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* feat(run_eval): add checkpoint resume functionality and update example documentation; - update new bootcamp benchmark dataset * refactor(data_pipeline): optimize data generation pipeline; add multiple preset configurations for data generation * docs: update bootcamp list and add new scripts - Update Fulllist_InternBootcamp.md with new bootcamps and categories - Add new scripts to .gitignore: - examples/pipelines/filter_autogen_configs.py - examples/pipelines/quickgen_data_configs_from_eval_meta.py - Update dependencies in setup.py: - Add scipy and scikit-learn * refactor(internbootcamp): update bootcamp modules and improve error handling - Update import statements in __init__.py files - Add timestamp to target directory name in verl_data_preprocess.py - Improve error handling and scoring logic in bootcamp_judger.py - Remove unnecessary comments and update puzzle descriptions in multiple files
111 lines
4.4 KiB
Python
Executable file
111 lines
4.4 KiB
Python
Executable file
"""### 谜题描述
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Kakurasu is a logic puzzle played on a rectangular grid (typically N×N). Each cell in the grid can be either shaded or unshaded. The puzzle provides target values for each row and column, and the goal is to shade cells such that:
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1. **Row Constraints**: For every row, the sum of the *column indices* of the shaded cells in that row equals the target value specified for that row.
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- Example: If a row's target is 7, you might shade cells in columns 3 and 4 (since 3 + 4 = 7).
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2. **Column Constraints**: For every column, the sum of the *row indices* of the shaded cells in that column equals the target value specified for that column.
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- Example: If a column's target is 5, you might shade cells in rows 2 and 3 (since 2 + 3 = 5).
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3. **Unique Contributions**: A shaded cell at position (row *i*, column *j*) contributes its *column index* **j** to its row's sum and its *row index* **i** to its column's sum. These dual contributions must satisfy both the row and column targets simultaneously.
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4. **No Overlapping Rules**: Unlike Sudoku, there are no region constraints—only row and column sums matter. However, cells cannot be \"partially\" shaded; they are either fully shaded or unshaded.
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The puzzle is solved when all row and column targets are satisfied without contradiction.
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请完成上述谜题的训练场环境类实现，包括所有必要的方法。
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"""
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from bootcamp import Basebootcamp
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import random
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import re
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import ast
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class Kakurasubootcamp(Basebootcamp):
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def __init__(self, n=5):
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self.n = n
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def case_generator(self):
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n = self.n
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grid = [[random.choice([0, 1]) for _ in range(n)] for _ in range(n)]
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row_targets = []
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for i in range(n):
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total = sum((j + 1) * cell for j, cell in enumerate(grid[i]))
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row_targets.append(total)
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col_targets = []
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for j in range(n):
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total = sum((i + 1) * grid[i][j] for i in range(n))
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col_targets.append(total)
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return {
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'n': n,
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'row_targets': row_targets,
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'col_targets': col_targets
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}
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@staticmethod
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def prompt_func(question_case) -> str:
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n = question_case['n']
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row_targets = question_case['row_targets']
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col_targets = question_case['col_targets']
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return f"""你正在解决一个Kakurasu谜题。这是一个{n}x{n}的网格谜题，目标是根据行和列的约束条件涂黑单元格。
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规则：
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1. 每行中被涂黑单元格的列索引（从左到右为1到{n}）之和等于该行的目标值。
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2. 每列中被涂黑单元格的行索引（从上到下为1到{n}）之和等于该列的目标值。
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3. 每个单元格必须明确涂黑（1）或未涂黑（0）。
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当前谜题的行目标值（从上到下）：{row_targets}
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当前谜题的列目标值（从左到右）：{col_targets}
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请将你的解答格式化为{n}x{n}的二维数组，其中1表示涂黑，0表示未涂黑，并用[answer]和[/answer]标签包裹。例如：
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[answer]
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[[1, 0, 0], [0, 1, 0], [0, 0, 1]]
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[/answer]
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"""
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@staticmethod
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def extract_output(output):
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matches = re.findall(r'\[answer\](.*?)\[/answer\]', output, re.DOTALL)
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if not matches:
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return None
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last_match = matches[-1].strip()
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try:
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solution = ast.literal_eval(last_match)
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return solution
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except (SyntaxError, ValueError):
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return None
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@classmethod
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def _verify_correction(cls, solution, identity):
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n = identity['n']
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row_targets = identity['row_targets']
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col_targets = identity['col_targets']
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# 验证答案结构
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if not isinstance(solution, list) or len(solution) != n:
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return False
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for row in solution:
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if not isinstance(row, list) or len(row) != n:
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return False
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for cell in row:
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if cell not in (0, 1):
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return False
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# 验证行约束
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for i in range(n):
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row_sum = sum((j + 1) * cell for j, cell in enumerate(solution[i]))
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if row_sum != row_targets[i]:
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return False
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# 验证列约束
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for j in range(n):
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col_sum = sum((i + 1) * solution[i][j] for i in range(n))
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if col_sum != col_targets[j]:
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return False
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return True
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