ExpressionCatalog.Expression Metode
Definisi
Penting
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Membuat sebuah 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
Parameter
- catalog
- TransformsCatalog
- outputColumnName
- String
Nama kolom yang dihasilkan dari transformasi inputColumnNames
.
Jenis data kolom ini akan sama dengan kolom input.
- expression
- String
Ekspresi yang akan diterapkan untuk inputColumnNames
membuat kolom outputColumnName
.
- inputColumnNames
- String[]
Nama kolom input.
Mengembalikan
Contoh
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; }
}
}
}