在 PySpark 中运行 KMeans 聚类 [英] Running KMeans clustering in PySpark

问题描述

这是我第一次尝试在 Spark 中运行 KMeans 聚类分析，所以，很抱歉问一个愚蠢的问题.

it's my very first time trying to run KMeans cluster analysis in Spark, so, I am sorry for a stupid question.

我有一个包含许多列的 spark 数据框 mydataframe.我只想在两列上运行 kmeans:lat 和 long(纬度和经度)，将它们用作简单值.我想仅基于那 2 列提取 7 个集群.我试过了:

I have a spark dataframe mydataframe with many columns. I want to run kmeans on only two columns: lat and long (latitude & longitude) using them as simple values. I want to extract 7 clusters based on just those 2 columns. I've tried:

from numpy import array
from math import sqrt
from pyspark.mllib.clustering import KMeans, KMeansModel

# Prepare a data frame with just 2 columns:
data = mydataframe.select('lat', 'long')

# Build the model (cluster the data)
clusters = KMeans.train(data, 7, maxIterations=15, initializationMode="random")

但我收到一个错误:

'DataFrame' 对象没有属性 'map'

'DataFrame' object has no attribute 'map'

提供给 KMeans.train 的对象应该是什么?显然，它不接受 DataFrame.我应该如何准备用于分析的数据框?

What should be the object one feeds to KMeans.train? Clearly, it doesn't accept a DataFrame. How should I prepare my data frame for the analysis?

非常感谢！

推荐答案

KMeans.train 方法将 RDD 而不是数据帧(数据)作为输入.因此，您只需要将数据转换为 rdd:data.rdd.希望有帮助.

the method KMeans.train takes as imput an RDD and not a dataframe (data). So, you just have to convert data to rdd: data.rdd. Hope it helps.

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