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[AutoML] Enable style cop rules & resolve errors (dotnet#3823)
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‎Microsoft.ML.AutoML.sln

Lines changed: 19 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -12,6 +12,10 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "mlnet.Tests", "test\mlnet.T
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EndProject
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.AutoML.Samples", "docs\samples\Microsoft.ML.AutoML.Samples\Microsoft.ML.AutoML.Samples.csproj", "{6E84E7C5-FECE-45A9-AD4C-4B0F39F78904}"
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EndProject
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.InternalCodeAnalyzer", "tools-local\Microsoft.ML.InternalCodeAnalyzer\Microsoft.ML.InternalCodeAnalyzer.csproj", "{954F14B0-BBDF-4A30-811E-2D86FDB5B399}"
16+
EndProject
17+
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "tools-local", "tools-local", "{2DEB692B-D982-4301-A074-DAA095658230}"
18+
EndProject
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Global
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GlobalSection(SolutionConfigurationPlatforms) = preSolution
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Debug|Any CPU = Debug|Any CPU
@@ -82,10 +86,25 @@ Global
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{6E84E7C5-FECE-45A9-AD4C-4B0F39F78904}.Release-Intrinsics|Any CPU.Build.0 = Release-Intrinsics|Any CPU
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{6E84E7C5-FECE-45A9-AD4C-4B0F39F78904}.Release-netfx|Any CPU.ActiveCfg = Release-netfx|Any CPU
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{6E84E7C5-FECE-45A9-AD4C-4B0F39F78904}.Release-netfx|Any CPU.Build.0 = Release-netfx|Any CPU
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{954F14B0-BBDF-4A30-811E-2D86FDB5B399}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
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{954F14B0-BBDF-4A30-811E-2D86FDB5B399}.Debug|Any CPU.Build.0 = Debug|Any CPU
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{954F14B0-BBDF-4A30-811E-2D86FDB5B399}.Debug-Intrinsics|Any CPU.ActiveCfg = Debug-Intrinsics|Any CPU
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{954F14B0-BBDF-4A30-811E-2D86FDB5B399}.Debug-Intrinsics|Any CPU.Build.0 = Debug-Intrinsics|Any CPU
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{954F14B0-BBDF-4A30-811E-2D86FDB5B399}.Debug-netfx|Any CPU.ActiveCfg = Debug-netfx|Any CPU
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{954F14B0-BBDF-4A30-811E-2D86FDB5B399}.Debug-netfx|Any CPU.Build.0 = Debug-netfx|Any CPU
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{954F14B0-BBDF-4A30-811E-2D86FDB5B399}.Release|Any CPU.ActiveCfg = Release|Any CPU
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{954F14B0-BBDF-4A30-811E-2D86FDB5B399}.Release|Any CPU.Build.0 = Release|Any CPU
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{954F14B0-BBDF-4A30-811E-2D86FDB5B399}.Release-Intrinsics|Any CPU.ActiveCfg = Release-Intrinsics|Any CPU
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{954F14B0-BBDF-4A30-811E-2D86FDB5B399}.Release-Intrinsics|Any CPU.Build.0 = Release-Intrinsics|Any CPU
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{954F14B0-BBDF-4A30-811E-2D86FDB5B399}.Release-netfx|Any CPU.ActiveCfg = Release-netfx|Any CPU
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{954F14B0-BBDF-4A30-811E-2D86FDB5B399}.Release-netfx|Any CPU.Build.0 = Release-netfx|Any CPU
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EndGlobalSection
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GlobalSection(SolutionProperties) = preSolution
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HideSolutionNode = FALSE
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EndGlobalSection
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GlobalSection(NestedProjects) = preSolution
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{954F14B0-BBDF-4A30-811E-2D86FDB5B399} = {2DEB692B-D982-4301-A074-DAA095658230}
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EndGlobalSection
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GlobalSection(ExtensibilityGlobals) = postSolution
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SolutionGuid = {8C1BC26C-B87E-47CD-928E-00EFE4353B40}
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EndGlobalSection

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

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Original file line numberDiff line numberDiff line change
@@ -27,9 +27,9 @@ internal AutoCatalog(MLContext context)
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/// <para>See <see cref="RegressionExperiment"/> for a more detailed code example of an AutoML regression experiment.</para>
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/// <para>An experiment may run for longer than <paramref name="maxExperimentTimeInSeconds"/>.
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/// This is because once AutoML starts training an ML.NET model, AutoML lets the
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/// model train to completion. For instance, if the first model
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/// model train to completion. For instance, if the first model
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/// AutoML trains takes 4 hours, and the second model trained takes 5 hours,
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/// but <paramref name="maxExperimentTimeInSeconds"/> was the number of seconds in 6 hours,
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/// but <paramref name="maxExperimentTimeInSeconds"/> was the number of seconds in 6 hours,
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/// the experiment will run for 4 + 5 = 9 hours (not 6 hours).</para>
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/// </remarks>
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public RegressionExperiment CreateRegressionExperiment(uint maxExperimentTimeInSeconds)
@@ -62,9 +62,9 @@ public RegressionExperiment CreateRegressionExperiment(RegressionExperimentSetti
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/// <para>See <see cref="BinaryClassificationExperiment"/> for a more detailed code example of an AutoML binary classification experiment.</para>
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/// <para>An experiment may run for longer than <paramref name="maxExperimentTimeInSeconds"/>.
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/// This is because once AutoML starts training an ML.NET model, AutoML lets the
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/// model train to completion. For instance, if the first model
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/// model train to completion. For instance, if the first model
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/// AutoML trains takes 4 hours, and the second model trained takes 5 hours,
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/// but <paramref name="maxExperimentTimeInSeconds"/> was the number of seconds in 6 hours,
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/// but <paramref name="maxExperimentTimeInSeconds"/> was the number of seconds in 6 hours,
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/// the experiment will run for 4 + 5 = 9 hours (not 6 hours).</para>
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/// </remarks>
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public BinaryClassificationExperiment CreateBinaryClassificationExperiment(uint maxExperimentTimeInSeconds)
@@ -97,9 +97,9 @@ public BinaryClassificationExperiment CreateBinaryClassificationExperiment(Binar
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/// <para>See <see cref="MulticlassClassificationExperiment"/> for a more detailed code example of an AutoML multiclass classification experiment.</para>
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/// <para>An experiment may run for longer than <paramref name="maxExperimentTimeInSeconds"/>.
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/// This is because once AutoML starts training an ML.NET model, AutoML lets the
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/// model train to completion. For instance, if the first model
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/// model train to completion. For instance, if the first model
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/// AutoML trains takes 4 hours, and the second model trained takes 5 hours,
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/// but <paramref name="maxExperimentTimeInSeconds"/> was the number of seconds in 6 hours,
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/// but <paramref name="maxExperimentTimeInSeconds"/> was the number of seconds in 6 hours,
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/// the experiment will run for 4 + 5 = 9 hours (not 6 hours).</para>
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/// </remarks>
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public MulticlassClassificationExperiment CreateMulticlassClassificationExperiment(uint maxExperimentTimeInSeconds)
@@ -136,15 +136,15 @@ public MulticlassClassificationExperiment CreateMulticlassClassificationExperime
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/// <returns>Information inferred about the columns in the provided dataset.</returns>
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/// <remarks>
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/// Infers information about the name, data type, and purpose of each column.
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/// The returned <see cref="ColumnInferenceResults.TextLoaderOptions" /> can be used to
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/// instantiate a <see cref="TextLoader" />. The <see cref="TextLoader" /> can be used to
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/// obtain an <see cref="IDataView"/> that can be fed into an AutoML experiment,
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/// The returned <see cref="ColumnInferenceResults.TextLoaderOptions" /> can be used to
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/// instantiate a <see cref="TextLoader" />. The <see cref="TextLoader" /> can be used to
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/// obtain an <see cref="IDataView"/> that can be fed into an AutoML experiment,
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/// or used elsewhere in the ML.NET ecosystem (ie in <see cref="IEstimator{TTransformer}.Fit(IDataView)"/>.
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/// The <see cref="ColumnInformation"/> contains the inferred purpose of each column in the dataset.
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/// (For instance, is the column categorical, numeric, or text data? Should the column be ignored? Etc.)
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/// The <see cref="ColumnInformation"/> can be inspected and modified (or kept as is) and used by an AutoML experiment.
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/// </remarks>
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public ColumnInferenceResults InferColumns(string path, string labelColumnName = DefaultColumnNames.Label, char? separatorChar = null, bool? allowQuoting = null,
147+
public ColumnInferenceResults InferColumns(string path, string labelColumnName = DefaultColumnNames.Label, char? separatorChar = null, bool? allowQuoting = null,
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bool? allowSparse = null, bool trimWhitespace = false, bool groupColumns = true)
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{
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UserInputValidationUtil.ValidateInferColumnsArgs(path, labelColumnName);
@@ -164,9 +164,9 @@ public ColumnInferenceResults InferColumns(string path, string labelColumnName =
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/// <returns>Information inferred about the columns in the provided dataset.</returns>
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/// <remarks>
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/// Infers information about the name, data type, and purpose of each column.
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/// The returned <see cref="ColumnInferenceResults.TextLoaderOptions" /> can be used to
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/// instantiate a <see cref="TextLoader" />. The <see cref="TextLoader" /> can be used to
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/// obtain an <see cref="IDataView"/> that can be fed into an AutoML experiment,
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/// The returned <see cref="ColumnInferenceResults.TextLoaderOptions" /> can be used to
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/// instantiate a <see cref="TextLoader" />. The <see cref="TextLoader" /> can be used to
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/// obtain an <see cref="IDataView"/> that can be fed into an AutoML experiment,
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/// or used elsewhere in the ML.NET ecosystem (ie in <see cref="IEstimator{TTransformer}.Fit(IDataView)"/>.
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/// The <see cref="ColumnInformation"/> contains the inferred purpose of each column in the dataset.
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/// (For instance, is the column categorical, numeric, or text data? Should the column be ignored? Etc.)
@@ -194,15 +194,15 @@ public ColumnInferenceResults InferColumns(string path, ColumnInformation column
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/// <returns>Information inferred about the columns in the provided dataset.</returns>
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/// <remarks>
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/// Infers information about the name, data type, and purpose of each column.
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/// The returned <see cref="ColumnInferenceResults.TextLoaderOptions" /> can be used to
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/// instantiate a <see cref="TextLoader" />. The <see cref="TextLoader" /> can be used to
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/// obtain an <see cref="IDataView"/> that can be fed into an AutoML experiment,
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/// The returned <see cref="ColumnInferenceResults.TextLoaderOptions" /> can be used to
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/// instantiate a <see cref="TextLoader" />. The <see cref="TextLoader" /> can be used to
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/// obtain an <see cref="IDataView"/> that can be fed into an AutoML experiment,
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/// or used elsewhere in the ML.NET ecosystem (ie in <see cref="IEstimator{TTransformer}.Fit(IDataView)"/>.
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/// The <see cref="ColumnInformation"/> contains the inferred purpose of each column in the dataset.
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/// (For instance, is the column categorical, numeric, or text data? Should the column be ignored? Etc.)
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/// The <see cref="ColumnInformation"/> can be inspected and modified (or kept as is) and used by an AutoML experiment.
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/// </remarks>
205-
public ColumnInferenceResults InferColumns(string path, uint labelColumnIndex, bool hasHeader = false, char? separatorChar = null,
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public ColumnInferenceResults InferColumns(string path, uint labelColumnIndex, bool hasHeader = false, char? separatorChar = null,
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bool? allowQuoting = null, bool? allowSparse = null, bool trimWhitespace = false, bool groupColumns = true)
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{
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UserInputValidationUtil.ValidateInferColumnsArgs(path);

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

Lines changed: 14 additions & 7 deletions
Original file line numberDiff line numberDiff line change
@@ -13,23 +13,30 @@
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namespace Microsoft.ML.AutoML
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{
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/// <summary>
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/// Settings for AutoML experiments on binary classification datasets.
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/// Settings for AutoML experiments on binary classification datasets.
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/// </summary>
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public sealed class BinaryExperimentSettings : ExperimentSettings
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{
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/// <summary>
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/// Metric that AutoML will try to optimize over the course of the experiment.
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/// </summary>
23-
public BinaryClassificationMetric OptimizingMetric { get; set; } = BinaryClassificationMetric.Accuracy;
23+
/// <value>The default value is <see cref="BinaryClassificationMetric.Accuracy"/>.</value>
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public BinaryClassificationMetric OptimizingMetric { get; set; }
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/// <summary>
2627
/// Collection of trainers the AutoML experiment can leverage.
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/// </summary>
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/// <remarks>
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/// The collection is auto-populated with all possible trainers (all values of <see cref="BinaryClassificationTrainer" />).
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/// </remarks>
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public ICollection<BinaryClassificationTrainer> Trainers { get; } =
32-
Enum.GetValues(typeof(BinaryClassificationTrainer)).OfType<BinaryClassificationTrainer>().ToList();
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/// <value>The default value is a collection auto-populated with all possible trainers (all values of <see cref="BinaryClassificationTrainer" />).</value>
30+
public ICollection<BinaryClassificationTrainer> Trainers { get; }
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/// <summary>
33+
/// Initializes a new instance of <see cref="BinaryExperimentSettings"/>.
34+
/// </summary>
35+
public BinaryExperimentSettings()
36+
{
37+
OptimizingMetric = BinaryClassificationMetric.Accuracy;
38+
Trainers = Enum.GetValues(typeof(BinaryClassificationTrainer)).OfType<BinaryClassificationTrainer>().ToList();
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}
3340
}
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/// <summary>

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

Lines changed: 25 additions & 11 deletions
Original file line numberDiff line numberDiff line change
@@ -20,26 +20,26 @@ public sealed class ColumnInferenceResults
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/// Can be used to instantiate a new <see cref="TextLoader" /> to load
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/// data into an <see cref="IDataView" />.
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/// </remarks>
23-
public TextLoader.Options TextLoaderOptions { get; internal set; } = new TextLoader.Options();
23+
public TextLoader.Options TextLoaderOptions { get; internal set; }
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2525
/// <summary>
2626
/// Information about the inferred columns in the dataset.
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/// </summary>
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/// <remarks>
2929
/// <para>Contains the inferred purposes of each column. See <see cref="AutoML.ColumnInformation"/> for more details.</para>
30-
/// <para>This can be fed to the AutoML API when running an experiment.
30+
/// <para>This can be fed to the AutoML API when running an experiment.
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/// See <typeref cref="ExperimentBase{TMetrics, TExperimentSettings}.Execute(IDataView, ColumnInformation, IEstimator{ITransformer}, System.IProgress{RunDetail{TMetrics}})" />
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/// for example.</para>
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/// </remarks>
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public ColumnInformation ColumnInformation { get; internal set; } = new ColumnInformation();
34+
public ColumnInformation ColumnInformation { get; internal set; }
3535
}
3636

3737
/// <summary>
3838
/// Information about the columns in a dataset.
3939
/// </summary>
4040
/// <remarks>
4141
/// <para>Contains information about the purpose of each column in the dataset. For instance,
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/// it enumerates the dataset columns that AutoML should treat as categorical,
42+
/// it enumerates the dataset columns that AutoML should treat as categorical,
4343
/// the columns AutoML should ignore, which column is the label, etc.</para>
4444
/// <para><see cref="ColumnInformation"/> can be fed to the AutoML API when running an experiment.
4545
/// See <typeref cref="ExperimentBase{TMetrics, TExperimentSettings}.Execute(IDataView, ColumnInformation, IEstimator{ITransformer}, System.IProgress{RunDetail{TMetrics}})" />
@@ -50,17 +50,18 @@ public sealed class ColumnInformation
5050
/// <summary>
5151
/// The dataset column to use as the label.
5252
/// </summary>
53-
public string LabelColumnName { get; set; } = DefaultColumnNames.Label;
53+
/// <value>The default value is "Label".</value>
54+
public string LabelColumnName { get; set; }
5455

5556
/// <summary>
5657
/// The dataset column to use for example weight.
5758
/// </summary>
5859
public string ExampleWeightColumnName { get; set; }
5960

6061
/// <summary>
61-
/// The dataset column to use for grouping rows.
62+
/// The dataset column to use for grouping rows.
6263
/// If two examples share the same sampling key column name,
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/// they are guaranteed to appear in the same subset (train or test).
64+
/// they are guaranteed to appear in the same subset (train or test).
6465
/// This can be used to ensure no label leakage from the train to the test set.
6566
/// If <see langword="null"/>, no row grouping will be performed.
6667
/// </summary>
@@ -69,24 +70,37 @@ public sealed class ColumnInformation
6970
/// <summary>
7071
/// The dataset columns that are categorical.
7172
/// </summary>
73+
/// <value>The default value is a new, empty <see cref="Collection{String}"/>.</value>
7274
/// <remarks>
7375
/// Categorical data columns should generally be columns that contain a small number of unique values.
7476
/// </remarks>
75-
public ICollection<string> CategoricalColumnNames { get; } = new Collection<string>();
77+
public ICollection<string> CategoricalColumnNames { get; }
7678

7779
/// <summary>
7880
/// The dataset columns that are numeric.
7981
/// </summary>
80-
public ICollection<string> NumericColumnNames { get; } = new Collection<string>();
82+
/// <value>The default value is a new, empty <see cref="Collection{String}"/>.</value>
83+
public ICollection<string> NumericColumnNames { get; }
8184

8285
/// <summary>
8386
/// The dataset columns that are text.
8487
/// </summary>
85-
public ICollection<string> TextColumnNames { get; } = new Collection<string>();
88+
/// <value>The default value is a new, empty <see cref="Collection{String}"/>.</value>
89+
public ICollection<string> TextColumnNames { get; }
8690

8791
/// <summary>
8892
/// The dataset columns that AutoML should ignore.
8993
/// </summary>
90-
public ICollection<string> IgnoredColumnNames { get; } = new Collection<string>();
94+
/// <value>The default value is a new, empty <see cref="Collection{String}"/>.</value>
95+
public ICollection<string> IgnoredColumnNames { get; }
96+
97+
public ColumnInformation()
98+
{
99+
LabelColumnName = DefaultColumnNames.Label;
100+
CategoricalColumnNames = new Collection<string>();
101+
NumericColumnNames = new Collection<string>();
102+
TextColumnNames = new Collection<string>();
103+
IgnoredColumnNames = new Collection<string>();
104+
}
91105
}
92106
}

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