hdp - Hierarchical Dirichlet Process (or Hierarichical LDA)

Authors: Eduardo Coronado and Andrew Carr (Duke University)

The hdp package provides tools to set-up and train a Hierarchical Dirichlet Process (HDP) for topic modeling. This is similar to a Latent Dirichlet Allocation (LDA) model, with one major difference - HDPs are non-parametric in that the topics are learned from the data rather than user-specified.

• Dependencies
• Installation
• Getting Started
• API
• Testing

Dependencies

This packages has the following dependencies

• Python >=3.0 (stability not tested on Python 2)
• Numpy >= 1.15
• Pandas >= 1.0
• Scipy >= 1.2

• Pybind11 = 2.5

• Eigen (C++ linear algebra library)

To install the Python dependencies, open up the terminal and run the following commands

pip3 install -r requirements.txt

To install the external C++ libraries, run the following command on terminal (note: it is important that you do so on the hdp root folder).

python3 ./src/clone_extensions.py

The scripts won’t install anything if everything is up to date.

Installation

Once the dependencies are installed, run the following script on terminal

chmod 755 ./INSTALL.sh
./INSTALL.sh

or individuallyou run the following commands

python3 setup.py build
python3 setup.py install

Getting Started

Let’s get started by importing the package and it’s main functions run_preprocess and run_hdp. The former provides an API to preprocess text data in CSV format while the later computes the inference.

import hdp
from hdp.text_prep import run_preprocess
from hdp.HDP import run_hdp

You can test these functions with some test data in the ./data folder. For example, to preprocess files and obtain the corpus vocabulary and an inference-ready document structure use the following commands

url = './data/tm_test_data.csv'
vocab, docs = run_preprocess(url)

Subsequently, you can generate inferences using the hdp function with the following commands

import numpy as np
# number of iteration
it = 5 
# Hyperparameters (user-defined)
beta = 0.5                   # topic concentration (LDA), can be user-defined
alpha = np.random.gamma(1,1) # DP mixture hyperparam (or user-defined float > 0)
gamma = np.random.gamma(1,1) # Base DP hyperparam (or user-defined float >0)
doc_arrays, topic_idx, n_kv, m_k, perplex = run_hdp(docs, vocab, gamma, alpha, beta, epochs=it)

For more information on how to run these functions see the api section below or visit the ./report folder which provides context and theory behind the implementation.

API

run_preprocess(file_url)

Parameters

• file_url: string
  • Path to CSV formatted data. Note: This should be 1 column only, where each row is the text data to be pre-processed (e.g. abstracts)

Returns

• vocab:array

  • corpus vocabulary

• docs:list(sub_list), len = num of docs

  • list containing document sublists, each sublists is composed of word unique indexes from vocabulary

run_hdp(docs, vocab, gamma, alpha, beta, epochs)

Computes inference on document corpus and vocabulary over a user defined number of epochs (iterations). Additionally, user must provide prior distribution hyperparameters similar to those needed in LDA

Parameters

• docs:list(sub_list), len = num of docs

  • list containing document sublists, each sublists is composed of word unique indexes from vocabulary

• vocab:array

  • corpus vocabulary

• gamma:float

  • Base Dirichlet Process distribution $G_0$ hyperparameter

• alpha:float

  • Dirichlet Process Mixture $G_j$ hyperparameter

• beta:float

  • Word concentration parameter (similar to LDA)

• epochs:int (default = 1)

  • Defines number of iterations for which to train the model

Returns

• doc_arrays: list(Dict), len = num of docs

  • For each document, there’s a dictionary with the following keys to the following arrays (all the same length)
    • t_j: array of table indexes in document
    • k_jt: array of topics assigned to tables in t_j
    • n_jt: count array of words assigned to each table/topic

• topic_idx: list

  • Inferred topic indexes

• n_kv: ndarray

  • Word-topic matrix, rows = words and columns = topics. Note: num of cols will not equal topic_idx since this is not dynamically updated as topic_idx during inference. Use the topic_idx to select the appropriate cols.
  • Using n_kv.sum(axis=0) provides a summary of the total number of words assigned per topic

• m_k:array

  • Number of tables per topic

• perplex:list

  • Perplexity score computed at end of each epoch

Testing

Unit tests can be run using the following command on terminal in the package root folder

python3 setup.py test