mirror of
https://github.com/saymrwulf/prophet.git
synced 2026-09-16 22:20:23 +00:00
Custom seasonality priors Py
This commit is contained in:
bl
parent
a620a6c9f9
commit
8d27643339
2 changed files with 77 additions and 25 deletions
|
|
@ -63,7 +63,8 @@ class Prophet(object):
|
|||
that holiday.
|
||||
seasonality_prior_scale: Parameter modulating the strength of the
|
||||
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
|
||||
components model, unless overriden in the holidays input.
|
||||
changepoint_prior_scale: Parameter modulating the flexibility of the
|
||||
|
|
@ -406,6 +407,8 @@ class Prophet(object):
|
|||
raise ValueError(
|
||||
'Holiday {} does not have consistent prior scale '
|
||||
'specification.'.format(row.holiday))
|
||||
if ps <= 0:
|
||||
raise ValueError('Prior scale must be > 0')
|
||||
prior_scales[row.holiday] = ps
|
||||
|
||||
for offset in range(lw, uw + 1):
|
||||
|
|
@ -470,19 +473,25 @@ class Prophet(object):
|
|||
}
|
||||
return self
|
||||
|
||||
def add_seasonality(self, name, period, fourier_order):
|
||||
"""Add a seasonal component with specified period and number of Fourier
|
||||
components.
|
||||
def add_seasonality(self, name, period, fourier_order, prior_scale=None):
|
||||
"""Add a seasonal component with specified period, number of Fourier
|
||||
components, and prior scale.
|
||||
|
||||
Increasing the number of Fourier components allows the seasonality to
|
||||
change more quickly (at risk of overfitting). Default values for yearly
|
||||
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
|
||||
----------
|
||||
name: string name of the seasonality component.
|
||||
period: float number of days in one period.
|
||||
fourier_order: int number of Fourier components to use.
|
||||
prior_scale: float prior scale for this component.
|
||||
|
||||
Returns
|
||||
-------
|
||||
|
|
@ -494,7 +503,17 @@ class Prophet(object):
|
|||
if name not in ['daily', 'weekly', 'yearly']:
|
||||
# Allow overwriting built-in seasonalities
|
||||
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
|
||||
|
||||
def make_all_seasonality_features(self, df):
|
||||
|
|
@ -516,16 +535,16 @@ class Prophet(object):
|
|||
prior_scales = []
|
||||
|
||||
# Seasonality features
|
||||
for name, (period, series_order) in self.seasonalities.items():
|
||||
for name, props in self.seasonalities.items():
|
||||
features = self.make_seasonality_features(
|
||||
df['ds'],
|
||||
period,
|
||||
series_order,
|
||||
props['period'],
|
||||
props['fourier_order'],
|
||||
name,
|
||||
)
|
||||
seasonal_features.append(features)
|
||||
prior_scales.extend(
|
||||
[self.seasonality_prior_scale] * features.shape[1])
|
||||
[props['prior_scale']] * features.shape[1])
|
||||
|
||||
# Holiday features
|
||||
if self.holidays is not None:
|
||||
|
|
@ -600,7 +619,11 @@ class Prophet(object):
|
|||
fourier_order = self.parse_seasonality_args(
|
||||
'yearly', self.yearly_seasonality, yearly_disable, 10)
|
||||
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_disable = ((last - first < pd.Timedelta(weeks=2)) or
|
||||
|
|
@ -608,7 +631,11 @@ class Prophet(object):
|
|||
fourier_order = self.parse_seasonality_args(
|
||||
'weekly', self.weekly_seasonality, weekly_disable, 3)
|
||||
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_disable = ((last - first < pd.Timedelta(days=2)) or
|
||||
|
|
@ -616,7 +643,11 @@ class Prophet(object):
|
|||
fourier_order = self.parse_seasonality_args(
|
||||
'daily', self.daily_seasonality, daily_disable, 4)
|
||||
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
|
||||
def linear_growth_init(df):
|
||||
|
|
@ -1407,7 +1438,7 @@ class Prophet(object):
|
|||
ax = fig.add_subplot(111)
|
||||
# Compute seasonality from Jan 1 through a single period.
|
||||
start = pd.to_datetime('2017-01-01 0000')
|
||||
period = self.seasonalities[name][0]
|
||||
period = self.seasonalities[name]['period']
|
||||
end = start + pd.Timedelta(days=period)
|
||||
plot_points = 200
|
||||
days = pd.to_datetime(np.linspace(start.value, end.value, plot_points))
|
||||
|
|
|
|||
|
|
@ -278,7 +278,7 @@ class TestProphet(TestCase):
|
|||
})
|
||||
holidays2 = pd.concat((holidays, holidays2))
|
||||
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
|
||||
holidays = pd.DataFrame({
|
||||
'ds': pd.to_datetime(['2016-12-25', '2017-12-25']),
|
||||
|
|
@ -327,7 +327,8 @@ class TestProphet(TestCase):
|
|||
self.assertEqual(m.weekly_seasonality, 'auto')
|
||||
m.fit(train)
|
||||
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
|
||||
N = 9
|
||||
train = DATA.head(N)
|
||||
|
|
@ -342,9 +343,10 @@ class TestProphet(TestCase):
|
|||
m = Prophet()
|
||||
m.fit(train)
|
||||
self.assertNotIn('weekly', m.seasonalities)
|
||||
m = Prophet(weekly_seasonality=2)
|
||||
m = Prophet(weekly_seasonality=2, seasonality_prior_scale=3.)
|
||||
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):
|
||||
# Should be enabled
|
||||
|
|
@ -352,7 +354,10 @@ class TestProphet(TestCase):
|
|||
self.assertEqual(m.yearly_seasonality, 'auto')
|
||||
m.fit(DATA)
|
||||
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
|
||||
N = 240
|
||||
train = DATA.head(N)
|
||||
|
|
@ -362,9 +367,12 @@ class TestProphet(TestCase):
|
|||
m = Prophet(yearly_seasonality=True)
|
||||
m.fit(train)
|
||||
self.assertIn('yearly', m.seasonalities)
|
||||
m = Prophet(yearly_seasonality=7)
|
||||
m = Prophet(yearly_seasonality=7, seasonality_prior_scale=3.)
|
||||
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):
|
||||
# Should be enabled
|
||||
|
|
@ -372,7 +380,8 @@ class TestProphet(TestCase):
|
|||
self.assertEqual(m.daily_seasonality, 'auto')
|
||||
m.fit(DATA2)
|
||||
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
|
||||
N = 430
|
||||
train = DATA2.head(N)
|
||||
|
|
@ -382,9 +391,10 @@ class TestProphet(TestCase):
|
|||
m = Prophet(daily_seasonality=True)
|
||||
m.fit(train)
|
||||
self.assertIn('daily', m.seasonalities)
|
||||
m = Prophet(daily_seasonality=7)
|
||||
m = Prophet(daily_seasonality=7, seasonality_prior_scale=3.)
|
||||
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.fit(DATA)
|
||||
self.assertNotIn('daily', m.seasonalities)
|
||||
|
|
@ -403,15 +413,26 @@ class TestProphet(TestCase):
|
|||
holidays = pd.DataFrame({
|
||||
'ds': pd.to_datetime(['2017-01-02']),
|
||||
'holiday': ['special_day'],
|
||||
'prior_scale': [4.],
|
||||
})
|
||||
m = Prophet(holidays=holidays)
|
||||
m.add_seasonality(name='monthly', period=30, fourier_order=5)
|
||||
self.assertEqual(m.seasonalities['monthly'], (30, 5))
|
||||
m.add_seasonality(name='monthly', period=30, fourier_order=5,
|
||||
prior_scale=2.)
|
||||
self.assertEqual(m.seasonalities['monthly'],
|
||||
{'period': 30, 'fourier_order': 5, 'prior_scale': 2.})
|
||||
with self.assertRaises(ValueError):
|
||||
m.add_seasonality(name='special_day', period=30, fourier_order=5)
|
||||
with self.assertRaises(ValueError):
|
||||
m.add_seasonality(name='trend', 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):
|
||||
m = Prophet()
|
||||
|
|
|
|||
Loading…
Reference in a new issue