Cleaning TrainCatalog and RecommenderCatalog (#2973)

Author: artidoro

docs/code/MlNetCookBook.md

@@ -376,7 +376,7 @@ var testData = mlContext.Data.LoadFromTextFile<AdultData>(testDataPath,
     separatorChar: ','
 );
 // Calculate metrics of the model on the test data.
-var metrics = mlContext.Regression.Evaluate(model.Transform(testData), label: "Target");
+var metrics = mlContext.Regression.Evaluate(model.Transform(testData), labelColumnName: "Target");
 ```
 
 ## How do I save and load the model?

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/PriorTrainerSample.cs

@@ -47,7 +47,7 @@ public static void Example()
 
             // Step 4: Evaluate on the test set
             var transformedData = trainedPipeline.Transform(loader.Load(testFile));
-            var evalMetrics = mlContext.BinaryClassification.Evaluate(transformedData, label: "Sentiment");
+            var evalMetrics = mlContext.BinaryClassification.Evaluate(transformedData, labelColumnName: "Sentiment");
             SamplesUtils.ConsoleUtils.PrintMetrics(evalMetrics);
 
             // The Prior trainer outputs the proportion of a label in the dataset as the probability of that label.

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/StochasticDualCoordinateAscent.cs

@@ -52,7 +52,7 @@ public static void Example()
                     .Append(mlContext.BinaryClassification.Trainers.SdcaNonCalibrated(labelColumnName: "Sentiment", featureColumnName: "Features", l2Regularization: 0.001f));
 
             // Step 3: Run Cross-Validation on this pipeline.
-            var cvResults = mlContext.BinaryClassification.CrossValidate(data, pipeline, labelColumn: "Sentiment");
+            var cvResults = mlContext.BinaryClassification.CrossValidate(data, pipeline, labelColumnName: "Sentiment");
 
             var accuracies = cvResults.Select(r => r.Metrics.Accuracy);
             Console.WriteLine(accuracies.Average());
@@ -69,7 +69,7 @@ public static void Example()
                                       }));
 
             // Run Cross-Validation on this second pipeline.
-            var cvResults_advancedPipeline = mlContext.BinaryClassification.CrossValidate(data, pipeline, labelColumn: "Sentiment", numFolds: 3);
+            var cvResults_advancedPipeline = mlContext.BinaryClassification.CrossValidate(data, pipeline, labelColumnName: "Sentiment", numberOfFolds: 3);
             accuracies = cvResults_advancedPipeline.Select(r => r.Metrics.Accuracy);
             Console.WriteLine(accuracies.Average());
 

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

@@ -46,7 +46,7 @@ public static void Example()
             var dataWithPredictions = model.Transform(split.TestSet);
 
             // Evaluate the trained model using the test set.
-            var metrics = mlContext.MulticlassClassification.Evaluate(dataWithPredictions, label: "LabelIndex");
+            var metrics = mlContext.MulticlassClassification.Evaluate(dataWithPredictions, labelColumnName: "LabelIndex");
 
             // Check if metrics are reasonable.
             Console.WriteLine($"Macro accuracy: {metrics.MacroAccuracy:F4}, Micro accuracy: {metrics.MicroAccuracy:F4}.");

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

@@ -57,7 +57,7 @@ public static void Example()
             var dataWithPredictions = model.Transform(split.TestSet);
 
             // Evaluate the trained model using the test set.
-            var metrics = mlContext.MulticlassClassification.Evaluate(dataWithPredictions, label: "LabelIndex");
+            var metrics = mlContext.MulticlassClassification.Evaluate(dataWithPredictions, labelColumnName: "LabelIndex");
 
             // Check if metrics are reasonable.
             Console.WriteLine($"Macro accuracy: {metrics.MacroAccuracy:F4}, Micro accuracy: {metrics.MicroAccuracy:F4}.");

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

@@ -14,7 +14,7 @@ public static void Example()
             // Leave out 10% of the dataset for testing. Since this is a ranking problem, we must ensure that the split
             // respects the GroupId column, i.e. rows with the same GroupId are either all in the train split or all in
             // the test split. The samplingKeyColumn parameter in Data.TrainTestSplit is used for this purpose.
-            var split = mlContext.Data.TrainTestSplit(dataview, testFraction: 0.1, samplingKeyColumn: "GroupId");
+            var split = mlContext.Data.TrainTestSplit(dataview, testFraction: 0.1, samplingKeyColumnName: "GroupId");
 
             // Create the Estimator pipeline. For simplicity, we will train a small tree with 4 leaves and 2 boosting iterations.
             var pipeline = mlContext.Ranking.Trainers.LightGbm(

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

@@ -17,7 +17,7 @@ public static void Example()
             // Leave out 10% of the dataset for testing. Since this is a ranking problem, we must ensure that the split
             // respects the GroupId column, i.e. rows with the same GroupId are either all in the train split or all in
             // the test split. The samplingKeyColumn parameter in Data.TrainTestSplit is used for this purpose.
-            var split = mlContext.Data.TrainTestSplit(dataview, testFraction: 0.1, samplingKeyColumn: "GroupId");
+            var split = mlContext.Data.TrainTestSplit(dataview, testFraction: 0.1, samplingKeyColumnName: "GroupId");
 
             // Create the Estimator pipeline. For simplicity, we will train a small tree with 4 leaves and 2 boosting iterations.
             var pipeline = mlContext.Ranking.Trainers.LightGbm(

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorization.cs

@@ -36,7 +36,7 @@ public static void Example()
 
             // Calculate regression matrices for the prediction result.
             var metrics = mlContext.Recommendation().Evaluate(prediction,
-                label: nameof(MatrixElement.Value), score: nameof(MatrixElementForScore.Score));
+                labelColumnName: nameof(MatrixElement.Value), scoreColumnName: nameof(MatrixElementForScore.Score));
             // Print out some metrics for checking the model's quality.
             SamplesUtils.ConsoleUtils.PrintMetrics(metrics);
             // L1: 0.17

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorizationWithOptions.cs

@@ -46,7 +46,7 @@ public static void Example()
 
             // Calculate regression matrices for the prediction result.
             var metrics = mlContext.Recommendation().Evaluate(prediction,
-                label: nameof(MatrixElement.Value), score: nameof(MatrixElementForScore.Score));
+                labelColumnName: nameof(MatrixElement.Value), scoreColumnName: nameof(MatrixElementForScore.Score));
             // Print out some metrics for checking the model's quality.
             SamplesUtils.ConsoleUtils.PrintMetrics(metrics);
             // L1: 0.16

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

@@ -51,7 +51,7 @@ public static void Example()
 
             // Evaluate how the model is doing on the test data.
             var dataWithPredictions = model.Transform(split.TestSet);
-            var metrics = mlContext.Regression.Evaluate(dataWithPredictions, label: labelName);
+            var metrics = mlContext.Regression.Evaluate(dataWithPredictions, labelColumnName: labelName);
             SamplesUtils.ConsoleUtils.PrintMetrics(metrics);
 
             // Expected output