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: all

Binding: rust

Bug

What happened:

We are only calling flush in file backend's put_obj implementation, which only flushes data into kernel buffer.

What you expected to happen:

sync_call should be called instead to persist data from kernel buffer into disk.

How to reproduce it:

More details:

From http

Hi,

We’re running into an odd issue with our Development Account deployment regarding the instance pools. We have terraform modules for our databricks workspaces and all resources inside of them. We have folders for the clusters, instance-pools, cluster policies etc which when you drop json config files inside of them they will deploy/update what is needed.

All of our environments (6 total)

Connect-Databricks.ps1 uses "https://login.microsoftonline.com" as part of the URI to connect. When retrieving a token for a non-AzureCloud tenant (e.g. AzureUSGovernment) the URI root would be different (e.g., "https://login.microsoftonline.us"). As such, cannot use this task to deploy to other tenant types. Would be helpful to be able to specify an Azure Environment and connect to the right e