戒酒的李白
39 lines
1.3 KiB
Python
39 lines
1.3 KiB
Python
import copy
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import pytest
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from bertopic.vectorizers import OnlineCountVectorizer
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@pytest.mark.parametrize(
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"model",
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[
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("kmeans_pca_topic_model"),
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("custom_topic_model"),
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("merged_topic_model"),
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("reduced_topic_model"),
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("online_topic_model"),
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],
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)
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def test_online_cv(model, documents, request):
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topic_model = copy.deepcopy(request.getfixturevalue(model))
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vectorizer_model = OnlineCountVectorizer(stop_words="english", ngram_range=(2, 2))
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topics = [topic_model.get_topic(topic) for topic in set(topic_model.topics_)]
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topic_model.update_topics(documents, vectorizer_model=vectorizer_model)
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new_topics = [topic_model.get_topic(topic) for topic in set(topic_model.topics_)]
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for old_topic, new_topic in zip(topics, new_topics):
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if old_topic[0][0] != "":
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assert old_topic != new_topic
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@pytest.mark.parametrize("model", [("online_topic_model")])
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def test_clean_bow(model, request):
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topic_model = copy.deepcopy(request.getfixturevalue(model))
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original_shape = topic_model.vectorizer_model.X_.shape
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topic_model.vectorizer_model.delete_min_df = 2
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topic_model.vectorizer_model._clean_bow()
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assert original_shape[0] == topic_model.vectorizer_model.X_.shape[0]
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assert original_shape[1] > topic_model.vectorizer_model.X_.shape[1]
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