I have a simple regression task (using a LightGBMRegressor) where I want to penalize negative predictions more than positive ones. Is there a way to achieve this with the default regression LightGBM objectives (see https://lightgbm.readthedocs.io/en/latest/Parameters.html)? If not, is it somehow possible to define (many example for default LightGBM model) and pass a custom regression objective?

This is to track implementation of the ML-Features: https://spark.apache.org/docs/latest/ml-features

Bucketizer has been implemented in dotnet/spark#378 but there are more features that should be implemented.

• Feature Extractors
  • TF-IDF
  • Word2Vec (dotnet/spark#491)
  • CountVectorizer (https://github.com/dotnet/spark/p

Environment

Delta-rs version:
rust-v0.4.0, rust-v0.4.1 (head)

Binding:
rust

Environment:

• Cloud provider: AWS
• OS: Linux / OS X
• Other:

Bug

What happened: Attempting to use delta-rs to read a streaming Delta table with a watermark fails with "Error: Failed to apply transaction log: Invalid JSON in log record"

**What you expected to