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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102
6
章
ML
パッケージ
さ あ 、す べ て の 特 徴 を 1つ の 列 に ま と め ま し ょ う 。そ れ に は
VectorAssembler
メソッドを使 います。
featuresCreator = ft.VectorAssembler(
inputCols=[
col[0]
for col
in labels[2:]] + \
[encoder.getOutputCol()],
outputCol='features'
)
VectorAssembler
オブジェクトに渡された
inputCols
パラメータは 1つにまとめる対象となる
すべての列のリストで、それらから
outputCol
、すなわち
'features'
が形作られます。注目してほ
しいのは、エンコーダーオブジェクトの出力を（
.getOutputCol()
メソッドを呼びだして）使うこと
によって、どこかの時点でエンコーダーオブジェクト内の出力列の名前を変更したとしても、このパラ
メータの値を変更しなくても良くなっていることです。
さあ、最初の estimatorを生成しましょう。
6.2.3
estimator
の生成
この例では、（再び）ロジスティック回帰モデルを使います。とはいえ、本章では後ほど PySpark ML
のモデル群の
.classification
からさらに複雑なモデルを紹介します。そのため、インポートはまと
めて済ませておきましょう。
import pyspark.ml.classification as cl ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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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.