Allocate_budget_to_maximize_response Takes a lot of time to work

Questions v5
1

I’m following the examples in order to do a POC for my job. I was doing fine until doing budget allocation. The allocate_budget_to_maximize_response functions has been running for hours and hasn’t given any results.
The data consist of three media channels spanning 7 months of daily data.
Here are the code snippets where I define the functions and its arguments

scaler = 1000

total_budget = 1000/scaler  # Imagine is 5K or 5M
# Define your channels
channel_list = ["BRAND", "DEVOLUCION", "SEGURO DE VIDA"]
# The initial split per channel
budget_per_channel = total_budget / len(channel_columns)
# Initial budget per channel as dictionary.
initial_budget_dict = {channel: budget_per_channel for channel in channel_columns}
# bounds for each channel
min_budget, max_budget = 100/scaler, 1000/scaler
budget_bounds = {channel: [min_budget, max_budget] for channel in channel_columns}

response = mmm.allocate_budget_to_maximize_response(
    budget=total_budget,
    num_days=5,
    time_granularity="daily",
    budget_bounds=budget_bounds,
)

It’s not an issue of installation as I’ve done several clean installs with the same result.
What could be the reason for this sluggishness?

2

hey @TheJarmanitor ! i’m running into the same issues with the 0.9.0 version as well…I’m running it on python 3.11 and with 16 vCPUs and 128GB memory… In the 0.8.0 version, i was able to run it perfectly fine… Have you found any solution to this? Would you mind sharing what machine you are running it on?
cc: @cluhmann @juanitorduz to shed some light on this too :slight_smile: thanks very much!

3

Hello, can you share the PyMC-Marketing version used? Additionally, would be nice to see a picture of your idata and model scalers.