Approximate Metric Optimization
This example demonstrates how to use the Rax transformations to build approximate metric losses and use them to optimize a linear model on the WEB10K dataset.
Instructions
Clone the Rax repo:
git clone git@github.com:google/rax.git
cd rax
Install the example dependencies:
pip install -r requirements/requirements-examples.txt
And then run the example code:
python examples/approx_metrics/main.py
You should see the following expected output in the form of a JSON dictionary containing the different approximate metrics and their results.
{
"ApproxAP": {
"AP": 0.5947682857513428,
"NDCG": 0.6587949991226196,
"R@50": 0.5807018876075745
},
"ApproxNDCG": {
"AP": 0.587692141532898,
"NDCG": 0.6700138449668884,
"R@50": 0.5744442939758301
},
"ApproxR@50": {
"AP": 0.5849018096923828,
"NDCG": 0.6449851989746094,
"R@50": 0.5746314525604248
}
}