TransformExtensionsCatalog.SelectColumns Method
Definition
Important
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Overloads
| SelectColumns(TransformsCatalog, String[]) |
Create a ColumnSelectingEstimator, which keeps a given list of columns in an IDataView and drops the others. |
| SelectColumns(TransformsCatalog, String[], Boolean) |
Create a ColumnSelectingEstimator, which keeps a given list of columns in an IDataView and drops the others. |
SelectColumns(TransformsCatalog, String[])
Create a ColumnSelectingEstimator, which keeps a given list of columns in an IDataView and drops the others.
public static Microsoft.ML.Transforms.ColumnSelectingEstimator SelectColumns (this Microsoft.ML.TransformsCatalog catalog, params string[] columnNames);static member SelectColumns : Microsoft.ML.TransformsCatalog * string[] -> Microsoft.ML.Transforms.ColumnSelectingEstimator<Extension()>
Public Function SelectColumns (catalog As TransformsCatalog, ParamArray columnNames As String()) As ColumnSelectingEstimatorParameters
- catalog
- TransformsCatalog
The transform's catalog.
- columnNames
- String[]
The array of column names to keep.
Returns
Examples
using System;
using System.Collections.Generic;
using Microsoft.ML;
namespace Samples.Dynamic
{
public static class SelectColumns
{
public static void Example()
{
// Create a new ML context, for ML.NET operations. It can be used for
// exception tracking and logging, as well as the source of randomness.
var mlContext = new MLContext();
// Create a small dataset as an IEnumerable.
var samples = new List<InputData>()
{
new InputData(){ Age = 21, Gender = "Male", Education = "BS",
ExtraColumn = 1 },
new InputData(){ Age = 23, Gender = "Female", Education = "MBA",
ExtraColumn = 2 },
new InputData(){ Age = 28, Gender = "Male", Education = "PhD",
ExtraColumn = 3 },
new InputData(){ Age = 22, Gender = "Male", Education = "BS",
ExtraColumn = 4 },
new InputData(){ Age = 23, Gender = "Female", Education = "MS",
ExtraColumn = 5 },
new InputData(){ Age = 27, Gender = "Female", Education = "PhD",
ExtraColumn = 6 },
};
// Convert training data to IDataView.
var dataview = mlContext.Data.LoadFromEnumerable(samples);
// Select a subset of columns to keep.
var pipeline = mlContext.Transforms.SelectColumns("Age", "Education");
// Now we can transform the data and look at the output to confirm the
// behavior of SelectColumns. Don't forget that this operation doesn't
// actually evaluate data until we read the data below, as
// transformations are lazy in ML.NET.
var transformedData = pipeline.Fit(dataview).Transform(dataview);
// Print the number of columns in the schema
Console.WriteLine($"There are {transformedData.Schema.Count} columns" +
$" in the dataset.");
// Expected output:
// There are 2 columns in the dataset.
// We can extract the newly created column as an IEnumerable of
// TransformedData, the class we define below.
var rowEnumerable = mlContext.Data.CreateEnumerable<TransformedData>(
transformedData, reuseRowObject: false);
// And finally, we can write out the rows of the dataset, looking at the
// columns of interest.
Console.WriteLine($"Age and Educations columns obtained " +
$"post-transformation.");
foreach (var row in rowEnumerable)
Console.WriteLine($"Age: {row.Age} Education: {row.Education}");
// Expected output:
// Age and Educations columns obtained post-transformation.
// Age: 21 Education: BS
// Age: 23 Education: MBA
// Age: 28 Education: PhD
// Age: 22 Education: BS
// Age: 23 Education: MS
// Age: 27 Education: PhD
}
private class InputData
{
public int Age { get; set; }
public string Gender { get; set; }
public string Education { get; set; }
public float ExtraColumn { get; set; }
}
private class TransformedData
{
public int Age { get; set; }
public string Education { get; set; }
}
}
}
Applies to
SelectColumns(TransformsCatalog, String[], Boolean)
Create a ColumnSelectingEstimator, which keeps a given list of columns in an IDataView and drops the others.
public static Microsoft.ML.Transforms.ColumnSelectingEstimator SelectColumns (this Microsoft.ML.TransformsCatalog catalog, string[] columnNames, bool keepHidden);static member SelectColumns : Microsoft.ML.TransformsCatalog * string[] * bool -> Microsoft.ML.Transforms.ColumnSelectingEstimator<Extension()>
Public Function SelectColumns (catalog As TransformsCatalog, columnNames As String(), keepHidden As Boolean) As ColumnSelectingEstimatorParameters
- catalog
- TransformsCatalog
The transform's catalog.
- columnNames
- String[]
The array of column names to keep.
- keepHidden
- Boolean
If true will keep hidden columns and false will remove hidden columns.
Keeping hidden columns, instead of dropping them, is recommended when it is necessary to understand how the inputs of a pipeline
map to outputs of the pipeline, for debugging purposes.
Returns
Examples
using System;
using System.Collections.Generic;
using Microsoft.ML;
namespace Samples.Dynamic
{
public static class SelectColumns
{
public static void Example()
{
// Create a new ML context, for ML.NET operations. It can be used for
// exception tracking and logging, as well as the source of randomness.
var mlContext = new MLContext();
// Create a small dataset as an IEnumerable.
var samples = new List<InputData>()
{
new InputData(){ Age = 21, Gender = "Male", Education = "BS",
ExtraColumn = 1 },
new InputData(){ Age = 23, Gender = "Female", Education = "MBA",
ExtraColumn = 2 },
new InputData(){ Age = 28, Gender = "Male", Education = "PhD",
ExtraColumn = 3 },
new InputData(){ Age = 22, Gender = "Male", Education = "BS",
ExtraColumn = 4 },
new InputData(){ Age = 23, Gender = "Female", Education = "MS",
ExtraColumn = 5 },
new InputData(){ Age = 27, Gender = "Female", Education = "PhD",
ExtraColumn = 6 },
};
// Convert training data to IDataView.
var dataview = mlContext.Data.LoadFromEnumerable(samples);
// Select a subset of columns to keep.
var pipeline = mlContext.Transforms.SelectColumns("Age", "Education");
// Now we can transform the data and look at the output to confirm the
// behavior of SelectColumns. Don't forget that this operation doesn't
// actually evaluate data until we read the data below, as
// transformations are lazy in ML.NET.
var transformedData = pipeline.Fit(dataview).Transform(dataview);
// Print the number of columns in the schema
Console.WriteLine($"There are {transformedData.Schema.Count} columns" +
$" in the dataset.");
// Expected output:
// There are 2 columns in the dataset.
// We can extract the newly created column as an IEnumerable of
// TransformedData, the class we define below.
var rowEnumerable = mlContext.Data.CreateEnumerable<TransformedData>(
transformedData, reuseRowObject: false);
// And finally, we can write out the rows of the dataset, looking at the
// columns of interest.
Console.WriteLine($"Age and Educations columns obtained " +
$"post-transformation.");
foreach (var row in rowEnumerable)
Console.WriteLine($"Age: {row.Age} Education: {row.Education}");
// Expected output:
// Age and Educations columns obtained post-transformation.
// Age: 21 Education: BS
// Age: 23 Education: MBA
// Age: 28 Education: PhD
// Age: 22 Education: BS
// Age: 23 Education: MS
// Age: 27 Education: PhD
}
private class InputData
{
public int Age { get; set; }
public string Gender { get; set; }
public string Education { get; set; }
public float ExtraColumn { get; set; }
}
private class TransformedData
{
public int Age { get; set; }
public string Education { get; set; }
}
}
}