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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; }
}
}
}
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