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Jan 5, 2019
Merged

Remove collectionsdatasource tests. #2032

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235 changes: 2 additions & 233 deletions ...oft.ML.Tests/CollectionDataSourceTests.cs → ...osoft.ML.Tests/CollectionsDataViewTest.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -7,251 +7,20 @@
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;

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;
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