November 2017
Intermediate to advanced
328 pages
6h 51m
Japanese
Content preview from 入門 PySpark ―PythonとJupyterで活用するSpark 2エコシステム
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6.4
PySpark ML
のその他の特徴
111
6.4.1
特徴抽出
ここまでに、PySparkのサブモジュールに含まれるモデルをかなりたくさん使ってきました。このセ
クションでは、（筆者の見解では）最も役立つモデルの使い方を説明します。
NLP
関連の特徴の
extractor
すでに述べたとおり、
NGram
モデルはトークン化されたテキストのリストを引数に取り、語のペア
（あるいは
n
-gram）を生成します。
この例では、PySparkのドキュメンテーションからの抜粋を素材とし、
NGram
モデルに渡す前のテキ
ストのクリーンアップの方法を紹介します。次にデータセットを示します（読みやすいように省略して
あ り ま す ）。
次のコードの全体を把握したい場合は、筆者の
GitHub
のリポジトリ
https://github.com/
drabastomek/learningPySpark
からコードをダウンロードしてください。ここでは
Pipeline
における
DataFrame
の使い方の説明から、
4
つの段落をコピーしています（
http://spark.
apache.org/docs/latest/ml-pipeline.html#dataframe
）。
text_data = spark.createDataFrame([
['''Machine learning can be applied to a wide variety
of data types, such as vectors, text, ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.