Fixing Invisible Clusters In Parameter Plots

Hey everyone! 👋 Let's talk about a tricky issue that can pop up when you're working with parameter plots, especially when dealing with large clusters of data. Have you ever noticed that your large clusters of points seem to disappear in your parameter plots, even though they're clearly visible in other plots, like waterfall plots? This is a common issue, and we're going to dive deep into why it happens and how you can fix it. This is specifically related to the pypesto library, and how it handles cluster visualization.

The Mystery of the Missing Clusters

So, you've generated some data, and you're eager to visualize it using a custom version of pypesto.visualize.parameters.parameters. You expect to see your distinct clusters nicely displayed, but then... poof! They're gone. This can be super frustrating, especially when your converged points, which are the most important part of the data, seem to vanish. This is where the detective work begins. We need to figure out why these clusters are hiding and how to bring them back into view. In the world of data visualization, understanding the nuances of how colors and transparency are handled is essential. The issue mainly lies in the way the colors are assigned to these clusters. This is because, in this particular plot, the balance_alpha=True option is set by default. The balance_alpha setting is designed to make sure all clusters are easily visible, but in practice, its behavior causes large clusters to turn into white color in this plot, making them invisible. Let's delve deeper into the code and the mechanics behind this issue.

Unveiling the Culprit: assign_colors and balance_alpha

After some investigation, it turns out the culprit is often the color assignment in pypesto.visualizes.clust_color.assign_colors. More specifically, the balance_alpha flag within this function. When balance_alpha=True (which, as mentioned, is the default), this can lead to a white color being assigned to large clusters, effectively making them invisible in your plot. Think of it like this: the function tries to balance the alpha (transparency) values across all clusters. For smaller clusters, this works fine. But for really large clusters, the balancing algorithm can end up making them so transparent that they blend into the background. And when the color becomes white, the clusters become invisible, which is exactly the opposite of what you want.

To really see what's happening, you can create a simple example. Suppose you generate a bunch of data points that belong to a few clusters. Then you try visualizing these clusters using assign_colors. Here is an MWE:

test_vals = np.array([np.full(10000, i) for i in np.linspace(1, 150, 5)]).flatten()

test_colors = visualize.clust_color.assign_colors(test_vals, highlight_global=True, balance_alpha=True)
plt.hlines([0.2 for i in range(cluster_size[0])], xmin=0.0, xmax=1.0, color=test_colors[0])

test_colors = visualize.clust_color.assign_colors(test_vals, highlight_global=True, balance_alpha=False)
plt.hlines([0.2 for i in range(cluster_size[0])], xmin=0.0, xmax=1.0, color=test_colors[0])

Running this code will show that with balance_alpha=True, the plot remains blank, and with balance_alpha=False, the red line is shown, representing the largest cluster. This is particularly noticeable in the parameter plots. However, waterfall plots can handle this more gracefully, because they assign colors to each cluster separately. This is why you might see your clusters in a waterfall plot but not in a parameter plot.

The balance_alpha Flag: A Closer Look

The balance_alpha flag is designed to improve the visibility of all the clusters, regardless of their size. However, for large clusters, it often has the opposite effect. The idea behind balance_alpha is to adjust the transparency of the colors so that you can see all clusters clearly. Smaller clusters would get a high alpha value (less transparent), and the larger clusters would get a low alpha value (more transparent). But in practice, the balancing algorithm can go overboard with very large clusters. The alpha value becomes so small that they blend into the background, which is a big problem. This becomes a real issue when visualizing large datasets with many points per cluster, because it becomes difficult to interpret the results.

Finding a Solution: What Can We Do?

So, what's the best way to handle this? There are several potential solutions:

1. Disable balance_alpha for large clusters: This is probably the easiest fix. You could modify the assign_colors function to automatically disable balance_alpha if it detects a large cluster. This would prevent the issue from occurring in the first place.
2. Provide a warning: You could add a warning message to the user if balance_alpha is enabled and large clusters are detected. This would alert the user to the potential problem and give them the chance to adjust the settings.
3. Adjust the balancing method: A more sophisticated approach would be to change how the alpha values are balanced. Instead of making large clusters almost transparent, you could adjust the balancing algorithm to use a different scaling method. This could help prevent large clusters from disappearing while still maintaining the visibility of smaller clusters.

Choosing the best solution involves a trade-off. Disabling balance_alpha might make it harder to see smaller clusters, but it would ensure that large clusters remain visible. Providing a warning would alert the user to the problem, but it wouldn't fix it automatically. Adjusting the balancing method is the most complex solution, but it could offer the best of both worlds.

Impact on Other Plots and Why This Matters

This behavior is mainly problematic in the parameter plots. The flag's behavior doesn't cause problems in waterfall plots, because the colors are assigned to each cluster separately. For cluster numbers equal to 1, the flag doesn't do anything, which is why the issue doesn't appear in those cases. This is crucial because parameter plots are a very important way to visualize the parameter values and explore the results of the optimization process. When your results are not visible, it can be very difficult to understand your data and make informed decisions.

Conclusion: Making Your Plots Pop!

In conclusion, the vanishing cluster problem in parameter plots, caused by the balance_alpha flag, can be solved by disabling balance_alpha for large clusters, giving a warning to the user, or by changing the balancing method. The best solution depends on the specific requirements of your analysis. By understanding this issue, you can make your parameter plots more informative and effective. Now you can visualize your large clusters and clearly see all the important information in your data. It's all about making sure your data tells the full story!