mirror of
https://github.com/open-thought/reasoning-gym.git
synced 2026-04-19 12:58:07 +00:00
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).
65 lines
2 KiB
Python
65 lines
2 KiB
Python
import pytest
|
|
|
|
from reasoning_gym.optimization.linear_programming import (
|
|
LinearProgrammingConfig,
|
|
LinearProgrammingCurriculum,
|
|
LinearProgrammingDataset,
|
|
)
|
|
|
|
|
|
def test_config_validation():
|
|
with pytest.raises(AssertionError):
|
|
config = LinearProgrammingConfig(min_coeff=0)
|
|
config.validate()
|
|
|
|
with pytest.raises(AssertionError):
|
|
config = LinearProgrammingConfig(num_constraints=1)
|
|
config.validate()
|
|
|
|
|
|
def test_deterministic():
|
|
config = LinearProgrammingConfig(seed=42, size=10)
|
|
ds1 = LinearProgrammingDataset(config)
|
|
ds2 = LinearProgrammingDataset(config)
|
|
for i in range(len(ds1)):
|
|
assert ds1[i] == ds2[i]
|
|
|
|
|
|
def test_item_structure():
|
|
config = LinearProgrammingConfig(seed=42, size=50)
|
|
ds = LinearProgrammingDataset(config)
|
|
for i in range(len(ds)):
|
|
item = ds[i]
|
|
assert isinstance(item, dict)
|
|
assert "question" in item
|
|
assert "answer" in item
|
|
assert "metadata" in item
|
|
assert item["metadata"]["source_dataset"] == "linear_programming"
|
|
|
|
|
|
def test_answer_correctness():
|
|
config = LinearProgrammingConfig(seed=42, size=50)
|
|
ds = LinearProgrammingDataset(config)
|
|
for i in range(len(ds)):
|
|
item = ds[i]
|
|
score = ds.score_answer(item["answer"], item)
|
|
assert score >= 1.0, f"Item {i}: oracle answer scored {score}"
|
|
|
|
|
|
def test_score_wrong_answer():
|
|
config = LinearProgrammingConfig(seed=42, size=10)
|
|
ds = LinearProgrammingDataset(config)
|
|
item = ds[0]
|
|
assert ds.score_answer(None, item) == 0.0
|
|
assert ds.score_answer("not a number", item) == 0.0
|
|
|
|
|
|
def test_curriculum():
|
|
curriculum = LinearProgrammingCurriculum()
|
|
base_value = {"size": 50, "seed": 1}
|
|
base_cfg = curriculum.generate_configuration(base_value)
|
|
assert base_cfg.seed == 1
|
|
|
|
curriculum.increment_attr_level("num_constraints")
|
|
increased_cfg = curriculum.generate_configuration(base_value)
|
|
assert increased_cfg.num_constraints >= base_cfg.num_constraints
|