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Custom prior scales for holidays Py
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bletham
parent
3c09448018
commit
a620a6c9f9
2 changed files with 62 additions and 11 deletions
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@ -58,12 +58,14 @@ class Prophet(object):
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holidays: pd.DataFrame with columns holiday (string) and ds (date type)
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and optionally columns lower_window and upper_window which specify a
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range of days around the date to be included as holidays.
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lower_window=-2 will include 2 days prior to the date as holidays.
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lower_window=-2 will include 2 days prior to the date as holidays. Also
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optionally can have a column prior_scale specifying the prior scale for
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that holiday.
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seasonality_prior_scale: Parameter modulating the strength of the
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seasonality model. Larger values allow the model to fit larger seasonal
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fluctuations, smaller values dampen the seasonality.
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holidays_prior_scale: Parameter modulating the strength of the holiday
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components model.
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components model, unless overriden in the holidays input.
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changepoint_prior_scale: Parameter modulating the flexibility of the
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automatic changepoint selection. Large values will allow many
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changepoints, small values will allow few changepoints.
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@ -376,10 +378,12 @@ class Prophet(object):
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Returns
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-------
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pd.DataFrame with a column for each holiday.
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holiday_features: pd.DataFrame with a column for each holiday.
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prior_scale_list: List of prior scales for each holiday column.
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"""
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# Holds columns of our future matrix.
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expanded_holidays = defaultdict(lambda: np.zeros(dates.shape[0]))
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prior_scales = {}
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# Makes an index so we can perform `get_loc` below.
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# Strip to just dates.
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row_index = pd.DatetimeIndex(dates.apply(lambda x: x.date()))
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@ -392,6 +396,18 @@ class Prophet(object):
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except ValueError:
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lw = 0
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uw = 0
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try:
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ps = float(row.get('prior_scale', self.holidays_prior_scale))
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except ValueError:
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ps = float(self.holidays_prior_scale)
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if (
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row.holiday in prior_scales and prior_scales[row.holiday] != ps
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):
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raise ValueError(
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'Holiday {} does not have consistent prior scale '
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'specification.'.format(row.holiday))
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prior_scales[row.holiday] = ps
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for offset in range(lw, uw + 1):
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occurrence = dt + timedelta(days=offset)
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try:
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@ -409,9 +425,12 @@ class Prophet(object):
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else:
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# Access key to generate value
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expanded_holidays[key]
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# This relies pretty importantly on pandas keeping the columns in order.
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return pd.DataFrame(expanded_holidays)
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holiday_features = pd.DataFrame(expanded_holidays)
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prior_scale_list = [
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prior_scales[h.split('_delim_')[0]]
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for h in holiday_features.columns
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]
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return holiday_features, prior_scale_list
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def add_regressor(self, name, prior_scale=None, standardize='auto'):
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"""Add an additional regressor to be used for fitting and predicting.
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@ -510,10 +529,9 @@ class Prophet(object):
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# Holiday features
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if self.holidays is not None:
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features = self.make_holiday_features(df['ds'])
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features, holiday_priors = self.make_holiday_features(df['ds'])
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seasonal_features.append(features)
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prior_scales.extend(
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[self.holidays_prior_scale] * features.shape[1])
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prior_scales.extend(holiday_priors)
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# Additional regressors
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for name, props in self.extra_regressors.items():
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@ -242,10 +242,11 @@ class TestProphet(TestCase):
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df = pd.DataFrame({
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'ds': pd.date_range('2016-12-20', '2016-12-31')
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})
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feats = model.make_holiday_features(df['ds'])
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feats, priors = model.make_holiday_features(df['ds'])
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# 11 columns generated even though only 8 overlap
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self.assertEqual(feats.shape, (df.shape[0], 2))
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self.assertEqual((feats.sum(0) - np.array([1.0, 1.0])).sum(), 0)
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self.assertEqual(priors, [10., 10.]) # Default prior
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holidays = pd.DataFrame({
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'ds': pd.to_datetime(['2016-12-25']),
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@ -253,9 +254,41 @@ class TestProphet(TestCase):
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'lower_window': [-1],
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'upper_window': [10],
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})
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feats = Prophet(holidays=holidays).make_holiday_features(df['ds'])
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feats, priors = Prophet(holidays=holidays).make_holiday_features(df['ds'])
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# 12 columns generated even though only 8 overlap
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self.assertEqual(feats.shape, (df.shape[0], 12))
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self.assertEqual(priors, list(10. * np.ones(12)))
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# Check prior specifications
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holidays = pd.DataFrame({
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'ds': pd.to_datetime(['2016-12-25', '2017-12-25']),
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'holiday': ['xmas', 'xmas'],
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'lower_window': [-1, -1],
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'upper_window': [0, 0],
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'prior_scale': [5., 5.],
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})
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feats, priors = Prophet(holidays=holidays).make_holiday_features(df['ds'])
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self.assertEqual(priors, [5., 5.])
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# 2 different priors
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holidays2 = pd.DataFrame({
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'ds': pd.to_datetime(['2012-06-06', '2013-06-06']),
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'holiday': ['seans-bday'] * 2,
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'lower_window': [0] * 2,
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'upper_window': [1] * 2,
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'prior_scale': [8] * 2,
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})
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holidays2 = pd.concat((holidays, holidays2))
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feats, priors = Prophet(holidays=holidays2).make_holiday_features(df['ds'])
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self.assertEqual(sum(priors), 26)
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# Check incompatible priors
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holidays = pd.DataFrame({
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'ds': pd.to_datetime(['2016-12-25', '2017-12-25']),
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'holiday': ['xmas', 'xmas'],
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'lower_window': [-1, -1],
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'upper_window': [0, 0],
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'prior_scale': [5., 6.],
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})
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with self.assertRaises(ValueError):
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Prophet(holidays=holidays).make_holiday_features(df['ds'])
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def test_fit_with_holidays(self):
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holidays = pd.DataFrame({
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