Topic Modeling API

This API is built to dynamically perform training, inference, and evaluation for different topic modeling techniques. The API grant common interfaces and command for accessing the different models, make easier to compare them.

A demo is available at http://hyperted.eurecom.fr/topic.

Models

In this repository, we provide:

• Code to perform training, inference, and evaluation for 9 Topic Modeling packages:
  • LDA from the Mallet package.
  • LFTM - paper.
  • Doc2Topic
  • GSDMM - paper
  • Non-Negative Matrix factorization (NMF)
  • Hierarchical Dirichlet Processing Model (HDP)
  • Latent Semantic Indexing (LSI)
  • Paragraph Vector Topic Model (PVTM) - paper
  • Context Topic Model (CTM) - paper
• A set of pre-trained models, downloadable from here. **NOTE: Newly trained models are by default stored in .\models, replacing the old ones, unless a new model path is given **
• Data files containing pre-processed corpus:
  • 20ng.txt and 20ng_labels.txt, with 11314 news from the 20 NewsGroup dataset
  • ted.txt with 51898 subtitles of TED Talks
  • test.txt and test_labels.txt, an extraction of 30 documents from 20_ng.txt, used for testing reason

Each model expose the following functions:

Training the model

m.train(data, num_topics, preprocessing) # => 'success'

Print the list of computed topics

for i, x in enumerate(m.topics):
    print(f'Topic {i}')
    for word, weight in zip(x['words'], x['weights']):
        print(f'- {word} => {weight}')

Access to the info about a specific topic

x = m.topic(0)
words = x['words']
weights= x['weights']

Access to the predictions computed on the training corpus

for i, p in enumerate(m.get_corpus_predictions(topn=3)): # predictions for each document
    print(f'Predictions on document {i}')
    for topic, confidence in p:
        print(f'- Topic {topic} with confidence {confidence}')
        # - Topic 21 with confidence 0.03927058187976461

Predict the topic of a new text

pred = m.predict(text, topn=3)
for topic, confidence in pred:
    print(f'- Topic {topic} with confidence {confidence}')
     # - Topic 21 with confidence 0.03927058187976461

Computing the coherence against a corpus

# coherence: Type of coherence to compute, among <c_v, c_npmi, c_uci, u_mass>. See https://radimrehurek.com/gensim/models/coherencemodel.html#gensim.models.coherencemodel.CoherenceModel
pred = m.coherence(mycorpus, metric='c_v')
print(pred)
#{
#  "c_v": 0.5186710138972105,
#  "c_v_std": 0.1810477961008996,
#  "c_v_per_topic": [
#    0.5845048872767505,
#    0.30693460230781777,
#    0.2611738203246824,
#    ...
#  ]
#}

Evaluating against a grount truth

# metric: Metric for computing the evaluation, among <purity, homogeneity, completeness, v-measure, nmi>.
res = m.get_corpus_predictions(topn=1)
v = m.evaluate(res, ground_truth_labels, metric='purity')
# 0.7825333630516738

The possible parameters can differ depending on the model.

Use in a Python enviroment

Install this package

pip install tomodapi

Use it in a Python script

from tomodapi import LdaModel

# init the model 
m = LdaModel(model_path=path_location) 
# train on a corpus
m.train(my_corpus, preprocessing=False, num_topics=10) 
# infer topic of a sentence
best_topics = m.predict("In the time since the industrial revolution the climate has increasingly been affected by human activities that are causing global warming and climate change") 
topic,confidence = best_topics[0] 
# get top words for a given topic
print(m.topic(topic)) # 

If the model_path is not specified, the library will load/save the model from/under models/<model_name>.

Web API

A web API is provided for accessing to the library as a service

Install dependencies

You should install 2 dependencies:

• mallet 2.0.8 to be placed in app\builtin
• glove.6B.50d.txt to be placed in app\builtin\glove

Under UNIX, you can use the download_dep.sh script.

sh download_dep.sh

Start the server

python server.py

Docker

Alternatively, you can run a docker container with

docker-compose -f docker-compose.yml up

The container uses mounted volumes so that you can easily update/access to the computed models and the data files.

Manual Docker installation

docker build -t hyperted/topic .
docker run -p 27020:5000 --env APP_BASE_PATH=http://hyperted.eurecom.fr/topic/api -d -v /home/semantic/hyperted/tomodapi/models:/models -v /home/semantic/hyperted/tomodapi/data:/data --name hyperted_topic hyperted/topic

# Uninstall
docker stop hyperted_topic
docker rm hyperted_topic
docker rmi hyperted/topic

Publications

If you find this library or API useful in your research, please consider citing our paper:

@inproceedings{Lisena:NLPOSS2020,
   author = {Pasquale Lisena and Ismail Harrando and Oussama Kandakji and Raphael Troncy},
   title =  {{ToModAPI: A Topic Modeling API to Train, Use and Compare Topic Models}},
   booktitle = {2$^{nd}$ International Workshop for Natural Language Processing Open Source Software (NLP-OSS)},
   year =   {2020}
}