Add BERTopic.

LLMTopicDetection_BERTopic/tests/test_sub_models/test_dim_reduction.py

@@ -0,0 +1,40 @@
+import copy
+import pytest
+import numpy as np
+from umap import UMAP
+from sklearn.decomposition import PCA
+
+from bertopic import BERTopic
+
+
+@pytest.mark.parametrize("dim_model", [UMAP, PCA])
+@pytest.mark.parametrize(
+    "embeddings,shape,n_components",
+    [
+        (np.random.rand(100, 128), 100, 5),
+        (np.random.rand(10, 256), 10, 5),
+        (np.random.rand(50, 15), 50, 10),
+    ],
+)
+def test_reduce_dimensionality(dim_model, embeddings, shape, n_components):
+    model = BERTopic(umap_model=dim_model(n_components=n_components))
+    umap_embeddings = model._reduce_dimensionality(embeddings)
+    assert umap_embeddings.shape == (shape, n_components)
+
+
+@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_custom_reduce_dimensionality(model, request):
+    embeddings = np.random.rand(500, 128)
+    topic_model = copy.deepcopy(request.getfixturevalue(model))
+    umap_embeddings = topic_model._reduce_dimensionality(embeddings)
+    assert umap_embeddings.shape[1] < embeddings.shape[1]