Fixing parameters in ML.NET Public API

docs/code/MlNetCookBook.md

@@ -959,7 +959,7 @@ public static ITransformer TrainModel(MLContext mlContext, IDataView trainData)
     // Construct the learning pipeline.
     var estimator = mlContext.Transforms.CustomMapping(mapping, null)
         .AppendCacheCheckpoint(mlContext)
-        .Append(mlContext.BinaryClassification.Trainers.FastTree(label: "Label"));
+        .Append(mlContext.BinaryClassification.Trainers.FastTree(labelColumnName: "Label"));
 
     return estimator.Fit(trainData);
 }
@@ -998,7 +998,7 @@ public class CustomMappings : CustomMappingFactory<InputRow, OutputRow>
 // Construct the learning pipeline. Note that we are now providing a contract name for the custom mapping:
 // otherwise we will not be able to save the model.
 var estimator = mlContext.Transforms.CustomMapping<InputRow, OutputRow>(CustomMappings.IncomeMapping, nameof(CustomMappings.IncomeMapping))
-    .Append(mlContext.BinaryClassification.Trainers.FastTree(label: "Label"));
+    .Append(mlContext.BinaryClassification.Trainers.FastTree(labelColumnName: "Label"));
 
 // If memory is enough, we can cache the data in-memory to avoid reading them from file
 // when it will be accessed multiple times. 

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

@@ -50,8 +50,8 @@ public static void Example()
             // the "Features" column produced by FeaturizeText as the features column. 
             var pipeline = mlContext.Transforms.Text.FeaturizeText("SentimentText", "Features")
                     .Append(mlContext.BinaryClassification.Trainers.StochasticDualCoordinateAscentNonCalibrated(
-                        labelColumn: "Sentiment", 
-                        featureColumn: "Features", 
+                        labelColumnName: "Sentiment", 
+                        featureColumnName: "Features", 
                         l2Const: 0.001f, 
                         loss: new HingeLoss())); // By specifying loss: new HingeLoss(), StochasticDualCoordinateAscent will train a support vector machine (SVM).
 

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

@@ -29,7 +29,7 @@ public static void Example()
             // We will train a FastTreeRegression model with 1 tree on these two columns to predict Age.
             string outputColumnName = "Features";
             var pipeline = ml.Transforms.Concatenate(outputColumnName, new[] { "Parity", "Induced" })
-                .Append(ml.Regression.Trainers.FastTree(labelColumn: "Age", featureColumn: outputColumnName, numTrees: 1, numLeaves: 2, minDatapointsInLeaves: 1));
+                .Append(ml.Regression.Trainers.FastTree(labelColumnName: "Age", featureColumnName: outputColumnName, numTrees: 1, numLeaves: 2, minDatapointsInLeaves: 1));
 
             var model = pipeline.Fit(trainData);
 

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

@@ -46,7 +46,7 @@ public static void Example()
             // the "Features" column produced by FeaturizeText as the features column.
             var pipeline = mlContext.Transforms.Text.FeaturizeText("SentimentText", "Features")
                     .AppendCacheCheckpoint(mlContext) // Add a data-cache step within a pipeline.
-                    .Append(mlContext.BinaryClassification.Trainers.FieldAwareFactorizationMachine(labelColumn: "Sentiment", featureColumns: new[] { "Features" }));
+                    .Append(mlContext.BinaryClassification.Trainers.FieldAwareFactorizationMachine(labelColumnName: "Sentiment", featureColumnNames: new[] { "Features" }));
 
             // Fit the model.
             var model = pipeline.Fit(data);

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

@@ -28,7 +28,7 @@ public static void Example()
                 .ToArray();
             var pipeline = mlContext.Transforms.Concatenate("Features", featureNames)
                     .Append(mlContext.Regression.Trainers.GeneralizedAdditiveModels(
-                        labelColumn: labelName, featureColumn: "Features", maxBins: 16));
+                        labelColumnName: labelName, featureColumnName: "Features", maxBins: 16));
             var fitPipeline = pipeline.Fit(data);
 
             // Extract the model from the pipeline

docs/samples/Microsoft.ML.Samples/Dynamic/PermutationFeatureImportance/PfiBinaryClassificationExample.cs

@@ -23,7 +23,7 @@ public static void Example()
             var pipeline = mlContext.Transforms.Concatenate("Features", featureNames)
                     .Append(mlContext.Transforms.Normalize("Features"))
                     .Append(mlContext.BinaryClassification.Trainers.LogisticRegression(
-                        labelColumn: labelName, featureColumn: "Features"));
+                        labelColumnName: labelName, featureColumnName: "Features"));
             var model = pipeline.Fit(data);
 
             // Extract the model from the pipeline

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

@@ -49,7 +49,7 @@ public static void Example()
             // the "Features" column produced by FeaturizeText as the features column.
             var pipeline = mlContext.Transforms.Text.FeaturizeText("SentimentText", "Features")
                     .AppendCacheCheckpoint(mlContext) // Add a data-cache step within a pipeline.
-                    .Append(mlContext.BinaryClassification.Trainers.StochasticDualCoordinateAscent(labelColumn: "Sentiment", featureColumn: "Features", l2Const: 0.001f));
+                    .Append(mlContext.BinaryClassification.Trainers.StochasticDualCoordinateAscent(labelColumnName: "Sentiment", featureColumnName: "Features", l2Const: 0.001f));
 
             // Step 3: Run Cross-Validation on this pipeline.
             var cvResults = mlContext.BinaryClassification.CrossValidate(data, pipeline, labelColumn: "Sentiment");

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

@@ -41,7 +41,7 @@ public static void Example()
             // Step 2: Create a binary classifier. This trainer may produce a logistic regression model.
             // We set the "Label" column as the label of the dataset, and the "Features" column as the features column.
             var pipeline = mlContext.BinaryClassification.Trainers.StochasticDualCoordinateAscentNonCalibrated(
-                labelColumn: "Label", featureColumn: "Features", loss: new HingeLoss(), l2Const: 0.001f);
+                labelColumnName: "Label", featureColumnName: "Features", loss: new HingeLoss(), l2Const: 0.001f);
 
             // Step 3: Train the pipeline created.
             var model = pipeline.Fit(data);

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

@@ -31,7 +31,7 @@ public static void Example()
             //  - Convert the string labels into key types.
             //  - Apply LightGbm multiclass trainer.
             var pipeline = mlContext.Transforms.Conversion.MapValueToKey("LabelIndex", "Label")
-                        .Append(mlContext.MulticlassClassification.Trainers.LightGbm(labelColumn: "LabelIndex"))
+                        .Append(mlContext.MulticlassClassification.Trainers.LightGbm(labelColumnName: "LabelIndex"))
                         .Append(mlContext.Transforms.Conversion.MapValueToKey("PredictedLabelIndex", "PredictedLabel"))
                         .Append(mlContext.Transforms.CopyColumns("Scores", "Score"));
 

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

@@ -44,7 +44,7 @@ public static void Example()
             // the "Features" column produced by FeaturizeText as the features column.
             var pipeline = mlContext.Transforms.Text.FeaturizeText("Features", "SentimentText")
                     .AppendCacheCheckpoint(mlContext) // Add a data-cache step within a pipeline.
-                    .Append(mlContext.BinaryClassification.Trainers.Prior(labelColumn: "Sentiment"));
+                    .Append(mlContext.BinaryClassification.Trainers.Prior(labelColumnName: "Sentiment"));
 
             // Step 3: Train the pipeline
             var trainedPipeline = pipeline.Fit(trainTestData.TrainSet);

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

@@ -34,7 +34,7 @@ public static void Example()
                 .ToArray();
             var pipeline = mlContext.Transforms.Concatenate("Features", featureNames)
                            .Append(mlContext.Regression.Trainers.LightGbm(
-                                            labelColumn: labelName,
+                                            labelColumnName: labelName,
                                             numLeaves: 4,
                                             minDataPerLeaf: 6,
                                             learningRate: 0.001));

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/CustomMappingSample.cs

@@ -47,7 +47,7 @@ public static void Example()
                 // It is useful to add a caching checkpoint before a trainer that does several passes over the data.
                 .AppendCacheCheckpoint(mlContext)
                 // We use binary FastTree to predict the label column that was generated by the custom mapping at the first step of the pipeline.
-                .Append(mlContext.BinaryClassification.Trainers.FastTree(labelColumn: "IsUnderThirty"));
+                .Append(mlContext.BinaryClassification.Trainers.FastTree(labelColumnName: "IsUnderThirty"));
 
             // We can train the pipeline and use it to transform data.
             transformedData = pipeline.Fit(trainData).Transform(trainData);