This project contains several experiments that used the LDA machine learning algorithm to generate topics from pages on GOV.UK and tag them with those topics.

• Document: a chunk of text representing a page on GOV.UK.
• Base path: The relative URL to a page on GOV.UK.
• Corpus: a set of documents.
• Term: a single word, phrase, or n-gram. We break a document into many terms before running the LDA algorithm.
• Dictionary: a data structure that maps every term to an integer ID.
• Stopwords: terms we want the algorithm to ignore - these won't be included in the dictionary.
• Document term matrix: a data structure that captures how frequently terms appear in different documents.
• TF-IDF: Term Frequency - Inverse Document Frequency. A measure that shows how important a word is to a document in a corpus.
• LDA: Latent Dirichlet Allocation - the algorithm we're using to model topics.

The best way to run these scripts is by using the govuk-lda-tagger-image docker container, which will ensure that python 2.7 and all the necessary dependencies are installed.

Before execution, the EXPERIMENT_DIR environment variable needs to be set to the folder in which you want your experiments to be saved. When using within a docker container, this should default to /mnt/experiments to allow the experiments to be mounted as a volume.

The train_lda.py script is a command line interface (CLI) to the LDA tagger. You can customise the input dataset, the preprocessing, and the parameters passed to the underlying LDA library.

Using the early years data from the HTML pages to derive topics, and tagging every document to those topics:

train_lda.py import --experiment early_years input/early-years.csv

The --experiment option defines the output directory under experiments. It defaults to one generated from the current time.

Pass a curated dictionary using the --input-dictionary option. By default the dictionary is generated from the corpus, excluding a number of predefined stopwords (defined in the stopwords directory).

train_lda.py import input/audits_with_content.csv --input-dictionary input/dictionary.txt

If you already ran an experiment, but something went wrong, you can use the refine subcommand to train it again, but reuse the corpus generated in the first run. The final argument is the original experiment directory name, which will be overwritten.

train_lda.py --numtopics 100 refine early-years

In gensim_engine.py there is a class that can be used to train and run an LDA model programatically.

This has the following API:

# Instantiate an object
engine = GensimEngine(documents, log=True)

# Train the model with the data provided
experiment = engine.train(number_of_topics=20)

# Tag all documents in the corpus
tags = experiment.tag()

documents is expected to be a list of dictionaries, where each dictionary has a base_path key and a text key.

When we started the project we created two simple scripts to test the libraries we used.

You can run either of these to see some sample topics.

Run python run_lda.py in order to use the LDA library to generate topics and categorise the documents listed in the input file.

Run python run_gensim.py in order to use the gensim library to generate topics and categorise the documents listed in the input file.

In order to fetch data from the search API, prepare a CSV input file containing one column (with the URL header) and the base_path of the links we wish to fetch content for.

Then run the following command:

python import_indexable_content.py --environemnt https://www.gov.uk input_file.csv

This script outputs CSV rows with the title, description, indexable content, topic names and organisation names.

In order to fetch PDF text from a number of GOV.UK base paths, prepare a CSV input file containing one column (with the URL header) and the base_path of the links we wish to fetch content for.

Then, run the following command:

python fetch_pdf_content.py input_file.csv output_file.csv

The output file will include the same base paths and also the text found in all PDF attachments, merged into one big string.

The python tool CSVKit can be used to combine the separate CSVs into one:

Note that because the columns are very wide, you will need to increase the default maximum field size:

csvjoin -c url all_audits_for_education.csv all_audits_for_education_with_pdf_data.csv > all_audits_for_education_with_pdf_and_indexable_content.csv --maxfieldsize [a big number]

The resulting CSV can then be passed to data_import/combine_csv_columns.py to merge everything into one "words" column.

python data_import/combine_csv_columns.py < all_audits_for_education_with_pdf_and_indexable_content.csv > all_audits_for_education_words.csv

MIT License