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Datasets:
DolphinNie
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dungeon-dataset

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End of preview. Expand in Dataset Viewer.

Brogue Map Dataset

To clone this repo, use:

git clone https://huggingface.co/datasets/DolphinNie/dungeon-dataset

1. Data Explanation

This is the Map dataset from the open-sourced game Brogue. It contains 49,000 train dataset, 14,000 test dataset and 7,000 validation dataset.

Each map is stored in a .csv file. The map is a (32x32) array, which is the map size.

Each cell in the array is a int number ranged from 0 to 13, which represented 14 tiles.

  "G_NONE": 0,
  "G_GROUND": 1,
  "G_SAND": 2,
  "G_WATER": 3,
  "G_BOG": 4,
  "G_LAVA": 5,
  "G_ICE": 6,
  "G_GRASS": 7,
  "G_FUNGUS": 8,
  "G_ASHES": 9,
  "G_STONE": 10,
  "G_CRYSTAL": 11,
  "G_FIRE": 12,
  "G_BRIDGE": 13

An example map datapoint is in the format of

0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
0,0,1,1,1,8,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
0,1,1,1,8,8,8,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0
0,1,1,8,8,8,8,0,0,0,1,1,1,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0
0,1,1,1,8,8,0,0,0,1,1,1,1,1,1,1,1,0,0,1,0,0,0,0,0,0,0,0
0,1,1,1,1,1,0,0,0,1,1,1,1,1,1,1,1,0,1,8,0,0,1,1,1,1,0,0
0,1,1,1,1,1,0,0,0,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,0
0,0,1,1,1,0,0,0,0,0,1,1,1,1,1,0,0,0,1,1,1,1,1,1,1,1,1,0
0,0,0,0,1,0,0,0,0,0,0,1,1,1,0,0,0,0,1,1,1,1,0,0,1,1,1,9
0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,1,1,0
0,0,1,1,1,1,1,0,0,0,0,0,0,1,1,1,1,0,0,0,1,0,0,0,0,0,0,0
0,0,1,1,1,1,1,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0
0,1,1,1,1,1,1,0,0,0,0,0,0,1,1,1,1,0,1,1,1,1,0,0,0,0,0,0
0,1,8,1,1,1,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0
0,1,8,8,8,8,1,8,0,0,0,0,1,8,1,1,0,0,0,0,0,0,0,0,0,1,1,1
0,0,8,8,8,8,8,8,0,0,0,8,8,8,8,8,1,0,0,0,1,1,0,0,0,1,1,1
0,0,1,8,8,8,8,8,8,0,1,8,8,8,8,8,1,0,0,0,1,1,0,0,0,0,1,1
0,0,0,1,8,8,8,8,8,0,1,1,1,8,8,1,0,0,0,0,1,1,0,1,0,1,1,1
0,0,0,8,8,8,8,8,8,1,1,1,1,8,1,1,0,0,0,0,1,1,1,1,0,1,1,0
0,0,0,8,8,8,8,1,0,0,0,3,1,0,1,0,0,0,0,0,0,1,1,1,0,1,1,0
0,0,0,0,8,8,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0
0,0,0,1,1,0,0,0,0,0,0,0,0,0,11,1,1,1,1,1,1,1,1,1,1,1,0,0
0,1,1,1,8,1,0,0,0,0,0,0,0,0,11,11,11,1,1,1,1,1,1,1,1,1,1,0
0,0,1,1,1,1,0,0,0,0,0,0,0,0,11,11,0,0,1,1,0,0,1,1,1,1,1,1
0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0

2. Data processing

Huggingface does not store the map data in the correct format. To get each correct map data, use the following code:

from datasets import load_dataset
import numpy as np
import matplotlib.pyplot as plt

# Load dataset from hugging face
dataset = load_dataset("DolphinNie/dungeon-dataset")


def get_processed_dataset(load_dataset_from_pickle=False,
                          save_dataset_to_pickle=False,
                          pickle_save_path='dungeon-dataset.pkl'):
    dataset = pull_hugging_face_dataset(load_dataset_from_pickle,
                                        save_dataset_to_pickle,
                                        pickle_save_path)
    dataset_train, dataset_test, dataset_valid = convert_dataset(dataset)
    return dataset_train, dataset_test, dataset_valid

def convert_dataset(dataset):
    dataset_train = list()
    dataset_test = list()
    dataset_valid = list()
    datasets = [dataset_train, dataset_test, dataset_valid]
    name = ['train', 'test', 'validation']
    for i in range(3):
        datapoint_num = int(dataset[name[i]].num_rows / 32)
        dataset_tf = dataset[name[i]].to_pandas()
        for n in range(datapoint_num):
            env_num = dataset_tf[n * 32:(n + 1) * 32]
            datasets[i].append(env_num)
    return dataset_train, dataset_test, dataset_valid

dataset_train, dataset_test, dataset_valid = get_processed_dataset(load_dataset_from_pickle, save_dataset_to_pickle)

# Visualize the datapoints if you want
def visualize_map(dungeon_map):
    plt.imshow(dungeon_map, cmap='viridis', interpolation='nearest')
    plt.title('dungeon map')
    plt.show()
    
visualize_map(dataset_train[10000])
image-20240411203604268

Note that this dataset contains a two-dimensional representation of the map, not a three-dimensional one-hot representation. If you need to train a new model, you need to further process the data set.

Downloads last month
57
Size of downloaded dataset files:
30 MB
Size of the auto-converted Parquet files:
710 MB
Number of rows:
2,240,000