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One of the most useful things sneer does during optimization is provide a plot of the current state of the output configuration. Details of that gets its own section: Visualization. This section covers both the textual part of the logging, its frequency and how it knows when to stop.

Console Logging

During optimization, various information is logged to the screen. Some of it appears during input initialization (see the Input Initialization section). We’ll cover some of the rest here.

The optimization logging messages look like:

Iteration #50 cost = 0.503 norm = 0.3987 rtol = 20

The Iteration value is the iteration number. Nothing surprising there.

The cost is the value of the cost function that’s specific to the embedding method. For example, for t-SNE, the cost is the Kullback-Leibler divergence. For Sammon Mapping, the cost is the Sammon Stress. It’s not particularly interesting what the number is, as long as you see the number going down. For a given parameterization, the smaller the number, the better. However, comparing costs between different methods or different data sets is not meaningful.

It’s also a bit hard to tell on an absolute scale how well you’re doing. A perfect embedding will give a cost of 0, but is cost = 0.503 good or bad? The norm value attempts to provide a normalization that helps. 0 is still perfect, but a norm of 1 is intended to represent the value you’d get if you completely ignored all the input data you were given and made all the distances (or probabilities) equal, which you can achieve by setting all the distances to zero. Hence, if you initialize the output coordinates with the small gaussian distribution (init = "r" and see Output Initialization for more details), your initial norm cost will be very close to 1 for most embedding methods.

If you use Sammon Mapping (method = "sammon"), the cost used is the Sammon Stress, which is already normalized, so no norm value is provided.

Additionally, for PCA (method = "pca"), metric MDS (method = "mmds") and Sammon Mapping, a kruskal_stress value is provided. This reports Kruskal Stress formula 1, which is related to the square loss of the input and output distances. It’s not reported for other embedding methods which aren’t concerned with preserving distances.

tol

The rtol value in the output measures the relative tolerance between the currently reported cost value and that of the previous report. As mentioned in the section on Optimization, the embedding will stop if the rtol value falls below the argument provided to the tol parameter.

tsne_iris <- sneer(iris, tol = 0.01)

report_every

You can change how often optimization progress is logged to screen by changing the report_every value. For example, if you set it 100, it will report every 100 iterations.

Watch out if you set the value low. First, if you are plotting the embedding, I’ve had the R session in RStudio crash on me with a complaint about graphics. This may be something to do with trying to get a graphics plot to refresh too quickly.

Second, because the tolerance is checked after every logged cost value, if you report too often and set tol too high, the optimization will converge because the cost won’t have changed enough between reports. The best course of action is to set tol = 0 under these conditions.

# If you like a talkative embedding
tsne_iris <- sneer(iris, report_every = 1, tol = 0)

The other way to affect how many times the information is logged to console is to change the max_iter argument that determines the total number of iterations. Also see the Optimization section for more on that.

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