As we can see, for K-Means clustering for small datasets (first 4 datasets) faiss-based version is slower for training a

ned, I eat healthily overall every day, but I regularly treat myself to cake and cheat meals. As the year has gone by, I have probably seen my body tone up a bit, and I’ve lost some weight. But I haven’t turned into a fitness machine, and probably you won’t either. Seeing noticeable and lasting results often requires more intensity and a flawless lifestyle.

As we can see from the example, the core of the algorithm is searching for nearest neighbors, specifically nearest centroids, both for training and prediction. And that’s where faiss is orders of magnitude faster than Scikit-learn! It leverages great C implementation, concurrency wherever possible and even GPU, if you want.

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In my last article on faiss library I’ve shown how to make kNN up to 300 times faster than Scikit-learn’s in 20 lines using Facebook’s faiss library. But we can do much more with it, including both faster and more accurate K-Means clustering, in just 25 lines!

I’ve chosen a few popular datasets available in Scikit-learn for comparison. The train times andpredict times are compared. For easier reading, I’ve explicitly written how many times faster is the faiss-based clustering than Scikit-learn’s. For error comparison I’ve just written how many times lower error the faiss-based clustering achieves (since numbers are large and not very informative).

A great feature of faiss is that it has both installation and build instructions (installation docs) and an excellent documentation with examples (getting started docs). After the installation, we can write the actual clustering. The code is quite simple, since we just mimic the Scikit-learn API.

In both libraries we have to specify algorithm hyperparameters: number of clusters, number of restarts (each starting with other initial guesses) and maximal number of iterations.

The final quality of clustering is calculated as a sum of in-cluster distances, where for each cluster we calculate a sum of Euclidean distances between points in that cluster and its centroid. This is also called inertia.

K-Means is an iterative algorithm, which clusters the data points into k clusters, each represented with a mean / center point (a centroid). Training starts with some initial guesses and then alternates between two steps: assignment and update.

in prediction we perform kNN search with k = 1, returning indices of nearest centroids from self.cluster_centers_ (index [1], since index.search() returns distances and indices)

All of those times have been measured with the time.process_time() function, that measures process time instead of wall clock time, for more accurate results. Results are averages of 100 runs, except for MNIST, where it took too long for Scikit-learn and I had to do 5 runs.

In the assignment phase we assign each point to the nearest cluster (using Euclidean distance between point and centroids) and in the update step we recalculate each centroid, calculating a mean point from all points assigned to that cluster in the current step.

Soon enough, when you finally change the tape that is playing in your head, you get unstuck from that story. Why is this so important? Because whatever your story says, know that it’s influencing your expectations. It’s creating your perception about the world; your assumptions about what life is. Your belief about who you are, your feelings, your moods, your thoughts — You.

The purpose of these exercises is to shift from looking from your thoughts to looking at your thoughts. Use the one or the ones that suit you better. There is no end to this practice, so don’t think you have to accomplish something with it — it’s a process, and it will take time. As always, practice is key.

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As we can see, for K-Means clustering for small datasets (first 4 datasets) faiss-based version is slower for training a

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