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Ensemble with NumerBay

In this tutorial we are going to make a simple ensemble Numerai predictions file from predictions bought on NumerBay.

Ensembling tends to lower variance and improve accuracy, and we want these for the Numerai tournaments.

Replace with your credentials below and change products_to_ensemble to the full names of the prediction files you have bought on NumerBay. Products you buy need to be listed in File mode so that you can download the files.

A working example notebook for this tutorial is available at

Checkout the Python Client Reference docs to learn about all available methods, parameters and returned values.

Using Python Client


If you have not installed the numerbay client, install by:

pip install -U numerbay

Initialize Python client

import pandas as pd
from numerbay import NumerBay

api = NumerBay(username="myusername", password="mypassword")

# exported NumerBay key file used for decryption, optional
numerbay_key_path = './numerbay.json'

Download predictions from NumerBay

Assuming you bought two products: numerai-predictions-numerbay and numerai-predictions-numerbay2

products_to_ensemble = ["numerai-predictions-numerbay", "numerai-predictions-numerbay2"]

for product_name in products_to_ensemble:
api.download_artifact(f"{product_name}.csv", product_full_name=product_name, key_path=numerbay_key_path)

Read downloaded predictions

all_preds = [pd.read_csv(f"{product_name}.csv", index_col=0).add_suffix(f"_{product_name}") 
for product_name in products_to_ensemble]

concat_preds = pd.concat(all_preds, axis=1, names=products_to_ensemble).dropna(how='any')
# drop NaNs because one submission file is a legacy file and the other a v2 file

Ensemble by simple average

For demo purpose we do a simple average ensemble here. You can of course try other methods such as rank-averaged predictions, etc.

ensemble_preds = concat_preds.mean(axis=1).rename('prediction').to_frame()