戒酒的李白
35 lines
1.3 KiB
Python
35 lines
1.3 KiB
Python
import copy
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import pytest
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@pytest.mark.parametrize("batch_size", [50, None])
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@pytest.mark.parametrize("padding", [True, False])
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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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("base_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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],
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)
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def test_approximate_distribution(batch_size, padding, model, documents, request):
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topic_model = copy.deepcopy(request.getfixturevalue(model))
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# Calculate only on a document-level based on tokensets
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topic_distr, _ = topic_model.approximate_distribution(documents, padding=padding, batch_size=batch_size)
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assert topic_distr.shape[1] == len(topic_model.topic_labels_) - topic_model._outliers
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# Use the distribution visualization
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for i in range(3):
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topic_model.visualize_distribution(topic_distr[i])
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# Calculate distribution on a token-level
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topic_distr, topic_token_distr = topic_model.approximate_distribution(documents[:100], calculate_tokens=True)
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assert topic_distr.shape[1] == len(topic_model.topic_labels_) - topic_model._outliers
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assert len(topic_token_distr) == len(documents[:100])
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for token_distr in topic_token_distr:
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assert token_distr.shape[1] == len(topic_model.topic_labels_) - topic_model._outliers
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