2019-08-23 Float, double with ONNX

Replicating what a library does, scikit-learn for example, is different from implementing a function defined in a paper. Every trick needs to be replicated. scikit-learn trees implement a prediction function which takes float features and compares them to double thresholds. Knowning the ONNX assumes that comparison only happens numbers of the same type, you end up with discrepencies.

To be honest, I did not imagine that scikit-learn would mix types when comparing numbers, I just assumed it was using double as many other predictors do. As none of my other ideas seemed to work, I went back to scikit-learn code, and discovered this mixed type comparison. It makes sense as it uses less memory and probably has a small impact on the performance. From an ONNX point of view, I had to change the thresholds to replicate scikit-learn behaviour. Notebook Tricky detail when converting a random forest from scikit-learn into ONNX shows how I handled it.