Rapids Galaxy Prediction: Easy Guide for Beginners 2024

Okay, so today I decided to dive into this whole “galaxy prediction” thing using RAPIDS. I’d heard about it and figured, why not give it a shot? It sounded pretty cool, and honestly, I was itching to try something new.

Getting Started

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First things first, I needed to get my environment set up. I already had RAPIDS installed, but if you don’t, their website has some pretty straightforward instructions. It’s basically getting the right conda environment going. The main thing is making sure you have a compatible NVIDIA GPU and drivers – that’s the key to making this whole thing run fast.

Data Wrangling

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Next up, the data. I grabbed a sample dataset of galaxy information. I think it was from some astronomy website, but don’t quote me on that. It was a pretty big CSV file, with tons of columns about galaxy sizes, brightness, redshifts, and a bunch of other stuff I barely understood. Seriously, who knew there were so many ways to measure a galaxy?

I used cuDF, which is RAPIDS’ version of pandas, to load the data. It was surprisingly easy. It felt just like using pandas, but way, way faster. Like, ridiculously faster. We’re talking loading a huge file in seconds, not minutes. Here’s the basic idea:

import cudf
gdf = *_csv('my_galaxy_*')

Then came the messy part: data cleaning. There were missing values, outliers, and all sorts of weirdness. I spent a good chunk of time just figuring out what each column even meant and deciding how to handle the problems. For missing values, I mostly just filled them with the average value for that column. It’s not perfect, but it’s a good enough starting point.

# Example: Fill missing values in 'galaxy_size' with the mean
gdf['galaxy_size'] = gdf['galaxy_size'].fillna(gdf['galaxy_size'].mean())
#outliers would require a much more complex method to properly clean the data

Building the Model

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Once the data was (relatively) clean, I started thinking about the actual prediction model. I decided to keep it simple and use a Random Forest model, since those are usually pretty good for this kind of tabular data. RAPIDS has cuML, which has all the machine learning stuff. Again, it’s super similar to scikit-learn, but designed for GPUs.

from * import RandomForestClassifier
from *_selection import train_test_split
#Let's make some fake target data first.

gdf['is_spiral'] = (gdf['galaxy_shape_code'] == 1)
# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(gdf, gdf['is_spiral'], test_size=0.2)
# Create and train the model
model = RandomForestClassifier()
*(X_train, y_train)

I split the data into training and testing sets, then created and trained the model. That part was pretty straightforward, thanks to cuML’s familiar API.

Making Predictions and Checking the Results

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Finally, I used the trained model to make predictions on the test set and see how well it did. cuML has functions for calculating accuracy, precision, recall, and all that good stuff.

from * import accuracy_score
# Make predictions
predictions = *(X_test)
# Calculate accuracy
accuracy = accuracy_score(y_test, predictions)
print(f'Accuracy: {accuracy}')

My initial results weren’t amazing, but they were decent enough to show that the whole thing was working. I played around with some of the model parameters (like the number of trees in the forest) to see if I could improve the accuracy. That’s where the real experimentation comes in.

Wrapping Up

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Overall, it was a fun little project. The best part was definitely seeing how much faster RAPIDS made everything. Things that would have taken forever with regular pandas and scikit-learn were happening in the blink of an eye. It really opens up possibilities for working with much larger datasets and doing more complex modeling. I definitely plan to explore this more – maybe try some different models and see if I can get those prediction results even better!