def test_categories_above_threshold():
ct = classifytext.ClassifyText()
p = {'a': 0.2, 'b': 0.49, 'c': 0.60}
res = ct.categories_above_threshold(p, 0.3)
assert set(res) == set(['b', 'c'])
res = ct.categories_above_threshold(p, 0.61)
assert ['Other'] == res
def test_probability_per_category():
ct = classifytext.ClassifyText()
p = ct.probability_per_category('This is a random text, shoppen, winkelen')
assert isinstance(p, dict)
for category, prob in p.items():
assert isinstance(category, str)
assert isinstance(prob, float)
def test_category_with_threshold():
ct = classifytext.ClassifyText()
p = {'a': 0.2, 'b': 0.49}
res = ct.category_with_threshold(p, 0.49)
assert 'b' == res
assert isinstance(res, str)
res = ct.category_with_threshold(p, 0.5)
assert 'Other' == res
assert isinstance(res, str)
def test_init():
ct = classifytext.ClassifyText()
assert isinstance(ct.mm, sgdc_modelmanager.SGDCModelManager)
assert isinstance(ct.mm.clf, Pipeline)
assert isinstance(ct.mm.clf.named_steps['clf'], SGDClassifier)
def test_predict():
ct = classifytext.ClassifyText()
p = ct.predict('This is a random text, shoppen, winkelen')
assert isinstance(p, ndarray)
assert isinstance(p[0], str)
def test_init_mnb():
ct = classifytext.ClassifyText(type=classifytext.MNB)
assert isinstance(ct.mm, mnb_modelmanager.MNBModelManager)
assert isinstance(ct.mm.clf, Pipeline)