def test_save_no_clf(mock_method):
mm = modelmanager.ModelManager()
mm.save(TEST_FILENAME)
assert mock_method.call_count == 0
def test_save(mock_method):
mm = modelmanager.ModelManager(filename='sgdcmodel.pickle')
mm.save(TEST_FILENAME)
assert mock_method.call_count == 1
def test_propabilities():
mm = modelmanager.ModelManager(filename='sgdcmodel.pickle')
prediction = mm.probabilities(['ssdfsaf dsfsadfds dsfsdafsd'])
assert prediction is not None
assert isinstance(prediction[0], ndarray)
def test_propabilities_no_clf():
mm = modelmanager.ModelManager()
prediction = mm.probabilities(['ssdfsaf dsfsadfds dsfsdafsd'])
assert prediction is None
def test_predict():
mm = modelmanager.ModelManager(filename='sgdcmodel.pickle')
prediction = mm.predict(['ssdfsaf dsfsadfds dsfsdafsd'])
assert prediction
assert isinstance(prediction[0], str)
def test_load_validationset(mock_method):
mm = modelmanager.ModelManager()
mm.load_validationset(TEST_FILENAME)
assert mock_method.call_count == 1
def test_load(filename='sgdcmodel.pickle'):
mm = modelmanager.ModelManager()
assert mm.clf is None
mm.clf = mm.load('sgdcmodel.pickle')
assert isinstance(mm.clf, Pipeline)
def test_init():
mm = modelmanager.ModelManager(filename='sgdcmodel.pickle')
assert mm.clf is not None
assert isinstance(mm.clf, Pipeline)
def test_init_no_file():
mm = modelmanager.ModelManager()
assert mm.clf is None