ExpressionCatalog.Expression 方法

定義

命名空間:

Microsoft.ML

組件:

Microsoft.ML.Transforms.dll

套件:

Microsoft.ML v2.0.0

建立 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

參數

catalog

TransformsCatalog

TransformsCatalog。

outputColumnName

String

轉換所產生的 inputColumnNames 資料行名稱。 此資料行的資料類型會與輸入資料行的資料類型相同。

expression

String

要套用至 inputColumnNames 以建立資料行 outputColumnName 的運算式。

inputColumnNames

String[]

輸入資料行的名稱。

傳回

ExpressionEstimator

範例

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; }
        }
    }
}