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Modification of holiday features (#644)

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* Allow both both hoidays and append holidays; match holidays in predict and fit

* Allow both both hoidays and append holidays; match holidays in predict and fit

* Add test for append_holiday features; minor fixes

* Add column name validation for append_holidays names; allow only one country
This commit is contained in:
ziye666 2018-08-27 13:52:34 -07:00 committed by Ben Letham
parent f473c743c9
commit 817f0306a4
6 changed files with 1378 additions and 27 deletions
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1
python/fbprophet/diagnostics.py
View file

@ -159,6 +159,7 @@ def prophet_copy(m, cutoff=None):
weekly_seasonality=False,
daily_seasonality=False,
holidays=m.holidays,
append_holidays=m.append_holidays,
seasonality_mode=m.seasonality_mode,
seasonality_prior_scale=m.seasonality_prior_scale,
changepoint_prior_scale=m.changepoint_prior_scale,

101
python/fbprophet/forecaster.py
View file

@ -14,16 +14,12 @@ from collections import defaultdict
from datetime import timedelta
import logging
import warnings
import numpy as np
import pandas as pd
try:
import pystan # noqa F401
except ImportError:
logger.exception('You cannot run fbprophet without pystan installed')
from fbprophet.diagnostics import prophet_copy
from fbprophet.models import prophet_stan_model
from fbprophet.make_holidays import get_holiday_names, make_holidays_df
from fbprophet.plot import (
plot,
plot_components,
@ -38,6 +34,11 @@ logging.basicConfig()
logger = logging.getLogger(__name__)
warnings.filterwarnings("default", category=DeprecationWarning)
try:
import pystan # noqa F401
except ImportError:
logger.exception('You cannot run fbprophet without pystan installed')
class Prophet(object):
"""Prophet forecaster.
@ -68,6 +69,7 @@ class Prophet(object):
lower_window=-2 will include 2 days prior to the date as holidays. Also
optionally can have a column prior_scale specifying the prior scale for
that holiday.
append_holidays: country name or abbreviation; must be string
seasonality_mode: 'additive' (default) or 'multiplicative'.
seasonality_prior_scale: Parameter modulating the strength of the
seasonality model. Larger values allow the model to fit larger seasonal
@ -100,6 +102,7 @@ class Prophet(object):
weekly_seasonality='auto',
daily_seasonality='auto',
holidays=None,
append_holidays=None,
seasonality_mode='additive',
seasonality_prior_scale=10.0,
holidays_prior_scale=10.0,
@ -134,6 +137,13 @@ class Prophet(object):
holidays['ds'] = pd.to_datetime(holidays['ds'])
self.holidays = holidays
if append_holidays is not None:
if not (
isinstance(append_holidays, str)
):
raise ValueError("append_holidays must be a string")
self.append_holidays = append_holidays
self.seasonality_mode = seasonality_mode
self.seasonality_prior_scale = float(seasonality_prior_scale)
self.changepoint_prior_scale = float(changepoint_prior_scale)
@ -157,6 +167,7 @@ class Prophet(object):
self.history_dates = None
self.train_component_cols = None
self.component_modes = None
self.train_holiday_names = None
self.validate_inputs()
def validate_inputs(self):
@ -199,7 +210,7 @@ class Prophet(object):
raise ValueError('Name cannot contain "_delim_"')
reserved_names = [
'trend', 'additive_terms', 'daily', 'weekly', 'yearly',
'holidays', 'zeros', 'extra_regressors_additive','yhat',
'holidays', 'zeros', 'extra_regressors_additive', 'yhat',
'extra_regressors_multiplicative', 'multiplicative_terms',
]
rn_l = [n + '_lower' for n in reserved_names]
@ -214,6 +225,10 @@ class Prophet(object):
name in self.holidays['holiday'].unique()):
raise ValueError(
'Name "{}" already used for a holiday.'.format(name))
if (check_holidays and self.append_holidays is not None and
name in get_holiday_names(self.append_holidays)):
raise ValueError(
'Name "{}" is a holiday name in {}.'.format(name, self.append_holidays))
if check_seasonalities and name in self.seasonalities:
raise ValueError(
'Name "{}" already used for a seasonality.'.format(name))
@ -351,8 +366,8 @@ class Prophet(object):
if self.n_changepoints > 0:
cp_indexes = (
np.linspace(0, hist_size - 1, self.n_changepoints + 1)
.round()
.astype(np.int)
.round()
.astype(np.int)
)
self.changepoints = (
self.history.iloc[cp_indexes]['ds'].tail(-1)
@ -384,8 +399,8 @@ class Prophet(object):
# convert to days since epoch
t = np.array(
(dates - pd.datetime(1970, 1, 1))
.dt.total_seconds()
.astype(np.float)
.dt.total_seconds()
.astype(np.float)
) / (3600 * 24.)
return np.column_stack([
fun((2.0 * (i + 1) * np.pi * t / period))
@ -429,6 +444,32 @@ class Prophet(object):
prior_scale_list: List of prior scales for each holiday column.
holiday_names: List of names of holidays
"""
# Concatenate holidays and append_holidays
all_holidays = self.holidays
if self.append_holidays is not None:
year_list = list({x.year for x in dates})
append_holidays_df = make_holidays_df(
year_list=year_list,
country=self.append_holidays)
all_holidays = pd.concat((all_holidays, append_holidays_df), sort=False)
all_holidays.reset_index(drop=True, inplace=True)
# Make fit and predict holidays components match
if self.train_holiday_names is not None:
train_holidays = self.train_holiday_names
# Remove holiday names didn't show up in fit
index_to_drop = all_holidays.index[
np.logical_not(
all_holidays.holiday.isin(train_holidays))]
all_holidays = all_holidays.drop(index_to_drop)
# Add holiday names show up in fit but not in predict with ds as NA
holidays_to_add = pd.DataFrame(
{'holiday':
train_holidays[
np.logical_not(
train_holidays.isin(all_holidays.holiday))]})
all_holidays = pd.concat((all_holidays, holidays_to_add), sort=False)
all_holidays.reset_index(drop=True, inplace=True)
# Holds columns of our future matrix.
expanded_holidays = defaultdict(lambda: np.zeros(dates.shape[0]))
prior_scales = {}
@ -436,7 +477,7 @@ class Prophet(object):
# Strip to just dates.
row_index = pd.DatetimeIndex(dates.apply(lambda x: x.date()))
for _ix, row in self.holidays.iterrows():
for _ix, row in all_holidays.iterrows():
dt = row.ds.date()
try:
lw = int(row.get('lower_window', 0))
@ -448,7 +489,7 @@ class Prophet(object):
if np.isnan(ps):
ps = float(self.holidays_prior_scale)
if (
row.holiday in prior_scales and prior_scales[row.holiday] != ps
row.holiday in prior_scales and prior_scales[row.holiday] != ps
):
raise ValueError(
'Holiday {} does not have consistent prior scale '
@ -475,14 +516,20 @@ class Prophet(object):
# Access key to generate value
expanded_holidays[key]
holiday_features = pd.DataFrame(expanded_holidays)
# Make sure fit and predict component_cols perfectly equal
holiday_features = holiday_features[sorted(holiday_features.columns.tolist())]
prior_scale_list = [
prior_scales[h.split('_delim_')[0]]
for h in holiday_features.columns
]
return holiday_features, prior_scale_list, list(prior_scales.keys())
holiday_names = list(prior_scales.keys())
# Store holiday names used in fit
if self.train_holiday_names is None:
self.train_holiday_names = pd.Series(holiday_names)
return holiday_features, prior_scale_list, holiday_names
def add_regressor(
self, name, prior_scale=None, standardize='auto', mode=None
self, name, prior_scale=None, standardize='auto', mode=None
):
"""Add an additional regressor to be used for fitting and predicting.
@ -534,7 +581,7 @@ class Prophet(object):
return self
def add_seasonality(
self, name, period, fourier_order, prior_scale=None, mode=None
self, name, period, fourier_order, prior_scale=None, mode=None
):
"""Add a seasonal component with specified period, number of Fourier
components, and prior scale.
@ -626,7 +673,7 @@ class Prophet(object):
modes[props['mode']].append(name)
# Holiday features
if self.holidays is not None:
if self.holidays is not None or self.append_holidays is not None:
features, holiday_priors, holiday_names = (
self.make_holiday_features(df['ds'])
)
@ -679,9 +726,9 @@ class Prophet(object):
],
})
# Add total for holidays
if self.holidays is not None:
if self.train_holiday_names is not None:
components = self.add_group_component(
components, 'holidays', self.holidays['holiday'].unique())
components, 'holidays', self.train_holiday_names.unique())
# Add totals additive and multiplicative components, and regressors
for mode in ['additive', 'multiplicative']:
components = self.add_group_component(
@ -710,8 +757,8 @@ class Prophet(object):
component_cols.drop('zeros', axis=1, inplace=True, errors='ignore')
# Validation
if (
max(component_cols['additive_terms']
+ component_cols['multiplicative_terms']) > 1
max(component_cols['additive_terms']
+ component_cols['multiplicative_terms']) > 1
):
raise Exception('A bug occurred in seasonal components.')
# Compare to the training, if set.
@ -979,8 +1026,8 @@ class Prophet(object):
}
if (
(history['y'].min() == history['y'].max())
and self.growth == 'linear'
(history['y'].min() == history['y'].max())
and self.growth == 'linear'
):
# Nothing to fit.
self.params = stan_init()
@ -1050,8 +1097,8 @@ class Prophet(object):
# Add in forecast components
df2 = pd.concat((df[cols], intervals, seasonal_components), axis=1)
df2['yhat'] = (
df2['trend'] * (1 + df2['multiplicative_terms'])
+ df2['additive_terms']
df2['trend'] * (1 + df2['multiplicative_terms'])
+ df2['additive_terms']
)
return df2
@ -1104,8 +1151,8 @@ class Prophet(object):
gammas = np.zeros(len(changepoint_ts))
for i, t_s in enumerate(changepoint_ts):
gammas[i] = (
(t_s - m - np.sum(gammas))
* (1 - k_cum[i] / k_cum[i + 1]) # noqa W503
(t_s - m - np.sum(gammas))
* (1 - k_cum[i] / k_cum[i + 1]) # noqa W503
)
# Get cumulative rate and offset at each t
k_t = k * np.ones_like(t)
@ -1165,7 +1212,7 @@ class Prophet(object):
comp = np.matmul(X, beta_c.transpose())
if component in self.component_modes['additive']:
comp *= self.y_scale
comp *= self.y_scale
data[component] = np.nanmean(comp, axis=1)
data[component + '_lower'] = np.nanpercentile(
comp, lower_p, axis=1,

1206
python/fbprophet/hdays.py Normal file
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70
python/fbprophet/make_holidays.py Normal file
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@ -0,0 +1,70 @@
# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree. An additional grant
# of patent rights can be found in the PATENTS file in the same directory.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import pandas as pd
import numpy as np
import warnings
import holidays as hdays_part1
import fbprophet.hdays as hdays_part2
def get_holiday_names(country):
"""Return all possible holiday names of given country
Parameters
----------
country: country name
Returns
------- a
Dataframe with 'ds' and 'holiday', which can directly feed
to 'holidays' params in Prophet
"""
years = np.arange(1995, 2045)
try:
with warnings.catch_warnings():
warnings.simplefilter("ignore")
holiday_names = getattr(hdays_part2, country)(years=years).values()
except AttributeError:
try:
holiday_names = getattr(hdays_part1, country)(years=years).values()
except AttributeError:
raise AttributeError(
"Holidays in {} are not currently supported!".format(country))
return set(holiday_names)
def make_holidays_df(year_list, country):
"""Make dataframe of holidays for given years and countries
Parameters
----------
year_list: a list of years
country: country name
Returns
-------
Dataframe with 'ds' and 'holiday', which can directly feed
to 'holidays' params in Prophet
"""
try:
holidays = getattr(hdays_part2, country)(years=year_list)
except AttributeError:
try:
holidays = getattr(hdays_part1, country)(years=year_list)
except AttributeError:
raise AttributeError(
"Holidays in {} are not currently supported!".format(country))
holidays_df = pd.DataFrame(list(holidays.items()), columns=['ds', 'holiday'])
holidays_df.reset_index(inplace=True, drop=True)
holidays_df['ds'] = pd.to_datetime(holidays_df['ds'])
return (holidays_df)

24
python/fbprophet/tests/test_prophet.py
View file

@ -358,6 +358,30 @@ class TestProphet(TestCase):
model = Prophet(holidays=holidays, uncertainty_samples=0)
model.fit(DATA).predict()
def test_fit_predict_with_append_holidays(self):
holidays = pd.DataFrame({
'ds': pd.to_datetime(['2012-06-06', '2013-06-06']),
'holiday': ['seans-bday'] * 2,
'lower_window': [0] * 2,
'upper_window': [1] * 2,
})
append_holidays = 'US'
# Test with holidays and append_holidays
model = Prophet(holidays=holidays,
append_holidays=append_holidays,
uncertainty_samples=0)
model.fit(DATA).predict()
# There are training holidays missing in the test set
train = DATA.head(154)
future = DATA.tail(355)
model = Prophet(append_holidays=append_holidays, uncertainty_samples=0)
model.fit(train).predict(future)
# There are test holidays missing in the training set
train = DATA.tail(355)
future = DATA2
model = Prophet(append_holidays=append_holidays, uncertainty_samples=0)
model.fit(train).predict(future)
def test_make_future_dataframe(self):
N = 468
train = DATA.head(N // 2)

3
python/requirements.txt
View file

@ -3,3 +3,6 @@ pystan>=2.14
numpy>=1.10.0
pandas>=0.20.1
matplotlib>=2.0.0
lunardate>=0.1.5
convertdate>=2.1.2
holidays>=0.9.5