Rev public API (dotnet#163)

Author: daholste

src/Microsoft.ML.Auto/API/AutoFitSettings.cs

@@ -0,0 +1,69 @@
+// 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.
+
+namespace Microsoft.ML.Auto
+{
+    public class AutoInferenceCatalog
+    {
+        private readonly MLContext _context;
+
+        internal AutoInferenceCatalog(MLContext context)
+        {
+            _context = context;
+        }
+
+        public RegressionExperiment CreateRegressionExperiment(uint maxInferenceTimeInSeconds)
+        {
+            return new RegressionExperiment(_context, new RegressionExperimentSettings()
+            {
+                MaxInferenceTimeInSeconds = maxInferenceTimeInSeconds
+            });
+        }
+
+        public RegressionExperiment CreateRegressionExperiment(RegressionExperimentSettings experimentSettings)
+        {
+            return new RegressionExperiment(_context, experimentSettings);
+        }
+
+        public BinaryClassificationExperiment CreateBinaryClassificationExperiment(uint maxInferenceTimeInSeconds)
+        {
+            return new BinaryClassificationExperiment(_context, new BinaryExperimentSettings()
+            {
+                MaxInferenceTimeInSeconds = maxInferenceTimeInSeconds
+            });
+        }
+
+        public BinaryClassificationExperiment CreateBinaryClassificationExperiment(BinaryExperimentSettings experimentSettings)
+        {
+            return new BinaryClassificationExperiment(_context, experimentSettings);
+        }
+
+        public MulticlassClassificationExperiment CreateMulticlassClassificationExperiment(uint maxInferenceTimeInSeconds)
+        {
+            return new MulticlassClassificationExperiment(_context, new MulticlassExperimentSettings()
+            {
+                MaxInferenceTimeInSeconds = maxInferenceTimeInSeconds
+            });
+        }
+
+        public MulticlassClassificationExperiment CreateMulticlassClassificationExperiment(MulticlassExperimentSettings experimentSettings)
+        {
+            return new MulticlassClassificationExperiment(_context, experimentSettings);
+        }
+
+        public ColumnInferenceResults InferColumns(string path, string label,char? separatorChar = null, bool? allowQuotedStrings = null, 
+            bool? supportSparse = null, bool trimWhitespace = false, bool groupColumns = true)
+        {
+            //UserInputValidationUtil.ValidateInferColumnsArgs(path, label);
+            return ColumnInferenceApi.InferColumns(_context, path, label, separatorChar, allowQuotedStrings, supportSparse, trimWhitespace, groupColumns);
+        }
+
+        public ColumnInferenceResults InferColumns(string path, uint labelColumnIndex, bool hasHeader = false, char? separatorChar = null, 
+            bool? allowQuotedStrings = null, bool? supportSparse = null, bool trimWhitespace = false, bool groupColumns = true)
+        {
+            //UserInputValidationUtil.ValidateInferColumnsArgs(path);
+            return ColumnInferenceApi.InferColumns(_context, path, labelColumnIndex, hasHeader, separatorChar, allowQuotedStrings, supportSparse, trimWhitespace, groupColumns);
+        }
+    }
+}

src/Microsoft.ML.Auto/API/AutoInferenceCatalog.cs

@@ -0,0 +1,83 @@
+// 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.Linq;
+using Microsoft.Data.DataView;
+using Microsoft.ML.Core.Data;
+using Microsoft.ML.Data;
+
+namespace Microsoft.ML.Auto
+{
+    public class BinaryExperimentSettings : ExperimentSettings
+    {
+        public IProgress<RunResult<BinaryClassificationMetrics>> ProgressCallback;
+        public BinaryClassificationMetric OptimizingMetric;
+        public BinaryClassificationTrainer[] WhitelistedTrainers;
+    }
+
+    public enum BinaryClassificationMetric
+    {
+        Accuracy
+    }
+
+    public enum BinaryClassificationTrainer
+    {
+        LightGbm
+    }
+
+    public class BinaryClassificationExperiment
+    {
+        private readonly MLContext _context;
+        private readonly BinaryExperimentSettings _settings;
+
+        internal BinaryClassificationExperiment(MLContext context, BinaryExperimentSettings settings)
+        {
+            _context = context;
+            _settings = settings;
+        }
+
+        public IEnumerable<RunResult<BinaryClassificationMetrics>> Execute(IDataView trainData, ColumnInformation columnInformation = null, IEstimator<ITransformer> preFeaturizers = null)
+        {
+            return Execute(_context, trainData, columnInformation, null, preFeaturizers);
+        }
+
+        public IEnumerable<RunResult<BinaryClassificationMetrics>> Execute(IDataView trainData, IDataView validationData, ColumnInformation columnInformation = null, IEstimator<ITransformer> preFeaturizers = null)
+        {
+            return Execute(_context, trainData, columnInformation, validationData, preFeaturizers);
+        }
+
+        internal RunResult<BinaryClassificationMetrics> Execute(IDataView trainData, uint numberOfCVFolds, ColumnInformation columnInformation = null, IEstimator<ITransformer> preFeaturizers = null)
+        {
+            throw new NotImplementedException();
+        }
+
+        internal IEnumerable<RunResult<BinaryClassificationMetrics>> Execute(MLContext context,
+            IDataView trainData,
+            ColumnInformation columnInfo,
+            IDataView validationData = null,
+            IEstimator<ITransformer> preFeaturizers = null)
+        {
+            columnInfo = columnInfo ?? new ColumnInformation();
+            //UserInputValidationUtil.ValidateAutoFitArgs(trainData, labelColunName, validationData, settings, columnPurposes)
+
+            // run autofit & get all pipelines run in that process
+            var autoFitter = new AutoFitter<BinaryClassificationMetrics>(context, TaskKind.BinaryClassification, trainData, columnInfo, 
+                validationData, preFeaturizers, OptimizingMetric.Accuracy, _settings?.ProgressCallback,
+                _settings);
+
+            return autoFitter.Fit();
+        }
+    }
+
+    public static class BinaryExperimentResultExtensions
+    {
+        public static RunResult<BinaryClassificationMetrics> Best(this IEnumerable<RunResult<BinaryClassificationMetrics>> results)
+        {
+            double maxScore = results.Select(r => r.Metrics.Accuracy).Max();
+            return results.First(r => r.Metrics.Accuracy == maxScore);
+        }
+    }
+}

src/Microsoft.ML.Auto/API/BinaryClassificationExperiment.cs

@@ -0,0 +1,27 @@
+// 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.Collections.Generic;
+using Microsoft.ML.Data;
+
+namespace Microsoft.ML.Auto
+{
+    public class ColumnInferenceResults
+    {
+        public TextLoader.Arguments TextLoaderArgs { get; set; }
+        public ColumnInformation ColumnInformation { get; set; }
+    }
+
+    public class ColumnInformation
+    {
+        public string LabelColumn = DefaultColumnNames.Label;
+        public string NameColumn = DefaultColumnNames.Name;
+        public string GroupIdColumn = DefaultColumnNames.GroupId;
+        public string WeightColumn = DefaultColumnNames.Weight;
+        public IEnumerable<string> CategoricalColumns { get; set; }
+        public IEnumerable<string> NumericColumns { get; set; }
+        public IEnumerable<string> TextColumns { get; set; }
+        public IEnumerable<string> IgnoredColumns { get; set; }
+    }
+}