| | 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)) |
| |
|
