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;
+ }
+ }
+}

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;
+ }
+ }
+}

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;
+ }
+ }
+}

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;
+ }
+ }
+}

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;
+ }
+ }
+}

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;
+ }
+ }
+}

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;
+ }
+ }
+}

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;
+ }
+ }
+}