spark-issues mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From "zhengruifeng (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-19053) Supporting multiple evaluation metrics in DataFrame-based API: discussion
Date Wed, 29 May 2019 06:04:00 GMT

    [ https://issues.apache.org/jira/browse/SPARK-19053?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16850527#comment-16850527
] 

zhengruifeng commented on SPARK-19053:
--------------------------------------

Just remember this ticket.

Currently I find another method to 'support' multi-metric evaluation,  in  [SPARK-27867|https://issues.apache.org/jira/browse/SPARK-27867]


the idea is to cache the lastest inputs and interenal mllib.metrics, and then if the next
evaluate call keep the inputs (except the metricName), then we can directly obtain the metric
from the internal intermediate summary, without any computation.

[~josephkb]  [~yuhaoyan] [~imatiach] how do you think about this?

> Supporting multiple evaluation metrics in DataFrame-based API: discussion
> -------------------------------------------------------------------------
>
>                 Key: SPARK-19053
>                 URL: https://issues.apache.org/jira/browse/SPARK-19053
>             Project: Spark
>          Issue Type: Brainstorming
>          Components: ML
>            Reporter: Joseph K. Bradley
>            Priority: Major
>
> This JIRA is to discuss supporting the computation of multiple evaluation metrics efficiently
in the DataFrame-based API for MLlib.
> In the RDD-based API, RegressionMetrics and other *Metrics classes support efficient
computation of multiple metrics.
> In the DataFrame-based API, there are a few options:
> * model/result summaries (e.g., LogisticRegressionSummary): These currently provide the
desired functionality, but they require a model and do not let users compute metrics manually
from DataFrames of predictions and true labels.
> * Evaluator classes (e.g., RegressionEvaluator): These only support computing a single
metric in one pass over the data, but they do not require a model.
> * new class analogous to Metrics: We could introduce a class analogous to Metrics.  Model/result
summaries could use this internally as a replacement for spark.mllib Metrics classes, or they
could (maybe) inherit from these classes.
> Thoughts?



--
This message was sent by Atlassian JIRA
(v7.6.3#76005)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org


Mime
View raw message