"Fossies" - the Fresh Open Source Software Archive  

Source code changes of the file "python/prophet/tests/test_diagnostics.py" between
prophet-1.1.tar.gz and prophet-1.1.1.tar.gz

About: Prophet is a tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

test_diagnostics.py  (prophet-1.1)	:	test_diagnostics.py  (prophet-1.1.1)
skipping to change at line 57		skipping to change at line 57
methods = [None, 'processes', 'threads', CustomParallelBackend()]		methods = [None, 'processes', 'threads', CustomParallelBackend()]
try:		try:
from dask.distributed import Client		from dask.distributed import Client
client = Client(processes=False) # noqa		client = Client(processes=False) # noqa
methods.append("dask")		methods.append("dask")
except ImportError:		except ImportError:
pass		pass
for parallel in methods:		for parallel in methods:
df_cv = diagnostics.cross_validation(		with self.subTest(i=parallel):
m, horizon='4 days', period='10 days', initial='115 days',		df_cv = diagnostics.cross_validation(
parallel=parallel)		m, horizon='4 days', period='10 days', initial='115 days',
self.assertEqual(len(np.unique(df_cv['cutoff'])), 3)		parallel=parallel)
self.assertEqual(max(df_cv['ds'] - df_cv['cutoff']), horizon)		self.assertEqual(len(np.unique(df_cv['cutoff'])), 3)
self.assertTrue(min(df_cv['cutoff']) >= min(self.__df['ds']) + initi		self.assertEqual(max(df_cv['ds'] - df_cv['cutoff']), horizon)
al)		self.assertTrue(min(df_cv['cutoff']) >= min(self.__df['ds']) + i
dc = df_cv['cutoff'].diff()		nitial)
dc = dc[dc > pd.Timedelta(0)].min()		dc = df_cv['cutoff'].diff()
self.assertTrue(dc >= period)		dc = dc[dc > pd.Timedelta(0)].min()
self.assertTrue((df_cv['cutoff'] < df_cv['ds']).all())		self.assertTrue(dc >= period)
# Each y in df_cv and self.__df with same ds should be equal		self.assertTrue((df_cv['cutoff'] < df_cv['ds']).all())
df_merged = pd.merge(df_cv, self.__df, 'left', on='ds')		# Each y in df_cv and self.__df with same ds should be equal
self.assertAlmostEqual(		df_merged = pd.merge(df_cv, self.__df, 'left', on='ds')
np.sum((df_merged['y_x'] - df_merged['y_y']) ** 2), 0.0)		self.assertAlmostEqual(
df_cv = diagnostics.cross_validation(		np.sum((df_merged['y_x'] - df_merged['y_y']) ** 2), 0.0)
m, horizon='4 days', period='10 days', initial='135 days')		df_cv = diagnostics.cross_validation(
self.assertEqual(len(np.unique(df_cv['cutoff'])), 1)		m, horizon='4 days', period='10 days', initial='135 days')
with self.assertRaises(ValueError):		self.assertEqual(len(np.unique(df_cv['cutoff'])), 1)
diagnostics.cross_validation(		with self.assertRaises(ValueError):
m, horizon='10 days', period='10 days', initial='140 days')		diagnostics.cross_validation(
		m, horizon='10 days', period='10 days', initial='140 day
		s')
# invalid alias		# invalid alias
with self.assertRaisesRegex(ValueError, "'parallel' should be one"):		with self.assertRaisesRegex(ValueError, "'parallel' should be one"):
diagnostics.cross_validation(m, horizon="4 days", parallel="bad")		diagnostics.cross_validation(m, horizon="4 days", parallel="bad")
# no map method		# no map method
with self.assertRaisesRegex(ValueError, "'parallel' should be one"):		with self.assertRaisesRegex(ValueError, "'parallel' should be one"):
diagnostics.cross_validation(m, horizon="4 days", parallel=object())		diagnostics.cross_validation(m, horizon="4 days", parallel=object())
def test_check_single_cutoff_forecast_func_calls(self):		def test_check_single_cutoff_forecast_func_calls(self):
End of changes. 1 change blocks.
21 lines changed or deleted		23 lines changed or added

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