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