GameOfLife Prediction Dataset

The GameOfLife dataset is an algorithmically generated dataset based off John Horton Conway's Game of Life. Conway's Game of Life follows a strict set off rules at each "generation" (simulation step) where cells alternate between a dead and alive state based on number of surrounding alive cells. These rules can be found on the Game of Life's [Wikipedia page](https://en.wikipedia.org/wiki/Conway's_Game_of_Life.)This dataset is one of the three hidden datasets used by the 2025 NAS Unseen-Data Challenge at AutoML.The goal of this dataset is to predict the number of cells alive in the next generation. This task is relatively simple for a human to do if a bit tedious, and should theoretically be simple for Machine Learning algorithms. Each cells's state is calculated based off the number of alive neighbour's in the previous step. Effectively for every cell we only need to look at the surrounding eight cells (3x3 square, minus the centre) which means all information for each cell can be found from a 3x3 Convolution, which is a very common kernel size to use. The dataset was used to make sure that participants appraoches could handle simple tasks along with the more complicated tasks to make sure they did not overcomplicate their submission.There are 70,000 images in the dataset where each image is a randomly generated starting configuration of the Game of Life, with a random level of density (number of initial alive cells).The data is stored in a channels-first format with a shape of (n, 1, 10, 10) where n is the number of samples in the corresponding set (50,000 for training, 10,000 for validation, and 10,000 for testing).There are 25 classes in this dataset, where the label (0..24) represents the number of alive celss in the next generation and images are evenly distributed by class across the dataset (2800 each, 2000, 400, 400 for training, validation and testing respectively). We limit the data to 25 classes despite theoretically a limit of 0-100, we do this as the higher classes are increasingly unlikely to occur, and would take much longer to create a balanced dataset. Excluding 0, the lower numbers also get increasingly unlikely, though more likely than higher numbers, we wanted to prevent gaps and therefore limited to 25 contiguous classesNumPy (.npy) files can be opened through the NumPy Python library, using the `numpy.load()` function by inputting the path to the file into the function as a parameter. The metadata file contains some basic information about the datasets, and can be opened in many text editors such as vim, nano, notepad++, notepad, etc.