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