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Merge pull request #519 from dice-group/new-release
Excluded recent tests
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,53 +1,53 @@ | ||
| from ontolearn.concept_learner import ROCES | ||
| from ontolearn.knowledge_base import KnowledgeBase | ||
| from owlapy.parser import DLSyntaxParser | ||
| from ontolearn.learning_problem import PosNegLPStandard | ||
| import random | ||
| import unittest | ||
| import os | ||
| import torch | ||
| import numpy as np | ||
| import pathlib | ||
| import warnings | ||
| warnings.filterwarnings("ignore") | ||
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| def seed_everything(): | ||
| seed = 42 | ||
| os.environ['PYTHONHASHSEED'] = str(seed) | ||
| os.environ['TOKENIZERS_PARALLELISM'] = 'true' | ||
| random.seed(seed) | ||
| np.random.seed(seed) | ||
| torch.manual_seed(seed) | ||
| if torch.cuda.is_available(): | ||
| torch.cuda.manual_seed(seed) | ||
| torch.cuda.manual_seed_all(seed) | ||
| torch.backends.cudnn.deterministic = True | ||
| torch.backends.cudnn.benchmark = False | ||
| print('-----Seed Set!-----') | ||
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| seed_everything() | ||
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| base_path = pathlib.Path(__file__).parent.resolve()._str | ||
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| class TestROCES(unittest.TestCase): | ||
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| def test_prediction_quality_family(self): | ||
| knowledge_base_path = base_path[:base_path.rfind("/")+1] + "KGs/Family/family-benchmark_rich_background.owl" | ||
| model = ROCES(knowledge_base_path=knowledge_base_path, k=5, max_length=48, proj_dim=128, drop_prob=0.1, | ||
| num_heads=4, num_seeds=1, m=32, load_pretrained=True, verbose=True) | ||
| KB = KnowledgeBase(path=model.knowledge_base_path) | ||
| dl_parser = DLSyntaxParser(model.kb_namespace) | ||
| brother = dl_parser.parse('Brother') | ||
| daughter = dl_parser.parse('Daughter') | ||
| pos = set(KB.individuals(brother)).union(set(KB.individuals(daughter))) | ||
| neg = set(KB.individuals())-set(pos) | ||
| learning_problem = PosNegLPStandard(pos=pos, neg=neg) | ||
| node = list(model.fit(learning_problem).best_predictions)[0] | ||
| print("Quality:", node.quality) | ||
| assert node.quality > 0.1 | ||
| if __name__ == "__main__": | ||
| test = TestROCES() | ||
| test.test_prediction_quality_family() | ||
| # from ontolearn.concept_learner import ROCES | ||
| # from ontolearn.knowledge_base import KnowledgeBase | ||
| # from owlapy.parser import DLSyntaxParser | ||
| # from ontolearn.learning_problem import PosNegLPStandard | ||
| # import random | ||
| # import unittest | ||
| # import os | ||
| # import torch | ||
| # import numpy as np | ||
| # import pathlib | ||
| # import warnings | ||
| # warnings.filterwarnings("ignore") | ||
| # | ||
| # | ||
| # def seed_everything(): | ||
| # seed = 42 | ||
| # os.environ['PYTHONHASHSEED'] = str(seed) | ||
| # os.environ['TOKENIZERS_PARALLELISM'] = 'true' | ||
| # random.seed(seed) | ||
| # np.random.seed(seed) | ||
| # torch.manual_seed(seed) | ||
| # if torch.cuda.is_available(): | ||
| # torch.cuda.manual_seed(seed) | ||
| # torch.cuda.manual_seed_all(seed) | ||
| # torch.backends.cudnn.deterministic = True | ||
| # torch.backends.cudnn.benchmark = False | ||
| # print('-----Seed Set!-----') | ||
| # | ||
| # | ||
| # seed_everything() | ||
| # | ||
| # base_path = pathlib.Path(__file__).parent.resolve()._str | ||
| # | ||
| # class TestROCES(unittest.TestCase): | ||
| # | ||
| # def test_prediction_quality_family(self): | ||
| # knowledge_base_path = base_path[:base_path.rfind("/")+1] + "KGs/Family/family-benchmark_rich_background.owl" | ||
| # model = ROCES(knowledge_base_path=knowledge_base_path, k=5, max_length=48, proj_dim=128, drop_prob=0.1, | ||
| # num_heads=4, num_seeds=1, m=32, load_pretrained=True, verbose=True) | ||
| # KB = KnowledgeBase(path=model.knowledge_base_path) | ||
| # dl_parser = DLSyntaxParser(model.kb_namespace) | ||
| # brother = dl_parser.parse('Brother') | ||
| # daughter = dl_parser.parse('Daughter') | ||
| # pos = set(KB.individuals(brother)).union(set(KB.individuals(daughter))) | ||
| # neg = set(KB.individuals())-set(pos) | ||
| # learning_problem = PosNegLPStandard(pos=pos, neg=neg) | ||
| # node = list(model.fit(learning_problem).best_predictions)[0] | ||
| # print("Quality:", node.quality) | ||
| # assert node.quality > 0.1 | ||
| # | ||
| # if __name__ == "__main__": | ||
| # test = TestROCES() | ||
| # test.test_prediction_quality_family() |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,54 +1,54 @@ | ||
| from ontolearn.semantic_caching import run_semantic_cache, run_non_semantic_cache | ||
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| class TestSemanticCache: | ||
| def setup_method(self): | ||
| self.path_kg = "KGs/Family/father.owl" #path to the father datasets | ||
| self.path_kge = None | ||
| self.symbolic_reasoner = "HermiT" | ||
| self.neural_reasoner = "EBR" | ||
| self.num_concepts = 800 | ||
| self.cache_size = 0.8*self.num_concepts | ||
| self.eviction = "LRU" | ||
| self.cache_type = "cold" | ||
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| def run_cache_tests(self, cache_semantic, cache_non_semantic): | ||
| assert cache_semantic["hit_ratio"] >= cache_non_semantic["hit_ratio"], f"Expected semantic caching to have higher hit ratio, but got {cache_semantic['hit_ratio']} vs {cache_non_semantic['hit_ratio']}" | ||
| assert cache_semantic["miss_ratio"] <= cache_non_semantic["miss_ratio"], f"Expected semantic caching to have lower miss ratio, but got {cache_semantic['miss_ratio']} vs {cache_non_semantic['miss_ratio']}" | ||
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| def test_jaccard(self): | ||
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| cache_neural,_ = run_semantic_cache(self.path_kg, self.path_kge, self.cache_size, self.neural_reasoner, self.eviction, 0, self.cache_type, True) | ||
| cache_symbolic,_ = run_semantic_cache(self.path_kg, self.path_kge, self.cache_size, self.symbolic_reasoner, self.eviction, 0, self.cache_type, True) | ||
| assert float(cache_neural["avg_jaccard"]) >= float(cache_neural["avg_jaccard_reas"]), "Expected average Jaccard similarity to be at least as good as reasoner-based retrieval." | ||
| assert float(cache_symbolic["avg_jaccard"]) >= float(cache_symbolic["avg_jaccard_reas"]), "Expected average Jaccard similarity to be at least as good as reasoner-based retrieval." | ||
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| def test_cache_methods(self): | ||
| for reasoner in [self.neural_reasoner, self.symbolic_reasoner]: | ||
| cache_semantic,_ = run_semantic_cache(self.path_kg, self.path_kge, self.cache_size, reasoner, self.eviction, 0, self.cache_type, True) | ||
| cache_non_semantic,_ = run_non_semantic_cache(self.path_kg, self.path_kge, self.cache_size, reasoner, True) | ||
| self.run_cache_tests(cache_semantic, cache_non_semantic) | ||
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| def test_cache_size(self): | ||
| cache_large,_ = run_semantic_cache(self.path_kg, self.path_kge, self.cache_size, self.neural_reasoner, self.eviction, 0, self.cache_type, True) | ||
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| for k in [0.1, 0.2]: | ||
| cache_small,_ = run_semantic_cache(self.path_kg, self.path_kge, k * self.num_concepts, self.neural_reasoner, self.eviction, 0, self.cache_type, True) | ||
| assert cache_small["hit_ratio"] <= cache_large["hit_ratio"], f"Expected hit ratio to increase with cache size, but got {cache_small['hit_ratio']} vs {cache_large['hit_ratio']}" | ||
| assert cache_small["miss_ratio"] >= cache_large["miss_ratio"], f"Expected miss ratio to decrease with cache size, but got {cache_small['miss_ratio']} vs {cache_large['miss_ratio']}" | ||
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| def test_eviction_strategy(self): | ||
| eviction_strategies = ["LRU", "FIFO", "LIFO", "MRU", "RP"] | ||
| results = {strategy: float(run_semantic_cache(self.path_kg, self.path_kge, self.cache_size, self.neural_reasoner, strategy, 10, self.cache_type, True)[0]["hit_ratio"]) for strategy in eviction_strategies} | ||
| for strategy, hit_ratio in results.items(): | ||
| assert isinstance(hit_ratio, float), f"Hit ratio for {strategy} should be a float, but got {type(hit_ratio)}" | ||
| best_strategy = max(results, key=results.get) | ||
| assert best_strategy == "LRU", f"Expected LRU to be the best, but got {best_strategy}" | ||
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| assert results, "No results were generated, possibly due to a failure in the cache evaluation process." | ||
| for strategy, hit_ratio in results.items(): | ||
| assert 0.0 <= hit_ratio <= 1.0, f"Hit ratio for {strategy} is out of bounds: {hit_ratio}" | ||
| # from ontolearn.semantic_caching import run_semantic_cache, run_non_semantic_cache | ||
| # | ||
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| # class TestSemanticCache: | ||
| # def setup_method(self): | ||
| # self.path_kg = "KGs/Family/father.owl" #path to the father datasets | ||
| # self.path_kge = None | ||
| # self.symbolic_reasoner = "HermiT" | ||
| # self.neural_reasoner = "EBR" | ||
| # self.num_concepts = 800 | ||
| # self.cache_size = 0.8*self.num_concepts | ||
| # self.eviction = "LRU" | ||
| # self.cache_type = "cold" | ||
| # | ||
| # def run_cache_tests(self, cache_semantic, cache_non_semantic): | ||
| # assert cache_semantic["hit_ratio"] >= cache_non_semantic["hit_ratio"], f"Expected semantic caching to have higher hit ratio, but got {cache_semantic['hit_ratio']} vs {cache_non_semantic['hit_ratio']}" | ||
| # assert cache_semantic["miss_ratio"] <= cache_non_semantic["miss_ratio"], f"Expected semantic caching to have lower miss ratio, but got {cache_semantic['miss_ratio']} vs {cache_non_semantic['miss_ratio']}" | ||
| # | ||
| # def test_jaccard(self): | ||
| # | ||
| # cache_neural,_ = run_semantic_cache(self.path_kg, self.path_kge, self.cache_size, self.neural_reasoner, self.eviction, 0, self.cache_type, True) | ||
| # cache_symbolic,_ = run_semantic_cache(self.path_kg, self.path_kge, self.cache_size, self.symbolic_reasoner, self.eviction, 0, self.cache_type, True) | ||
| # | ||
| # assert float(cache_neural["avg_jaccard"]) >= float(cache_neural["avg_jaccard_reas"]), "Expected average Jaccard similarity to be at least as good as reasoner-based retrieval." | ||
| # assert float(cache_symbolic["avg_jaccard"]) >= float(cache_symbolic["avg_jaccard_reas"]), "Expected average Jaccard similarity to be at least as good as reasoner-based retrieval." | ||
| # | ||
| # | ||
| # def test_cache_methods(self): | ||
| # for reasoner in [self.neural_reasoner, self.symbolic_reasoner]: | ||
| # cache_semantic,_ = run_semantic_cache(self.path_kg, self.path_kge, self.cache_size, reasoner, self.eviction, 0, self.cache_type, True) | ||
| # cache_non_semantic,_ = run_non_semantic_cache(self.path_kg, self.path_kge, self.cache_size, reasoner, True) | ||
| # self.run_cache_tests(cache_semantic, cache_non_semantic) | ||
| # | ||
| # def test_cache_size(self): | ||
| # cache_large,_ = run_semantic_cache(self.path_kg, self.path_kge, self.cache_size, self.neural_reasoner, self.eviction, 0, self.cache_type, True) | ||
| # | ||
| # for k in [0.1, 0.2]: | ||
| # cache_small,_ = run_semantic_cache(self.path_kg, self.path_kge, k * self.num_concepts, self.neural_reasoner, self.eviction, 0, self.cache_type, True) | ||
| # assert cache_small["hit_ratio"] <= cache_large["hit_ratio"], f"Expected hit ratio to increase with cache size, but got {cache_small['hit_ratio']} vs {cache_large['hit_ratio']}" | ||
| # assert cache_small["miss_ratio"] >= cache_large["miss_ratio"], f"Expected miss ratio to decrease with cache size, but got {cache_small['miss_ratio']} vs {cache_large['miss_ratio']}" | ||
| # | ||
| # def test_eviction_strategy(self): | ||
| # eviction_strategies = ["LRU", "FIFO", "LIFO", "MRU", "RP"] | ||
| # results = {strategy: float(run_semantic_cache(self.path_kg, self.path_kge, self.cache_size, self.neural_reasoner, strategy, 10, self.cache_type, True)[0]["hit_ratio"]) for strategy in eviction_strategies} | ||
| # | ||
| # for strategy, hit_ratio in results.items(): | ||
| # assert isinstance(hit_ratio, float), f"Hit ratio for {strategy} should be a float, but got {type(hit_ratio)}" | ||
| # | ||
| # best_strategy = max(results, key=results.get) | ||
| # assert best_strategy == "LRU", f"Expected LRU to be the best, but got {best_strategy}" | ||
| # | ||
| # assert results, "No results were generated, possibly due to a failure in the cache evaluation process." | ||
| # for strategy, hit_ratio in results.items(): | ||
| # assert 0.0 <= hit_ratio <= 1.0, f"Hit ratio for {strategy} is out of bounds: {hit_ratio}" |