gaussianlda 0.2.14

Description:

gaussianlda 0.2.14

Gaussian LDA
Another implementation of the paper
Gaussian LDA for Topic Models with Word Embeddings.
This is a Python implementation based as closely as possible on
the Java implementation
released by the paper's authors.
Installation
You'll first need to install the choldate package, following its installation
instructions. (It's not
possible to include this as a dependency for the PyPi package.)
Then install gaussianlda using Pip:
pip install gaussianlda

Usage
The package provides two classes for training Gaussian LDA:

Cholesky only, gaussianlda.GaussianLDATrainer: Simple Gibbs sampler
with optional Cholesky decomposition trick.
Cholesky+aliasing, gaussianlda.GaussianLDAAliasTrainer:
Cholesky decomposition (not optional) and the Vose aliasing trick.

The trainer is prepared by instantiating the training class:

corpus: List of documents, where each document is a list of int IDs
of words. These are IDs into the vocabulary and the embeddings matrix.
vocab_embeddings: (V, D) Numpy array, where V is the number of words
in the vocabulary and D is the dimensionality of the embeddings.
vocab: Vocabulary, given as a list of words, whose position corresponds
to the indices using in the data. This is not strictly needed for training,
but is used to output topics.
num_tables: Number of topics to learn.
alpha, kappa: Hyperparameters to the doc-topic Dirichlet and
the inverse Wishart prior
save_path: Path to write the model out to after each iteration.
mh_steps (aliasing only): Number of Montecarlo-Hastings steps for
each topic sample.

Then you set the sampler running for a specified number of iterations
over the training data by calling trainer.sample(num_iters).
Example
import numpy as np
from gaussianlda import GaussianLDAAliasTrainer

# A small vocabulary as a list of words
vocab = "money business bank finance sheep cow goat pig".split()
# A random embedding for each word
# Really, you'd want to load something more useful!
embeddings = np.random.sample((8, 100), dtype=np.float32)
corpus = [
[0, 2, 1, 1, 3, 0, 6, 1],
[3, 1, 1, 3, 7, 0, 1, 2],
[7, 5, 4, 7, 7, 4, 6],
[5, 6, 1, 7, 7, 5, 6, 4],
]
# Prepare a trainer
trainer = GaussianLDAAliasTrainer(
corpus, embeddings, vocab, 2, 0.1, 0.1
)
# Set training running
trainer.sample(10)