https://github.com/andreaferretti/lda

Latent Dirichlet Allocation
https://github.com/andreaferretti/lda

lda nim topic-modeling

Last synced: 9 days ago
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Latent Dirichlet Allocation

• Host: GitHub
• URL: https://github.com/andreaferretti/lda
• Owner: andreaferretti
• License: apache-2.0
• Created: 2018-05-18T17:15:45.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2018-05-18T17:17:20.000Z (over 6 years ago)
• Last Synced: 2024-12-02T07:31:18.199Z (2 months ago)
• Topics: lda, nim, topic-modeling
• Language: Nim
• Size: 503 KB
• Stars: 6
• Watchers: 12
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
LDA

===

This library implements a form of text clustering and topic modeling called

[Latent Dirichlet Allocation](http://ethen8181.github.io/machine-learning/clustering_old/topic_model/LDA.html).

In order to use it, you have to have a seq of documents, each one being itself

a seq of strings. These documents can then be indexed through the use of a

vocabulary, as follows:

```nim

import sequtils, strutils

import lda

let

  rawDocs = @[

      "eat turkey on turkey day holiday",

      "i like to eat cake on holiday",

      "turkey trot race on thanksgiving holiday",

      "snail race the turtle",

      "time travel space race",

      "movie on thanksgiving",

      "movie at air and space museum is cool movie",

      "aspiring movie star"

    ]

  docWords = rawDocs.mapIt(it.split(' '))

  vocab = makeVocab(docWords)

  docs = makeDocs(docWords, vocab)

```

Once you have the vocabulary `vocab` , which is just the seq of all word appearing

through all documents, and the preoprocessed documents, which are a nested

sequence of integer indices, you can traing the model through Collapsed Gibbs

Sampling using

```nim

let ldaResult = lda(docs, vocabLen = vocab.len, K = 3, iterations = 1000)

```

Here `K` denotes the number of desired topics and `iterations` the number of

rounds in the training phase. The result contains a document/topic matrix

and a word/topic matrix. These can be used to find the most descriptive

words for a topic:

```nim

for t in 0 ..< 3:

  echo "TOPIC ", t

  echo bestWords(ldaResult, vocab, t)

```

or to find the most relevant topics for a document:

```nim

for d in 0 ..< docs.len:

  echo "> ", rawDocs[d]

  echo "topic: ", ldaResult.bestTopic(d)

```

or even to generate text with the same topic distribution as a given document:

```nim

echo sample(ldaResult, vocab, doc = 6)

```

## TODO

* parallel training

* variational Bayes sampling

* modified model to account for stop words