| |
|
| | 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] |
| |
|
