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[AutoML] Add AutoML XML documentation to all public members; migrate AutoML projects & tests into ML.NET solution; AutoML test fixes #3351

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Merged
merged 17 commits into from
Apr 16, 2019
Merged

[AutoML] Add AutoML XML documentation to all public members; migrate AutoML projects & tests into ML.NET solution; AutoML test fixes #3351

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69a566f
documentation -- first pass
daholste Apr 15, 2019
71aefd1
add samples; convert nuget to project references; add projects to mai…
daholste Apr 15, 2019
0f1572f
delete AutoML sln; add AutoML test projs to ML.NET sln
daholste Apr 16, 2019
f2f498a
test fixes
daholste Apr 16, 2019
e539942
comments rev
daholste Apr 16, 2019
44dea9c
remove examples for restricting available trainers
daholste Apr 16, 2019
71950a6
wording rev
daholste Apr 16, 2019
06a12df
wording rev
daholste Apr 16, 2019
45f7b4e
trainer fixes
daholste Apr 16, 2019
01e4b4c
wording rev
daholste Apr 16, 2019
c8f1b21
Update src/Microsoft.ML.Auto/API/AutoCatalog.cs
justinormont Apr 16, 2019
e30a6bb
Update src/Microsoft.ML.Auto/API/AutoCatalog.cs
justinormont Apr 16, 2019
d46e95a
data points --> data elements
daholste Apr 16, 2019
de53fa3
wording rev
daholste Apr 16, 2019
4e2a3f7
wording rev
daholste Apr 16, 2019
5313141
wording revs
daholste Apr 16, 2019
0099daf
wording rev
daholste Apr 16, 2019
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91 changes: 0 additions & 91 deletions Microsoft.ML.AutoML.sln
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60 changes: 60 additions & 0 deletions Microsoft.ML.sln
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Expand Up @@ -274,6 +274,14 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Microsoft.ML.FastTree", "Mi
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Expand Down Expand Up @@ -968,6 +976,54 @@ Global
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Expand Down Expand Up @@ -1055,6 +1111,10 @@ Global
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Expand Down
1 change: 0 additions & 1 deletion build.proj
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Expand Up @@ -22,7 +22,6 @@

<ItemGroup>
<Project Include="Microsoft.ML.sln" />
<Project Include="Microsoft.ML.AutoML.sln" />
</ItemGroup>

<Import Project="dir.traversal.targets" />
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23 changes: 23 additions & 0 deletions docs/samples/Microsoft.ML.Samples/AutoML/BinaryClassificationExperiment.cs
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@@ -0,0 +1,23 @@
using Microsoft.ML;
using Microsoft.ML.Auto;

namespace Samples.AutoML
{
public static class BinaryClassificationExperiment
{
public static void Example()
{
// Create a new context for ML.NET operations. It can be used for exception tracking and logging,
// as a catalog of available operations and as the source of randomness.
var mlContext = new MLContext();

// Download and featurize the dataset.
var dataView = Microsoft.ML.SamplesUtils.DatasetUtils.LoadFeaturizedAdultDataset(mlContext);

// Run an AutoML experiment
var experimentResult = mlContext.Auto()
.CreateBinaryClassificationExperiment(60)
.Execute(dataView, "IsOver50K");
}
}
}
27 changes: 27 additions & 0 deletions ...mples/Microsoft.ML.Samples/AutoML/BinaryClassificationExperimentWithExperimentSettings.cs
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using Microsoft.ML;
using Microsoft.ML.Auto;

namespace Samples.AutoML
{
public static class BinaryClassificationExperimentWithExperimentSettings
{
public static void Example()
{
// Create a new context for ML.NET operations. It can be used for exception tracking and logging,
// as a catalog of available operations and as the source of randomness.
var mlContext = new MLContext();

// Download and featurize the dataset.
var dataView = Microsoft.ML.SamplesUtils.DatasetUtils.LoadFeaturizedAdultDataset(mlContext);

// Run an AutoML experiment
var experimentSettings = new BinaryExperimentSettings()
{
MaxExperimentTimeInSeconds = 60
};
var experimentResult = mlContext.Auto()
.CreateBinaryClassificationExperiment(experimentSettings)
.Execute(dataView, "IsOver50K");
}
}
}
28 changes: 28 additions & 0 deletions docs/samples/Microsoft.ML.Samples/AutoML/MulticlassClassificationExperiment.cs
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using Microsoft.ML;
using Microsoft.ML.Auto;
using Microsoft.ML.SamplesUtils;

namespace Samples.AutoML
{
public static class MulticlassClassificationExperiment
{
public static void Example()
{
// Create a new context for ML.NET operations. It can be used for exception tracking and logging,
// as a catalog of available operations and as the source of randomness.
var mlContext = new MLContext();

// Download and featurize the dataset.
// Create a list of data examples.
var examples = DatasetUtils.GenerateRandomMulticlassClassificationExamples(1000);

// Convert the examples list to an IDataView object, which is consumable by ML.NET API.
var dataView = mlContext.Data.LoadFromEnumerable(examples);

// Run an AutoML experiment
var experimentResult = mlContext.Auto()
.CreateMulticlassClassificationExperiment(60)
.Execute(dataView);
}
}
}
32 changes: 32 additions & 0 deletions ...s/Microsoft.ML.Samples/AutoML/MulticlassClassificationExperimentWithExperimentSettings.cs
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using Microsoft.ML;
using Microsoft.ML.Auto;
using Microsoft.ML.SamplesUtils;

namespace Samples.AutoML
{
public static class MulticlassClassificationExperimentWithExperimentSettings
{
public static void Example()
{
// Create a new context for ML.NET operations. It can be used for exception tracking and logging,
// as a catalog of available operations and as the source of randomness.
var mlContext = new MLContext();

// Download and featurize the dataset.
// Create a list of data examples.
var examples = DatasetUtils.GenerateRandomMulticlassClassificationExamples(1000);

// Convert the examples list to an IDataView object, which is consumable by ML.NET API.
var dataView = mlContext.Data.LoadFromEnumerable(examples);

// Run an AutoML experiment
var experimentSettings = new MulticlassExperimentSettings()
{
MaxExperimentTimeInSeconds = 60
};
var experimentResult = mlContext.Auto()
.CreateMulticlassClassificationExperiment(experimentSettings)
.Execute(dataView);
}
}
}
39 changes: 39 additions & 0 deletions docs/samples/Microsoft.ML.Samples/AutoML/RegressionExperiment.cs
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using Microsoft.ML;
using Microsoft.ML.Auto;
using Microsoft.ML.Data;

namespace Samples.AutoML
{
public static class RegressionExperiment
{
public static void Example()
{
// Downloading a regression dataset from github.com/dotnet/machinelearning
string dataFile = Microsoft.ML.SamplesUtils.DatasetUtils.DownloadHousingRegressionDataset();

// Create a new context for ML.NET operations. It can be used for exception tracking and logging,
// as a catalog of available operations and as the source of randomness.
var mlContext = new MLContext();

// Creating a data loader, based on the format of the data
// The data is tab separated with all numeric columns.
// The first column being the label and rest are numeric features
// Here only seven numeric columns are used as features
var dataView = mlContext.Data.LoadFromTextFile(dataFile, new TextLoader.Options
{
Separators = new[] { '\t' },
HasHeader = true,
Columns = new[]
{
new TextLoader.Column("Label", DataKind.Single, 0),
new TextLoader.Column("Features", DataKind.Single, 1, 6)
}
});

// Run an AutoML experiment
var experimentResult = mlContext.Auto()
.CreateRegressionExperiment(60)
.Execute(dataView);
}
}
}
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