Adding documentation about the rest of the classes involved on generating the CSharpAPI

src/Microsoft.ML.Core/Utilities/ReservoirSampler.cs

@@ -47,7 +47,7 @@ public interface IReservoirSampler<T>
  /// This class produces a sample without replacement from a stream of data of type <typeparamref name="T"/>. 
  /// It is instantiated with a delegate that gets the next data point, and builds a reservoir in one pass by calling <see cref="Sample"/> 
  /// for every data point in the stream. In case the next data point does not get 'picked' into the reservoir, the delegate is not invoked.
- /// Sampling is done according to the algorithm in this paper: <see href="http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53"/>.
+ /// Sampling is done according to the algorithm in this paper: <a href="http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53">http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53</a>.
  /// </summary>
  public sealed class ReservoirSamplerWithoutReplacement<T> : IReservoirSampler<T>
  {
@@ -120,7 +120,7 @@ public IEnumerable<T> GetSample()
  /// This class produces a sample with replacement from a stream of data of type <typeparamref name="T"/>. 
  /// It is instantiated with a delegate that gets the next data point, and builds a reservoir in one pass by calling <see cref="Sample"/> 
  /// for every data point in the stream. In case the next data point does not get 'picked' into the reservoir, the delegate is not invoked.
- /// Sampling is done according to the algorithm in this paper: <see href="http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53"/>.
+ /// Sampling is done according to the algorithm in this paper: <a href="http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53">http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53</a>.
  /// </summary>
  public sealed class ReservoirSamplerWithReplacement<T> : IReservoirSampler<T>
  {

src/Microsoft.ML.Data/Evaluators/AucAggregator.cs

@@ -408,7 +408,7 @@ public UnweightedAuPrcAggregator(IRandom rand, int reservoirSize)
 
  /// <summary>
  /// Compute the AUPRC using the "lower trapesoid" estimator, as described in the paper
- /// <see href="http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf"/>.
+ /// <a href="http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf">http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf</a>.
  /// </summary>
  protected override Double ComputeWeightedAuPrcCore(out Double unweighted)
  {
@@ -482,7 +482,7 @@ public WeightedAuPrcAggregator(IRandom rand, int reservoirSize)
 
  /// <summary>
  /// Compute the AUPRC using the "lower trapesoid" estimator, as described in the paper
- /// <see href="http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf"/>.
+ /// <a href="http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf">http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf</a>.
  /// </summary>
  protected override Double ComputeWeightedAuPrcCore(out Double unweighted)
  {

src/Microsoft.ML.Data/Transforms/NAFilter.cs

@@ -26,6 +26,7 @@
 
 namespace Microsoft.ML.Runtime.Data
 {
+ /// <include file='doc.xml' path='doc/members/member[@name="NAFilter"]'/>
  public sealed class NAFilter : FilterBase
  {
  private static class Defaults

src/Microsoft.ML.Data/Transforms/TermTransform.cs

@@ -29,14 +29,14 @@
 
 namespace Microsoft.ML.Runtime.Data
 {
- /// <summary>
- /// TermTransform builds up term vocabularies (dictionaries).
- /// Notes:
- /// * Each column builds/uses exactly one "vocabulary" (dictionary).
- /// * Output columns are KeyType-valued.
- /// * The Key value is the one-based index of the item in the dictionary.
- /// * Not found is assigned the value zero.
- /// </summary>
+
+ // TermTransform builds up term vocabularies (dictionaries).
+ // Notes:
+ // * Each column builds/uses exactly one "vocabulary" (dictionary).
+ // * Output columns are KeyType-valued.
+ // * The Key value is the one-based index of the item in the dictionary.
+ // * Not found is assigned the value zero.
+ /// <include file='doc.xml' path='doc/members/member[@name="TextToKey"]/*' />
  public sealed partial class TermTransform : OneToOneTransformBase, ITransformTemplate
  {
  public abstract class ColumnBase : OneToOneColumn

src/Microsoft.ML.Data/Transforms/doc.xml

@@ -0,0 +1,54 @@
+<?xml version="1.0" encoding="utf-8" ?>
+<doc>
+ <members>
+ <member name="NAFilter">
+ <summary>
+ Removes missing values from vector type columns.
+ </summary>
+ <remarks>
+ This transform removes the entire row if any of the input columns have a missing value in that row.
+ This preprocessing is required for many ML algorithms that cannot work with missing values.
+ Useful if any missing entry invalidates the entire row.
+ If the <see cref="Microsoft.ML.Runtime.Data.NAFilter.Defaults.Complement"/> is set to true, this transform would do the exact opposite,
+ it will keep only the rows that have missing values.
+ </remarks>
+ <seealso cref="Microsoft.ML.Runtime.Data.MetadataUtils.Kinds.HasMissingValues"></seealso>
+ </member>
+ <example name="NAFilter">
+ <example>
+ <code language="csharp">
+ pipeline.Add(new MissingValuesRowDropper(&quot;Column1&quot;));
+ </code>
+ </example>
+ </example>
+
+ <member name="TextToKey">
+ <summary>
+ Converts input values (words, numbers, etc.) to index in a dictionary.
+ </summary>
+ <remarks>
+ The TextToKeyConverter transform builds up term vocabularies (dictionaries).
+ The TextToKey Converter and the <see cref="T:Microsoft.ML.Transforms.HashConverter"/> are the two one primary mechanisms by which raw input is transformed into keys.
+ If multiple columns are used, each column builds/uses exactly one vocabulary.
+ The output columns are KeyType-valued.
+ The Key value is the one-based index of the item in the dictionary.
+ If the key is not found in the dictionary, it is assigned the missing value indicator.
+ This dictionary mapping values to keys is most commonly learnt from the unique values in input data,
+ but can be defined through other means: either with the mapping defined directly on the command line, or as loaded from an external file.
+ </remarks>
+ <seealso cref="T:Microsoft.ML.Transforms.HashConverter"/>
+ <seealso cref="T:Microsoft.ML.Transforms.KeyToTextConverter"/>
+ </member>
+ <example name="TextToKey">
+ <example>
+ <code language="csharp">
+ pipeline.Add(new TextToKeyConverter((&quot;Column&quot;, &quot;OutColumn&quot;))
+ { 
+ Sort = TermTransformSortOrder.Occurrence 
+ });
+ </code>
+ </example>
+ </example>
+
+ </members>
+</doc>

src/Microsoft.ML.FastTree/FastTreeArguments.cs

@@ -20,7 +20,7 @@ public interface IFastTreeTrainerFactory : IComponentFactory<ITrainer>
  {
  }
 
- /// <include file='./doc.xml' path='docs/members/member[@name="FastTree"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastTree"]/*' />
  public sealed partial class FastTreeBinaryClassificationTrainer
  {
  [TlcModule.Component(Name = LoadNameValue, FriendlyName = UserNameValue, Desc = Summary)]

src/Microsoft.ML.FastTree/FastTreeClassification.cs

@@ -100,7 +100,7 @@ public static IPredictorProducing<Float> Create(IHostEnvironment env, ModelLoadC
  public override PredictionKind PredictionKind => PredictionKind.BinaryClassification;
  }
 
- /// <include file = './doc.xml' path='docs/members/member[@name="FastTree"]/*' />
+ /// <include file = 'doc.xml' path='doc/members/member[@name="FastTree"]/*' />
  public sealed partial class FastTreeBinaryClassificationTrainer :
  BoostingFastTreeTrainerBase<FastTreeBinaryClassificationTrainer.Arguments, IPredictorWithFeatureWeights<Float>>
  {
@@ -342,7 +342,8 @@ public static partial class FastTree
  Desc = FastTreeBinaryClassificationTrainer.Summary,
  UserName = FastTreeBinaryClassificationTrainer.UserNameValue,
  ShortName = FastTreeBinaryClassificationTrainer.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastTree""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastTree""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastTreeBinaryClassifier""]/*' />" })]
  public static CommonOutputs.BinaryClassificationOutput TrainBinary(IHostEnvironment env, FastTreeBinaryClassificationTrainer.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/FastTreeRanking.cs

@@ -38,7 +38,7 @@
 
 namespace Microsoft.ML.Runtime.FastTree
 {
- /// <include file='./doc.xml' path='docs/members/member[@name="FastTree"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastTree"]/*' />
  public sealed partial class FastTreeRankingTrainer : BoostingFastTreeTrainerBase<FastTreeRankingTrainer.Arguments, FastTreeRankingPredictor>,
  IHasLabelGains
  {
@@ -1096,7 +1096,8 @@ public static partial class FastTree
  Desc = FastTreeRankingTrainer.Summary,
  UserName = FastTreeRankingTrainer.UserNameValue,
  ShortName = FastTreeRankingTrainer.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastTree""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastTree""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastTreeRanker""]/*' />"})]
  public static CommonOutputs.RankingOutput TrainRanking(IHostEnvironment env, FastTreeRankingTrainer.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/FastTreeRegression.cs

@@ -31,7 +31,7 @@
 
 namespace Microsoft.ML.Runtime.FastTree
 {
- /// <include file='./doc.xml' path='docs/members/member[@name="FastTree"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastTree"]/*' />
  public sealed partial class FastTreeRegressionTrainer : BoostingFastTreeTrainerBase<FastTreeRegressionTrainer.Arguments, FastTreeRegressionPredictor>
  {
  public const string LoadNameValue = "FastTreeRegression";
@@ -445,7 +445,8 @@ public static partial class FastTree
  Desc = FastTreeRegressionTrainer.Summary,
  UserName = FastTreeRegressionTrainer.UserNameValue,
  ShortName = FastTreeRegressionTrainer.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastTree""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastTree""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastTreeRegressor""]/*' />"})]
  public static CommonOutputs.RegressionOutput TrainRegression(IHostEnvironment env, FastTreeRegressionTrainer.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/FastTreeTweedie.cs

@@ -30,7 +30,7 @@ namespace Microsoft.ML.Runtime.FastTree
  // The Tweedie boosting model follows the mathematics established in:
  // Yang, Quan, and Zou. "Insurance Premium Prediction via Gradient Tree-Boosted Tweedie Compound Poisson Models."
  // https://arxiv.org/pdf/1508.06378.pdf
- /// <include file='./doc.xml' path='docs/members/member[@name="FastTreeTweedieRegression"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastTreeTweedieRegression"]/*' />
  public sealed partial class FastTreeTweedieTrainer : BoostingFastTreeTrainerBase<FastTreeTweedieTrainer.Arguments, FastTreeTweediePredictor>
  {
  public const string LoadNameValue = "FastTreeTweedieRegression";
@@ -454,7 +454,7 @@ public static partial class FastTree
  Desc = FastTreeTweedieTrainer.Summary,
  UserName = FastTreeTweedieTrainer.UserNameValue,
  ShortName = FastTreeTweedieTrainer.ShortName,
- XmlInclude = new [] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastTreeTweedieRegression""]/*' />" })]
+ XmlInclude = new [] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastTreeTweedieRegression""]/*' />" })]
  public static CommonOutputs.RegressionOutput TrainTweedieRegression(IHostEnvironment env, FastTreeTweedieTrainer.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/RandomForestClassification.cs

@@ -106,7 +106,7 @@ public static IPredictorProducing<Float> Create(IHostEnvironment env, ModelLoadC
  }
  }
 
- /// <include file='./doc.xml' path='docs/members/member[@name="FastForest"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastForest"]/*' />
  public sealed partial class FastForestClassification :
  RandomForestTrainerBase<FastForestClassification.Arguments, IPredictorWithFeatureWeights<Float>>
  {
@@ -206,7 +206,8 @@ public static partial class FastForest
  Desc = FastForestClassification.Summary,
  UserName = FastForestClassification.UserNameValue,
  ShortName = FastForestClassification.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastForest""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastForest""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastForestBinaryClassifier""]/*' />"})]
  public static CommonOutputs.BinaryClassificationOutput TrainBinary(IHostEnvironment env, FastForestClassification.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/RandomForestRegression.cs

@@ -137,7 +137,7 @@ public ISchemaBindableMapper CreateMapper(Double[] quantiles)
  }
  }
 
- /// <include file='./doc.xml' path='docs/members/member[@name="FastForest"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastForest"]/*' />
  public sealed partial class FastForestRegression : RandomForestTrainerBase<FastForestRegression.Arguments, FastForestRegressionPredictor>
  {
  public sealed class Arguments : FastForestArgumentsBase
@@ -277,7 +277,8 @@ public static partial class FastForest
  Desc = FastForestRegression.Summary,
  UserName = FastForestRegression.LoadNameValue,
  ShortName = FastForestRegression.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastForest""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastForest""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastForestRegressor""]/*' />"})]
  public static CommonOutputs.RegressionOutput TrainRegression(IHostEnvironment env, FastForestRegression.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/Training/Parallel/IParallelTraining.cs

@@ -33,20 +33,20 @@ public delegate void FindBestThresholdFromRawArrayFun(LeafSplitCandidates leafSp
  /// <summary>
  /// Interface used for parallel training.
  /// Mainly contains three parts:
- /// 1. interactive with IO: <see href="GetLocalBinConstructionFeatures" />, <see href="SyncGlobalBoundary" />.
+ /// 1. interactive with IO: <see cref="GetLocalBinConstructionFeatures" />, <see cref="SyncGlobalBoundary" />.
  /// Data will be partitioned by rows in Data parallel and Voting Parallel.
  /// To speed up the find bin process, it let different workers to find bins for different features.
  /// Then perform global sync up.
  /// In Feature parallel, every machines holds all data, so this is unneeded.
- /// 2. interactive with TreeLearner: <see href="InitIteration" />, <see href="CacheHistogram" />, <see href="IsNeedFindLocalBestSplit" />, 
- /// <see href="IsSkipNonSplittableHistogram" />, <see href="FindGlobalBestSplit" />, <see href="GetGlobalDataCountInLeaf" />, <see href="PerformGlobalSplit" />.
+ /// 2. interactive with TreeLearner: <see cref="InitIteration" />, <see cref="CacheHistogram" />, <see cref="IsNeedFindLocalBestSplit" />, 
+ /// <see cref="IsSkipNonSplittableHistogram" />, <see cref="FindGlobalBestSplit" />, <see cref="GetGlobalDataCountInLeaf" />, <see cref="PerformGlobalSplit" />.
  /// A full process is:
- /// Use <see href="InitIteration" /> to alter local active features.
- /// Use <see href="GetGlobalDataCountInLeaf" /> to check smaller leaf and larger leaf.
- /// Use <see href="CacheHistogram" />, <see href="IsNeedFindLocalBestSplit" /> and <see href="IsSkipNonSplittableHistogram" /> to interactive with Feature histograms.
- /// Use <see href="FindGlobalBestSplit" /> to sync up global best split
- /// Use <see href="PerformGlobalSplit" /> to record global num_data in leaves.
- /// 3. interactive with Application : <see href="GlobalMean" />.
+ /// Use <see cref="InitIteration" /> to alter local active features.
+ /// Use <see cref="GetGlobalDataCountInLeaf" /> to check smaller leaf and larger leaf.
+ /// Use <see cref="CacheHistogram" />, <see cref="IsNeedFindLocalBestSplit" /> and <see cref="IsSkipNonSplittableHistogram" /> to interactive with Feature histograms.
+ /// Use <see cref="FindGlobalBestSplit" /> to sync up global best split
+ /// Use <see cref="PerformGlobalSplit" /> to record global num_data in leaves.
+ /// 3. interactive with Application : <see cref="GlobalMean" />.
  /// Output of leaves is calculated by newton step ( - sum(first_order_gradients) / sum(second_order_gradients)).
  /// If data is partitioned by row, it needs to a sync up for these sum result.
  /// So It needs to call this to get the real output of leaves.

src/Microsoft.ML.FastTree/TreeEnsembleFeaturizer.cs

@@ -544,6 +544,7 @@ public ISchemaBoundMapper Bind(IHostEnvironment env, RoleMappedSchema schema)
  }
  }
 
+ /// <include file='doc.xml' path='doc/members/member[@name="TreeEnsembleFeaturizerTransform"]'/>
  public static class TreeEnsembleFeaturizerTransform
  {
  public sealed class Arguments : TrainAndScoreTransform.ArgumentsBase<SignatureTreeEnsembleTrainer>
@@ -802,7 +803,11 @@ private static IDataView AppendLabelTransform(IHostEnvironment env, IChannel ch,
 
  public static partial class TreeFeaturize
  {
- [TlcModule.EntryPoint(Name = "Transforms.TreeLeafFeaturizer", Desc = TreeEnsembleFeaturizerTransform.TreeEnsembleSummary, UserName = TreeEnsembleFeaturizerTransform.UserName, ShortName = TreeEnsembleFeaturizerBindableMapper.LoadNameShort)]
+ [TlcModule.EntryPoint(Name = "Transforms.TreeLeafFeaturizer", 
+ Desc = TreeEnsembleFeaturizerTransform.TreeEnsembleSummary, 
+ UserName = TreeEnsembleFeaturizerTransform.UserName, 
+ ShortName = TreeEnsembleFeaturizerBindableMapper.LoadNameShort,
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""TreeEnsembleFeaturizerTransform""]'/>" })]
  public static CommonOutputs.TransformOutput Featurizer(IHostEnvironment env, TreeEnsembleFeaturizerTransform.ArgumentsForEntryPoint input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.Core/Utilities/ReservoirSampler.cs

@@ -47,7 +47,7 @@ public interface IReservoirSampler<T>
  /// This class produces a sample without replacement from a stream of data of type <typeparamref name="T"/>. 
  /// It is instantiated with a delegate that gets the next data point, and builds a reservoir in one pass by calling <see cref="Sample"/> 
  /// for every data point in the stream. In case the next data point does not get 'picked' into the reservoir, the delegate is not invoked.
- /// Sampling is done according to the algorithm in this paper: <see href="http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53"/>.
+ /// Sampling is done according to the algorithm in this paper: <a href="http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53">http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53</a>.
  /// </summary>
  public sealed class ReservoirSamplerWithoutReplacement<T> : IReservoirSampler<T>
  {
@@ -120,7 +120,7 @@ public IEnumerable<T> GetSample()
  /// This class produces a sample with replacement from a stream of data of type <typeparamref name="T"/>. 
  /// It is instantiated with a delegate that gets the next data point, and builds a reservoir in one pass by calling <see cref="Sample"/> 
  /// for every data point in the stream. In case the next data point does not get 'picked' into the reservoir, the delegate is not invoked.
- /// Sampling is done according to the algorithm in this paper: <see href="http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53"/>.
+ /// Sampling is done according to the algorithm in this paper: <a href="http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53">http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53</a>.
  /// </summary>
  public sealed class ReservoirSamplerWithReplacement<T> : IReservoirSampler<T>
  {

src/Microsoft.ML.Data/Evaluators/AucAggregator.cs

@@ -408,7 +408,7 @@ public UnweightedAuPrcAggregator(IRandom rand, int reservoirSize)
 
  /// <summary>
  /// Compute the AUPRC using the "lower trapesoid" estimator, as described in the paper
- /// <see href="http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf"/>.
+ /// <a href="http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf">http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf</a>.
  /// </summary>
  protected override Double ComputeWeightedAuPrcCore(out Double unweighted)
  {
@@ -482,7 +482,7 @@ public WeightedAuPrcAggregator(IRandom rand, int reservoirSize)
 
  /// <summary>
  /// Compute the AUPRC using the "lower trapesoid" estimator, as described in the paper
- /// <see href="http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf"/>.
+ /// <a href="http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf">http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf</a>.
  /// </summary>
  protected override Double ComputeWeightedAuPrcCore(out Double unweighted)
  {

src/Microsoft.ML.Data/Transforms/NAFilter.cs

@@ -26,6 +26,7 @@
 
 namespace Microsoft.ML.Runtime.Data
 {
+ /// <include file='doc.xml' path='doc/members/member[@name="NAFilter"]'/>
  public sealed class NAFilter : FilterBase
  {
  private static class Defaults

src/Microsoft.ML.Data/Transforms/TermTransform.cs

@@ -29,14 +29,14 @@
 
 namespace Microsoft.ML.Runtime.Data
 {
- /// <summary>
- /// TermTransform builds up term vocabularies (dictionaries).
- /// Notes:
- /// * Each column builds/uses exactly one "vocabulary" (dictionary).
- /// * Output columns are KeyType-valued.
- /// * The Key value is the one-based index of the item in the dictionary.
- /// * Not found is assigned the value zero.
- /// </summary>
+
+ // TermTransform builds up term vocabularies (dictionaries).
+ // Notes:
+ // * Each column builds/uses exactly one "vocabulary" (dictionary).
+ // * Output columns are KeyType-valued.
+ // * The Key value is the one-based index of the item in the dictionary.
+ // * Not found is assigned the value zero.
+ /// <include file='doc.xml' path='doc/members/member[@name="TextToKey"]/*' />
  public sealed partial class TermTransform : OneToOneTransformBase, ITransformTemplate
  {
  public abstract class ColumnBase : OneToOneColumn

src/Microsoft.ML.Data/Transforms/doc.xml

@@ -0,0 +1,54 @@
+<?xml version="1.0" encoding="utf-8" ?>
+<doc>
+ <members>
+ <member name="NAFilter">
+ <summary>
+ Removes missing values from vector type columns.
+ </summary>
+ <remarks>
+ This transform removes the entire row if any of the input columns have a missing value in that row.
+ This preprocessing is required for many ML algorithms that cannot work with missing values.
+ Useful if any missing entry invalidates the entire row.
+ If the <see cref="Microsoft.ML.Runtime.Data.NAFilter.Defaults.Complement"/> is set to true, this transform would do the exact opposite,
+ it will keep only the rows that have missing values.
+ </remarks>
+ <seealso cref="Microsoft.ML.Runtime.Data.MetadataUtils.Kinds.HasMissingValues"></seealso>
+ </member>
+ <example name="NAFilter">
+ <example>
+ <code language="csharp">
+ pipeline.Add(new MissingValuesRowDropper(&quot;Column1&quot;));
+ </code>
+ </example>
+ </example>
+
+ <member name="TextToKey">
+ <summary>
+ Converts input values (words, numbers, etc.) to index in a dictionary.
+ </summary>
+ <remarks>
+ The TextToKeyConverter transform builds up term vocabularies (dictionaries).
+ The TextToKey Converter and the <see cref="T:Microsoft.ML.Transforms.HashConverter"/> are the two one primary mechanisms by which raw input is transformed into keys.
+ If multiple columns are used, each column builds/uses exactly one vocabulary.
+ The output columns are KeyType-valued.
+ The Key value is the one-based index of the item in the dictionary.
+ If the key is not found in the dictionary, it is assigned the missing value indicator.
+ This dictionary mapping values to keys is most commonly learnt from the unique values in input data,
+ but can be defined through other means: either with the mapping defined directly on the command line, or as loaded from an external file.
+ </remarks>
+ <seealso cref="T:Microsoft.ML.Transforms.HashConverter"/>
+ <seealso cref="T:Microsoft.ML.Transforms.KeyToTextConverter"/>
+ </member>
+ <example name="TextToKey">
+ <example>
+ <code language="csharp">
+ pipeline.Add(new TextToKeyConverter((&quot;Column&quot;, &quot;OutColumn&quot;))
+ { 
+ Sort = TermTransformSortOrder.Occurrence 
+ });
+ </code>
+ </example>
+ </example>
+
+ </members>
+</doc>

src/Microsoft.ML.FastTree/FastTreeArguments.cs

@@ -20,7 +20,7 @@ public interface IFastTreeTrainerFactory : IComponentFactory<ITrainer>
  {
  }
 
- /// <include file='./doc.xml' path='docs/members/member[@name="FastTree"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastTree"]/*' />
  public sealed partial class FastTreeBinaryClassificationTrainer
  {
  [TlcModule.Component(Name = LoadNameValue, FriendlyName = UserNameValue, Desc = Summary)]

src/Microsoft.ML.FastTree/FastTreeClassification.cs

@@ -100,7 +100,7 @@ public static IPredictorProducing<Float> Create(IHostEnvironment env, ModelLoadC
  public override PredictionKind PredictionKind => PredictionKind.BinaryClassification;
  }
 
- /// <include file = './doc.xml' path='docs/members/member[@name="FastTree"]/*' />
+ /// <include file = 'doc.xml' path='doc/members/member[@name="FastTree"]/*' />
  public sealed partial class FastTreeBinaryClassificationTrainer :
  BoostingFastTreeTrainerBase<FastTreeBinaryClassificationTrainer.Arguments, IPredictorWithFeatureWeights<Float>>
  {
@@ -342,7 +342,8 @@ public static partial class FastTree
  Desc = FastTreeBinaryClassificationTrainer.Summary,
  UserName = FastTreeBinaryClassificationTrainer.UserNameValue,
  ShortName = FastTreeBinaryClassificationTrainer.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastTree""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastTree""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastTreeBinaryClassifier""]/*' />" })]
  public static CommonOutputs.BinaryClassificationOutput TrainBinary(IHostEnvironment env, FastTreeBinaryClassificationTrainer.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/FastTreeRanking.cs

@@ -38,7 +38,7 @@
 
 namespace Microsoft.ML.Runtime.FastTree
 {
- /// <include file='./doc.xml' path='docs/members/member[@name="FastTree"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastTree"]/*' />
  public sealed partial class FastTreeRankingTrainer : BoostingFastTreeTrainerBase<FastTreeRankingTrainer.Arguments, FastTreeRankingPredictor>,
  IHasLabelGains
  {
@@ -1096,7 +1096,8 @@ public static partial class FastTree
  Desc = FastTreeRankingTrainer.Summary,
  UserName = FastTreeRankingTrainer.UserNameValue,
  ShortName = FastTreeRankingTrainer.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastTree""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastTree""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastTreeRanker""]/*' />"})]
  public static CommonOutputs.RankingOutput TrainRanking(IHostEnvironment env, FastTreeRankingTrainer.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/FastTreeRegression.cs

@@ -31,7 +31,7 @@
 
 namespace Microsoft.ML.Runtime.FastTree
 {
- /// <include file='./doc.xml' path='docs/members/member[@name="FastTree"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastTree"]/*' />
  public sealed partial class FastTreeRegressionTrainer : BoostingFastTreeTrainerBase<FastTreeRegressionTrainer.Arguments, FastTreeRegressionPredictor>
  {
  public const string LoadNameValue = "FastTreeRegression";
@@ -445,7 +445,8 @@ public static partial class FastTree
  Desc = FastTreeRegressionTrainer.Summary,
  UserName = FastTreeRegressionTrainer.UserNameValue,
  ShortName = FastTreeRegressionTrainer.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastTree""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastTree""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastTreeRegressor""]/*' />"})]
  public static CommonOutputs.RegressionOutput TrainRegression(IHostEnvironment env, FastTreeRegressionTrainer.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/FastTreeTweedie.cs

@@ -30,7 +30,7 @@ namespace Microsoft.ML.Runtime.FastTree
  // The Tweedie boosting model follows the mathematics established in:
  // Yang, Quan, and Zou. "Insurance Premium Prediction via Gradient Tree-Boosted Tweedie Compound Poisson Models."
  // https://arxiv.org/pdf/1508.06378.pdf
- /// <include file='./doc.xml' path='docs/members/member[@name="FastTreeTweedieRegression"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastTreeTweedieRegression"]/*' />
  public sealed partial class FastTreeTweedieTrainer : BoostingFastTreeTrainerBase<FastTreeTweedieTrainer.Arguments, FastTreeTweediePredictor>
  {
  public const string LoadNameValue = "FastTreeTweedieRegression";
@@ -454,7 +454,7 @@ public static partial class FastTree
  Desc = FastTreeTweedieTrainer.Summary,
  UserName = FastTreeTweedieTrainer.UserNameValue,
  ShortName = FastTreeTweedieTrainer.ShortName,
- XmlInclude = new [] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastTreeTweedieRegression""]/*' />" })]
+ XmlInclude = new [] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastTreeTweedieRegression""]/*' />" })]
  public static CommonOutputs.RegressionOutput TrainTweedieRegression(IHostEnvironment env, FastTreeTweedieTrainer.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/RandomForestClassification.cs

@@ -106,7 +106,7 @@ public static IPredictorProducing<Float> Create(IHostEnvironment env, ModelLoadC
  }
  }
 
- /// <include file='./doc.xml' path='docs/members/member[@name="FastForest"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastForest"]/*' />
  public sealed partial class FastForestClassification :
  RandomForestTrainerBase<FastForestClassification.Arguments, IPredictorWithFeatureWeights<Float>>
  {
@@ -206,7 +206,8 @@ public static partial class FastForest
  Desc = FastForestClassification.Summary,
  UserName = FastForestClassification.UserNameValue,
  ShortName = FastForestClassification.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastForest""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastForest""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastForestBinaryClassifier""]/*' />"})]
  public static CommonOutputs.BinaryClassificationOutput TrainBinary(IHostEnvironment env, FastForestClassification.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/RandomForestRegression.cs

@@ -137,7 +137,7 @@ public ISchemaBindableMapper CreateMapper(Double[] quantiles)
  }
  }
 
- /// <include file='./doc.xml' path='docs/members/member[@name="FastForest"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastForest"]/*' />
  public sealed partial class FastForestRegression : RandomForestTrainerBase<FastForestRegression.Arguments, FastForestRegressionPredictor>
  {
  public sealed class Arguments : FastForestArgumentsBase
@@ -277,7 +277,8 @@ public static partial class FastForest
  Desc = FastForestRegression.Summary,
  UserName = FastForestRegression.LoadNameValue,
  ShortName = FastForestRegression.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastForest""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastForest""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastForestRegressor""]/*' />"})]
  public static CommonOutputs.RegressionOutput TrainRegression(IHostEnvironment env, FastForestRegression.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/Training/Parallel/IParallelTraining.cs

@@ -33,20 +33,20 @@ public delegate void FindBestThresholdFromRawArrayFun(LeafSplitCandidates leafSp
  /// <summary>
  /// Interface used for parallel training.
  /// Mainly contains three parts:
- /// 1. interactive with IO: <see href="GetLocalBinConstructionFeatures" />, <see href="SyncGlobalBoundary" />.
+ /// 1. interactive with IO: <see cref="GetLocalBinConstructionFeatures" />, <see cref="SyncGlobalBoundary" />.
  /// Data will be partitioned by rows in Data parallel and Voting Parallel.
  /// To speed up the find bin process, it let different workers to find bins for different features.
  /// Then perform global sync up.
  /// In Feature parallel, every machines holds all data, so this is unneeded.
- /// 2. interactive with TreeLearner: <see href="InitIteration" />, <see href="CacheHistogram" />, <see href="IsNeedFindLocalBestSplit" />, 
- /// <see href="IsSkipNonSplittableHistogram" />, <see href="FindGlobalBestSplit" />, <see href="GetGlobalDataCountInLeaf" />, <see href="PerformGlobalSplit" />.
+ /// 2. interactive with TreeLearner: <see cref="InitIteration" />, <see cref="CacheHistogram" />, <see cref="IsNeedFindLocalBestSplit" />, 
+ /// <see cref="IsSkipNonSplittableHistogram" />, <see cref="FindGlobalBestSplit" />, <see cref="GetGlobalDataCountInLeaf" />, <see cref="PerformGlobalSplit" />.
  /// A full process is:
- /// Use <see href="InitIteration" /> to alter local active features.
- /// Use <see href="GetGlobalDataCountInLeaf" /> to check smaller leaf and larger leaf.
- /// Use <see href="CacheHistogram" />, <see href="IsNeedFindLocalBestSplit" /> and <see href="IsSkipNonSplittableHistogram" /> to interactive with Feature histograms.
- /// Use <see href="FindGlobalBestSplit" /> to sync up global best split
- /// Use <see href="PerformGlobalSplit" /> to record global num_data in leaves.
- /// 3. interactive with Application : <see href="GlobalMean" />.
+ /// Use <see cref="InitIteration" /> to alter local active features.
+ /// Use <see cref="GetGlobalDataCountInLeaf" /> to check smaller leaf and larger leaf.
+ /// Use <see cref="CacheHistogram" />, <see cref="IsNeedFindLocalBestSplit" /> and <see cref="IsSkipNonSplittableHistogram" /> to interactive with Feature histograms.
+ /// Use <see cref="FindGlobalBestSplit" /> to sync up global best split
+ /// Use <see cref="PerformGlobalSplit" /> to record global num_data in leaves.
+ /// 3. interactive with Application : <see cref="GlobalMean" />.
  /// Output of leaves is calculated by newton step ( - sum(first_order_gradients) / sum(second_order_gradients)).
  /// If data is partitioned by row, it needs to a sync up for these sum result.
  /// So It needs to call this to get the real output of leaves.

src/Microsoft.ML.FastTree/TreeEnsembleFeaturizer.cs

@@ -544,6 +544,7 @@ public ISchemaBoundMapper Bind(IHostEnvironment env, RoleMappedSchema schema)
  }
  }
 
+ /// <include file='doc.xml' path='doc/members/member[@name="TreeEnsembleFeaturizerTransform"]'/>
  public static class TreeEnsembleFeaturizerTransform
  {
  public sealed class Arguments : TrainAndScoreTransform.ArgumentsBase<SignatureTreeEnsembleTrainer>
@@ -802,7 +803,11 @@ private static IDataView AppendLabelTransform(IHostEnvironment env, IChannel ch,
 
  public static partial class TreeFeaturize
  {
- [TlcModule.EntryPoint(Name = "Transforms.TreeLeafFeaturizer", Desc = TreeEnsembleFeaturizerTransform.TreeEnsembleSummary, UserName = TreeEnsembleFeaturizerTransform.UserName, ShortName = TreeEnsembleFeaturizerBindableMapper.LoadNameShort)]
+ [TlcModule.EntryPoint(Name = "Transforms.TreeLeafFeaturizer", 
+ Desc = TreeEnsembleFeaturizerTransform.TreeEnsembleSummary, 
+ UserName = TreeEnsembleFeaturizerTransform.UserName, 
+ ShortName = TreeEnsembleFeaturizerBindableMapper.LoadNameShort,
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""TreeEnsembleFeaturizerTransform""]'/>" })]
  public static CommonOutputs.TransformOutput Featurizer(IHostEnvironment env, TreeEnsembleFeaturizerTransform.ArgumentsForEntryPoint input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.Core/Utilities/ReservoirSampler.cs

@@ -47,7 +47,7 @@ public interface IReservoirSampler<T>
  /// This class produces a sample without replacement from a stream of data of type <typeparamref name="T"/>. 
  /// It is instantiated with a delegate that gets the next data point, and builds a reservoir in one pass by calling <see cref="Sample"/> 
  /// for every data point in the stream. In case the next data point does not get 'picked' into the reservoir, the delegate is not invoked.
- /// Sampling is done according to the algorithm in this paper: <see href="http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53"/>.
+ /// Sampling is done according to the algorithm in this paper: <a href="http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53">http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53</a>.
  /// </summary>
  public sealed class ReservoirSamplerWithoutReplacement<T> : IReservoirSampler<T>
  {
@@ -120,7 +120,7 @@ public IEnumerable<T> GetSample()
  /// This class produces a sample with replacement from a stream of data of type <typeparamref name="T"/>. 
  /// It is instantiated with a delegate that gets the next data point, and builds a reservoir in one pass by calling <see cref="Sample"/> 
  /// for every data point in the stream. In case the next data point does not get 'picked' into the reservoir, the delegate is not invoked.
- /// Sampling is done according to the algorithm in this paper: <see href="http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53"/>.
+ /// Sampling is done according to the algorithm in this paper: <a href="http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53">http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53</a>.
  /// </summary>
  public sealed class ReservoirSamplerWithReplacement<T> : IReservoirSampler<T>
  {

src/Microsoft.ML.Data/Evaluators/AucAggregator.cs

@@ -408,7 +408,7 @@ public UnweightedAuPrcAggregator(IRandom rand, int reservoirSize)
 
  /// <summary>
  /// Compute the AUPRC using the "lower trapesoid" estimator, as described in the paper
- /// <see href="http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf"/>.
+ /// <a href="http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf">http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf</a>.
  /// </summary>
  protected override Double ComputeWeightedAuPrcCore(out Double unweighted)
  {
@@ -482,7 +482,7 @@ public WeightedAuPrcAggregator(IRandom rand, int reservoirSize)
 
  /// <summary>
  /// Compute the AUPRC using the "lower trapesoid" estimator, as described in the paper
- /// <see href="http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf"/>.
+ /// <a href="http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf">http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf</a>.
  /// </summary>
  protected override Double ComputeWeightedAuPrcCore(out Double unweighted)
  {

src/Microsoft.ML.Data/Transforms/NAFilter.cs

@@ -26,6 +26,7 @@
 
 namespace Microsoft.ML.Runtime.Data
 {
+ /// <include file='doc.xml' path='doc/members/member[@name="NAFilter"]'/>
  public sealed class NAFilter : FilterBase
  {
  private static class Defaults

src/Microsoft.ML.Data/Transforms/TermTransform.cs

@@ -29,14 +29,14 @@
 
 namespace Microsoft.ML.Runtime.Data
 {
- /// <summary>
- /// TermTransform builds up term vocabularies (dictionaries).
- /// Notes:
- /// * Each column builds/uses exactly one "vocabulary" (dictionary).
- /// * Output columns are KeyType-valued.
- /// * The Key value is the one-based index of the item in the dictionary.
- /// * Not found is assigned the value zero.
- /// </summary>
+
+ // TermTransform builds up term vocabularies (dictionaries).
+ // Notes:
+ // * Each column builds/uses exactly one "vocabulary" (dictionary).
+ // * Output columns are KeyType-valued.
+ // * The Key value is the one-based index of the item in the dictionary.
+ // * Not found is assigned the value zero.
+ /// <include file='doc.xml' path='doc/members/member[@name="TextToKey"]/*' />
  public sealed partial class TermTransform : OneToOneTransformBase, ITransformTemplate
  {
  public abstract class ColumnBase : OneToOneColumn

src/Microsoft.ML.Data/Transforms/doc.xml

@@ -0,0 +1,54 @@
+<?xml version="1.0" encoding="utf-8" ?>
+<doc>
+ <members>
+ <member name="NAFilter">
+ <summary>
+ Removes missing values from vector type columns.
+ </summary>
+ <remarks>
+ This transform removes the entire row if any of the input columns have a missing value in that row.
+ This preprocessing is required for many ML algorithms that cannot work with missing values.
+ Useful if any missing entry invalidates the entire row.
+ If the <see cref="Microsoft.ML.Runtime.Data.NAFilter.Defaults.Complement"/> is set to true, this transform would do the exact opposite,
+ it will keep only the rows that have missing values.
+ </remarks>
+ <seealso cref="Microsoft.ML.Runtime.Data.MetadataUtils.Kinds.HasMissingValues"></seealso>
+ </member>
+ <example name="NAFilter">
+ <example>
+ <code language="csharp">
+ pipeline.Add(new MissingValuesRowDropper(&quot;Column1&quot;));
+ </code>
+ </example>
+ </example>
+
+ <member name="TextToKey">
+ <summary>
+ Converts input values (words, numbers, etc.) to index in a dictionary.
+ </summary>
+ <remarks>
+ The TextToKeyConverter transform builds up term vocabularies (dictionaries).
+ The TextToKey Converter and the <see cref="T:Microsoft.ML.Transforms.HashConverter"/> are the two one primary mechanisms by which raw input is transformed into keys.
+ If multiple columns are used, each column builds/uses exactly one vocabulary.
+ The output columns are KeyType-valued.
+ The Key value is the one-based index of the item in the dictionary.
+ If the key is not found in the dictionary, it is assigned the missing value indicator.
+ This dictionary mapping values to keys is most commonly learnt from the unique values in input data,
+ but can be defined through other means: either with the mapping defined directly on the command line, or as loaded from an external file.
+ </remarks>
+ <seealso cref="T:Microsoft.ML.Transforms.HashConverter"/>
+ <seealso cref="T:Microsoft.ML.Transforms.KeyToTextConverter"/>
+ </member>
+ <example name="TextToKey">
+ <example>
+ <code language="csharp">
+ pipeline.Add(new TextToKeyConverter((&quot;Column&quot;, &quot;OutColumn&quot;))
+ { 
+ Sort = TermTransformSortOrder.Occurrence 
+ });
+ </code>
+ </example>
+ </example>
+
+ </members>
+</doc>

src/Microsoft.ML.FastTree/FastTreeArguments.cs

@@ -20,7 +20,7 @@ public interface IFastTreeTrainerFactory : IComponentFactory<ITrainer>
  {
  }
 
- /// <include file='./doc.xml' path='docs/members/member[@name="FastTree"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastTree"]/*' />
  public sealed partial class FastTreeBinaryClassificationTrainer
  {
  [TlcModule.Component(Name = LoadNameValue, FriendlyName = UserNameValue, Desc = Summary)]

src/Microsoft.ML.FastTree/FastTreeClassification.cs

@@ -100,7 +100,7 @@ public static IPredictorProducing<Float> Create(IHostEnvironment env, ModelLoadC
  public override PredictionKind PredictionKind => PredictionKind.BinaryClassification;
  }
 
- /// <include file = './doc.xml' path='docs/members/member[@name="FastTree"]/*' />
+ /// <include file = 'doc.xml' path='doc/members/member[@name="FastTree"]/*' />
  public sealed partial class FastTreeBinaryClassificationTrainer :
  BoostingFastTreeTrainerBase<FastTreeBinaryClassificationTrainer.Arguments, IPredictorWithFeatureWeights<Float>>
  {
@@ -342,7 +342,8 @@ public static partial class FastTree
  Desc = FastTreeBinaryClassificationTrainer.Summary,
  UserName = FastTreeBinaryClassificationTrainer.UserNameValue,
  ShortName = FastTreeBinaryClassificationTrainer.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastTree""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastTree""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastTreeBinaryClassifier""]/*' />" })]
  public static CommonOutputs.BinaryClassificationOutput TrainBinary(IHostEnvironment env, FastTreeBinaryClassificationTrainer.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/FastTreeRanking.cs

@@ -38,7 +38,7 @@
 
 namespace Microsoft.ML.Runtime.FastTree
 {
- /// <include file='./doc.xml' path='docs/members/member[@name="FastTree"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastTree"]/*' />
  public sealed partial class FastTreeRankingTrainer : BoostingFastTreeTrainerBase<FastTreeRankingTrainer.Arguments, FastTreeRankingPredictor>,
  IHasLabelGains
  {
@@ -1096,7 +1096,8 @@ public static partial class FastTree
  Desc = FastTreeRankingTrainer.Summary,
  UserName = FastTreeRankingTrainer.UserNameValue,
  ShortName = FastTreeRankingTrainer.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastTree""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastTree""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastTreeRanker""]/*' />"})]
  public static CommonOutputs.RankingOutput TrainRanking(IHostEnvironment env, FastTreeRankingTrainer.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/FastTreeRegression.cs

@@ -31,7 +31,7 @@
 
 namespace Microsoft.ML.Runtime.FastTree
 {
- /// <include file='./doc.xml' path='docs/members/member[@name="FastTree"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastTree"]/*' />
  public sealed partial class FastTreeRegressionTrainer : BoostingFastTreeTrainerBase<FastTreeRegressionTrainer.Arguments, FastTreeRegressionPredictor>
  {
  public const string LoadNameValue = "FastTreeRegression";
@@ -445,7 +445,8 @@ public static partial class FastTree
  Desc = FastTreeRegressionTrainer.Summary,
  UserName = FastTreeRegressionTrainer.UserNameValue,
  ShortName = FastTreeRegressionTrainer.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastTree""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastTree""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastTreeRegressor""]/*' />"})]
  public static CommonOutputs.RegressionOutput TrainRegression(IHostEnvironment env, FastTreeRegressionTrainer.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/FastTreeTweedie.cs

@@ -30,7 +30,7 @@ namespace Microsoft.ML.Runtime.FastTree
  // The Tweedie boosting model follows the mathematics established in:
  // Yang, Quan, and Zou. "Insurance Premium Prediction via Gradient Tree-Boosted Tweedie Compound Poisson Models."
  // https://arxiv.org/pdf/1508.06378.pdf
- /// <include file='./doc.xml' path='docs/members/member[@name="FastTreeTweedieRegression"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastTreeTweedieRegression"]/*' />
  public sealed partial class FastTreeTweedieTrainer : BoostingFastTreeTrainerBase<FastTreeTweedieTrainer.Arguments, FastTreeTweediePredictor>
  {
  public const string LoadNameValue = "FastTreeTweedieRegression";
@@ -454,7 +454,7 @@ public static partial class FastTree
  Desc = FastTreeTweedieTrainer.Summary,
  UserName = FastTreeTweedieTrainer.UserNameValue,
  ShortName = FastTreeTweedieTrainer.ShortName,
- XmlInclude = new [] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastTreeTweedieRegression""]/*' />" })]
+ XmlInclude = new [] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastTreeTweedieRegression""]/*' />" })]
  public static CommonOutputs.RegressionOutput TrainTweedieRegression(IHostEnvironment env, FastTreeTweedieTrainer.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/RandomForestClassification.cs

@@ -106,7 +106,7 @@ public static IPredictorProducing<Float> Create(IHostEnvironment env, ModelLoadC
  }
  }
 
- /// <include file='./doc.xml' path='docs/members/member[@name="FastForest"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastForest"]/*' />
  public sealed partial class FastForestClassification :
  RandomForestTrainerBase<FastForestClassification.Arguments, IPredictorWithFeatureWeights<Float>>
  {
@@ -206,7 +206,8 @@ public static partial class FastForest
  Desc = FastForestClassification.Summary,
  UserName = FastForestClassification.UserNameValue,
  ShortName = FastForestClassification.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastForest""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastForest""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastForestBinaryClassifier""]/*' />"})]
  public static CommonOutputs.BinaryClassificationOutput TrainBinary(IHostEnvironment env, FastForestClassification.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/RandomForestRegression.cs

@@ -137,7 +137,7 @@ public ISchemaBindableMapper CreateMapper(Double[] quantiles)
  }
  }
 
- /// <include file='./doc.xml' path='docs/members/member[@name="FastForest"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastForest"]/*' />
  public sealed partial class FastForestRegression : RandomForestTrainerBase<FastForestRegression.Arguments, FastForestRegressionPredictor>
  {
  public sealed class Arguments : FastForestArgumentsBase
@@ -277,7 +277,8 @@ public static partial class FastForest
  Desc = FastForestRegression.Summary,
  UserName = FastForestRegression.LoadNameValue,
  ShortName = FastForestRegression.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastForest""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastForest""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastForestRegressor""]/*' />"})]
  public static CommonOutputs.RegressionOutput TrainRegression(IHostEnvironment env, FastForestRegression.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/Training/Parallel/IParallelTraining.cs

@@ -33,20 +33,20 @@ public delegate void FindBestThresholdFromRawArrayFun(LeafSplitCandidates leafSp
  /// <summary>
  /// Interface used for parallel training.
  /// Mainly contains three parts:
- /// 1. interactive with IO: <see href="GetLocalBinConstructionFeatures" />, <see href="SyncGlobalBoundary" />.
+ /// 1. interactive with IO: <see cref="GetLocalBinConstructionFeatures" />, <see cref="SyncGlobalBoundary" />.
  /// Data will be partitioned by rows in Data parallel and Voting Parallel.
  /// To speed up the find bin process, it let different workers to find bins for different features.
  /// Then perform global sync up.
  /// In Feature parallel, every machines holds all data, so this is unneeded.
- /// 2. interactive with TreeLearner: <see href="InitIteration" />, <see href="CacheHistogram" />, <see href="IsNeedFindLocalBestSplit" />, 
- /// <see href="IsSkipNonSplittableHistogram" />, <see href="FindGlobalBestSplit" />, <see href="GetGlobalDataCountInLeaf" />, <see href="PerformGlobalSplit" />.
+ /// 2. interactive with TreeLearner: <see cref="InitIteration" />, <see cref="CacheHistogram" />, <see cref="IsNeedFindLocalBestSplit" />, 
+ /// <see cref="IsSkipNonSplittableHistogram" />, <see cref="FindGlobalBestSplit" />, <see cref="GetGlobalDataCountInLeaf" />, <see cref="PerformGlobalSplit" />.
  /// A full process is:
- /// Use <see href="InitIteration" /> to alter local active features.
- /// Use <see href="GetGlobalDataCountInLeaf" /> to check smaller leaf and larger leaf.
- /// Use <see href="CacheHistogram" />, <see href="IsNeedFindLocalBestSplit" /> and <see href="IsSkipNonSplittableHistogram" /> to interactive with Feature histograms.
- /// Use <see href="FindGlobalBestSplit" /> to sync up global best split
- /// Use <see href="PerformGlobalSplit" /> to record global num_data in leaves.
- /// 3. interactive with Application : <see href="GlobalMean" />.
+ /// Use <see cref="InitIteration" /> to alter local active features.
+ /// Use <see cref="GetGlobalDataCountInLeaf" /> to check smaller leaf and larger leaf.
+ /// Use <see cref="CacheHistogram" />, <see cref="IsNeedFindLocalBestSplit" /> and <see cref="IsSkipNonSplittableHistogram" /> to interactive with Feature histograms.
+ /// Use <see cref="FindGlobalBestSplit" /> to sync up global best split
+ /// Use <see cref="PerformGlobalSplit" /> to record global num_data in leaves.
+ /// 3. interactive with Application : <see cref="GlobalMean" />.
  /// Output of leaves is calculated by newton step ( - sum(first_order_gradients) / sum(second_order_gradients)).
  /// If data is partitioned by row, it needs to a sync up for these sum result.
  /// So It needs to call this to get the real output of leaves.

src/Microsoft.ML.FastTree/TreeEnsembleFeaturizer.cs

@@ -544,6 +544,7 @@ public ISchemaBoundMapper Bind(IHostEnvironment env, RoleMappedSchema schema)
  }
  }
 
+ /// <include file='doc.xml' path='doc/members/member[@name="TreeEnsembleFeaturizerTransform"]'/>
  public static class TreeEnsembleFeaturizerTransform
  {
  public sealed class Arguments : TrainAndScoreTransform.ArgumentsBase<SignatureTreeEnsembleTrainer>
@@ -802,7 +803,11 @@ private static IDataView AppendLabelTransform(IHostEnvironment env, IChannel ch,
 
  public static partial class TreeFeaturize
  {
- [TlcModule.EntryPoint(Name = "Transforms.TreeLeafFeaturizer", Desc = TreeEnsembleFeaturizerTransform.TreeEnsembleSummary, UserName = TreeEnsembleFeaturizerTransform.UserName, ShortName = TreeEnsembleFeaturizerBindableMapper.LoadNameShort)]
+ [TlcModule.EntryPoint(Name = "Transforms.TreeLeafFeaturizer", 
+ Desc = TreeEnsembleFeaturizerTransform.TreeEnsembleSummary, 
+ UserName = TreeEnsembleFeaturizerTransform.UserName, 
+ ShortName = TreeEnsembleFeaturizerBindableMapper.LoadNameShort,
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""TreeEnsembleFeaturizerTransform""]'/>" })]
  public static CommonOutputs.TransformOutput Featurizer(IHostEnvironment env, TreeEnsembleFeaturizerTransform.ArgumentsForEntryPoint input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.Core/Utilities/ReservoirSampler.cs

@@ -47,7 +47,7 @@ public interface IReservoirSampler<T>
  /// This class produces a sample without replacement from a stream of data of type <typeparamref name="T"/>. 
  /// It is instantiated with a delegate that gets the next data point, and builds a reservoir in one pass by calling <see cref="Sample"/> 
  /// for every data point in the stream. In case the next data point does not get 'picked' into the reservoir, the delegate is not invoked.
- /// Sampling is done according to the algorithm in this paper: <see href="http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53"/>.
+ /// Sampling is done according to the algorithm in this paper: <a href="http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53">http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53</a>.
  /// </summary>
  public sealed class ReservoirSamplerWithoutReplacement<T> : IReservoirSampler<T>
  {
@@ -120,7 +120,7 @@ public IEnumerable<T> GetSample()
  /// This class produces a sample with replacement from a stream of data of type <typeparamref name="T"/>. 
  /// It is instantiated with a delegate that gets the next data point, and builds a reservoir in one pass by calling <see cref="Sample"/> 
  /// for every data point in the stream. In case the next data point does not get 'picked' into the reservoir, the delegate is not invoked.
- /// Sampling is done according to the algorithm in this paper: <see href="http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53"/>.
+ /// Sampling is done according to the algorithm in this paper: <a href="http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53">http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53</a>.
  /// </summary>
  public sealed class ReservoirSamplerWithReplacement<T> : IReservoirSampler<T>
  {

src/Microsoft.ML.Data/Evaluators/AucAggregator.cs

@@ -408,7 +408,7 @@ public UnweightedAuPrcAggregator(IRandom rand, int reservoirSize)
 
  /// <summary>
  /// Compute the AUPRC using the "lower trapesoid" estimator, as described in the paper
- /// <see href="http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf"/>.
+ /// <a href="http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf">http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf</a>.
  /// </summary>
  protected override Double ComputeWeightedAuPrcCore(out Double unweighted)
  {
@@ -482,7 +482,7 @@ public WeightedAuPrcAggregator(IRandom rand, int reservoirSize)
 
  /// <summary>
  /// Compute the AUPRC using the "lower trapesoid" estimator, as described in the paper
- /// <see href="http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf"/>.
+ /// <a href="http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf">http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf</a>.
  /// </summary>
  protected override Double ComputeWeightedAuPrcCore(out Double unweighted)
  {

src/Microsoft.ML.Data/Transforms/NAFilter.cs

@@ -26,6 +26,7 @@
 
 namespace Microsoft.ML.Runtime.Data
 {
+ /// <include file='doc.xml' path='doc/members/member[@name="NAFilter"]'/>
  public sealed class NAFilter : FilterBase
  {
  private static class Defaults

src/Microsoft.ML.Data/Transforms/TermTransform.cs

@@ -29,14 +29,14 @@
 
 namespace Microsoft.ML.Runtime.Data
 {
- /// <summary>
- /// TermTransform builds up term vocabularies (dictionaries).
- /// Notes:
- /// * Each column builds/uses exactly one "vocabulary" (dictionary).
- /// * Output columns are KeyType-valued.
- /// * The Key value is the one-based index of the item in the dictionary.
- /// * Not found is assigned the value zero.
- /// </summary>
+
+ // TermTransform builds up term vocabularies (dictionaries).
+ // Notes:
+ // * Each column builds/uses exactly one "vocabulary" (dictionary).
+ // * Output columns are KeyType-valued.
+ // * The Key value is the one-based index of the item in the dictionary.
+ // * Not found is assigned the value zero.
+ /// <include file='doc.xml' path='doc/members/member[@name="TextToKey"]/*' />
  public sealed partial class TermTransform : OneToOneTransformBase, ITransformTemplate
  {
  public abstract class ColumnBase : OneToOneColumn

src/Microsoft.ML.Data/Transforms/doc.xml

@@ -0,0 +1,54 @@
+<?xml version="1.0" encoding="utf-8" ?>
+<doc>
+ <members>
+ <member name="NAFilter">
+ <summary>
+ Removes missing values from vector type columns.
+ </summary>
+ <remarks>
+ This transform removes the entire row if any of the input columns have a missing value in that row.
+ This preprocessing is required for many ML algorithms that cannot work with missing values.
+ Useful if any missing entry invalidates the entire row.
+ If the <see cref="Microsoft.ML.Runtime.Data.NAFilter.Defaults.Complement"/> is set to true, this transform would do the exact opposite,
+ it will keep only the rows that have missing values.
+ </remarks>
+ <seealso cref="Microsoft.ML.Runtime.Data.MetadataUtils.Kinds.HasMissingValues"></seealso>
+ </member>
+ <example name="NAFilter">
+ <example>
+ <code language="csharp">
+ pipeline.Add(new MissingValuesRowDropper(&quot;Column1&quot;));
+ </code>
+ </example>
+ </example>
+
+ <member name="TextToKey">
+ <summary>
+ Converts input values (words, numbers, etc.) to index in a dictionary.
+ </summary>
+ <remarks>
+ The TextToKeyConverter transform builds up term vocabularies (dictionaries).
+ The TextToKey Converter and the <see cref="T:Microsoft.ML.Transforms.HashConverter"/> are the two one primary mechanisms by which raw input is transformed into keys.
+ If multiple columns are used, each column builds/uses exactly one vocabulary.
+ The output columns are KeyType-valued.
+ The Key value is the one-based index of the item in the dictionary.
+ If the key is not found in the dictionary, it is assigned the missing value indicator.
+ This dictionary mapping values to keys is most commonly learnt from the unique values in input data,
+ but can be defined through other means: either with the mapping defined directly on the command line, or as loaded from an external file.
+ </remarks>
+ <seealso cref="T:Microsoft.ML.Transforms.HashConverter"/>
+ <seealso cref="T:Microsoft.ML.Transforms.KeyToTextConverter"/>
+ </member>
+ <example name="TextToKey">
+ <example>
+ <code language="csharp">
+ pipeline.Add(new TextToKeyConverter((&quot;Column&quot;, &quot;OutColumn&quot;))
+ { 
+ Sort = TermTransformSortOrder.Occurrence 
+ });
+ </code>
+ </example>
+ </example>
+
+ </members>
+</doc>

src/Microsoft.ML.FastTree/FastTreeArguments.cs

@@ -20,7 +20,7 @@ public interface IFastTreeTrainerFactory : IComponentFactory<ITrainer>
  {
  }
 
- /// <include file='./doc.xml' path='docs/members/member[@name="FastTree"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastTree"]/*' />
  public sealed partial class FastTreeBinaryClassificationTrainer
  {
  [TlcModule.Component(Name = LoadNameValue, FriendlyName = UserNameValue, Desc = Summary)]

src/Microsoft.ML.FastTree/FastTreeClassification.cs

@@ -100,7 +100,7 @@ public static IPredictorProducing<Float> Create(IHostEnvironment env, ModelLoadC
  public override PredictionKind PredictionKind => PredictionKind.BinaryClassification;
  }
 
- /// <include file = './doc.xml' path='docs/members/member[@name="FastTree"]/*' />
+ /// <include file = 'doc.xml' path='doc/members/member[@name="FastTree"]/*' />
  public sealed partial class FastTreeBinaryClassificationTrainer :
  BoostingFastTreeTrainerBase<FastTreeBinaryClassificationTrainer.Arguments, IPredictorWithFeatureWeights<Float>>
  {
@@ -342,7 +342,8 @@ public static partial class FastTree
  Desc = FastTreeBinaryClassificationTrainer.Summary,
  UserName = FastTreeBinaryClassificationTrainer.UserNameValue,
  ShortName = FastTreeBinaryClassificationTrainer.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastTree""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastTree""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastTreeBinaryClassifier""]/*' />" })]
  public static CommonOutputs.BinaryClassificationOutput TrainBinary(IHostEnvironment env, FastTreeBinaryClassificationTrainer.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/FastTreeRanking.cs

@@ -38,7 +38,7 @@
 
 namespace Microsoft.ML.Runtime.FastTree
 {
- /// <include file='./doc.xml' path='docs/members/member[@name="FastTree"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastTree"]/*' />
  public sealed partial class FastTreeRankingTrainer : BoostingFastTreeTrainerBase<FastTreeRankingTrainer.Arguments, FastTreeRankingPredictor>,
  IHasLabelGains
  {
@@ -1096,7 +1096,8 @@ public static partial class FastTree
  Desc = FastTreeRankingTrainer.Summary,
  UserName = FastTreeRankingTrainer.UserNameValue,
  ShortName = FastTreeRankingTrainer.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastTree""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastTree""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastTreeRanker""]/*' />"})]
  public static CommonOutputs.RankingOutput TrainRanking(IHostEnvironment env, FastTreeRankingTrainer.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/FastTreeRegression.cs

@@ -31,7 +31,7 @@
 
 namespace Microsoft.ML.Runtime.FastTree
 {
- /// <include file='./doc.xml' path='docs/members/member[@name="FastTree"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastTree"]/*' />
  public sealed partial class FastTreeRegressionTrainer : BoostingFastTreeTrainerBase<FastTreeRegressionTrainer.Arguments, FastTreeRegressionPredictor>
  {
  public const string LoadNameValue = "FastTreeRegression";
@@ -445,7 +445,8 @@ public static partial class FastTree
  Desc = FastTreeRegressionTrainer.Summary,
  UserName = FastTreeRegressionTrainer.UserNameValue,
  ShortName = FastTreeRegressionTrainer.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastTree""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastTree""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastTreeRegressor""]/*' />"})]
  public static CommonOutputs.RegressionOutput TrainRegression(IHostEnvironment env, FastTreeRegressionTrainer.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/FastTreeTweedie.cs

@@ -30,7 +30,7 @@ namespace Microsoft.ML.Runtime.FastTree
  // The Tweedie boosting model follows the mathematics established in:
  // Yang, Quan, and Zou. "Insurance Premium Prediction via Gradient Tree-Boosted Tweedie Compound Poisson Models."
  // https://arxiv.org/pdf/1508.06378.pdf
- /// <include file='./doc.xml' path='docs/members/member[@name="FastTreeTweedieRegression"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastTreeTweedieRegression"]/*' />
  public sealed partial class FastTreeTweedieTrainer : BoostingFastTreeTrainerBase<FastTreeTweedieTrainer.Arguments, FastTreeTweediePredictor>
  {
  public const string LoadNameValue = "FastTreeTweedieRegression";
@@ -454,7 +454,7 @@ public static partial class FastTree
  Desc = FastTreeTweedieTrainer.Summary,
  UserName = FastTreeTweedieTrainer.UserNameValue,
  ShortName = FastTreeTweedieTrainer.ShortName,
- XmlInclude = new [] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastTreeTweedieRegression""]/*' />" })]
+ XmlInclude = new [] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastTreeTweedieRegression""]/*' />" })]
  public static CommonOutputs.RegressionOutput TrainTweedieRegression(IHostEnvironment env, FastTreeTweedieTrainer.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/RandomForestClassification.cs

@@ -106,7 +106,7 @@ public static IPredictorProducing<Float> Create(IHostEnvironment env, ModelLoadC
  }
  }
 
- /// <include file='./doc.xml' path='docs/members/member[@name="FastForest"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastForest"]/*' />
  public sealed partial class FastForestClassification :
  RandomForestTrainerBase<FastForestClassification.Arguments, IPredictorWithFeatureWeights<Float>>
  {
@@ -206,7 +206,8 @@ public static partial class FastForest
  Desc = FastForestClassification.Summary,
  UserName = FastForestClassification.UserNameValue,
  ShortName = FastForestClassification.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastForest""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastForest""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastForestBinaryClassifier""]/*' />"})]
  public static CommonOutputs.BinaryClassificationOutput TrainBinary(IHostEnvironment env, FastForestClassification.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/RandomForestRegression.cs

@@ -137,7 +137,7 @@ public ISchemaBindableMapper CreateMapper(Double[] quantiles)
  }
  }
 
- /// <include file='./doc.xml' path='docs/members/member[@name="FastForest"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastForest"]/*' />
  public sealed partial class FastForestRegression : RandomForestTrainerBase<FastForestRegression.Arguments, FastForestRegressionPredictor>
  {
  public sealed class Arguments : FastForestArgumentsBase
@@ -277,7 +277,8 @@ public static partial class FastForest
  Desc = FastForestRegression.Summary,
  UserName = FastForestRegression.LoadNameValue,
  ShortName = FastForestRegression.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastForest""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastForest""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastForestRegressor""]/*' />"})]
  public static CommonOutputs.RegressionOutput TrainRegression(IHostEnvironment env, FastForestRegression.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/Training/Parallel/IParallelTraining.cs

@@ -33,20 +33,20 @@ public delegate void FindBestThresholdFromRawArrayFun(LeafSplitCandidates leafSp
  /// <summary>
  /// Interface used for parallel training.
  /// Mainly contains three parts:
- /// 1. interactive with IO: <see href="GetLocalBinConstructionFeatures" />, <see href="SyncGlobalBoundary" />.
+ /// 1. interactive with IO: <see cref="GetLocalBinConstructionFeatures" />, <see cref="SyncGlobalBoundary" />.
  /// Data will be partitioned by rows in Data parallel and Voting Parallel.
  /// To speed up the find bin process, it let different workers to find bins for different features.
  /// Then perform global sync up.
  /// In Feature parallel, every machines holds all data, so this is unneeded.
- /// 2. interactive with TreeLearner: <see href="InitIteration" />, <see href="CacheHistogram" />, <see href="IsNeedFindLocalBestSplit" />, 
- /// <see href="IsSkipNonSplittableHistogram" />, <see href="FindGlobalBestSplit" />, <see href="GetGlobalDataCountInLeaf" />, <see href="PerformGlobalSplit" />.
+ /// 2. interactive with TreeLearner: <see cref="InitIteration" />, <see cref="CacheHistogram" />, <see cref="IsNeedFindLocalBestSplit" />, 
+ /// <see cref="IsSkipNonSplittableHistogram" />, <see cref="FindGlobalBestSplit" />, <see cref="GetGlobalDataCountInLeaf" />, <see cref="PerformGlobalSplit" />.
  /// A full process is:
- /// Use <see href="InitIteration" /> to alter local active features.
- /// Use <see href="GetGlobalDataCountInLeaf" /> to check smaller leaf and larger leaf.
- /// Use <see href="CacheHistogram" />, <see href="IsNeedFindLocalBestSplit" /> and <see href="IsSkipNonSplittableHistogram" /> to interactive with Feature histograms.
- /// Use <see href="FindGlobalBestSplit" /> to sync up global best split
- /// Use <see href="PerformGlobalSplit" /> to record global num_data in leaves.
- /// 3. interactive with Application : <see href="GlobalMean" />.
+ /// Use <see cref="InitIteration" /> to alter local active features.
+ /// Use <see cref="GetGlobalDataCountInLeaf" /> to check smaller leaf and larger leaf.
+ /// Use <see cref="CacheHistogram" />, <see cref="IsNeedFindLocalBestSplit" /> and <see cref="IsSkipNonSplittableHistogram" /> to interactive with Feature histograms.
+ /// Use <see cref="FindGlobalBestSplit" /> to sync up global best split
+ /// Use <see cref="PerformGlobalSplit" /> to record global num_data in leaves.
+ /// 3. interactive with Application : <see cref="GlobalMean" />.
  /// Output of leaves is calculated by newton step ( - sum(first_order_gradients) / sum(second_order_gradients)).
  /// If data is partitioned by row, it needs to a sync up for these sum result.
  /// So It needs to call this to get the real output of leaves.

src/Microsoft.ML.FastTree/TreeEnsembleFeaturizer.cs

@@ -544,6 +544,7 @@ public ISchemaBoundMapper Bind(IHostEnvironment env, RoleMappedSchema schema)
  }
  }
 
+ /// <include file='doc.xml' path='doc/members/member[@name="TreeEnsembleFeaturizerTransform"]'/>
  public static class TreeEnsembleFeaturizerTransform
  {
  public sealed class Arguments : TrainAndScoreTransform.ArgumentsBase<SignatureTreeEnsembleTrainer>
@@ -802,7 +803,11 @@ private static IDataView AppendLabelTransform(IHostEnvironment env, IChannel ch,
 
  public static partial class TreeFeaturize
  {
- [TlcModule.EntryPoint(Name = "Transforms.TreeLeafFeaturizer", Desc = TreeEnsembleFeaturizerTransform.TreeEnsembleSummary, UserName = TreeEnsembleFeaturizerTransform.UserName, ShortName = TreeEnsembleFeaturizerBindableMapper.LoadNameShort)]
+ [TlcModule.EntryPoint(Name = "Transforms.TreeLeafFeaturizer", 
+ Desc = TreeEnsembleFeaturizerTransform.TreeEnsembleSummary, 
+ UserName = TreeEnsembleFeaturizerTransform.UserName, 
+ ShortName = TreeEnsembleFeaturizerBindableMapper.LoadNameShort,
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""TreeEnsembleFeaturizerTransform""]'/>" })]
  public static CommonOutputs.TransformOutput Featurizer(IHostEnvironment env, TreeEnsembleFeaturizerTransform.ArgumentsForEntryPoint input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.Core/Utilities/ReservoirSampler.cs

@@ -47,7 +47,7 @@ public interface IReservoirSampler<T>
  /// This class produces a sample without replacement from a stream of data of type <typeparamref name="T"/>. 
  /// It is instantiated with a delegate that gets the next data point, and builds a reservoir in one pass by calling <see cref="Sample"/> 
  /// for every data point in the stream. In case the next data point does not get 'picked' into the reservoir, the delegate is not invoked.
- /// Sampling is done according to the algorithm in this paper: <see href="http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53"/>.
+ /// Sampling is done according to the algorithm in this paper: <a href="http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53">http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53</a>.
  /// </summary>
  public sealed class ReservoirSamplerWithoutReplacement<T> : IReservoirSampler<T>
  {
@@ -120,7 +120,7 @@ public IEnumerable<T> GetSample()
  /// This class produces a sample with replacement from a stream of data of type <typeparamref name="T"/>. 
  /// It is instantiated with a delegate that gets the next data point, and builds a reservoir in one pass by calling <see cref="Sample"/> 
  /// for every data point in the stream. In case the next data point does not get 'picked' into the reservoir, the delegate is not invoked.
- /// Sampling is done according to the algorithm in this paper: <see href="http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53"/>.
+ /// Sampling is done according to the algorithm in this paper: <a href="http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53">http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53</a>.
  /// </summary>
  public sealed class ReservoirSamplerWithReplacement<T> : IReservoirSampler<T>
  {

src/Microsoft.ML.Data/Evaluators/AucAggregator.cs

@@ -408,7 +408,7 @@ public UnweightedAuPrcAggregator(IRandom rand, int reservoirSize)
 
  /// <summary>
  /// Compute the AUPRC using the "lower trapesoid" estimator, as described in the paper
- /// <see href="http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf"/>.
+ /// <a href="http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf">http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf</a>.
  /// </summary>
  protected override Double ComputeWeightedAuPrcCore(out Double unweighted)
  {
@@ -482,7 +482,7 @@ public WeightedAuPrcAggregator(IRandom rand, int reservoirSize)
 
  /// <summary>
  /// Compute the AUPRC using the "lower trapesoid" estimator, as described in the paper
- /// <see href="http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf"/>.
+ /// <a href="http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf">http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf</a>.
  /// </summary>
  protected override Double ComputeWeightedAuPrcCore(out Double unweighted)
  {

src/Microsoft.ML.Data/Transforms/NAFilter.cs

@@ -26,6 +26,7 @@
 
 namespace Microsoft.ML.Runtime.Data
 {
+ /// <include file='doc.xml' path='doc/members/member[@name="NAFilter"]'/>
  public sealed class NAFilter : FilterBase
  {
  private static class Defaults

src/Microsoft.ML.Data/Transforms/TermTransform.cs

@@ -29,14 +29,14 @@
 
 namespace Microsoft.ML.Runtime.Data
 {
- /// <summary>
- /// TermTransform builds up term vocabularies (dictionaries).
- /// Notes:
- /// * Each column builds/uses exactly one "vocabulary" (dictionary).
- /// * Output columns are KeyType-valued.
- /// * The Key value is the one-based index of the item in the dictionary.
- /// * Not found is assigned the value zero.
- /// </summary>
+
+ // TermTransform builds up term vocabularies (dictionaries).
+ // Notes:
+ // * Each column builds/uses exactly one "vocabulary" (dictionary).
+ // * Output columns are KeyType-valued.
+ // * The Key value is the one-based index of the item in the dictionary.
+ // * Not found is assigned the value zero.
+ /// <include file='doc.xml' path='doc/members/member[@name="TextToKey"]/*' />
  public sealed partial class TermTransform : OneToOneTransformBase, ITransformTemplate
  {
  public abstract class ColumnBase : OneToOneColumn

src/Microsoft.ML.Data/Transforms/doc.xml

@@ -0,0 +1,54 @@
+<?xml version="1.0" encoding="utf-8" ?>
+<doc>
+ <members>
+ <member name="NAFilter">
+ <summary>
+ Removes missing values from vector type columns.
+ </summary>
+ <remarks>
+ This transform removes the entire row if any of the input columns have a missing value in that row.
+ This preprocessing is required for many ML algorithms that cannot work with missing values.
+ Useful if any missing entry invalidates the entire row.
+ If the <see cref="Microsoft.ML.Runtime.Data.NAFilter.Defaults.Complement"/> is set to true, this transform would do the exact opposite,
+ it will keep only the rows that have missing values.
+ </remarks>
+ <seealso cref="Microsoft.ML.Runtime.Data.MetadataUtils.Kinds.HasMissingValues"></seealso>
+ </member>
+ <example name="NAFilter">
+ <example>
+ <code language="csharp">
+ pipeline.Add(new MissingValuesRowDropper(&quot;Column1&quot;));
+ </code>
+ </example>
+ </example>
+
+ <member name="TextToKey">
+ <summary>
+ Converts input values (words, numbers, etc.) to index in a dictionary.
+ </summary>
+ <remarks>
+ The TextToKeyConverter transform builds up term vocabularies (dictionaries).
+ The TextToKey Converter and the <see cref="T:Microsoft.ML.Transforms.HashConverter"/> are the two one primary mechanisms by which raw input is transformed into keys.
+ If multiple columns are used, each column builds/uses exactly one vocabulary.
+ The output columns are KeyType-valued.
+ The Key value is the one-based index of the item in the dictionary.
+ If the key is not found in the dictionary, it is assigned the missing value indicator.
+ This dictionary mapping values to keys is most commonly learnt from the unique values in input data,
+ but can be defined through other means: either with the mapping defined directly on the command line, or as loaded from an external file.
+ </remarks>
+ <seealso cref="T:Microsoft.ML.Transforms.HashConverter"/>
+ <seealso cref="T:Microsoft.ML.Transforms.KeyToTextConverter"/>
+ </member>
+ <example name="TextToKey">
+ <example>
+ <code language="csharp">
+ pipeline.Add(new TextToKeyConverter((&quot;Column&quot;, &quot;OutColumn&quot;))
+ { 
+ Sort = TermTransformSortOrder.Occurrence 
+ });
+ </code>
+ </example>
+ </example>
+
+ </members>
+</doc>

src/Microsoft.ML.FastTree/FastTreeArguments.cs

@@ -20,7 +20,7 @@ public interface IFastTreeTrainerFactory : IComponentFactory<ITrainer>
  {
  }
 
- /// <include file='./doc.xml' path='docs/members/member[@name="FastTree"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastTree"]/*' />
  public sealed partial class FastTreeBinaryClassificationTrainer
  {
  [TlcModule.Component(Name = LoadNameValue, FriendlyName = UserNameValue, Desc = Summary)]

src/Microsoft.ML.FastTree/FastTreeClassification.cs

@@ -100,7 +100,7 @@ public static IPredictorProducing<Float> Create(IHostEnvironment env, ModelLoadC
  public override PredictionKind PredictionKind => PredictionKind.BinaryClassification;
  }
 
- /// <include file = './doc.xml' path='docs/members/member[@name="FastTree"]/*' />
+ /// <include file = 'doc.xml' path='doc/members/member[@name="FastTree"]/*' />
  public sealed partial class FastTreeBinaryClassificationTrainer :
  BoostingFastTreeTrainerBase<FastTreeBinaryClassificationTrainer.Arguments, IPredictorWithFeatureWeights<Float>>
  {
@@ -342,7 +342,8 @@ public static partial class FastTree
  Desc = FastTreeBinaryClassificationTrainer.Summary,
  UserName = FastTreeBinaryClassificationTrainer.UserNameValue,
  ShortName = FastTreeBinaryClassificationTrainer.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastTree""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastTree""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastTreeBinaryClassifier""]/*' />" })]
  public static CommonOutputs.BinaryClassificationOutput TrainBinary(IHostEnvironment env, FastTreeBinaryClassificationTrainer.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/FastTreeRanking.cs

@@ -38,7 +38,7 @@
 
 namespace Microsoft.ML.Runtime.FastTree
 {
- /// <include file='./doc.xml' path='docs/members/member[@name="FastTree"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastTree"]/*' />
  public sealed partial class FastTreeRankingTrainer : BoostingFastTreeTrainerBase<FastTreeRankingTrainer.Arguments, FastTreeRankingPredictor>,
  IHasLabelGains
  {
@@ -1096,7 +1096,8 @@ public static partial class FastTree
  Desc = FastTreeRankingTrainer.Summary,
  UserName = FastTreeRankingTrainer.UserNameValue,
  ShortName = FastTreeRankingTrainer.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastTree""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastTree""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastTreeRanker""]/*' />"})]
  public static CommonOutputs.RankingOutput TrainRanking(IHostEnvironment env, FastTreeRankingTrainer.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/FastTreeRegression.cs

@@ -31,7 +31,7 @@
 
 namespace Microsoft.ML.Runtime.FastTree
 {
- /// <include file='./doc.xml' path='docs/members/member[@name="FastTree"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastTree"]/*' />
  public sealed partial class FastTreeRegressionTrainer : BoostingFastTreeTrainerBase<FastTreeRegressionTrainer.Arguments, FastTreeRegressionPredictor>
  {
  public const string LoadNameValue = "FastTreeRegression";
@@ -445,7 +445,8 @@ public static partial class FastTree
  Desc = FastTreeRegressionTrainer.Summary,
  UserName = FastTreeRegressionTrainer.UserNameValue,
  ShortName = FastTreeRegressionTrainer.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastTree""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastTree""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastTreeRegressor""]/*' />"})]
  public static CommonOutputs.RegressionOutput TrainRegression(IHostEnvironment env, FastTreeRegressionTrainer.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/FastTreeTweedie.cs

@@ -30,7 +30,7 @@ namespace Microsoft.ML.Runtime.FastTree
  // The Tweedie boosting model follows the mathematics established in:
  // Yang, Quan, and Zou. "Insurance Premium Prediction via Gradient Tree-Boosted Tweedie Compound Poisson Models."
  // https://arxiv.org/pdf/1508.06378.pdf
- /// <include file='./doc.xml' path='docs/members/member[@name="FastTreeTweedieRegression"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastTreeTweedieRegression"]/*' />
  public sealed partial class FastTreeTweedieTrainer : BoostingFastTreeTrainerBase<FastTreeTweedieTrainer.Arguments, FastTreeTweediePredictor>
  {
  public const string LoadNameValue = "FastTreeTweedieRegression";
@@ -454,7 +454,7 @@ public static partial class FastTree
  Desc = FastTreeTweedieTrainer.Summary,
  UserName = FastTreeTweedieTrainer.UserNameValue,
  ShortName = FastTreeTweedieTrainer.ShortName,
- XmlInclude = new [] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastTreeTweedieRegression""]/*' />" })]
+ XmlInclude = new [] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastTreeTweedieRegression""]/*' />" })]
  public static CommonOutputs.RegressionOutput TrainTweedieRegression(IHostEnvironment env, FastTreeTweedieTrainer.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/RandomForestClassification.cs

@@ -106,7 +106,7 @@ public static IPredictorProducing<Float> Create(IHostEnvironment env, ModelLoadC
  }
  }
 
- /// <include file='./doc.xml' path='docs/members/member[@name="FastForest"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastForest"]/*' />
  public sealed partial class FastForestClassification :
  RandomForestTrainerBase<FastForestClassification.Arguments, IPredictorWithFeatureWeights<Float>>
  {
@@ -206,7 +206,8 @@ public static partial class FastForest
  Desc = FastForestClassification.Summary,
  UserName = FastForestClassification.UserNameValue,
  ShortName = FastForestClassification.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastForest""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastForest""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastForestBinaryClassifier""]/*' />"})]
  public static CommonOutputs.BinaryClassificationOutput TrainBinary(IHostEnvironment env, FastForestClassification.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/RandomForestRegression.cs

@@ -137,7 +137,7 @@ public ISchemaBindableMapper CreateMapper(Double[] quantiles)
  }
  }
 
- /// <include file='./doc.xml' path='docs/members/member[@name="FastForest"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastForest"]/*' />
  public sealed partial class FastForestRegression : RandomForestTrainerBase<FastForestRegression.Arguments, FastForestRegressionPredictor>
  {
  public sealed class Arguments : FastForestArgumentsBase
@@ -277,7 +277,8 @@ public static partial class FastForest
  Desc = FastForestRegression.Summary,
  UserName = FastForestRegression.LoadNameValue,
  ShortName = FastForestRegression.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastForest""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastForest""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastForestRegressor""]/*' />"})]
  public static CommonOutputs.RegressionOutput TrainRegression(IHostEnvironment env, FastForestRegression.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/Training/Parallel/IParallelTraining.cs

@@ -33,20 +33,20 @@ public delegate void FindBestThresholdFromRawArrayFun(LeafSplitCandidates leafSp
  /// <summary>
  /// Interface used for parallel training.
  /// Mainly contains three parts:
- /// 1. interactive with IO: <see href="GetLocalBinConstructionFeatures" />, <see href="SyncGlobalBoundary" />.
+ /// 1. interactive with IO: <see cref="GetLocalBinConstructionFeatures" />, <see cref="SyncGlobalBoundary" />.
  /// Data will be partitioned by rows in Data parallel and Voting Parallel.
  /// To speed up the find bin process, it let different workers to find bins for different features.
  /// Then perform global sync up.
  /// In Feature parallel, every machines holds all data, so this is unneeded.
- /// 2. interactive with TreeLearner: <see href="InitIteration" />, <see href="CacheHistogram" />, <see href="IsNeedFindLocalBestSplit" />, 
- /// <see href="IsSkipNonSplittableHistogram" />, <see href="FindGlobalBestSplit" />, <see href="GetGlobalDataCountInLeaf" />, <see href="PerformGlobalSplit" />.
+ /// 2. interactive with TreeLearner: <see cref="InitIteration" />, <see cref="CacheHistogram" />, <see cref="IsNeedFindLocalBestSplit" />, 
+ /// <see cref="IsSkipNonSplittableHistogram" />, <see cref="FindGlobalBestSplit" />, <see cref="GetGlobalDataCountInLeaf" />, <see cref="PerformGlobalSplit" />.
  /// A full process is:
- /// Use <see href="InitIteration" /> to alter local active features.
- /// Use <see href="GetGlobalDataCountInLeaf" /> to check smaller leaf and larger leaf.
- /// Use <see href="CacheHistogram" />, <see href="IsNeedFindLocalBestSplit" /> and <see href="IsSkipNonSplittableHistogram" /> to interactive with Feature histograms.
- /// Use <see href="FindGlobalBestSplit" /> to sync up global best split
- /// Use <see href="PerformGlobalSplit" /> to record global num_data in leaves.
- /// 3. interactive with Application : <see href="GlobalMean" />.
+ /// Use <see cref="InitIteration" /> to alter local active features.
+ /// Use <see cref="GetGlobalDataCountInLeaf" /> to check smaller leaf and larger leaf.
+ /// Use <see cref="CacheHistogram" />, <see cref="IsNeedFindLocalBestSplit" /> and <see cref="IsSkipNonSplittableHistogram" /> to interactive with Feature histograms.
+ /// Use <see cref="FindGlobalBestSplit" /> to sync up global best split
+ /// Use <see cref="PerformGlobalSplit" /> to record global num_data in leaves.
+ /// 3. interactive with Application : <see cref="GlobalMean" />.
  /// Output of leaves is calculated by newton step ( - sum(first_order_gradients) / sum(second_order_gradients)).
  /// If data is partitioned by row, it needs to a sync up for these sum result.
  /// So It needs to call this to get the real output of leaves.

src/Microsoft.ML.FastTree/TreeEnsembleFeaturizer.cs

@@ -544,6 +544,7 @@ public ISchemaBoundMapper Bind(IHostEnvironment env, RoleMappedSchema schema)
  }
  }
 
+ /// <include file='doc.xml' path='doc/members/member[@name="TreeEnsembleFeaturizerTransform"]'/>
  public static class TreeEnsembleFeaturizerTransform
  {
  public sealed class Arguments : TrainAndScoreTransform.ArgumentsBase<SignatureTreeEnsembleTrainer>
@@ -802,7 +803,11 @@ private static IDataView AppendLabelTransform(IHostEnvironment env, IChannel ch,
 
  public static partial class TreeFeaturize
  {
- [TlcModule.EntryPoint(Name = "Transforms.TreeLeafFeaturizer", Desc = TreeEnsembleFeaturizerTransform.TreeEnsembleSummary, UserName = TreeEnsembleFeaturizerTransform.UserName, ShortName = TreeEnsembleFeaturizerBindableMapper.LoadNameShort)]
+ [TlcModule.EntryPoint(Name = "Transforms.TreeLeafFeaturizer", 
+ Desc = TreeEnsembleFeaturizerTransform.TreeEnsembleSummary, 
+ UserName = TreeEnsembleFeaturizerTransform.UserName, 
+ ShortName = TreeEnsembleFeaturizerBindableMapper.LoadNameShort,
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""TreeEnsembleFeaturizerTransform""]'/>" })]
  public static CommonOutputs.TransformOutput Featurizer(IHostEnvironment env, TreeEnsembleFeaturizerTransform.ArgumentsForEntryPoint input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.Core/Utilities/ReservoirSampler.cs

@@ -47,7 +47,7 @@ public interface IReservoirSampler<T>
  /// This class produces a sample without replacement from a stream of data of type <typeparamref name="T"/>. 
  /// It is instantiated with a delegate that gets the next data point, and builds a reservoir in one pass by calling <see cref="Sample"/> 
  /// for every data point in the stream. In case the next data point does not get 'picked' into the reservoir, the delegate is not invoked.
- /// Sampling is done according to the algorithm in this paper: <see href="http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53"/>.
+ /// Sampling is done according to the algorithm in this paper: <a href="http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53">http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53</a>.
  /// </summary>
  public sealed class ReservoirSamplerWithoutReplacement<T> : IReservoirSampler<T>
  {
@@ -120,7 +120,7 @@ public IEnumerable<T> GetSample()
  /// This class produces a sample with replacement from a stream of data of type <typeparamref name="T"/>. 
  /// It is instantiated with a delegate that gets the next data point, and builds a reservoir in one pass by calling <see cref="Sample"/> 
  /// for every data point in the stream. In case the next data point does not get 'picked' into the reservoir, the delegate is not invoked.
- /// Sampling is done according to the algorithm in this paper: <see href="http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53"/>.
+ /// Sampling is done according to the algorithm in this paper: <a href="http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53">http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53</a>.
  /// </summary>
  public sealed class ReservoirSamplerWithReplacement<T> : IReservoirSampler<T>
  {

src/Microsoft.ML.Data/Evaluators/AucAggregator.cs

@@ -408,7 +408,7 @@ public UnweightedAuPrcAggregator(IRandom rand, int reservoirSize)
 
  /// <summary>
  /// Compute the AUPRC using the "lower trapesoid" estimator, as described in the paper
- /// <see href="http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf"/>.
+ /// <a href="http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf">http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf</a>.
  /// </summary>
  protected override Double ComputeWeightedAuPrcCore(out Double unweighted)
  {
@@ -482,7 +482,7 @@ public WeightedAuPrcAggregator(IRandom rand, int reservoirSize)
 
  /// <summary>
  /// Compute the AUPRC using the "lower trapesoid" estimator, as described in the paper
- /// <see href="http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf"/>.
+ /// <a href="http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf">http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf</a>.
  /// </summary>
  protected override Double ComputeWeightedAuPrcCore(out Double unweighted)
  {

src/Microsoft.ML.Data/Transforms/NAFilter.cs

@@ -26,6 +26,7 @@
 
 namespace Microsoft.ML.Runtime.Data
 {
+ /// <include file='doc.xml' path='doc/members/member[@name="NAFilter"]'/>
  public sealed class NAFilter : FilterBase
  {
  private static class Defaults

src/Microsoft.ML.Data/Transforms/TermTransform.cs

@@ -29,14 +29,14 @@
 
 namespace Microsoft.ML.Runtime.Data
 {
- /// <summary>
- /// TermTransform builds up term vocabularies (dictionaries).
- /// Notes:
- /// * Each column builds/uses exactly one "vocabulary" (dictionary).
- /// * Output columns are KeyType-valued.
- /// * The Key value is the one-based index of the item in the dictionary.
- /// * Not found is assigned the value zero.
- /// </summary>
+
+ // TermTransform builds up term vocabularies (dictionaries).
+ // Notes:
+ // * Each column builds/uses exactly one "vocabulary" (dictionary).
+ // * Output columns are KeyType-valued.
+ // * The Key value is the one-based index of the item in the dictionary.
+ // * Not found is assigned the value zero.
+ /// <include file='doc.xml' path='doc/members/member[@name="TextToKey"]/*' />
  public sealed partial class TermTransform : OneToOneTransformBase, ITransformTemplate
  {
  public abstract class ColumnBase : OneToOneColumn

src/Microsoft.ML.Data/Transforms/doc.xml

@@ -0,0 +1,54 @@
+<?xml version="1.0" encoding="utf-8" ?>
+<doc>
+ <members>
+ <member name="NAFilter">
+ <summary>
+ Removes missing values from vector type columns.
+ </summary>
+ <remarks>
+ This transform removes the entire row if any of the input columns have a missing value in that row.
+ This preprocessing is required for many ML algorithms that cannot work with missing values.
+ Useful if any missing entry invalidates the entire row.
+ If the <see cref="Microsoft.ML.Runtime.Data.NAFilter.Defaults.Complement"/> is set to true, this transform would do the exact opposite,
+ it will keep only the rows that have missing values.
+ </remarks>
+ <seealso cref="Microsoft.ML.Runtime.Data.MetadataUtils.Kinds.HasMissingValues"></seealso>
+ </member>
+ <example name="NAFilter">
+ <example>
+ <code language="csharp">
+ pipeline.Add(new MissingValuesRowDropper(&quot;Column1&quot;));
+ </code>
+ </example>
+ </example>
+
+ <member name="TextToKey">
+ <summary>
+ Converts input values (words, numbers, etc.) to index in a dictionary.
+ </summary>
+ <remarks>
+ The TextToKeyConverter transform builds up term vocabularies (dictionaries).
+ The TextToKey Converter and the <see cref="T:Microsoft.ML.Transforms.HashConverter"/> are the two one primary mechanisms by which raw input is transformed into keys.
+ If multiple columns are used, each column builds/uses exactly one vocabulary.
+ The output columns are KeyType-valued.
+ The Key value is the one-based index of the item in the dictionary.
+ If the key is not found in the dictionary, it is assigned the missing value indicator.
+ This dictionary mapping values to keys is most commonly learnt from the unique values in input data,
+ but can be defined through other means: either with the mapping defined directly on the command line, or as loaded from an external file.
+ </remarks>
+ <seealso cref="T:Microsoft.ML.Transforms.HashConverter"/>
+ <seealso cref="T:Microsoft.ML.Transforms.KeyToTextConverter"/>
+ </member>
+ <example name="TextToKey">
+ <example>
+ <code language="csharp">
+ pipeline.Add(new TextToKeyConverter((&quot;Column&quot;, &quot;OutColumn&quot;))
+ { 
+ Sort = TermTransformSortOrder.Occurrence 
+ });
+ </code>
+ </example>
+ </example>
+
+ </members>
+</doc>

src/Microsoft.ML.FastTree/FastTreeArguments.cs

@@ -20,7 +20,7 @@ public interface IFastTreeTrainerFactory : IComponentFactory<ITrainer>
  {
  }
 
- /// <include file='./doc.xml' path='docs/members/member[@name="FastTree"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastTree"]/*' />
  public sealed partial class FastTreeBinaryClassificationTrainer
  {
  [TlcModule.Component(Name = LoadNameValue, FriendlyName = UserNameValue, Desc = Summary)]

src/Microsoft.ML.FastTree/FastTreeClassification.cs

@@ -100,7 +100,7 @@ public static IPredictorProducing<Float> Create(IHostEnvironment env, ModelLoadC
  public override PredictionKind PredictionKind => PredictionKind.BinaryClassification;
  }
 
- /// <include file = './doc.xml' path='docs/members/member[@name="FastTree"]/*' />
+ /// <include file = 'doc.xml' path='doc/members/member[@name="FastTree"]/*' />
  public sealed partial class FastTreeBinaryClassificationTrainer :
  BoostingFastTreeTrainerBase<FastTreeBinaryClassificationTrainer.Arguments, IPredictorWithFeatureWeights<Float>>
  {
@@ -342,7 +342,8 @@ public static partial class FastTree
  Desc = FastTreeBinaryClassificationTrainer.Summary,
  UserName = FastTreeBinaryClassificationTrainer.UserNameValue,
  ShortName = FastTreeBinaryClassificationTrainer.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastTree""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastTree""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastTreeBinaryClassifier""]/*' />" })]
  public static CommonOutputs.BinaryClassificationOutput TrainBinary(IHostEnvironment env, FastTreeBinaryClassificationTrainer.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/FastTreeRanking.cs

@@ -38,7 +38,7 @@
 
 namespace Microsoft.ML.Runtime.FastTree
 {
- /// <include file='./doc.xml' path='docs/members/member[@name="FastTree"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastTree"]/*' />
  public sealed partial class FastTreeRankingTrainer : BoostingFastTreeTrainerBase<FastTreeRankingTrainer.Arguments, FastTreeRankingPredictor>,
  IHasLabelGains
  {
@@ -1096,7 +1096,8 @@ public static partial class FastTree
  Desc = FastTreeRankingTrainer.Summary,
  UserName = FastTreeRankingTrainer.UserNameValue,
  ShortName = FastTreeRankingTrainer.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastTree""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastTree""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastTreeRanker""]/*' />"})]
  public static CommonOutputs.RankingOutput TrainRanking(IHostEnvironment env, FastTreeRankingTrainer.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/FastTreeRegression.cs

@@ -31,7 +31,7 @@
 
 namespace Microsoft.ML.Runtime.FastTree
 {
- /// <include file='./doc.xml' path='docs/members/member[@name="FastTree"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastTree"]/*' />
  public sealed partial class FastTreeRegressionTrainer : BoostingFastTreeTrainerBase<FastTreeRegressionTrainer.Arguments, FastTreeRegressionPredictor>
  {
  public const string LoadNameValue = "FastTreeRegression";
@@ -445,7 +445,8 @@ public static partial class FastTree
  Desc = FastTreeRegressionTrainer.Summary,
  UserName = FastTreeRegressionTrainer.UserNameValue,
  ShortName = FastTreeRegressionTrainer.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastTree""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastTree""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastTreeRegressor""]/*' />"})]
  public static CommonOutputs.RegressionOutput TrainRegression(IHostEnvironment env, FastTreeRegressionTrainer.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/FastTreeTweedie.cs

@@ -30,7 +30,7 @@ namespace Microsoft.ML.Runtime.FastTree
  // The Tweedie boosting model follows the mathematics established in:
  // Yang, Quan, and Zou. "Insurance Premium Prediction via Gradient Tree-Boosted Tweedie Compound Poisson Models."
  // https://arxiv.org/pdf/1508.06378.pdf
- /// <include file='./doc.xml' path='docs/members/member[@name="FastTreeTweedieRegression"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastTreeTweedieRegression"]/*' />
  public sealed partial class FastTreeTweedieTrainer : BoostingFastTreeTrainerBase<FastTreeTweedieTrainer.Arguments, FastTreeTweediePredictor>
  {
  public const string LoadNameValue = "FastTreeTweedieRegression";
@@ -454,7 +454,7 @@ public static partial class FastTree
  Desc = FastTreeTweedieTrainer.Summary,
  UserName = FastTreeTweedieTrainer.UserNameValue,
  ShortName = FastTreeTweedieTrainer.ShortName,
- XmlInclude = new [] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastTreeTweedieRegression""]/*' />" })]
+ XmlInclude = new [] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastTreeTweedieRegression""]/*' />" })]
  public static CommonOutputs.RegressionOutput TrainTweedieRegression(IHostEnvironment env, FastTreeTweedieTrainer.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/RandomForestClassification.cs

@@ -106,7 +106,7 @@ public static IPredictorProducing<Float> Create(IHostEnvironment env, ModelLoadC
  }
  }
 
- /// <include file='./doc.xml' path='docs/members/member[@name="FastForest"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastForest"]/*' />
  public sealed partial class FastForestClassification :
  RandomForestTrainerBase<FastForestClassification.Arguments, IPredictorWithFeatureWeights<Float>>
  {
@@ -206,7 +206,8 @@ public static partial class FastForest
  Desc = FastForestClassification.Summary,
  UserName = FastForestClassification.UserNameValue,
  ShortName = FastForestClassification.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastForest""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastForest""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastForestBinaryClassifier""]/*' />"})]
  public static CommonOutputs.BinaryClassificationOutput TrainBinary(IHostEnvironment env, FastForestClassification.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/RandomForestRegression.cs

@@ -137,7 +137,7 @@ public ISchemaBindableMapper CreateMapper(Double[] quantiles)
  }
  }
 
- /// <include file='./doc.xml' path='docs/members/member[@name="FastForest"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastForest"]/*' />
  public sealed partial class FastForestRegression : RandomForestTrainerBase<FastForestRegression.Arguments, FastForestRegressionPredictor>
  {
  public sealed class Arguments : FastForestArgumentsBase
@@ -277,7 +277,8 @@ public static partial class FastForest
  Desc = FastForestRegression.Summary,
  UserName = FastForestRegression.LoadNameValue,
  ShortName = FastForestRegression.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastForest""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastForest""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastForestRegressor""]/*' />"})]
  public static CommonOutputs.RegressionOutput TrainRegression(IHostEnvironment env, FastForestRegression.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/Training/Parallel/IParallelTraining.cs

@@ -33,20 +33,20 @@ public delegate void FindBestThresholdFromRawArrayFun(LeafSplitCandidates leafSp
  /// <summary>
  /// Interface used for parallel training.
  /// Mainly contains three parts:
- /// 1. interactive with IO: <see href="GetLocalBinConstructionFeatures" />, <see href="SyncGlobalBoundary" />.
+ /// 1. interactive with IO: <see cref="GetLocalBinConstructionFeatures" />, <see cref="SyncGlobalBoundary" />.
  /// Data will be partitioned by rows in Data parallel and Voting Parallel.
  /// To speed up the find bin process, it let different workers to find bins for different features.
  /// Then perform global sync up.
  /// In Feature parallel, every machines holds all data, so this is unneeded.
- /// 2. interactive with TreeLearner: <see href="InitIteration" />, <see href="CacheHistogram" />, <see href="IsNeedFindLocalBestSplit" />, 
- /// <see href="IsSkipNonSplittableHistogram" />, <see href="FindGlobalBestSplit" />, <see href="GetGlobalDataCountInLeaf" />, <see href="PerformGlobalSplit" />.
+ /// 2. interactive with TreeLearner: <see cref="InitIteration" />, <see cref="CacheHistogram" />, <see cref="IsNeedFindLocalBestSplit" />, 
+ /// <see cref="IsSkipNonSplittableHistogram" />, <see cref="FindGlobalBestSplit" />, <see cref="GetGlobalDataCountInLeaf" />, <see cref="PerformGlobalSplit" />.
  /// A full process is:
- /// Use <see href="InitIteration" /> to alter local active features.
- /// Use <see href="GetGlobalDataCountInLeaf" /> to check smaller leaf and larger leaf.
- /// Use <see href="CacheHistogram" />, <see href="IsNeedFindLocalBestSplit" /> and <see href="IsSkipNonSplittableHistogram" /> to interactive with Feature histograms.
- /// Use <see href="FindGlobalBestSplit" /> to sync up global best split
- /// Use <see href="PerformGlobalSplit" /> to record global num_data in leaves.
- /// 3. interactive with Application : <see href="GlobalMean" />.
+ /// Use <see cref="InitIteration" /> to alter local active features.
+ /// Use <see cref="GetGlobalDataCountInLeaf" /> to check smaller leaf and larger leaf.
+ /// Use <see cref="CacheHistogram" />, <see cref="IsNeedFindLocalBestSplit" /> and <see cref="IsSkipNonSplittableHistogram" /> to interactive with Feature histograms.
+ /// Use <see cref="FindGlobalBestSplit" /> to sync up global best split
+ /// Use <see cref="PerformGlobalSplit" /> to record global num_data in leaves.
+ /// 3. interactive with Application : <see cref="GlobalMean" />.
  /// Output of leaves is calculated by newton step ( - sum(first_order_gradients) / sum(second_order_gradients)).
  /// If data is partitioned by row, it needs to a sync up for these sum result.
  /// So It needs to call this to get the real output of leaves.

src/Microsoft.ML.FastTree/TreeEnsembleFeaturizer.cs

@@ -544,6 +544,7 @@ public ISchemaBoundMapper Bind(IHostEnvironment env, RoleMappedSchema schema)
  }
  }
 
+ /// <include file='doc.xml' path='doc/members/member[@name="TreeEnsembleFeaturizerTransform"]'/>
  public static class TreeEnsembleFeaturizerTransform
  {
  public sealed class Arguments : TrainAndScoreTransform.ArgumentsBase<SignatureTreeEnsembleTrainer>
@@ -802,7 +803,11 @@ private static IDataView AppendLabelTransform(IHostEnvironment env, IChannel ch,
 
  public static partial class TreeFeaturize
  {
- [TlcModule.EntryPoint(Name = "Transforms.TreeLeafFeaturizer", Desc = TreeEnsembleFeaturizerTransform.TreeEnsembleSummary, UserName = TreeEnsembleFeaturizerTransform.UserName, ShortName = TreeEnsembleFeaturizerBindableMapper.LoadNameShort)]
+ [TlcModule.EntryPoint(Name = "Transforms.TreeLeafFeaturizer", 
+ Desc = TreeEnsembleFeaturizerTransform.TreeEnsembleSummary, 
+ UserName = TreeEnsembleFeaturizerTransform.UserName, 
+ ShortName = TreeEnsembleFeaturizerBindableMapper.LoadNameShort,
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""TreeEnsembleFeaturizerTransform""]'/>" })]
  public static CommonOutputs.TransformOutput Featurizer(IHostEnvironment env, TreeEnsembleFeaturizerTransform.ArgumentsForEntryPoint input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.Core/Utilities/ReservoirSampler.cs

@@ -47,7 +47,7 @@ public interface IReservoirSampler<T>
  /// This class produces a sample without replacement from a stream of data of type <typeparamref name="T"/>. 
  /// It is instantiated with a delegate that gets the next data point, and builds a reservoir in one pass by calling <see cref="Sample"/> 
  /// for every data point in the stream. In case the next data point does not get 'picked' into the reservoir, the delegate is not invoked.
- /// Sampling is done according to the algorithm in this paper: <see href="http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53"/>.
+ /// Sampling is done according to the algorithm in this paper: <a href="http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53">http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53</a>.
  /// </summary>
  public sealed class ReservoirSamplerWithoutReplacement<T> : IReservoirSampler<T>
  {
@@ -120,7 +120,7 @@ public IEnumerable<T> GetSample()
  /// This class produces a sample with replacement from a stream of data of type <typeparamref name="T"/>. 
  /// It is instantiated with a delegate that gets the next data point, and builds a reservoir in one pass by calling <see cref="Sample"/> 
  /// for every data point in the stream. In case the next data point does not get 'picked' into the reservoir, the delegate is not invoked.
- /// Sampling is done according to the algorithm in this paper: <see href="http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53"/>.
+ /// Sampling is done according to the algorithm in this paper: <a href="http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53">http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53</a>.
  /// </summary>
  public sealed class ReservoirSamplerWithReplacement<T> : IReservoirSampler<T>
  {

src/Microsoft.ML.Data/Evaluators/AucAggregator.cs

@@ -408,7 +408,7 @@ public UnweightedAuPrcAggregator(IRandom rand, int reservoirSize)
 
  /// <summary>
  /// Compute the AUPRC using the "lower trapesoid" estimator, as described in the paper
- /// <see href="http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf"/>.
+ /// <a href="http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf">http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf</a>.
  /// </summary>
  protected override Double ComputeWeightedAuPrcCore(out Double unweighted)
  {
@@ -482,7 +482,7 @@ public WeightedAuPrcAggregator(IRandom rand, int reservoirSize)
 
  /// <summary>
  /// Compute the AUPRC using the "lower trapesoid" estimator, as described in the paper
- /// <see href="http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf"/>.
+ /// <a href="http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf">http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf</a>.
  /// </summary>
  protected override Double ComputeWeightedAuPrcCore(out Double unweighted)
  {

src/Microsoft.ML.Data/Transforms/NAFilter.cs

@@ -26,6 +26,7 @@
 
 namespace Microsoft.ML.Runtime.Data
 {
+ /// <include file='doc.xml' path='doc/members/member[@name="NAFilter"]'/>
  public sealed class NAFilter : FilterBase
  {
  private static class Defaults

src/Microsoft.ML.Data/Transforms/TermTransform.cs

@@ -29,14 +29,14 @@
 
 namespace Microsoft.ML.Runtime.Data
 {
- /// <summary>
- /// TermTransform builds up term vocabularies (dictionaries).
- /// Notes:
- /// * Each column builds/uses exactly one "vocabulary" (dictionary).
- /// * Output columns are KeyType-valued.
- /// * The Key value is the one-based index of the item in the dictionary.
- /// * Not found is assigned the value zero.
- /// </summary>
+
+ // TermTransform builds up term vocabularies (dictionaries).
+ // Notes:
+ // * Each column builds/uses exactly one "vocabulary" (dictionary).
+ // * Output columns are KeyType-valued.
+ // * The Key value is the one-based index of the item in the dictionary.
+ // * Not found is assigned the value zero.
+ /// <include file='doc.xml' path='doc/members/member[@name="TextToKey"]/*' />
  public sealed partial class TermTransform : OneToOneTransformBase, ITransformTemplate
  {
  public abstract class ColumnBase : OneToOneColumn

src/Microsoft.ML.Data/Transforms/doc.xml

@@ -0,0 +1,54 @@
+<?xml version="1.0" encoding="utf-8" ?>
+<doc>
+ <members>
+ <member name="NAFilter">
+ <summary>
+ Removes missing values from vector type columns.
+ </summary>
+ <remarks>
+ This transform removes the entire row if any of the input columns have a missing value in that row.
+ This preprocessing is required for many ML algorithms that cannot work with missing values.
+ Useful if any missing entry invalidates the entire row.
+ If the <see cref="Microsoft.ML.Runtime.Data.NAFilter.Defaults.Complement"/> is set to true, this transform would do the exact opposite,
+ it will keep only the rows that have missing values.
+ </remarks>
+ <seealso cref="Microsoft.ML.Runtime.Data.MetadataUtils.Kinds.HasMissingValues"></seealso>
+ </member>
+ <example name="NAFilter">
+ <example>
+ <code language="csharp">
+ pipeline.Add(new MissingValuesRowDropper(&quot;Column1&quot;));
+ </code>
+ </example>
+ </example>
+
+ <member name="TextToKey">
+ <summary>
+ Converts input values (words, numbers, etc.) to index in a dictionary.
+ </summary>
+ <remarks>
+ The TextToKeyConverter transform builds up term vocabularies (dictionaries).
+ The TextToKey Converter and the <see cref="T:Microsoft.ML.Transforms.HashConverter"/> are the two one primary mechanisms by which raw input is transformed into keys.
+ If multiple columns are used, each column builds/uses exactly one vocabulary.
+ The output columns are KeyType-valued.
+ The Key value is the one-based index of the item in the dictionary.
+ If the key is not found in the dictionary, it is assigned the missing value indicator.
+ This dictionary mapping values to keys is most commonly learnt from the unique values in input data,
+ but can be defined through other means: either with the mapping defined directly on the command line, or as loaded from an external file.
+ </remarks>
+ <seealso cref="T:Microsoft.ML.Transforms.HashConverter"/>
+ <seealso cref="T:Microsoft.ML.Transforms.KeyToTextConverter"/>
+ </member>
+ <example name="TextToKey">
+ <example>
+ <code language="csharp">
+ pipeline.Add(new TextToKeyConverter((&quot;Column&quot;, &quot;OutColumn&quot;))
+ { 
+ Sort = TermTransformSortOrder.Occurrence 
+ });
+ </code>
+ </example>
+ </example>
+
+ </members>
+</doc>

src/Microsoft.ML.FastTree/FastTreeArguments.cs

@@ -20,7 +20,7 @@ public interface IFastTreeTrainerFactory : IComponentFactory<ITrainer>
  {
  }
 
- /// <include file='./doc.xml' path='docs/members/member[@name="FastTree"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastTree"]/*' />
  public sealed partial class FastTreeBinaryClassificationTrainer
  {
  [TlcModule.Component(Name = LoadNameValue, FriendlyName = UserNameValue, Desc = Summary)]

src/Microsoft.ML.FastTree/FastTreeClassification.cs

@@ -100,7 +100,7 @@ public static IPredictorProducing<Float> Create(IHostEnvironment env, ModelLoadC
  public override PredictionKind PredictionKind => PredictionKind.BinaryClassification;
  }
 
- /// <include file = './doc.xml' path='docs/members/member[@name="FastTree"]/*' />
+ /// <include file = 'doc.xml' path='doc/members/member[@name="FastTree"]/*' />
  public sealed partial class FastTreeBinaryClassificationTrainer :
  BoostingFastTreeTrainerBase<FastTreeBinaryClassificationTrainer.Arguments, IPredictorWithFeatureWeights<Float>>
  {
@@ -342,7 +342,8 @@ public static partial class FastTree
  Desc = FastTreeBinaryClassificationTrainer.Summary,
  UserName = FastTreeBinaryClassificationTrainer.UserNameValue,
  ShortName = FastTreeBinaryClassificationTrainer.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastTree""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastTree""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastTreeBinaryClassifier""]/*' />" })]
  public static CommonOutputs.BinaryClassificationOutput TrainBinary(IHostEnvironment env, FastTreeBinaryClassificationTrainer.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/FastTreeRanking.cs

@@ -38,7 +38,7 @@
 
 namespace Microsoft.ML.Runtime.FastTree
 {
- /// <include file='./doc.xml' path='docs/members/member[@name="FastTree"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastTree"]/*' />
  public sealed partial class FastTreeRankingTrainer : BoostingFastTreeTrainerBase<FastTreeRankingTrainer.Arguments, FastTreeRankingPredictor>,
  IHasLabelGains
  {
@@ -1096,7 +1096,8 @@ public static partial class FastTree
  Desc = FastTreeRankingTrainer.Summary,
  UserName = FastTreeRankingTrainer.UserNameValue,
  ShortName = FastTreeRankingTrainer.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastTree""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastTree""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastTreeRanker""]/*' />"})]
  public static CommonOutputs.RankingOutput TrainRanking(IHostEnvironment env, FastTreeRankingTrainer.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/FastTreeRegression.cs

@@ -31,7 +31,7 @@
 
 namespace Microsoft.ML.Runtime.FastTree
 {
- /// <include file='./doc.xml' path='docs/members/member[@name="FastTree"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastTree"]/*' />
  public sealed partial class FastTreeRegressionTrainer : BoostingFastTreeTrainerBase<FastTreeRegressionTrainer.Arguments, FastTreeRegressionPredictor>
  {
  public const string LoadNameValue = "FastTreeRegression";
@@ -445,7 +445,8 @@ public static partial class FastTree
  Desc = FastTreeRegressionTrainer.Summary,
  UserName = FastTreeRegressionTrainer.UserNameValue,
  ShortName = FastTreeRegressionTrainer.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastTree""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastTree""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastTreeRegressor""]/*' />"})]
  public static CommonOutputs.RegressionOutput TrainRegression(IHostEnvironment env, FastTreeRegressionTrainer.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/FastTreeTweedie.cs

@@ -30,7 +30,7 @@ namespace Microsoft.ML.Runtime.FastTree
  // The Tweedie boosting model follows the mathematics established in:
  // Yang, Quan, and Zou. "Insurance Premium Prediction via Gradient Tree-Boosted Tweedie Compound Poisson Models."
  // https://arxiv.org/pdf/1508.06378.pdf
- /// <include file='./doc.xml' path='docs/members/member[@name="FastTreeTweedieRegression"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastTreeTweedieRegression"]/*' />
  public sealed partial class FastTreeTweedieTrainer : BoostingFastTreeTrainerBase<FastTreeTweedieTrainer.Arguments, FastTreeTweediePredictor>
  {
  public const string LoadNameValue = "FastTreeTweedieRegression";
@@ -454,7 +454,7 @@ public static partial class FastTree
  Desc = FastTreeTweedieTrainer.Summary,
  UserName = FastTreeTweedieTrainer.UserNameValue,
  ShortName = FastTreeTweedieTrainer.ShortName,
- XmlInclude = new [] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastTreeTweedieRegression""]/*' />" })]
+ XmlInclude = new [] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastTreeTweedieRegression""]/*' />" })]
  public static CommonOutputs.RegressionOutput TrainTweedieRegression(IHostEnvironment env, FastTreeTweedieTrainer.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/RandomForestClassification.cs

@@ -106,7 +106,7 @@ public static IPredictorProducing<Float> Create(IHostEnvironment env, ModelLoadC
  }
  }
 
- /// <include file='./doc.xml' path='docs/members/member[@name="FastForest"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastForest"]/*' />
  public sealed partial class FastForestClassification :
  RandomForestTrainerBase<FastForestClassification.Arguments, IPredictorWithFeatureWeights<Float>>
  {
@@ -206,7 +206,8 @@ public static partial class FastForest
  Desc = FastForestClassification.Summary,
  UserName = FastForestClassification.UserNameValue,
  ShortName = FastForestClassification.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastForest""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastForest""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastForestBinaryClassifier""]/*' />"})]
  public static CommonOutputs.BinaryClassificationOutput TrainBinary(IHostEnvironment env, FastForestClassification.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/RandomForestRegression.cs

@@ -137,7 +137,7 @@ public ISchemaBindableMapper CreateMapper(Double[] quantiles)
  }
  }
 
- /// <include file='./doc.xml' path='docs/members/member[@name="FastForest"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastForest"]/*' />
  public sealed partial class FastForestRegression : RandomForestTrainerBase<FastForestRegression.Arguments, FastForestRegressionPredictor>
  {
  public sealed class Arguments : FastForestArgumentsBase
@@ -277,7 +277,8 @@ public static partial class FastForest
  Desc = FastForestRegression.Summary,
  UserName = FastForestRegression.LoadNameValue,
  ShortName = FastForestRegression.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastForest""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastForest""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastForestRegressor""]/*' />"})]
  public static CommonOutputs.RegressionOutput TrainRegression(IHostEnvironment env, FastForestRegression.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/Training/Parallel/IParallelTraining.cs

@@ -33,20 +33,20 @@ public delegate void FindBestThresholdFromRawArrayFun(LeafSplitCandidates leafSp
  /// <summary>
  /// Interface used for parallel training.
  /// Mainly contains three parts:
- /// 1. interactive with IO: <see href="GetLocalBinConstructionFeatures" />, <see href="SyncGlobalBoundary" />.
+ /// 1. interactive with IO: <see cref="GetLocalBinConstructionFeatures" />, <see cref="SyncGlobalBoundary" />.
  /// Data will be partitioned by rows in Data parallel and Voting Parallel.
  /// To speed up the find bin process, it let different workers to find bins for different features.
  /// Then perform global sync up.
  /// In Feature parallel, every machines holds all data, so this is unneeded.
- /// 2. interactive with TreeLearner: <see href="InitIteration" />, <see href="CacheHistogram" />, <see href="IsNeedFindLocalBestSplit" />, 
- /// <see href="IsSkipNonSplittableHistogram" />, <see href="FindGlobalBestSplit" />, <see href="GetGlobalDataCountInLeaf" />, <see href="PerformGlobalSplit" />.
+ /// 2. interactive with TreeLearner: <see cref="InitIteration" />, <see cref="CacheHistogram" />, <see cref="IsNeedFindLocalBestSplit" />, 
+ /// <see cref="IsSkipNonSplittableHistogram" />, <see cref="FindGlobalBestSplit" />, <see cref="GetGlobalDataCountInLeaf" />, <see cref="PerformGlobalSplit" />.
  /// A full process is:
- /// Use <see href="InitIteration" /> to alter local active features.
- /// Use <see href="GetGlobalDataCountInLeaf" /> to check smaller leaf and larger leaf.
- /// Use <see href="CacheHistogram" />, <see href="IsNeedFindLocalBestSplit" /> and <see href="IsSkipNonSplittableHistogram" /> to interactive with Feature histograms.
- /// Use <see href="FindGlobalBestSplit" /> to sync up global best split
- /// Use <see href="PerformGlobalSplit" /> to record global num_data in leaves.
- /// 3. interactive with Application : <see href="GlobalMean" />.
+ /// Use <see cref="InitIteration" /> to alter local active features.
+ /// Use <see cref="GetGlobalDataCountInLeaf" /> to check smaller leaf and larger leaf.
+ /// Use <see cref="CacheHistogram" />, <see cref="IsNeedFindLocalBestSplit" /> and <see cref="IsSkipNonSplittableHistogram" /> to interactive with Feature histograms.
+ /// Use <see cref="FindGlobalBestSplit" /> to sync up global best split
+ /// Use <see cref="PerformGlobalSplit" /> to record global num_data in leaves.
+ /// 3. interactive with Application : <see cref="GlobalMean" />.
  /// Output of leaves is calculated by newton step ( - sum(first_order_gradients) / sum(second_order_gradients)).
  /// If data is partitioned by row, it needs to a sync up for these sum result.
  /// So It needs to call this to get the real output of leaves.

src/Microsoft.ML.FastTree/TreeEnsembleFeaturizer.cs

@@ -544,6 +544,7 @@ public ISchemaBoundMapper Bind(IHostEnvironment env, RoleMappedSchema schema)
  }
  }
 
+ /// <include file='doc.xml' path='doc/members/member[@name="TreeEnsembleFeaturizerTransform"]'/>
  public static class TreeEnsembleFeaturizerTransform
  {
  public sealed class Arguments : TrainAndScoreTransform.ArgumentsBase<SignatureTreeEnsembleTrainer>
@@ -802,7 +803,11 @@ private static IDataView AppendLabelTransform(IHostEnvironment env, IChannel ch,
 
  public static partial class TreeFeaturize
  {
- [TlcModule.EntryPoint(Name = "Transforms.TreeLeafFeaturizer", Desc = TreeEnsembleFeaturizerTransform.TreeEnsembleSummary, UserName = TreeEnsembleFeaturizerTransform.UserName, ShortName = TreeEnsembleFeaturizerBindableMapper.LoadNameShort)]
+ [TlcModule.EntryPoint(Name = "Transforms.TreeLeafFeaturizer", 
+ Desc = TreeEnsembleFeaturizerTransform.TreeEnsembleSummary, 
+ UserName = TreeEnsembleFeaturizerTransform.UserName, 
+ ShortName = TreeEnsembleFeaturizerBindableMapper.LoadNameShort,
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""TreeEnsembleFeaturizerTransform""]'/>" })]
  public static CommonOutputs.TransformOutput Featurizer(IHostEnvironment env, TreeEnsembleFeaturizerTransform.ArgumentsForEntryPoint input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.Core/Utilities/ReservoirSampler.cs

@@ -47,7 +47,7 @@ public interface IReservoirSampler<T>
  /// This class produces a sample without replacement from a stream of data of type <typeparamref name="T"/>. 
  /// It is instantiated with a delegate that gets the next data point, and builds a reservoir in one pass by calling <see cref="Sample"/> 
  /// for every data point in the stream. In case the next data point does not get 'picked' into the reservoir, the delegate is not invoked.
- /// Sampling is done according to the algorithm in this paper: <see href="http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53"/>.
+ /// Sampling is done according to the algorithm in this paper: <a href="http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53">http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53</a>.
  /// </summary>
  public sealed class ReservoirSamplerWithoutReplacement<T> : IReservoirSampler<T>
  {
@@ -120,7 +120,7 @@ public IEnumerable<T> GetSample()
  /// This class produces a sample with replacement from a stream of data of type <typeparamref name="T"/>. 
  /// It is instantiated with a delegate that gets the next data point, and builds a reservoir in one pass by calling <see cref="Sample"/> 
  /// for every data point in the stream. In case the next data point does not get 'picked' into the reservoir, the delegate is not invoked.
- /// Sampling is done according to the algorithm in this paper: <see href="http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53"/>.
+ /// Sampling is done according to the algorithm in this paper: <a href="http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53">http://epubs.siam.org/doi/pdf/10.1137/1.9781611972740.53</a>.
  /// </summary>
  public sealed class ReservoirSamplerWithReplacement<T> : IReservoirSampler<T>
  {

src/Microsoft.ML.Data/Evaluators/AucAggregator.cs

@@ -408,7 +408,7 @@ public UnweightedAuPrcAggregator(IRandom rand, int reservoirSize)
 
  /// <summary>
  /// Compute the AUPRC using the "lower trapesoid" estimator, as described in the paper
- /// <see href="http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf"/>.
+ /// <a href="http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf">http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf</a>.
  /// </summary>
  protected override Double ComputeWeightedAuPrcCore(out Double unweighted)
  {
@@ -482,7 +482,7 @@ public WeightedAuPrcAggregator(IRandom rand, int reservoirSize)
 
  /// <summary>
  /// Compute the AUPRC using the "lower trapesoid" estimator, as described in the paper
- /// <see href="http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf"/>.
+ /// <a href="http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf">http://www.ecmlpkdd2013.org/wp-content/uploads/2013/07/aucpr_2013ecml_corrected.pdf</a>.
  /// </summary>
  protected override Double ComputeWeightedAuPrcCore(out Double unweighted)
  {

src/Microsoft.ML.Data/Transforms/NAFilter.cs

@@ -26,6 +26,7 @@
 
 namespace Microsoft.ML.Runtime.Data
 {
+ /// <include file='doc.xml' path='doc/members/member[@name="NAFilter"]'/>
  public sealed class NAFilter : FilterBase
  {
  private static class Defaults

src/Microsoft.ML.Data/Transforms/TermTransform.cs

@@ -29,14 +29,14 @@
 
 namespace Microsoft.ML.Runtime.Data
 {
- /// <summary>
- /// TermTransform builds up term vocabularies (dictionaries).
- /// Notes:
- /// * Each column builds/uses exactly one "vocabulary" (dictionary).
- /// * Output columns are KeyType-valued.
- /// * The Key value is the one-based index of the item in the dictionary.
- /// * Not found is assigned the value zero.
- /// </summary>
+
+ // TermTransform builds up term vocabularies (dictionaries).
+ // Notes:
+ // * Each column builds/uses exactly one "vocabulary" (dictionary).
+ // * Output columns are KeyType-valued.
+ // * The Key value is the one-based index of the item in the dictionary.
+ // * Not found is assigned the value zero.
+ /// <include file='doc.xml' path='doc/members/member[@name="TextToKey"]/*' />
  public sealed partial class TermTransform : OneToOneTransformBase, ITransformTemplate
  {
  public abstract class ColumnBase : OneToOneColumn

src/Microsoft.ML.Data/Transforms/doc.xml

@@ -0,0 +1,54 @@
+<?xml version="1.0" encoding="utf-8" ?>
+<doc>
+ <members>
+ <member name="NAFilter">
+ <summary>
+ Removes missing values from vector type columns.
+ </summary>
+ <remarks>
+ This transform removes the entire row if any of the input columns have a missing value in that row.
+ This preprocessing is required for many ML algorithms that cannot work with missing values.
+ Useful if any missing entry invalidates the entire row.
+ If the <see cref="Microsoft.ML.Runtime.Data.NAFilter.Defaults.Complement"/> is set to true, this transform would do the exact opposite,
+ it will keep only the rows that have missing values.
+ </remarks>
+ <seealso cref="Microsoft.ML.Runtime.Data.MetadataUtils.Kinds.HasMissingValues"></seealso>
+ </member>
+ <example name="NAFilter">
+ <example>
+ <code language="csharp">
+ pipeline.Add(new MissingValuesRowDropper(&quot;Column1&quot;));
+ </code>
+ </example>
+ </example>
+
+ <member name="TextToKey">
+ <summary>
+ Converts input values (words, numbers, etc.) to index in a dictionary.
+ </summary>
+ <remarks>
+ The TextToKeyConverter transform builds up term vocabularies (dictionaries).
+ The TextToKey Converter and the <see cref="T:Microsoft.ML.Transforms.HashConverter"/> are the two one primary mechanisms by which raw input is transformed into keys.
+ If multiple columns are used, each column builds/uses exactly one vocabulary.
+ The output columns are KeyType-valued.
+ The Key value is the one-based index of the item in the dictionary.
+ If the key is not found in the dictionary, it is assigned the missing value indicator.
+ This dictionary mapping values to keys is most commonly learnt from the unique values in input data,
+ but can be defined through other means: either with the mapping defined directly on the command line, or as loaded from an external file.
+ </remarks>
+ <seealso cref="T:Microsoft.ML.Transforms.HashConverter"/>
+ <seealso cref="T:Microsoft.ML.Transforms.KeyToTextConverter"/>
+ </member>
+ <example name="TextToKey">
+ <example>
+ <code language="csharp">
+ pipeline.Add(new TextToKeyConverter((&quot;Column&quot;, &quot;OutColumn&quot;))
+ { 
+ Sort = TermTransformSortOrder.Occurrence 
+ });
+ </code>
+ </example>
+ </example>
+
+ </members>
+</doc>

src/Microsoft.ML.FastTree/FastTreeArguments.cs

@@ -20,7 +20,7 @@ public interface IFastTreeTrainerFactory : IComponentFactory<ITrainer>
  {
  }
 
- /// <include file='./doc.xml' path='docs/members/member[@name="FastTree"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastTree"]/*' />
  public sealed partial class FastTreeBinaryClassificationTrainer
  {
  [TlcModule.Component(Name = LoadNameValue, FriendlyName = UserNameValue, Desc = Summary)]

src/Microsoft.ML.FastTree/FastTreeClassification.cs

@@ -100,7 +100,7 @@ public static IPredictorProducing<Float> Create(IHostEnvironment env, ModelLoadC
  public override PredictionKind PredictionKind => PredictionKind.BinaryClassification;
  }
 
- /// <include file = './doc.xml' path='docs/members/member[@name="FastTree"]/*' />
+ /// <include file = 'doc.xml' path='doc/members/member[@name="FastTree"]/*' />
  public sealed partial class FastTreeBinaryClassificationTrainer :
  BoostingFastTreeTrainerBase<FastTreeBinaryClassificationTrainer.Arguments, IPredictorWithFeatureWeights<Float>>
  {
@@ -342,7 +342,8 @@ public static partial class FastTree
  Desc = FastTreeBinaryClassificationTrainer.Summary,
  UserName = FastTreeBinaryClassificationTrainer.UserNameValue,
  ShortName = FastTreeBinaryClassificationTrainer.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastTree""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastTree""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastTreeBinaryClassifier""]/*' />" })]
  public static CommonOutputs.BinaryClassificationOutput TrainBinary(IHostEnvironment env, FastTreeBinaryClassificationTrainer.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/FastTreeRanking.cs

@@ -38,7 +38,7 @@
 
 namespace Microsoft.ML.Runtime.FastTree
 {
- /// <include file='./doc.xml' path='docs/members/member[@name="FastTree"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastTree"]/*' />
  public sealed partial class FastTreeRankingTrainer : BoostingFastTreeTrainerBase<FastTreeRankingTrainer.Arguments, FastTreeRankingPredictor>,
  IHasLabelGains
  {
@@ -1096,7 +1096,8 @@ public static partial class FastTree
  Desc = FastTreeRankingTrainer.Summary,
  UserName = FastTreeRankingTrainer.UserNameValue,
  ShortName = FastTreeRankingTrainer.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastTree""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastTree""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastTreeRanker""]/*' />"})]
  public static CommonOutputs.RankingOutput TrainRanking(IHostEnvironment env, FastTreeRankingTrainer.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/FastTreeRegression.cs

@@ -31,7 +31,7 @@
 
 namespace Microsoft.ML.Runtime.FastTree
 {
- /// <include file='./doc.xml' path='docs/members/member[@name="FastTree"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastTree"]/*' />
  public sealed partial class FastTreeRegressionTrainer : BoostingFastTreeTrainerBase<FastTreeRegressionTrainer.Arguments, FastTreeRegressionPredictor>
  {
  public const string LoadNameValue = "FastTreeRegression";
@@ -445,7 +445,8 @@ public static partial class FastTree
  Desc = FastTreeRegressionTrainer.Summary,
  UserName = FastTreeRegressionTrainer.UserNameValue,
  ShortName = FastTreeRegressionTrainer.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastTree""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastTree""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastTreeRegressor""]/*' />"})]
  public static CommonOutputs.RegressionOutput TrainRegression(IHostEnvironment env, FastTreeRegressionTrainer.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/FastTreeTweedie.cs

@@ -30,7 +30,7 @@ namespace Microsoft.ML.Runtime.FastTree
  // The Tweedie boosting model follows the mathematics established in:
  // Yang, Quan, and Zou. "Insurance Premium Prediction via Gradient Tree-Boosted Tweedie Compound Poisson Models."
  // https://arxiv.org/pdf/1508.06378.pdf
- /// <include file='./doc.xml' path='docs/members/member[@name="FastTreeTweedieRegression"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastTreeTweedieRegression"]/*' />
  public sealed partial class FastTreeTweedieTrainer : BoostingFastTreeTrainerBase<FastTreeTweedieTrainer.Arguments, FastTreeTweediePredictor>
  {
  public const string LoadNameValue = "FastTreeTweedieRegression";
@@ -454,7 +454,7 @@ public static partial class FastTree
  Desc = FastTreeTweedieTrainer.Summary,
  UserName = FastTreeTweedieTrainer.UserNameValue,
  ShortName = FastTreeTweedieTrainer.ShortName,
- XmlInclude = new [] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastTreeTweedieRegression""]/*' />" })]
+ XmlInclude = new [] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastTreeTweedieRegression""]/*' />" })]
  public static CommonOutputs.RegressionOutput TrainTweedieRegression(IHostEnvironment env, FastTreeTweedieTrainer.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/RandomForestClassification.cs

@@ -106,7 +106,7 @@ public static IPredictorProducing<Float> Create(IHostEnvironment env, ModelLoadC
  }
  }
 
- /// <include file='./doc.xml' path='docs/members/member[@name="FastForest"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastForest"]/*' />
  public sealed partial class FastForestClassification :
  RandomForestTrainerBase<FastForestClassification.Arguments, IPredictorWithFeatureWeights<Float>>
  {
@@ -206,7 +206,8 @@ public static partial class FastForest
  Desc = FastForestClassification.Summary,
  UserName = FastForestClassification.UserNameValue,
  ShortName = FastForestClassification.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastForest""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastForest""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastForestBinaryClassifier""]/*' />"})]
  public static CommonOutputs.BinaryClassificationOutput TrainBinary(IHostEnvironment env, FastForestClassification.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/RandomForestRegression.cs

@@ -137,7 +137,7 @@ public ISchemaBindableMapper CreateMapper(Double[] quantiles)
  }
  }
 
- /// <include file='./doc.xml' path='docs/members/member[@name="FastForest"]/*' />
+ /// <include file='doc.xml' path='doc/members/member[@name="FastForest"]/*' />
  public sealed partial class FastForestRegression : RandomForestTrainerBase<FastForestRegression.Arguments, FastForestRegressionPredictor>
  {
  public sealed class Arguments : FastForestArgumentsBase
@@ -277,7 +277,8 @@ public static partial class FastForest
  Desc = FastForestRegression.Summary,
  UserName = FastForestRegression.LoadNameValue,
  ShortName = FastForestRegression.ShortName,
- XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='docs/members/member[@name=""FastForest""]/*' />" })]
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""FastForest""]/*' />",
+ @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/example[@name=""FastForestRegressor""]/*' />"})]
  public static CommonOutputs.RegressionOutput TrainRegression(IHostEnvironment env, FastForestRegression.Arguments input)
  {
  Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.FastTree/Training/Parallel/IParallelTraining.cs

@@ -33,20 +33,20 @@ public delegate void FindBestThresholdFromRawArrayFun(LeafSplitCandidates leafSp
  /// <summary>
  /// Interface used for parallel training.
  /// Mainly contains three parts:
- /// 1. interactive with IO: <see href="GetLocalBinConstructionFeatures" />, <see href="SyncGlobalBoundary" />.
+ /// 1. interactive with IO: <see cref="GetLocalBinConstructionFeatures" />, <see cref="SyncGlobalBoundary" />.
  /// Data will be partitioned by rows in Data parallel and Voting Parallel.
  /// To speed up the find bin process, it let different workers to find bins for different features.
  /// Then perform global sync up.
  /// In Feature parallel, every machines holds all data, so this is unneeded.
- /// 2. interactive with TreeLearner: <see href="InitIteration" />, <see href="CacheHistogram" />, <see href="IsNeedFindLocalBestSplit" />, 
- /// <see href="IsSkipNonSplittableHistogram" />, <see href="FindGlobalBestSplit" />, <see href="GetGlobalDataCountInLeaf" />, <see href="PerformGlobalSplit" />.
+ /// 2. interactive with TreeLearner: <see cref="InitIteration" />, <see cref="CacheHistogram" />, <see cref="IsNeedFindLocalBestSplit" />, 
+ /// <see cref="IsSkipNonSplittableHistogram" />, <see cref="FindGlobalBestSplit" />, <see cref="GetGlobalDataCountInLeaf" />, <see cref="PerformGlobalSplit" />.
  /// A full process is:
- /// Use <see href="InitIteration" /> to alter local active features.
- /// Use <see href="GetGlobalDataCountInLeaf" /> to check smaller leaf and larger leaf.
- /// Use <see href="CacheHistogram" />, <see href="IsNeedFindLocalBestSplit" /> and <see href="IsSkipNonSplittableHistogram" /> to interactive with Feature histograms.
- /// Use <see href="FindGlobalBestSplit" /> to sync up global best split
- /// Use <see href="PerformGlobalSplit" /> to record global num_data in leaves.
- /// 3. interactive with Application : <see href="GlobalMean" />.
+ /// Use <see cref="InitIteration" /> to alter local active features.
+ /// Use <see cref="GetGlobalDataCountInLeaf" /> to check smaller leaf and larger leaf.
+ /// Use <see cref="CacheHistogram" />, <see cref="IsNeedFindLocalBestSplit" /> and <see cref="IsSkipNonSplittableHistogram" /> to interactive with Feature histograms.
+ /// Use <see cref="FindGlobalBestSplit" /> to sync up global best split
+ /// Use <see cref="PerformGlobalSplit" /> to record global num_data in leaves.
+ /// 3. interactive with Application : <see cref="GlobalMean" />.
  /// Output of leaves is calculated by newton step ( - sum(first_order_gradients) / sum(second_order_gradients)).
  /// If data is partitioned by row, it needs to a sync up for these sum result.
  /// So It needs to call this to get the real output of leaves.

src/Microsoft.ML.FastTree/TreeEnsembleFeaturizer.cs

@@ -544,6 +544,7 @@ public ISchemaBoundMapper Bind(IHostEnvironment env, RoleMappedSchema schema)
  }
  }
 
+ /// <include file='doc.xml' path='doc/members/member[@name="TreeEnsembleFeaturizerTransform"]'/>
  public static class TreeEnsembleFeaturizerTransform
  {
  public sealed class Arguments : TrainAndScoreTransform.ArgumentsBase<SignatureTreeEnsembleTrainer>
@@ -802,7 +803,11 @@ private static IDataView AppendLabelTransform(IHostEnvironment env, IChannel ch,
 
  public static partial class TreeFeaturize
  {
- [TlcModule.EntryPoint(Name = "Transforms.TreeLeafFeaturizer", Desc = TreeEnsembleFeaturizerTransform.TreeEnsembleSummary, UserName = TreeEnsembleFeaturizerTransform.UserName, ShortName = TreeEnsembleFeaturizerBindableMapper.LoadNameShort)]
+ [TlcModule.EntryPoint(Name = "Transforms.TreeLeafFeaturizer", 
+ Desc = TreeEnsembleFeaturizerTransform.TreeEnsembleSummary, 
+ UserName = TreeEnsembleFeaturizerTransform.UserName, 
+ ShortName = TreeEnsembleFeaturizerBindableMapper.LoadNameShort,
+ XmlInclude = new[] { @"<include file='../Microsoft.ML.FastTree/doc.xml' path='doc/members/member[@name=""TreeEnsembleFeaturizerTransform""]'/>" })]
  public static CommonOutputs.TransformOutput Featurizer(IHostEnvironment env, TreeEnsembleFeaturizerTransform.ArgumentsForEntryPoint input)
  {
  Contracts.CheckValue(env, nameof(env));