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