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https://github.com/open-thought/reasoning-gym.git
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77 lines
2.6 KiB
Python
77 lines
2.6 KiB
Python
import pytest
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from reasoning_gym.probability.conditional_probability import (
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ConditionalProbabilityConfig,
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ConditionalProbabilityCurriculum,
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ConditionalProbabilityDataset,
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)
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def test_config_validation():
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with pytest.raises(AssertionError):
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config = ConditionalProbabilityConfig(min_total_items=1)
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config.validate()
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with pytest.raises(AssertionError):
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config = ConditionalProbabilityConfig(min_total_items=20, max_total_items=5)
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config.validate()
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def test_deterministic():
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config = ConditionalProbabilityConfig(seed=42, size=10)
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ds1 = ConditionalProbabilityDataset(config)
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ds2 = ConditionalProbabilityDataset(config)
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for i in range(len(ds1)):
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assert ds1[i] == ds2[i]
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def test_item_structure():
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config = ConditionalProbabilityConfig(seed=42, size=50)
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ds = ConditionalProbabilityDataset(config)
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for i in range(len(ds)):
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item = ds[i]
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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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assert item["metadata"]["source_dataset"] == "conditional_probability"
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def test_answer_correctness():
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config = ConditionalProbabilityConfig(seed=42, size=50)
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ds = ConditionalProbabilityDataset(config)
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for i in range(len(ds)):
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item = ds[i]
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score = ds.score_answer(item["answer"], item)
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assert score >= 1.0, f"Item {i}: oracle answer scored {score}"
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def test_score_wrong_answer():
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config = ConditionalProbabilityConfig(seed=42, size=10)
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ds = ConditionalProbabilityDataset(config)
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item = ds[0]
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assert ds.score_answer(None, item) == 0.0
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assert ds.score_answer("not a fraction", item) == 0.0
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def test_curriculum():
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curriculum = ConditionalProbabilityCurriculum()
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base_value = {"size": 50, "seed": 1}
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base_cfg = curriculum.generate_configuration(base_value)
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assert base_cfg.seed == 1
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assert base_cfg.size == 50
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curriculum.increment_attr_level("total_items")
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increased_cfg = curriculum.generate_configuration(base_value)
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assert increased_cfg.max_total_items >= base_cfg.max_total_items
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def test_task_types():
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for task_type in ("bayes", "dependent_draws", "contingency_table"):
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config = ConditionalProbabilityConfig(seed=42, size=10, task_types=(task_type,), task_weights=[1.0])
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ds = ConditionalProbabilityDataset(config)
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for i in range(len(ds)):
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item = ds[i]
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assert item["metadata"]["task_type"] == task_type
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score = ds.score_answer(item["answer"], item)
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assert score >= 1.0, f"Task {task_type}, item {i}: oracle scored {score}"
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