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)
def make_extrapolatad_plot(params=None):
mock = get_mock_with_dates()
plot_mock(mock)
timescale = (1.0, 'month')
model = ZaggyModel(mock['dates'], mock['y'], timescale=timescale,
params=params)
model.fit()
plot_model(model)
dates = date_range(2022, 1, 2024, 12)
results = model.predict(dates)
plt.plot(dates, results, color='orange', alpha=0.7, marker='d',
label='predictions')
dates = date_range(2014, 1, 2016, 1)
results = model.predict(dates)
plt.plot(dates, results, color='orange', alpha=0.7, marker='s',
label='predictions (earlier)')
plt.legend(loc='upper left')
return model
