API Reference

API Reference

Modules:

• interfaces – Core gensim interfaces
• utils – Various utility functions
• matutils – Math utils
• corpora.bleicorpus – Corpus in Blei’s LDA-C format
• corpora.csvcorpus – Corpus in CSV format
• corpora.dictionary – Construct word<->id mappings
• corpora.hashdictionary – Construct word<->id mappings
• corpora.indexedcorpus – Random access to corpus documents
• corpora.lowcorpus – Corpus in List-of-Words format
• corpora.malletcorpus – Corpus in Mallet format of List-Of-Words.
• corpora.mmcorpus – Corpus in Matrix Market format
• corpora.sharded_corpus – Corpus stored in separate files
• corpora.svmlightcorpus – Corpus in SVMlight format
• corpora.textcorpus – Building corpora with dictionaries
• corpora.ucicorpus – Corpus in UCI bag-of-words format
• corpora.wikicorpus – Corpus from a Wikipedia dump
• models.ldamodel – Latent Dirichlet Allocation
• models.ldamulticore – parallelized Latent Dirichlet Allocation
• models.lsimodel – Latent Semantic Indexing
• models.ldaseqmodel – Dynamic Topic Modeling in Python
• models.tfidfmodel – TF-IDF model
• models.rpmodel – Random Projections
• models.hdpmodel – Hierarchical Dirichlet Process
• models.logentropy_model – LogEntropy model
• models.normmodel – Normalization model
• models.lsi_dispatcher – Dispatcher for distributed LSI
• models.lsi_worker – Worker for distributed LSI
• models.lda_dispatcher – Dispatcher for distributed LDA
• models.lda_worker – Worker for distributed LDA
• models.atmodel – Author-topic models
• models.word2vec – Deep learning with word2vec
• models.keyedvectors – Store and query word vectors
• models.doc2vec – Deep learning with paragraph2vec
• models.phrases – Phrase (collocation) detection
• models.coherencemodel – Topic coherence pipeline
• models.wrappers.ldamallet – Latent Dirichlet Allocation via Mallet
• models.wrappers.dtmmodel – Dynamic Topic Models (DTM) and Dynamic Influence Models (DIM)
• models.wrappers.ldavowpalwabbit – Latent Dirichlet Allocation via Vowpal Wabbit
• models.wrappers.wordrank – Word Embeddings from WordRank
• models.wrappers.varembed – VarEmbed Word Embeddings
• models.wrappers.fasttext – FastText Word Embeddings
• similarities.docsim – Document similarity queries
  • How It Works
• similarities.index – Fast Approximate Nearest Neighbor Similarity with Annoy package
• topic_coherence.aggregation – Aggregation module
• topic_coherence.direct_confirmation_measure – Direct confirmation measure module
• topic_coherence.indirect_confirmation_measure – Indirect confirmation measure module
• topic_coherence.probability_estimation – Probability estimation module
• topic_coherence.segmentation – Segmentation module
• scripts.glove2word2vec – Convert glove format to word2vec
• scripts.make_wikicorpus – Convert articles from a Wikipedia dump to vectors.
• scripts.word2vec_standalone – Train word2vec on text file CORPUS
• parsing.porter – Porter Stemming Algorithm
• parsing.preprocessing – Functions to preprocess raw text
• summarization.bm25 – BM25 ranking function
• summarization.commons – Common graph functions
• summarization.graph – TextRank graph
• summarization.keywords – Keywords for TextRank summarization algorithm
• summarization.pagerank_weighted – Weighted PageRank algorithm
• summarization.summarizer – TextRank Summariser
• summarization.syntactic_unit – Syntactic Unit class
• summarization.textcleaner – Summarization pre-processing
• sklearn_integration.sklearn_wrapper_gensim_ldamodel – Scikit learn wrapper for Latent Dirichlet Allocation