Svmlight loader and saver

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

@@ -0,0 +1,116 @@
+using System;
+using System.Collections.Generic;
+using System.IO;
+using System.Text;
+using Microsoft.ML;
+using Microsoft.ML.Data;
+using Microsoft.ML.Transforms;
+using Microsoft.VisualBasic.CompilerServices;
+using Tensorflow;
+
+namespace Samples.Dynamic.DataOperations
+{
+    public static class LoadingSvmLight
+    {
+        // This examples shows all the ways to load data with TextLoader.
+        public static void Example()
+        {
+            // Create a random SVM light format file.
+            var random = new Random(42);
+            var dataDirectoryName = "DataDir";
+            Directory.CreateDirectory(dataDirectoryName);
+            var fileName = Path.Combine(dataDirectoryName, $"SVM_Data.csv");
+            using (var fs = File.CreateText(fileName))
+            {
+                // Write random lines in SVM light format
+                for (int line = 0; line < 10; line++)
+                {
+                    var sb = new StringBuilder();
+                    if (random.NextDouble() > 0.5)
+                        sb.Append("1 ");
+                    else
+                        sb.Append("-1 ");
+                    if (line % 2 == 0)
+                        sb.Append("cost:1");
+                    else
+                        sb.Append("cost:2");
+                    for (int i = 1; i <= 10; i++)
+                    {
+                        if (random.NextDouble() > 0.5)
+                            continue;
+                        sb.Append($"{i}:{random.NextDouble()} ");
+                    }
+                    fs.WriteLine(sb.ToString());
+                }
+            }
+
+            // Create an SvmLightLoader.
+            var mlContext = new MLContext();
+            var file = new MultiFileSource(fileName);
+            var loader = mlContext.Data.CreateSvmLightLoader(dataSample: file);
+
+            // Load a single file from path.
+            var svmData = loader.Load(file);
+
+            PrintSchema(svmData);
+
+            // Expected Output:
+            // Column Label type Single
+            // Column Weight type Single
+            // Column GroupId type Key<UInt64, 0 - 18446744073709551613>
+            // Column Comment type String
+            // Column Features type Vector<Single, 10>
+
+            PrintData(svmData);
+
+            // Expected Output:
+            // 1 1 0 0 0.2625927 0 0 0.7612506 0.2573214 0 0.3809696 0.5174511
+            // -1 1 0 0 0 0.7051522 0 0 0.7111546 0.9062127 0 0
+            // -1 1 0 0 0 0.535722 0 0 0.1491191 0.05100901 0 0
+            // -1 1 0 0.6481459 0.04449836 0 0 0.4203662 0 0 0.01325378 0.2674384
+            // -1 1 0 0 0.7978093 0.5134962 0.008952909 0 0.003074009 0.6541431 0.9135142 0
+            // -1 1 0 0.3727672 0.4369507 0 0 0.2973725 0 0 0 0.8816807
+            // 1 1 0 0.1031429 0.3332489 0 0.1346936 0.5916625 0 0 0 0
+            // 1 1 0 0 0 0.3454075 0 0.2197472 0.03848049 0.5923384 0.09373277 0
+            // -1 1 0 0.7511514 0 0.0420841 0 0 0.9262196 0 0.545344 0
+            // 1 1 0 0.02958358 0.9334617 0 0 0.8833956 0.2947684 0 0 0
+
+            // If the loader is created without a data sample we need to specify the number of features expected in the file.
+            loader = mlContext.Data.CreateSvmLightLoader(inputSize: 10);
+            svmData = loader.Load(file);
+
+            PrintSchema(svmData);
+            PrintData(svmData);
+        }
+
+        private static void PrintSchema(IDataView svmData)
+        {
+            foreach (var col in svmData.Schema)
+                Console.WriteLine($"Column {col.Name} type {col.Type}");
+        }
+
+        private static void PrintData(IDataView svmData)
+        {
+            using (var cursor = svmData.GetRowCursor(svmData.Schema))
+            {
+                var labelGetter = cursor.GetGetter<float>(svmData.Schema["Label"]);
+                var weightGetter = cursor.GetGetter<float>(svmData.Schema["Weight"]);
+                var featuresGetter = cursor.GetGetter<VBuffer<float>>(svmData.Schema["Features"]);
+
+                VBuffer<float> features = default;
+                while (cursor.MoveNext())
+                {
+                    float label = default;
+                    labelGetter(ref label);
+
+                    float weight = default;
+                    weightGetter(ref weight);
+
+                    featuresGetter(ref features);
+
+                    Console.WriteLine($"{label} {weight} {string.Join(' ', features.DenseValues())}");
+                }
+            }
+        }
+    }
+}

docs/samples/Microsoft.ML.Samples/Microsoft.ML.Samples.csproj

@@ -24,6 +24,7 @@
     <ProjectReference Include="..\..\..\src\Microsoft.ML.TensorFlow\Microsoft.ML.TensorFlow.csproj" />
     <ProjectReference Include="..\..\..\src\Microsoft.ML.TimeSeries\Microsoft.ML.TimeSeries.csproj" />
     <ProjectReference Include="..\..\..\src\Microsoft.ML.DnnImageFeaturizer.ResNet18\Microsoft.ML.DnnImageFeaturizer.ResNet18.csproj" />
+    <ProjectReference Include="..\..\..\src\Microsoft.ML.Transforms\Microsoft.ML.Transforms.csproj" />
 
     <NativeAssemblyReference Include="CpuMathNative" />
     <NativeAssemblyReference Include="FastTreeNative" />