|
|
| 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] |
|
|
