戒酒的李白
53 lines
1.5 KiB
Python
53 lines
1.5 KiB
Python
import copy
|
|
import pytest
|
|
|
|
|
|
@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_topics(model, request):
|
|
topic_model = copy.deepcopy(request.getfixturevalue(model))
|
|
fig = topic_model.visualize_topics()
|
|
for slider in fig.to_dict()["layout"]["sliders"]:
|
|
for step in slider["steps"]:
|
|
assert int(step["label"].split(" ")[-1]) != -1
|
|
|
|
fig = topic_model.visualize_topics(top_n_topics=5)
|
|
for slider in fig.to_dict()["layout"]["sliders"]:
|
|
for step in slider["steps"]:
|
|
assert int(step["label"].split(" ")[-1]) != -1
|
|
|
|
|
|
@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_topics_outlier(model, request):
|
|
topic_model = copy.deepcopy(request.getfixturevalue(model))
|
|
topic_model.topic_sizes_[-1] = 4
|
|
fig = topic_model.visualize_topics()
|
|
|
|
for slider in fig.to_dict()["layout"]["sliders"]:
|
|
for step in slider["steps"]:
|
|
assert int(step["label"].split(" ")[-1]) != -1
|
|
|
|
fig = topic_model.visualize_topics(top_n_topics=5)
|
|
for slider in fig.to_dict()["layout"]["sliders"]:
|
|
for step in slider["steps"]:
|
|
assert int(step["label"].split(" ")[-1]) != -1
|