mirror of
https://github.com/open-thought/reasoning-gym.git
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3f6b2fc807
* feat: Add Coach and ScoreBoard classes for performance tracking and difficulty adjustment * feat: Add GroupedScores class to wrap aggregated scores * refactor: Create ScoreStats class with tuple-based score statistics * feat: Add unit test for Coach with CompositeDataset and multiple datasets * fix: Add difficulty metadata to leg counting dataset * feat: Add clear() method to ScoreBoard to reset all stored data * feat: Add __len__ method to ScoreBoard to return number of scores * feat: Add update_dataset_config method to CompositeDataset * cleanup __init__ & imports
58 lines
2 KiB
Python
58 lines
2 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 ..data import read_data_file
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from ..factory import ProceduralDataset, register_dataset
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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 = rng.randint(self.config.min_words, self.config.max_words)
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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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question = ", ".join(words)
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answer = ", ".join(reversed(words))
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return {
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"question": f"Reverse this list of words: {question}",
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"answer": answer,
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"metadata": {"num_words": num_words, "words": words},
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}
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register_dataset("word_sequence_reversal", WordSequenceReversalDataset, WordSequenceReversalConfig)
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