ExpressionCatalog.Expression Метод

Определение

Пространство имен:

Microsoft.ML

Сборка:

Microsoft.ML.Transforms.dll

Пакет:

Microsoft.ML v2.0.0

Создает ExpressionEstimator.

public static Microsoft.ML.Transforms.ExpressionEstimator Expression (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string expression, params string[] inputColumnNames);

static member Expression : Microsoft.ML.TransformsCatalog * string * string * string[] -> Microsoft.ML.Transforms.ExpressionEstimator

<Extension()>
Public Function Expression (catalog As TransformsCatalog, outputColumnName As String, expression As String, ParamArray inputColumnNames As String()) As ExpressionEstimator

Параметры

catalog

TransformsCatalog

TransformsCatalog.

outputColumnName

String

Имя столбца, полученного из преобразования inputColumnNames. Тип данных этого столбца будет совпадать с типом входного столбца.

expression

String

Выражение, применяемого для inputColumnNames создания столбца outputColumnName.

inputColumnNames

String[]

Имена входных столбцов.

Возвращаемое значение

ExpressionEstimator

Примеры

using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic.Transforms
{
    public static class Expression
    {
        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(0.5f, new[] { 1f, 0.2f }, 3, "hi", true, new[] { "zero", "one" }),
                new InputData(-2.7f, new[] { 3.5f, -0.1f }, 2, "bye", false, new[] { "a", "b" }),
                new InputData(1.3f, new[] { 1.9f, 3.3f }, 39, "hi", false, new[] { "0", "1" }),
                new InputData(3, new[] { 3f, 3f }, 4, "hello", true, new[] { "c", "d" }),
                new InputData(0, new[] { 1f, 1f }, 1, "hi", true, new[] { "zero", "one" }),
                new InputData(30.4f, new[] { 10f, 4f }, 9, "bye", true, new[] { "e", "f" }),
                new InputData(5.6f, new[] { 1.1f, 2.2f }, 0, "hey", false, new[] { "g", "h" }),
            };

            // Convert training data to IDataView.
            var dataview = mlContext.Data.LoadFromEnumerable(samples);

            // A pipeline that applies various expressions to the input columns.
            var pipeline = mlContext.Transforms.Expression("Expr1", "(x,y)=>log(y)+x",
                    nameof(InputData.FloatColumn), nameof(InputData.FloatVectorColumn))
                .Append(mlContext.Transforms.Expression("Expr2", "(b,s,i)=>b ? len(s) : i",
                    nameof(InputData.BooleanColumn), nameof(InputData.StringVectorColumn), nameof(InputData.IntColumn)))
                .Append(mlContext.Transforms.Expression("Expr3", "(s,f1,f2,i)=>len(concat(s,\"a\"))+f1+f2+i",
                    nameof(InputData.StringColumn), nameof(InputData.FloatVectorColumn), nameof(InputData.FloatColumn), nameof(InputData.IntColumn)))
                .Append(mlContext.Transforms.Expression("Expr4", "(x,y)=>cos(x+pi())*y",
                    nameof(InputData.FloatColumn), nameof(InputData.IntColumn)));

            // The transformed data.
            var transformedData = pipeline.Fit(dataview).Transform(dataview);

            // Now let's take a look at what this concatenation did.
            // We can extract the newly created column as an IEnumerable of
            // TransformedData.
            var featuresColumn = mlContext.Data.CreateEnumerable<TransformedData>(
                transformedData, reuseRowObject: false);

            // And we can write out a few rows
            Console.WriteLine($"Features column obtained post-transformation.");
            foreach (var featureRow in featuresColumn)
            {
                Console.Write(string.Join(" ", featureRow.Expr1));
                Console.Write(" ");
                Console.Write(string.Join(" ", featureRow.Expr2));
                Console.Write(" ");
                Console.Write(string.Join(" ", featureRow.Expr3));
                Console.Write(" ");
                Console.WriteLine(featureRow.Expr4);
            }

            // Expected output:
            //  Features column obtained post-transformation.
            //  0.5 - 1.109438 4 3 7.5 6.7 - 2.63274768567112
            //  - 1.447237 NaN 2 2 6.8 3.2 1.80814432479224
            //  1.941854 2.493922 39 39 45.2 46.6 - 10.4324561082543
            //  4.098612 4.098612 1 1 16 16 3.95996998640178
            //  0 0 4 3 5 5 - 1
            //  32.70258 31.78629 1 1 53.4 47.4 - 4.74149076052604
            //  5.69531 6.388457 0 0 10.7 11.8 0
        }

        private class InputData
        {
            public float FloatColumn;
            [VectorType(3)]
            public float[] FloatVectorColumn;
            public int IntColumn;
            public string StringColumn;
            public bool BooleanColumn;
            [VectorType(2)]
            public string[] StringVectorColumn;

            public InputData(float f, float[] fv, int i, string s, bool b, string[] sv)
            {
                FloatColumn = f;
                FloatVectorColumn = fv;
                IntColumn = i;
                StringColumn = s;
                BooleanColumn = b;
                StringVectorColumn = sv;
            }
        }

        private sealed class TransformedData
        {
            public float[] Expr1 { get; set; }
            public int[] Expr2 { get; set; }
            public float[] Expr3 { get; set; }
            public double Expr4 { get; set; }
        }
    }
}