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test_zaggy_model.py
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test_zaggy_model.py
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import unittest
from unittest import TestCase
from date_scaling import date_range
from mocks import make_mock
from zaggy_model import ZaggyModel
from numpy import ndarray
class TestZaggyModel(TestCase):
def setUp(self):
mock = make_mock()
self.y = mock['y']
num = len(self.y)
self.dates = date_range(2015, 1, 2029, 12)[0: num]
def test_zaggy_model_runs(self):
self.model = ZaggyModel(self.dates, self.y)
assert True
def test_zaggy_model_fit_runs(self):
self.model = ZaggyModel(self.dates, self.y)
self.assertIsNone(self.model.solution)
self.model.fit()
self.assertIsNotNone(self.model.solution)
self.assertIsNotNone(self.model.slope)
self.assertIsNotNone(self.model.offset)
self.assertIsNotNone(self.model.seasonal)
self.assertIsNotNone(self.model.interpolate)
self.assertIsNotNone(self.model.extrapolate_without_seasonal)
self.assertIsNotNone(self.model.seasonality_function)
self.assertIsNotNone(self.model.date_to_seasonal_component)
self.assertIsNotNone(self.model.compression_dict)
def test_zaggy_predict_on_fitted_points(self):
self.model = ZaggyModel(self.dates, self.y)
self.model.fit()
results = self.model.predict(self.dates)
assert isinstance(results, ndarray)
for expected, result in zip(self.model.solution['model'], results):
self.assertEquals(expected, result)
def test_zaggy_predict_is_zero_on_dates_before_first_data_point(self):
self.model = ZaggyModel(self.dates, self.y)
self.model.fit()
old_dates = date_range(2013, 1, 2014, 12)
results = self.model.predict(old_dates)
for expected, result in zip(self.model.solution['model'], results):
self.assertEquals(result, 0.0)
if __name__ == "__main__":
unittest.main()