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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
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