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license: cc0-1.0 |
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--- |
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# GambitFlow Opening Database |
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A curated, high-quality chess opening theory database in SQLite format, designed for chess engines and analysis tools. This dataset contains aggregated move statistics derived from thousands of recent, high-rated (2600+ ELO) grandmaster-level games. |
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The goal of this database is to provide a powerful, statistically-backed foundation for understanding modern opening theory, without relying on traditional, human-annotated opening books. |
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## How to Use |
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The database is a single SQLite file (`opening_theory.db`). You can download it directly from the "Files" tab or use the `huggingface_hub` library to access it programmatically. |
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Here is a Python example to query the database for the starting position: |
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```python |
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import sqlite3 |
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import json |
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from huggingface_hub import hf_hub_download |
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# 1. Download the database file |
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db_path = hf_hub_download( |
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repo_id="GambitFlow/Opening-Database", |
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filename="opening_theory.db", |
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repo_type="dataset" |
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) |
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# 2. Connect to the database |
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conn = sqlite3.connect(db_path) |
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cursor = conn.cursor() |
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# 3. Query a position (FEN) |
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# The FEN should be "canonical" (position, turn, castling, en passant) |
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start_fen = "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq -" |
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cursor.execute("SELECT move_data FROM openings WHERE fen = ?", (start_fen,)) |
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row = cursor.fetchone() |
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if row: |
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# 4. Parse the JSON data |
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moves_data = json.loads(row) |
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# Sort moves by frequency |
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sorted_moves = sorted( |
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moves_data.items(), |
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key=lambda item: item['frequency'], |
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reverse=True |
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) |
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print(f"Top moves for FEN: {start_fen}\n") |
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for move, data in sorted_moves[:5]: |
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print(f"- Move: {move}") |
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print(f" Frequency: {data['frequency']:,}") |
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print(f" Avg Score: {data.get('avg_score', 0.0):.3f}\n") |
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conn.close() |
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``` |
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## Data Collection and Processing |
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This dataset was meticulously created through a multi-stage pipeline to ensure the highest quality and statistical relevance. |
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**1. Data Source:** |
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The foundation of this dataset is the **Lichess Elite Database**, a filtered collection of games from high-rated players, originally curated by [Nikonoel](https://database.nikonoel.fr/). We used multiple monthly PGN files from the 2023-2024 period to capture modern theory. |
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**2. Filtering Criteria:** |
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Only games meeting the following strict criteria were processed: |
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* **Minimum ELO:** Both players had a rating of **2600 or higher**. |
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* **Maximum Depth:** Only the first **25 moves** of each game were analyzed to focus on opening theory. |
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**3. Processing Pipeline:** |
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* **Distributed Processing:** The PGN files were processed in parallel across multiple sessions to handle the large volume of games efficiently. |
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* **Perspective-Aware Scoring:** Each move's quality was scored from the perspective of the player whose turn it was. A win scored `1.0`, a loss `0.0`, and a draw `0.5`. |
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* **Aggregation:** For each unique board position (FEN), statistics for every move played were aggregated, including frequency and the sum of scores. |
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* **Merging:** The databases from the distributed sessions were merged into a single master file. This process recalculated weighted averages for ELO and move scores, ensuring statistical accuracy. |
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## Dataset Structure |
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The database contains a single table named `openings`. |
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**File:** `opening_theory.db` |
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**Table:** `openings` |
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| Column | Type | Description | |
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|---------------|---------|-------------------------------------------------------------| |
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| `fen` | TEXT | The canonical board position (FEN string, Primary Key). | |
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| `move_data` | TEXT | A JSON object containing statistics for each move played. | |
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| `total_games` | INTEGER | The total number of times this position was seen. | |
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| `avg_elo` | INTEGER | The average ELO of players who reached this position. | |
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### `move_data` JSON Structure |
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The `move_data` column contains a JSON string with the following structure: |
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```json |
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{ |
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"e4": { |
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"frequency": 153944, |
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"score_sum": 76972.0, |
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"avg_score": 0.5 |
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}, |
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"d4": { |
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"frequency": 65604, |
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"score_sum": 32802.0, |
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"avg_score": 0.5 |
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} |
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} |
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``` |
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* **`frequency`**: The number of times this move was played from the given position. |
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* **`score_sum`**: The sum of all perspective-aware scores for this move. |
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* **`avg_score`**: The average score, calculated as `score_sum / frequency`. |
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## Citation |
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If you use this dataset in your work, please consider citing it: |
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```bibtex |
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@dataset{gambitflow_opening_database_2024, |
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author = {GambitFlow Project}, |
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title = {GambitFlow Opening Database}, |
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year = {2024}, |
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publisher = {Hugging Face}, |
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url = {https://huggingface.co/datasets/GambitFlow/Opening-Database} |
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} |
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``` |
