mirror of
https://github.com/open-thought/reasoning-gym.git
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6eb252ae32
New dataset categories: combinatorics, statistics, optimization, and formal languages. Extended existing algebra, arithmetic, probability, logic, and graphs packages with complex_advanced, linear_algebra, limits, number_theory, conditional_probability, set_operations, and job_scheduling. Each dataset includes config validation, deterministic seeding, custom scoring, curriculum support, and comprehensive unit tests (92 new tests).
89 lines
2.7 KiB
Python
89 lines
2.7 KiB
Python
import pytest
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from reasoning_gym.algebra.linear_algebra import (
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LinearAlgebraConfig,
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LinearAlgebraCurriculum,
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LinearAlgebraDataset,
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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 = LinearAlgebraConfig(min_dim=1)
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config.validate()
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with pytest.raises(AssertionError):
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config = LinearAlgebraConfig(max_dim=5)
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config.validate()
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def test_deterministic():
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config = LinearAlgebraConfig(seed=42, size=10)
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ds1 = LinearAlgebraDataset(config)
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ds2 = LinearAlgebraDataset(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 = LinearAlgebraConfig(seed=42, size=50)
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ds = LinearAlgebraDataset(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"] == "linear_algebra"
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def test_answer_correctness():
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config = LinearAlgebraConfig(seed=42, size=50)
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ds = LinearAlgebraDataset(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_solve_system_verification():
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config = LinearAlgebraConfig(
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seed=42, size=20, task_types=("solve_system",), task_weights=[1.0]
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)
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ds = LinearAlgebraDataset(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
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def test_score_wrong_answer():
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config = LinearAlgebraConfig(seed=42, size=10)
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ds = LinearAlgebraDataset(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("totally wrong", item) == 0.0
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def test_curriculum():
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curriculum = LinearAlgebraCurriculum()
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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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curriculum.increment_attr_level("max_dim")
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increased_cfg = curriculum.generate_configuration(base_value)
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assert increased_cfg.max_dim >= base_cfg.max_dim
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def test_task_types():
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for task_type in ("matrix_multiply", "determinant", "inverse", "solve_system", "eigenvalues"):
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config = LinearAlgebraConfig(
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seed=42, size=10, task_types=(task_type,), task_weights=[1.0]
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)
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ds = LinearAlgebraDataset(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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