TextCatalog.LatentDirichletAllocation Method

Definition

Namespace:

Microsoft.ML

Assembly:

Microsoft.ML.Transforms.dll

Package:

Microsoft.ML v3.0.1

Create a LatentDirichletAllocationEstimator, which uses LightLDA to transform text (represented as a vector of floats) into a vector of Single indicating the similarity of the text with each topic identified.

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

Parameters

catalog

TransformsCatalog.TextTransforms

The transform's catalog.

outputColumnName

String

Name of the column resulting from the transformation of inputColumnName. This estimator outputs a vector of Single.

inputColumnName

String

Name of the column to transform. If set to null, the value of the outputColumnName will be used as source. This estimator operates over a vector of Single.

numberOfTopics

Int32

The number of topics.

alphaSum

Single

Dirichlet prior on document-topic vectors.

beta

Single

Dirichlet prior on vocab-topic vectors.

samplingStepCount

Int32

Number of Metropolis Hasting step.

maximumNumberOfIterations

Int32

Number of iterations.

likelihoodInterval

Int32

Compute log likelihood over local dataset on this iteration interval.

numberOfThreads

Int32

The number of training threads. Default value depends on number of logical processors.

maximumTokenCountPerDocument

Int32

The threshold of maximum count of tokens per doc.

numberOfSummaryTermsPerTopic

Int32

The number of words to summarize the topic.

numberOfBurninIterations

Int32

The number of burn-in iterations.

resetRandomGenerator

Boolean

Reset the random number generator for each document.

Returns

LatentDirichletAllocationEstimator

Examples

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; }
        }
    }
}