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https://github.com/open-thought/reasoning-gym.git
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141 lines
4.8 KiB
Python
141 lines
4.8 KiB
Python
"""Perform average / max pooling on a matrix"""
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from dataclasses import dataclass
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from random import Random
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from typing import Any, Optional
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import numpy as np
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from ..factory import ProceduralDataset, register_dataset
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QUESTION_TEMPLATE = """Your job is to perform max/average pooling on the given matrix.
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The stride is equal to the kernel size, meaning there is no overlap between the pooling regions.
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Example 1:
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- Input: Perform max pooling on the following matrix with a kernel size of 2:
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1 2 3 4
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5 6 7 8
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9 10 11 12
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13 14 15 16
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- Output:
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6 8
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14 16
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Example 2:
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- Input: Perform average pooling on the following matrix with a kernel size of 2:
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1 2 3 4
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5 6 7 8
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9 10 11 12
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13 14 15 16
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- Output:
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3.5 5.5
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11.5 13.5
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Perform {pool_type} pooling on the following matrix with a kernel size of {pool_size}:
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{matrix}
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"""
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@dataclass
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class PoolMatrixConfig:
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"""Configuration for Pool Matrix dataset generation"""
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min_rows: int = 2 # Minimum rows of the matrix
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min_cols: int = 2 # Minimum columns of the matrix
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max_rows: int = 10 # Maximum rows of the matrix
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max_cols: int = 10 # Maximum columns of the matrix
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max_pool_size: int = 3 # Maximum pooling size
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size: int = 500 # Virtual dataset size
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seed: Optional[int] = None
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def validate(self):
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"""Validate configuration parameters"""
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assert 2 <= self.min_rows, "min_rows must be at least 2"
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assert 2 <= self.min_cols, "min_cols must be at least 2"
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assert self.min_rows <= self.max_rows, "max_rows must be at least min_rows"
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assert self.min_cols <= self.max_cols, "max_cols must be at least min_cols"
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assert 1 <= self.max_pool_size, "max_pool_size must be at least 1"
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class PoolMatrixDataset(ProceduralDataset):
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"""Generates Pool Matrix exercises with configurable difficulty"""
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def __init__(self, config: PoolMatrixConfig):
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super().__init__(config=config, seed=config.seed, size=config.size)
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def _get_matrix(self, rng: Random) -> np.ndarray:
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"""Generate a random matrix"""
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rows = rng.randint(self.config.min_rows, self.config.max_rows)
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cols = rng.randint(self.config.min_rows, self.config.max_cols)
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return np.random.randint(0, 10, (rows, cols))
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def _matrix_to_str(self, matrix: np.ndarray) -> str:
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"""Get a string representation of the matrix"""
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return "\n".join(" ".join(str(round(x, 2)) for x in row) for row in matrix)
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def _max_pool(self, matrix: np.ndarray, pool_size: int) -> np.ndarray:
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"""Perform max pooling on the matrix"""
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rows, cols = matrix.shape
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return np.array(
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[
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[np.max(matrix[i : i + pool_size, j : j + pool_size]) for j in range(0, cols, pool_size)]
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for i in range(0, rows, pool_size)
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]
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)
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def _average_pool(self, matrix: np.ndarray, pool_size: int) -> np.ndarray:
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"""Perform average pooling on the matrix"""
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rows, cols = matrix.shape
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return np.array(
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[
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[np.mean(matrix[i : i + pool_size, j : j + pool_size]) for j in range(0, cols, pool_size)]
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for i in range(0, rows, pool_size)
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]
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)
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def score_answer(self, answer: Optional[str], entry: dict[str, Any]) -> float:
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"""Score the answer based on the metadata"""
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reward = 0.0
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try:
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if answer is not None:
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oracle_answer = np.array(entry["answer"])
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answer = np.array(answer)
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if oracle_answer.shape == answer.shape and np.allclose(oracle_answer, answer):
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reward = 1.0
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if oracle_answer.shape == answer.shape:
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reward = 0.1
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else:
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reward = 0.01
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except:
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print("Error in scoring answer for Pool Matrix")
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return reward
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def __getitem__(self, idx: int) -> dict:
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"""Generate a single Pool Matrix question"""
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rng = Random(self.seed + idx)
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np.random.seed(self.seed + idx)
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matrix = self._get_matrix(rng)
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matrix_str = self._matrix_to_str(matrix)
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pool_size = rng.randint(1, self.config.max_pool_size)
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pool_type = rng.choice(["average", "max"])
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answer = self._average_pool(matrix, pool_size) if pool_type == "average" else self._max_pool(matrix, pool_size)
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answer_str = self._matrix_to_str(answer)
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return {
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"question": QUESTION_TEMPLATE.format(matrix=matrix_str, pool_type=pool_type, pool_size=pool_size),
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"answer": answer_str,
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"metadata": {
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"matrix": matrix.tolist(),
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"pool_type": pool_type,
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"pool_size": pool_size,
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"solution": answer.tolist(),
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},
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}
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register_dataset("pool_matrix", PoolMatrixDataset, PoolMatrixConfig)
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