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