0

我正在使用 Prophet 模型来预测我公司的收入，我目前面临的挑战之一是能够修改代码以利用超参数调整功能来处理月度数据。据我了解，FB 先知网站上的代码旨在调整每日数据，而不是每月。但是，我已经在某个地方（似乎找不到该帖子）阅读过可以针对每月数据进行调整的地方。

有没有人能够解决这个问题？希望得到一些帮助！我不是程序员，并且一直在利用低代码平台来构建它，所以非常感谢程序员同事帮助解决这个问题！

这是我正在使用的代码：

# Conditional Install
cond.install <- function(package.name){
options(repos = "http://cran.rstudio.com") #set repo
#check for package in library, if package is missing install
if(package.name%in%rownames(installed.packages())==FALSE) {
install.packages(package.name, .libPaths()[2])}else{require(package.name, character.only = TRUE)}}

# conditionally install package
cond.install('forecast')
cond.install('prophet')
cond.install('rBayesianOptimization')
cond.install('dplyr')
cond.install('lubridate')


library(dplyr)
library(lubridate)
library(forecast)
library(prophet)
library(rBayesianOptimization)



#reading data
cv_set <- read.Alteryx("#1", mode="data.frame")
valid <- read.Alteryx("#2", mode="data.frame")

#make sure the date format is defined

cv_set$ds <- as.Date(cv_set$ds)
date_seq <- as.Date(valid$ds)

#define hyper search parameter
rand_search_grid =  data.frame( 
  changepoint_prior_scale = sort(runif(10, 0.01, 20)),
  seasonality_prior_scale = c(sort(sample(c(runif(5, 0.01, 0.05), runif(5, 1, 20)), 5, replace = F)),
                              sort(sample(c(runif(5, 0.01, 0.05), runif(5, 1, 20)), 5, replace = F))),
  n_changepoints          = sample(5:50, 10, replace = F)

)

#Define deafult function for prophet. Change Linear to Logistic cap setting
prophet_fit_bayes = function(changepoint_prior_scale, seasonality_prior_scale, n_changepoints) {
    
    error = c()
    for (d in date_seq) {
      train = subset(cv_set, ds < d)
      test  = subset(cv_set, ds == d)
    
      m = prophet(train, growth = 'linear',
                 seasonality.prior.scale = seasonality_prior_scale, 
                 changepoint.prior.scale = changepoint_prior_scale,
                 n.changepoints = n_changepoints,
                 weekly.seasonality = F,
                 daily.seasonality = F)
    
      future = make_future_dataframe(m, periods = 1)
    
    # NOTE: There's a problem in function names with library(caret)
      forecast = predict(m, future)
      forecast$ds = as.Date(forecast$ds)
    
      error_d = forecast::accuracy(forecast[forecast$ds %in% test$ds, 'yhat'], test$y)[ , 'MAPE']
      error = c(error, error_d)
  }
  
    ## The function wants to _maximize_ the outcome so we return 
    ## the negative of the resampled MAPE value. `Pred` can be used
    ## to return predicted values but we'll avoid that and use zero
    list(Score = -mean(error), Pred = 0)
}
  
changepoint_bounds    = range(rand_search_grid$changepoint_prior_scale)
n_changepoint_bounds  = as.integer(range(rand_search_grid$n_changepoints))
seasonality_bounds    = range(rand_search_grid$seasonality_prior_scale)

bayesian_search_bounds = list(changepoint_prior_scale = changepoint_bounds,
                              seasonality_prior_scale = seasonality_bounds,
                              n_changepoints = as.integer(n_changepoint_bounds))


#rBayesian parameters. Assume n_iteration is 1 for demo purpose
ba_search = BayesianOptimization(prophet_fit_bayes,
                                    bounds = bayesian_search_bounds,
                                    init_grid_dt = rand_search_grid, 
                                    init_points = 1, 
                                    n_iter = %Question.iteration.var%,
                                    acq = 'ucb', 
                                    kappa = 1, 
                                    eps = 0,
                                    verbose = TRUE)


best_params_ba  = c(ba_search$Best_Par)



#Start Prophet 

# Holiday Setting
custom1 <- data_frame(
  holiday = 'custom1',
  ds = as.Date(c('1991-12-31')))

custom2 <- data_frame(
  holiday = 'custom2',
  ds = as.Date(c('1992-12-31', '1993-01-01')))
holidays <- bind_rows(custom1, custom2)


if ('%Question.noholiday.var%' == "True") {
    m = prophet(cv_set, growth = 'linear',
                 seasonality.prior.scale = best_params_ba[['seasonality_prior_scale']], 
                 changepoint.prior.scale = best_params_ba[['changepoint_prior_scale']],
                 n.changepoints = best_params_ba[['n_changepoints']])
}
if ('%Question.holiday.var%' == "True") {
    m <- prophet(holidays = holidays, growth = 'linear',
                 seasonality.prior.scale = best_params_ba[['seasonality_prior_scale']], 
                 changepoint.prior.scale = best_params_ba[['changepoint_prior_scale']],
                 n.changepoints = best_params_ba[['n_changepoints']])
    m <- add_country_holidays(m, country_name = '%Question.country.var%')
    m <- fit.prophet(m, cv_set)
}

future <- make_future_dataframe(m, periods = %Question.forecast.var%)
forecast <- predict(m, future)

yhat <- as.data.frame(forecast$yhat)
yhat_l <- as.data.frame(forecast$yhat_lower)
yhat_u <-as.data.frame(forecast$yhat_upper)
trend <- as.data.frame(forecast$trend)

df1 <- cbind(yhat, yhat_l, yhat_u, trend)

write.Alteryx(df1, 1)

AlteryxGraph(3, width=576, height=576)
plot(m, forecast) + add_changepoints_to_plot(m)
invisible(dev.off())


AlteryxGraph(4, width=576, height=576)
prophet_plot_components(m, forecast)
invisible(dev.off())


#Output best params for reference
df5 <- best_params_ba

write.Alteryx(df5, 5)
4

0 回答 0