Adding prediction benchmarks using legacy LearningPipeline API (#1126)

See #1013 for the benchmark results

Author: najeeb-kazmi

test/Microsoft.ML.Benchmarks/LegacyPredictionEngineBench.cs

@@ -0,0 +1,110 @@
+// Licensed to the .NET Foundation under one or more agreements.
+// The .NET Foundation licenses this file to you under the MIT license.
+// See the LICENSE file in the project root for more information.
+
+using BenchmarkDotNet.Attributes;
+using Microsoft.ML.Runtime;
+using Microsoft.ML.Runtime.Api;
+using Microsoft.ML.Runtime.Learners;
+using Microsoft.ML.Legacy;
+using Microsoft.ML.Legacy.Data;
+using Microsoft.ML.Legacy.Transforms;
+using Microsoft.ML.Legacy.Trainers;
+
+namespace Microsoft.ML.Benchmarks
+{
+    public class LegacyPredictionEngineBench
+    {
+        private IrisData _irisExample;
+        private PredictionModel<IrisData, IrisPrediction> _irisModel;
+
+        private SentimentData _sentimentExample;
+        private PredictionModel<SentimentData, SentimentPrediction> _sentimentModel;
+
+        private BreastCancerData _breastCancerExample;
+        private PredictionModel<BreastCancerData, BreastCancerPrediction> _breastCancerModel;
+
+        [GlobalSetup(Target = nameof(MakeIrisPredictions))]
+        public void SetupIrisPipeline()
+        {
+            _irisExample = new IrisData()
+            {
+                SepalLength = 3.3f,
+                SepalWidth = 1.6f,
+                PetalLength = 0.2f,
+                PetalWidth = 5.1f,
+            };
+
+            string _irisDataPath = Program.GetInvariantCultureDataPath("iris.txt");
+
+            var pipeline = new LearningPipeline();
+            pipeline.Add(new TextLoader(_irisDataPath).CreateFrom<IrisData>(useHeader: true, separator: '\t'));
+            pipeline.Add(new ColumnConcatenator("Features", new[] { "SepalLength", "SepalWidth", "PetalLength", "PetalWidth" }));
+            pipeline.Add(new StochasticDualCoordinateAscentClassifier() { NumThreads = 1, ConvergenceTolerance = 1e-2f });
+
+            _irisModel = pipeline.Train<IrisData, IrisPrediction>();
+        }
+
+        [GlobalSetup(Target = nameof(MakeSentimentPredictions))]
+        public void SetupSentimentPipeline()
+        {
+            _sentimentExample = new SentimentData()
+            {
+                SentimentText = "Not a big fan of this."
+            };
+
+            string _sentimentDataPath = Program.GetInvariantCultureDataPath("wikipedia-detox-250-line-data.tsv");
+
+            var pipeline = new LearningPipeline();
+            pipeline.Add(new TextLoader(_sentimentDataPath).CreateFrom<SentimentData>(useHeader: true, separator: '\t'));
+            pipeline.Add(new TextFeaturizer("Features", "SentimentText"));
+            pipeline.Add(new StochasticDualCoordinateAscentBinaryClassifier() { NumThreads = 1, ConvergenceTolerance = 1e-2f });
+
+            _sentimentModel = pipeline.Train<SentimentData, SentimentPrediction>();
+        }
+
+        [GlobalSetup(Target = nameof(MakeBreastCancerPredictions))]
+        public void SetupBreastCancerPipeline()
+        {
+            _breastCancerExample = new BreastCancerData()
+            {
+                Features = new[] { 5f, 1f, 1f, 1f, 2f, 1f, 3f, 1f, 1f }
+            };
+
+            string _breastCancerDataPath = Program.GetInvariantCultureDataPath("breast-cancer.txt");
+
+            var pipeline = new LearningPipeline();
+            pipeline.Add(new TextLoader(_breastCancerDataPath).CreateFrom<BreastCancerData>(useHeader: false, separator: '\t'));
+            pipeline.Add(new StochasticDualCoordinateAscentBinaryClassifier() { NumThreads = 1, ConvergenceTolerance = 1e-2f });
+
+            _breastCancerModel = pipeline.Train<BreastCancerData, BreastCancerPrediction>();
+        }
+
+        [Benchmark]
+        public void MakeIrisPredictions()
+        {
+            for (int i = 0; i < 10000; i++)
+            {
+                _irisModel.Predict(_irisExample);
+            }
+        }
+
+        [Benchmark]
+        public void MakeSentimentPredictions()
+        {
+            for (int i = 0; i < 10000; i++)
+            {
+                _sentimentModel.Predict(_sentimentExample);
+            }
+        }
+
+        [Benchmark]
+        public void MakeBreastCancerPredictions()
+        {
+            for (int i = 0; i < 10000; i++)
+            {
+                _breastCancerModel.Predict(_breastCancerExample);
+            }
+        }
+    }
+}

test/Microsoft.ML.Benchmarks/PredictionEngineBench.cs

@@ -161,9 +161,10 @@ public void MakeBreastCancerPredictions()
 
     public class SentimentData
     {
-        [ColumnName("Label")]
+        [ColumnName("Label"), Column("0")]
         public bool Sentiment;
 
+        [Column("1")]
         public string SentimentText;
     }
 
@@ -177,10 +178,10 @@ public class SentimentPrediction
 
     public class BreastCancerData
     {
-        [ColumnName("Label")]
+        [ColumnName("Label"), Column("0")]
         public bool Label;
 
-        [ColumnName("Features"), VectorType(9)]
+        [ColumnName("Features"), Column("1-9"), VectorType(9)]
         public float[] Features;
     }