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--- |
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license: mit |
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tags: |
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- code |
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size_categories: |
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- 10K<n<100K |
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--- |
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# Brogue Map Dataset |
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To clone this repo, use: |
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``` |
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git clone https://huggingface.co/datasets/DolphinNie/dungeon-dataset |
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``` |
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## 1. Data Explanation |
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This is the Map dataset from the open-sourced game [Brogue](https://github.com/tmewett/BrogueCE). It contains 49,000 train dataset, 14,000 test dataset and 7,000 validation dataset. |
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Each map is stored in a `.csv` file. The map is a `(32x32)` array, which is the map size. |
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Each cell in the array is a `int` number ranged from 0 to 13, which represented 14 tiles. |
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```json |
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"G_NONE": 0, |
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"G_GROUND": 1, |
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"G_SAND": 2, |
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"G_WATER": 3, |
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"G_BOG": 4, |
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"G_LAVA": 5, |
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"G_ICE": 6, |
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"G_GRASS": 7, |
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"G_FUNGUS": 8, |
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"G_ASHES": 9, |
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"G_STONE": 10, |
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"G_CRYSTAL": 11, |
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"G_FIRE": 12, |
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"G_BRIDGE": 13 |
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``` |
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An example map datapoint is in the format of |
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``` |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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``` |
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## 2. Data processing |
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Huggingface does not store the map data in the correct format. To get each correct map data, use the following code: |
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```python |
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from datasets import load_dataset |
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import numpy as np |
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import matplotlib.pyplot as plt |
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# Load dataset from hugging face |
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dataset = load_dataset("DolphinNie/dungeon-dataset") |
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def get_processed_dataset(load_dataset_from_pickle=False, |
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save_dataset_to_pickle=False, |
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pickle_save_path='dungeon-dataset.pkl'): |
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dataset = pull_hugging_face_dataset(load_dataset_from_pickle, |
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save_dataset_to_pickle, |
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pickle_save_path) |
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dataset_train, dataset_test, dataset_valid = convert_dataset(dataset) |
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return dataset_train, dataset_test, dataset_valid |
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def convert_dataset(dataset): |
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dataset_train = list() |
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dataset_test = list() |
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dataset_valid = list() |
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datasets = [dataset_train, dataset_test, dataset_valid] |
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name = ['train', 'test', 'validation'] |
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for i in range(3): |
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datapoint_num = int(dataset[name[i]].num_rows / 32) |
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dataset_tf = dataset[name[i]].to_pandas() |
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for n in range(datapoint_num): |
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env_num = dataset_tf[n * 32:(n + 1) * 32] |
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datasets[i].append(env_num) |
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return dataset_train, dataset_test, dataset_valid |
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dataset_train, dataset_test, dataset_valid = get_processed_dataset(load_dataset_from_pickle, save_dataset_to_pickle) |
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# Visualize the datapoints if you want |
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def visualize_map(dungeon_map): |
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plt.imshow(dungeon_map, cmap='viridis', interpolation='nearest') |
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plt.title('dungeon map') |
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plt.show() |
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visualize_map(dataset_train[10000]) |
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``` |
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<img src="./README.assets/image-20240411203604268.png" alt="image-20240411203604268" style="zoom:50%;" /> |
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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. |
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