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Custom seasonality priors Py

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bl 2017-08-27 14:32:39 -07:00
parent a620a6c9f9
commit 8d27643339
2 changed files with 77 additions and 25 deletions
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57
python/fbprophet/forecaster.py
View file

@ -63,7 +63,8 @@ class Prophet(object):
that holiday. that holiday.
seasonality_prior_scale: Parameter modulating the strength of the seasonality_prior_scale: Parameter modulating the strength of the
seasonality model. Larger values allow the model to fit larger seasonal seasonality model. Larger values allow the model to fit larger seasonal
fluctuations, smaller values dampen the seasonality. fluctuations, smaller values dampen the seasonality. Can be specified
for individual seasonalities using add_seasonality.
holidays_prior_scale: Parameter modulating the strength of the holiday holidays_prior_scale: Parameter modulating the strength of the holiday
components model, unless overriden in the holidays input. components model, unless overriden in the holidays input.
changepoint_prior_scale: Parameter modulating the flexibility of the changepoint_prior_scale: Parameter modulating the flexibility of the
@ -406,6 +407,8 @@ class Prophet(object):
raise ValueError( raise ValueError(
'Holiday {} does not have consistent prior scale ' 'Holiday {} does not have consistent prior scale '
'specification.'.format(row.holiday)) 'specification.'.format(row.holiday))
if ps <= 0:
raise ValueError('Prior scale must be > 0')
prior_scales[row.holiday] = ps prior_scales[row.holiday] = ps
for offset in range(lw, uw + 1): for offset in range(lw, uw + 1):
@ -470,19 +473,25 @@ class Prophet(object):
} }
return self return self
def add_seasonality(self, name, period, fourier_order): def add_seasonality(self, name, period, fourier_order, prior_scale=None):
"""Add a seasonal component with specified period and number of Fourier """Add a seasonal component with specified period, number of Fourier
components. components, and prior scale.
Increasing the number of Fourier components allows the seasonality to Increasing the number of Fourier components allows the seasonality to
change more quickly (at risk of overfitting). Default values for yearly change more quickly (at risk of overfitting). Default values for yearly
and weekly seasonalities are 10 and 3 respectively. and weekly seasonalities are 10 and 3 respectively.
Increasing prior scale will allow this seasonality component more
flexibility, decreasing will dampen it. If not provided, will use the
seasonality_prior_scale provided on Prophet initialization (defaults
to 10).
Parameters Parameters
---------- ----------
name: string name of the seasonality component. name: string name of the seasonality component.
period: float number of days in one period. period: float number of days in one period.
fourier_order: int number of Fourier components to use. fourier_order: int number of Fourier components to use.
prior_scale: float prior scale for this component.
Returns Returns
------- -------
@ -494,7 +503,17 @@ class Prophet(object):
if name not in ['daily', 'weekly', 'yearly']: if name not in ['daily', 'weekly', 'yearly']:
# Allow overwriting built-in seasonalities # Allow overwriting built-in seasonalities
self.validate_column_name(name, check_seasonalities=False) self.validate_column_name(name, check_seasonalities=False)
self.seasonalities[name] = (period, fourier_order) if prior_scale is None:
ps = self.seasonality_prior_scale
else:
ps = float(prior_scale)
if ps <= 0:
raise ValueError('Prior scale must be > 0')
self.seasonalities[name] = {
'period': period,
'fourier_order': fourier_order,
'prior_scale': ps,
}
return self return self
def make_all_seasonality_features(self, df): def make_all_seasonality_features(self, df):
@ -516,16 +535,16 @@ class Prophet(object):
prior_scales = [] prior_scales = []
# Seasonality features # Seasonality features
for name, (period, series_order) in self.seasonalities.items(): for name, props in self.seasonalities.items():
features = self.make_seasonality_features( features = self.make_seasonality_features(
df['ds'], df['ds'],
period, props['period'],
series_order, props['fourier_order'],
name, name,
) )
seasonal_features.append(features) seasonal_features.append(features)
prior_scales.extend( prior_scales.extend(
[self.seasonality_prior_scale] * features.shape[1]) [props['prior_scale']] * features.shape[1])
# Holiday features # Holiday features
if self.holidays is not None: if self.holidays is not None:
@ -600,7 +619,11 @@ class Prophet(object):
fourier_order = self.parse_seasonality_args( fourier_order = self.parse_seasonality_args(
'yearly', self.yearly_seasonality, yearly_disable, 10) 'yearly', self.yearly_seasonality, yearly_disable, 10)
if fourier_order > 0: if fourier_order > 0:
self.seasonalities['yearly'] = (365.25, fourier_order) self.seasonalities['yearly'] = {
'period': 365.25,
'fourier_order': fourier_order,
'prior_scale': self.seasonality_prior_scale,
}
# Weekly seasonality # Weekly seasonality
weekly_disable = ((last - first < pd.Timedelta(weeks=2)) or weekly_disable = ((last - first < pd.Timedelta(weeks=2)) or
@ -608,7 +631,11 @@ class Prophet(object):
fourier_order = self.parse_seasonality_args( fourier_order = self.parse_seasonality_args(
'weekly', self.weekly_seasonality, weekly_disable, 3) 'weekly', self.weekly_seasonality, weekly_disable, 3)
if fourier_order > 0: if fourier_order > 0:
self.seasonalities['weekly'] = (7, fourier_order) self.seasonalities['weekly'] = {
'period': 7,
'fourier_order': fourier_order,
'prior_scale': self.seasonality_prior_scale,
}
# Daily seasonality # Daily seasonality
daily_disable = ((last - first < pd.Timedelta(days=2)) or daily_disable = ((last - first < pd.Timedelta(days=2)) or
@ -616,7 +643,11 @@ class Prophet(object):
fourier_order = self.parse_seasonality_args( fourier_order = self.parse_seasonality_args(
'daily', self.daily_seasonality, daily_disable, 4) 'daily', self.daily_seasonality, daily_disable, 4)
if fourier_order > 0: if fourier_order > 0:
self.seasonalities['daily'] = (1, fourier_order) self.seasonalities['daily'] = {
'period': 1,
'fourier_order': fourier_order,
'prior_scale': self.seasonality_prior_scale,
}
@staticmethod @staticmethod
def linear_growth_init(df): def linear_growth_init(df):
@ -1407,7 +1438,7 @@ class Prophet(object):
ax = fig.add_subplot(111) ax = fig.add_subplot(111)
# Compute seasonality from Jan 1 through a single period. # Compute seasonality from Jan 1 through a single period.
start = pd.to_datetime('2017-01-01 0000') start = pd.to_datetime('2017-01-01 0000')
period = self.seasonalities[name][0] period = self.seasonalities[name]['period']
end = start + pd.Timedelta(days=period) end = start + pd.Timedelta(days=period)
plot_points = 200 plot_points = 200
days = pd.to_datetime(np.linspace(start.value, end.value, plot_points)) days = pd.to_datetime(np.linspace(start.value, end.value, plot_points))

45
python/fbprophet/tests/test_prophet.py
View file

@ -278,7 +278,7 @@ class TestProphet(TestCase):
}) })
holidays2 = pd.concat((holidays, holidays2)) holidays2 = pd.concat((holidays, holidays2))
feats, priors = Prophet(holidays=holidays2).make_holiday_features(df['ds']) feats, priors = Prophet(holidays=holidays2).make_holiday_features(df['ds'])
self.assertEqual(sum(priors), 26) self.assertEqual(priors, [8., 8., 5., 5.])
# Check incompatible priors # Check incompatible priors
holidays = pd.DataFrame({ holidays = pd.DataFrame({
'ds': pd.to_datetime(['2016-12-25', '2017-12-25']), 'ds': pd.to_datetime(['2016-12-25', '2017-12-25']),
@ -327,7 +327,8 @@ class TestProphet(TestCase):
self.assertEqual(m.weekly_seasonality, 'auto') self.assertEqual(m.weekly_seasonality, 'auto')
m.fit(train) m.fit(train)
self.assertIn('weekly', m.seasonalities) self.assertIn('weekly', m.seasonalities)
self.assertEqual(m.seasonalities['weekly'], (7, 3)) self.assertEqual(m.seasonalities['weekly'],
{'period': 7, 'fourier_order': 3, 'prior_scale': 10.})
# Should be disabled due to too short history # Should be disabled due to too short history
N = 9 N = 9
train = DATA.head(N) train = DATA.head(N)
@ -342,9 +343,10 @@ class TestProphet(TestCase):
m = Prophet() m = Prophet()
m.fit(train) m.fit(train)
self.assertNotIn('weekly', m.seasonalities) self.assertNotIn('weekly', m.seasonalities)
m = Prophet(weekly_seasonality=2) m = Prophet(weekly_seasonality=2, seasonality_prior_scale=3.)
m.fit(DATA) m.fit(DATA)
self.assertEqual(m.seasonalities['weekly'], (7, 2)) self.assertEqual(m.seasonalities['weekly'],
{'period': 7, 'fourier_order': 2, 'prior_scale': 3.})
def test_auto_yearly_seasonality(self): def test_auto_yearly_seasonality(self):
# Should be enabled # Should be enabled
@ -352,7 +354,10 @@ class TestProphet(TestCase):
self.assertEqual(m.yearly_seasonality, 'auto') self.assertEqual(m.yearly_seasonality, 'auto')
m.fit(DATA) m.fit(DATA)
self.assertIn('yearly', m.seasonalities) self.assertIn('yearly', m.seasonalities)
self.assertEqual(m.seasonalities['yearly'], (365.25, 10)) self.assertEqual(
m.seasonalities['yearly'],
{'period': 365.25, 'fourier_order': 10, 'prior_scale': 10.},
)
# Should be disabled due to too short history # Should be disabled due to too short history
N = 240 N = 240
train = DATA.head(N) train = DATA.head(N)
@ -362,9 +367,12 @@ class TestProphet(TestCase):
m = Prophet(yearly_seasonality=True) m = Prophet(yearly_seasonality=True)
m.fit(train) m.fit(train)
self.assertIn('yearly', m.seasonalities) self.assertIn('yearly', m.seasonalities)
m = Prophet(yearly_seasonality=7) m = Prophet(yearly_seasonality=7, seasonality_prior_scale=3.)
m.fit(DATA) m.fit(DATA)
self.assertEqual(m.seasonalities['yearly'], (365.25, 7)) self.assertEqual(
m.seasonalities['yearly'],
{'period': 365.25, 'fourier_order': 7, 'prior_scale': 3.},
)
def test_auto_daily_seasonality(self): def test_auto_daily_seasonality(self):
# Should be enabled # Should be enabled
@ -372,7 +380,8 @@ class TestProphet(TestCase):
self.assertEqual(m.daily_seasonality, 'auto') self.assertEqual(m.daily_seasonality, 'auto')
m.fit(DATA2) m.fit(DATA2)
self.assertIn('daily', m.seasonalities) self.assertIn('daily', m.seasonalities)
self.assertEqual(m.seasonalities['daily'], (1, 4)) self.assertEqual(m.seasonalities['daily'],
{'period': 1, 'fourier_order': 4, 'prior_scale': 10.})
# Should be disabled due to too short history # Should be disabled due to too short history
N = 430 N = 430
train = DATA2.head(N) train = DATA2.head(N)
@ -382,9 +391,10 @@ class TestProphet(TestCase):
m = Prophet(daily_seasonality=True) m = Prophet(daily_seasonality=True)
m.fit(train) m.fit(train)
self.assertIn('daily', m.seasonalities) self.assertIn('daily', m.seasonalities)
m = Prophet(daily_seasonality=7) m = Prophet(daily_seasonality=7, seasonality_prior_scale=3.)
m.fit(DATA2) m.fit(DATA2)
self.assertEqual(m.seasonalities['daily'], (1, 7)) self.assertEqual(m.seasonalities['daily'],
{'period': 1, 'fourier_order': 7, 'prior_scale': 3.})
m = Prophet() m = Prophet()
m.fit(DATA) m.fit(DATA)
self.assertNotIn('daily', m.seasonalities) self.assertNotIn('daily', m.seasonalities)
@ -403,15 +413,26 @@ class TestProphet(TestCase):
holidays = pd.DataFrame({ holidays = pd.DataFrame({
'ds': pd.to_datetime(['2017-01-02']), 'ds': pd.to_datetime(['2017-01-02']),
'holiday': ['special_day'], 'holiday': ['special_day'],
'prior_scale': [4.],
}) })
m = Prophet(holidays=holidays) m = Prophet(holidays=holidays)
m.add_seasonality(name='monthly', period=30, fourier_order=5) m.add_seasonality(name='monthly', period=30, fourier_order=5,
self.assertEqual(m.seasonalities['monthly'], (30, 5)) prior_scale=2.)
self.assertEqual(m.seasonalities['monthly'],
{'period': 30, 'fourier_order': 5, 'prior_scale': 2.})
with self.assertRaises(ValueError): with self.assertRaises(ValueError):
m.add_seasonality(name='special_day', period=30, fourier_order=5) m.add_seasonality(name='special_day', period=30, fourier_order=5)
with self.assertRaises(ValueError): with self.assertRaises(ValueError):
m.add_seasonality(name='trend', period=30, fourier_order=5) m.add_seasonality(name='trend', period=30, fourier_order=5)
m.add_seasonality(name='weekly', period=30, fourier_order=5) m.add_seasonality(name='weekly', period=30, fourier_order=5)
# Test priors
m = Prophet(holidays=holidays, yearly_seasonality=False)
m.add_seasonality(name='monthly', period=30, fourier_order=5,
prior_scale=2.)
m.fit(DATA.copy())
seasonal_features, prior_scales = m.make_all_seasonality_features(
m.history)
self.assertEqual(prior_scales, [2.] * 10 + [10.] * 6 + [4.])
def test_added_regressors(self): def test_added_regressors(self):
m = Prophet() m = Prophet()