reasoning-gym/tests/test_word_sorting.py
2025-02-14 12:57:31 +01:00

118 lines
4.1 KiB
Python

"""Tests for word sorting task generation"""
import pytest
from reasoning_gym.algorithmic.word_sorting import TextTransformation, WordSortingConfig, WordSortingDataset
def test_word_sorting_config_validation():
"""Test that invalid configs raise appropriate errors"""
with pytest.raises(AssertionError):
config = WordSortingConfig(min_words=0)
config.validate()
with pytest.raises(AssertionError):
config = WordSortingConfig(min_words=10, max_words=5)
config.validate()
with pytest.raises(AssertionError):
config = WordSortingConfig(min_word_length=0)
config.validate()
with pytest.raises(AssertionError):
config = WordSortingConfig(min_word_length=10, max_word_length=5)
config.validate()
def test_word_sorting_dataset_deterministic():
"""Test that dataset generates same items with same seed"""
config = WordSortingConfig(seed=42, size=10)
dataset1 = WordSortingDataset(config)
dataset2 = WordSortingDataset(config)
for i in range(len(dataset1)):
assert dataset1[i] == dataset2[i]
def test_word_sorting_transformations():
"""Test different text transformations"""
seed = 42
size = 5
# Test LOWERCASE
config = WordSortingConfig(transformation=TextTransformation.LOWERCASE, seed=seed, size=size)
dataset = WordSortingDataset(config)
for item in dataset:
for word in item["metadata"]["transformed_words"]:
if word.isalpha(): # Only test alphabetic strings
assert word.islower()
# Test UPPERCASE
config = WordSortingConfig(transformation=TextTransformation.UPPERCASE, seed=seed, size=size)
dataset = WordSortingDataset(config)
for item in dataset:
for word in item["metadata"]["transformed_words"]:
if word.isalpha(): # Only test alphabetic strings
assert word.isupper()
# Test ORIGINAL
config = WordSortingConfig(transformation=TextTransformation.ORIGINAL, seed=seed, size=size)
dataset = WordSortingDataset(config)
for item in dataset:
assert item["metadata"]["original_words"] == item["metadata"]["transformed_words"]
def test_word_sorting_dataset_items():
"""Test basic properties of generated items"""
config = WordSortingConfig(min_words=3, max_words=6, min_word_length=3, max_word_length=8, size=10, seed=42)
dataset = WordSortingDataset(config)
for i in range(len(dataset)):
item = dataset[i]
# Check item structure
assert isinstance(item, dict)
assert "question" in item
assert "answer" in item
assert "metadata" in item
# Check metadata
assert "original_words" in item["metadata"]
assert "transformed_words" in item["metadata"]
assert "direction" in item["metadata"]
assert "transformation" in item["metadata"]
assert "sorted_words" in item["metadata"]
# Verify word count constraints
words = item["metadata"]["transformed_words"]
assert len(words) >= config.min_words
assert len(words) <= config.max_words
# Verify word length constraints
for word in words:
assert len(word) >= config.min_word_length
assert len(word) <= config.max_word_length
# Verify sorting
direction = item["metadata"]["direction"]
sorted_words = item["answer"].split(", ")
if direction == "ascending":
assert sorted_words == sorted(sorted_words)
else:
assert sorted_words == sorted(sorted_words, reverse=True)
# Test the scoring
assert dataset.score_answer(answer=item["answer"], entry=item) == 1.0
assert dataset.score_answer(answer="gibberish", entry=item) == 0.01
assert dataset.score_answer(answer=None, entry=item) == 0.0
def test_word_sorting_dataset_iteration():
"""Test that iteration respects dataset size"""
config = WordSortingConfig(size=5, seed=42)
dataset = WordSortingDataset(config)
items = list(dataset)
assert len(items) == config.size
# Test multiple iterations yield same items
assert items == list(dataset)