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mmlspark
brunocous
brunocous commented Sep 2, 2020

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?

Jonathanpro
Jonathanpro commented Jan 2, 2019

Hello everyone,
Recently I tried to set up petastorm on my company's hadoop cluster.
However as the cluster uses Kerberos for authentication using petastorm failed.
I figured out that petastorm relies on pyarrow which actually supports kerberos authentication.

I hacked "petastorm/petastorm/hdfs/namenode.py" line 250
and replaced it with

driver = 'libhdfs'
return pyarrow.hdfs.c

80+ DevOps & Data CLI Tools - AWS, GCP, GCF Python Cloud Function, Log Anonymizer, Spark, Hadoop, HBase, Hive, Impala, Linux, Docker, Spark Data Converters & Validators (Avro/Parquet/JSON/CSV/INI/XML/YAML), Travis CI, AWS CloudFormation, Elasticsearch, Solr etc.

  • Updated Nov 23, 2020
  • Python

MorphL Community Edition uses big data and machine learning to predict user behaviors in digital products and services with the end goal of increasing KPIs (click-through rates, conversion rates, etc.) through personalization

  • Updated Oct 2, 2019
  • Python
Dee-Pac
Dee-Pac commented Apr 24, 2018

These files belong to the Gimel Discovery Service, which is still Work-In-Progress in PayPal & not yet open sourced. In addition, the logic in these files are outdated & hence it does not make sense to have these files in the repo.

https://github.com/paypal/gimel/search?l=Shell
Remove --> gimel-dataapi/gimel-core/src/main/scripts/tools/bin/hbase/hbase_ddl_creator.sh

https://github.com/paypa

AlvaroMarquesAndrade
AlvaroMarquesAndrade commented Sep 17, 2020

Pivot missing categories breaks FeatureSet/AggregatedFeatureSet

Summary

When defining a feature set, it's expected that pivot will have all categories and, as a consequence, the resulting Source dataframe will be suitable to be transformed. When a different behavior happens, FeatureSet and AggregatedFeatureSet breaks.

Feature related:

Age: legacy

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