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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('model', [('kmeans_pca_topic_model'), ('custom_topic_model'), ('merged_topic_model'), ('reduced_topic_model'), ('online_topic_model')]) |
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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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