First iteration (#2852)

Author: Ivanidzo4ka

docs/samples/Microsoft.ML.Samples/Dynamic/FeatureSelectionTransform.cs

@@ -55,7 +55,7 @@ public static void Example()
             // specify the parameter `numBins', which controls the number of bins used in the approximation of the mutual information
             // between features and label.
             var mutualInfoEst = ml.Transforms.FeatureSelection.SelectFeaturesBasedOnMutualInformation(
-                outputColumnName: "FeaturesMISelect", inputColumnName: "FeaturesCountSelect", labelColumn: "Label", slotsInOutput: 5);
+                outputColumnName: "FeaturesMISelect", inputColumnName: "FeaturesCountSelect", labelColumnName: "Label", slotsInOutput: 5);
 
             // Now, we can put the previous two transformations together in a pipeline.
             var pipeline = countSelectEst.Append(mutualInfoEst);

src/Microsoft.ML.Transforms/CountFeatureSelection.cs

@@ -10,7 +10,6 @@
 using Microsoft.ML;
 using Microsoft.ML.CommandLine;
 using Microsoft.ML.Data;
-using Microsoft.ML.EntryPoints;
 using Microsoft.ML.Internal.Utilities;
 using Microsoft.ML.Transforms.FeatureSelection;
 
@@ -54,23 +53,25 @@ public sealed class ColumnOptions
             public readonly string Name;
             /// <summary> Name of the column to transform.</summary>
             public readonly string InputColumnName;
-            /// <summary> If the count of non-default values for a slot is greater than or equal to this threshold in the training data, the slot is preserved.</summary>
-            public readonly long MinCount;
+            /// <summary>If the count of non-default values for a slot is greater than or equal to this threshold in the training data, the slot is preserved.</summary>
+            public readonly long Count;
 
             /// <summary>
             /// Describes the parameters of the feature selection process for a column pair.
             /// </summary>
             /// <param name="name">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
             /// <param name="inputColumnName">Name of the column to transform. If set to <see langword="null"/>, the value of the <paramref name="name"/> will be used as source.</param>
-            /// <param name="minCount">If the count of non-default values for a slot is greater than or equal to this threshold in the training data, the slot is preserved.</param>
-            public ColumnOptions(string name, string inputColumnName = null, long minCount = Defaults.Count)
+            /// <param name="count">If the count of non-default values for a slot is greater than or equal to this threshold in the training data, the slot is preserved.</param>
+
+            public ColumnOptions(string name, string inputColumnName = null, long count = Defaults.Count)
             {
                 Name = name;
                 Contracts.CheckValue(Name, nameof(Name));
 
                 InputColumnName = inputColumnName ?? name;
                 Contracts.CheckValue(InputColumnName, nameof(InputColumnName));
-                MinCount = minCount;
+                Contracts.CheckParam(count >= 0, nameof(count), "Must be non-negative.");
+                Count = count;
             }
         }
 
@@ -183,7 +184,7 @@ internal static IDataTransform Create(IHostEnvironment env, Options options, IDa
             host.CheckUserArg(Utils.Size(options.Columns) > 0, nameof(options.Columns));
             host.CheckUserArg(options.Count > 0, nameof(options.Count));
 
-            var columnOptions = options.Columns.Select(inColName => new ColumnOptions(inColName, minCount: options.Count)).ToArray();
+            var columnOptions = options.Columns.Select(inColName => new ColumnOptions(inColName, count: options.Count)).ToArray();
 
             return new CountFeatureSelectingEstimator(env, columnOptions).Fit(input).Transform(input) as IDataTransform;
         }
@@ -206,11 +207,11 @@ private static void CreateDropAndCopyColumns(ColumnOptions[] columnOptions, int
                 selectedCount[i] = 0;
                 for (int j = 0; j < score.Length; j++)
                 {
-                    if (score[j] < columnOptions[i].MinCount)
+                    if (score[j] < columnOptions[i].Count)
                     {
                         // Adjacent slots are combined into a single range.
                         int min = j;
-                        while (j < score.Length && score[j] < columnOptions[i].MinCount)
+                        while (j < score.Length && score[j] < columnOptions[i].Count)
                             j++;
                         int max = j - 1;
                         slots.Add((min, max));

src/Microsoft.ML.Transforms/FeatureSelectionCatalog.cs

@@ -14,9 +14,9 @@ public static class FeatureSelectionCatalog
     {
         /// <include file='doc.xml' path='doc/members/member[@name="MutualInformationFeatureSelection"]/*' />
         /// <param name="catalog">The transform's catalog.</param>
-        /// <param name="labelColumn">Name of the column to use for labels.</param>
+        /// <param name="labelColumnName">The name of the label column.</param>
         /// <param name="slotsInOutput">The maximum number of slots to preserve in the output. The number of slots to preserve is taken across all input columns.</param>
-        /// <param name="numBins">Max number of bins used to approximate mutual information between each input column and the label column. Power of 2 recommended.</param>
+        /// <param name="numberOfBins">Max number of bins used to approximate mutual information between each input column and the label column. Power of 2 recommended.</param>
         /// <param name="columns">Specifies the names of the input columns for the transformation, and their respective output column names.</param>
         /// <example>
         /// <format type="text/markdown">
@@ -26,20 +26,20 @@ public static class FeatureSelectionCatalog
         /// </format>
         /// </example>
         public static MutualInformationFeatureSelectingEstimator SelectFeaturesBasedOnMutualInformation(this TransformsCatalog.FeatureSelectionTransforms catalog,
-            string labelColumn = MutualInfoSelectDefaults.LabelColumn,
+            string labelColumnName = MutualInfoSelectDefaults.LabelColumn,
             int slotsInOutput = MutualInfoSelectDefaults.SlotsInOutput,
-            int numBins = MutualInfoSelectDefaults.NumBins,
+            int numberOfBins = MutualInfoSelectDefaults.NumBins,
             params ColumnOptions[] columns)
-            => new MutualInformationFeatureSelectingEstimator(CatalogUtils.GetEnvironment(catalog), labelColumn, slotsInOutput, numBins,
+            => new MutualInformationFeatureSelectingEstimator(CatalogUtils.GetEnvironment(catalog), labelColumnName, slotsInOutput, numberOfBins,
                 ColumnOptions.ConvertToValueTuples(columns));
 
         /// <include file='doc.xml' path='doc/members/member[@name="MutualInformationFeatureSelection"]/*' />
         /// <param name="catalog">The transform's catalog.</param>
         /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
         /// <param name="inputColumnName">Name of column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
-        /// <param name="labelColumn">Name of the column to use for labels.</param>
+        /// <param name="labelColumnName">The name of the label column.</param>
         /// <param name="slotsInOutput">The maximum number of slots to preserve in the output. The number of slots to preserve is taken across all input columns.</param>
-        /// <param name="numBins">Max number of bins used to approximate mutual information between each input column and the label column. Power of 2 recommended.</param>
+        /// <param name="numberOfBins">Max number of bins used to approximate mutual information between each input column and the label column. Power of 2 recommended.</param>
         /// <example>
         /// <format type="text/markdown">
         /// <![CDATA[
@@ -49,10 +49,10 @@ public static MutualInformationFeatureSelectingEstimator SelectFeaturesBasedOnMu
         /// </example>
         public static MutualInformationFeatureSelectingEstimator SelectFeaturesBasedOnMutualInformation(this TransformsCatalog.FeatureSelectionTransforms catalog,
             string outputColumnName, string inputColumnName = null,
-            string labelColumn = MutualInfoSelectDefaults.LabelColumn,
+            string labelColumnName = MutualInfoSelectDefaults.LabelColumn,
             int slotsInOutput = MutualInfoSelectDefaults.SlotsInOutput,
-            int numBins = MutualInfoSelectDefaults.NumBins)
-            => new MutualInformationFeatureSelectingEstimator(CatalogUtils.GetEnvironment(catalog), outputColumnName, inputColumnName, labelColumn, slotsInOutput, numBins);
+            int numberOfBins = MutualInfoSelectDefaults.NumBins)
+            => new MutualInformationFeatureSelectingEstimator(CatalogUtils.GetEnvironment(catalog), outputColumnName, inputColumnName, labelColumnName, slotsInOutput, numberOfBins);
 
         /// <include file='doc.xml' path='doc/members/member[@name="CountFeatureSelection"]' />
         /// <param name="catalog">The transform's catalog.</param>

src/Microsoft.ML.Transforms/MutualInformationFeatureSelection.cs

@@ -66,7 +66,7 @@ internal sealed class Options : TransformInputBase
         /// <param name="env">The environment to use.</param>
         /// <param name="labelColumn">Name of the column to use for labels.</param>
         /// <param name="slotsInOutput">The maximum number of slots to preserve in the output. The number of slots to preserve is taken across all input columns.</param>
-        /// <param name="numBins">Max number of bins used to approximate mutual information between each input column and the label column. Power of 2 recommended.</param>
+        /// <param name="numberOfBins">Max number of bins used to approximate mutual information between each input column and the label column. Power of 2 recommended.</param>
         /// <param name="columns">Specifies the names of the input columns for the transformation, and their respective output column names.</param>
         /// <example>
         /// <format type="text/markdown">
@@ -78,7 +78,7 @@ internal sealed class Options : TransformInputBase
         internal MutualInformationFeatureSelectingEstimator(IHostEnvironment env,
             string labelColumn = Defaults.LabelColumn,
             int slotsInOutput = Defaults.SlotsInOutput,
-            int numBins = Defaults.NumBins,
+            int numberOfBins = Defaults.NumBins,
             params (string outputColumnName, string inputColumnName)[] columns)
         {
             Contracts.CheckValue(env, nameof(env));
@@ -87,12 +87,12 @@ internal MutualInformationFeatureSelectingEstimator(IHostEnvironment env,
             _host.CheckUserArg(Utils.Size(columns) > 0, nameof(columns));
             _host.CheckUserArg(slotsInOutput > 0, nameof(slotsInOutput));
             _host.CheckNonWhiteSpace(labelColumn, nameof(labelColumn));
-            _host.Check(numBins > 1, "numBins must be greater than 1.");
+            _host.Check(numberOfBins > 1, "numBins must be greater than 1.");
 
             _columns = columns;
             _labelColumn = labelColumn;
             _slotsInOutput = slotsInOutput;
-            _numBins = numBins;
+            _numBins = numberOfBins;
         }
 
         /// <include file='doc.xml' path='doc/members/member[@name="MutualInformationFeatureSelection"]/*' />

test/Microsoft.ML.Tests/Transformers/FeatureSelectionTests.cs

@@ -42,7 +42,7 @@ public void FeatureSelectionWorkout()
             var est = new WordBagEstimator(ML, "bag_of_words", "text")
                 .AppendCacheCheckpoint(ML)
                 .Append(ML.Transforms.FeatureSelection.SelectFeaturesBasedOnCount("bag_of_words_count", "bag_of_words", 10)
-                .Append(ML.Transforms.FeatureSelection.SelectFeaturesBasedOnMutualInformation("bag_of_words_mi", "bag_of_words", labelColumn: "label")));
+                .Append(ML.Transforms.FeatureSelection.SelectFeaturesBasedOnMutualInformation("bag_of_words_mi", "bag_of_words", labelColumnName: "label")));
 
             var outputPath = GetOutputPath("FeatureSelection", "featureselection.tsv");
             using (var ch = Env.Start("save"))
@@ -115,11 +115,11 @@ public void CountFeatureSelectionWorkout()
             var data = ML.Data.Cache(reader.Load(new MultiFileSource(dataPath)).AsDynamic);
 
             var columns = new[] {
-                new CountFeatureSelectingEstimator.ColumnOptions("FeatureSelectDouble", "VectorDouble", minCount: 1),
-                new CountFeatureSelectingEstimator.ColumnOptions("ScalFeatureSelectMissing690", "ScalarFloat", minCount: 690),
-                new CountFeatureSelectingEstimator.ColumnOptions("ScalFeatureSelectMissing100", "ScalarFloat", minCount: 100),
-                new CountFeatureSelectingEstimator.ColumnOptions("VecFeatureSelectMissing690", "VectorDouble", minCount: 690),
-                new CountFeatureSelectingEstimator.ColumnOptions("VecFeatureSelectMissing100", "VectorDouble", minCount: 100)
+                new CountFeatureSelectingEstimator.ColumnOptions("FeatureSelectDouble", "VectorDouble", count: 1),
+                new CountFeatureSelectingEstimator.ColumnOptions("ScalFeatureSelectMissing690", "ScalarFloat", count: 690),
+                new CountFeatureSelectingEstimator.ColumnOptions("ScalFeatureSelectMissing100", "ScalarFloat", count: 100),
+                new CountFeatureSelectingEstimator.ColumnOptions("VecFeatureSelectMissing690", "VectorDouble", count: 690),
+                new CountFeatureSelectingEstimator.ColumnOptions("VecFeatureSelectMissing100", "VectorDouble", count: 100)
             };
             var est = ML.Transforms.FeatureSelection.SelectFeaturesBasedOnCount("FeatureSelect", "VectorFloat", count: 1)
                 .Append(ML.Transforms.FeatureSelection.SelectFeaturesBasedOnCount(columns));
@@ -182,8 +182,8 @@ public void MutualInformationSelectionWorkout()
 
             var data = reader.Load(new MultiFileSource(dataPath)).AsDynamic;
 
-            var est = ML.Transforms.FeatureSelection.SelectFeaturesBasedOnMutualInformation("FeatureSelect", "VectorFloat", slotsInOutput: 1, labelColumn: "Label")
-                .Append(ML.Transforms.FeatureSelection.SelectFeaturesBasedOnMutualInformation(labelColumn: "Label", slotsInOutput: 2, numBins: 100,
+            var est = ML.Transforms.FeatureSelection.SelectFeaturesBasedOnMutualInformation("FeatureSelect", "VectorFloat", slotsInOutput: 1, labelColumnName: "Label")
+                .Append(ML.Transforms.FeatureSelection.SelectFeaturesBasedOnMutualInformation(labelColumnName: "Label", slotsInOutput: 2, numberOfBins: 100,
                     columns: new ColumnOptions[] {
                         ("out1", "VectorFloat"),
                         ("out2", "VectorDouble")
@@ -220,7 +220,7 @@ public void TestMutualInformationOldSavingAndLoading()
 
             var dataView = reader.Load(new MultiFileSource(dataPath)).AsDynamic;
 
-            var pipe = ML.Transforms.FeatureSelection.SelectFeaturesBasedOnMutualInformation("FeatureSelect", "VectorFloat", slotsInOutput: 1, labelColumn: "Label");
+            var pipe = ML.Transforms.FeatureSelection.SelectFeaturesBasedOnMutualInformation("FeatureSelect", "VectorFloat", slotsInOutput: 1, labelColumnName: "Label");
 
             var result = pipe.Fit(dataView).Transform(dataView);
             var resultRoles = new RoleMappedData(result);