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https://github.com/VCUWrightCenter/TopEx

Project description

CI

Documentation is available at https://VCUWrightCenter.github.io/TopEx/.

TopEx

Topic exploration in unstructured text documents.

Requirements

First install TopEx

pip install topex

Then install the SciSpacy model used for tokenization and/or NER

pip install https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.0/en_core_sci_sm-0.5.0.tar.gz

How to use

A template pipeline is provided below using a test dataset. You can read more about the test_data dataset here

Each step of the pipeline has configuration options for experimenting with various methods. These are detailed in the documentation for each method. Notably, the import_docs, get_cluster_topics, visualize_clustering, and evaluate methods all include the option to save results to a file.

Example Pipeline

Import data

Import and pre-process documents from a text file containing a list of all documents. The ner option alows users to run clustering over biomedical entities extracted using SciSpacy's en_core_sci_sm model. If that doesn't mean anything to you, just omit that option and clustering will run over words.

import topex.core as topex
data, doc_df = topex.import_from_files('test_data/corpus_file_list.txt', stop_words_file='stop_words.txt', 
                                       save_results = False, ner=False)

You can also consolidate your documents into a single, pipe-delimited csv file with the columns "doc_name" and "text".

data, doc_df = topex.import_from_csv('test_data/corpus.txt', stop_words_file='stop_words.txt', save_results = False, 
                                     ner=False)

Transform data

Create word vectors from the most expressive phrase in each sentence of the imported documents. Expansion documents can be passed as a single CSV similar to corpus documents in the import step. Options for tfidf_corpus are ('clustering', 'expansion', 'both')

  • Clustering Corpus: The set of documents the user wants to analyze and cluster.
  • Expansion Corpus: A set of additional documents uploaded by the user that are either 1) added to the Clustering Corpus to create the TF-IDF, or 2) are the only set of documents used to create the TF-IDF.
  • Background Corpus: The set of documents used to create the TF-IDF matrix. Can be composed of 1) only the Clustering Corpus, 2) only the Expansion Corpus, or 3) the concatenation of the Clustering and Expansion Corpus.
tfidf_corpus='both'
tfidf, dictionary = topex.create_tfidf(tfidf_corpus, doc_df, path_to_expansion_file_list='test_data/expansion_file_list.txt')
data = topex.get_phrases(data, dictionary.token2id, tfidf, tfidf_corpus=tfidf_corpus, include_sentiment=True)
data = topex.get_vectors("svd", data, dictionary = dictionary, tfidf = tfidf, dimensions=min(200,tfidf.shape[1]-1))

Cluster data

Cluster the sentences into groups expressing similar ideas or topics. If you aren't sure how many true clusters exist in the data, try running assign_clusters with the optional parameter show_chart = True to visual cluster quality with varying numbers of clusters. When using method='hac', you can also use show_dendrogram = True see the cluster dendrogram.

data, linkage_matrix, max_height, height = topex.assign_clusters(data, method = "hac", show_chart = False)
viz_df = topex.visualize_clustering(data, method="umap", show_chart=True, return_data=True)

Cluster size exploration

Determining the correct number of clusters can often be as much art as science, so we've included a mechanism to quickly iterate through various values of k or cut heights for the HAC tree. Mapping the data points into the x,y coordinate plane need only be performed once as it depends solely on the vector representation of each sentence and the visualize_clustering method returns those values in a dataframe, which can be used to quickly redraw the visualization after updating cluster assignments using a different k or height. Similarly, computing the tree for HAC clustering can be done once and then cut at different heights to produce different clusters. assign_clusters returns the linkage_matrix and max_height at which you can cut the tree.

Use the recluster method to experiment with different values of k or height. You can also, remove noise from the visualization by setting the min_cluster_size parameter. This only hides points from the visualization and does not remove them from data.

# data, cluster_df = topex.recluster(data, viz_df, cluster_method='kmeans', k=25, min_cluster_size=6, show_chart=False)
data, cluster_df = topex.recluster(data, viz_df, linkage_matrix=linkage_matrix, cluster_method='hac', height=height+1, 
                                   min_cluster_size=5, show_chart=False)

Evaluate results

gold_file = "test_data/gold.txt"
cluster_df = topex.get_cluster_topics(data, doc_df, save_results = False)
results_df = topex.evaluate(data, gold_file="test_data/gold.txt", save_results = False)

Document Clustering

IMPORTANT: This feature is still in alpha, meaning that we have adapted the pipeline to accomodate the clustering of documents, but have made no rigorous efforts the ensure that it works well.

To cluster documents, simply import data and create the TF-IDF as above, but extract phrase, create the vectors, and cluster using the doc_df dataframe. Passing the parameter window_size=-1 to get_phrases tells the method to use all tokens instead of selecting a subset of length window_size.

tfidf_corpus='both'
doc_df = topex.get_phrases(doc_df, dictionary.token2id, tfidf, tfidf_corpus=tfidf_corpus, window_size=-1)
doc_df = topex.get_vectors("svd", doc_df, dictionary = dictionary, tfidf = tfidf)
doc_df = topex.assign_clusters(doc_df, method = "kmeans", k=4)
cluster_df = topex.get_cluster_topics(data, doc_df, save_results = False)
topex.visualize_clustering(data, method = "umap", show_chart = False)

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