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use native types List->list, Dict->dict, Set->set, Tuple->tuple

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Andreas Koepf 2025-02-21 15:13:19 +01:00
parent 5d02064b5a
commit 3e7ff3b084
95 changed files with 754 additions and 760 deletions
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14
reasoning_gym/algorithmic/word_ladder.py
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@ -3,7 +3,7 @@
from collections import deque
from dataclasses import dataclass
from random import Random
from typing import Dict, List, Optional, Set, Tuple
from typing import Any, Optional
from ..data import get_data_file_path
from ..factory import ProceduralDataset, register_dataset
@ -82,7 +82,7 @@ class WordLadderDataset(ProceduralDataset):
super().__init__(config=config, seed=config.seed, size=config.size)
@classmethod
def _load_words_from_csv(cls, min_length: int = 3, max_length: int = 5) -> Dict[int, Set[str]]:
def _load_words_from_csv(cls, min_length: int = 3, max_length: int = 5) -> dict[int, set[str]]:
"""Load words from CSV file organized by length"""
# Validate length range before processing
assert 3 <= min_length <= max_length <= 5, "Word length must be between 3 and 5 inclusive"
@ -117,7 +117,7 @@ class WordLadderDataset(ProceduralDataset):
return word_sets
def _get_neighbors(self, word: str, word_set: Set[str]) -> Set[str]:
def _get_neighbors(self, word: str, word_set: set[str]) -> set[str]:
"""Get neighbors from either precomputed graph or by computing on demand"""
# Try precomputed graph first
if len(word) in self.word_graphs and word in self.word_graphs[len(word)]:
@ -132,7 +132,7 @@ class WordLadderDataset(ProceduralDataset):
neighbors.add(neighbor)
return neighbors
def _build_word_graph(self, word_length: int) -> Dict[str, Set[str]]:
def _build_word_graph(self, word_length: int) -> dict[str, set[str]]:
"""Build graph of word connections for given length, using caching"""
# Return cached graph if it exists
if word_length in self.word_graphs:
@ -156,7 +156,7 @@ class WordLadderDataset(ProceduralDataset):
self.word_graphs[word_length] = graph
return self.word_graphs[word_length]
def _find_path(self, start: str, end: str, word_set: Set[str]) -> Optional[List[str]]:
def _find_path(self, start: str, end: str, word_set: set[str]) -> Optional[list[str]]:
"""Simplified path finding using BFS for shortest paths"""
# Early exit if words are direct neighbors
if end in self._get_neighbors(start, word_set):
@ -181,7 +181,7 @@ class WordLadderDataset(ProceduralDataset):
return None
def _generate_word_pair(self, rng: Random, length: int) -> Tuple[str, str, List[str]]:
def _generate_word_pair(self, rng: Random, length: int) -> tuple[str, str, list[str]]:
"""Simplified word pair generation"""
word_set = self.word_sets[length]
words_list = sorted(word_set)
@ -220,7 +220,7 @@ class WordLadderDataset(ProceduralDataset):
"metadata": {"start_word": start, "end_word": end, "word_length": length, "chain_length": len(path)},
}
def score_answer(self, answer: Optional[str], entry: Dict[str, any]) -> float:
def score_answer(self, answer: Optional[str], entry: dict[str, Any]) -> float:
if answer is None:
return 0