Fixed exception: "InvalidOperationException: Source column 'Label' is required but not found."

src/Microsoft.ML.Api/SchemaDefinition.cs

@@ -332,8 +332,9 @@ public static SchemaDefinition Create(Type userType)
 
                 if (fieldInfo.GetCustomAttribute<NoColumnAttribute>() != null)
                     continue;
-                var mappingAttr = fieldInfo.GetCustomAttribute<ColumnNameAttribute>();
-                var name = mappingAttr == null ? fieldInfo.Name : (mappingAttr.Name ?? fieldInfo.Name);
+                var mappingAttr = fieldInfo.GetCustomAttribute<ColumnAttribute>();
+                var mappingNameAttr = fieldInfo.GetCustomAttribute<ColumnNameAttribute>();
+                string name = mappingAttr?.Name ?? mappingNameAttr?.Name ?? fieldInfo.Name;
                 // Disallow duplicate names, because the field enumeration order is not actually
                 // well defined, so we are not gauranteed to have consistent "hiding" from run to
                 // run, across different .NET versions.

test/Microsoft.ML.Tests/Scenarios/IrisPlantClassificationWithStringLabelTests.cs

@@ -0,0 +1,136 @@
+// Licensed to the .NET Foundation under one or more agreements.
+// The .NET Foundation licenses this file to you under the MIT license.
+// See the LICENSE file in the project root for more information.
+
+using Microsoft.ML.Models;
+using Microsoft.ML.Runtime.Api;
+using Microsoft.ML.Trainers;
+using Microsoft.ML.Transforms;
+using Xunit;
+
+namespace Microsoft.ML.Scenarios
+{
+    public partial class ScenariosTests
+    {
+        [Fact]
+        public void TrainAndPredictIrisModelWithStringLabelTest()
+        {
+            string dataPath = GetDataPath("iris.data");
+
+            var pipeline = new LearningPipeline();
+
+            pipeline.Add(new TextLoader<IrisDataWithStringLabel>(dataPath, useHeader: false, separator: ","));
+
+            pipeline.Add(new Dictionarizer("Label"));  // "IrisPlantType" is used as "Label" because of column attribute name on the field.
+
+            pipeline.Add(new ColumnConcatenator(outputColumn: "Features",
+                 "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"));
+
+            pipeline.Add(new StochasticDualCoordinateAscentClassifier());
+
+            PredictionModel<IrisDataWithStringLabel, IrisPrediction> model = pipeline.Train<IrisDataWithStringLabel, IrisPrediction>();
+
+            IrisPrediction prediction = model.Predict(new IrisDataWithStringLabel()
+            {
+                SepalLength = 3.3f,
+                SepalWidth = 1.6f,
+                PetalLength = 0.2f,
+                PetalWidth = 5.1f,
+            });
+
+            Assert.Equal(1, prediction.PredictedLabels[0], 2);
+            Assert.Equal(0, prediction.PredictedLabels[1], 2);
+            Assert.Equal(0, prediction.PredictedLabels[2], 2);
+
+            prediction = model.Predict(new IrisDataWithStringLabel()
+            {
+                SepalLength = 3.1f,
+                SepalWidth = 5.5f,
+                PetalLength = 2.2f,
+                PetalWidth = 6.4f,
+            });
+
+            Assert.Equal(0, prediction.PredictedLabels[0], 2);
+            Assert.Equal(0, prediction.PredictedLabels[1], 2);
+            Assert.Equal(1, prediction.PredictedLabels[2], 2);
+
+            prediction = model.Predict(new IrisDataWithStringLabel()
+            {
+                SepalLength = 3.1f,
+                SepalWidth = 2.5f,
+                PetalLength = 1.2f,
+                PetalWidth = 4.4f,
+            });
+
+            Assert.Equal(.2, prediction.PredictedLabels[0], 1);
+            Assert.Equal(.8, prediction.PredictedLabels[1], 1);
+            Assert.Equal(0, prediction.PredictedLabels[2], 2);
+
+            // Note: Testing against the same data set as a simple way to test evaluation.
+            // This isn't appropriate in real-world scenarios.
+            string testDataPath = GetDataPath("iris.data");
+            var testData = new TextLoader<IrisDataWithStringLabel>(testDataPath, useHeader: false, separator: ",");
+
+            var evaluator = new ClassificationEvaluator();
+            evaluator.OutputTopKAcc = 3;
+            ClassificationMetrics metrics = evaluator.Evaluate(model, testData);
+
+            Assert.Equal(.98, metrics.AccuracyMacro);
+            Assert.Equal(.98, metrics.AccuracyMicro, 2);
+            Assert.Equal(.06, metrics.LogLoss, 2);
+            Assert.InRange(metrics.LogLossReduction, 94, 96);
+            Assert.Equal(1, metrics.TopKAccuracy);
+
+            Assert.Equal(3, metrics.PerClassLogLoss.Length);
+            Assert.Equal(0, metrics.PerClassLogLoss[0], 1);
+            Assert.Equal(.1, metrics.PerClassLogLoss[1], 1);
+            Assert.Equal(.1, metrics.PerClassLogLoss[2], 1);
+
+            ConfusionMatrix matrix = metrics.ConfusionMatrix;
+            Assert.Equal(3, matrix.Order);
+            Assert.Equal(3, matrix.ClassNames.Count);
+            Assert.Equal("Iris-setosa", matrix.ClassNames[0]);
+            Assert.Equal("Iris-versicolor", matrix.ClassNames[1]);
+            Assert.Equal("Iris-virginica", matrix.ClassNames[2]);
+
+            Assert.Equal(50, matrix[0, 0]);
+            Assert.Equal(50, matrix["Iris-setosa", "Iris-setosa"]);
+            Assert.Equal(0, matrix[0, 1]);
+            Assert.Equal(0, matrix["Iris-setosa", "Iris-versicolor"]);
+            Assert.Equal(0, matrix[0, 2]);
+            Assert.Equal(0, matrix["Iris-setosa", "Iris-virginica"]);
+
+            Assert.Equal(0, matrix[1, 0]);
+            Assert.Equal(0, matrix["Iris-versicolor", "Iris-setosa"]);
+            Assert.Equal(48, matrix[1, 1]);
+            Assert.Equal(48, matrix["Iris-versicolor", "Iris-versicolor"]);
+            Assert.Equal(2, matrix[1, 2]);
+            Assert.Equal(2, matrix["Iris-versicolor", "Iris-virginica"]);
+
+            Assert.Equal(0, matrix[2, 0]);
+            Assert.Equal(0, matrix["Iris-virginica", "Iris-setosa"]);
+            Assert.Equal(1, matrix[2, 1]);
+            Assert.Equal(1, matrix["Iris-virginica", "Iris-versicolor"]);
+            Assert.Equal(49, matrix[2, 2]);
+            Assert.Equal(49, matrix["Iris-virginica", "Iris-virginica"]);
+        }
+
+        public class IrisDataWithStringLabel
+        {
+            [Column("0")]
+            public float PetalWidth;
+
+            [Column("1")]
+            public float SepalLength;
+
+            [Column("2")]
+            public float SepalWidth;
+
+            [Column("3")]
+            public float PetalLength;
+
+            [Column("4", name: "Label")]
+            public string IrisPlantType;
+        }
+    }
+}