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import copy |
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import pytest |
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from bertopic.vectorizers import OnlineCountVectorizer |
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@pytest.mark.parametrize('model', [('kmeans_pca_topic_model'), ('custom_topic_model'), ('merged_topic_model'), ('reduced_topic_model'), ('online_topic_model')]) |
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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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