| | --- |
| | license: mit |
| | task_categories: |
| | - token-classification |
| | - text-classification |
| | - text-generation |
| | language: |
| | - en |
| | pretty_name: ChessSet-Community |
| | size_categories: |
| | - 1K<n<10K |
| | tags: |
| | - ChessAI-Community |
| | --- |
| | # Chess Positions with Stockfish Evaluations |
| |
|
| | This dataset contains a collection of chess positions in Forsyth-Edwards Notation (FEN), each paired with a corresponding evaluation from the Stockfish chess engine. It is designed for use in training machine learning models for chess, analyzing chess positions, or for any application requiring a large set of evaluated positions. |
| |
|
| | ## Data Format |
| |
|
| | The data is stored in a simple CSV (Comma-Separated Values) format without a header row. Each line in the file represents a single chess position and its evaluation. |
| |
|
| | The format is as follows: |
| | `"FEN_string",evaluation` |
| |
|
| | ### Fields |
| |
|
| | | Field | Description | |
| | |-------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | | `FEN_string`| A standard FEN string representing a specific board state. This includes piece placement, active color, castling availability, en passant target square, halfmove clock, and fullmove number. [Learn more about FEN](https://en.wikipedia.org/wiki/Forsyth%E2%80%93Edwards_Notation). | |
| | | `evaluation`| The Stockfish engine's evaluation of the position in centipawns. <br> - A positive value (`+110`) indicates an advantage for White. <br> - A negative value (`-95`) indicates an advantage for Black. <br> - A value near zero suggests a balanced position. <br> - Mate-in-N is represented as `#N` (e.g., `#3` for mate in 3 for the side to move) or `#-N` (e.g., `#-2` if the side to move is being mated in 2). | |
| |
|
| | ### Example Data |
| |
|
| | ```csv |
| | "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1",+25 |
| | "r1bqkbnr/pp1ppppp/2n5/2p5/4P3/5N2/PPPP1PPP/RNBQKB1R w KQkq - 2 3",+15 |
| | "r4rk1/pp1n1ppp/2pbp3/q2n2B1/3PN3/3Q1N2/PPP2PPP/1K1R3R b - - 5 13",-80 |
| | "4r1k1/pp1r1p1p/1qp1n1p1/4P3/3p1P2/2Q4P/PP1R2P1/3R2K1 w - - 0 29",#4 |
| | ``` |
| |
|
| | ## How to Use |
| |
|
| | Here is a basic Python script to read and parse the data from a file named `chess_data.csv`. |
| |
|
| | ```python |
| | import csv |
| | |
| | def load_chess_data(file_path='chess_data.csv'): |
| | """ |
| | Loads and prints chess positions and their evaluations from the dataset. |
| | """ |
| | positions = [] |
| | try: |
| | with open(file_path, 'r', encoding='utf-8') as f: |
| | reader = csv.reader(f) |
| | for row in reader: |
| | if not row: |
| | continue |
| | fen_string = row[0] |
| | evaluation = row[1] |
| | positions.append({'fen': fen_string, 'eval': evaluation}) |
| | print(f"FEN: {fen_string}, Evaluation: {evaluation}") |
| | except FileNotFoundError: |
| | print(f"Error: The file {file_path} was not found.") |
| | except IndexError: |
| | print(f"Error: Malformed row found in {file_path}.") |
| | |
| | return positions |
| | |
| | if __name__ == '__main__': |
| | # Assuming your data is in 'chess_data.csv' |
| | chess_dataset = load_chess_data() |
| | if chess_dataset: |
| | print(f" |
| | Successfully loaded {len(chess_dataset)} positions.") |
| | |
| | ``` |
| |
|
| | ## Data Generation (Example) |
| |
|
| | This dataset was generated using: |
| | * **Engine**: Stockfish 16 |
| | * **Search**: 15 seconds per position |
| | * **Source**: A curated list of positions from grandmaster games. |
| |
|
| | ## License |
| |
|
| | This dataset is released under the [MIT License](https://opensource.org/licenses/MIT). You are free to use, modify, and distribute it for any purpose. |
