flâneur — a map of the web's best reading

Debugging a pipeline - River

riverml.xyz · 725 words · saved by 1 readers

Machine learning for data streams in Python

Debugging a pipeline ¶ River encourages users to make use of pipelines. The biggest pain point of pipelines is that it can be hard to understand what's happening to the data, especially when the pipeline is complex. Fortunately the Pipeline class has a debug_one method that can help out. Let's look at a fairly complex pipeline for predicting the number of bikes in 5 bike stations from the city of Toulouse. It doesn't matter if you understand the pipeline or not; the point of this notebook is to learn how to introspect a pipeline. import datetime as dt from river import compose from river

Explore this link on the map →

saved by

related reading