DocNADE

A TensorFlow implementation of the DocNADE model, published in A Neural Autoregressive Topic Model.

Requirements

Requires Python 3 (tested with 3.6.1). The remaining dependencies can then be installed via:

    $ pip install -r requirements.txt
    $ python -c "import nltk; nltk.download('punkt')"

Data format and preprocessing

You first need to preprocess any input data into the format expected by the model:

    $ python preprocess.py --input <path to input dataset> --output <path to output dataset> --vocab <path to vocab file>

where <path to input directory> points to a directory containing an input dataset (described below), <path to output directory> gives the path to a newly created output dataset directory (containing the preprocessed data), and <path to vocab file> gives the path to a vocabulary file (described below).

Datasets: A directory containing CSV files. There is expected to be 1 CSV file per set or collection, with separate sets for training, validation and test. The CSV files in the directory must be named accordingly: training.csv, validation.csv, test.csv. For this task, each CSV file (prior to preprocessing) consists of 2 string fields with a comma delimiter - the first is the label and the second is the document body.

Vocabulary files: A plain text file, with 1 vocabulary token per line (note that this must be created in advance, we do not provide a script for creating vocabularies). We do provide the vocabulary file used in our 20 Newsgroups experiment in data/20newsgroups.vocab.

Training

The default parameters should achieve good perplexity results, you just need to pass the input dataset and model output directories:

    $ python train.py --dataset <path to preprocessed dataset> --model <path to model output directory>

To view additional parameters (which may yield better document representations):

    $ python train.py --help

Extracting topic words

To get topic words of 10 topic clusters:

    $ python get_topics.py --dataset <path to preprocessed dataset> --model <path to trained model directory>

Extracting document vectors and evaluating results

To evaluate the retrieval results:

    $ python evaluate.py --dataset <path to preprocessed dataset> --model <path to trained model directory>

To extract document vectors (will be saved in NumPy text format to the model directory):