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* 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
82 lines
3 KiB
Python
82 lines
3 KiB
Python
"""Caesar cipher task generator"""
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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 reasoning_gym.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 CaesarCipherConfig:
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"""Configuration for Caesar cipher task generation"""
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delimiter: str = "." # Delimiter for splitting text into sentences
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min_words: int = 3 # Minimum words per sentence
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max_words: int = 20 # Maximum words per sentence
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min_rotation: int = 1 # Minimum Caesar rotation
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max_rotation: int = 25 # Maximum Caesar rotation
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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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assert 0 < self.min_rotation <= self.max_rotation < 26, "rotation must be in range [1,25]"
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class CaesarCipherDataset(ProceduralDataset):
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"""Generates Caesar cipher encryption/decryption tasks"""
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def __init__(self, config: CaesarCipherConfig):
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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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# Split into sentences and filter
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sentences = [s.strip() for s in text.split(config.delimiter) if s.strip()]
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# Process each sentence
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self.valid_sentences = []
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for sentence in sentences:
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# Split into words and filter for alpha-only
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words = [w.upper() for w in sentence.split() if w.isalpha()]
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if self.config.min_words <= len(words) <= self.config.max_words:
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self.valid_sentences.append(" ".join(words))
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def _caesar_encrypt(self, text: str, rotation: int) -> str:
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"""Apply Caesar cipher encryption with given rotation"""
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result = []
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for char in text:
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if char.isalpha():
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# Convert to 0-25 range, rotate, convert back to ASCII
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base = ord("A")
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rotated = (ord(char) - base + rotation) % 26
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result.append(chr(base + rotated))
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else:
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result.append(char)
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return "".join(result)
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def __getitem__(self, idx: int) -> dict:
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"""Generate a single Caesar cipher task"""
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rng = Random(self.seed + idx)
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# Select random sentence and rotation
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sentence = rng.choice(self.valid_sentences)
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rotation = rng.randint(self.config.min_rotation, self.config.max_rotation)
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# Generate cipher text
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cipher_text = self._caesar_encrypt(sentence, rotation)
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return {
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"question": f"Decrypt this Caesar cipher text: {cipher_text}",
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"answer": sentence,
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"metadata": {"rotation": rotation, "cipher_text": cipher_text, "clear_text": sentence},
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}
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register_dataset("caesar_cipher", CaesarCipherDataset, CaesarCipherConfig)
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