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Jun 6, 2019
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[AutoML] Enable style cop rules & resolve errors #3823

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3f0a6b8
Remove code that ignores style guidelines
daholste Jun 1, 2019
dd37fa6
Merge remote-tracking branch 'upstream/features/automl' into style
daholste Jun 4, 2019
0ec7d11
AutoML project building
daholste Jun 4, 2019
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Get CLI building
daholste Jun 4, 2019
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shift value tags to one line
daholste Jun 4, 2019
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comment rev
daholste Jun 4, 2019
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daholste Jun 4, 2019
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name update
daholste Jun 4, 2019
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add project-referenced code analyzer to tools-local folder of AutoML sln
daholste Jun 6, 2019
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34 changes: 17 additions & 17 deletions src/Microsoft.ML.AutoML/API/AutoCatalog.cs
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Expand Up @@ -27,9 +27,9 @@ internal AutoCatalog(MLContext context)
/// <para>See <see cref="RegressionExperiment"/> for a more detailed code example of an AutoML regression experiment.</para>
/// <para>An experiment may run for longer than <paramref name="maxExperimentTimeInSeconds"/>.
/// This is because once AutoML starts training an ML.NET model, AutoML lets the
/// model train to completion. For instance, if the first model
/// model train to completion. For instance, if the first model
/// AutoML trains takes 4 hours, and the second model trained takes 5 hours,
/// but <paramref name="maxExperimentTimeInSeconds"/> was the number of seconds in 6 hours,
/// but <paramref name="maxExperimentTimeInSeconds"/> was the number of seconds in 6 hours,
/// the experiment will run for 4 + 5 = 9 hours (not 6 hours).</para>
/// </remarks>
public RegressionExperiment CreateRegressionExperiment(uint maxExperimentTimeInSeconds)
Expand Down Expand Up @@ -62,9 +62,9 @@ public RegressionExperiment CreateRegressionExperiment(RegressionExperimentSetti
/// <para>See <see cref="BinaryClassificationExperiment"/> for a more detailed code example of an AutoML binary classification experiment.</para>
/// <para>An experiment may run for longer than <paramref name="maxExperimentTimeInSeconds"/>.
/// This is because once AutoML starts training an ML.NET model, AutoML lets the
/// model train to completion. For instance, if the first model
/// model train to completion. For instance, if the first model
/// AutoML trains takes 4 hours, and the second model trained takes 5 hours,
/// but <paramref name="maxExperimentTimeInSeconds"/> was the number of seconds in 6 hours,
/// but <paramref name="maxExperimentTimeInSeconds"/> was the number of seconds in 6 hours,
/// the experiment will run for 4 + 5 = 9 hours (not 6 hours).</para>
/// </remarks>
public BinaryClassificationExperiment CreateBinaryClassificationExperiment(uint maxExperimentTimeInSeconds)
Expand Down Expand Up @@ -97,9 +97,9 @@ public BinaryClassificationExperiment CreateBinaryClassificationExperiment(Binar
/// <para>See <see cref="MulticlassClassificationExperiment"/> for a more detailed code example of an AutoML multiclass classification experiment.</para>
/// <para>An experiment may run for longer than <paramref name="maxExperimentTimeInSeconds"/>.
/// This is because once AutoML starts training an ML.NET model, AutoML lets the
/// model train to completion. For instance, if the first model
/// model train to completion. For instance, if the first model
/// AutoML trains takes 4 hours, and the second model trained takes 5 hours,
/// but <paramref name="maxExperimentTimeInSeconds"/> was the number of seconds in 6 hours,
/// but <paramref name="maxExperimentTimeInSeconds"/> was the number of seconds in 6 hours,
/// the experiment will run for 4 + 5 = 9 hours (not 6 hours).</para>
/// </remarks>
public MulticlassClassificationExperiment CreateMulticlassClassificationExperiment(uint maxExperimentTimeInSeconds)
Expand Down Expand Up @@ -136,15 +136,15 @@ public MulticlassClassificationExperiment CreateMulticlassClassificationExperime
/// <returns>Information inferred about the columns in the provided dataset.</returns>
/// <remarks>
/// Infers information about the name, data type, and purpose of each column.
/// The returned <see cref="ColumnInferenceResults.TextLoaderOptions" /> can be used to
/// instantiate a <see cref="TextLoader" />. The <see cref="TextLoader" /> can be used to
/// obtain an <see cref="IDataView"/> that can be fed into an AutoML experiment,
/// The returned <see cref="ColumnInferenceResults.TextLoaderOptions" /> can be used to
/// instantiate a <see cref="TextLoader" />. The <see cref="TextLoader" /> can be used to
/// obtain an <see cref="IDataView"/> that can be fed into an AutoML experiment,
/// or used elsewhere in the ML.NET ecosystem (ie in <see cref="IEstimator{TTransformer}.Fit(IDataView)"/>.
/// The <see cref="ColumnInformation"/> contains the inferred purpose of each column in the dataset.
/// (For instance, is the column categorical, numeric, or text data? Should the column be ignored? Etc.)
/// The <see cref="ColumnInformation"/> can be inspected and modified (or kept as is) and used by an AutoML experiment.
/// </remarks>
public ColumnInferenceResults InferColumns(string path, string labelColumnName = DefaultColumnNames.Label, char? separatorChar = null, bool? allowQuoting = null,
public ColumnInferenceResults InferColumns(string path, string labelColumnName = DefaultColumnNames.Label, char? separatorChar = null, bool? allowQuoting = null,
bool? allowSparse = null, bool trimWhitespace = false, bool groupColumns = true)
{
UserInputValidationUtil.ValidateInferColumnsArgs(path, labelColumnName);
Expand All @@ -164,9 +164,9 @@ public ColumnInferenceResults InferColumns(string path, string labelColumnName =
/// <returns>Information inferred about the columns in the provided dataset.</returns>
/// <remarks>
/// Infers information about the name, data type, and purpose of each column.
/// The returned <see cref="ColumnInferenceResults.TextLoaderOptions" /> can be used to
/// instantiate a <see cref="TextLoader" />. The <see cref="TextLoader" /> can be used to
/// obtain an <see cref="IDataView"/> that can be fed into an AutoML experiment,
/// The returned <see cref="ColumnInferenceResults.TextLoaderOptions" /> can be used to
/// instantiate a <see cref="TextLoader" />. The <see cref="TextLoader" /> can be used to
/// obtain an <see cref="IDataView"/> that can be fed into an AutoML experiment,
/// or used elsewhere in the ML.NET ecosystem (ie in <see cref="IEstimator{TTransformer}.Fit(IDataView)"/>.
/// The <see cref="ColumnInformation"/> contains the inferred purpose of each column in the dataset.
/// (For instance, is the column categorical, numeric, or text data? Should the column be ignored? Etc.)
Expand Down Expand Up @@ -194,15 +194,15 @@ public ColumnInferenceResults InferColumns(string path, ColumnInformation column
/// <returns>Information inferred about the columns in the provided dataset.</returns>
/// <remarks>
/// Infers information about the name, data type, and purpose of each column.
/// The returned <see cref="ColumnInferenceResults.TextLoaderOptions" /> can be used to
/// instantiate a <see cref="TextLoader" />. The <see cref="TextLoader" /> can be used to
/// obtain an <see cref="IDataView"/> that can be fed into an AutoML experiment,
/// The returned <see cref="ColumnInferenceResults.TextLoaderOptions" /> can be used to
/// instantiate a <see cref="TextLoader" />. The <see cref="TextLoader" /> can be used to
/// obtain an <see cref="IDataView"/> that can be fed into an AutoML experiment,
/// or used elsewhere in the ML.NET ecosystem (ie in <see cref="IEstimator{TTransformer}.Fit(IDataView)"/>.
/// The <see cref="ColumnInformation"/> contains the inferred purpose of each column in the dataset.
/// (For instance, is the column categorical, numeric, or text data? Should the column be ignored? Etc.)
/// The <see cref="ColumnInformation"/> can be inspected and modified (or kept as is) and used by an AutoML experiment.
/// </remarks>
public ColumnInferenceResults InferColumns(string path, uint labelColumnIndex, bool hasHeader = false, char? separatorChar = null,
public ColumnInferenceResults InferColumns(string path, uint labelColumnIndex, bool hasHeader = false, char? separatorChar = null,
bool? allowQuoting = null, bool? allowSparse = null, bool trimWhitespace = false, bool groupColumns = true)
{
UserInputValidationUtil.ValidateInferColumnsArgs(path);
Expand Down
21 changes: 14 additions & 7 deletions src/Microsoft.ML.AutoML/API/BinaryClassificationExperiment.cs
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Expand Up @@ -13,23 +13,30 @@
namespace Microsoft.ML.AutoML
{
/// <summary>
/// Settings for AutoML experiments on binary classification datasets.
/// Settings for AutoML experiments on binary classification datasets.
/// </summary>
public sealed class BinaryExperimentSettings : ExperimentSettings
{
/// <summary>
/// Metric that AutoML will try to optimize over the course of the experiment.
/// </summary>
public BinaryClassificationMetric OptimizingMetric { get; set; } = BinaryClassificationMetric.Accuracy;
/// <value>The default value is <see cref="BinaryClassificationMetric.Accuracy"/>.</value>
public BinaryClassificationMetric OptimizingMetric { get; set; }

/// <summary>
/// Collection of trainers the AutoML experiment can leverage.
/// </summary>
/// <remarks>
/// The collection is auto-populated with all possible trainers (all values of <see cref="BinaryClassificationTrainer" />).
/// </remarks>
public ICollection<BinaryClassificationTrainer> Trainers { get; } =
Enum.GetValues(typeof(BinaryClassificationTrainer)).OfType<BinaryClassificationTrainer>().ToList();
/// <value>The default value is a collection auto-populated with all possible trainers (all values of <see cref="BinaryClassificationTrainer" />).</value>
public ICollection<BinaryClassificationTrainer> Trainers { get; }

/// <summary>
/// Initializes a new instance of <see cref="BinaryExperimentSettings"/>.
/// </summary>
public BinaryExperimentSettings()
{
OptimizingMetric = BinaryClassificationMetric.Accuracy;
Trainers = Enum.GetValues(typeof(BinaryClassificationTrainer)).OfType<BinaryClassificationTrainer>().ToList();
}
}

/// <summary>
Expand Down
36 changes: 25 additions & 11 deletions src/Microsoft.ML.AutoML/API/ColumnInference.cs
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Expand Up @@ -20,26 +20,26 @@ public sealed class ColumnInferenceResults
/// Can be used to instantiate a new <see cref="TextLoader" /> to load
/// data into an <see cref="IDataView" />.
/// </remarks>
public TextLoader.Options TextLoaderOptions { get; internal set; } = new TextLoader.Options();
public TextLoader.Options TextLoaderOptions { get; internal set; }

/// <summary>
/// Information about the inferred columns in the dataset.
/// </summary>
/// <remarks>
/// <para>Contains the inferred purposes of each column. See <see cref="AutoML.ColumnInformation"/> for more details.</para>
/// <para>This can be fed to the AutoML API when running an experiment.
/// <para>This can be fed to the AutoML API when running an experiment.
/// See <typeref cref="ExperimentBase{TMetrics, TExperimentSettings}.Execute(IDataView, ColumnInformation, IEstimator{ITransformer}, System.IProgress{RunDetail{TMetrics}})" />
/// for example.</para>
/// </remarks>
public ColumnInformation ColumnInformation { get; internal set; } = new ColumnInformation();
public ColumnInformation ColumnInformation { get; internal set; }
}

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

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

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

/// <summary>
/// The dataset columns that are numeric.
/// </summary>
public ICollection<string> NumericColumnNames { get; } = new Collection<string>();
/// <value>The default value is a new, empty <see cref="Collection{String}"/>.</value>
public ICollection<string> NumericColumnNames { get; }

/// <summary>
/// The dataset columns that are text.
/// </summary>
public ICollection<string> TextColumnNames { get; } = new Collection<string>();
/// <value>The default value is a new, empty <see cref="Collection{String}"/>.</value>
public ICollection<string> TextColumnNames { get; }

/// <summary>
/// The dataset columns that AutoML should ignore.
/// </summary>
public ICollection<string> IgnoredColumnNames { get; } = new Collection<string>();
/// <value>The default value is a new, empty <see cref="Collection{String}"/>.</value>
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📝 This should really be Collection{T}. See dotnet/csharplang#401.

public ICollection<string> IgnoredColumnNames { get; }

public ColumnInformation()
{
LabelColumnName = DefaultColumnNames.Label;
CategoricalColumnNames = new Collection<string>();
NumericColumnNames = new Collection<string>();
TextColumnNames = new Collection<string>();
IgnoredColumnNames = new Collection<string>();
}
}
}
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