Supervised topic model generating different topics to training data

I am trying to run a supervised topic model, but when i look at the results the model produces topic numbers that are different to those that i trained it on. Am I misunderstanding something. I thought the supervised model would produce the exact same results as the training data - i appreciate that results for test data will depend on the accuracy of the model.

Here is some sample code to show the problem.

# Note: I have a dataframe "combined_df_clean_doc_info" that contains the training docs and target topic numbers.

# Import the relevant libraries

from bertopic.vectorizers import ClassTfidfTransformer
from bertopic.dimensionality import BaseDimensionalityReduction
from sklearn.linear_model import LogisticRegression

 # Get the data for training the supervised model - the documents and the topic numbers

training_titles = combined_df_clean_doc_info["Document"].to_list()
training_topic_numbers = combined_df_clean_doc_info["Topic"].to_list()

# Skip over dimensionality reduction, replace cluster model with classifier,
# and reduce frequent words while we are at it.

empty_dimensionality_model = BaseDimensionalityReduction()
clf = LogisticRegression()
ctfidf_model = ClassTfidfTransformer(reduce_frequent_words=True)

# Create a fully supervised BERTopic instance
manual_topic_model= BERTopic(
        umap_model=empty_dimensionality_model,
        hdbscan_model=clf,
        ctfidf_model=ctfidf_model
)

topic = manual_topic_model.fit_transform(training_titles, y=training_topic_numbers)

Now I look to compare the model generated topic numbers with the original topic numbers:

pd.DataFrame({"training_title": training_titles, #i.e. the training titles
"training_topic_number": training_topic_numbers, #i.e. the training topics
"model_topic_title": manual_topic_model.get_document_info(training_titles)["Document"],
"model_topic_number": manual_topic_model.get_document_info(training_titles)["Topic"]})

Gives:

training_title				training_topic_number	model_topic_title			model_topic_number
0 !!CALL OSHA!! Oregon Amazon warehouse workers		4	!!CALL OSHA!! Oregon Amazon warehouse workers		5
1 " She described physical “misery” from walking...	4	" She described physical “misery” from walking...	5
2 "#PrimeDay makes this one of the most dangerou...	4	"#PrimeDay makes this one of the most dangerou...	5
3 "...Amazon workers say intense focus on speed ...	4	"...Amazon workers say intense focus on speed ...	5
4 "50 to 100" Amazon workers are trapped under r...	4	"50 to 100" Amazon workers are trapped under r...	5
...	...	...	...	...
8490 “It’s sheer slavery” Amazon warehouse worker i...	2	“It’s sheer slavery” Amazon warehouse worker i...	3
8491 “I’m an Amazon Warehouse Worker. This Crisis I...	2	“I’m an Amazon Warehouse Worker. This Crisis I...	3
8492 “The Only Amazon Prime Day Guide You’ll Need” ...	2	“The Only Amazon Prime Day Guide You’ll Need” ...	3
8493 “Why don’t you get a job at Amazon instead?”	-1	“Why don’t you get a job at Amazon instead?”		-1
8494 米Amazonの倉庫作業員8人が新型コロナで死亡		2	米Amazonの倉庫作業員8人が新型コロナで死亡			3

The reason behind doing all this, is that i am analyzing social media (Reddit) data (on Amazon). The data is full of re-posts that distort my clusters. So I generate unique posts before modelling. However i also want to look at (and topic model) the comments that flow from posts in each post-cluster. Some of those comments sit in re-posts that were initially excluded. So what i am trying to do here is generate the topic numbers for the full data (including the re-posts). The steps are essentially: Clean the data to get unique documents, model the topics (unsupervised), use the topics derived to generate a classifier (supervised), run the classifier on the whole dataset (i.e. including re-posts). My assumption was that all the training data would be correctly categorized and so would any "test data" that is identical to the training data.

What the above is showing me, however, is that the model is generating different topic classifications to the data it was trained on. This means that the "test" data won't be classified correctly. Is this expected behavior?

Start by reproducing the supplied Python example with BERTopic.fit_transform and get_document_info, using the shown LogisticRegression classifier and training labels. Trace how topic numbers are assigned and compare the returned labels with the input labels; done means the expected behavior and any required correction are clearly established.