ExtensionsCatalog.IndicateMissingValues メソッド
定義
重要
一部の情報は、リリース前に大きく変更される可能性があるプレリリースされた製品に関するものです。 Microsoft は、ここに記載されている情報について、明示または黙示を問わず、一切保証しません。
オーバーロード
| IndicateMissingValues(TransformsCatalog, InputOutputColumnPair[]) |
新しい列に MissingValueIndicatorEstimator指定された InputColumnName 列からデータをコピーする 、を作成します OutputColumnName。 |
| IndicateMissingValues(TransformsCatalog, String, String) |
MissingValueIndicatorEstimatorで指定された |
IndicateMissingValues(TransformsCatalog, InputOutputColumnPair[])
新しい列に MissingValueIndicatorEstimator指定された InputColumnName 列からデータをコピーする 、を作成します OutputColumnName。
public static Microsoft.ML.Transforms.MissingValueIndicatorEstimator IndicateMissingValues (this Microsoft.ML.TransformsCatalog catalog, Microsoft.ML.InputOutputColumnPair[] columns);static member IndicateMissingValues : Microsoft.ML.TransformsCatalog * Microsoft.ML.InputOutputColumnPair[] -> Microsoft.ML.Transforms.MissingValueIndicatorEstimator<Extension()>
Public Function IndicateMissingValues (catalog As TransformsCatalog, columns As InputOutputColumnPair()) As MissingValueIndicatorEstimatorパラメーター
- catalog
- TransformsCatalog
変換のカタログ。
- columns
- InputOutputColumnPair[]
戻り値
例
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic
{
public static class IndicateMissingValuesMultiColumn
{
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 and convert it to an IDataView.
var samples = new List<DataPoint>()
{
new DataPoint(){ Features1 = new float[3] {1, 1, 0}, Features2 =
new float[2] {1, 1} },
new DataPoint(){ Features1 = new float[3] {0, float.NaN, 1},
Features2 = new float[2] {float.NaN, 1} },
new DataPoint(){ Features1 = new float[3] {-1, float.NaN, -3},
Features2 = new float[2] {1, float.PositiveInfinity} },
};
var data = mlContext.Data.LoadFromEnumerable(samples);
// IndicateMissingValues is used to create a boolean containing 'true'
// where the value in the input column is missing. For floats and
// doubles, missing values are NaN. We can use an array of
// InputOutputColumnPair to apply the MissingValueIndicatorEstimator
// to multiple columns in one pass over the data.
var pipeline = mlContext.Transforms.IndicateMissingValues(new[] {
new InputOutputColumnPair("MissingIndicator1", "Features1"),
new InputOutputColumnPair("MissingIndicator2", "Features2")
});
// Now we can transform the data and look at the output to confirm the
// behavior of the estimator. This operation doesn't actually evaluate
// data until we read the data below.
var tansformer = pipeline.Fit(data);
var transformedData = tansformer.Transform(data);
// We can extract the newly created column as an IEnumerable of
// SampleDataTransformed, the class we define below.
var rowEnumerable = mlContext.Data.CreateEnumerable<
SampleDataTransformed>(transformedData, reuseRowObject: false);
// And finally, we can write out the rows of the dataset, looking at the
// columns of interest.
foreach (var row in rowEnumerable)
Console.WriteLine("Features1: [" + string.Join(", ", row
.Features1) + "]\t MissingIndicator1: [" + string.Join(", ",
row.MissingIndicator1) + "]\t Features2: [" + string.Join(", ",
row.Features2) + "]\t MissingIndicator2: [" + string.Join(", ",
row.MissingIndicator2) + "]");
// Expected output:
// Features1: [1, 1, 0] MissingIndicator1: [False, False, False] Features2: [1, 1] MissingIndicator2: [False, False]
// Features1: [0, NaN, 1] MissingIndicator1: [False, True, False] Features2: [NaN, 1] MissingIndicator2: [True, False]
// Features1: [-1, NaN, -3] MissingIndicator1: [False, True, False] Features2: [1, ∞] MissingIndicator2: [False, False]
}
private class DataPoint
{
[VectorType(3)]
public float[] Features1 { get; set; }
[VectorType(2)]
public float[] Features2 { get; set; }
}
private sealed class SampleDataTransformed : DataPoint
{
public bool[] MissingIndicator1 { get; set; }
public bool[] MissingIndicator2 { get; set; }
}
}
}
注釈
この変換は、複数の列に対して動作できます。
適用対象
IndicateMissingValues(TransformsCatalog, String, String)
MissingValueIndicatorEstimatorで指定されたinputColumnName列からデータをスキャンし、列データ内outputColumnNameの i 番目の要素に欠損false値がある場合の値trueを持つ bool のベクトルで指定された新しい列を埋めます。
public static Microsoft.ML.Transforms.MissingValueIndicatorEstimator IndicateMissingValues (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName = default);static member IndicateMissingValues : Microsoft.ML.TransformsCatalog * string * string -> Microsoft.ML.Transforms.MissingValueIndicatorEstimator<Extension()>
Public Function IndicateMissingValues (catalog As TransformsCatalog, outputColumnName As String, Optional inputColumnName As String = Nothing) As MissingValueIndicatorEstimatorパラメーター
- catalog
- TransformsCatalog
変換のカタログ。
戻り値
例
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic
{
public static class IndicateMissingValues
{
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 and convert it to an IDataView.
var samples = new List<DataPoint>()
{
new DataPoint(){ Features = new float[3] {1, 1, 0} },
new DataPoint(){ Features = new float[3] {0, float.NaN, 1} },
new DataPoint(){ Features = new float[3] {-1, float.NaN, -3} },
};
var data = mlContext.Data.LoadFromEnumerable(samples);
// IndicateMissingValues is used to create a boolean containing 'true'
// where the value in the input column is missing. For floats and
// doubles, missing values are represented as NaN.
var pipeline = mlContext.Transforms.IndicateMissingValues(
"MissingIndicator", "Features");
// Now we can transform the data and look at the output to confirm the
// behavior of the estimator. This operation doesn't actually evaluate
// data until we read the data below.
var tansformer = pipeline.Fit(data);
var transformedData = tansformer.Transform(data);
// We can extract the newly created column as an IEnumerable of
// SampleDataTransformed, the class we define below.
var rowEnumerable = mlContext.Data.CreateEnumerable<
SampleDataTransformed>(transformedData, reuseRowObject: false);
// And finally, we can write out the rows of the dataset, looking at the
// columns of interest.
foreach (var row in rowEnumerable)
Console.WriteLine("Features: [" + string.Join(", ", row.Features) +
"]\t MissingIndicator: [" + string.Join(", ", row
.MissingIndicator) + "]");
// Expected output:
// Features: [1, 1, 0] MissingIndicator: [False, False, False]
// Features: [0, NaN, 1] MissingIndicator: [False, True, False]
// Features: [-1, NaN, -3] MissingIndicator: [False, True, False]
}
private class DataPoint
{
[VectorType(3)]
public float[] Features { get; set; }
}
private sealed class SampleDataTransformed : DataPoint
{
public bool[] MissingIndicator { get; set; }
}
}
}