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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('model', [('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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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('model', [('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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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('model', [('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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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('model', [('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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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('model', [('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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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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