TextCatalog.LatentDirichletAllocation Metode

Definisi

Ruang nama:

Microsoft.ML

Rakitan:

Microsoft.ML.Transforms.dll

Paket:

Microsoft.ML v3.0.1

Buat LatentDirichletAllocationEstimator, yang menggunakan LightLDA untuk mengubah teks (direpresentasikan sebagai vektor float) menjadi vektor Single yang menunjukkan kesamaan teks dengan setiap topik yang diidentifikasi.

public static Microsoft.ML.Transforms.Text.LatentDirichletAllocationEstimator LatentDirichletAllocation (this Microsoft.ML.TransformsCatalog.TextTransforms catalog, string outputColumnName, string inputColumnName = default, int numberOfTopics = 100, float alphaSum = 100, float beta = 0.01, int samplingStepCount = 4, int maximumNumberOfIterations = 200, int likelihoodInterval = 5, int numberOfThreads = 0, int maximumTokenCountPerDocument = 512, int numberOfSummaryTermsPerTopic = 10, int numberOfBurninIterations = 10, bool resetRandomGenerator = false);

static member LatentDirichletAllocation : Microsoft.ML.TransformsCatalog.TextTransforms * string * string * int * single * single * int * int * int * int * int * int * int * bool -> Microsoft.ML.Transforms.Text.LatentDirichletAllocationEstimator

<Extension()>
Public Function LatentDirichletAllocation (catalog As TransformsCatalog.TextTransforms, outputColumnName As String, Optional inputColumnName As String = Nothing, Optional numberOfTopics As Integer = 100, Optional alphaSum As Single = 100, Optional beta As Single = 0.01, Optional samplingStepCount As Integer = 4, Optional maximumNumberOfIterations As Integer = 200, Optional likelihoodInterval As Integer = 5, Optional numberOfThreads As Integer = 0, Optional maximumTokenCountPerDocument As Integer = 512, Optional numberOfSummaryTermsPerTopic As Integer = 10, Optional numberOfBurninIterations As Integer = 10, Optional resetRandomGenerator As Boolean = false) As LatentDirichletAllocationEstimator

Parameter

catalog

TransformsCatalog.TextTransforms

Katalog transformasi.

outputColumnName

String

Nama kolom yang dihasilkan dari transformasi inputColumnName. Estimator ini menghasilkan vektor .Single

inputColumnName

String

Nama kolom yang akan diubah. Jika diatur ke null, nilai outputColumnName akan digunakan sebagai sumber. Estimator ini beroperasi melalui vektor Single.

numberOfTopics

Int32

Jumlah topik.

alphaSum

Single

Dirichlet sebelumnya pada vektor topik dokumen.

beta

Single

Dirichlet sebelumnya menggunakan vektor topik vocab.

samplingStepCount

Int32

Jumlah langkah Metropolis Hasting.

maximumNumberOfIterations

Int32

Jumlah perulangan.

likelihoodInterval

Int32

Kemungkinan log komputasi atas himpunan data lokal pada interval perulangan ini.

numberOfThreads

Int32

Jumlah utas pelatihan. Nilai default tergantung pada jumlah prosesor logis.

maximumTokenCountPerDocument

Int32

Ambang batas jumlah maksimum token per dokumen.

numberOfSummaryTermsPerTopic

Int32

Jumlah kata untuk meringkas topik.

numberOfBurninIterations

Int32

Jumlah iterasi burn-in.

resetRandomGenerator

Boolean

Reset generator angka acak untuk setiap dokumen.

Mengembalikan

LatentDirichletAllocationEstimator

Contoh

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

namespace Samples.Dynamic
{
    public static class LatentDirichletAllocation
    {
        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<TextData>()
            {
                new TextData(){ Text = "ML.NET's LatentDirichletAllocation API " +
                "computes topic models." },

                new TextData(){ Text = "ML.NET's LatentDirichletAllocation API " +
                "is the best for topic models." },

                new TextData(){ Text = "I like to eat broccoli and bananas." },
                new TextData(){ Text = "I eat bananas for breakfast." },
                new TextData(){ Text = "This car is expensive compared to last " +
                "week's price." },

                new TextData(){ Text = "This car was $X last week." },
            };

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

            // A pipeline for featurizing the text/string using 
            // LatentDirichletAllocation API. o be more accurate in computing the
            // LDA features, the pipeline first normalizes text and removes stop
            // words before passing tokens (the individual words, lower cased, with
            // common words removed) to LatentDirichletAllocation.
            var pipeline = mlContext.Transforms.Text.NormalizeText("NormalizedText",
                "Text")
                .Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens",
                    "NormalizedText"))
                .Append(mlContext.Transforms.Text.RemoveDefaultStopWords("Tokens"))
                .Append(mlContext.Transforms.Conversion.MapValueToKey("Tokens"))
                .Append(mlContext.Transforms.Text.ProduceNgrams("Tokens"))
                .Append(mlContext.Transforms.Text.LatentDirichletAllocation(
                    "Features", "Tokens", numberOfTopics: 3));

            // Fit to data.
            var transformer = pipeline.Fit(dataview);

            // Create the prediction engine to get the LDA features extracted from
            // the text.
            var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData,
                TransformedTextData>(transformer);

            // Convert the sample text into LDA features and print it.
            PrintLdaFeatures(predictionEngine.Predict(samples[0]));
            PrintLdaFeatures(predictionEngine.Predict(samples[1]));

            // Features obtained post-transformation.
            // For LatentDirichletAllocation, we had specified numTopic:3. Hence
            // each prediction has been featurized as a vector of floats with length
            // 3.

            //  Topic1  Topic2  Topic3
            //  0.6364  0.2727  0.0909
            //  0.5455  0.1818  0.2727
        }

        private static void PrintLdaFeatures(TransformedTextData prediction)
        {
            for (int i = 0; i < prediction.Features.Length; i++)
                Console.Write($"{prediction.Features[i]:F4}  ");
            Console.WriteLine();
        }

        private class TextData
        {
            public string Text { get; set; }
        }

        private class TransformedTextData : TextData
        {
            public float[] Features { get; set; }
        }
    }
}