Rename IDataLoader, IDataReader and IDataReaderEstimator (#2731)

Author: artidoro

docs/code/MlNetCookBook.md

@@ -28,7 +28,7 @@ public static void Example()
             var mlContext = new MLContext();
 
             // Create a text loader.
-            var reader = mlContext.Data.CreateTextLoader(new TextLoader.Options()
+            var loader = mlContext.Data.CreateTextLoader(new TextLoader.Options()
             {
                 Separators = new[] { '\t' },
                 HasHeader = true,
@@ -39,8 +39,8 @@ public static void Example()
                     }
             });
 
-            // Read the data
-            var data = reader.Read(dataFile);
+            // Load the data
+            var data = loader.Load(dataFile);
 
             // Split the dataset into two parts: one used for training, the other to train the calibrator
             var split = mlContext.BinaryClassification.TrainTestSplit(data, testFraction: 0.1);

docs/code/experimental/MlNetCookBookStaticApi.md

@@ -14,7 +14,7 @@ public static void Example()
 
             // Get a small dataset as an IEnumerable and them read it as ML.NET's data type.
             IEnumerable<SamplesUtils.DatasetUtils.BinaryLabelFloatFeatureVectorSample> enumerableOfData = SamplesUtils.DatasetUtils.GenerateBinaryLabelFloatFeatureVectorSamples(5);
-            var data = mlContext.Data.ReadFromEnumerable(enumerableOfData);
+            var data = mlContext.Data.LoadFromEnumerable(enumerableOfData);
 
             // Look at the original dataset
             Console.WriteLine($"Label\tFeatures[0]");

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

@@ -20,8 +20,8 @@ public static void Example()
             // Get a small dataset as an IEnumerable.
             IEnumerable<DatasetUtils.SampleTemperatureData> enumerableOfData = DatasetUtils.GetSampleTemperatureData(5);
 
-            // Read in into an IDataView using ReadFromEnumerable. 
-            IDataView data = mlContext.Data.ReadFromEnumerable(enumerableOfData);
+            // Load dataset into an IDataView. 
+            IDataView data = mlContext.Data.LoadFromEnumerable(enumerableOfData);
 
             // We can now examine the records in the IDataView. We first create an enumerable of rows in the IDataView.
             var rowEnumerable = mlContext.Data.CreateEnumerable<DatasetUtils.SampleTemperatureData>(data, reuseRowObject: true);

docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/BootstrapSample.cs

@@ -17,7 +17,7 @@ public static void Example()
 
             // Get a small dataset as an IEnumerable.
             IEnumerable<SamplesUtils.DatasetUtils.SampleTemperatureData> enumerableOfData = SamplesUtils.DatasetUtils.GetSampleTemperatureData(10);
-            var data = mlContext.Data.ReadFromEnumerable(enumerableOfData);
+            var data = mlContext.Data.LoadFromEnumerable(enumerableOfData);
 
             // Before we apply a filter, examine all the records in the dataset.
             Console.WriteLine($"Date\tTemperature");

docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/DataViewEnumerable.cs

@@ -20,7 +20,7 @@ public static void Example()
 
             // Get a small dataset as an IEnumerable.
             IEnumerable<MulticlassClassificationExample> enumerableOfData = DatasetUtils.GenerateRandomMulticlassClassificationExamples(10);
-            var data = mlContext.Data.ReadFromEnumerable(enumerableOfData);
+            var data = mlContext.Data.LoadFromEnumerable(enumerableOfData);
 
             // Convert the string labels to keys
             var pipeline = mlContext.Transforms.Conversion.MapValueToKey("Label");

docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/FilterRowsByColumn.cs

@@ -17,7 +17,7 @@ public static void Example()
 
             // Get a small dataset as an IEnumerable.
             var dataEnumerable = DatasetUtils.GenerateFloatLabelFloatFeatureVectorSamples(10, naRate: 0.05);
-            var data = mlContext.Data.ReadFromEnumerable(dataEnumerable);
+            var data = mlContext.Data.LoadFromEnumerable(dataEnumerable);
 
             // Look at the original dataset
             Console.WriteLine($"Label\tFeatures");

docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/FilterRowsByKeyColumnFraction.cs

@@ -17,7 +17,7 @@ public static void Example()
 
             // Get a small dataset as an IEnumerable.
             var enumerableOfData = DatasetUtils.GetSampleTemperatureData(5);
-            var data = mlContext.Data.ReadFromEnumerable(enumerableOfData);
+            var data = mlContext.Data.LoadFromEnumerable(enumerableOfData);
 
             // Before we apply a filter, examine all the records in the dataset.
             Console.WriteLine($"Date\tTemperature");

docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/FilterRowsByMissingValues.cs

@@ -17,7 +17,7 @@ public static void Example()
 
             // Get a small dataset as an IEnumerable.
             var enumerableOfData = SamplesUtils.DatasetUtils.GetSampleTemperatureData(10);
-            var data = mlContext.Data.ReadFromEnumerable(enumerableOfData);
+            var data = mlContext.Data.LoadFromEnumerable(enumerableOfData);
 
             // Before we apply a filter, examine all the records in the dataset.
             Console.WriteLine($"Date\tTemperature");