Fix topic_sizes_ not updated in zero-shot topic modeling (#2384)

bertopic/_bertopic.py

@@ -505,6 +505,9 @@ def fit_transform(
             documents = assigned_documents
             embeddings = assigned_embeddings
 
+            # Update topic_sizes_ when all documents are assigned to zero-shot topics
+            self._update_topic_size(documents)
+
         # Sort and Map Topic IDs by their frequency
         if not self.nr_topics:
             documents = self._sort_mappings_by_frequency(documents)

docs/changelog.md

@@ -5,6 +5,12 @@ hide:
 
 # Changelog
 
+## **Unreleased**
+
+<h3><b>Fixes:</a></b></h3>
+
+* Fix `topic_sizes_` not being updated in zero-shot topic modeling when using `nr_topics` parameter ([#2384](https://github.com/MaartenGr/BERTopic/issues/2384))
+
 ## **Version 0.17.3**
 *Release date: 8 July, 2025*
 

tests/test_variations/test_zeroshot.py

@@ -0,0 +1,185 @@
+"""
+Tests for zero-shot topic modeling functionality.
+
+This module tests various aspects of zero-shot topic modeling, including
+edge cases with the nr_topics parameter and topic_sizes_ consistency.
+"""
+
+from bertopic import BERTopic
+from umap import UMAP
+
+
+def test_zeroshot_with_nr_topics():
+    """Test zero-shot topic modeling with nr_topics parameter."""
+    docs = [
+        "This is about machine learning and artificial intelligence",
+        "Deep learning neural networks are powerful",
+        "Python programming for data science",
+        "Machine learning algorithms and models",
+        "Artificial intelligence and deep learning",
+        "Data science with Python programming",
+        "Neural networks and machine learning",
+        "Programming in Python for AI",
+        "Deep learning models and algorithms",
+        "Artificial intelligence programming",
+    ]
+
+    zeroshot_topics = ["Technology and Programming"]
+
+    topic_model = BERTopic(
+        zeroshot_topic_list=zeroshot_topics, zeroshot_min_similarity=0.1, nr_topics=2, min_topic_size=2
+    )
+
+    topics, probs = topic_model.fit_transform(docs)
+
+    # Verify topic_sizes_ is properly populated
+    assert topic_model.topic_sizes_ is not None
+    assert len(topic_model.topic_sizes_) > 0
+
+    # Verify total document count matches
+    total_in_sizes = sum(topic_model.topic_sizes_.values())
+    assert total_in_sizes == len(docs)
+
+    # Verify all topics are accounted for
+    for topic in set(topics):
+        assert topic in topic_model.topic_sizes_
+
+
+def test_zeroshot_all_documents_assigned():
+    """Test edge case where all documents are assigned to zero-shot topics."""
+    docs = [
+        "Technology is advancing rapidly",
+        "Software development is important",
+        "Programming languages are evolving",
+        "Computer science research continues",
+        "Digital transformation is happening",
+        "Innovation in technology sector",
+        "Software engineering best practices",
+        "Modern programming techniques",
+        "Computer systems and architecture",
+        "Digital solutions and platforms",
+        "Technology trends and developments",
+        "Software design patterns",
+        "Programming paradigms evolution",
+        "Computing infrastructure advances",
+        "Digital innovation strategies",
+    ]
+
+    zeroshot_topics = ["Technology"]
+    umap_model = UMAP(n_neighbors=5, n_components=2, min_dist=0.0, metric="cosine", random_state=42)
+
+    topic_model = BERTopic(
+        zeroshot_topic_list=zeroshot_topics,
+        zeroshot_min_similarity=0.05,
+        nr_topics=2,
+        min_topic_size=1,
+        umap_model=umap_model,
+    )
+
+    topics, probs = topic_model.fit_transform(docs)
+
+    # Verify all documents are accounted for
+    total_in_sizes = sum(topic_model.topic_sizes_.values())
+    assert total_in_sizes == len(docs)
+    assert topic_model.topic_sizes_ is not None
+
+
+def test_zeroshot_topic_info_consistency():
+    """Test consistency between topic_sizes_ and get_topic_info()."""
+    docs = [
+        "AI and machine learning research",
+        "Deep learning neural networks",
+        "Neural network architectures",
+        "Machine learning algorithms",
+        "Artificial intelligence systems",
+        "Deep learning models training",
+        "Neural network optimization",
+        "Machine learning applications",
+        "AI research and development",
+        "Deep learning frameworks",
+    ]
+    zeroshot_topics = ["Artificial Intelligence"]
+    umap_model = UMAP(n_neighbors=5, n_components=2, min_dist=0.0, metric="cosine", random_state=42)
+
+    topic_model = BERTopic(
+        zeroshot_topic_list=zeroshot_topics,
+        zeroshot_min_similarity=0.1,
+        nr_topics=2,
+        min_topic_size=1,
+        umap_model=umap_model,
+    )
+
+    topics, probs = topic_model.fit_transform(docs)
+
+    # Verify topic info consistency
+    topic_info = topic_model.get_topic_info()
+    assert not topic_info.empty
+    assert topic_info.shape[0] > 0
+
+    # Verify topic_sizes_ and topic_info are consistent
+    topic_info_counts = dict(zip(topic_info.Topic, topic_info.Count))
+    for topic_id, count in topic_model.topic_sizes_.items():
+        assert topic_id in topic_info_counts
+        assert topic_info_counts[topic_id] == count
+
+
+def test_github_issue_2384_reproduction():
+    """Test exact reproduction case from GitHub issue #2384."""
+    # Exact reproduction case from GitHub issue #2384
+    docs = ["I need help with my voucher", "Gift card not working", "Customer service was poor"] * 50
+    zeroshot_topics = ["Voucher inquiries", "Gift card issues", "Customer service feedback"]
+
+    model = BERTopic(
+        zeroshot_topic_list=zeroshot_topics,
+        zeroshot_min_similarity=-1,
+        nr_topics=4,
+    )
+
+    topics, _ = model.fit_transform(docs)
+
+    # Verify the fix
+    assert model.topic_sizes_ is not None
+    assert len(model.topic_sizes_) > 0
+
+    # Verify get_topic_info() works
+    topic_info = model.get_topic_info()
+    assert not topic_info.empty
+    assert topic_info.shape[0] > 0
+
+    # Verify total document count matches
+    total_docs_in_sizes = sum(model.topic_sizes_.values())
+    assert total_docs_in_sizes == len(docs)
+
+    # Verify topic_representations_ still works (no regression)
+    assert model.topic_representations_ is not None
+    assert len(model.topic_representations_) > 0
+
+
+def test_zeroshot_nr_topics_consistency():
+    """Test consistency between using nr_topics and not using it."""
+    docs = ["I need help with my voucher", "Gift card not working", "Customer service was poor"] * 20
+    zeroshot_topics = ["Voucher inquiries", "Gift card issues", "Customer service feedback"]
+
+    # Test without nr_topics
+    model_without = BERTopic(zeroshot_topic_list=zeroshot_topics, zeroshot_min_similarity=-1)
+    topics_without, _ = model_without.fit_transform(docs)
+
+    # Test with nr_topics
+    model_with = BERTopic(zeroshot_topic_list=zeroshot_topics, zeroshot_min_similarity=-1, nr_topics=4)
+    topics_with, _ = model_with.fit_transform(docs)
+
+    # Both should have properly populated topic_sizes_
+    assert model_without.topic_sizes_ is not None
+    assert model_with.topic_sizes_ is not None
+
+    # Both should have same total document count
+    total_without = sum(model_without.topic_sizes_.values())
+    total_with = sum(model_with.topic_sizes_.values())
+    assert total_without == len(docs)
+    assert total_with == len(docs)
+
+    # Both should have working get_topic_info()
+    info_without = model_without.get_topic_info()
+    info_with = model_with.get_topic_info()
+    assert not info_without.empty
+    assert not info_with.empty