Clean up metrics classes. (#2700)

* Convert short names to full names.

* PR feedback.

* Build break.

* PR feedback.

* rebase errors.

* PR feedback.

* rebase errors.

Author: codemzs

docs/samples/Microsoft.ML.Samples/Dynamic/LogisticRegression.cs

@@ -73,7 +73,7 @@ public static void Example()
             var metrics = ml.BinaryClassification.Evaluate(dataWithPredictions);
 
             Console.WriteLine($"Accuracy: {metrics.Accuracy}"); // 0.80
-            Console.WriteLine($"AUC: {metrics.Auc}"); // 0.64
+            Console.WriteLine($"AUC: {metrics.AreaUnderRocCurve}"); // 0.64
             Console.WriteLine($"F1 Score: {metrics.F1Score}"); // 0.39
 
             Console.WriteLine($"Negative Precision: {metrics.NegativePrecision}"); // 0.81

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LightGbm.cs

@@ -49,7 +49,7 @@ public static void Example()
             var metrics = mlContext.MulticlassClassification.Evaluate(dataWithPredictions, label: "LabelIndex");
 
             // Check if metrics are reasonable.
-            Console.WriteLine($"Macro accuracy: {metrics.AccuracyMacro:F4}, Micro accuracy: {metrics.AccuracyMicro:F4}.");
+            Console.WriteLine($"Macro accuracy: {metrics.MacroAccuracy:F4}, Micro accuracy: {metrics.MicroAccuracy:F4}.");
             // Console output:
             //   Macro accuracy: 0.8655, Micro accuracy: 0.8651.
 

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LightGbmWithOptions.cs

@@ -60,7 +60,7 @@ public static void Example()
             var metrics = mlContext.MulticlassClassification.Evaluate(dataWithPredictions, label: "LabelIndex");
 
             // Check if metrics are reasonable.
-            Console.WriteLine($"Macro accuracy: {metrics.AccuracyMacro:F4}, Micro accuracy: {metrics.AccuracyMicro:F4}.");
+            Console.WriteLine($"Macro accuracy: {metrics.MacroAccuracy:F4}, Micro accuracy: {metrics.MicroAccuracy:F4}.");
             // Console output:
             //   Macro accuracy: 0.8619, Micro accuracy: 0.8611.
 

docs/samples/Microsoft.ML.Samples/Static/AveragedPerceptronBinaryClassification.cs

@@ -92,7 +92,7 @@ public static void AveragedPerceptronBinaryClassification()
             var metrics = mlContext.BinaryClassification.Evaluate(dataWithPredictions, row => row.Label, row => row.Score);
 
             Console.WriteLine($"Accuracy: {metrics.Accuracy}"); // 0.83
-            Console.WriteLine($"AUC: {metrics.Auc}"); // 0.88
+            Console.WriteLine($"AUC: {metrics.AreaUnderRocCurve}"); // 0.88
             Console.WriteLine($"F1 Score: {metrics.F1Score}"); // 0.63
 
             Console.WriteLine($"Negative Precision: {metrics.NegativePrecision}"); // 0.89

docs/samples/Microsoft.ML.Samples/Static/FastTreeBinaryClassification.cs

@@ -95,7 +95,7 @@ public static void FastTreeBinaryClassification()
             var metrics = mlContext.BinaryClassification.Evaluate(dataWithPredictions, row => row.Label, row => row.Score);
 
             Console.WriteLine($"Accuracy: {metrics.Accuracy}"); // 0.84
-            Console.WriteLine($"AUC: {metrics.Auc}"); // 0.89
+            Console.WriteLine($"AUC: {metrics.AreaUnderRocCurve}"); // 0.89
             Console.WriteLine($"F1 Score: {metrics.F1Score}"); // 0.64
 
             Console.WriteLine($"Negative Precision: {metrics.NegativePrecision}"); // 0.88

docs/samples/Microsoft.ML.Samples/Static/FastTreeRegression.cs

@@ -47,10 +47,10 @@ public static void FastTreeRegression()
 
             var cvResults = mlContext.Regression.CrossValidate(data, learningPipeline, r => r.label, numFolds: 5);
             var averagedMetrics = (
-                L1: cvResults.Select(r => r.metrics.L1).Average(),
-                L2: cvResults.Select(r => r.metrics.L2).Average(),
-                LossFn: cvResults.Select(r => r.metrics.LossFn).Average(),
-                Rms: cvResults.Select(r => r.metrics.Rms).Average(),
+                L1: cvResults.Select(r => r.metrics.MeanAbsoluteError).Average(),
+                L2: cvResults.Select(r => r.metrics.MeanSquaredError).Average(),
+                LossFn: cvResults.Select(r => r.metrics.LossFunction).Average(),
+                Rms: cvResults.Select(r => r.metrics.RootMeanSquaredError).Average(),
                 RSquared: cvResults.Select(r => r.metrics.RSquared).Average()
             );
             Console.WriteLine($"L1 - {averagedMetrics.L1}");    // 3.091095

docs/samples/Microsoft.ML.Samples/Static/LightGBMBinaryClassification.cs

@@ -95,7 +95,7 @@ public static void LightGbmBinaryClassification()
             var metrics = mlContext.BinaryClassification.Evaluate(dataWithPredictions, row => row.Label, row => row.Score);
 
             Console.WriteLine($"Accuracy: {metrics.Accuracy}"); // 0.84
-            Console.WriteLine($"AUC: {metrics.Auc}"); // 0.89
+            Console.WriteLine($"AUC: {metrics.AreaUnderRocCurve}"); // 0.89
             Console.WriteLine($"F1 Score: {metrics.F1Score}"); // 0.64
 
             Console.WriteLine($"Negative Precision: {metrics.NegativePrecision}"); // 0.88

docs/samples/Microsoft.ML.Samples/Static/LightGBMRegression.cs

@@ -60,10 +60,10 @@ public static void LightGbmRegression()
             var dataWithPredictions = model.Transform(testData);
             var metrics = mlContext.Regression.Evaluate(dataWithPredictions, r => r.label, r => r.score);
 
-            Console.WriteLine($"L1 - {metrics.L1}");    // 4.9669731
-            Console.WriteLine($"L2 - {metrics.L2}");    // 51.37296
-            Console.WriteLine($"LossFunction - {metrics.LossFn}");  // 51.37296
-            Console.WriteLine($"RMS - {metrics.Rms}");              // 7.167493
+            Console.WriteLine($"L1 - {metrics.MeanAbsoluteError}");    // 4.9669731
+            Console.WriteLine($"L2 - {metrics.MeanSquaredError}");    // 51.37296
+            Console.WriteLine($"LossFunction - {metrics.LossFunction}");  // 51.37296
+            Console.WriteLine($"RMS - {metrics.RootMeanSquaredError}");              // 7.167493
             Console.WriteLine($"RSquared - {metrics.RSquared}");    // 0.079478
         }
     }

docs/samples/Microsoft.ML.Samples/Static/SDCABinaryClassification.cs

@@ -92,7 +92,7 @@ public static void SdcaBinaryClassification()
             var metrics = mlContext.BinaryClassification.Evaluate(dataWithPredictions, row => row.Label, row => row.Score);
 
             Console.WriteLine($"Accuracy: {metrics.Accuracy}"); // 0.83
-            Console.WriteLine($"AUC: {metrics.Auc}"); // 0.88
+            Console.WriteLine($"AUC: {metrics.AreaUnderRocCurve}"); // 0.88
             Console.WriteLine($"F1 Score: {metrics.F1Score}"); // 0.59
 
             Console.WriteLine($"Negative Precision: {metrics.NegativePrecision}"); // 0.87

docs/samples/Microsoft.ML.Samples/Static/SDCARegression.cs

@@ -57,10 +57,10 @@ public static void SdcaRegression()
             var dataWithPredictions = model.Transform(testData);
             var metrics = mlContext.Regression.Evaluate(dataWithPredictions, r => r.label, r => r.score);
 
-            Console.WriteLine($"L1 - {metrics.L1}");  // 3.7226085
-            Console.WriteLine($"L2 - {metrics.L2}");  // 24.250636
-            Console.WriteLine($"LossFunction - {metrics.LossFn}");  // 24.25063
-            Console.WriteLine($"RMS - {metrics.Rms}");  // 4.924493
+            Console.WriteLine($"L1 - {metrics.MeanAbsoluteError}");  // 3.7226085
+            Console.WriteLine($"L2 - {metrics.MeanSquaredError}");  // 24.250636
+            Console.WriteLine($"LossFunction - {metrics.LossFunction}");  // 24.25063
+            Console.WriteLine($"RMS - {metrics.RootMeanSquaredError}");  // 4.924493
             Console.WriteLine($"RSquared - {metrics.RSquared}");  // 0.565467
         }
     }