戒酒的李白
127 lines
3.6 KiB
Python
127 lines
3.6 KiB
Python
import copy
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import pytest
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import numpy as np
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import pandas as pd
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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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("online_topic_model"),
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],
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)
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def test_get_topic(model, request):
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topic_model = copy.deepcopy(request.getfixturevalue(model))
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topics = [topic_model.get_topic(topic) for topic in set(topic_model.topics_)]
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unknown_topic = topic_model.get_topic(500)
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for topic in topics:
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assert topic is not False
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assert len(topics) == len(topic_model.get_topic_info())
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assert not unknown_topic
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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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("online_topic_model"),
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],
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)
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def test_get_topics(model, request):
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topic_model = copy.deepcopy(request.getfixturevalue(model))
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topics = topic_model.get_topics()
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assert topics == topic_model.topic_representations_
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assert len(topics.keys()) == len(set(topic_model.topics_))
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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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("online_topic_model"),
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],
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)
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def test_get_topic_freq(model, request):
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topic_model = copy.deepcopy(request.getfixturevalue(model))
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for topic in set(topic_model.topics_):
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assert not isinstance(topic_model.get_topic_freq(topic), pd.DataFrame)
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topic_freq = topic_model.get_topic_freq()
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unique_topics = set(topic_model.topics_)
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topics_in_mapper = set(np.array(topic_model.topic_mapper_.mappings_)[:, -1])
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assert isinstance(topic_freq, pd.DataFrame)
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assert len(topic_freq) == len(set(topic_model.topics_))
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assert len(topics_in_mapper.difference(unique_topics)) == 0
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assert len(unique_topics.difference(topics_in_mapper)) == 0
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@pytest.mark.parametrize(
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"model",
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[
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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_get_representative_docs(model, request):
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topic_model = copy.deepcopy(request.getfixturevalue(model))
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all_docs = topic_model.get_representative_docs()
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unique_topics = set(topic_model.topics_)
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topics_in_mapper = set(np.array(topic_model.topic_mapper_.mappings_)[:, -1])
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assert len(all_docs) == len(topic_model.topic_sizes_.keys())
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assert len(all_docs) == len(topics_in_mapper)
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assert len(all_docs) == topic_model.c_tf_idf_.shape[0]
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assert len(all_docs) == len(topic_model.topic_labels_)
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assert all([True if len(docs) == 3 else False for docs in all_docs.values()])
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topics = set(list(all_docs.keys()))
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assert len(topics.difference(unique_topics)) == 0
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assert len(topics.difference(topics_in_mapper)) == 0
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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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("online_topic_model"),
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],
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)
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def test_get_topic_info(model, request):
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topic_model = copy.deepcopy(request.getfixturevalue(model))
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info = topic_model.get_topic_info()
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if topic_model._outliers:
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assert info.iloc[0].Topic == -1
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else:
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assert info.iloc[0].Topic == 0
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for topic in set(topic_model.topics_):
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assert len(topic_model.get_topic_info(topic)) == 1
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assert len(topic_model.get_topic_info(200)) == 0
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