ConversionsExtensionsCatalog.MapValue Method
Definition
Important
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Overloads
| MapValue(TransformsCatalog+ConversionTransforms, String, IDataView, DataViewSchema+Column, DataViewSchema+Column, String) |
Create a ValueMappingEstimator, which converts value types into keys, loading the keys to use from the |
| MapValue<TInputType,TOutputType>(TransformsCatalog+ConversionTransforms, String, IEnumerable<KeyValuePair<TInputType,TOutputType[]>>, String) |
Create a ValueMappingEstimator, which converts value types into keys, loading the keys to use from |
| MapValue<TInputType,TOutputType>(TransformsCatalog+ConversionTransforms, String, IEnumerable<KeyValuePair<TInputType,TOutputType>>, String, Boolean) |
Create a ValueMappingEstimator, which converts value types into keys, loading the keys to use from |
MapValue(TransformsCatalog+ConversionTransforms, String, IDataView, DataViewSchema+Column, DataViewSchema+Column, String)
Create a ValueMappingEstimator, which converts value types into keys, loading the keys to use from the lookupMap where the keyColumn
specifies the keys, and the valueColumn the respective value.
public static Microsoft.ML.Transforms.ValueMappingEstimator MapValue (this Microsoft.ML.TransformsCatalog.ConversionTransforms catalog, string outputColumnName, Microsoft.ML.IDataView lookupMap, Microsoft.ML.DataViewSchema.Column keyColumn, Microsoft.ML.DataViewSchema.Column valueColumn, string inputColumnName = default);static member MapValue : Microsoft.ML.TransformsCatalog.ConversionTransforms * string * Microsoft.ML.IDataView * Microsoft.ML.DataViewSchema.Column * Microsoft.ML.DataViewSchema.Column * string -> Microsoft.ML.Transforms.ValueMappingEstimator<Extension()>
Public Function MapValue (catalog As TransformsCatalog.ConversionTransforms, outputColumnName As String, lookupMap As IDataView, keyColumn As DataViewSchema.Column, valueColumn As DataViewSchema.Column, Optional inputColumnName As String = Nothing) As ValueMappingEstimatorParameters
The conversion transform's catalog
- outputColumnName
- String
Name of the column resulting from the transformation of inputColumnName.
The data types can be primitives or vectors of numeric, text, boolean, DateTime, DateTimeOffset
or DataViewRowId types.
- keyColumn
- DataViewSchema.Column
The key column in lookupMap.
- valueColumn
- DataViewSchema.Column
The value column in lookupMap.
- inputColumnName
- String
Name of the column to transform. If set to null, the value of the outputColumnName will be used as source.
The data types can be primitives or vectors of numeric, text, boolean, DateTime, DateTimeOffset
or DataViewRowId types.
Returns
Examples
using System;
using System.Collections.Generic;
using Microsoft.ML;
namespace Samples.Dynamic
{
public static class MapValueIdvLookup
{
/// This example demonstrates the use of MapValue by mapping floats to
/// strings, looking up the mapping in an IDataView. This is useful to map
/// types to a grouping.
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();
// Get a small dataset as an IEnumerable.
var rawData = new[] {
new DataPoint() { Price = 3.14f },
new DataPoint() { Price = 2000f },
new DataPoint() { Price = 1.19f },
new DataPoint() { Price = 2.17f },
new DataPoint() { Price = 33.784f },
};
// Convert to IDataView
var data = mlContext.Data.LoadFromEnumerable(rawData);
// Create the lookup map data IEnumerable.
var lookupData = new[] {
new LookupMap { Value = 3.14f, Category = "Low" },
new LookupMap { Value = 1.19f , Category = "Low" },
new LookupMap { Value = 2.17f , Category = "Low" },
new LookupMap { Value = 33.784f, Category = "Medium" },
new LookupMap { Value = 2000f, Category = "High"}
};
// Convert to IDataView
var lookupIdvMap = mlContext.Data.LoadFromEnumerable(lookupData);
// Constructs the ValueMappingEstimator making the ML.NET pipeline
var pipeline = mlContext.Transforms.Conversion.MapValue("PriceCategory",
lookupIdvMap, lookupIdvMap.Schema["Value"], lookupIdvMap.Schema[
"Category"], "Price");
// Fits the ValueMappingEstimator and transforms the data converting the
// Price to PriceCategory.
IDataView transformedData = pipeline.Fit(data).Transform(data);
// Getting the resulting data as an IEnumerable.
IEnumerable<TransformedData> features = mlContext.Data.CreateEnumerable<
TransformedData>(transformedData, reuseRowObject: false);
Console.WriteLine($" Price PriceCategory");
foreach (var featureRow in features)
Console.WriteLine($"{featureRow.Price}\t\t" +
$"{featureRow.PriceCategory}");
// TransformedData obtained post-transformation.
//
// Price PriceCategory
// 3.14 Low
// 2000 High
// 1.19 Low
// 2.17 Low
// 33.784 Medium
}
// Type for the IDataView that will be serving as the map
private class LookupMap
{
public float Value { get; set; }
public string Category { get; set; }
}
private class DataPoint
{
public float Price { get; set; }
}
private class TransformedData : DataPoint
{
public string PriceCategory { get; set; }
}
}
}
Applies to
MapValue<TInputType,TOutputType>(TransformsCatalog+ConversionTransforms, String, IEnumerable<KeyValuePair<TInputType,TOutputType[]>>, String)
Create a ValueMappingEstimator, which converts value types into keys, loading the keys to use from keyValuePairs.
public static Microsoft.ML.Transforms.ValueMappingEstimator<TInputType,TOutputType> MapValue<TInputType,TOutputType> (this Microsoft.ML.TransformsCatalog.ConversionTransforms catalog, string outputColumnName, System.Collections.Generic.IEnumerable<System.Collections.Generic.KeyValuePair<TInputType,TOutputType[]>> keyValuePairs, string inputColumnName = default);static member MapValue : Microsoft.ML.TransformsCatalog.ConversionTransforms * string * seq<System.Collections.Generic.KeyValuePair<'InputType, 'OutputType[]>> * string -> Microsoft.ML.Transforms.ValueMappingEstimator<'InputType, 'OutputType><Extension()>
Public Function MapValue(Of TInputType, TOutputType) (catalog As TransformsCatalog.ConversionTransforms, outputColumnName As String, keyValuePairs As IEnumerable(Of KeyValuePair(Of TInputType, TOutputType())), Optional inputColumnName As String = Nothing) As ValueMappingEstimator(Of TInputType, TOutputType)Type Parameters
- TInputType
The key type.
- TOutputType
The value type.
Parameters
The conversion transform's catalog
- outputColumnName
- String
Name of the column resulting from the transformation of inputColumnName.
The data types can be primitives or vectors of numeric, text, boolean, DateTime, DateTimeOffset or DataViewRowId types,
as specified in the TOutputType.
- keyValuePairs
- IEnumerable<KeyValuePair<TInputType,TOutputType[]>>
Specifies the mapping that will be performed. The keys will be mapped to the values as specified in the keyValuePairs.
- inputColumnName
- String
Name of the column to transform. If set to null, the value of the outputColumnName will be used as source.
The data types can be primitives or vectors of numeric, text, boolean, DateTime, DateTimeOffset or DataViewRowId types,
as specified in the TInputType.
Returns
ValueMappingEstimator<TInputType,TOutputType>
Examples
using System;
using System.Collections.Generic;
using Microsoft.ML;
namespace Samples.Dynamic
{
public static class MapValueToArray
{
/// This example demonstrates the use of MapValue by mapping strings to
/// array values, which allows for mapping data to numeric arrays. This
/// functionality is useful when the generated column will serve as the
/// Features column for a trainer. Most of the trainers take a numeric
/// vector, as the Features column. In this example, we are mapping the
/// Timeframe data to arbitrary integer arrays.
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();
// Get a small dataset as an IEnumerable.
var rawData = new[] {
new DataPoint() { Timeframe = "0-4yrs" },
new DataPoint() { Timeframe = "6-11yrs" },
new DataPoint() { Timeframe = "12-25yrs" },
new DataPoint() { Timeframe = "0-5yrs" },
new DataPoint() { Timeframe = "12-25yrs" },
new DataPoint() { Timeframe = "25+yrs" },
};
var data = mlContext.Data.LoadFromEnumerable(rawData);
// Creating a list of key-value pairs to indicate the mapping between
// the DataPoint values, and the arrays they should map to.
var timeframeMap = new Dictionary<string, int[]>();
timeframeMap["0-4yrs"] = new int[] { 0, 5, 300 };
timeframeMap["0-5yrs"] = new int[] { 0, 5, 300 };
timeframeMap["6-11yrs"] = new int[] { 6, 11, 300 };
timeframeMap["12-25yrs"] = new int[] { 12, 50, 300 };
timeframeMap["25+yrs"] = new int[] { 12, 50, 300 };
// Constructs the ValueMappingEstimator making the ML.NET pipeline.
var pipeline = mlContext.Transforms.Conversion.MapValue("Features",
timeframeMap, "Timeframe");
// Fits the ValueMappingEstimator and transforms the data adding the
// Features column.
IDataView transformedData = pipeline.Fit(data).Transform(data);
// Getting the resulting data as an IEnumerable.
IEnumerable<TransformedData> featuresColumn = mlContext.Data
.CreateEnumerable<TransformedData>(transformedData, reuseRowObject:
false);
Console.WriteLine($"Timeframe Features");
foreach (var featureRow in featuresColumn)
Console.WriteLine($"{featureRow.Timeframe}\t\t " +
$"{string.Join(",", featureRow.Features)}");
// Timeframe Features
// 0-4yrs 0, 5, 300
// 6-11yrs 6, 11, 300
// 12-25yrs 12, 50, 300
// 0-5yrs 0, 5, 300
// 12-25yrs 12, 50,300
// 25+yrs 12, 50, 300
}
public class DataPoint
{
public string Timeframe { get; set; }
}
public class TransformedData : DataPoint
{
public int[] Features { get; set; }
}
}
}
Applies to
MapValue<TInputType,TOutputType>(TransformsCatalog+ConversionTransforms, String, IEnumerable<KeyValuePair<TInputType,TOutputType>>, String, Boolean)
Create a ValueMappingEstimator, which converts value types into keys, loading the keys to use from keyValuePairs.
public static Microsoft.ML.Transforms.ValueMappingEstimator<TInputType,TOutputType> MapValue<TInputType,TOutputType> (this Microsoft.ML.TransformsCatalog.ConversionTransforms catalog, string outputColumnName, System.Collections.Generic.IEnumerable<System.Collections.Generic.KeyValuePair<TInputType,TOutputType>> keyValuePairs, string inputColumnName = default, bool treatValuesAsKeyType = false);static member MapValue : Microsoft.ML.TransformsCatalog.ConversionTransforms * string * seq<System.Collections.Generic.KeyValuePair<'InputType, 'OutputType>> * string * bool -> Microsoft.ML.Transforms.ValueMappingEstimator<'InputType, 'OutputType><Extension()>
Public Function MapValue(Of TInputType, TOutputType) (catalog As TransformsCatalog.ConversionTransforms, outputColumnName As String, keyValuePairs As IEnumerable(Of KeyValuePair(Of TInputType, TOutputType)), Optional inputColumnName As String = Nothing, Optional treatValuesAsKeyType As Boolean = false) As ValueMappingEstimator(Of TInputType, TOutputType)Type Parameters
- TInputType
The key type.
- TOutputType
The value type.
Parameters
The conversion transform's catalog
- outputColumnName
- String
Name of the column resulting from the transformation of inputColumnName.
The output data types can be primitives or vectors of numeric, text, boolean, DateTime, DateTimeOffset or DataViewRowId types.
- keyValuePairs
- IEnumerable<KeyValuePair<TInputType,TOutputType>>
Specifies the mapping that will be performed. The keys will be mapped to the values as specified in the keyValuePairs.
- inputColumnName
- String
Name of the column to transform.
If set to null, the value of the outputColumnName will be used as source.
The input data types can be primitives or vectors of numeric, text, boolean, DateTime, DateTimeOffset or DataViewRowId types.
- treatValuesAsKeyType
- Boolean
Whether to treat the values as a key.
Returns
ValueMappingEstimator<TInputType,TOutputType>
An instance of the ValueMappingEstimator
Examples
using System;
using System.Collections.Generic;
using Microsoft.ML;
namespace Samples.Dynamic
{
public static class MapValue
{
/// This example demonstrates the use of the ValueMappingEstimator by
/// mapping strings to other string values, or floats to strings. This is
/// useful to map types to a category.
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();
// Get a small dataset as an IEnumerable.
var rawData = new[] {
new DataPoint() { Timeframe = "0-4yrs" , Score = 1 },
new DataPoint() { Timeframe = "6-11yrs" , Score = 2 },
new DataPoint() { Timeframe = "12-25yrs" , Score = 3 },
new DataPoint() { Timeframe = "0-5yrs" , Score = 4 },
new DataPoint() { Timeframe = "12-25yrs" , Score = 5 },
new DataPoint() { Timeframe = "25+yrs" , Score = 5 },
};
var data = mlContext.Data.LoadFromEnumerable(rawData);
// Construct the mapping to other strings for the Timeframe column.
var timeframeMap = new Dictionary<string, string>();
timeframeMap["0-4yrs"] = "Short";
timeframeMap["0-5yrs"] = "Short";
timeframeMap["6-11yrs"] = "Medium";
timeframeMap["12-25yrs"] = "Long";
timeframeMap["25+yrs"] = "Long";
// Construct the mapping of strings to keys(uints) for the Timeframe
// column.
var timeframeKeyMap = new Dictionary<string, uint>();
timeframeKeyMap["0-4yrs"] = 1;
timeframeKeyMap["0-5yrs"] = 1;
timeframeKeyMap["6-11yrs"] = 2;
timeframeKeyMap["12-25yrs"] = 3;
timeframeKeyMap["25+yrs"] = 3;
// Construct the mapping of ints to strings for the Score column.
var scoreMap = new Dictionary<int, string>();
scoreMap[1] = "Low";
scoreMap[2] = "Low";
scoreMap[3] = "Average";
scoreMap[4] = "High";
scoreMap[5] = "High";
// Constructs the ML.net pipeline
var pipeline = mlContext.Transforms.Conversion.MapValue(
"TimeframeCategory", timeframeMap, "Timeframe").Append(mlContext.
Transforms.Conversion.MapValue("ScoreCategory", scoreMap, "Score"))
// on the MapValue below, the treatValuesAsKeyType is set to true.
// The type of the Label column will be a KeyDataViewType type,
// and it can be used as input for trainers performing multiclass
// classification.
.Append(mlContext.Transforms.Conversion.MapValue("Label",
timeframeKeyMap, "Timeframe", treatValuesAsKeyType: true));
// Fits the pipeline to the data.
IDataView transformedData = pipeline.Fit(data).Transform(data);
// Getting the resulting data as an IEnumerable.
// This will contain the newly created columns.
IEnumerable<TransformedData> features = mlContext.Data.CreateEnumerable<
TransformedData>(transformedData, reuseRowObject: false);
Console.WriteLine(" Timeframe TimeframeCategory Label Score " +
"ScoreCategory");
foreach (var featureRow in features)
Console.WriteLine($"{featureRow.Timeframe}\t\t" +
$"{featureRow.TimeframeCategory}\t\t\t{featureRow.Label}\t\t" +
$"{featureRow.Score}\t{featureRow.ScoreCategory}");
// TransformedData obtained post-transformation.
//
// Timeframe TimeframeCategory Label Score ScoreCategory
// 0-4yrs Short 1 1 Low
// 6-11yrs Medium 2 2 Low
// 12-25yrs Long 3 3 Average
// 0-5yrs Short 1 4 High
// 12-25yrs Long 3 5 High
// 25+yrs Long 3 5 High
}
private class DataPoint
{
public string Timeframe { get; set; }
public int Score { get; set; }
}
private class TransformedData : DataPoint
{
public string TimeframeCategory { get; set; }
public string ScoreCategory { get; set; }
public uint Label { get; set; }
}
}
}