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transform method handle a single document with provided embedidngs #2043

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transform method handle a single document with provided embedidngs #2043

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8 changes: 6 additions & 2 deletions bertopic/_bertopic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -497,10 +497,14 @@ def transform(self,
```
"""
check_is_fitted(self)
check_embeddings_shape(embeddings, documents)


if isinstance(documents, str) or documents is None:
documents = [documents]

if len(documents) == 1 and isinstance(embeddings, np.ndarray) and embeddings.ndim == 1:
embeddings = embeddings.reshape(1,-1)
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The embeddings that you can pass to .transform is typed as np.ndarray which is actually quite misleading (sorry!) as it can technically take the form of any iterable (but mostly just np.ndarray-like structures, such as a scipy sparse matrix). As such, doing something like embeddings.ndim == 1 might break here.


check_embeddings_shape(embeddings, documents)

if embeddings is None:
embeddings = self._extract_embeddings(documents,
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24 changes: 24 additions & 0 deletions tests/test_bertopic.py
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Expand Up @@ -138,3 +138,27 @@ def test_full_model(model, documents, request):

assert len(merged_model.get_topic_info()) > len(topic_model1.get_topic_info())
assert len(merged_model.get_topic_info()) > len(topic_model.get_topic_info())

def test_transform_flexibility(documents, document_embeddings, request):

topic_model = copy.deepcopy(request.getfixturevalue('base_topic_model'))
print(document_embeddings[0].shape)
try:
topic_model.transform(documents[0], document_embeddings[0])
except ValueError:
pytest.fail('Error thrown for transform with single document and embeddings')

try:
topic_model.transform(documents[0:2], document_embeddings[0:2])
except ValueError:
pytest.fail('Error thrown for transform with multiple documents and embeddings')

with pytest.raises(ValueError):
topic_model.transform(documents[0], document_embeddings[0:2])

with pytest.raises(ValueError):
topic_model.transform(documents[0:2], document_embeddings[0])

with pytest.raises(ValueError):
topic_model.transform(documents[0], [1, 2, 3])