问题描述
我有一个很大的 pyspark 数据框.我想得到它的相关矩阵.我知道如何使用 Pandas 数据框获取它.但是我的数据太大而无法转换为 Pandas.所以我需要用 pyspark 数据框得到结果.我搜索了其他类似的问题，答案对我不起作用.有谁能够帮我?谢谢！
I have a big pyspark data frame. I want to get its correlation matrix. I know how to get it with a pandas data frame.But my data is too big to convert to pandas. So I need to get the result with pyspark data frame.I searched other similar questions, the answers don't work for me.Can any body help me? thanks!
数据示例:数据示例
推荐答案
欢迎来到 SO！
我准备了一些虚拟数据以便于复制(也许下次你也可以提供一些易于复制的数据;-)):
I prepared some dummy data for easier replication (perhaps next time you may supply some easy to copy data, too ;-)):
data = pd.DataFrame(np.random.random((10, 5)),
columns=["x{}".format(x) for x in range(5)])
df = spark.createDataFrame(data)
df.show()
这是数据:
+-------------------+-------------------+-------------------+-------------------+--------------------+
| x0| x1| x2| x3| x4|
+-------------------+-------------------+-------------------+-------------------+--------------------+
| 0.9965335347601945|0.09311299224360992| 0.9273393764180728| 0.8523333283310564| 0.5040716744686445|
| 0.2341313103221958| 0.9356109544246494| 0.6377089480113576| 0.8129047787928055| 0.22215891357547046|
| 0.6310473705907303| 0.2040705293700683|0.17329601185489396| 0.9062007987480959| 0.44105687572209895|
|0.27711903958232764| 0.9434521502343274| 0.9300724702792151| 0.9916836130997986| 0.6869145183972896|
| 0.8247010263098201| 0.6029990758603708|0.07266306799434707| 0.6808038838294564| 0.27937146479120245|
| 0.7786370627473335|0.17583334607075107| 0.8467715537463528| 0.67702427694934| 0.8976402177586831|
|0.40620117097757724| 0.5080531043890719| 0.3722402520743703|0.14555317396545808| 0.7954133091360741|
|0.20876805543974553| 0.9755867281355178| 0.7570617946515066| 0.6974893162590945|0.054708580878511825|
|0.47979629269402546| 0.1851379589735923| 0.4786682088989791| 0.6809358266732168| 0.8829180507209633|
| 0.1122983875801804|0.45310988757198734| 0.4713203140134805|0.45333792855503807| 0.9189083355172629|
+-------------------+-------------------+-------------------+-------------------+--------------------+
解决方案
ml 子包中有相关函数pyspark.ml.stat.但是，它要求您提供 Vector 类型的列.因此，您需要首先使用 将您的列转换为向量列VectorAssembler 然后应用相关性:
Solution
There is a correlation function in the ml subpackage pyspark.ml.stat. However, it requires you to provide a column of type Vector. So you need to convert your columns into a vector column first using the VectorAssembler and then apply the correlation:
from pyspark.ml.stat import Correlation
from pyspark.ml.feature import VectorAssembler
# convert to vector column first
vector_col = "corr_features"
assembler = VectorAssembler(inputCols=df.columns, outputCol=vector_col)
df_vector = assembler.transform(df).select(vector_col)
# get correlation matrix
matrix = Correlation.corr(df_vector, vector_col)
如果您想将结果作为 numpy 数组(在您的驱动程序上)，您可以使用以下命令:
If you want to get the result as a numpy array (on your driver), you can use the following:
matrix.collect()[0]["pearson({})".format(vector_col)].values
array([ 1. , -0.66882741, -0.06459055, 0.21802534, 0.00113399,
-0.66882741, 1. , 0.14854203, 0.09711389, -0.5408654 ,
-0.06459055, 0.14854203, 1. , 0.33513733, 0.09001684,
0.21802534, 0.09711389, 0.33513733, 1. , -0.37871581,
0.00113399, -0.5408654 , 0.09001684, -0.37871581, 1. ])
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