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@@ -29,7 +29,7 @@ class TestDiagnostics(TestCase):
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def test_simulated_historical_forecasts(self):
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m = Prophet()
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m.fit(self.__df)
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- k = 3
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+ k = 2
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for p in [1, 10]:
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for h in [1, 3]:
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period = '{} days'.format(p)
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@@ -39,6 +39,10 @@ class TestDiagnostics(TestCase):
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self.assertTrue((df_shf['cutoff'] < df_shf['ds']).all())
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# The unique size of output cutoff should be equal to 'k'
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self.assertEqual(len(np.unique(df_shf['cutoff'])), k)
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+ self.assertEqual(max(df_shf['ds'] - df_shf['cutoff']), pd.Timedelta(horizon))
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+ dc = df_shf['cutoff'].diff()
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+ dc = dc[dc > pd.Timedelta(0)].min()
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+ self.assertTrue(dc >= pd.Timedelta(period))
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# Each y in df_shf and self.__df with same ds should be equal
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df_merged = pd.merge(df_shf, self.__df, 'left', on='ds')
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self.assertAlmostEqual(np.sum((df_merged['y_x'] - df_merged['y_y']) ** 2), 0.0)
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@@ -72,17 +76,21 @@ class TestDiagnostics(TestCase):
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te = self.__df['ds'].max()
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ts = self.__df['ds'].min()
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horizon = pd.Timedelta('4 days')
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- period = pd.Timedelta('1 days')
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+ period = pd.Timedelta('10 days')
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initial = pd.Timedelta('90 days')
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k = int(np.floor(((te - horizon) - (ts + initial)) / period))
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df_cv = diagnostics.cross_validation(m, horizon=horizon, period=period, initial=initial)
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# The unique size of output cutoff should be equal to 'k'
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self.assertEqual(len(np.unique(df_cv['cutoff'])), k)
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+ self.assertEqual(max(df_cv['ds'] - df_cv['cutoff']), horizon)
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+ dc = df_cv['cutoff'].diff()
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+ dc = dc[dc > pd.Timedelta(0)].min()
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+ self.assertTrue(dc >= period)
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def test_cross_validation_default_value_check(self):
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m = Prophet()
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m.fit(self.__df)
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# Default value of initial should be equal to 3 * horizon
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- df_cv1 = diagnostics.cross_validation(m, horizon='32 days', period='1 days')
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- df_cv2 = diagnostics.cross_validation(m, horizon='32 days', period='1 days', initial='96 days')
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+ df_cv1 = diagnostics.cross_validation(m, horizon='32 days', period='10 days')
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+ df_cv2 = diagnostics.cross_validation(m, horizon='32 days', period='10 days', initial='96 days')
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self.assertAlmostEqual(((df_cv1 - df_cv2)**2)[['y', 'yhat']].sum().sum(), 0.0)
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bletham