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Allow shifting the start date of the yearly seasonality plot

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Ben Letham 2017-04-13 02:08:34 -07:00
parent cacfdc635e
commit df4b6158eb
4 changed files with 35 additions and 11 deletions
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18
R/R/prophet.R
View file

@ -990,13 +990,17 @@ plot.prophet <- function(x, fcst, uncertainty = TRUE, plot_cap = TRUE,
#' @param weekly_start Integer specifying the start day of the weekly
#' seasonality plot. 0 (default) starts the week on Sunday. 1 shifts by 1 day
#' to Monday, and so on.
#' @param yearly_start Integer specifying the start day of the yearly
#' seasonality plot. 0 (default) starts the year on Jan 1. 1 shifts by 1 day
#' to Jan 2, and so on.
#'
#' @return Invisibly return a list containing the plotted ggplot objects
#'
#' @export
#' @importFrom dplyr "%>%"
prophet_plot_components <- function(m, fcst, uncertainty = TRUE,
plot_cap = TRUE, weekly_start = 0) {
prophet_plot_components <- function(
m, fcst, uncertainty = TRUE, plot_cap = TRUE, weekly_start = 0,
yearly_start = 0) {
df <- df_for_plotting(m, fcst)
# Plot the trend
panels <- list(plot_trend(df, uncertainty, plot_cap))
@ -1010,7 +1014,7 @@ prophet_plot_components <- function(m, fcst, uncertainty = TRUE,
}
# Plot yearly seasonality, if present
if ("yearly" %in% colnames(df)) {
panels[[length(panels) + 1]] <- plot_yearly(m, uncertainty)
panels[[length(panels) + 1]] <- plot_yearly(m, uncertainty, yearly_start)
}
# Make the plot.
grid::grid.newpage()
@ -1119,12 +1123,16 @@ plot_weekly <- function(m, uncertainty = TRUE, weekly_start = 0) {
#'
#' @param m Prophet model object.
#' @param uncertainty Boolean to plot uncertainty intervals.
#' @param yearly_start Integer specifying the start day of the yearly
#' seasonality plot. 0 (default) starts the year on Jan 1. 1 shifts by 1 day
#' to Jan 2, and so on.
#'
#' @return A ggplot2 plot.
plot_yearly <- function(m, uncertainty = TRUE) {
plot_yearly <- function(m, uncertainty = TRUE, yearly_start = 0) {
# Compute yearly seasonality for a Jan 1 - Dec 31 sequence of dates.
df.y <- data.frame(
ds=seq.Date(zoo::as.Date('2017-01-01'), by='d', length.out=365), cap=1.)
ds=seq.Date(zoo::as.Date('2017-01-01'), by='d', length.out=365) +
yearly_start, cap=1.)
df.y <- setup_dataframe(m, df.y)$df
seas <- predict_seasonal_components(m, df.y)
seas$ds <- df.y$ds

6
R/man/plot_yearly.Rd
View file

@ -4,12 +4,16 @@
\alias{plot_yearly}
\title{Plot the yearly component of the forecast.}
\usage{
plot_yearly(m, uncertainty = TRUE)
plot_yearly(m, uncertainty = TRUE, yearly_start = 0)
}
\arguments{
\item{m}{Prophet model object.}
\item{uncertainty}{Boolean to plot uncertainty intervals.}
\item{yearly_start}{Integer specifying the start day of the yearly
seasonality plot. 0 (default) starts the year on Jan 1. 1 shifts by 1 day
to Jan 2, and so on.}
}
\value{
A ggplot2 plot.

6
R/man/prophet_plot_components.Rd
View file

@ -7,7 +7,7 @@ Prints a ggplot2 with panels for trend, weekly and yearly seasonalities if
present, and holidays if present.}
\usage{
prophet_plot_components(m, fcst, uncertainty = TRUE, plot_cap = TRUE,
weekly_start = 0)
weekly_start = 0, yearly_start = 0)
}
\arguments{
\item{m}{Prophet object.}
@ -23,6 +23,10 @@ figure, if available.}
\item{weekly_start}{Integer specifying the start day of the weekly
seasonality plot. 0 (default) starts the week on Sunday. 1 shifts by 1 day
to Monday, and so on.}
\item{yearly_start}{Integer specifying the start day of the yearly
seasonality plot. 0 (default) starts the year on Jan 1. 1 shifts by 1 day
to Jan 2, and so on.}
}
\value{
Invisibly return a list containing the plotted ggplot objects

16
python/fbprophet/forecaster.py
View file

@ -915,7 +915,7 @@ class Prophet(object):
return fig
def plot_components(self, fcst, uncertainty=True, plot_cap=True,
weekly_start=0):
weekly_start=0, yearly_start=0):
"""Plot the Prophet forecast components.
Will plot whichever are available of: trend, holidays, weekly
@ -930,6 +930,9 @@ class Prophet(object):
weekly_start: Optional int specifying the start day of the weekly
seasonality plot. 0 (default) starts the week on Sunday. 1 shifts
by 1 day to Monday, and so on.
yearly_start: Optional int specifying the start day of the yearly
seasonality plot. 0 (default) starts the year on Jan 1. 1 shifts
by 1 day to Jan 2, and so on.
Returns
-------
@ -958,7 +961,8 @@ class Prophet(object):
artists += self.plot_weekly(ax=ax, uncertainty=uncertainty,
weekly_start=weekly_start)
elif plot == 'yearly':
artists += self.plot_yearly(ax=ax, uncertainty=uncertainty)
artists += self.plot_yearly(ax=ax, uncertainty=uncertainty,
yearly_start=yearly_start)
fig.tight_layout()
return artists
@ -1072,7 +1076,7 @@ class Prophet(object):
ax.set_ylabel('weekly')
return artists
def plot_yearly(self, ax=None, uncertainty=True):
def plot_yearly(self, ax=None, uncertainty=True, yearly_start=0):
"""Plot the yearly component of the forecast.
Parameters
@ -1080,6 +1084,9 @@ class Prophet(object):
ax: Optional matplotlib Axes to plot on. One will be created if
this is not provided.
uncertainty: Optional boolean to plot uncertainty intervals.
yearly_start: Optional int specifying the start day of the yearly
seasonality plot. 0 (default) starts the year on Jan 1. 1 shifts
by 1 day to Jan 2, and so on.
Returns
-------
@ -1091,7 +1098,8 @@ class Prophet(object):
ax = fig.add_subplot(111)
# Compute yearly seasonality for a Jan 1 - Dec 31 sequence of dates.
df_y = pd.DataFrame(
{'ds': pd.date_range(start='2017-01-01', periods=365), 'cap': 1.})
{'ds': pd.date_range(start='2017-01-01', periods=365) +
pd.Timedelta(days=yearly_start), 'cap': 1.})
df_y = self.setup_dataframe(df_y)
seas = self.predict_seasonal_components(df_y)
artists += ax.plot(df_y['ds'], seas['yearly'], ls='-',