mirror of
https://github.com/open-thought/reasoning-gym.git
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160 lines
5.2 KiB
Python
160 lines
5.2 KiB
Python
import random
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from dataclasses import dataclass
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from typing import Optional
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@dataclass
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class ChainSumConfig:
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"""Configuration for chain sum task generation"""
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min_terms: int = 2
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max_terms: int = 6
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min_digits: int = 1
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max_digits: int = 4
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allow_negation: bool = False
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seed: Optional[int] = None
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size: int = 500
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def validate(self):
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"""Validate configuration parameters"""
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assert self.min_terms > 0, "min_terms must be positive"
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assert self.max_terms >= self.min_terms, "max_terms must be >= min_terms"
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assert self.min_digits > 0, "min_digits must be positive"
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assert self.max_digits >= self.min_digits, "max_digits must be >= min_digits"
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# Validate digit ranges make sense
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if self.min_digits > 1:
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assert 10 ** (self.min_digits - 1) >= 1, "min_digits would result in invalid number range"
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class ChainSum:
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"""Generates simple arithmetic tasks using only + and - operators"""
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def __init__(self, config: ChainSumConfig):
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self.config = config
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self.config.validate()
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# Generate base seed if none provided
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self.seed = config.seed if config.seed is not None else random.randint(0, 2**32)
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def __len__(self) -> int:
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return self.config.size
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def __getitem__(self, idx: int) -> dict:
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"""Generate a single chain sum task
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Args:
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idx: Index of the item to generate
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Returns:
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dict with keys:
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- question: str, the formatted arithmetic expression
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- answer: str, the ground truth result
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- metadata: dict with generation parameters
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"""
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# Create deterministic RNG from base seed and idx
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item_rng = random.Random(self.seed + idx)
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num_terms = item_rng.randint(self.config.min_terms, self.config.max_terms)
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num_digits = item_rng.randint(self.config.min_digits, self.config.max_digits)
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# Calculate value ranges based on number of digits
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min_value = 0 if num_digits == 1 else 10 ** (num_digits - 1) # Special case for 1 digit
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max_value = (10**num_digits) - 1 # e.g., 999 for 3 digits
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expression, result = self._generate_task(item_rng, num_terms, min_value, max_value)
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return {
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"question": f"{expression} =",
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"answer": str(result),
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"metadata": {
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"num_terms": num_terms,
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"num_digits": num_digits,
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"expression": expression,
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},
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}
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def __iter__(self):
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"""Make the dataset iterable"""
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self._current_idx = 0
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return self
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def __next__(self):
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"""Get next item in iteration"""
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if self._current_idx >= self.config.size:
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raise StopIteration
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item = self[self._current_idx]
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self._current_idx += 1
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return item
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def _generate_task(self, rng: random.Random, num_terms: int, min_value: int, max_value: int) -> tuple[str, int]:
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"""Generate a chain sum task
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Args:
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rng: Random number generator
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num_terms: Number of terms in the expression
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min_value: Minimum value for generated numbers
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max_value: Maximum value for generated numbers
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Returns:
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Tuple of (expression string, result integer)
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"""
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if self.config.allow_negation:
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# Allow both positive and negative numbers in the range
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constants = [rng.randint(-max_value, max_value) for _ in range(num_terms)]
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else:
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# Only positive numbers
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constants = [rng.randint(min_value, max_value) for _ in range(num_terms)]
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operators = [rng.choice(["+", "-"]) for _ in range(num_terms - 1)]
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# Build expression and compute result
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expression_parts = []
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result = constants[0]
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expression_parts.append(str(constants[0]))
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for i, op in enumerate(operators):
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c = constants[i + 1]
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expression_parts.append(op)
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expression_parts.append(str(c))
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if op == "+":
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result += c
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else: # op == "-"
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result -= c
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expression = " ".join(expression_parts)
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return expression, result
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def chain_sum_dataset(
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min_terms: int = 2,
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max_terms: int = 6,
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min_digits: int = 1,
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max_digits: int = 4,
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allow_negation: bool = False,
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seed: Optional[int] = None,
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size: int = 500,
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) -> ChainSum:
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"""Create a ChainSum dataset with the given configuration.
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Args:
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min_terms: Minimum number of terms in expressions
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max_terms: Maximum number of terms in expressions
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min_digits: Minimum number of digits in numbers
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max_digits: Maximum number of digits in numbers
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allow_negation: Whether to allow negative numbers
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seed: Random seed for reproducibility
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size: Virtual size of the dataset
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Returns:
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ChainSum: Configured dataset instance
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"""
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config = ChainSumConfig(
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min_terms=min_terms,
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max_terms=max_terms,
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min_digits=min_digits,
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max_digits=max_digits,
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allow_negation=allow_negation,
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seed=seed,
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size=size,
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)
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return ChainSum(config)
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