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https://github.com/saymrwulf/prophet.git
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Move built-in country holidays to a method
This commit is contained in:
Ben Letham
parent
ebf7b3da0a
commit
92f955d25a
2 changed files with 107 additions and 57 deletions
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@ -68,7 +68,6 @@ class Prophet(object):
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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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append_holidays: country name or abbreviation; must be string
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seasonality_mode: 'additive' (default) or 'multiplicative'.
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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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@ -101,7 +100,6 @@ class Prophet(object):
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weekly_seasonality='auto',
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daily_seasonality='auto',
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holidays=None,
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append_holidays=None,
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seasonality_mode='additive',
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seasonality_prior_scale=10.0,
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holidays_prior_scale=10.0,
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@ -136,13 +134,6 @@ class Prophet(object):
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holidays['ds'] = pd.to_datetime(holidays['ds'])
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self.holidays = holidays
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if append_holidays is not None:
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if not (
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isinstance(append_holidays, str)
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):
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raise ValueError("append_holidays must be a string")
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self.append_holidays = append_holidays
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self.seasonality_mode = seasonality_mode
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self.seasonality_prior_scale = float(seasonality_prior_scale)
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self.changepoint_prior_scale = float(changepoint_prior_scale)
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@ -152,7 +143,7 @@ class Prophet(object):
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self.interval_width = interval_width
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self.uncertainty_samples = uncertainty_samples
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# Set during fitting
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# Set during fitting or by other methods
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self.start = None
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self.y_scale = None
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self.logistic_floor = False
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@ -160,6 +151,7 @@ class Prophet(object):
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self.changepoints_t = None
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self.seasonalities = {}
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self.extra_regressors = OrderedDict({})
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self.country_holidays = None
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self.stan_fit = None
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self.params = {}
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self.history = None
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@ -224,10 +216,10 @@ class Prophet(object):
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name in self.holidays['holiday'].unique()):
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raise ValueError(
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'Name "{}" already used for a holiday.'.format(name))
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if (check_holidays and self.append_holidays is not None and
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name in get_holiday_names(self.append_holidays)):
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if (check_holidays and self.country_holidays is not None and
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name in get_holiday_names(self.country_holidays)):
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raise ValueError(
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'Name "{}" is a holiday name in {}.'.format(name, self.append_holidays))
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'Name "{}" is a holiday name in {}.'.format(name, self.country_holidays))
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if check_seasonalities and name in self.seasonalities:
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raise ValueError(
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'Name "{}" already used for a seasonality.'.format(name))
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@ -430,12 +422,57 @@ class Prophet(object):
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]
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return pd.DataFrame(features, columns=columns)
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def make_holiday_features(self, dates):
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def construct_holiday_dataframe(self, dates):
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"""Construct a dataframe of holiday dates.
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Will combine self.holidays with the built-in country holidays
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corresponding to input dates, if self.country_holidays is set.
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Parameters
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----------
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dates: pd.Series containing timestamps used for computing seasonality.
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Returns
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-------
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"""
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all_holidays = pd.DataFrame()
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if self.holidays is not None:
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all_holidays = pd.concat((all_holidays, self.holidays))
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if self.country_holidays is not None:
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year_list = list({x.year for x in dates})
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country_holidays_df = make_holidays_df(
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year_list=year_list, country=self.country_holidays
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)
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all_holidays = pd.concat((all_holidays, country_holidays_df), sort=False)
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all_holidays.reset_index(drop=True, inplace=True)
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# If the model has already been fit with a certain set of holidays,
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# make sure we are using those same ones.
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if self.train_holiday_names is not None:
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# Remove holiday names didn't show up in fit
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index_to_drop = all_holidays.index[
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np.logical_not(
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all_holidays.holiday.isin(self.train_holiday_names)
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)
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]
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all_holidays = all_holidays.drop(index_to_drop)
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# Add holiday names in fit but not in predict with ds as NA
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holidays_to_add = pd.DataFrame({
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'holiday': self.train_holiday_names[
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np.logical_not(self.train_holiday_names.isin(all_holidays.holiday))
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]
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})
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all_holidays = pd.concat((all_holidays, holidays_to_add), sort=False)
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all_holidays.reset_index(drop=True, inplace=True)
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return all_holidays
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def make_holiday_features(self, dates, holidays):
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"""Construct a dataframe of holiday features.
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Parameters
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----------
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dates: pd.Series containing timestamps used for computing seasonality.
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holidays: pd.Dataframe containing holidays, as returned by
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construct_holiday_dataframe.
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Returns
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-------
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@ -443,32 +480,6 @@ class Prophet(object):
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prior_scale_list: List of prior scales for each holiday column.
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holiday_names: List of names of holidays
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"""
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# Concatenate holidays and append_holidays
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all_holidays = self.holidays
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if self.append_holidays is not None:
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year_list = list({x.year for x in dates})
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append_holidays_df = make_holidays_df(
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year_list=year_list,
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country=self.append_holidays)
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all_holidays = pd.concat((all_holidays, append_holidays_df), sort=False)
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all_holidays.reset_index(drop=True, inplace=True)
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# Make fit and predict holidays components match
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if self.train_holiday_names is not None:
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train_holidays = self.train_holiday_names
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# Remove holiday names didn't show up in fit
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index_to_drop = all_holidays.index[
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np.logical_not(
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all_holidays.holiday.isin(train_holidays))]
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all_holidays = all_holidays.drop(index_to_drop)
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# Add holiday names show up in fit but not in predict with ds as NA
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holidays_to_add = pd.DataFrame(
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{'holiday':
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train_holidays[
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np.logical_not(
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train_holidays.isin(all_holidays.holiday))]})
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all_holidays = pd.concat((all_holidays, holidays_to_add), sort=False)
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all_holidays.reset_index(drop=True, inplace=True)
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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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@ -476,7 +487,7 @@ class Prophet(object):
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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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for _ix, row in all_holidays.iterrows():
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for _ix, row in holidays.iterrows():
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dt = row.ds.date()
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try:
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lw = int(row.get('lower_window', 0))
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@ -635,6 +646,44 @@ class Prophet(object):
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}
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return self
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def add_country_holidays(self, country_name):
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"""Add in built-in holidays for the specified country.
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These holidays will be included in addition to any specified on model
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initialization.
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Holidays will be calculated for arbitrary date ranges in the history
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and future. See the online documentation for the list of countries with
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built-in holidays.
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Built-in country holidays can only be set for a single country.
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Parameters
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----------
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country_name: Name of the country, like 'UnitedStates' or 'US'
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Returns
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-------
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The prophet object.
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"""
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if self.history is not None:
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raise Exception(
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"Country holidays must be added prior to model fitting."
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)
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# Validate names.
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for name in get_holiday_names(country_name):
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# Allow merging with existing holidays
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self.validate_column_name(name, check_holidays=False)
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# Set the holidays.
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if self.country_holidays is not None:
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logger.warning(
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'Changing country holidays from {} to {}'.format(
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self.country_holidays, country_name
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)
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)
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self.country_holidays = country_name
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return self
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def make_all_seasonality_features(self, df):
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"""Dataframe with seasonality features.
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@ -672,9 +721,10 @@ class Prophet(object):
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modes[props['mode']].append(name)
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# Holiday features
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if self.holidays is not None or self.append_holidays is not None:
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holidays = self.construct_holiday_dataframe(df['ds'])
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if len(holidays) > 0:
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features, holiday_priors, holiday_names = (
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self.make_holiday_features(df['ds'])
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self.make_holiday_features(df['ds'], holidays)
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)
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seasonal_features.append(features)
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prior_scales.extend(holiday_priors)
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@ -281,7 +281,7 @@ 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, priors, names = model.make_holiday_features(df['ds'])
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feats, priors, names = model.make_holiday_features(df['ds'], model.holidays)
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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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@ -295,7 +295,7 @@ class TestProphet(TestCase):
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'upper_window': [10],
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})
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m = Prophet(holidays=holidays)
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feats, priors, names = m.make_holiday_features(df['ds'])
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feats, priors, names = m.make_holiday_features(df['ds'], m.holidays)
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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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@ -309,7 +309,7 @@ class TestProphet(TestCase):
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'prior_scale': [5., 5.],
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})
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m = Prophet(holidays=holidays)
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feats, priors, names = m.make_holiday_features(df['ds'])
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feats, priors, names = m.make_holiday_features(df['ds'], m.holidays)
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self.assertEqual(priors, [5., 5.])
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self.assertEqual(names, ['xmas'])
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# 2 different priors
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@ -322,7 +322,7 @@ class TestProphet(TestCase):
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})
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holidays2 = pd.concat((holidays, holidays2))
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m = Prophet(holidays=holidays2)
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feats, priors, names = m.make_holiday_features(df['ds'])
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feats, priors, names = m.make_holiday_features(df['ds'], m.holidays)
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pn = zip(priors, [s.split('_delim_')[0] for s in feats.columns])
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for t in pn:
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self.assertIn(t, [(8., 'seans-bday'), (5., 'xmas')])
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@ -335,7 +335,7 @@ class TestProphet(TestCase):
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holidays2 = pd.concat((holidays, holidays2))
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feats, priors, names = Prophet(
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holidays=holidays2, holidays_prior_scale=4
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).make_holiday_features(df['ds'])
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).make_holiday_features(df['ds'], holidays2)
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self.assertEqual(set(priors), {4., 5.})
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# Check incompatible priors
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holidays = pd.DataFrame({
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@ -346,7 +346,7 @@ class TestProphet(TestCase):
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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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Prophet(holidays=holidays).make_holiday_features(df['ds'], holidays)
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def test_fit_with_holidays(self):
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holidays = pd.DataFrame({
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@ -358,28 +358,28 @@ class TestProphet(TestCase):
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model = Prophet(holidays=holidays, uncertainty_samples=0)
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model.fit(DATA).predict()
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def test_fit_predict_with_append_holidays(self):
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def test_fit_predict_with_country_holidays(self):
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holidays = 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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})
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append_holidays = 'US'
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# Test with holidays and append_holidays
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model = Prophet(holidays=holidays,
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append_holidays=append_holidays,
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uncertainty_samples=0)
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# Test with holidays and country_holidays
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model = Prophet(holidays=holidays, uncertainty_samples=0)
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model.add_country_holidays(country_name='US')
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model.fit(DATA).predict()
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# There are training holidays missing in the test set
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train = DATA.head(154)
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future = DATA.tail(355)
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model = Prophet(append_holidays=append_holidays, uncertainty_samples=0)
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model = Prophet(uncertainty_samples=0)
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model.add_country_holidays(country_name='US')
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model.fit(train).predict(future)
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# There are test holidays missing in the training set
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train = DATA.tail(355)
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future = DATA2
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model = Prophet(append_holidays=append_holidays, uncertainty_samples=0)
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model = Prophet(uncertainty_samples=0)
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model.add_country_holidays(country_name='US')
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model.fit(train).predict(future)
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def test_make_future_dataframe(self):
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