add append function to pipeline

src/Microsoft.ML/LearningPipeline.cs

@@ -66,7 +66,7 @@ public LearningPipeline()
         /// </summary>
         /// <param name="seed">Specify seed for random generator</param>
         /// <param name="conc">Specify concurrency factor (default value - autoselection)</param>
-        internal LearningPipeline(int? seed=null, int conc=0)
+        internal LearningPipeline(int? seed = null, int conc = 0)
         {
             _seed = seed;
             _conc = conc;
@@ -109,6 +109,16 @@ internal LearningPipeline(int? seed=null, int conc=0)
         /// <param name="item">Any ML component (data loader, transform or trainer) defined as <see cref="ILearningPipelineItem"/>.</param>
         public void Add(ILearningPipelineItem item) => Items.Add(item);
 
+        /// <summary>
+        /// Add a data loader, transform or trainer into the pipeline.
+        /// </summary>
+        /// <param name="item">Any ML component (data loader, transform or trainer) defined as <see cref="ILearningPipelineItem"/>.</param>
+        /// <returns>Pipeline with added item</returns>
+        public LearningPipeline Append(ILearningPipelineItem item)
+        {
+            Add(item);
+            return this;
+        }
         /// <summary>
         /// Remove all the loaders/transforms/trainers from the pipeline.
         /// </summary>
@@ -152,7 +162,7 @@ public PredictionModel<TInput, TOutput> Train<TInput, TOutput>()
             where TInput : class
             where TOutput : class, new()
         {
-            using (var environment = new TlcEnvironment(seed:_seed, conc:_conc))
+            using (var environment = new TlcEnvironment(seed: _seed, conc: _conc))
             {
                 Experiment experiment = environment.CreateExperiment();
                 ILearningPipelineStep step = null;

test/Microsoft.ML.Tests/LearningPipelineTests.cs

@@ -167,5 +167,22 @@ public void NullableBooleanLabelPipeline()
             pipeline.Add(new FastForestBinaryClassifier());
             var model = pipeline.Train<Data, Prediction>();
         }
+
+        [Fact]
+        public void AppendPipeline()
+        {
+            var pipeline = new LearningPipeline();
+            pipeline.Append(new CategoricalOneHotVectorizer("String1", "String2"))
+                .Append(new ColumnConcatenator(outputColumn: "Features", "String1", "String2", "Number1", "Number2"))
+                .Append(new StochasticDualCoordinateAscentRegressor());
+            Assert.NotNull(pipeline);
+            Assert.Equal(3, pipeline.Count);
+
+            pipeline.Remove(pipeline.ElementAt(2));
+            Assert.Equal(2, pipeline.Count);
+
+            pipeline.Append(new StochasticDualCoordinateAscentRegressor());
+            Assert.Equal(3, pipeline.Count);
+        }
     }
 }

test/Microsoft.ML.Tests/Scenarios/IrisPlantClassificationTests.cs

@@ -18,7 +18,7 @@ public void TrainAndPredictIrisModelTest()
         {
             string dataPath = GetDataPath("iris.txt");
 
-            var pipeline = new LearningPipeline();
+            var pipeline = new LearningPipeline(seed:1, conc:1);
 
             pipeline.Add(new TextLoader(dataPath).CreateFrom<IrisData>(useHeader: false));
             pipeline.Add(new ColumnConcatenator(outputColumn: "Features",