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Posted to issues@ignite.apache.org by "Aleksey Zinoviev (JIRA)" <ji...@apache.org> on 2018/08/16 13:05:00 UTC
[jira] [Updated] (IGNITE-9285) [ML] Add MaxAbsScaler as a
preprocessing stage
[ https://issues.apache.org/jira/browse/IGNITE-9285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Aleksey Zinoviev updated IGNITE-9285:
-------------------------------------
Description:
Add analogue of [http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MaxAbsScaler.html#sklearn.preprocessing.MaxAbsScaler]
Please look at the MinMaxScaler or Normalization packages in preprocessing package.
Add classes if required
1) Preprocessor
2) Trainer
3) custom PartitionData if shuffling is a step of algorithm
Requirements for successful PR:
# PartitionedDataset usage
# Trainer-Model paradigm support
# Tests for Model and for Trainer (and other stuff)
# Example of usage with small, but famous dataset like IRIS, Titanic or House Prices
# Javadocs/codestyle according guidelines
was:
Add analogue of [http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MaxAbsScaler.html#sklearn.preprocessing.MaxAbsScaler]
Please look at the MinMaxScaler or Normalization packages in preprocessing package.
Add classes if required
1) Preprocessor
2) Trainer
3) custom PartitionData if shuffling is a step of algorithm
> [ML] Add MaxAbsScaler as a preprocessing stage
> ----------------------------------------------
>
> Key: IGNITE-9285
> URL: https://issues.apache.org/jira/browse/IGNITE-9285
> Project: Ignite
> Issue Type: Sub-task
> Components: ml
> Reporter: Aleksey Zinoviev
> Priority: Major
>
> Add analogue of [http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MaxAbsScaler.html#sklearn.preprocessing.MaxAbsScaler]
> Please look at the MinMaxScaler or Normalization packages in preprocessing package.
> Add classes if required
> 1) Preprocessor
> 2) Trainer
> 3) custom PartitionData if shuffling is a step of algorithm
>
> Requirements for successful PR:
> # PartitionedDataset usage
> # Trainer-Model paradigm support
> # Tests for Model and for Trainer (and other stuff)
> # Example of usage with small, but famous dataset like IRIS, Titanic or House Prices
> # Javadocs/codestyle according guidelines
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