| import copy |
| import pytest |
| from sklearn.datasets import fetch_20newsgroups |
|
|
| data = fetch_20newsgroups(subset="all", remove=('headers', 'footers', 'quotes')) |
| classes = [data["target_names"][i] for i in data["target"]][:1000] |
|
|
|
|
| @pytest.mark.parametrize('model', [('kmeans_pca_topic_model'), ('custom_topic_model'), ('merged_topic_model'), ('reduced_topic_model'), ('online_topic_model')]) |
| def test_class(model, documents, request): |
| topic_model = copy.deepcopy(request.getfixturevalue(model)) |
| topics_per_class_global = topic_model.topics_per_class(documents, classes=classes, global_tuning=True) |
| topics_per_class_local = topic_model.topics_per_class(documents, classes=classes, global_tuning=False) |
|
|
| assert topics_per_class_global.Frequency.sum() == len(documents) |
| assert topics_per_class_local.Frequency.sum() == len(documents) |
| assert set(topics_per_class_global.Topic.unique()) == set(topic_model.topics_) |
| assert set(topics_per_class_local.Topic.unique()) == set(topic_model.topics_) |
| assert len(topics_per_class_global.Class.unique()) == len(set(classes)) |
| assert len(topics_per_class_local.Class.unique()) == len(set(classes)) |
|
|
