,
-                SepalWidth = 1.6f,
-                PetalLength = 0.2f,
-                PetalWidth = 5.1f,
-            });
-
-            pipeline = new Legacy.LearningPipeline();
-            collection = CollectionDataSource.Create(data.AsEnumerable());
-            pipeline.Add(collection);
-            pipeline.Add(new ColumnConcatenator(outputColumn: "Features",
-                "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"));
-            pipeline.Add(new StochasticDualCoordinateAscentClassifier());
-            model = pipeline.Train<IrisData, IrisPrediction>();
-
-            prediction = model.Predict(new IrisData()
-            {
-                SepalLength = 3.3f,
-                SepalWidth = 1.6f,
-                PetalLength = 0.2f,
-                PetalWidth = 5.1f,
-            });
-
-        }
-
-        [Fact]
-        public void CanTrainProperties()
-        {
-            var pipeline = new Legacy.LearningPipeline();
-            var data = new List<IrisData>() {
-                new IrisData { SepalLength = 1f, SepalWidth = 1f, PetalLength=0.3f, PetalWidth=5.1f, Label=1},
-                new IrisData { SepalLength = 1f, SepalWidth = 1f, PetalLength=0.3f, PetalWidth=5.1f, Label=1},
-                new IrisData { SepalLength = 1.2f, SepalWidth = 0.5f, PetalLength=0.3f, PetalWidth=5.1f, Label=0}
-            };
-            var collection = CollectionDataSource.Create(data);
-
-            pipeline.Add(collection);
-            pipeline.Add(new ColumnConcatenator(outputColumn: "Features",
-                "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"));
-            pipeline.Add(new StochasticDualCoordinateAscentClassifier());
-            var model = pipeline.Train<IrisData, IrisPredictionProperties>();
-
-            IrisPredictionProperties prediction = model.Predict(new IrisData
-            {
-                SepalLength = 3.3f,
-                SepalWidth = 1.6f,
-                PetalLength = 0.2f,
-                PetalWidth = 5.1f,
-            });
-
-            pipeline = new Legacy.LearningPipeline();
-            collection = CollectionDataSource.Create(data.AsEnumerable());
-            pipeline.Add(collection);
-            pipeline.Add(new ColumnConcatenator(outputColumn: "Features",
-                "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"));
-            pipeline.Add(new StochasticDualCoordinateAscentClassifier());
-            model = pipeline.Train<IrisData, IrisPredictionProperties>();
-
-            prediction = model.Predict(new IrisData
-            {
-                SepalLength = 3.3f,
-                SepalWidth = 1.6f,
-                PetalLength = 0.2f,
-                PetalWidth = 5.1f,
-            });
-
-        }
-
-        public class Input
-        {
-            [LoadColumn(0)]
-            public float Number1;
-
-            [LoadColumn(1)]
-            public string String1;
-        }
-
-        public class IrisData
-        {
-            [LoadColumn(0)]
-            public float Label;
-
-            [LoadColumn(1)]
-            public float SepalLength;
-
-            [LoadColumn(2)]
-            public float SepalWidth;
-
-            [LoadColumn(3)]
-            public float PetalLength;
-
-            [LoadColumn(4)]
-            public float PetalWidth;
-        }
-
-        public class IrisPrediction
-        {
-            [ColumnName("Score")]
-            public float[] PredictedLabels;
-        }
-
-        public class IrisPredictionProperties
-        {
-            private float[] _PredictedLabels;
-            [ColumnName("Score")]
-            public float[] PredictedLabels { get { return _PredictedLabels; } set { _PredictedLabels = value; } }
-        }
-
         public class ConversionSimpleClass
         {
             public int fInt;