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