Add tests covering LoadColumnNameAttribute

Author: tannergooding

test/Microsoft.ML.Tests/DatabaseLoaderTests.cs

@@ -77,6 +77,52 @@ public void IrisLightGbm()
             }).PredictedLabel);
         }
 
+        [LightGBMFact]
+        public void IrisLightGbmWithLoadColumnName()
+        {
+            if (!RuntimeInformation.IsOSPlatform(OSPlatform.Windows))
+            {
+                // https://github.com/dotnet/machinelearning/issues/4156
+                return;
+            }
+
+            var mlContext = new MLContext(seed: 1);
+
+            var connectionString = GetConnectionString(TestDatasets.irisDb.name);
+            var commandText = $@"SELECT * FROM ""{TestDatasets.irisDb.trainFilename}""";
+
+            var loader = mlContext.Data.CreateDatabaseLoader<IrisDataWithLoadColumnName>();
+
+            var databaseSource = new DatabaseSource(SqlClientFactory.Instance, connectionString, commandText);
+
+            var trainingData = loader.Load(databaseSource);
+
+            IEstimator<ITransformer> pipeline = mlContext.Transforms.Conversion.MapValueToKey("Label")
+                .Append(mlContext.Transforms.Concatenate("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"))
+                .Append(mlContext.MulticlassClassification.Trainers.LightGbm())
+                .Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
+
+            var model = pipeline.Fit(trainingData);
+
+            var engine = mlContext.Model.CreatePredictionEngine<IrisData, IrisPrediction>(model);
+
+            Assert.Equal(0, engine.Predict(new IrisData()
+            {
+                SepalLength = 4.5f,
+                SepalWidth = 5.6f,
+                PetalLength = 0.5f,
+                PetalWidth = 0.5f,
+            }).PredictedLabel);
+
+            Assert.Equal(1, engine.Predict(new IrisData()
+            {
+                SepalLength = 4.9f,
+                SepalWidth = 2.4f,
+                PetalLength = 3.3f,
+                PetalWidth = 1.0f,
+            }).PredictedLabel);
+        }
+
         [LightGBMFact]
         public void IrisVectorLightGbm()
         {
@@ -119,6 +165,48 @@ public void IrisVectorLightGbm()
             }).PredictedLabel);
         }
 
+        [LightGBMFact]
+        public void IrisVectorLightGbmWithLoadColumnName()
+        {
+            if (!RuntimeInformation.IsOSPlatform(OSPlatform.Windows))
+            {
+                // https://github.com/dotnet/machinelearning/issues/4156
+                return;
+            }
+
+            var mlContext = new MLContext(seed: 1);
+
+            var connectionString = GetConnectionString(TestDatasets.irisDb.name);
+            var commandText = $@"SELECT * FROM ""{TestDatasets.irisDb.trainFilename}""";
+
+            var loader = mlContext.Data.CreateDatabaseLoader<IrisVectorDataWithLoadColumnName>();
+
+            var databaseSource = new DatabaseSource(SqlClientFactory.Instance, connectionString, commandText);
+
+            var trainingData = loader.Load(databaseSource);
+
+            IEstimator<ITransformer> pipeline = mlContext.Transforms.Conversion.MapValueToKey("Label")
+                .Append(mlContext.Transforms.Concatenate("Features", "SepalInfo", "PetalInfo"))
+                .Append(mlContext.MulticlassClassification.Trainers.LightGbm())
+                .Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
+
+            var model = pipeline.Fit(trainingData);
+
+            var engine = mlContext.Model.CreatePredictionEngine<IrisVectorData, IrisPrediction>(model);
+
+            Assert.Equal(0, engine.Predict(new IrisVectorData()
+            {
+                SepalInfo = new float[] { 4.5f, 5.6f },
+                PetalInfo = new float[] { 0.5f, 0.5f },
+            }).PredictedLabel);
+
+            Assert.Equal(1, engine.Predict(new IrisVectorData()
+            {
+                SepalInfo = new float[] { 4.9f, 2.4f },
+                PetalInfo = new float[] { 3.3f, 1.0f },
+            }).PredictedLabel);
+        }
+
         [Fact]
         public void IrisSdcaMaximumEntropy()
         {
@@ -189,6 +277,21 @@ public class IrisData
             public float PetalWidth;
         }
 
+        public class IrisDataWithLoadColumnName
+        {
+            [LoadColumnName("Label")]
+            [ColumnName("Label")]
+            public int Kind;
+
+            public float SepalLength;
+
+            public float SepalWidth;
+
+            public float PetalLength;
+
+            public float PetalWidth;
+        }
+
         public class IrisVectorData
         {
             public int Label;
@@ -202,6 +305,19 @@ public class IrisVectorData
             public float[] PetalInfo;
         }
 
+        public class IrisVectorDataWithLoadColumnName
+        {
+            public int Label;
+
+            [LoadColumnName("SepalLength", "SepalWidth")]
+            [VectorType(2)]
+            public float[] SepalInfo;
+
+            [LoadColumnName("PetalLength", "PetalWidth")]
+            [VectorType(2)]
+            public float[] PetalInfo;
+        }
+
         public class IrisPrediction
         {
             public int PredictedLabel;