Add BERTopic.

LLMTopicDetection_BERTopic/tests/test_plotting/test_approximate.py

@@ -0,0 +1,34 @@
+import copy
+import pytest
+
+
+@pytest.mark.parametrize("batch_size", [50, None])
+@pytest.mark.parametrize("padding", [True, False])
+@pytest.mark.parametrize(
+    "model",
+    [
+        ("kmeans_pca_topic_model"),
+        ("base_topic_model"),
+        ("custom_topic_model"),
+        ("merged_topic_model"),
+        ("reduced_topic_model"),
+    ],
+)
+def test_approximate_distribution(batch_size, padding, model, documents, request):
+    topic_model = copy.deepcopy(request.getfixturevalue(model))
+
+    # Calculate only on a document-level based on tokensets
+    topic_distr, _ = topic_model.approximate_distribution(documents, padding=padding, batch_size=batch_size)
+    assert topic_distr.shape[1] == len(topic_model.topic_labels_) - topic_model._outliers
+
+    # Use the distribution visualization
+    for i in range(3):
+        topic_model.visualize_distribution(topic_distr[i])
+
+    # Calculate distribution on a token-level
+    topic_distr, topic_token_distr = topic_model.approximate_distribution(documents[:100], calculate_tokens=True)
+    assert topic_distr.shape[1] == len(topic_model.topic_labels_) - topic_model._outliers
+    assert len(topic_token_distr) == len(documents[:100])
+
+    for token_distr in topic_token_distr:
+        assert token_distr.shape[1] == len(topic_model.topic_labels_) - topic_model._outliers