ConversionsExtensionsCatalog.MapKeyToVector 方法
定义
重要
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重载
MapKeyToVector(TransformsCatalog+ConversionTransforms, InputOutputColumnPair[], Boolean)
创建一个 KeyToVectorMappingEstimator，它将键的值映射到表示值的浮点向量。
public static Microsoft.ML.Transforms.KeyToVectorMappingEstimator MapKeyToVector (this Microsoft.ML.TransformsCatalog.ConversionTransforms catalog, Microsoft.ML.InputOutputColumnPair[] columns, bool outputCountVector = false);static member MapKeyToVector : Microsoft.ML.TransformsCatalog.ConversionTransforms * Microsoft.ML.InputOutputColumnPair[] * bool -> Microsoft.ML.Transforms.KeyToVectorMappingEstimator<Extension()>
Public Function MapKeyToVector (catalog As TransformsCatalog.ConversionTransforms, columns As InputOutputColumnPair(), Optional outputCountVector As Boolean = false) As KeyToVectorMappingEstimator参数
转换转换的目录。
- columns
- InputOutputColumnPair[]
输入和输出列。 新列的数据类型是表示原始值的向量 Single 。
- outputCountVector
- Boolean
是否将多个指示器向量合并为一个计数向量，而不是将它们串联在一起。 仅当输入列是键的向量时，才相关。
返回
示例
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic
{
public class MapKeyToVectorMultiColumn
{
/// This example demonstrates the use of MapKeyToVector by mapping keys to
/// floats[] for multiple columns at once. Because the ML.NET KeyType maps
/// the missing value to zero, counting starts at 1, so the uint values
/// converted to KeyTypes will appear skewed by one.
/// See https://github.com/dotnet/machinelearning/blob/main/docs/code/IDataViewTypeSystem.md#key-types
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 = 9, Category = 5 },
new DataPoint() { Timeframe = 8, Category = 4 },
new DataPoint() { Timeframe = 8, Category = 4 },
new DataPoint() { Timeframe = 9, Category = 3 },
new DataPoint() { Timeframe = 2, Category = 3 },
new DataPoint() { Timeframe = 3, Category = 5 }
};
var data = mlContext.Data.LoadFromEnumerable(rawData);
// Constructs the ML.net pipeline
var pipeline = mlContext.Transforms.Conversion.MapKeyToVector(new[]{
new InputOutputColumnPair ("TimeframeVector", "Timeframe"),
new InputOutputColumnPair ("CategoryVector", "Category")
});
// 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 TimeframeVector " +
$"Category CategoryVector");
foreach (var featureRow in features)
Console.WriteLine(featureRow.Timeframe + " " +
string.Join(',', featureRow.TimeframeVector) + " " +
featureRow.Category + " " +
string.Join(',', featureRow.CategoryVector));
// TransformedData obtained post-transformation.
//
// Timeframe TimeframeVector Category CategoryVector
// 10 0,0,0,0,0,0,0,0,0,1 6 0,0,0,0,0
// 9 0,0,0,0,0,0,0,0,1,0 5 0,0,0,0,1
// 9 0,0,0,0,0,0,0,0,1,0 5 0,0,0,0,1
// 10 0,0,0,0,0,0,0,0,0,1 4 0,0,0,1,0
// 3 0,0,1,0,0,0,0,0,0,0 4 0,0,0,1,0
// 4 0,0,0,1,0,0,0,0,0,0 6 0,0,0,0,0
}
private class DataPoint
{
// The maximal value used is 9; but since 0 is reserved for missing
// value, we set the count to 10.
[KeyType(10)]
public uint Timeframe { get; set; }
[KeyType(6)]
public uint Category { get; set; }
}
private class TransformedData : DataPoint
{
public float[] TimeframeVector { get; set; }
public float[] CategoryVector { get; set; }
}
}
}
注解
此转换可以针对多个键列进行操作。
适用于
MapKeyToVector(TransformsCatalog+ConversionTransforms, String, String, Boolean)
创建一个 KeyToVectorMappingEstimator，它将键的值映射到表示值的浮点向量。
public static Microsoft.ML.Transforms.KeyToVectorMappingEstimator MapKeyToVector (this Microsoft.ML.TransformsCatalog.ConversionTransforms catalog, string outputColumnName, string inputColumnName = default, bool outputCountVector = false);static member MapKeyToVector : Microsoft.ML.TransformsCatalog.ConversionTransforms * string * string * bool -> Microsoft.ML.Transforms.KeyToVectorMappingEstimator<Extension()>
Public Function MapKeyToVector (catalog As TransformsCatalog.ConversionTransforms, outputColumnName As String, Optional inputColumnName As String = Nothing, Optional outputCountVector As Boolean = false) As KeyToVectorMappingEstimator参数
转换转换的目录。
- inputColumnName
- String
要转换的列的名称。 If set to null, the value of the outputColumnName will be used as source.
此转换通过键操作。
- outputCountVector
- Boolean
是否将多个指示器向量合并为一个计数向量，而不是将它们串联在一起。 仅当输入列是键的向量时，才相关。
返回
示例
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic
{
class MapKeyToVector
{
/// This example demonstrates the use of MapKeyToVector by mapping keys to
/// floats[]. Because the ML.NET KeyType maps the missing value to zero,
/// counting starts at 1, so the uint values converted to KeyTypes will
/// appear skewed by one. See https://github.com/dotnet/machinelearning/blob/main/docs/code/IDataViewTypeSystem.md#key-types
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 = 8, PartA=1, PartB=2},
new DataPoint() { Timeframe = 7, PartA=2, PartB=1},
new DataPoint() { Timeframe = 8, PartA=3, PartB=2},
new DataPoint() { Timeframe = 3, PartA=3, PartB=3}
};
var data = mlContext.Data.LoadFromEnumerable(rawData);
// First transform just maps key type to indicator vector. i.e. it's
// produces vector filled with zeros with size of key cardinality and
// set 1 to corresponding key's value index in that array. After that we
// concatenate two columns with single int values into vector of ints.
// Third transform will create vector of keys, where key type is shared
// across whole vector. Forth transform output data as count vector and
// that vector would have size equal to shared key type cardinality and
// put key counts to corresponding indexes in array. Fifth transform
// output indicator vector for each key and concatenate them together.
// Result vector would be size of key cardinality multiplied by size of
// original vector.
var pipeline = mlContext.Transforms.Conversion.MapKeyToVector(
"TimeframeVector", "Timeframe")
.Append(mlContext.Transforms.Concatenate("Parts", "PartA", "PartB"))
.Append(mlContext.Transforms.Conversion.MapValueToKey("Parts"))
.Append(mlContext.Transforms.Conversion.MapKeyToVector(
"PartsCount", "Parts", outputCountVector: true))
.Append(mlContext.Transforms.Conversion.MapKeyToVector(
"PartsNoCount", "Parts"));
// 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 TimeframeVector PartsCount " +
"PartsNoCount");
foreach (var featureRow in features)
Console.WriteLine(featureRow.Timeframe + " " +
string.Join(',', featureRow.TimeframeVector.Select(x => x)) + " "
+ string.Join(',', featureRow.PartsCount.Select(x => x)) +
" " + string.Join(',', featureRow.PartsNoCount.Select(
x => x)));
// Expected output:
// Timeframe TimeframeVector PartsCount PartsNoCount
// 9 0,0,0,0,0,0,0,0,1 1,1,0 1,0,0,0,1,0
// 8 0,0,0,0,0,0,0,1,0 1,1,0 0,1,0,1,0,0
// 9 0,0,0,0,0,0,0,0,1 0,1,1 0,0,1,0,1,0
// 4 0,0,0,1,0,0,0,0,0 0,0,2 0,0,1,0,0,1
}
private class DataPoint
{
[KeyType(9)]
public uint Timeframe { get; set; }
public int PartA { get; set; }
public int PartB { get; set; }
}
private class TransformedData : DataPoint
{
public float[] TimeframeVector { get; set; }
public float[] PartsCount { get; set; }
public float[] PartsNoCount { get; set; }
}
}
}