reasoning-gym/reasoning_gym/arithmetic/power_function.py
Zafir Stojanovski a8c39ddcfb
Power Function (#102)
* power function dataset + tests
2025-02-10 22:04:58 +01:00

62 lines
2 KiB
Python

"""Computhe the power of a number."""
from dataclasses import dataclass
from math import pow
from random import Random
from typing import Dict, Optional
from ..factory import ProceduralDataset, register_dataset
QUESTION_TEMPLATE = """Compute {base}^{exponent}"""
@dataclass
class PowerFunctionConfig:
"""Configuration for Power Function dataset generation"""
min_base: float = -(10**6) # Minimum base value
max_base: float = 10**6 # Maximum base value
min_exponent: int = -50 # Minimum exponent value
max_exponent: int = 50 # Maximum exponent value
size: int = 500 # Virtual dataset size
seed: Optional[int] = None
class PowerFunctionDataset(ProceduralDataset):
"""Generates Power Function exercises with configurable difficulty"""
def __init__(self, config: PowerFunctionConfig):
super().__init__(config=config, seed=config.seed, size=config.size)
def score_answer(self, answer: Optional[str], entry: Dict[str, any]) -> float:
"""Overwrite this method in derived classes if a single oracle answer is not available."""
oracle_answer = entry["answer"]
reward = 0.0
if answer is not None:
difference = abs(float(answer) - float(oracle_answer))
if difference < 1e-6:
reward = 1.0
elif difference < 1e-1:
reward = 0.5
else:
reward = 0.01
return reward
def __getitem__(self, idx: int) -> dict:
"""Generate a single Power Function question"""
rng = Random(self.seed + idx)
base = rng.uniform(self.config.min_base, self.config.max_base)
exponent = rng.randint(self.config.min_exponent, self.config.max_exponent)
answer = pow(base, exponent)
return {
"question": f"Compute {base}^{exponent}",
"answer": str(answer),
"metadata": {"base": base, "exponent": exponent, "solution": answer},
}
register_dataset("power_function", PowerFunctionDataset, PowerFunctionConfig)