Visualisation after merge_models

Hi @MaartenGr ,

I am trying to visualise the topic after merging the models but getting an error. Could you please guide me to fix it?

Fitted model using two different datasets:

keybert = KeyBERTInspired()

mmr = MaximalMarginalRelevance(diversity=0.3)

representation_models = [keybert,mmr]

topic_model = BERTopic(language="english",
top_n_words=100,
verbose=True,
representation_model=representation_models,
vectorizer_model=CountVectorizer(ngram_range=(1, 10) , stop_words="english")
)
print("Fitting model")
topic_model.fit(docs)

This resulted in two models model1 & model2

loading models to merge
topic_model1 = BERTopic.load("model1")
topic_model2 = BERTopic.load("model2")

merging model
merged_model = BERTopic.merge_models([topic_model1, topic_model2], min_similarity=0.99)

Visualisation (topic over time):

timestamps = df['Date'].to_list()
topics_over_time = merged_model.topics_over_time(docs, timestamps, nr_bins=10, global_tuning = True, evolution_tuning = True)
topic_over_time=merged_model.visualize_topics_over_time(topics_over_time, topics=[1,2,5,7,14 ], title='', width=800, height=400, custom_labels=True)
topic_over_time.write_html('topics_over_time_test.html')

ERROR:
topics_over_time = merged_model.topics_over_time(docs, timestamps, nr_bins=10, global_tuning = True, evolution_tuning = True)
File ".conda\envs\bertopic2\lib\site-packages\bertopic_bertopic.py", line 768, in topics_over_time
global_c_tf_idf = normalize(self.c_tf_idf_, axis=1, norm='l1', copy=False)
File ".conda\envs\bertopic2\lib\site-packages\sklearn\utils_param_validation.py", line 204, in wrapper
validate_parameter_constraints(
File ".conda\envs\bertopic2\lib\site-packages\sklearn\utils_param_validation.py", line 96, in validate_parameter_constraints
raise InvalidParameterError(
sklearn.utils._param_validation.InvalidParameterError: The 'X' parameter of normalize must be an array-like or a sparse matrix. Got None instead.

Thanks

Start with BERTopic's merge_models and topics_over_time entry points, then inspect bertopic.py around line 768 where c_tf_idf is passed to sklearn's normalize. Reproduce the two-model merge and topic-over-time call from the issue, and determine how the merged model should provide the required data without raising this error.