diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/LdaTransform.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/LdaTransform.cs
index 73512830086..9d0a040591a 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/LdaTransform.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/LdaTransform.cs
@@ -4,7 +4,7 @@
namespace Microsoft.ML.Samples.Dynamic
{
- public static class LdaTransform
+ public static class LatentDirichletAllocationTransform
{
public static void Example()
{
diff --git a/src/Microsoft.ML.StaticPipe/LdaStaticExtensions.cs b/src/Microsoft.ML.StaticPipe/LdaStaticExtensions.cs
index c08d0aeed1b..25a2337c35c 100644
--- a/src/Microsoft.ML.StaticPipe/LdaStaticExtensions.cs
+++ b/src/Microsoft.ML.StaticPipe/LdaStaticExtensions.cs
@@ -11,65 +11,65 @@ namespace Microsoft.ML.StaticPipe
///
/// Information on the result of fitting a LDA transform.
///
- public sealed class LdaFitResult
+ public sealed class LatentDirichletAllocationFitResult
{
///
/// For user defined delegates that accept instances of the containing type.
///
///
- public delegate void OnFit(LdaFitResult result);
+ public delegate void OnFit(LatentDirichletAllocationFitResult result);
public LatentDirichletAllocationTransformer.LdaSummary LdaTopicSummary;
- public LdaFitResult(LatentDirichletAllocationTransformer.LdaSummary ldaTopicSummary)
+ public LatentDirichletAllocationFitResult(LatentDirichletAllocationTransformer.LdaSummary ldaTopicSummary)
{
LdaTopicSummary = ldaTopicSummary;
}
}
- public static class LdaStaticExtensions
+ public static class LatentDirichletAllocationStaticExtensions
{
private struct Config
{
- public readonly int NumTopic;
+ public readonly int NumberOfTopics;
public readonly Single AlphaSum;
public readonly Single Beta;
- public readonly int MHStep;
- public readonly int NumIter;
+ public readonly int SamplingStepCount;
+ public readonly int MaximumNumberOfIterations;
public readonly int LikelihoodInterval;
- public readonly int NumThread;
- public readonly int NumMaxDocToken;
- public readonly int NumSummaryTermPerTopic;
- public readonly int NumBurninIter;
+ public readonly int NumberOfThreads;
+ public readonly int MaximumTokenCountPerDocument;
+ public readonly int NumberOfSummaryTermsPerTopic;
+ public readonly int NumberOfBurninIterations;
public readonly bool ResetRandomGenerator;
public readonly Action OnFit;
- public Config(int numTopic, Single alphaSum, Single beta, int mhStep, int numIter, int likelihoodInterval,
- int numThread, int numMaxDocToken, int numSummaryTermPerTopic, int numBurninIter, bool resetRandomGenerator,
+ public Config(int numberOfTopics, Single alphaSum, Single beta, int samplingStepCount, int maximumNumberOfIterations, int likelihoodInterval,
+ int numberOfThreads, int maximumTokenCountPerDocument, int numberOfSummaryTermsPerTopic, int numberOfBurninIterations, bool resetRandomGenerator,
Action onFit)
{
- NumTopic = numTopic;
+ NumberOfTopics = numberOfTopics;
AlphaSum = alphaSum;
Beta = beta;
- MHStep = mhStep;
- NumIter = numIter;
+ SamplingStepCount = samplingStepCount;
+ MaximumNumberOfIterations = maximumNumberOfIterations;
LikelihoodInterval = likelihoodInterval;
- NumThread = numThread;
- NumMaxDocToken = numMaxDocToken;
- NumSummaryTermPerTopic = numSummaryTermPerTopic;
- NumBurninIter = numBurninIter;
+ NumberOfThreads = numberOfThreads;
+ MaximumTokenCountPerDocument = maximumTokenCountPerDocument;
+ NumberOfSummaryTermsPerTopic = numberOfSummaryTermsPerTopic;
+ NumberOfBurninIterations = numberOfBurninIterations;
ResetRandomGenerator = resetRandomGenerator;
OnFit = onFit;
}
}
- private static Action Wrap(LdaFitResult.OnFit onFit)
+ private static Action Wrap(LatentDirichletAllocationFitResult.OnFit onFit)
{
if (onFit == null)
return null;
- return ldaTopicSummary => onFit(new LdaFitResult(ldaTopicSummary));
+ return ldaTopicSummary => onFit(new LatentDirichletAllocationFitResult(ldaTopicSummary));
}
private interface ILdaCol
@@ -107,16 +107,16 @@ public override IEstimator Reconcile(IHostEnvironment env,
infos[i] = new LatentDirichletAllocationEstimator.ColumnOptions(outputNames[toOutput[i]],
inputNames[tcol.Input],
- tcol.Config.NumTopic,
+ tcol.Config.NumberOfTopics,
tcol.Config.AlphaSum,
tcol.Config.Beta,
- tcol.Config.MHStep,
- tcol.Config.NumIter,
+ tcol.Config.SamplingStepCount,
+ tcol.Config.MaximumNumberOfIterations,
tcol.Config.LikelihoodInterval,
- tcol.Config.NumThread,
- tcol.Config.NumMaxDocToken,
- tcol.Config.NumSummaryTermPerTopic,
- tcol.Config.NumBurninIter,
+ tcol.Config.NumberOfThreads,
+ tcol.Config.MaximumTokenCountPerDocument,
+ tcol.Config.NumberOfSummaryTermsPerTopic,
+ tcol.Config.NumberOfBurninIterations,
tcol.Config.ResetRandomGenerator);
if (tcol.Config.OnFit != null)
@@ -136,36 +136,36 @@ public override IEstimator Reconcile(IHostEnvironment env,
///
/// A vector of floats representing the document.
- /// The number of topics.
+ /// The number of topics.
/// Dirichlet prior on document-topic vectors.
/// Dirichlet prior on vocab-topic vectors.
- /// Number of Metropolis Hasting step.
- /// Number of iterations.
+ /// Number of Metropolis Hasting step.
+ /// Number of iterations.
/// Compute log likelihood over local dataset on this iteration interval.
- /// The number of training threads. Default value depends on number of logical processors.
- /// The threshold of maximum count of tokens per doc.
- /// The number of words to summarize the topic.
- /// The number of burn-in iterations.
+ /// The number of training threads. Default value depends on number of logical processors.
+ /// The threshold of maximum count of tokens per doc.
+ /// The number of words to summarize the topic.
+ /// The number of burn-in iterations.
/// Reset the random number generator for each document.
/// Called upon fitting with the learnt enumeration on the dataset.
- public static Vector ToLdaTopicVector(this Vector input,
- int numTopic = LatentDirichletAllocationEstimator.Defaults.NumberOfTopics,
+ public static Vector ToLatentDirichletAllocationTopicVector(this Vector input,
+ int numberOfTopics = LatentDirichletAllocationEstimator.Defaults.NumberOfTopics,
Single alphaSum = LatentDirichletAllocationEstimator.Defaults.AlphaSum,
Single beta = LatentDirichletAllocationEstimator.Defaults.Beta,
- int mhstep = LatentDirichletAllocationEstimator.Defaults.SamplingStepCount,
- int numIterations = LatentDirichletAllocationEstimator.Defaults.MaximumNumberOfIterations,
+ int samplingStepCount = LatentDirichletAllocationEstimator.Defaults.SamplingStepCount,
+ int maximumNumberOfIterations = LatentDirichletAllocationEstimator.Defaults.MaximumNumberOfIterations,
int likelihoodInterval = LatentDirichletAllocationEstimator.Defaults.LikelihoodInterval,
- int numThreads = LatentDirichletAllocationEstimator.Defaults.NumThreads,
- int numMaxDocToken = LatentDirichletAllocationEstimator.Defaults.NumMaxDocToken,
- int numSummaryTermPerTopic = LatentDirichletAllocationEstimator.Defaults.NumSummaryTermPerTopic,
- int numBurninIterations = LatentDirichletAllocationEstimator.Defaults.NumBurninIterations,
+ int numberOfThreads = LatentDirichletAllocationEstimator.Defaults.NumThreads,
+ int maximumTokenCountPerDocument = LatentDirichletAllocationEstimator.Defaults.NumMaxDocToken,
+ int numberOfSummaryTermsPerTopic = LatentDirichletAllocationEstimator.Defaults.NumSummaryTermPerTopic,
+ int numberOfBurninIterations = LatentDirichletAllocationEstimator.Defaults.NumBurninIterations,
bool resetRandomGenerator = LatentDirichletAllocationEstimator.Defaults.ResetRandomGenerator,
- LdaFitResult.OnFit onFit = null)
+ LatentDirichletAllocationFitResult.OnFit onFit = null)
{
Contracts.CheckValue(input, nameof(input));
return new ImplVector(input,
- new Config(numTopic, alphaSum, beta, mhstep, numIterations, likelihoodInterval, numThreads, numMaxDocToken, numSummaryTermPerTopic,
- numBurninIterations, resetRandomGenerator, Wrap(onFit)));
+ new Config(numberOfTopics, alphaSum, beta, samplingStepCount, maximumNumberOfIterations, likelihoodInterval, numberOfThreads, maximumTokenCountPerDocument, numberOfSummaryTermsPerTopic,
+ numberOfBurninIterations, resetRandomGenerator, Wrap(onFit)));
}
}
}
\ No newline at end of file
diff --git a/src/Microsoft.ML.Transforms/Text/LdaTransform.cs b/src/Microsoft.ML.Transforms/Text/LdaTransform.cs
index 442e41c1caa..8ff5752def0 100644
--- a/src/Microsoft.ML.Transforms/Text/LdaTransform.cs
+++ b/src/Microsoft.ML.Transforms/Text/LdaTransform.cs
@@ -14,7 +14,6 @@
using Microsoft.ML.EntryPoints;
using Microsoft.ML.Internal.Internallearn;
using Microsoft.ML.Internal.Utilities;
-using Microsoft.ML.Model;
using Microsoft.ML.TextAnalytics;
using Microsoft.ML.Transforms.Text;
diff --git a/test/Microsoft.ML.StaticPipelineTesting/StaticPipeTests.cs b/test/Microsoft.ML.StaticPipelineTesting/StaticPipeTests.cs
index 0c6fb3c5105..8197b274d13 100644
--- a/test/Microsoft.ML.StaticPipelineTesting/StaticPipeTests.cs
+++ b/test/Microsoft.ML.StaticPipelineTesting/StaticPipeTests.cs
@@ -674,7 +674,7 @@ public void LdaTopicModel()
var est = data.MakeNewEstimator()
.Append(r => (
r.label,
- topics: r.text.ToBagofWords().ToLdaTopicVector(numTopic: 3, numSummaryTermPerTopic:5, alphaSum: 10, onFit: m => ldaSummary = m.LdaTopicSummary)));
+ topics: r.text.ToBagofWords().ToLatentDirichletAllocationTopicVector(numberOfTopics: 3, numberOfSummaryTermsPerTopic:5, alphaSum: 10, onFit: m => ldaSummary = m.LdaTopicSummary)));
var transformer = est.Fit(data);
var tdata = transformer.Transform(data);