mirror of
https://github.com/open-thought/reasoning-gym.git
synced 2026-04-19 12:58:07 +00:00
dced3bfc45
* init * fix tests * unify codeio * filtered for libraries not present in reasoning-gym * fix more bounds * puzzle24 * knight swap curriculum * fix number sorting * fix attributes * add validation of config in creation of dataset * dry run for instantiating and validating the datasets * remove unused imports * fix curriculum tests to reference newly updated attribute names
101 lines
3.1 KiB
Python
101 lines
3.1 KiB
Python
import json
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import pytest
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from reasoning_gym.algorithmic.graph_color import GraphColorConfig, GraphColorCurriculum, GraphColorDataset
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from reasoning_gym.coaching.base_curriculum import DefaultCurriculumContext, RangeAttributeMode
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def test_graph_color():
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"""Test basic properties and solution of generated items"""
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config = GraphColorConfig(
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seed=42,
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size=10,
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min_num_vertices=10,
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max_num_vertices=10,
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num_colors=4,
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edge_probability=0.4,
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)
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dataset = GraphColorDataset(config)
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# easy
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for item in dataset:
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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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# Test the scoring
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assert dataset.score_answer(answer=json.dumps(item["metadata"]["possible_answer"]), entry=item) == 1.0
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assert dataset.score_answer(answer=None, entry=item) == 0.0
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# medium
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config = GraphColorConfig(
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seed=42,
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size=1,
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min_num_vertices=10,
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max_num_vertices=10,
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num_colors=3,
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edge_probability=0.1,
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)
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dataset = GraphColorDataset(config)
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for item in dataset:
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assert dataset.score_answer(answer=json.dumps(item["metadata"]["possible_answer"]), entry=item) == 1.0
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assert dataset.score_answer(answer=None, entry=item) == 0.0
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# hard
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config = GraphColorConfig(
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seed=42,
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size=1,
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min_num_vertices=15,
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max_num_vertices=15,
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num_colors=3,
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edge_probability=0.1,
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)
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dataset = GraphColorDataset(config)
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for item in dataset:
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assert dataset.score_answer(answer=json.dumps(item["metadata"]["possible_answer"]), entry=item) == 1.0
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assert dataset.score_answer(answer=None, entry=item) == 0.0
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# v hard
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config = GraphColorConfig(
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seed=42,
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size=1,
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min_num_vertices=50,
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max_num_vertices=50,
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num_colors=3,
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edge_probability=0.1,
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)
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dataset = GraphColorDataset(config)
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for item in dataset:
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assert dataset.score_answer(answer=json.dumps(item["metadata"]["possible_answer"]), entry=item) == 1.0
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assert dataset.score_answer(answer=None, entry=item) == 0.0
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def test_graph_color_curriculum():
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curriculum = GraphColorCurriculum()
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base_value = {"size": 150, "seed": 1}
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context = DefaultCurriculumContext(mode=RangeAttributeMode.UPPER_BOUND)
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base_cfg: GraphColorConfig = curriculum.generate_configuration(base_value, context=context)
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assert base_cfg.size == 150
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assert base_cfg.seed == 1
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assert base_cfg.min_num_vertices == 6
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assert base_cfg.max_num_vertices == 10
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assert base_cfg.num_colors == 5
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curriculum.increment_attr_level("num_vertices")
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cfg = curriculum.generate_configuration(base_value, context=context)
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assert cfg.min_num_vertices == 10
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assert cfg.max_num_vertices == 20
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curriculum.increment_attr_level("num_colors")
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cfg = curriculum.generate_configuration(base_value)
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assert cfg.num_colors == 4
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curriculum.increment_attr_level("num_colors")
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cfg = curriculum.generate_configuration(base_value)
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assert cfg.num_colors == 3
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