# Your Easy Guide to Latent Dirichlet Allocation

Before we get started, I made a tool (here’s the source) that runs LDA right inside your browser (it’s pretty neat). Be sure to have that open as I go along. All of the emoji I use come from this page. Just select any one emoji and copy it into the input boxes. If you don’t see the emoji, try using Firefox version 50 or greater.

#### What is LDA?

LDA or latent Dirichlet allocation is a “generative probabilistic model” of a collection of composites made up of parts. In terms of topic modeling, the composites are documents and the parts are words and/or phrases (n-grams). But you could apply LDA to DNA and nucleotides, pizzas and toppings, molecules and atoms, employees and skills, or keyboards and crumbs.

The probabilistic topic model estimated by LDA consists of two tables (matrices). The first table describes the probability or chance of selecting a particular part when sampling a particular topic (category). The second table describes the chance of selecting a particular topic when sampling a particular document or composite.

Take the image up above for example. We have four emoji sequences (the composites) and three types of emoji (the parts). The first three documents have 10 of only one type of emoji while the last document has 10 of each.

After we run our emoji composites through LDA, we end up with the probabilistic topic model you see above. The left table has emoji versus topics and the right table has documents versus topics. Each column in the left table and each row in the right table sums to one (allowing for some truncation and precision loss). So if I were to sample (draw an emoji out of a bag) Topic 0, I’d almost certainly get the avocado emoji. If I sampled Document 3, there’s an equal (uniform) chance I’d get either Topic 0, 1, or 2.

The LDA algorithm assumes your composites were generated like so.

- Pick your unique set of parts.
- Pick how many composites you want.
- Pick how many parts you want per each composite (sample from a Poisson distribution).
- Pick how many topics (categories) you want.
- Pick a number between not zero and positive infinity and call it
*alpha*. - Pick a number between not zero and positive infinity and call it
*beta*. - Build the parts versus the topics table. For each column, draw a sample (spin the wheel) from a Dirichlet distribution (a distribution of distributions) using beta as the input. Each sample will fill out each column in the table, sum to one, and give the probability of each part per topic (column).
- Build the composites versus the topics table. For each row, draw a sample from a Dirichlet distribution using alpha as the input. Each sample will fill out each row in the table, sum to one, and give the probability of each topic (column) per composite.
- Build the actual composites. For each composite, 1) look up its row in the composites versus topics table, 2) sample a topic based on the probabilities in the row, 3) go to the parts versus topics table, 4) look up the topic sampled, 5) sample a part based on the probabilities in the column, 6) repeat from step 2 until you’ve reached how many parts this composite was set to have.

Now we know this algorithm (or generative procedure/process) is not how say articles are written but this — for better or worse — is the simplified model LDA assumes.

#### What is the Dirichlet distribution?

A bit about the Dirichlet distribution before we move on. What you see up above are iterations of taking 1000 samples from a Dirichlet distribution using an increasing alpha value. The Dirichlet distribution takes a number (called alpha in most places) for each topic (or category). In the GIF and for our purposes, every topic is given the same alpha value you see displayed. Each dot represents some distribution or mixture of the three topics like (1.0, 0.0, 0.0) or (0.4, 0.3, 0.3). Remember that each sample has to add up to one.

At low alpha values (less than one), most of the topic distribution samples are in the corners (near the topics). For really low alpha values, it’s likely you’ll end up sampling (1.0, 0.0, 0.0), (0.0, 1.0, 0.0), or (0.0, 0.0, 1.0). This would mean that a document would only ever have one topic if we were building a three topic probabilistic topic model from scratch.

At alpha equal to one, any space on the surface of the triangle (2-simplex) is fair game (uniformly distributed). You could equally likely end up with a sample favoring only one topic, a sample that gives an even mixture of all the topics, or something in between.

For alpha values greater than one, the samples start to congregate to the center. This means that as alpha gets bigger, your samples will more likely be uniform or an even mixture of all the topics.

The GIF demonstrates the sampling of topic mixtures for the documents but the Dirichlet distribution is also assumed the source (the Bayesian prior) for the mixture of parts per topic. Three topics were used as that works well when plotting in three dimensions but typically one shoots for more than three topics depending on the number of documents they have.

If you look at the tool mentioned earlier, you’ll see an alpha and beta slider. These two hyperparameters are required by LDA. The alpha controls the mixture of topics for any given document. Turn it down and the documents will likely have less of a mixture of topics. Turn it up and the documents will likely have more of a mixture of topics. The beta hyperparameter controls the distribution of words per topic. Turn it down and the topics will likely have less words. Turn it up and the topics will likely have more words.

Ideally we want our composites to be made up of only a few topics and our parts to belong to only some of the topics. With this in mind, alpha and beta are typically set below one.

#### Why use LDA?

If you view the number of topics as number of clusters and the probabilities as the proportion of cluster membership then using LDA is a way of soft clustering your composites and parts. Contrast this with say k-means where each entity can only belong to one cluster. These fuzzy memberships provide a more nuanced way of recommending similar items, finding duplicates, or discovering user profiles/personas.

You could analyze every GitHub repository’s topics/tags and infer themes like native desktop client, back-end web service, single-paged app, or flappy bird clone.

If you choose the number of topics to be less than the documents, using LDA is a way of reducing the dimensionality (the number of rows and columns) of the original composite versus part data set. With the documents now mapped to a lower dimensional latent/hidden topic/category space, you can now apply other machine learning algorithms which will benefit from the smaller number of dimensions. For example, you could run your documents through LDA and then hard cluster them using DBSCAN.

Of course the main reason you’d use latent Dirichlet allocation is to uncover the themes lurking in your data. By using LDA on pizza orders, you might infer pizza topping themes like spicy, salty, savory, and sweet. You could analyze every GitHub repository’s topics/tags and infer themes like native desktop client, back-end web service, single paged app, or flappy bird clone.

#### How does latent Dirichlet allocation work?

There are a few ways of implementing LDA. Still, like most — if not all — machine learning algorithms, it comes down to estimating one or more parameters. For LDA those parameters are phi and theta (sometimes they’re called something else). Phi is the parts versus topics matrix (or topics versus parts) and theta is the composites versus topics matrix.

To learn how it works, I’ll make this easy and step through a concrete example. The documents and emoji are shown in the image above. Our hyperparameters are alpha 0.5, beta 0.01, topics 2, and iterations 1. The following manual run through is based on the paper *Probabilistic Topic Models, M Steyvers, T Griffiths, Handbook of latent semantic analysis 427 (7), 424–440*.

Instead of estimating phi and theta directly, we will estimate the topic assignments for each of the four emoji using the Gibbs sampling algorithm. Once we have the estimated topic assignments, we can then estimate phi and theta.

To start, we need to randomly assign a topic to each emoji. Using a fair coin (sampling from a uniform distribution), we assign to the first cat Topic 0, the second cat Topic 1, the first dog Topic 1, and the second dog Topic 0.

`| | Cat 0 | Cat 1 | Dog 0 | Dog 1 |`

|---------|-------|-------|-------|-------|

| Topic 0 | * | | | * |

| Topic 1 | | * | * | |

This is our current topic assignment per each emoji.

`| | Topic 0 | Topic 1 |`

|-----|---------|---------|

| Cat | 1 | 1 |

| Dog | 1 | 1 |

This is our current emoji versus topic counts.

`| | Topic 0 | Topic 1 |`

|------------|---------|---------|

| Document 0 | 1 | 1 |

| Document 1 | 1 | 1 |

This our current document versus topic counts.

Now we need to update the topic assignment for the first cat. We subtract one from the emoji versus topic counts for Cat 0, subtract one from the document versus topic counts for Cat 0, calculate the probability of Topic 0 and 1 for Cat 0, flip a biased coin (sample from a categorical distribution), and then update the assignment and counts.

t0 =

(

(cat emoji with Topic 0 + beta)

/

(emoji with Topic 0 + unique emoji * beta)

)

*

(

(emoji in Document 0 with Topic 0 + alpha)

/

(emoji in Document 0 with a topic + number of topics * alpha)

) =

(

(0 + 0.01)

/

(1 + 2 * 0.01)

)

*

(

(0 + 0.5)

/

(1 + 2 * 0.5)

) = 0.0024509803921568627

t1 = ((1 + 0.01) / (2 + 2 * 0.01)) * ((1 + 0.5) / (1 + 2 * 0.5))

= 0.375

p(Cat 0 = Topic 0 | *) = t0 / (t0 + t1) = 0.006493506493506494

p(Cat 0 = Topic 1 | *) = t1 / (t0 + t1) = 0.9935064935064936

After flipping the biased coin, we surprisingly get the same Topic 0 for Cat 0 so our tables before updating Cat 0 remain the same.

Next we do for Cat 1 what we did for Cat 0. After the flipping the biased coin, we get Topic 0 so now our tables look like so.

`| | Cat 0 | Cat 1 | Dog 0 | Dog 1 |`

|---------|-------|-------|-------|-------|

| Topic 0 | * | * | | * |

| Topic 1 | | | * | |

This is our current topic assignment per each emoji.

`| | Topic 0 | Topic 1 |`

|-----|---------|---------|

| Cat | 2 | 0 |

| Dog | 1 | 1 |

This is our current emoji versus topic counts.

`| | Topic 0 | Topic 1 |`

|------------|---------|---------|

| Document 0 | 2 | 0 |

| Document 1 | 1 | 1 |

This our current document versus topic counts.

What we did for the two cat emoji we now do for the dog emoji. After flipping the biased coins, we end up assigning Topic 1 to Dog 0 and Topic 1 to Dog 1.

`| | Cat 0 | Cat 1 | Dog 0 | Dog 1 |`

|---------|-------|-------|-------|-------|

| Topic 0 | * | * | | |

| Topic 1 | | | * | * |

This is our current topic assignment per each emoji.

`| | Topic 0 | Topic 1 |`

|-----|---------|---------|

| Cat | 2 | 0 |

| Dog | 0 | 2 |

This is our current emoji versus topic counts.

`| | Topic 0 | Topic 1 |`

|------------|---------|---------|

| Document 0 | 2 | 0 |

| Document 1 | 0 | 2 |

This our current document versus topic counts.

Since we’ve completed one iteration, we are now done with the Gibbs sampling algorithm and we have our topic assignment estimates.

To estimate phi, we use the following equation for each row-column cell in the emoji versus topic count matrix.

Phi row column =

(emoji row with topic column + beta)

/

(all emoji with topic column + unique emoji * beta)

And for estimating theta we use the following equation for each row-column cell in the document versus topic count matrix.

Theta row column =

(emoji in document row with topic column + alpha)

/

(emoji in document row + number of topics * alpha)

At long last we are done. The image above contains the estimated phi and theta parameters of our probabilistic topic model.

If you enjoyed this article you might also like Boost your skills, how to easily write K-Means, k-Nearest Neighbors from Scratch, and Let’s make a Linear Regression Calculator with PureScript.

How have you used LDA? Do you prefer Gensim or MALLET? How cool would it be if Elasticsearch did topic modeling out of the box? Let me know in the responses below. Thanks!