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Speed up diagnostics unit tests

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This commit is contained in:
bletham 2017-08-22 14:14:46 -07:00
parent 047a0c3c23
commit 509666d1d2
1 changed files with 12 additions and 4 deletions
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16
python/fbprophet/tests/test_diagnostics.py
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@ -29,7 +29,7 @@ class TestDiagnostics(TestCase):
def test_simulated_historical_forecasts(self):
m = Prophet()
m.fit(self.__df)
k = 3
k = 2
for p in [1, 10]:
for h in [1, 3]:
period = '{} days'.format(p)
@ -39,6 +39,10 @@ class TestDiagnostics(TestCase):
self.assertTrue((df_shf['cutoff'] < df_shf['ds']).all())
# The unique size of output cutoff should be equal to 'k'
self.assertEqual(len(np.unique(df_shf['cutoff'])), k)
self.assertEqual(max(df_shf['ds'] - df_shf['cutoff']), pd.Timedelta(horizon))
dc = df_shf['cutoff'].diff()
dc = dc[dc > pd.Timedelta(0)].min()
self.assertTrue(dc >= pd.Timedelta(period))
# Each y in df_shf and self.__df with same ds should be equal
df_merged = pd.merge(df_shf, self.__df, 'left', on='ds')
self.assertAlmostEqual(np.sum((df_merged['y_x'] - df_merged['y_y']) ** 2), 0.0)
@ -72,17 +76,21 @@ class TestDiagnostics(TestCase):
te = self.__df['ds'].max()
ts = self.__df['ds'].min()
horizon = pd.Timedelta('4 days')
period = pd.Timedelta('1 days')
period = pd.Timedelta('10 days')
initial = pd.Timedelta('90 days')
k = int(np.floor(((te - horizon) - (ts + initial)) / period))
df_cv = diagnostics.cross_validation(m, horizon=horizon, period=period, initial=initial)
# The unique size of output cutoff should be equal to 'k'
self.assertEqual(len(np.unique(df_cv['cutoff'])), k)
self.assertEqual(max(df_cv['ds'] - df_cv['cutoff']), horizon)
dc = df_cv['cutoff'].diff()
dc = dc[dc > pd.Timedelta(0)].min()
self.assertTrue(dc >= period)
def test_cross_validation_default_value_check(self):
m = Prophet()
m.fit(self.__df)
# Default value of initial should be equal to 3 * horizon
df_cv1 = diagnostics.cross_validation(m, horizon='32 days', period='1 days')
df_cv2 = diagnostics.cross_validation(m, horizon='32 days', period='1 days', initial='96 days')
df_cv1 = diagnostics.cross_validation(m, horizon='32 days', period='10 days')
df_cv2 = diagnostics.cross_validation(m, horizon='32 days', period='10 days', initial='96 days')
self.assertAlmostEqual(((df_cv1 - df_cv2)**2)[['y', 'yhat']].sum().sum(), 0.0)