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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))
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