Add BERTopic.

LLMTopicDetection_BERTopic/tests/test_reduction/test_delete.py

@@ -0,0 +1,59 @@
+import copy
+import pytest
+
+
+@pytest.mark.parametrize(
+    "model",
+    [
+        ("kmeans_pca_topic_model"),
+        ("base_topic_model"),
+        ("custom_topic_model"),
+        ("merged_topic_model"),
+        ("reduced_topic_model"),
+        ("online_topic_model"),
+    ],
+)
+def test_delete(model, request):
+    topic_model = copy.deepcopy(request.getfixturevalue(model))
+    nr_topics = len(set(topic_model.topics_))
+    length_documents = len(topic_model.topics_)
+
+    # First deletion
+    topics_to_delete = [1, 2]
+    topic_model.delete_topics(topics_to_delete)
+    mappings = topic_model.topic_mapper_.get_mappings(list(topic_model.hdbscan_model.labels_))
+    mapped_labels = [mappings[label] for label in topic_model.hdbscan_model.labels_]
+
+    if model == "online_topic_model" or model == "kmeans_pca_topic_model":
+        assert nr_topics == len(set(topic_model.topics_)) + 1
+        assert topic_model.get_topic_info().Count.sum() == length_documents
+    else:
+        assert nr_topics == len(set(topic_model.topics_)) + 2
+        assert topic_model.get_topic_info().Count.sum() == length_documents
+
+    if model == "online_topic_model":
+        assert mapped_labels == topic_model.topics_[950:]
+    else:
+        assert mapped_labels == topic_model.topics_
+
+    # Find two existing topics for second deletion
+    remaining_topics = sorted(list(set(topic_model.topics_)))
+    remaining_topics = [t for t in remaining_topics if t != -1]  # Exclude outlier topic
+    topics_to_delete = remaining_topics[:2]  # Take first two remaining topics
+
+    # Second deletion
+    topic_model.delete_topics(topics_to_delete)
+    mappings = topic_model.topic_mapper_.get_mappings(list(topic_model.hdbscan_model.labels_))
+    mapped_labels = [mappings[label] for label in topic_model.hdbscan_model.labels_]
+
+    if model == "online_topic_model" or model == "kmeans_pca_topic_model":
+        assert nr_topics == len(set(topic_model.topics_)) + 3
+        assert topic_model.get_topic_info().Count.sum() == length_documents
+    else:
+        assert nr_topics == len(set(topic_model.topics_)) + 4
+        assert topic_model.get_topic_info().Count.sum() == length_documents
+
+    if model == "online_topic_model":
+        assert mapped_labels == topic_model.topics_[950:]
+    else:
+        assert mapped_labels == topic_model.topics_

LLMTopicDetection_BERTopic/tests/test_reduction/test_merge.py

@@ -0,0 +1,42 @@
+import copy
+import pytest
+
+
+@pytest.mark.parametrize(
+    "model",
+    [
+        ("kmeans_pca_topic_model"),
+        ("base_topic_model"),
+        ("custom_topic_model"),
+        ("merged_topic_model"),
+        ("reduced_topic_model"),
+        ("online_topic_model"),
+    ],
+)
+def test_merge(model, documents, request):
+    topic_model = copy.deepcopy(request.getfixturevalue(model))
+    nr_topics = len(set(topic_model.topics_))
+
+    topics_to_merge = [1, 2]
+    topic_model.merge_topics(documents, topics_to_merge)
+    mappings = topic_model.topic_mapper_.get_mappings(list(topic_model.hdbscan_model.labels_))
+    mapped_labels = [mappings[label] for label in topic_model.hdbscan_model.labels_]
+
+    assert nr_topics == len(set(topic_model.topics_)) + 1
+    assert topic_model.get_topic_info().Count.sum() == len(documents)
+    if model == "online_topic_model":
+        assert mapped_labels == topic_model.topics_[950:]
+    else:
+        assert mapped_labels == topic_model.topics_
+
+    topics_to_merge = [1, 2]
+    topic_model.merge_topics(documents, topics_to_merge)
+    mappings = topic_model.topic_mapper_.get_mappings(list(topic_model.hdbscan_model.labels_))
+    mapped_labels = [mappings[label] for label in topic_model.hdbscan_model.labels_]
+
+    assert nr_topics == len(set(topic_model.topics_)) + 2
+    assert topic_model.get_topic_info().Count.sum() == len(documents)
+    if model == "online_topic_model":
+        assert mapped_labels == topic_model.topics_[950:]
+    else:
+        assert mapped_labels == topic_model.topics_