Added ReceiverOperatingCharacteristic struct

src/Learners/Classifier/ClassifierEvaluator.cs

@@ -393,7 +393,7 @@ namespace Microsoft.ML.Probabilistic.Learners
         /// <param name="instanceSource">The instance source.</param>
         /// <param name="predictions">The predictions.</param>
         /// <returns>The computed receiver operating characteristic curve.</returns>
-        public IEnumerable<Pair<double, double>> ReceiverOperatingCharacteristicCurve(
+        public IEnumerable<ReceiverOperatingCharacteristic> ReceiverOperatingCharacteristicCurve(
             TLabel positiveClassLabel,
             TInstanceSource instanceSource,
             IEnumerable<IDictionary<TLabel, double>> predictions)
@@ -410,7 +410,7 @@ namespace Microsoft.ML.Probabilistic.Learners
         /// <param name="labelSource">The label source.</param>
         /// <param name="predictions">The predictions.</param>
         /// <returns>The computed receiver operating characteristic curve.</returns>
-        public IEnumerable<Pair<double, double>> ReceiverOperatingCharacteristicCurve(
+        public IEnumerable<ReceiverOperatingCharacteristic> ReceiverOperatingCharacteristicCurve(
             TLabel positiveClassLabel,
             TInstanceSource instanceSource,
             TLabelSource labelSource,
@@ -429,7 +429,7 @@ namespace Microsoft.ML.Probabilistic.Learners
         /// <param name="instanceSource">The instance source.</param>
         /// <param name="predictions">The predictions.</param>
         /// <returns>The computed receiver operating characteristic curve.</returns>
-        public IEnumerable<Pair<double, double>> ReceiverOperatingCharacteristicCurve(
+        public IEnumerable<ReceiverOperatingCharacteristic> ReceiverOperatingCharacteristicCurve(
             TLabel positiveClassLabel,
             TLabel negativeClassLabel,
             TInstanceSource instanceSource,
@@ -448,7 +448,7 @@ namespace Microsoft.ML.Probabilistic.Learners
         /// <param name="labelSource">The label source.</param>
         /// <param name="predictions">The predictions.</param>
         /// <returns>The computed receiver operating characteristic curve.</returns>
-        public IEnumerable<Pair<double, double>> ReceiverOperatingCharacteristicCurve(
+        public IEnumerable<ReceiverOperatingCharacteristic> ReceiverOperatingCharacteristicCurve(
             TLabel positiveClassLabel,
             TLabel negativeClassLabel,
             TInstanceSource instanceSource,

src/Learners/Core/Metrics.cs

@@ -487,7 +487,7 @@ namespace Microsoft.ML.Probabilistic.Learners
         /// <remarks>
         /// All instances not contained in <paramref name="positiveInstances"/> are assumed to belong to the 'negative' class.
         /// </remarks>
-        public static IEnumerable<Pair<double, double>> ReceiverOperatingCharacteristicCurve<TInstance>(
+        public static IEnumerable<ReceiverOperatingCharacteristic> ReceiverOperatingCharacteristicCurve<TInstance>(
             IEnumerable<TInstance> positiveInstances, IEnumerable<KeyValuePair<TInstance, double>> instanceScores)
         {
             if (positiveInstances == null)
@@ -523,7 +523,7 @@ namespace Microsoft.ML.Probabilistic.Learners
             double falsePositiveRate;
             double truePositiveRate;
             double previousScore = double.NaN;
-            var rocCurve = new List<Pair<double, double>>();
+            var rocCurve = new List<ReceiverOperatingCharacteristic>();
 
             foreach (var instance in sortedInstanceScores)
             {
@@ -532,7 +532,7 @@ namespace Microsoft.ML.Probabilistic.Learners
                 {
                     falsePositiveRate = falsePositivesCount / (double)negativesCount;
                     truePositiveRate = truePositivesCount / (double)positivesCount;
-                    rocCurve.Add(Pair.Create(falsePositiveRate, truePositiveRate));
+                    rocCurve.Add(new ReceiverOperatingCharacteristic(truePositiveRate, falsePositiveRate));
                     previousScore = score;
                 }
 
@@ -549,7 +549,7 @@ namespace Microsoft.ML.Probabilistic.Learners
             // Add point for (1,1)
             falsePositiveRate = falsePositivesCount / (double)negativesCount;
             truePositiveRate = truePositivesCount / (double)positivesCount;
-            rocCurve.Add(Pair.Create(falsePositiveRate, truePositiveRate));
+            rocCurve.Add(new ReceiverOperatingCharacteristic(truePositiveRate, falsePositiveRate));
 
             return rocCurve;
         }

src/Learners/Core/ReceiverOperatingCharacteristic.cs

@@ -0,0 +1,76 @@
+// 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.
+
+namespace Microsoft.ML.Probabilistic.Learners
+{
+    using Microsoft.ML.Probabilistic.Utilities;
+
+    /// <summary>
+    /// Struct which holds a single receiver operating characteristic (ROC)
+    /// </summary>
+    public struct ReceiverOperatingCharacteristic
+    {
+        /// <summary>
+        /// Initializes a new instance of the <see cref="ReceiverOperatingCharacteristic"/> struct.
+        /// </summary>
+        /// <param name="truePositiveRate">The true positive rate (TPR)</param>
+        /// /// <param name="falsePositiveRate">The false positive rate (FPR)</param>
+        public ReceiverOperatingCharacteristic(double truePositiveRate, double falsePositiveRate)
+            : this()
+        {
+            this.TruePositiveRate = truePositiveRate;
+            this.FalsePositiveRate = falsePositiveRate;
+        }
+
+        /// <summary>
+        /// Gets the <see cref="TruePositiveRate"/>
+        /// </summary>
+        public readonly double TruePositiveRate;
+
+        /// <summary>
+        /// Gets the <see cref="FalsePositiveRate"/>.
+        /// </summary>
+        public readonly double FalsePositiveRate;
+
+        /// <summary>
+        /// Gets the string representation of this <see cref="ReceiverOperatingCharacteristic"/>.
+        /// </summary>
+        /// <returns>The string representation of the <see cref="ReceiverOperatingCharacteristic"/>.</returns>
+        public override string ToString()
+        {
+            return $"{this.TruePositiveRate}, {this.FalsePositiveRate}";
+        }
+
+        /// <summary>
+        /// Checks if this object is equal to <paramref name="obj"/>.
+        /// </summary>
+        /// <param name="obj">The object to compare this object with.</param>
+        /// <returns>
+        /// <see langword="true"/> if this object is equal to <paramref name="obj"/>,
+        /// <see langword="false"/> otherwise.
+        /// </returns>
+        public override bool Equals(object obj)
+        {
+            if (obj is ReceiverOperatingCharacteristic receiverOperatingCharacteristic)
+            {
+                return object.Equals(this.TruePositiveRate, receiverOperatingCharacteristic.TruePositiveRate) && object.Equals(this.FalsePositiveRate, receiverOperatingCharacteristic.FalsePositiveRate);
+            }
+
+            return false;
+        }
+        /// <summary>
+        /// Computes the hash code of this object.
+        /// </summary>
+        /// <returns>The computed hash code.</returns>
+        public override int GetHashCode()
+        {
+            int result = Hash.Start;
+
+            result = Hash.Combine(result, this.TruePositiveRate.GetHashCode());
+            result = Hash.Combine(result, this.FalsePositiveRate.GetHashCode());
+
+            return result;
+        }
+    }
+}

src/Learners/Runners/CommandLine/Classifier/ClassifierEvaluationModule.cs

@@ -599,10 +599,10 @@ namespace Microsoft.ML.Probabilistic.Learners.Runners
                 writer.WriteLine("#");
                 writer.WriteLine("# Class '" + positiveClassLabel + "'     (versus the rest)");
                 writer.WriteLine("#");
-                writer.WriteLine("# False positive rate (FPR), true positive rate (TPR)");
+                writer.WriteLine("# True positive rate (TPR), False positive rate (FPR)");
                 foreach (var point in rocCurve)
                 {
-                    writer.WriteLine("{0}, {1}", point.First, point.Second);
+                    writer.WriteLine(point);
                 }
             }
         }

test/Learners/LearnersTests/ClassifierEvaluatorTests.cs

@@ -176,17 +176,17 @@ namespace Microsoft.ML.Probabilistic.Learners.Tests
         public void RocCurveTest()
         {
             // Curve for perfect predictions
-            var expected = new[] { Pair.Create(0.0, 0.0), Pair.Create(0.0, 1.0), Pair.Create(1.0, 1.0) };
+            var expected = new[] { new ReceiverOperatingCharacteristic(0.0, 0.0), new ReceiverOperatingCharacteristic(1.0, 0.0), new ReceiverOperatingCharacteristic(1.0, 1.0) };
             var actual = this.evaluator.ReceiverOperatingCharacteristicCurve(LabelSet[0], this.groundTruth, this.groundTruth).ToArray();
             Xunit.Assert.Equal(expected, actual);
 
             // Curve for imperfect predictions (one-versus-rest)
-            expected = new[] { Pair.Create(0.0, 0.0), Pair.Create(0.5, 0.0), Pair.Create(0.5, 1 / 3.0), Pair.Create(0.5, 2 / 3.0), Pair.Create(1.0, 1.0) };
+            expected = new[] { new ReceiverOperatingCharacteristic(0.0, 0.0), new ReceiverOperatingCharacteristic(0.0, 0.5), new ReceiverOperatingCharacteristic(1 / 3.0, 0.5), new ReceiverOperatingCharacteristic(2 / 3.0, 0.5), new ReceiverOperatingCharacteristic(1.0, 1.0) };
             actual = this.evaluator.ReceiverOperatingCharacteristicCurve(LabelSet[0], this.groundTruth, this.predictions).ToArray();
             Xunit.Assert.Equal(expected, actual); // matches below AUC = 5/12
 
             // Curve for imperfect predictions (one-versus-another)
-            expected = new[] { Pair.Create(0.0, 0.0), Pair.Create(0.0, 1 / 3.0), Pair.Create(0.0, 2 / 3.0), Pair.Create(1.0, 1.0) };
+            expected = new[] { new ReceiverOperatingCharacteristic(0.0, 0.0), new ReceiverOperatingCharacteristic(1 / 3.0, 0.0), new ReceiverOperatingCharacteristic(2 / 3.0, 0.0), new ReceiverOperatingCharacteristic(1.0, 1.0) };
             actual = this.evaluator.ReceiverOperatingCharacteristicCurve(LabelSet[0], LabelSet[1], this.groundTruth, this.predictions).ToArray();
             Xunit.Assert.Equal(expected, actual); // matches below AUC = 5/6
 

test/Learners/LearnersTests/MetricTests.cs

@@ -352,9 +352,9 @@ namespace Microsoft.ML.Probabilistic.Learners.Tests
             // Duplicate instance scores, duplicate positive instances
             var expectedCurve = new[]
                                     {
-                                        Pair.Create<double, double>(0, 0), 
-                                        Pair.Create<double, double>(0.5, 1), 
-                                        Pair.Create<double, double>(1, 1)
+                                        new ReceiverOperatingCharacteristic(0, 0), 
+                                        new ReceiverOperatingCharacteristic(1, 0.5),
+                                        new ReceiverOperatingCharacteristic(1, 1)
                                     };
             var computedCurve = Metrics.ReceiverOperatingCharacteristicCurve(new[] { 1, 1, 2 }, new Dictionary<int, double> { { 1, 0.5 }, { 2, 0.5 }, { 3, 0.5 }, { 4, 0 } }).ToArray();
             foreach (var tuple in computedCurve)
@@ -362,11 +362,7 @@ namespace Microsoft.ML.Probabilistic.Learners.Tests
                 Console.WriteLine(tuple);
             }
 
-            Assert.Equal(expectedCurve.Length, computedCurve.Length);
-            for (int i = 0; i < expectedCurve.Length; i++)
-            {
-                Assert.Equal(expectedCurve[i], computedCurve[i]);
-            }
+            Xunit.Assert.Equal(expectedCurve, computedCurve);
 
             // No positive instance scores
             Assert.Throws<ArgumentException>(() => Metrics.ReceiverOperatingCharacteristicCurve(new int[] { }, new Dictionary<int, double> { { 1, 1 } }));