[WIP] TT Template for managing samples...

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

@@ -0,0 +1,97 @@
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using Microsoft.ML.Data;
+
+namespace Microsoft.ML.Samples.Dynamic.Trainers.Regression
+{
+    public static class FastTree2
+    {
+        // This example requires installation of additional NuGet package
+        // <a href='https://www.nuget.org/packages/Microsoft.ML.FastTree/'>Microsoft.ML.FastTree</a>.
+        public static void Example()
+        {
+            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
+            // as a catalog of available operations and as the source of randomness.
+            // Setting the seed to a fixed number in this example to make outputs deterministic.
+            var mlContext = new MLContext(seed: 0);
+
+            // Create a list of training examples.
+            var examples = GenerateRandomDataPoints(1000);
+
+            // Convert the examples list to an IDataView object, which is consumable by ML.NET API.
+            var trainingData = mlContext.Data.LoadFromEnumerable(examples);
+
+            // Define the trainer.
+            var pipeline = mlContext.Regression.Trainers.FastTree();
+
+            // Train the model.
+            var model = pipeline.Fit(trainingData);
+
+            // Create testing examples. Use different random seed to make it different from training data.
+            var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123));
+
+            // Run the model on test data set.
+            var transformedTestData = model.Transform(testData);
+
+            // Convert IDataView object to a list.
+            var predictions = mlContext.Data.CreateEnumerable<Prediction>(transformedTestData, reuseRowObject: false).ToList();
+
+            // Look at 5 predictions
+            foreach (var p in predictions.Take(5))
+                Console.WriteLine($"Label: {p.Label:F3}, Prediction: {p.Score:F3}");
+
+            // Expected output:
+            //   Label: 0.985, Prediction: 0.938
+            //   Label: 0.155, Prediction: 0.131
+            //   Label: 0.515, Prediction: 0.517
+            //   Label: 0.566, Prediction: 0.519
+            //   Label: 0.096, Prediction: 0.089
+
+
+            // Evaluate the overall metrics
+            var metrics = mlContext.Regression.Evaluate(transformedTestData);
+            SamplesUtils.ConsoleUtils.PrintMetrics(metrics);
+
+            // Expected output:
+            //   Mean Absolute Error: 0.05
+            //   Mean Squared Error: 0.00
+            //   Root Mean Squared Error: 0.06
+            //   RSquared: 0.95
+        }
+
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
+        {
+            var random = new Random(seed);
+            float randomFloat() => (float)random.NextDouble();
+            for (int i = 0; i < count; i++)
+            {
+                var label = randomFloat();
+                yield return new DataPoint
+                {
+                    Label = label,
+                    // Create random features that are correlated with label.
+                    Features = Enumerable.Repeat(label, 50).Select(x => x + randomFloat()).ToArray()
+                };
+            }
+        }
+
+        // Example with label and 50 feature values. A data set is a collection of such examples.
+        private class DataPoint
+        {
+            public float Label { get; set; }
+            [VectorType(50)]
+            public float[] Features { get; set; }
+        }
+
+        // Class used to capture predictions.
+        private class Prediction
+        {
+            // Original label.
+            public float Label { get; set; }
+            // Predicted score from the trainer.
+            public float Score { get; set; }
+        }
+    }
+}
+

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastTreeTemplate.tt

@@ -0,0 +1,23 @@
+<#@ include file="RegressionTemplate.txt"#>
+
+<#+
+string ClassName="FastTree2";
+string Comments = @"// This example requires installation of additional NuGet package
+        // <a href='https://www.nuget.org/packages/Microsoft.ML.FastTree/'>Microsoft.ML.FastTree</a>.";
+
+string TrainingCode = @"var pipeline = mlContext.Regression.Trainers.FastTree();";
+
+string ExpectedOutputPerInstance= @"// Expected output:
+            //   Label: 0.985, Prediction: 0.938
+            //   Label: 0.155, Prediction: 0.131
+            //   Label: 0.515, Prediction: 0.517
+            //   Label: 0.566, Prediction: 0.519
+            //   Label: 0.096, Prediction: 0.089
+";
+
+string ExpectedOutput = @"// Expected output:
+            //   Mean Absolute Error: 0.05
+            //   Mean Squared Error: 0.00
+            //   Root Mean Squared Error: 0.06
+            //   RSquared: 0.95";
+#>

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/RegressionTemplate.txt

@@ -0,0 +1,85 @@
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using Microsoft.ML.Data;
+
+namespace Microsoft.ML.Samples.Dynamic.Trainers.Regression
+{
+    public static class <#=ClassName#>
+    {
+        <#=Comments#>
+        public static void Example()
+        {
+            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
+            // as a catalog of available operations and as the source of randomness.
+            // Setting the seed to a fixed number in this example to make outputs deterministic.
+            var mlContext = new MLContext(seed: 0);
+
+            // Create a list of training examples.
+            var examples = GenerateRandomDataPoints(1000);
+
+            // Convert the examples list to an IDataView object, which is consumable by ML.NET API.
+            var trainingData = mlContext.Data.LoadFromEnumerable(examples);
+
+            // Define the trainer.
+            <#=TrainingCode#>
+
+            // Train the model.
+            var model = pipeline.Fit(trainingData);
+
+            // Create testing examples. Use different random seed to make it different from training data.
+            var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123));
+
+            // Run the model on test data set.
+            var transformedTestData = model.Transform(testData);
+
+            // Convert IDataView object to a list.
+            var predictions = mlContext.Data.CreateEnumerable<Prediction>(transformedTestData, reuseRowObject: false).ToList();
+
+            // Look at 5 predictions
+            foreach (var p in predictions.Take(5))
+                Console.WriteLine($"Label: {p.Label:F3}, Prediction: {p.Score:F3}");
+
+            <#=ExpectedOutputPerInstance#>
+
+            // Evaluate the overall metrics
+            var metrics = mlContext.Regression.Evaluate(transformedTestData);
+            SamplesUtils.ConsoleUtils.PrintMetrics(metrics);
+
+            <#=ExpectedOutput#>
+        }
+
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
+        {
+            var random = new Random(seed);
+            float randomFloat() => (float)random.NextDouble();
+            for (int i = 0; i < count; i++)
+            {
+                var label = randomFloat();
+                yield return new DataPoint
+                {
+                    Label = label,
+                    // Create random features that are correlated with label.
+                    Features = Enumerable.Repeat(label, 50).Select(x => x + randomFloat()).ToArray()
+                };
+            }
+        }
+
+        // Example with label and 50 feature values. A data set is a collection of such examples.
+        private class DataPoint
+        {
+            public float Label { get; set; }
+            [VectorType(50)]
+            public float[] Features { get; set; }
+        }
+
+        // Class used to capture predictions.
+        private class Prediction
+        {
+            // Original label.
+            public float Label { get; set; }
+            // Predicted score from the trainer.
+            public float Score { get; set; }
+        }
+    }
+}

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

@@ -35,4 +35,23 @@
 
   </ItemGroup>
 
+  <ItemGroup>
+    <None Update="Dynamic\Trainers\Regression\FastTreeTemplate.tt">
+      <Generator>TextTemplatingFileGenerator</Generator>
+      <LastGenOutput>FastTreeTemplate.cs</LastGenOutput>
+    </None>
+  </ItemGroup>
+
+  <ItemGroup>
+    <Service Include="{508349b6-6b84-4df5-91f0-309beebad82d}" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <Compile Update="Dynamic\Trainers\Regression\FastTreeTemplate.cs">
+      <DesignTime>True</DesignTime>
+      <AutoGen>True</AutoGen>
+      <DependentUpon>FastTreeTemplate.tt</DependentUpon>
+    </Compile>
+  </ItemGroup>
+
 </Project>