戒酒的李白
178 lines
6.7 KiB
Python
178 lines
6.7 KiB
Python
"""
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This class is used to test the init and fit functions of the TopicGPT class
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"""
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import os
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import sys
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import inspect
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currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
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parentdir = os.path.dirname(currentdir)
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sys.path.insert(0, f"{parentdir}/src")
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from topicgpt.TopicGPT import TopicGPT
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sys.path.insert(0, parentdir)
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import openai
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import pickle
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import unittest
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from src.topicgpt.TopicRepresentation import Topic
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from src.topicgpt.Clustering import Clustering_and_DimRed
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from src.topicgpt.TopwordEnhancement import TopwordEnhancement
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from src.topicgpt.TopicPrompting import TopicPrompting
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class TestTopicGPT_init_and_fit(unittest.TestCase):
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"""
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Test the init and fit functions of the TopicGPT class
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"""
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@classmethod
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def setUpClass(cls, sample_size = 0.1):
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"""
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load the necessary data and only keep a sample of it
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"""
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print("Setting up class...")
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cls.api_key_openai = os.environ.get('api_key')
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# TODO: The 'openai.organization' option isn't read in the client API. You will need to pass it when you instantiate the client, e.g. 'OpenAI(organization=os.environ.get('OPENAI_ORG'))'
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# openai.organization = os.environ.get('OPENAI_ORG')
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with open("Data/Emebeddings/embeddings_20ng_raw.pkl", "rb") as f:
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data_raw = pickle.load(f)
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corpus = data_raw["corpus"]
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doc_embeddings = data_raw["embeddings"]
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n_docs = int(len(corpus) * sample_size)
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cls.corpus = corpus[:n_docs]
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cls.doc_embeddings = doc_embeddings[:n_docs]
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print("Using {} out of {} documents".format(n_docs, len(data_raw["corpus"])))
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with open("Data/Emebeddings/embeddings_20ng_vocab.pkl", "rb") as f:
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cls.embeddings_vocab = pickle.load(f)
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def test_init(self):
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"""
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test the init function of the TopicGPT class
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"""
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print("Testing init...")
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topicgpt = TopicGPT(api_key = self.api_key_openai)
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self.assertTrue(isinstance(topicgpt, TopicGPT))
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topicgpt = TopicGPT(api_key = self.api_key_openai,
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n_topics= 20)
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self.assertTrue(isinstance(topicgpt, TopicGPT))
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topicgpt = TopicGPT(api_key = self.api_key_openai,
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n_topics= 20,
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corpus_instruction="This is a corpus instruction",
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document_embeddings = self.doc_embeddings,
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vocab_embeddings= self.embeddings_vocab)
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self.assertTrue(isinstance(topicgpt, TopicGPT))
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# check if assertions are triggered
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with self.assertRaises(AssertionError):
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topicgpt = TopicGPT(api_key = None,
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n_topics= 32,
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openai_prompting_model="gpt-4",
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max_number_of_tokens=8000,
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corpus_instruction="This is a corpus instruction")
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with self.assertRaises(AssertionError):
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topicgpt = TopicGPT(api_key = self.api_key_openai,
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n_topics= 0,
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max_number_of_tokens=8000,
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corpus_instruction="This is a corpus instruction")
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with self.assertRaises(AssertionError):
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topicgpt = TopicGPT(api_key = self.api_key_openai,
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n_topics= 20,
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max_number_of_tokens=0,
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corpus_instruction="This is a corpus instruction")
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def test_fit(self):
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"""
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test the fit function of the TopicGPT class
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"""
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print("Testing fit...")
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def instance_test(topicgpt):
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topicgpt.fit(self.corpus)
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self.assertTrue(hasattr(topicgpt, "vocab"))
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self.assertTrue(hasattr(topicgpt, "topic_lis"))
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self.assertTrue(isinstance(topicgpt.vocab, list))
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self.assertTrue(isinstance(topicgpt.vocab[0], str))
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self.assertTrue(isinstance(topicgpt.topic_lis, list))
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try:
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self.assertTrue(type(topicgpt.topic_lis[0]) == Topic)
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except AssertionError as e:
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print(e)
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print(type(topicgpt.topic_lis[0]))
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print(topicgpt.topic_lis[0])
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if topicgpt.n_topics is not None:
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self.assertTrue(len(topicgpt.topic_lis) == topicgpt.n_topics)
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self.assertTrue(topicgpt.topic_lis == topicgpt.topic_prompting.topic_lis)
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self.assertTrue(topicgpt.vocab == topicgpt.topic_prompting.vocab)
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self.assertTrue(topicgpt.vocab_embeddings == topicgpt.topic_prompting.vocab_embeddings)
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topicgpt1 = TopicGPT(api_key = self.api_key_openai,
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n_topics= 20,
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document_embeddings = self.doc_embeddings,
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vocab_embeddings = self.embeddings_vocab)
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topicgpt2 = TopicGPT(api_key = self.api_key_openai,
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n_topics= None,
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document_embeddings = self.doc_embeddings,
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vocab_embeddings = self.embeddings_vocab)
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topicgpt3 = TopicGPT(api_key=self.api_key_openai,
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n_topics = 1,
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document_embeddings = self.doc_embeddings,
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vocab_embeddings = self.embeddings_vocab,
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n_topwords=10,
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n_topwords_description=10,
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topword_extraction_methods=["cosine_similarity"])
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clusterer4 = Clustering_and_DimRed(
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n_dims_umap = 10,
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n_neighbors_umap = 20,
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min_cluster_size_hdbscan = 10,
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number_clusters_hdbscan= 10 # use only 10 clusters
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)
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topword_enhancement4 = TopwordEnhancement(api_key = self.api_key_openai)
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topic_prompting4 = TopicPrompting(
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api_key = self.api_key_openai,
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enhancer = topword_enhancement4,
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topic_lis = None
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)
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topicgpt4 = TopicGPT(api_key=self.api_key_openai,
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n_topics= None,
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document_embeddings = self.doc_embeddings,
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vocab_embeddings = self.embeddings_vocab,
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topic_prompting = topic_prompting4,
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clusterer = clusterer4,
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topword_extraction_methods=["tfidf"])
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topic_gpt_list = [topicgpt1, topicgpt2, topicgpt3, topicgpt4]
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for topic_gpt in topic_gpt_list:
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instance_test(topic_gpt)
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if __name__ == "__main__":
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unittest.main() |