PipelineSweeperMacro for Multi-Class Classification (dotnet#539)

* failing test case for multiclass

* Refactored PipelineSweeperSupportedMetrics Class; added unit test for MultiClassClassification; refactored out unit tests for the PipelineSweeper

* take care of review comments; display transforms/learners + metrics in pipeline

* taking care of PR comments + refactor PipelineSweeperRunSummary

* taking care of review comments

Author: abgoswam

src/Microsoft.ML.PipelineInference/AutoInference.cs

@@ -51,67 +51,6 @@ public class LevelDependencyMap : Dictionary<ColumnInfo, List<TransformInference
         /// </summary>
         public class DependencyMap : Dictionary<int, LevelDependencyMap> { }
 
-        /// <summary>
-        /// AutoInference will support metrics as they are added here.
-        /// </summary>
-        public sealed class SupportedMetric
-        {
-            public static readonly SupportedMetric Auc = new SupportedMetric(FieldNames.PipelineSweeperSupportedMetrics.Auc, true);
-            public static readonly SupportedMetric AccuracyMicro = new SupportedMetric(FieldNames.PipelineSweeperSupportedMetrics.AccuracyMicro, true);
-            public static readonly SupportedMetric AccuracyMacro = new SupportedMetric(FieldNames.PipelineSweeperSupportedMetrics.AccuracyMacro, true);
-            public static readonly SupportedMetric L1 = new SupportedMetric(FieldNames.PipelineSweeperSupportedMetrics.L1, false);
-            public static readonly SupportedMetric L2 = new SupportedMetric(FieldNames.PipelineSweeperSupportedMetrics.L2, false);
-            public static readonly SupportedMetric F1 = new SupportedMetric(FieldNames.PipelineSweeperSupportedMetrics.F1, true);
-            public static readonly SupportedMetric AuPrc = new SupportedMetric(FieldNames.PipelineSweeperSupportedMetrics.AuPrc, true);
-            public static readonly SupportedMetric TopKAccuracy = new SupportedMetric(FieldNames.PipelineSweeperSupportedMetrics.TopKAccuracy, true);
-            public static readonly SupportedMetric Rms = new SupportedMetric(FieldNames.PipelineSweeperSupportedMetrics.Rms, false);
-            public static readonly SupportedMetric LossFn = new SupportedMetric(FieldNames.PipelineSweeperSupportedMetrics.LossFn, false);
-            public static readonly SupportedMetric RSquared = new SupportedMetric(FieldNames.PipelineSweeperSupportedMetrics.RSquared, false);
-            public static readonly SupportedMetric LogLoss = new SupportedMetric(FieldNames.PipelineSweeperSupportedMetrics.LogLoss, false);
-            public static readonly SupportedMetric LogLossReduction = new SupportedMetric(FieldNames.PipelineSweeperSupportedMetrics.LogLossReduction, true);
-            public static readonly SupportedMetric Ndcg = new SupportedMetric(FieldNames.PipelineSweeperSupportedMetrics.Ndcg, true);
-            public static readonly SupportedMetric Dcg = new SupportedMetric(FieldNames.PipelineSweeperSupportedMetrics.Dcg, true);
-            public static readonly SupportedMetric PositivePrecision = new SupportedMetric(FieldNames.PipelineSweeperSupportedMetrics.PositivePrecision, true);
-            public static readonly SupportedMetric PositiveRecall = new SupportedMetric(FieldNames.PipelineSweeperSupportedMetrics.PositiveRecall, true);
-            public static readonly SupportedMetric NegativePrecision = new SupportedMetric(FieldNames.PipelineSweeperSupportedMetrics.NegativePrecision, true);
-            public static readonly SupportedMetric NegativeRecall = new SupportedMetric(FieldNames.PipelineSweeperSupportedMetrics.NegativeRecall, true);
-            public static readonly SupportedMetric DrAtK = new SupportedMetric(FieldNames.PipelineSweeperSupportedMetrics.DrAtK, true);
-            public static readonly SupportedMetric DrAtPFpr = new SupportedMetric(FieldNames.PipelineSweeperSupportedMetrics.DrAtPFpr, true);
-            public static readonly SupportedMetric DrAtNumPos = new SupportedMetric(FieldNames.PipelineSweeperSupportedMetrics.DrAtNumPos, true);
-            public static readonly SupportedMetric NumAnomalies = new SupportedMetric(FieldNames.PipelineSweeperSupportedMetrics.NumAnomalies, true);
-            public static readonly SupportedMetric ThreshAtK = new SupportedMetric(FieldNames.PipelineSweeperSupportedMetrics.ThreshAtK, false);
-            public static readonly SupportedMetric ThreshAtP = new SupportedMetric(FieldNames.PipelineSweeperSupportedMetrics.ThreshAtP, false);
-            public static readonly SupportedMetric ThreshAtNumPos = new SupportedMetric(FieldNames.PipelineSweeperSupportedMetrics.ThreshAtNumPos, false);
-            public static readonly SupportedMetric Nmi = new SupportedMetric(FieldNames.PipelineSweeperSupportedMetrics.Nmi, true);
-            public static readonly SupportedMetric AvgMinScore = new SupportedMetric(FieldNames.PipelineSweeperSupportedMetrics.AvgMinScore, false);
-            public static readonly SupportedMetric Dbi = new SupportedMetric(FieldNames.PipelineSweeperSupportedMetrics.Dbi, false);
-
-            public string Name { get; }
-            public bool IsMaximizing { get; }
-
-            private SupportedMetric(string name, bool isMaximizing)
-            {
-                Name = name;
-                IsMaximizing = isMaximizing;
-            }
-
-            public static SupportedMetric ByName(string name)
-            {
-                var fields =
-                    typeof(SupportedMetric).GetFields(BindingFlags.Static | BindingFlags.Public);
-
-                foreach (var field in fields)
-                {
-                    var metric = (SupportedMetric)field.GetValue(Auc);
-                    if (name.Equals(metric.Name, StringComparison.OrdinalIgnoreCase))
-                        return metric;
-                }
-                throw new NotSupportedException($"Metric '{name}' not supported.");
-            }
-
-            public override string ToString() => Name;
-        }
-
         /// <summary>
         /// Class for encapsulating an entrypoint experiment graph
         /// and keeping track of the input and output nodes.
@@ -167,26 +106,6 @@ private bool GetDataVariableName(IExceptionContext ectx, string nameOfData, JTok
             }
         }
 
-        /// <summary>
-        /// Class containing some information about an exectuted pipeline.
-        /// These are analogous to IRunResult for smart sweepers.
-        /// </summary>
-        public sealed class RunSummary
-        {
-            public double MetricValue { get; }
-            public double TrainingMetricValue { get; }
-            public int NumRowsInTraining { get; }
-            public long RunTimeMilliseconds { get; }
-
-            public RunSummary(double metricValue, int numRows, long runTimeMilliseconds, double trainingMetricValue)
-            {
-                MetricValue = metricValue;
-                TrainingMetricValue = trainingMetricValue;
-                NumRowsInTraining = numRows;
-                RunTimeMilliseconds = runTimeMilliseconds;
-            }
-        }
-
         [TlcModule.ComponentKind("AutoMlStateBase")]
         public interface ISupportAutoMlStateFactory : IComponentFactory<IMlState>
         { }
@@ -218,42 +137,8 @@ public sealed class AutoMlMlState : IMlState
                 Desc = "State of an AutoML search and search space.")]
             public sealed class Arguments : ISupportAutoMlStateFactory
             {
-                // REVIEW: These should be the same as SupportedMetrics above. Not sure how to reference that class,
-                // without the C# API generator trying to create a version of that class in the API as well.
-                public enum Metrics
-                {
-                    Auc,
-                    AccuracyMicro,
-                    AccuracyMacro,
-                    L2,
-                    F1,
-                    AuPrc,
-                    TopKAccuracy,
-                    Rms,
-                    LossFn,
-                    RSquared,
-                    LogLoss,
-                    LogLossReduction,
-                    Ndcg,
-                    Dcg,
-                    PositivePrecision,
-                    PositiveRecall,
-                    NegativePrecision,
-                    NegativeRecall,
-                    DrAtK,
-                    DrAtPFpr,
-                    DrAtNumPos,
-                    NumAnomalies,
-                    ThreshAtK,
-                    ThreshAtP,
-                    ThreshAtNumPos,
-                    Nmi,
-                    AvgMinScore,
-                    Dbi
-                };
-
                 [Argument(ArgumentType.Required, HelpText = "Supported metric for evaluator.", ShortName = "metric")]
-                public Metrics Metric;
+                public PipelineSweeperSupportedMetrics.Metrics Metric;
 
                 [Argument(ArgumentType.Required, HelpText = "AutoML engine (pipeline optimizer) that generates next candidates.", ShortName = "engine")]
                 public ISupportIPipelineOptimizerFactory Engine;
@@ -271,7 +156,9 @@ public enum Metrics
             }
 
             public AutoMlMlState(IHostEnvironment env, Arguments args)
-                : this(env, SupportedMetric.ByName(Enum.GetName(typeof(Arguments.Metrics), args.Metric)), args.Engine.CreateComponent(env),
+                : this(env,
+                      PipelineSweeperSupportedMetrics.GetSupportedMetric(args.Metric), 
+                      args.Engine.CreateComponent(env),
                       args.TerminatorArgs.CreateComponent(env), args.TrainerKind, requestedLearners: args.RequestedLearners)
             {
             }
@@ -355,8 +242,7 @@ private void ProcessPipeline(Sweeper.Algorithms.SweeperProbabilityUtils utils, S
                     testMetricVal += 1e-10;
 
                 // Save performance score
-                candidate.PerformanceSummary =
-                    new RunSummary(testMetricVal, randomizedNumberOfRows, stopwatch.ElapsedMilliseconds, trainMetricVal);
+                candidate.PerformanceSummary = new PipelineSweeperRunSummary(testMetricVal, randomizedNumberOfRows, stopwatch.ElapsedMilliseconds, trainMetricVal);
                 _sortedSampledElements.Add(candidate.PerformanceSummary.MetricValue, candidate);
                 _history.Add(candidate);
             }
@@ -524,6 +410,21 @@ public void AddEvaluated(PipelinePattern pipeline)
                     d += 1e-3;
                 _sortedSampledElements.Add(d, pipeline);
                 _history.Add(pipeline);
+
+                using (var ch = _host.Start("Suggested Pipeline"))
+                {
+                    ch.Info($"PipelineSweeper Iteration Number : {_history.Count}");
+                    ch.Info($"PipelineSweeper Pipeline Id : {pipeline.UniqueId}");
+
+                    foreach (var transform in pipeline.Transforms)
+                    {
+                        ch.Info($"PipelineSweeper Transform : {transform.Transform}");
+                    }
+
+                    ch.Info($"PipelineSweeper Learner : {pipeline.Learner}");
+                    ch.Info($"PipelineSweeper Train Metric Value : {pipeline.PerformanceSummary.TrainingMetricValue}");
+                    ch.Info($"PipelineSweeper Test Metric Value : {pipeline.PerformanceSummary.MetricValue}");
+                }
             }
 
             public void AddEvaluated(PipelinePattern[] pipelines)
@@ -541,19 +442,6 @@ public PipelinePattern[] GetNextCandidates(int numberOfCandidates)
                     currentBatchSize = Math.Min(itr.RemainingIterations(_history), numberOfCandidates);
                 BatchCandidates = AutoMlEngine.GetNextCandidates(_sortedSampledElements.Select(kvp => kvp.Value), currentBatchSize, _dataRoles);
 
-                using (var ch = _host.Start("Suggested Pipeline"))
-                {
-                    foreach (var pipeline in BatchCandidates)
-                    {
-                        ch.Info($"AutoInference Pipeline Id : {pipeline.UniqueId}");
-                        foreach (var transform in pipeline.Transforms)
-                        {
-                            ch.Info($"AutoInference Transform : {transform.Transform}");
-                        }
-                        ch.Info($"AutoInference Learner : {pipeline.Learner}");
-                    }
-                }
-
                 return BatchCandidates;
             }
 

src/Microsoft.ML.PipelineInference/AutoMlUtils.cs

@@ -38,11 +38,15 @@ public static double ExtractValueFromIdv(IHostEnvironment env, IDataView result,
             return outputValue;
         }
 
-        public static AutoInference.RunSummary ExtractRunSummary(IHostEnvironment env, IDataView result, string metricColumnName, IDataView trainResult = null)
+        public static PipelineSweeperRunSummary ExtractRunSummary(IHostEnvironment env, IDataView result, string metricColumnName, IDataView trainResult = null)
         {
+            Contracts.CheckValue(env, nameof(env));
+            env.CheckValue(result, nameof(result));
+            env.CheckNonEmpty(metricColumnName, nameof(metricColumnName));
+
             double testingMetricValue = ExtractValueFromIdv(env, result, metricColumnName);
             double trainingMetricValue = trainResult != null ? ExtractValueFromIdv(env, trainResult, metricColumnName)  : double.MinValue;
-            return new AutoInference.RunSummary(testingMetricValue, 0, 0, trainingMetricValue);
+            return new PipelineSweeperRunSummary(testingMetricValue, 0, 0, trainingMetricValue);
         }
 
         public static CommonInputs.IEvaluatorInput CloneEvaluatorInstance(CommonInputs.IEvaluatorInput evalInput) =>
@@ -566,14 +570,15 @@ private static ParameterSet ConvertToParameterSet(TlcModule.SweepableParamAttrib
             return learner.PipelineNode.HyperSweeperParamSet;
         }
 
-        public static IRunResult ConvertToRunResult(RecipeInference.SuggestedRecipe.SuggestedLearner learner,
-            AutoInference.RunSummary rs, bool isMetricMaximizing) =>
-                new RunResult(ConvertToParameterSet(learner.PipelineNode.SweepParams, learner), rs.MetricValue, isMetricMaximizing);
-
-        public static IRunResult[] ConvertToRunResults(PipelinePattern[] history, bool isMetricMaximizing) =>
-            history.Select(h =>
-                ConvertToRunResult(h.Learner, h.PerformanceSummary, isMetricMaximizing)).ToArray();
+        public static IRunResult ConvertToRunResult(RecipeInference.SuggestedRecipe.SuggestedLearner learner, PipelineSweeperRunSummary rs, bool isMetricMaximizing)
+        {
+            return new RunResult(ConvertToParameterSet(learner.PipelineNode.SweepParams, learner), rs.MetricValue, isMetricMaximizing);
+        }
 
+        public static IRunResult[] ConvertToRunResults(PipelinePattern[] history, bool isMetricMaximizing)
+        {
+            return history.Select(h => ConvertToRunResult(h.Learner, h.PerformanceSummary, isMetricMaximizing)).ToArray();
+        }
         /// <summary>
         /// Method to convert set of sweepable hyperparameters into strings of a format understood
         /// by the current smart hyperparameter sweepers.

src/Microsoft.ML.PipelineInference/Macros/PipelineSweeperMacro.cs

@@ -239,8 +239,7 @@ public static CommonOutputs.MacroOutput<Output> PipelineSweep(
                 if (node.Context.TryGetVariable(ExperimentUtils.GenerateOverallMetricVarName(pipeline.UniqueId), out var v) &&
                     node.Context.TryGetVariable(AutoMlUtils.GenerateOverallTrainingMetricVarName(pipeline.UniqueId), out var v2))
                 {
-                    pipeline.PerformanceSummary =
-                        AutoMlUtils.ExtractRunSummary(env, (IDataView)v.Value, autoMlState.Metric.Name, (IDataView)v2.Value);
+                    pipeline.PerformanceSummary = AutoMlUtils.ExtractRunSummary(env, (IDataView)v.Value, autoMlState.Metric.Name, (IDataView)v2.Value);
                     autoMlState.AddEvaluated(pipeline);
                 }
             }

src/Microsoft.ML.PipelineInference/PipelinePattern.cs

@@ -55,13 +55,13 @@ public PipelineResultRow(string graphJson, double metricValue,
         private readonly IHostEnvironment _env;
         public readonly TransformInference.SuggestedTransform[] Transforms;
         public readonly RecipeInference.SuggestedRecipe.SuggestedLearner Learner;
-        public AutoInference.RunSummary PerformanceSummary { get; set; }
+        public PipelineSweeperRunSummary PerformanceSummary { get; set; }
         public string LoaderSettings { get; set; }
         public Guid UniqueId { get; }
 
         public PipelinePattern(TransformInference.SuggestedTransform[] transforms,
             RecipeInference.SuggestedRecipe.SuggestedLearner learner,
-            string loaderSettings, IHostEnvironment env, AutoInference.RunSummary summary = null)
+            string loaderSettings, IHostEnvironment env, PipelineSweeperRunSummary summary = null)
         {
             // Make sure internal pipeline nodes and sweep params are cloned, not shared.
             // Cloning the transforms and learner rather than assigning outright
@@ -205,7 +205,7 @@ public Models.TrainTestEvaluator.Output AddAsTrainTest(Var<IDataView> trainData,
         /// Runs a train-test experiment on the current pipeline, through entrypoints.
         /// </summary>
         public void RunTrainTestExperiment(IDataView trainData, IDataView testData,
-            AutoInference.SupportedMetric metric, MacroUtils.TrainerKinds trainerKind, out double testMetricValue,
+            SupportedMetric metric, MacroUtils.TrainerKinds trainerKind, out double testMetricValue,
             out double trainMetricValue)
         {
             var experiment = CreateTrainTestExperiment(trainData, testData, trainerKind, true, out var trainTestOutput);

src/Microsoft.ML.PipelineInference/PipelineSweeperRunSummary.cs

@@ -0,0 +1,38 @@
+// 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 System;
+using System.Collections.Generic;
+using System.Diagnostics;
+using System.Linq;
+using System.Reflection;
+using Microsoft.ML.Runtime.CommandLine;
+using Microsoft.ML.Runtime.EntryPoints;
+using Microsoft.ML.Runtime.Data;
+using Microsoft.ML.Runtime.PipelineInference;
+using Microsoft.ML.Runtime.EntryPoints.JsonUtils;
+using Newtonsoft.Json.Linq;
+
+namespace Microsoft.ML.Runtime.PipelineInference
+{
+    /// <summary>
+    /// Class containing some information about an exectuted pipeline.
+    /// These are analogous to IRunResult for smart sweepers.
+    /// </summary>
+    public sealed class PipelineSweeperRunSummary
+    {
+        public double MetricValue { get; }
+        public double TrainingMetricValue { get; }
+        public int NumRowsInTraining { get; }
+        public long RunTimeMilliseconds { get; }
+
+        public PipelineSweeperRunSummary(double metricValue, int numRows, long runTimeMilliseconds, double trainingMetricValue)
+        {
+            MetricValue = metricValue;
+            TrainingMetricValue = trainingMetricValue;
+            NumRowsInTraining = numRows;
+            RunTimeMilliseconds = runTimeMilliseconds;
+        }
+    }
+}