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robyn_two_var_tv_fb.R
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## Libraries
library(Robyn)
library(readr)
library(data.table)
library(dplyr)
library(rlist)
## force multicore when using RStudio
Sys.setenv(R_FUTURE_FORK_ENABLE="true")
options(future.fork.enable = TRUE)
library('reticulate')
#
# Key configuration items
#
window_start = "2021-01-01" # Robyn window start
window_end = "2021-12-31" # Robyn window end
trials = 5 # Robyn trials
iterations = 2000 # Robyn iterations
source_file = "../mmm-toy-data/data/robyn_toy_data_2paidvar_bal_eff2ratio_dimret_600000err.csv"
# csv file with the source data
robyn_version_expected = '3.5.0.2' # assert correct version of Robyn
output_working_directory = 'e:\\repo\\robyn-mmm' # where output will be stored
country_filter = "US" # for prophet to know country
envname = 'never2' # name of conda env setup for nevergrad
set.seed(45) # repeatability
options(digits=12) # dataframe printing
cores = 6
#
# Configure across experiments
#
target_variable = 'bookings_noiseless'
paid_media_vars = c("tv", "fb") # variables to be tested, could be tv, fb
paid_media_spends = c("tv", "fb") # variables to be tested, could be tv, fb
paid_media_signs = c("positive", "positive")
context_vars = c() # context, could be c('context_0_center')
context_signs = c() # could be c('default')
# match number of media variables here; may need fb
hyperparameters <- list(
tv_alphas = c(0.001, 2.0)
, tv_gammas = c(0.3, 1.0)
, tv_thetas = c(0, 0.2)
, fb_alphas = c(0.001, 2.0)
, fb_gammas = c(0.3, 1.0)
, fb_thetas = c(0, 0.2)
)
#
# Check version
#
pkg_version = packageVersion('Robyn')
if (pkg_version != robyn_version_expected) {
writeLines(as.character(pkg_version))
stop('Wrong pkg version')
} else {
writeLines('Libraries loaded. Robyn package version tested')
}
## Configuration -------------------------------------------
#
# User needs to adjust this part specifically
#
# Beware that these are relative to *working directory* not where this R file
# is stored. (In RStudio working directory can be changed under Tools/GlobalOptions)
setwd(output_working_directory)
## Testing conda
#
use_condaenv(envname,required = TRUE) # loads conva environment and verifies
py_config() # Check that everything is correct
adstock_model = 'geometric'
nevergrad_algo = "TwoPointsDE"
holidays_none = data_frame (
holiday = 'NoHoliday',
country = country_filter,
year = c(2008),
ds = as.Date(c('2008-02-01'))
)
# typically not modified
date_format = "%m/%d/%Y" # format of date in csv file
robyn_object <- paste0("output/mmm_", format(Sys.Date(), "%Y_%m_%d"), ".RDS") # where to store output from run
plot_folder_sub = paste0(format(Sys.time(), "%Y-%m-%d_%H.%M"), "_init")
optimal_cores = future::availableCores() - 2
src_dt<- read.csv(source_file, header=TRUE, sep=',')
################################################################
#### Step 2a: For first time user: Model specification in 4 steps
#### 2a-1: First, specify input data & model parameters
InputCollect <- robyn_inputs(
dt_input = src_dt
,dt_holidays = holidays_none
### set variables
,date_var = "date"
,dep_var = target_variable
,dep_var_type = "revenue"
,prophet_vars = c("trend")
,prophet_signs = c("positive")
,prophet_country = country_filter
,paid_media_vars = paid_media_vars
,paid_media_spends = paid_media_spends
, paid_media_signs = paid_media_signs
, context_vars = context_vars
, context_signs = context_signs
### set model parameters
## set cores for parallel computing
,cores = cores # I am using 6 cores from 8 on my local machine. Use future::availableCores() to find out cores
## set rolling window start
# doing full year model for test
,window_start = window_start
,window_end = window_end
## set model core features
,adstock = adstock_model # geometric or weibull. weibull is more flexible, yet has one more parameter and thus takes longer
,iterations = iterations
,nevergrad_algo = nevergrad_algo # recommended algorithm for Nevergrad, the gradient-free
# optimisation library https://facebookresearch.github.io/nevergrad/index.html
,trials = trials
)
#### 2a-2: Second, define and add hyperparameters
# helper plots: set plot to TRUE for transformation examples
plot_adstock(TRUE) # adstock transformation example plot,
# helping you understand geometric/theta and weibull/shape/scale transformation
plot_saturation(TRUE) # s-curve transformation example plot,
# helping you understand hill/alpha/gamma transformation
## 3. set each hyperparameter bounds. They either contains two values e.g. c(0, 0.5),
# or only one value (in which case you've "fixed" that hyperparameter)
# Run ?hyper_names to check parameter definition
hyper_names(adstock = InputCollect$adstock, all_media = InputCollect$all_media)
#### 2a-3: Third, add hyperparameters into robyn_inputs()
print("RStudio code: invoking robyn_inputs")
InputCollect <- robyn_inputs(InputCollect = InputCollect,
hyperparameters = hyperparameters)
print('RStudio code: robyn_inputs complete')
#### 2a-4: Fourth (optional), model calibration / add experimental input
# NA
################################################################
#### Step 2b: For known model specification, setup in one single step
################################################################
#### Step 3: Build initial model
# Run ?robyn_run to check parameter definition
print('robyn_run started')
OutputCollect <- robyn_run(
InputCollect = InputCollect # feed in all model specification
, plot_folder = robyn_object # plots will be saved in the same folder as robyn_object
, plot_folder_sub = plot_folder_sub
, pareto_fronts = 3
, csv_out = "all" # code modification to ensure all csv output in our fork of Robyn
# we are using seed above only and do not want to put it here as well , seed=seed
, plot_pareto = TRUE # can make FALSE To save time but then we dont have the output images
, unconstrained_intercept = TRUE # allows negative intercept in our fork of Robyn
, saveInputCollectSelector = TRUE # save the input collector because we will need it for the allocator to be pulled up separately
)
print('robyn_run complete')
print('intentional stop')
stop()
################################################################
#### Step 4: Select and save the initial model
# budget allocation
# select a particular model
select_model = '1_93_6'
robyn_save(robyn_object = robyn_object # model object location and name
, select_model = select_model # selected model ID
, InputCollect = InputCollect # all model input
, OutputCollect = OutputCollect # all model output
)
# this version has bug where channel_constr_low and channel_construct_up must have same length as paid_media_vars (2)
channel_constr_low = c(0.6, 0.6) # NOTE: this line will essentially restrict the range of solutions the optimizer could consider
channel_constr_up = c(20, 20)
alloc = robyn_allocator(InputCollect=InputCollect, OutputCollect=OutputCollect, select_model=select_model, channel_constr_low=channel_constr_low, channel_constr_up=channel_constr_up)
# run with future spend
if (FALSE) {
AllocatorCollect <- robyn_allocator(
InputCollect=InputCollect,
OutputCollect=OutputCollect,
select_model=select_model,
scenario = "max_response_expected_spend",
channel_constr_low = c(0.01, 0.01),
channel_constr_up = c(20, 20),
expected_spend = 3608511, # match historical for this test
expected_spend_days = 365
)
AllocatorCollect <- robyn_allocator(
InputCollect=InputCollect,
OutputCollect=OutputCollect,
select_model=select_model,
scenario = "max_response_expected_spend",
channel_constr_low = c(1, 1),
channel_constr_up = c(200, 1),
expected_spend = 3608511*2, # double th spend in the future
expected_spend_days = 365
)
AllocatorCollect <- robyn_allocator(
InputCollect=InputCollect,
OutputCollect=OutputCollect,
select_model=select_model,
scenario = "max_response_expected_spend",
channel_constr_low = c(2, 1),
channel_constr_up = c(2, 1),
expected_spend = 5406087, # ideally one would double the first channel and input the value here
expected_spend_days = 365
)
AllocatorCollect <- robyn_allocator(
InputCollect=InputCollect,
OutputCollect=OutputCollect,
select_model=select_model,
scenario = "max_response_expected_spend",
channel_constr_low = c(1.5, 0.8),
channel_constr_up = c(1.5, 0.8),
expected_spend = 4154463, # ideally one would double the first channel and input the value here
expected_spend_days = 365
)
}