Added tests for new API where components(Loaders/Transforms/Learners) are directly instantiated.

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

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+// 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 Microsoft.ML.Models;
+using Microsoft.ML.Runtime;
+using Microsoft.ML.Runtime.Api;
+using Microsoft.ML.Runtime.Data;
+using Microsoft.ML.Runtime.Learners;
+using Microsoft.ML.Runtime.Model;
+using System;
+using System.IO;
+using Xunit;
+
+namespace Microsoft.ML.Scenarios
+{
+    public partial class ScenariosTests
+    {
+        [Fact]
+        public void TrainAndPredictIrisModelUsingDirectInstantiationTest()
+        {
+            string dataPath = GetDataPath("iris.txt");
+            string testDataPath = dataPath;
+
+            using (var env = new TlcEnvironment(seed: 1, conc: 1))
+            {
+                // Pipeline
+                var loader = new TextLoader(env,
+                    new TextLoader.Arguments()
+                    {
+                        HasHeader = false,
+                        Column = new[] {
+                            new TextLoader.Column()
+                            {
+                                Name = "Label",
+                                Source = new [] { new TextLoader.Range() { Min = 0, Max = 0} },
+                                Type = DataKind.R4
+                            },
+                            new TextLoader.Column()
+                            {
+                                Name = "SepalLength",
+                                Source = new [] { new TextLoader.Range() { Min = 1, Max = 1} },
+                                Type = DataKind.R4
+                            },
+                            new TextLoader.Column()
+                            {
+                                Name = "SepalWidth",
+                                Source = new [] { new TextLoader.Range() { Min = 2, Max = 2} },
+                                Type = DataKind.R4
+                            },
+                            new TextLoader.Column()
+                            {
+                                Name = "PetalLength",
+                                Source = new [] { new TextLoader.Range() { Min = 3, Max = 3} },
+                                Type = DataKind.R4
+                            },
+                            new TextLoader.Column()
+                            {
+                                Name = "PetalWidth",
+                                Source = new [] { new TextLoader.Range() { Min = 4, Max = 4} },
+                                Type = DataKind.R4
+                            }
+                        }
+                    }, new MultiFileSource(dataPath));
+
+                IDataTransform trans = new ConcatTransform(env, loader, "Features",
+                    "SepalLength", "SepalWidth", "PetalLength", "PetalWidth");
+
+                // Normalizer is not automatically added though the trainer has 'NormalizeFeatures' On/Auto
+                trans = NormalizeTransform.CreateMinMaxNormalizer(env, trans, "Features");
+
+                // Train
+                var trainer = new SdcaMultiClassTrainer(env, new SdcaMultiClassTrainer.Arguments());
+
+                // Explicity adding CacheDataView since caching is not working though trainer has 'Caching' On/Auto
+                var cached = new CacheDataView(env, trans, prefetch: null);
+                var trainRoles = TrainUtils.CreateExamples(cached, label: "Label", feature: "Features");
+                trainer.Train(trainRoles);
+
+                // Get scorer and evaluate the predictions from test data
+                var pred = trainer.CreatePredictor();
+                IDataScorerTransform testDataScorer = GetScorer(env, trans, pred, testDataPath);
+                var metrics = Evaluate(env, testDataScorer);
+                CompareMatrics(metrics);
+
+                // Create prediction engine and test predictions
+                var model = env.CreatePredictionEngine<IrisData, IrisPrediction>(testDataScorer);
+                ComparePredictions(model);
+
+                // Get feature importance i.e. weight vector
+                var summary = ((MulticlassLogisticRegressionPredictor)pred).GetSummaryInKeyValuePairs(trainRoles.Schema);
+                Assert.Equal(7.757867, Convert.ToDouble(summary[0].Value), 5);
+            }
+        }
+
+        private void ComparePredictions(PredictionEngine<IrisData, IrisPrediction> model)
+        {
+            IrisPrediction prediction = model.Predict(new IrisData()
+            {
+                SepalLength = 3.3f,
+                SepalWidth = 1.6f,
+                PetalLength = 0.2f,
+                PetalWidth = 5.1f,
+            });
+
+            Assert.Equal(1, prediction.PredictedLabels[0], 2);
+            Assert.Equal(0, prediction.PredictedLabels[1], 2);
+            Assert.Equal(0, prediction.PredictedLabels[2], 2);
+
+            prediction = model.Predict(new IrisData()
+            {
+                SepalLength = 3.1f,
+                SepalWidth = 5.5f,
+                PetalLength = 2.2f,
+                PetalWidth = 6.4f,
+            });
+
+            Assert.Equal(0, prediction.PredictedLabels[0], 2);
+            Assert.Equal(0, prediction.PredictedLabels[1], 2);
+            Assert.Equal(1, prediction.PredictedLabels[2], 2);
+
+            prediction = model.Predict(new IrisData()
+            {
+                SepalLength = 3.1f,
+                SepalWidth = 2.5f,
+                PetalLength = 1.2f,
+                PetalWidth = 4.4f,
+            });
+
+            Assert.Equal(.2, prediction.PredictedLabels[0], 1);
+            Assert.Equal(.8, prediction.PredictedLabels[1], 1);
+            Assert.Equal(0, prediction.PredictedLabels[2], 2);
+        }
+
+        private void CompareMatrics(ClassificationMetrics metrics)
+        {
+            Assert.Equal(.98, metrics.AccuracyMacro);
+            Assert.Equal(.98, metrics.AccuracyMicro, 2);
+            Assert.Equal(.06, metrics.LogLoss, 2);
+            Assert.InRange(metrics.LogLossReduction, 94, 96);
+            Assert.Equal(1, metrics.TopKAccuracy);
+
+            Assert.Equal(3, metrics.PerClassLogLoss.Length);
+            Assert.Equal(0, metrics.PerClassLogLoss[0], 1);
+            Assert.Equal(.1, metrics.PerClassLogLoss[1], 1);
+            Assert.Equal(.1, metrics.PerClassLogLoss[2], 1);
+
+            ConfusionMatrix matrix = metrics.ConfusionMatrix;
+            Assert.Equal(3, matrix.Order);
+            Assert.Equal(3, matrix.ClassNames.Count);
+            Assert.Equal("0", matrix.ClassNames[0]);
+            Assert.Equal("1", matrix.ClassNames[1]);
+            Assert.Equal("2", matrix.ClassNames[2]);
+
+            Assert.Equal(50, matrix[0, 0]);
+            Assert.Equal(50, matrix["0", "0"]);
+            Assert.Equal(0, matrix[0, 1]);
+            Assert.Equal(0, matrix["0", "1"]);
+            Assert.Equal(0, matrix[0, 2]);
+            Assert.Equal(0, matrix["0", "2"]);
+
+            Assert.Equal(0, matrix[1, 0]);
+            Assert.Equal(0, matrix["1", "0"]);
+            Assert.Equal(48, matrix[1, 1]);
+            Assert.Equal(48, matrix["1", "1"]);
+            Assert.Equal(2, matrix[1, 2]);
+            Assert.Equal(2, matrix["1", "2"]);
+
+            Assert.Equal(0, matrix[2, 0]);
+            Assert.Equal(0, matrix["2", "0"]);
+            Assert.Equal(1, matrix[2, 1]);
+            Assert.Equal(1, matrix["2", "1"]);
+            Assert.Equal(49, matrix[2, 2]);
+            Assert.Equal(49, matrix["2", "2"]);
+        }
+
+        private ClassificationMetrics Evaluate(IHostEnvironment env, IDataView scoredData)
+        {
+            var dataEval = TrainUtils.CreateExamplesOpt(scoredData, label: "Label", feature: "Features");
+
+            // Evaluate.
+            // It does not work. It throws error "Failed to find 'Score' column" when Evaluate is called
+            //var evaluator = new MultiClassClassifierEvaluator(env, new MultiClassClassifierEvaluator.Arguments() { OutputTopKAcc = 3 });
+
+            var evaluator = new MultiClassMamlEvaluator(env, new MultiClassMamlEvaluator.Arguments() { OutputTopKAcc = 3 });
+            var metricsDic = evaluator.Evaluate(dataEval);
+
+            return ClassificationMetrics.FromMetrics(env, metricsDic["OverallMetrics"], metricsDic["ConfusionMatrix"])[0];
+        }
+
+        private IDataScorerTransform GetScorer(IHostEnvironment env, IDataView transforms, IPredictor pred, string testDataPath = null)
+        {
+            using (var ch = env.Start("Saving model"))
+            using (var memoryStream = new MemoryStream())
+            {
+                var trainRoles = TrainUtils.CreateExamples(transforms, label: "Label", feature: "Features");
+
+                // Model cannot be saved with CacheDataView
+                TrainUtils.SaveModel(env, ch, memoryStream, pred, trainRoles);
+                memoryStream.Position = 0;
+                using (var rep = RepositoryReader.Open(memoryStream, ch))
+                {
+                    IDataLoader testPipe = ModelFileUtils.LoadLoader(env, rep, new MultiFileSource(testDataPath), true);
+                    RoleMappedData testRoles = TrainUtils.CreateExamples(testPipe, label: "Label", feature: "Features");
+                    return ScoreUtils.GetScorer(pred, testRoles, env, testRoles.Schema);
+                }
+            }
+        }
+    }
+}

test/Microsoft.ML.Tests/ScenariosWithDirectInstantiation/SentimentPredictionTests.cs

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+// 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 Microsoft.ML.Models;
+using Microsoft.ML.Runtime;
+using Microsoft.ML.Runtime.Api;
+using Microsoft.ML.Runtime.Data;
+using Microsoft.ML.Runtime.FastTree;
+using Microsoft.ML.Runtime.Internal.Calibration;
+using Microsoft.ML.Runtime.Model;
+using Microsoft.ML.Trainers;
+using Microsoft.ML.Transforms;
+using System.Collections.Generic;
+using System.IO;
+using System.Linq;
+using Xunit;
+
+namespace Microsoft.ML.Scenarios
+{
+    public partial class ScenariosTests
+    {
+        [Fact]
+        public void TrainAndPredictSentimentModelWithDirectionInstantiationTest()
+        {
+            var dataPath = GetDataPath(SentimentDataPath);
+            var testDataPath = GetDataPath(SentimentTestPath);
+
+            using (var env = new TlcEnvironment(seed: 1, conc: 1))
+            {
+                // Pipeline
+                var loader = new TextLoader(env,
+                new TextLoader.Arguments()
+                {
+                    Separator = "tab",
+                    HasHeader = true,
+                    Column = new[]
+                    {
+                        new TextLoader.Column()
+                        {
+                            Name = "Label",
+                            Source = new [] { new TextLoader.Range() { Min=0, Max=0} },
+                            Type = DataKind.Num
+                        },
+
+                        new TextLoader.Column()
+                        {
+                            Name = "SentimentText",
+                            Source = new [] { new TextLoader.Range() { Min=1, Max=1} },
+                            Type = DataKind.Text
+                        }
+                    }
+                }, new MultiFileSource(dataPath));
+
+                var trans = TextTransform.Create(env, new TextTransform.Arguments()
+                {
+                    Column = new TextTransform.Column
+                    {
+                        Name = "Features",
+                        Source = new[] { "SentimentText" }
+                    },
+                    KeepDiacritics = false,
+                    KeepPunctuations = false,
+                    TextCase = Runtime.TextAnalytics.TextNormalizerTransform.CaseNormalizationMode.Lower,
+                    OutputTokens = true,
+                    StopWordsRemover = new Runtime.TextAnalytics.PredefinedStopWordsRemoverFactory(),
+                    VectorNormalizer = TextTransform.TextNormKind.L2,
+                    CharFeatureExtractor = new NgramExtractorTransform.NgramExtractorArguments() { NgramLength = 3, AllLengths = false },
+                    WordFeatureExtractor = new NgramExtractorTransform.NgramExtractorArguments() { NgramLength = 2, AllLengths = true },
+                },
+                loader);
+
+                // Train
+                var trainer = new FastTreeBinaryClassificationTrainer(env, new FastTreeBinaryClassificationTrainer.Arguments()
+                {
+                    NumLeaves = 5,
+                    NumTrees = 5,
+                    MinDocumentsInLeafs = 2
+                });
+
+                var trainRoles = TrainUtils.CreateExamples(trans, label: "Label", feature: "Features");
+                trainer.Train(trainRoles);
+
+                // Get scorer and evaluate the predictions from test data
+                var pred = trainer.CreatePredictor();
+                IDataScorerTransform testDataScorer = GetScorer(env, trans, pred, testDataPath);
+                var metrics = EvaluateBinary(env, testDataScorer);
+                ValidateBinaryMetrics(metrics);
+
+                // Create prediction engine and test predictions
+                var model = env.CreateBatchPredictionEngine<SentimentData, SentimentPrediction>(testDataScorer);
+                var sentiments = GetTestData();
+                var predictions = model.Predict(sentiments, false);
+                Assert.Equal(2, predictions.Count());
+                Assert.True(predictions.ElementAt(0).Sentiment.IsFalse);
+                Assert.True(predictions.ElementAt(1).Sentiment.IsTrue);
+
+                // Get feature importance based on feature gain during training
+                var summary = ((FeatureWeightsCalibratedPredictor)pred).GetSummaryInKeyValuePairs(trainRoles.Schema);
+                Assert.Equal(1.0, (double)summary[0].Value, 1);
+            }
+        }
+
+        private BinaryClassificationMetrics EvaluateBinary(IHostEnvironment env, IDataView scoredData)
+        {
+            var dataEval = TrainUtils.CreateExamplesOpt(scoredData, label: "Label", feature: "Features");
+
+            // Evaluate.
+            // It does not work. It throws error "Failed to find 'Score' column" when Evaluate is called
+            //var evaluator = new BinaryClassifierEvaluator(env, new BinaryClassifierEvaluator.Arguments());
+
+            var evaluator = new BinaryClassifierMamlEvaluator(env, new BinaryClassifierMamlEvaluator.Arguments());
+            var metricsDic = evaluator.Evaluate(dataEval);
+
+            return BinaryClassificationMetrics.FromMetrics(env, metricsDic["OverallMetrics"], metricsDic["ConfusionMatrix"])[0];
+        }
+    }
+}