make AutoMLExperiment public && some small refactor

src/Microsoft.ML.AutoML/API/AutoCatalog.cs

@@ -7,6 +7,7 @@
 using Microsoft.ML.AutoML.CodeGen;
 using Microsoft.ML.Data;
 using Microsoft.ML.SearchSpace;
+using Microsoft.ML.Trainers.FastTree;
 
 namespace Microsoft.ML.AutoML
 {
@@ -286,18 +287,43 @@ public ColumnInferenceResults InferColumns(string path, uint labelColumnIndex, b
         /// <summary>
         /// Create a sweepable estimator with a custom factory and search space.
         /// </summary>
-        internal SweepableEstimator CreateSweepableEstimator<T>(Func<MLContext, T, IEstimator<ITransformer>> factory, SearchSpace<T> ss = null)
+        public SweepableEstimator CreateSweepableEstimator<T>(Func<MLContext, T, IEstimator<ITransformer>> factory, SearchSpace<T> ss = null)
             where T : class, new()
         {
             return new SweepableEstimator((MLContext context, Parameter param) => factory(context, param.AsType<T>()), ss);
         }
 
-        internal AutoMLExperiment CreateExperiment()
+        /// <summary>
+        /// Create an <see cref="AutoMLExperiment"/>.
+        /// </summary>
+        public AutoMLExperiment CreateExperiment()
         {
             return new AutoMLExperiment(_context, new AutoMLExperiment.AutoMLExperimentSettings());
         }
 
-        internal SweepableEstimator[] BinaryClassification(string labelColumnName = DefaultColumnNames.Label, string featureColumnName = DefaultColumnNames.Features, string exampleWeightColumnName = null, bool useFastForest = true, bool useLgbm = true, bool useFastTree = true, bool useLbfgs = true, bool useSdca = true,
+        /// <summary>
+        /// Create a list of <see cref="SweepableEstimator"/> for binary classification.
+        /// </summary>
+        /// <param name="labelColumnName">label column name.</param>
+        /// <param name="featureColumnName">feature column name.</param>
+        /// <param name="exampleWeightColumnName">example weight column name.</param>
+        /// <param name="useFastForest">true if use fast forest as available trainer.</param>
+        /// <param name="useLgbm">true if use lgbm as available trainer.</param>
+        /// <param name="useFastTree">true if use fast tree as available trainer.</param>
+        /// <param name="useLbfgs">true if use lbfgs as available trainer.</param>
+        /// <param name="useSdca">true if use sdca as available trainer.</param>
+        /// <param name="fastTreeOption">if provided, use it as initial option for fast tree, otherwise the default option will be used.</param>
+        /// <param name="lgbmOption">if provided, use it as initial option for lgbm, otherwise the default option will be used.</param>
+        /// <param name="fastForestOption">if provided, use it as initial option for fast forest, otherwise the default option will be used.</param>
+        /// <param name="lbfgsOption">if provided, use it as initial option for lbfgs, otherwise the default option will be used.</param>
+        /// <param name="sdcaOption">if provided, use it as initial option for sdca, otherwise the default option will be used.</param>
+        /// <param name="fastTreeSearchSpace">if provided, use it as search space for fast tree, otherwise the default search space will be used.</param>
+        /// <param name="lgbmSearchSpace">if provided, use it as search space for lgbm, otherwise the default search space will be used.</param>
+        /// <param name="fastForestSearchSpace">if provided, use it as search space for fast forest, otherwise the default search space will be used.</param>
+        /// <param name="lbfgsSearchSpace">if provided, use it as search space for lbfgs, otherwise the default search space will be used.</param>
+        /// <param name="sdcaSearchSpace">if provided, use it as search space for sdca, otherwise the default search space will be used.</param>
+        /// <returns></returns>
+        public SweepableEstimator[] BinaryClassification(string labelColumnName = DefaultColumnNames.Label, string featureColumnName = DefaultColumnNames.Features, string exampleWeightColumnName = null, bool useFastForest = true, bool useLgbm = true, bool useFastTree = true, bool useLbfgs = true, bool useSdca = true,
             FastTreeOption fastTreeOption = null, LgbmOption lgbmOption = null, FastForestOption fastForestOption = null, LbfgsOption lbfgsOption = null, SdcaOption sdcaOption = null,
             SearchSpace<FastTreeOption> fastTreeSearchSpace = null, SearchSpace<LgbmOption> lgbmSearchSpace = null, SearchSpace<FastForestOption> fastForestSearchSpace = null, SearchSpace<LbfgsOption> lbfgsSearchSpace = null, SearchSpace<SdcaOption> sdcaSearchSpace = null)
         {
@@ -351,7 +377,29 @@ internal SweepableEstimator[] BinaryClassification(string labelColumnName = Defa
             return res.ToArray();
         }
 
-        internal SweepableEstimator[] MultiClassification(string labelColumnName = DefaultColumnNames.Label, string featureColumnName = DefaultColumnNames.Features, string exampleWeightColumnName = null, bool useFastForest = true, bool useLgbm = true, bool useFastTree = true, bool useLbfgs = true, bool useSdca = true,
+        /// <summary>
+        /// Create a list of <see cref="SweepableEstimator"/> for multiclass classification.
+        /// </summary>
+        /// <param name="labelColumnName">label column name.</param>
+        /// <param name="featureColumnName">feature column name.</param>
+        /// <param name="exampleWeightColumnName">example weight column name.</param>
+        /// <param name="useFastForest">true if use fast forest as available trainer.</param>
+        /// <param name="useLgbm">true if use lgbm as available trainer.</param>
+        /// <param name="useFastTree">true if use fast tree as available trainer.</param>
+        /// <param name="useLbfgs">true if use lbfgs as available trainer.</param>
+        /// <param name="useSdca">true if use sdca as available trainer.</param>
+        /// <param name="fastTreeOption">if provided, use it as initial option for fast tree, otherwise the default option will be used.</param>
+        /// <param name="lgbmOption">if provided, use it as initial option for lgbm, otherwise the default option will be used.</param>
+        /// <param name="fastForestOption">if provided, use it as initial option for fast forest, otherwise the default option will be used.</param>
+        /// <param name="lbfgsOption">if provided, use it as initial option for lbfgs, otherwise the default option will be used.</param>
+        /// <param name="sdcaOption">if provided, use it as initial option for sdca, otherwise the default option will be used.</param>
+        /// <param name="fastTreeSearchSpace">if provided, use it as search space for fast tree, otherwise the default search space will be used.</param>
+        /// <param name="lgbmSearchSpace">if provided, use it as search space for lgbm, otherwise the default search space will be used.</param>
+        /// <param name="fastForestSearchSpace">if provided, use it as search space for fast forest, otherwise the default search space will be used.</param>
+        /// <param name="lbfgsSearchSpace">if provided, use it as search space for lbfgs, otherwise the default search space will be used.</param>
+        /// <param name="sdcaSearchSpace">if provided, use it as search space for sdca, otherwise the default search space will be used.</param>
+        /// <returns></returns>
+        public SweepableEstimator[] MultiClassification(string labelColumnName = DefaultColumnNames.Label, string featureColumnName = DefaultColumnNames.Features, string exampleWeightColumnName = null, bool useFastForest = true, bool useLgbm = true, bool useFastTree = true, bool useLbfgs = true, bool useSdca = true,
             FastTreeOption fastTreeOption = null, LgbmOption lgbmOption = null, FastForestOption fastForestOption = null, LbfgsOption lbfgsOption = null, SdcaOption sdcaOption = null,
             SearchSpace<FastTreeOption> fastTreeSearchSpace = null, SearchSpace<LgbmOption> lgbmSearchSpace = null, SearchSpace<FastForestOption> fastForestSearchSpace = null, SearchSpace<LbfgsOption> lbfgsSearchSpace = null, SearchSpace<SdcaOption> sdcaSearchSpace = null)
         {
@@ -407,7 +455,29 @@ internal SweepableEstimator[] MultiClassification(string labelColumnName = Defau
             return res.ToArray();
         }
 
-        internal SweepableEstimator[] Regression(string labelColumnName = DefaultColumnNames.Label, string featureColumnName = DefaultColumnNames.Features, string exampleWeightColumnName = null, bool useFastForest = true, bool useLgbm = true, bool useFastTree = true, bool useLbfgs = true, bool useSdca = true,
+        /// <summary>
+        /// Create a list of <see cref="SweepableEstimator"/> for regression.
+        /// </summary>
+        /// <param name="labelColumnName">label column name.</param>
+        /// <param name="featureColumnName">feature column name.</param>
+        /// <param name="exampleWeightColumnName">example weight column name.</param>
+        /// <param name="useFastForest">true if use fast forest as available trainer.</param>
+        /// <param name="useLgbm">true if use lgbm as available trainer.</param>
+        /// <param name="useFastTree">true if use fast tree as available trainer.</param>
+        /// <param name="useLbfgs">true if use lbfgs as available trainer.</param>
+        /// <param name="useSdca">true if use sdca as available trainer.</param>
+        /// <param name="fastTreeOption">if provided, use it as initial option for fast tree, otherwise the default option will be used.</param>
+        /// <param name="lgbmOption">if provided, use it as initial option for lgbm, otherwise the default option will be used.</param>
+        /// <param name="fastForestOption">if provided, use it as initial option for fast forest, otherwise the default option will be used.</param>
+        /// <param name="lbfgsOption">if provided, use it as initial option for lbfgs, otherwise the default option will be used.</param>
+        /// <param name="sdcaOption">if provided, use it as initial option for sdca, otherwise the default option will be used.</param>
+        /// <param name="fastTreeSearchSpace">if provided, use it as search space for fast tree, otherwise the default search space will be used.</param>
+        /// <param name="lgbmSearchSpace">if provided, use it as search space for lgbm, otherwise the default search space will be used.</param>
+        /// <param name="fastForestSearchSpace">if provided, use it as search space for fast forest, otherwise the default search space will be used.</param>
+        /// <param name="lbfgsSearchSpace">if provided, use it as search space for lbfgs, otherwise the default search space will be used.</param>
+        /// <param name="sdcaSearchSpace">if provided, use it as search space for sdca, otherwise the default search space will be used.</param>
+        /// <returns></returns>
+        public SweepableEstimator[] Regression(string labelColumnName = DefaultColumnNames.Label, string featureColumnName = DefaultColumnNames.Features, string exampleWeightColumnName = null, bool useFastForest = true, bool useLgbm = true, bool useFastTree = true, bool useLbfgs = true, bool useSdca = true,
             FastTreeOption fastTreeOption = null, LgbmOption lgbmOption = null, FastForestOption fastForestOption = null, LbfgsOption lbfgsOption = null, SdcaOption sdcaOption = null,
             SearchSpace<FastTreeOption> fastTreeSearchSpace = null, SearchSpace<LgbmOption> lgbmSearchSpace = null, SearchSpace<FastForestOption> fastForestSearchSpace = null, SearchSpace<LbfgsOption> lbfgsSearchSpace = null, SearchSpace<SdcaOption> sdcaSearchSpace = null)
         {

src/Microsoft.ML.AutoML/API/SweepableExtension.cs

@@ -4,7 +4,7 @@
 
 namespace Microsoft.ML.AutoML
 {
-    internal static class SweepableExtension
+    public static class SweepableExtension
     {
         public static SweepableEstimatorPipeline Append(this IEstimator<ITransformer> estimator, SweepableEstimator estimator1)
         {

src/Microsoft.ML.AutoML/AutoMLExperiment/AutoMLExperiment.cs

@@ -12,7 +12,7 @@
 
 namespace Microsoft.ML.AutoML
 {
-    internal class AutoMLExperiment
+    public class AutoMLExperiment
     {
         private readonly AutoMLExperimentSettings _settings;
         private readonly MLContext _context;
@@ -52,12 +52,14 @@ public AutoMLExperiment SetTrainingTimeInSeconds(uint trainingTimeInSeconds)
 
         public AutoMLExperiment SetDataset(IDataView train, IDataView test)
         {
-            _settings.DatasetSettings = new TrainTestDatasetSettings()
+            var datasetManager = new TrainTestDatasetManager()
             {
                 TrainDataset = train,
                 TestDataset = test
             };
 
+            _serviceCollection.AddSingleton<IDatasetManager>(datasetManager);
+
             return this;
         }
 
@@ -70,12 +72,14 @@ public AutoMLExperiment SetDataset(TrainTestData trainTestSplit)
 
         public AutoMLExperiment SetDataset(IDataView dataset, int fold = 10)
         {
-            _settings.DatasetSettings = new CrossValidateDatasetSettings()
+            var datasetManager = new CrossValidateDatasetManager()
             {
                 Dataset = dataset,
                 Fold = fold,
             };
 
+            _serviceCollection.AddSingleton<IDatasetManager>(datasetManager);
+
             return this;
         }
 
@@ -116,8 +120,15 @@ public AutoMLExperiment SetPipeline(MultiModelPipeline pipeline)
             return this;
         }
 
-        public AutoMLExperiment SetTrialRunnerFactory(ITrialRunnerFactory factory)
+        public AutoMLExperiment SetIsMaximizeMetric(bool isMaximize)
         {
+            _settings.IsMaximizeMetric = isMaximize;
+            return this;
+        }
+
+        public AutoMLExperiment SetTrialRunner(ITrialRunner runner)
+        {
+            var factory = new CustomRunnerFactory(runner);
             var descriptor = new ServiceDescriptor(typeof(ITrialRunnerFactory), factory);
             if (_serviceCollection.Contains(descriptor))
             {
@@ -146,36 +157,42 @@ public AutoMLExperiment SetPipeline(SweepableEstimatorPipeline pipeline)
 
         public AutoMLExperiment SetEvaluateMetric(BinaryClassificationMetric metric, string labelColumn = "label", string predictedColumn = "Predicted")
         {
-            _settings.EvaluateMetric = new BinaryMetricSettings()
+            var metricManager = new BinaryMetricManager()
             {
                 Metric = metric,
                 PredictedColumn = predictedColumn,
                 LabelColumn = labelColumn,
             };
+            _serviceCollection.AddSingleton<IMetricManager>(metricManager);
+            this.SetIsMaximizeMetric(metricManager.IsMaximize);
 
             return this;
         }
 
         public AutoMLExperiment SetEvaluateMetric(MulticlassClassificationMetric metric, string labelColumn = "label", string predictedColumn = "Predicted")
         {
-            _settings.EvaluateMetric = new MultiClassMetricSettings()
+            var metricManager = new MultiClassMetricManager()
             {
                 Metric = metric,
                 PredictedColumn = predictedColumn,
                 LabelColumn = labelColumn,
             };
+            _serviceCollection.AddSingleton<IMetricManager>(metricManager);
+            this.SetIsMaximizeMetric(metricManager.IsMaximize);
 
             return this;
         }
 
         public AutoMLExperiment SetEvaluateMetric(RegressionMetric metric, string labelColumn = "label", string scoreColumn = "Score")
         {
-            _settings.EvaluateMetric = new RegressionMetricSettings()
+            var metricManager = new RegressionMetricManager()
             {
                 Metric = metric,
                 ScoreColumn = scoreColumn,
                 LabelColumn = labelColumn,
             };
+            _serviceCollection.AddSingleton<IMetricManager>(metricManager);
+            this.SetIsMaximizeMetric(metricManager.IsMaximize);
 
             return this;
         }
@@ -224,13 +241,13 @@ private async Task<TrialResult> RunAsync(CancellationToken ct)
                     setting = pipelineProposer.Propose(setting);
                     setting = hyperParameterProposer.Propose(setting);
                     monitor.ReportRunningTrial(setting);
-                    var runner = runnerFactory.CreateTrialRunner(setting);
-                    var trialResult = runner.Run(setting);
+                    var runner = runnerFactory.CreateTrialRunner();
+                    var trialResult = runner.Run(setting, serviceProvider);
                     monitor.ReportCompletedTrial(trialResult);
                     hyperParameterProposer.Update(setting, trialResult);
                     pipelineProposer.Update(setting, trialResult);
 
-                    var error = _settings.EvaluateMetric.IsMaximize ? 1 - trialResult.Metric : trialResult.Metric;
+                    var error = _settings.IsMaximizeMetric ? 1 - trialResult.Metric : trialResult.Metric;
                     if (error < _bestError)
                     {
                         _bestTrialResult = trialResult;
@@ -264,20 +281,16 @@ private async Task<TrialResult> RunAsync(CancellationToken ct)
         private void ValidateSettings()
         {
             Contracts.Assert(_settings.MaxExperimentTimeInSeconds > 0, $"{nameof(ExperimentSettings.MaxExperimentTimeInSeconds)} must be larger than 0");
-            Contracts.Assert(_settings.DatasetSettings != null, $"{nameof(_settings.DatasetSettings)} must be not null");
-            Contracts.Assert(_settings.EvaluateMetric != null, $"{nameof(_settings.EvaluateMetric)} must be not null");
         }
 
 
         public class AutoMLExperimentSettings : ExperimentSettings
         {
-            public IDatasetSettings DatasetSettings { get; set; }
-
-            public IMetricSettings EvaluateMetric { get; set; }
-
             public MultiModelPipeline Pipeline { get; set; }
 
             public int? Seed { get; set; }
+
+            public bool IsMaximizeMetric { get; set; }
         }
     }
 }

src/Microsoft.ML.AutoML/AutoMLExperiment/IDatasetSettings.cs → src/Microsoft.ML.AutoML/AutoMLExperiment/IDatasetManager.cs

@@ -4,18 +4,18 @@
 
 namespace Microsoft.ML.AutoML
 {
-    internal interface IDatasetSettings
+    public interface IDatasetManager
     {
     }
 
-    internal class TrainTestDatasetSettings : IDatasetSettings
+    public class TrainTestDatasetManager : IDatasetManager
     {
         public IDataView TrainDataset { get; set; }
 
         public IDataView TestDataset { get; set; }
     }
 
-    internal class CrossValidateDatasetSettings : IDatasetSettings
+    public class CrossValidateDatasetManager : IDatasetManager
     {
         public IDataView Dataset { get; set; }
 

src/Microsoft.ML.AutoML/AutoMLExperiment/IMetricSettings.cs → src/Microsoft.ML.AutoML/AutoMLExperiment/IMetricManager.cs

@@ -6,12 +6,12 @@
 
 namespace Microsoft.ML.AutoML
 {
-    internal interface IMetricSettings
+    internal interface IMetricManager
     {
         bool IsMaximize { get; }
     }
 
-    internal class BinaryMetricSettings : IMetricSettings
+    internal class BinaryMetricManager : IMetricManager
     {
         public BinaryClassificationMetric Metric { get; set; }
 
@@ -33,7 +33,7 @@ internal class BinaryMetricSettings : IMetricSettings
         };
     }
 
-    internal class MultiClassMetricSettings : IMetricSettings
+    internal class MultiClassMetricManager : IMetricManager
     {
         public MulticlassClassificationMetric Metric { get; set; }
 
@@ -52,7 +52,7 @@ internal class MultiClassMetricSettings : IMetricSettings
         };
     }
 
-    internal class RegressionMetricSettings : IMetricSettings
+    internal class RegressionMetricManager : IMetricManager
     {
         public RegressionMetric Metric { get; set; }
 

src/Microsoft.ML.AutoML/AutoMLExperiment/IMonitor.cs

@@ -8,7 +8,7 @@
 
 namespace Microsoft.ML.AutoML
 {
-    internal interface IMonitor
+    public interface IMonitor
     {
         void ReportCompletedTrial(TrialResult result);
 

src/Microsoft.ML.AutoML/AutoMLExperiment/TrialResult.cs

@@ -4,14 +4,16 @@
 
 namespace Microsoft.ML.AutoML
 {
-    internal class TrialResult
+    public class TrialResult
     {
         public TrialSettings TrialSettings { get; set; }
 
         public ITransformer Model { get; set; }
 
         public double Metric { get; set; }
 
+        public bool IsMaximize { get; set; }
+
         public double DurationInMilliseconds { get; set; }
     }
 }