Latent Dirichlet Allocation and related topic models are often presented in the form of complicated equations and confusing diagrams. I will present LDA as a generative model through probabilistic simulation in simple Python. Simulation will help data scientists to understand the model assumptions and limitations and more effectively use black box LDA implementations.
Those without training in probabilistic graphical models and measure theory, data scientist may have a hard time understanding Latent Dirichlet Allocation and other probabilistic topic models. However, because LDA is a generative model, we can write Python code to generated data based on the model assumptions.
The talk will progress as follows:
- Introduction to mixture models
- Simulation of mixture models
- Introduction to grouped data
- Simulation of latent Dirichlet allocation
- Fitting and visualizing LDA with Python
With Conda installed, run
$ git clone https://github.com/tdhopper/pydata-nyc-2015.git understanding-lda
$ cd understanding-lda
$ make install
$ source activate understanding-lda
To view the notebook, run
$ make
To view the notebook as a slideshow, run
make slides