mirror of
https://github.com/open-thought/reasoning-gym.git
synced 2026-04-19 12:58:07 +00:00
dced3bfc45
* init * fix tests * unify codeio * filtered for libraries not present in reasoning-gym * fix more bounds * puzzle24 * knight swap curriculum * fix number sorting * fix attributes * add validation of config in creation of dataset * dry run for instantiating and validating the datasets * remove unused imports * fix curriculum tests to reference newly updated attribute names
97 lines
3.1 KiB
Python
97 lines
3.1 KiB
Python
"""Word reversal task generator"""
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import re
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from dataclasses import dataclass
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from random import Random
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from typing import Optional
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from ..coaching import BaseCurriculum, RangeAttributeDefinition
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from ..data import read_data_file
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from ..factory import ProceduralDataset, register_dataset
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QUESTION_TEMPLATE = """Solve the following problem.
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Provide you answer as a comma-separated list of words with a space after the comma.
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Reverse this list of words: {words}
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"""
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DATASET_NAME = "word_sequence_reversal"
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@dataclass
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class WordSequenceReversalConfig:
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"""Configuration for word sequence reversal task generation"""
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min_words: int = 3 # Minimum words in list
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max_words: int = 8 # Maximum words in list
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seed: Optional[int] = None
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size: int = 500 # Virtual dataset size
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def validate(self) -> None:
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"""Validate configuration parameters"""
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assert self.min_words > 0, "min_words must be positive"
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assert self.max_words >= self.min_words, "max_words must be >= min_words"
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class WordSequenceReversalDataset(ProceduralDataset):
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"""Generates word sequence reversal tasks from text spans"""
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def __init__(self, config: WordSequenceReversalConfig):
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super().__init__(config=config, seed=config.seed, size=config.size)
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# Load and preprocess text
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text = read_data_file("in_the_year_2889.txt")
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# Extract words and clean them to contain only alphanumeric characters
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self.words = [word for word in re.findall(r"\b\w+\b", text) if word.isalnum()]
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def __getitem__(self, idx: int) -> dict:
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"""Generate a single word reversal task"""
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rng = Random(self.seed + idx)
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# Select random words
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num_words = min(
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rng.randint(self.config.min_words, self.config.max_words),
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len(self.words),
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)
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word_indices = rng.sample(range(len(self.words)), num_words)
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words = [self.words[i] for i in word_indices]
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# Create question and answer
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words_str = ", ".join(words)
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answer = ", ".join(reversed(words))
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return {
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"question": f"{QUESTION_TEMPLATE.format(words=words_str)}",
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"answer": answer,
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"metadata": {
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"source_dataset": DATASET_NAME,
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"source_index": idx,
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"num_words": num_words,
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"words": words,
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"difficulty": {
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"words": (self.config.min_words, self.config.max_words),
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},
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},
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}
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class WordSequenceReversalCurriculum(BaseCurriculum):
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def __init__(self):
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super().__init__(WordSequenceReversalCurriculum.__name__, WordSequenceReversalConfig)
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# Define attributes
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self._define_attributes(
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RangeAttributeDefinition(
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name="words",
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levels=[10, 25, 50, 100],
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description="Number of words in the list",
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lower_field_name="min_words",
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upper_field_name="max_words",
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ensure_interval=True,
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),
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)
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register_dataset(DATASET_NAME, WordSequenceReversalDataset, WordSequenceReversalConfig, WordSequenceReversalCurriculum)
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