review comments

Author: artidoro

docs/code/MlNetCookBook.md

@@ -115,9 +115,7 @@ If the schema of the data is not known at compile time, or too cumbersome, you c
 var mlContext = new MLContext();
 
 // Create the reader: define the data columns and where to find them in the text file.
-var reader = mlContext.Data.TextReader(new TextLoader.Arguments
-{
- Column = new[] {
+var reader = mlContext.Data.TextReader(new[] {
  // A boolean column depicting the 'label'.
  new TextLoader.Column("IsOver50K", DataKind.BL, 0),
  // Three text columns.
@@ -126,8 +124,8 @@ var reader = mlContext.Data.TextReader(new TextLoader.Arguments
  new TextLoader.Column("MaritalStatus", DataKind.TX, 3)
  },
  // First line of the file is a header, not a data row.
- HasHeader = true
-});
+ hasHeader: true
+);
 
 // Now read the file (remember though, readers are lazy, so the actual reading will happen when the data is accessed).
 var data = reader.Read(dataPath);
@@ -175,19 +173,17 @@ The code is very similar using the dynamic API:
 var mlContext = new MLContext();
 
 // Create the reader: define the data columns and where to find them in the text file.
-var reader = mlContext.Data.TextReader(new TextLoader.Arguments
-{
- Column = new[] {
+var reader = mlContext.Data.TextReader(new[] {
  // A boolean column depicting the 'label'.
- new TextLoader.Column("IsOver50k", DataKind.BL, 0),
+ new TextLoader.Column("IsOver50K", DataKind.BL, 0),
  // Three text columns.
  new TextLoader.Column("Workclass", DataKind.TX, 1),
  new TextLoader.Column("Education", DataKind.TX, 2),
  new TextLoader.Column("MaritalStatus", DataKind.TX, 3)
  },
  // First line of the file is a header, not a data row.
- HasHeader = true
-});
+ hasHeader: true
+);
 
 var data = reader.Read(exampleFile1, exampleFile2);
 ```
@@ -365,19 +361,17 @@ You can also use the dynamic API to create the equivalent of the previous pipeli
 var mlContext = new MLContext();
 
 // Create the reader: define the data columns and where to find them in the text file.
-var reader = mlContext.Data.TextReader(new TextLoader.Arguments
-{
- Column = new[] {
+var reader = mlContext.Data.TextReader(new[] {
  // A boolean column depicting the 'label'.
- new TextLoader.Column("IsOver50k", DataKind.BL, 0),
+ new TextLoader.Column("IsOver50K", DataKind.BL, 0),
  // Three text columns.
  new TextLoader.Column("Workclass", DataKind.TX, 1),
  new TextLoader.Column("Education", DataKind.TX, 2),
  new TextLoader.Column("MaritalStatus", DataKind.TX, 3)
  },
  // First line of the file is a header, not a data row.
- HasHeader = true
-});
+ hasHeader: true
+);
 
 // Start creating our processing pipeline. For now, let's just concatenate all the text columns
 // together into one.
@@ -468,20 +462,18 @@ var mlContext = new MLContext();
 
 // Step one: read the data as an IDataView.
 // First, we define the reader: specify the data columns and where to find them in the text file.
-var reader = mlContext.Data.TextReader(new TextLoader.Arguments
-{
- Column = new[] {
+var reader = mlContext.Data.TextReader(new[] {
  // We read the first 11 values as a single float vector.
  new TextLoader.Column("FeatureVector", DataKind.R4, 0, 10),
 
  // Separately, read the target variable.
  new TextLoader.Column("Target", DataKind.R4, 11),
  },
  // First line of the file is a header, not a data row.
- HasHeader = true,
+ hasHeader: true,
  // Default separator is tab, but we need a semicolon.
- Separator = ";"
-});
+ separatorChar: ';'
+);
 
 // Now read the file (remember though, readers are lazy, so the actual reading will happen when the data is accessed).
 var trainData = reader.Read(trainDataPath);
@@ -617,9 +609,7 @@ var mlContext = new MLContext();
 
 // Step one: read the data as an IDataView.
 // First, we define the reader: specify the data columns and where to find them in the text file.
-var reader = mlContext.Data.TextReader(new TextLoader.Arguments
-{
- Column = new[] {
+var reader = mlContext.Data.TextReader(new[] {
  new TextLoader.Column("SepalLength", DataKind.R4, 0),
  new TextLoader.Column("SepalWidth", DataKind.R4, 1),
  new TextLoader.Column("PetalLength", DataKind.R4, 2),
@@ -628,8 +618,8 @@ var reader = mlContext.Data.TextReader(new TextLoader.Arguments
  new TextLoader.Column("Label", DataKind.TX, 4),
  },
  // Default separator is tab, but the dataset has comma.
- Separator = ","
-});
+ separatorChar: ','
+);
 
 // Retrieve the training data.
 var trainData = reader.Read(irisDataPath);
@@ -910,17 +900,15 @@ You can achieve the same results using the dynamic API.
 var mlContext = new MLContext();
 
 // Define the reader: specify the data columns and where to find them in the text file.
-var reader = mlContext.Data.TextReader(new TextLoader.Arguments
-{
- Column = new[] {
+var reader = mlContext.Data.TextReader(new[] {
  // The four features of the Iris dataset will be grouped together as one Features column.
  new TextLoader.Column("Features", DataKind.R4, 0, 3),
  // Label: kind of iris.
  new TextLoader.Column("Label", DataKind.TX, 4),
  },
  // Default separator is tab, but the dataset has comma.
- Separator = ","
-});
+ separatorChar: ','
+);
 
 // Read the training data.
 var trainData = reader.Read(dataPath);
@@ -1027,9 +1015,8 @@ You can achieve the same results using the dynamic API.
 var mlContext = new MLContext();
 
 // Define the reader: specify the data columns and where to find them in the text file.
-var reader = mlContext.Data.TextReader(new TextLoader.Arguments
-{
- Column = new[] {
+var reader = mlContext.Data.TextReader(new[] 
+ {
  new TextLoader.Column("Label", DataKind.BL, 0),
  // We will load all the categorical features into one vector column of size 8.
  new TextLoader.Column("CategoricalFeatures", DataKind.TX, 1, 8),
@@ -1038,8 +1025,8 @@ var reader = mlContext.Data.TextReader(new TextLoader.Arguments
  // Let's also separately load the 'Workclass' column.
  new TextLoader.Column("Workclass", DataKind.TX, 1),
  },
- HasHeader = true
-});
+ hasHeader: true
+);
 
 // Read the data.
 var data = reader.Read(dataPath);
@@ -1154,14 +1141,13 @@ You can achieve the same results using the dynamic API.
 var mlContext = new MLContext();
 
 // Define the reader: specify the data columns and where to find them in the text file.
-var reader = mlContext.Data.TextReader(new TextLoader.Arguments
-{
- Column = new[] {
+var reader = mlContext.Data.TextReader(new[] 
+ {
  new TextLoader.Column("IsToxic", DataKind.BL, 0),
  new TextLoader.Column("Message", DataKind.TX, 1),
  },
- HasHeader = true
-});
+ hasHeader: true
+);
 
 // Read the data.
 var data = reader.Read(dataPath);
@@ -1274,9 +1260,8 @@ var mlContext = new MLContext();
 
 // Step one: read the data as an IDataView.
 // First, we define the reader: specify the data columns and where to find them in the text file.
-var reader = mlContext.Data.TextReader(new TextLoader.Arguments
-{
- Column = new[] {
+var reader = mlContext.Data.TextReader(new[] 
+ {
  // We read the first 11 values as a single float vector.
  new TextLoader.Column("SepalLength", DataKind.R4, 0),
  new TextLoader.Column("SepalWidth", DataKind.R4, 1),
@@ -1286,8 +1271,8 @@ var reader = mlContext.Data.TextReader(new TextLoader.Arguments
  new TextLoader.Column("Label", DataKind.TX, 4),
  },
  // Default separator is tab, but the dataset has comma.
- Separator = ","
-});
+ separatorChar: ','
+);
 
 // Read the data.
 var data = reader.Read(dataPath);

src/Microsoft.ML.Data/DataLoadSave/Text/TextLoader.cs

@@ -1033,11 +1033,12 @@ private static Arguments MakeArgs(Column[] columns, bool hasHeader, char[] separ
  /// <param name="env">The environment to use.</param>
  /// <param name="args">Defines the settings of the load operation.</param>
  /// <param name="dataSample">Allows to expose items that can be used for reading.</param>
- public TextLoader(IHostEnvironment env, Arguments args, IMultiStreamSource dataSample = null)
+ public TextLoader(IHostEnvironment env, Arguments args = null, IMultiStreamSource dataSample = null)
  {
+ args = args ?? new Arguments();
+
  Contracts.CheckValue(env, nameof(env));
  _host = env.Register(RegistrationName);
-
  _host.CheckValue(args, nameof(args));
  _host.CheckValueOrNull(dataSample);
 
@@ -1332,7 +1333,7 @@ public static IDataView ReadFile(IHostEnvironment env, IMultiStreamSource fileSo
  /// <param name="env">The environment to use.</param>
  /// <param name="fileSource">Specifies a file from which to read.</param>
  /// <param name="args">Defines the settings of the load operation.</param>
- public static IDataView ReadFile(IHostEnvironment env, IMultiStreamSource fileSource, Arguments args)
+ public static IDataView ReadFile(IHostEnvironment env, IMultiStreamSource fileSource, Arguments args = null)
  => new TextLoader(env, args, fileSource).Read(fileSource);
 
  public void Save(ModelSaveContext ctx)

src/Microsoft.ML.Data/DataLoadSave/Text/TextLoaderSaverCatalog.cs

@@ -19,7 +19,7 @@ public static class TextLoaderSaverCatalog
  /// <param name="catalog">The catalog.</param>
  /// <param name="columns">The columns of the schema.</param>
  /// <param name="hasHeader">Whether the file has a header.</param>
- /// <param name="separatorChar"> The character used as separator between data points in a row. By default the tab character is used as separator.</param>
+ /// <param name="separatorChar">The character used as separator between data points in a row. By default the tab character is used as separator.</param>
  /// <param name="dataSample">The optional location of a data sample.</param>
  public static TextLoader TextReader(this DataOperations catalog,
  Column[] columns, bool hasHeader = false, char separatorChar = '\t', IMultiStreamSource dataSample = null)
@@ -40,7 +40,7 @@ public static TextLoader TextReader(this DataOperations catalog, Arguments args,
  /// <param name="catalog">The catalog.</param>
  /// <param name="columns">The columns of the schema.</param>
  /// <param name="hasHeader">Whether the file has a header.</param>
- /// <param name="separatorChar"> The character used as separator between data points in a row. By default the tab character is used as separator.</param>
+ /// <param name="separatorChar">The character used as separator between data points in a row. By default the tab character is used as separator.</param>
  /// <param name="path">The path to the file.</param>
  /// <returns>The data view.</returns>
  public static IDataView ReadFromTextFile(this DataOperations catalog,

src/Microsoft.ML.Data/Utilities/ModelFileUtils.cs

@@ -283,7 +283,7 @@ public static IEnumerable<KeyValuePair<ColumnRole, string>> LoadRoleMappingsOrNu
  {
  // REVIEW: Should really validate the schema here, and consider
  // ignoring this stream if it isn't as expected.
- var loader = TextLoader.ReadFile(env, new RepositoryStreamWrapper(rep, DirTrainingInfo, RoleMappingFile), new TextLoader.Arguments());
+ var loader = TextLoader.ReadFile(env, new RepositoryStreamWrapper(rep, DirTrainingInfo, RoleMappingFile));
 
  using (var cursor = loader.GetRowCursor(c => true))
  {

src/Microsoft.ML.Transforms/TermLookupTransformer.cs

@@ -361,9 +361,6 @@ private static IComponentFactory<IMultiStreamSource, IDataLoader> GetLoaderFacto
  ulong max = ulong.MinValue;
  try
  {
- var txtArgs = new TextLoader.Arguments();
- bool parsed = CmdParser.ParseArguments(host, "col=Term:TX:0 col=Value:TX:1", txtArgs);
- host.Assert(parsed);
  var data = TextLoader.ReadFile(host, new MultiFileSource(filename), new[]
  {
  new TextLoader.Column("Term", DataKind.TX, 0),

test/Microsoft.ML.Benchmarks/StochasticDualCoordinateAscentClassifierBench.cs

@@ -64,7 +64,7 @@ public void TrainSentiment()
  {
  var env = new MLContext(seed: 1);
  // Pipeline
- var arguemnts = new TextLoader.Arguments()
+ var arguments = new TextLoader.Arguments()
  {
  Column = new TextLoader.Column[]
  {
@@ -86,7 +86,7 @@ public void TrainSentiment()
  AllowQuoting = false,
  AllowSparse = false
  };
- var loader = TextLoader.ReadFile(env, new MultiFileSource(_sentimentDataPath), arguemnts);
+ var loader = TextLoader.ReadFile(env, new MultiFileSource(_sentimentDataPath), arguments);
 
  var text = TextFeaturizingEstimator.Create(env,
  new TextFeaturizingEstimator.Arguments()

test/Microsoft.ML.Predictor.Tests/TestPredictors.cs

@@ -606,7 +606,7 @@ public void RankingLightGBMTest()
  public void TestTreeEnsembleCombiner()
  {
  var dataPath = GetDataPath("breast-cancer.txt");
- var dataView = TextLoader.ReadFile(Env, new MultiFileSource(dataPath), new TextLoader.Arguments());
+ var dataView = TextLoader.ReadFile(Env, new MultiFileSource(dataPath));
 
  var fastTrees = new IPredictorModel[3];
  for (int i = 0; i < 3; i++)
@@ -628,7 +628,7 @@ public void TestTreeEnsembleCombiner()
  public void TestTreeEnsembleCombinerWithCategoricalSplits()
  {
  var dataPath = GetDataPath("adult.tiny.with-schema.txt");
- var dataView = TextLoader.ReadFile(Env, new MultiFileSource(dataPath), new TextLoader.Arguments());
+ var dataView = TextLoader.ReadFile(Env, new MultiFileSource(dataPath));
 
  var cat = new OneHotEncodingEstimator(Env, "Categories", "Features").Fit(dataView).Transform(dataView);
  var fastTrees = new IPredictorModel[3];
@@ -729,7 +729,7 @@ private void CombineAndTestTreeEnsembles(IDataView idv, IPredictorModel[] fastTr
  public void TestEnsembleCombiner()
  {
  var dataPath = GetDataPath("breast-cancer.txt");
- var dataView = TextLoader.ReadFile(Env, new MultiFileSource(dataPath), new TextLoader.Arguments());
+ var dataView = TextLoader.ReadFile(Env, new MultiFileSource(dataPath));
 
  var predictors = new IPredictorModel[]
  {
@@ -775,7 +775,7 @@ public void TestEnsembleCombiner()
  public void TestMultiClassEnsembleCombiner()
  {
  var dataPath = GetDataPath("breast-cancer.txt");
- var dataView = TextLoader.ReadFile(Env, new MultiFileSource(dataPath), new TextLoader.Arguments());
+ var dataView = TextLoader.ReadFile(Env, new MultiFileSource(dataPath));
 
  var predictors = new IPredictorModel[]
  {

test/Microsoft.ML.TestFramework/DataPipe/TestDataPipeBase.cs

@@ -439,7 +439,7 @@ protected bool SaveLoadText(IDataView view, IHostEnvironment env,
 
  // Note that we don't pass in "args", but pass in a default args so we test
  // the auto-schema parsing.
- var loadedData = TextLoader.ReadFile(env, new MultiFileSource(pathData), new TextLoader.Arguments());
+ var loadedData = TextLoader.ReadFile(env, new MultiFileSource(pathData));
  if (!CheckMetadataTypes(loadedData.Schema))
  Failed();