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forecaster.py
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forecaster.py
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# -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import absolute_import, division, print_function
import logging
from collections import OrderedDict, defaultdict
from copy import deepcopy
from datetime import timedelta, datetime
import numpy as np
import pandas as pd
from fbprophet.make_holidays import get_holiday_names, make_holidays_df
from fbprophet.models import StanBackendEnum
from fbprophet.plot import (plot, plot_components)
logger = logging.getLogger('fbprophet')
logger.setLevel(logging.INFO)
class Prophet(object):
"""Prophet forecaster.
Parameters
----------
growth: String 'linear' or 'logistic' to specify a linear or logistic
trend.
changepoints: List of dates at which to include potential changepoints. If
not specified, potential changepoints are selected automatically.
n_changepoints: Number of potential changepoints to include. Not used
if input `changepoints` is supplied. If `changepoints` is not supplied,
then n_changepoints potential changepoints are selected uniformly from
the first `changepoint_range` proportion of the history.
changepoint_range: Proportion of history in which trend changepoints will
be estimated. Defaults to 0.8 for the first 80%. Not used if
`changepoints` is specified.
yearly_seasonality: Fit yearly seasonality.
Can be 'auto', True, False, or a number of Fourier terms to generate.
weekly_seasonality: Fit weekly seasonality.
Can be 'auto', True, False, or a number of Fourier terms to generate.
daily_seasonality: Fit daily seasonality.
Can be 'auto', True, False, or a number of Fourier terms to generate.
holidays: pd.DataFrame with columns holiday (string) and ds (date type)
and optionally columns lower_window and upper_window which specify a
range of days around the date to be included as holidays.
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.
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
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 overridden in the holidays input.
changepoint_prior_scale: Parameter modulating the flexibility of the
automatic changepoint selection. Large values will allow many
changepoints, small values will allow few changepoints.
mcmc_samples: Integer, if greater than 0, will do full Bayesian inference
with the specified number of MCMC samples. If 0, will do MAP
estimation.
interval_width: Float, width of the uncertainty intervals provided
for the forecast. If mcmc_samples=0, this will be only the uncertainty
in the trend using the MAP estimate of the extrapolated generative
model. If mcmc.samples>0, this will be integrated over all model
parameters, which will include uncertainty in seasonality.
uncertainty_samples: Number of simulated draws used to estimate
uncertainty intervals. Settings this value to 0 or False will disable
uncertainty estimation and speed up the calculation.
stan_backend: str as defined in StanBackendEnum default: None - will try to
iterate over all available backends and find the working one
"""
def __init__(
self,
growth='linear',
changepoints=None,
n_changepoints=25,
changepoint_range=0.8,
yearly_seasonality='auto',
weekly_seasonality='auto',
daily_seasonality='auto',
holidays=None,
seasonality_mode='additive',
seasonality_prior_scale=10.0,
holidays_prior_scale=10.0,
changepoint_prior_scale=0.05,
mcmc_samples=0,
interval_width=0.80,
uncertainty_samples=1000,
stan_backend=None
):
self.growth = growth
self.changepoints = changepoints
if self.changepoints is not None:
self.changepoints = pd.Series(pd.to_datetime(self.changepoints), name='ds')
self.n_changepoints = len(self.changepoints)
self.specified_changepoints = True
else:
self.n_changepoints = n_changepoints
self.specified_changepoints = False
self.changepoint_range = changepoint_range
self.yearly_seasonality = yearly_seasonality
self.weekly_seasonality = weekly_seasonality
self.daily_seasonality = daily_seasonality
self.holidays = holidays
self.seasonality_mode = seasonality_mode
self.seasonality_prior_scale = float(seasonality_prior_scale)
self.changepoint_prior_scale = float(changepoint_prior_scale)
self.holidays_prior_scale = float(holidays_prior_scale)
self.mcmc_samples = mcmc_samples
self.interval_width = interval_width
self.uncertainty_samples = uncertainty_samples
# Set during fitting or by other methods
self.start = None
self.y_scale = None
self.logistic_floor = False
self.t_scale = None
self.changepoints_t = None
self.seasonalities = OrderedDict({})
self.extra_regressors = OrderedDict({})
self.country_holidays = None
self.stan_fit = None
self.params = {}
self.history = None
self.history_dates = None
self.train_component_cols = None
self.component_modes = None
self.train_holiday_names = None
self.fit_kwargs = {}
self.validate_inputs()
self._load_stan_backend(stan_backend)
def _load_stan_backend(self, stan_backend):
if stan_backend is None:
for i in StanBackendEnum:
try:
logger.debug("Trying to load backend: %s", i.name)
return self._load_stan_backend(i.name)
except Exception as e:
logger.debug("Unable to load backend %s (%s), trying the next one", i.name, e)
else:
self.stan_backend = StanBackendEnum.get_backend_class(stan_backend)()
logger.debug("Loaded stan backend: %s", self.stan_backend.get_type())
def validate_inputs(self):
"""Validates the inputs to Prophet."""
if self.growth not in ('linear', 'logistic', 'flat'):
raise ValueError(
'Parameter "growth" should be "linear", "logistic" or "flat".')
if ((self.changepoint_range < 0) or (self.changepoint_range > 1)):
raise ValueError('Parameter "changepoint_range" must be in [0, 1]')
if self.holidays is not None:
if not (
isinstance(self.holidays, pd.DataFrame)
and 'ds' in self.holidays # noqa W503
and 'holiday' in self.holidays # noqa W503
):
raise ValueError('holidays must be a DataFrame with "ds" and '
'"holiday" columns.')
self.holidays['ds'] = pd.to_datetime(self.holidays['ds'])
has_lower = 'lower_window' in self.holidays
has_upper = 'upper_window' in self.holidays
if has_lower + has_upper == 1:
raise ValueError('Holidays must have both lower_window and ' +
'upper_window, or neither')
if has_lower:
if self.holidays['lower_window'].max() > 0:
raise ValueError('Holiday lower_window should be <= 0')
if self.holidays['upper_window'].min() < 0:
raise ValueError('Holiday upper_window should be >= 0')
for h in self.holidays['holiday'].unique():
self.validate_column_name(h, check_holidays=False)
if self.seasonality_mode not in ['additive', 'multiplicative']:
raise ValueError(
'seasonality_mode must be "additive" or "multiplicative"'
)
def validate_column_name(self, name, check_holidays=True,
check_seasonalities=True, check_regressors=True):
"""Validates the name of a seasonality, holiday, or regressor.
Parameters
----------
name: string
check_holidays: bool check if name already used for holiday
check_seasonalities: bool check if name already used for seasonality
check_regressors: bool check if name already used for regressor
"""
if '_delim_' in name:
raise ValueError('Name cannot contain "_delim_"')
reserved_names = [
'trend', 'additive_terms', 'daily', 'weekly', 'yearly',
'holidays', 'zeros', 'extra_regressors_additive', 'yhat',
'extra_regressors_multiplicative', 'multiplicative_terms',
]
rn_l = [n + '_lower' for n in reserved_names]
rn_u = [n + '_upper' for n in reserved_names]
reserved_names.extend(rn_l)
reserved_names.extend(rn_u)
reserved_names.extend([
'ds', 'y', 'cap', 'floor', 'y_scaled', 'cap_scaled'])
if name in reserved_names:
raise ValueError(
'Name {name!r} is reserved.'.format(name=name)
)
if (check_holidays and self.holidays is not None and
name in self.holidays['holiday'].unique()):
raise ValueError(
'Name {name!r} already used for a holiday.'.format(name=name)
)
if (check_holidays and self.country_holidays is not None and
name in get_holiday_names(self.country_holidays)):
raise ValueError(
'Name {name!r} is a holiday name in {country_holidays}.'
.format(name=name, country_holidays=self.country_holidays)
)
if check_seasonalities and name in self.seasonalities:
raise ValueError(
'Name {name!r} already used for a seasonality.'
.format(name=name)
)
if check_regressors and name in self.extra_regressors:
raise ValueError(
'Name {name!r} already used for an added regressor.'
.format(name=name)
)
def setup_dataframe(self, df, initialize_scales=False):
"""Prepare dataframe for fitting or predicting.
Adds a time index and scales y. Creates auxiliary columns 't', 't_ix',
'y_scaled', and 'cap_scaled'. These columns are used during both
fitting and predicting.
Parameters
----------
df: pd.DataFrame with columns ds, y, and cap if logistic growth. Any
specified additional regressors must also be present.
initialize_scales: Boolean set scaling factors in self from df.
Returns
-------
pd.DataFrame prepared for fitting or predicting.
"""
if 'y' in df: # 'y' will be in training data
df['y'] = pd.to_numeric(df['y'])
if np.isinf(df['y'].values).any():
raise ValueError('Found infinity in column y.')
if df['ds'].dtype == np.int64:
df['ds'] = df['ds'].astype(str)
df['ds'] = pd.to_datetime(df['ds'])
if df['ds'].dt.tz is not None:
raise ValueError(
'Column ds has timezone specified, which is not supported. '
'Remove timezone.'
)
if df['ds'].isnull().any():
raise ValueError('Found NaN in column ds.')
for name in self.extra_regressors:
if name not in df:
raise ValueError(
'Regressor {name!r} missing from dataframe'
.format(name=name)
)
df[name] = pd.to_numeric(df[name])
if df[name].isnull().any():
raise ValueError(
'Found NaN in column {name!r}'.format(name=name)
)
for props in self.seasonalities.values():
condition_name = props['condition_name']
if condition_name is not None:
if condition_name not in df:
raise ValueError(
'Condition {condition_name!r} missing from dataframe'
.format(condition_name=condition_name)
)
if not df[condition_name].isin([True, False]).all():
raise ValueError(
'Found non-boolean in column {condition_name!r}'
.format(condition_name=condition_name)
)
df[condition_name] = df[condition_name].astype('bool')
if df.index.name == 'ds':
df.index.name = None
df = df.sort_values('ds')
df = df.reset_index(drop=True)
self.initialize_scales(initialize_scales, df)
if self.logistic_floor:
if 'floor' not in df:
raise ValueError('Expected column "floor".')
else:
df['floor'] = 0
if self.growth == 'logistic':
if 'cap' not in df:
raise ValueError(
'Capacities must be supplied for logistic growth in '
'column "cap"'
)
if (df['cap'] <= df['floor']).any():
raise ValueError(
'cap must be greater than floor (which defaults to 0).'
)
df['cap_scaled'] = (df['cap'] - df['floor']) / self.y_scale
df['t'] = (df['ds'] - self.start) / self.t_scale
if 'y' in df:
df['y_scaled'] = (df['y'] - df['floor']) / self.y_scale
for name, props in self.extra_regressors.items():
df[name] = ((df[name] - props['mu']) / props['std'])
return df
def initialize_scales(self, initialize_scales, df):
"""Initialize model scales.
Sets model scaling factors using df.
Parameters
----------
initialize_scales: Boolean set the scales or not.
df: pd.DataFrame for setting scales.
"""
if not initialize_scales:
return
if self.growth == 'logistic' and 'floor' in df:
self.logistic_floor = True
floor = df['floor']
else:
floor = 0.
self.y_scale = (df['y'] - floor).abs().max()
if self.y_scale == 0:
self.y_scale = 1
self.start = df['ds'].min()
self.t_scale = df['ds'].max() - self.start
for name, props in self.extra_regressors.items():
standardize = props['standardize']
n_vals = len(df[name].unique())
if n_vals < 2:
standardize = False
if standardize == 'auto':
if set(df[name].unique()) == set([1, 0]):
standardize = False # Don't standardize binary variables.
else:
standardize = True
if standardize:
mu = df[name].mean()
std = df[name].std()
self.extra_regressors[name]['mu'] = mu
self.extra_regressors[name]['std'] = std
def set_changepoints(self):
"""Set changepoints
Sets m$changepoints to the dates of changepoints. Either:
1) The changepoints were passed in explicitly.
A) They are empty.
B) They are not empty, and need validation.
2) We are generating a grid of them.
3) The user prefers no changepoints be used.
"""
if self.changepoints is not None:
if len(self.changepoints) == 0:
pass
else:
too_low = min(self.changepoints) < self.history['ds'].min()
too_high = max(self.changepoints) > self.history['ds'].max()
if too_low or too_high:
raise ValueError(
'Changepoints must fall within training data.')
else:
# Place potential changepoints evenly through first
# `changepoint_range` proportion of the history
hist_size = int(np.floor(self.history.shape[0]
* self.changepoint_range))
if self.n_changepoints + 1 > hist_size:
self.n_changepoints = hist_size - 1
logger.info(
'n_changepoints greater than number of observations. '
'Using {n_changepoints}.'
.format(n_changepoints=self.n_changepoints)
)
if self.n_changepoints > 0:
cp_indexes = (
np.linspace(0, hist_size - 1, self.n_changepoints + 1)
.round()
.astype(np.int)
)
self.changepoints = (
self.history.iloc[cp_indexes]['ds'].tail(-1)
)
else:
# set empty changepoints
self.changepoints = pd.Series(pd.to_datetime([]), name='ds')
if len(self.changepoints) > 0:
self.changepoints_t = np.sort(np.array(
(self.changepoints - self.start) / self.t_scale))
else:
self.changepoints_t = np.array([0]) # dummy changepoint
@staticmethod
def fourier_series(dates, period, series_order):
"""Provides Fourier series components with the specified frequency
and order.
Parameters
----------
dates: pd.Series containing timestamps.
period: Number of days of the period.
series_order: Number of components.
Returns
-------
Matrix with seasonality features.
"""
# convert to days since epoch
t = np.array(
(dates - datetime(1970, 1, 1))
.dt.total_seconds()
.astype(np.float)
) / (3600 * 24.)
return np.column_stack([
fun((2.0 * (i + 1) * np.pi * t / period))
for i in range(series_order)
for fun in (np.sin, np.cos)
])
@classmethod
def make_seasonality_features(cls, dates, period, series_order, prefix):
"""Data frame with seasonality features.
Parameters
----------
cls: Prophet class.
dates: pd.Series containing timestamps.
period: Number of days of the period.
series_order: Number of components.
prefix: Column name prefix.
Returns
-------
pd.DataFrame with seasonality features.
"""
features = cls.fourier_series(dates, period, series_order)
columns = [
'{}_delim_{}'.format(prefix, i + 1)
for i in range(features.shape[1])
]
return pd.DataFrame(features, columns=columns)
def construct_holiday_dataframe(self, dates):
"""Construct a dataframe of holiday dates.
Will combine self.holidays with the built-in country holidays
corresponding to input dates, if self.country_holidays is set.
Parameters
----------
dates: pd.Series containing timestamps used for computing seasonality.
Returns
-------
dataframe of holiday dates, in holiday dataframe format used in
initialization.
"""
all_holidays = pd.DataFrame()
if self.holidays is not None:
all_holidays = self.holidays.copy()
if self.country_holidays is not None:
year_list = list({x.year for x in dates})
country_holidays_df = make_holidays_df(
year_list=year_list, country=self.country_holidays
)
all_holidays = pd.concat((all_holidays, country_holidays_df),
sort=False)
all_holidays.reset_index(drop=True, inplace=True)
# Drop future holidays not previously seen in training data
if self.train_holiday_names is not None:
# Remove holiday names didn't show up in fit
index_to_drop = all_holidays.index[
np.logical_not(
all_holidays.holiday.isin(self.train_holiday_names)
)
]
all_holidays = all_holidays.drop(index_to_drop)
# Add holiday names in fit but not in predict with ds as NA
holidays_to_add = pd.DataFrame({
'holiday': self.train_holiday_names[
np.logical_not(self.train_holiday_names
.isin(all_holidays.holiday))
]
})
all_holidays = pd.concat((all_holidays, holidays_to_add),
sort=False)
all_holidays.reset_index(drop=True, inplace=True)
return all_holidays
def make_holiday_features(self, dates, holidays):
"""Construct a dataframe of holiday features.
Parameters
----------
dates: pd.Series containing timestamps used for computing seasonality.
holidays: pd.Dataframe containing holidays, as returned by
construct_holiday_dataframe.
Returns
-------
holiday_features: pd.DataFrame with a column for each holiday.
prior_scale_list: List of prior scales for each holiday column.
holiday_names: List of names of holidays
"""
# Holds columns of our future matrix.
expanded_holidays = defaultdict(lambda: np.zeros(dates.shape[0]))
prior_scales = {}
# Makes an index so we can perform `get_loc` below.
# Strip to just dates.
row_index = pd.DatetimeIndex(dates.apply(lambda x: x.date()))
for _ix, row in holidays.iterrows():
dt = row.ds.date()
try:
lw = int(row.get('lower_window', 0))
uw = int(row.get('upper_window', 0))
except ValueError:
lw = 0
uw = 0
ps = float(row.get('prior_scale', self.holidays_prior_scale))
if np.isnan(ps):
ps = float(self.holidays_prior_scale)
if row.holiday in prior_scales and prior_scales[row.holiday] != ps:
raise ValueError(
'Holiday {holiday!r} does not have consistent prior '
'scale specification.'.format(holiday=row.holiday)
)
if ps <= 0:
raise ValueError('Prior scale must be > 0')
prior_scales[row.holiday] = ps
for offset in range(lw, uw + 1):
occurrence = pd.to_datetime(dt + timedelta(days=offset))
try:
loc = row_index.get_loc(occurrence)
except KeyError:
loc = None
key = '{}_delim_{}{}'.format(
row.holiday,
'+' if offset >= 0 else '-',
abs(offset)
)
if loc is not None:
expanded_holidays[key][loc] = 1.
else:
expanded_holidays[key] # Access key to generate value
holiday_features = pd.DataFrame(expanded_holidays)
# Make sure column order is consistent
holiday_features = holiday_features[sorted(holiday_features.columns
.tolist())]
prior_scale_list = [
prior_scales[h.split('_delim_')[0]]
for h in holiday_features.columns
]
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):
"""Add an additional regressor to be used for fitting and predicting.
The dataframe passed to `fit` and `predict` will have a column with the
specified name to be used as a regressor. When standardize='auto', the
regressor will be standardized unless it is binary. The regression
coefficient is given a prior with the specified scale parameter.
Decreasing the prior scale will add additional regularization. If no
prior scale is provided, self.holidays_prior_scale will be used.
Mode can be specified as either 'additive' or 'multiplicative'. If not
specified, self.seasonality_mode will be used. 'additive' means the
effect of the regressor will be added to the trend, 'multiplicative'
means it will multiply the trend.
Parameters
----------
name: string name of the regressor.
prior_scale: optional float scale for the normal prior. If not
provided, self.holidays_prior_scale will be used.
standardize: optional, specify whether this regressor will be
standardized prior to fitting. Can be 'auto' (standardize if not
binary), True, or False.
mode: optional, 'additive' or 'multiplicative'. Defaults to
self.seasonality_mode.
Returns
-------
The prophet object.
"""
if self.history is not None:
raise Exception(
"Regressors must be added prior to model fitting.")
self.validate_column_name(name, check_regressors=False)
if prior_scale is None:
prior_scale = float(self.holidays_prior_scale)
if mode is None:
mode = self.seasonality_mode
if prior_scale <= 0:
raise ValueError('Prior scale must be > 0')
if mode not in ['additive', 'multiplicative']:
raise ValueError("mode must be 'additive' or 'multiplicative'")
self.extra_regressors[name] = {
'prior_scale': prior_scale,
'standardize': standardize,
'mu': 0.,
'std': 1.,
'mode': mode,
}
return self
def add_seasonality(self, name, period, fourier_order, prior_scale=None,
mode=None, condition_name=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).
Mode can be specified as either 'additive' or 'multiplicative'. If not
specified, self.seasonality_mode will be used (defaults to additive).
Additive means the seasonality will be added to the trend,
multiplicative means it will multiply the trend.
If condition_name is provided, the dataframe passed to `fit` and
`predict` should have a column with the specified condition_name
containing booleans which decides when to apply seasonality.
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: optional float prior scale for this component.
mode: optional 'additive' or 'multiplicative'
condition_name: string name of the seasonality condition.
Returns
-------
The prophet object.
"""
if self.history is not None:
raise Exception(
'Seasonality must be added prior to model fitting.')
if name not in ['daily', 'weekly', 'yearly']:
# Allow overwriting built-in seasonalities
self.validate_column_name(name, check_seasonalities=False)
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')
if fourier_order <= 0:
raise ValueError('Fourier Order must be > 0')
if mode is None:
mode = self.seasonality_mode
if mode not in ['additive', 'multiplicative']:
raise ValueError('mode must be "additive" or "multiplicative"')
if condition_name is not None:
self.validate_column_name(condition_name)
self.seasonalities[name] = {
'period': period,
'fourier_order': fourier_order,
'prior_scale': ps,
'mode': mode,
'condition_name': condition_name,
}
return self
def add_country_holidays(self, country_name):
"""Add in built-in holidays for the specified country.
These holidays will be included in addition to any specified on model
initialization.
Holidays will be calculated for arbitrary date ranges in the history
and future. See the online documentation for the list of countries with
built-in holidays.
Built-in country holidays can only be set for a single country.
Parameters
----------
country_name: Name of the country, like 'UnitedStates' or 'US'
Returns
-------
The prophet object.
"""
if self.history is not None:
raise Exception(
"Country holidays must be added prior to model fitting."
)
# Validate names.
for name in get_holiday_names(country_name):
# Allow merging with existing holidays
self.validate_column_name(name, check_holidays=False)
# Set the holidays.
if self.country_holidays is not None:
logger.warning(
'Changing country holidays from {country_holidays!r} to '
'{country_name!r}.'
.format(
country_holidays=self.country_holidays,
country_name=country_name,
)
)
self.country_holidays = country_name
return self
def make_all_seasonality_features(self, df):
"""Dataframe with seasonality features.
Includes seasonality features, holiday features, and added regressors.
Parameters
----------
df: pd.DataFrame with dates for computing seasonality features and any
added regressors.
Returns
-------
pd.DataFrame with regression features.
list of prior scales for each column of the features dataframe.
Dataframe with indicators for which regression components correspond to
which columns.
Dictionary with keys 'additive' and 'multiplicative' listing the
component names for each mode of seasonality.
"""
seasonal_features = []
prior_scales = []
modes = {'additive': [], 'multiplicative': []}
# Seasonality features
for name, props in self.seasonalities.items():
features = self.make_seasonality_features(
df['ds'],
props['period'],
props['fourier_order'],
name,
)
if props['condition_name'] is not None:
features[~df[props['condition_name']]] = 0
seasonal_features.append(features)
prior_scales.extend(
[props['prior_scale']] * features.shape[1])
modes[props['mode']].append(name)
# Holiday features
holidays = self.construct_holiday_dataframe(df['ds'])
if len(holidays) > 0:
features, holiday_priors, holiday_names = (
self.make_holiday_features(df['ds'], holidays)
)
seasonal_features.append(features)
prior_scales.extend(holiday_priors)
modes[self.seasonality_mode].extend(holiday_names)
# Additional regressors
for name, props in self.extra_regressors.items():
seasonal_features.append(pd.DataFrame(df[name]))
prior_scales.append(props['prior_scale'])
modes[props['mode']].append(name)
# Dummy to prevent empty X
if len(seasonal_features) == 0:
seasonal_features.append(
pd.DataFrame({'zeros': np.zeros(df.shape[0])}))
prior_scales.append(1.)
seasonal_features = pd.concat(seasonal_features, axis=1)
component_cols, modes = self.regressor_column_matrix(
seasonal_features, modes
)
return seasonal_features, prior_scales, component_cols, modes
def regressor_column_matrix(self, seasonal_features, modes):
"""Dataframe indicating which columns of the feature matrix correspond
to which seasonality/regressor components.
Includes combination components, like 'additive_terms'. These
combination components will be added to the 'modes' input.
Parameters
----------
seasonal_features: Constructed seasonal features dataframe
modes: Dictionary with keys 'additive' and 'multiplicative' listing the
component names for each mode of seasonality.
Returns
-------
component_cols: A binary indicator dataframe with columns seasonal
components and rows columns in seasonal_features. Entry is 1 if
that columns is used in that component.
modes: Updated input with combination components.
"""
components = pd.DataFrame({
'col': np.arange(seasonal_features.shape[1]),
'component': [
x.split('_delim_')[0] for x in seasonal_features.columns
],
})
# Add total for holidays
if self.train_holiday_names is not None:
components = self.add_group_component(
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(
components, mode + '_terms', modes[mode]
)
regressors_by_mode = [
r for r, props in self.extra_regressors.items()
if props['mode'] == mode
]
components = self.add_group_component(
components, 'extra_regressors_' + mode, regressors_by_mode)
# Add combination components to modes
modes[mode].append(mode + '_terms')
modes[mode].append('extra_regressors_' + mode)
# After all of the additive/multiplicative groups have been added,
modes[self.seasonality_mode].append('holidays')
# Convert to a binary matrix
component_cols = pd.crosstab(
components['col'], components['component'],
).sort_index(level='col')
# Add columns for additive and multiplicative terms, if missing
for name in ['additive_terms', 'multiplicative_terms']:
if name not in component_cols:
component_cols[name] = 0
# Remove the placeholder
component_cols.drop('zeros', axis=1, inplace=True, errors='ignore')
# Validation
if (max(component_cols['additive_terms']
+ component_cols['multiplicative_terms']) > 1):
raise Exception('A bug occurred in seasonal components.')
# Compare to the training, if set.
if self.train_component_cols is not None:
component_cols = component_cols[self.train_component_cols.columns]
if not component_cols.equals(self.train_component_cols):
raise Exception('A bug occurred in constructing regressors.')
return component_cols, modes
def add_group_component(self, components, name, group):
"""Adds a component with given name that contains all of the components
in group.
Parameters
----------
components: Dataframe with components.
name: Name of new group component.
group: List of components that form the group.
Returns
-------
Dataframe with components.
"""
new_comp = components[components['component'].isin(set(group))].copy()
group_cols = new_comp['col'].unique()
if len(group_cols) > 0:
new_comp = pd.DataFrame({'col': group_cols, 'component': name})
components = components.append(new_comp)
return components
def parse_seasonality_args(self, name, arg, auto_disable, default_order):
"""Get number of fourier components for built-in seasonalities.
Parameters
----------
name: string name of the seasonality component.
arg: 'auto', True, False, or number of fourier components as provided.
auto_disable: bool if seasonality should be disabled when 'auto'.
default_order: int default fourier order
Returns
-------
Number of fourier components, or 0 for disabled.
"""
if arg == 'auto':
fourier_order = 0
if name in self.seasonalities:
logger.info(
'Found custom seasonality named {name!r}, disabling '
'built-in {name!r} seasonality.'.format(name=name)
)
elif auto_disable:
logger.info(
'Disabling {name} seasonality. Run prophet with '
'{name}_seasonality=True to override this.'
.format(name=name)
)
else:
fourier_order = default_order
elif arg is True:
fourier_order = default_order
elif arg is False:
fourier_order = 0
else:
fourier_order = int(arg)
return fourier_order
def set_auto_seasonalities(self):
"""Set seasonalities that were left on auto.
Turns on yearly seasonality if there is >=2 years of history.
Turns on weekly seasonality if there is >=2 weeks of history, and the
spacing between dates in the history is <7 days.
Turns on daily seasonality if there is >=2 days of history, and the
spacing between dates in the history is <1 day.
"""
first = self.history['ds'].min()
last = self.history['ds'].max()
dt = self.history['ds'].diff()
min_dt = dt.iloc[dt.values.nonzero()[0]].min()
# Yearly seasonality
yearly_disable = last - first < pd.Timedelta(days=730)
fourier_order = self.parse_seasonality_args(
'yearly', self.yearly_seasonality, yearly_disable, 10)
if fourier_order > 0:
self.seasonalities['yearly'] = {
'period': 365.25,
'fourier_order': fourier_order,
'prior_scale': self.seasonality_prior_scale,
'mode': self.seasonality_mode,
'condition_name': None
}
# Weekly seasonality
weekly_disable = ((last - first < pd.Timedelta(weeks=2)) or
(min_dt >= pd.Timedelta(weeks=1)))
fourier_order = self.parse_seasonality_args(
'weekly', self.weekly_seasonality, weekly_disable, 3)
if fourier_order > 0:
self.seasonalities['weekly'] = {
'period': 7,
'fourier_order': fourier_order,
'prior_scale': self.seasonality_prior_scale,
'mode': self.seasonality_mode,
'condition_name': None
}
# Daily seasonality
daily_disable = ((last - first < pd.Timedelta(days=2)) or
(min_dt >= pd.Timedelta(days=1)))
fourier_order = self.parse_seasonality_args(
'daily', self.daily_seasonality, daily_disable, 4)
if fourier_order > 0:
self.seasonalities['daily'] = {
'period': 1,
'fourier_order': fourier_order,
'prior_scale': self.seasonality_prior_scale,
'mode': self.seasonality_mode,
'condition_name': None
}
@staticmethod
def linear_growth_init(df):
"""Initialize linear growth.
Provides a strong initialization for linear growth by calculating the
growth and offset parameters that pass the function through the first
and last points in the time series.
Parameters
----------
df: pd.DataFrame with columns ds (date), y_scaled (scaled time series),
and t (scaled time).
Returns
-------