使用Python进行Spark矩阵乘法 [英] Spark Matrix multiplication with python
问题描述
我正在尝试使用Apache Spark和Python进行矩阵乘法.
I am trying to do matrix multiplication using Apache Spark and Python.
这是我的数据
from pyspark.mllib.linalg.distributed import RowMatrix
我的矢量RDD
rows_1 = sc.parallelize([[1, 2], [4, 5], [7, 8]])
rows_2 = sc.parallelize([[1, 2], [4, 5]])
我的maxtrix
mat1 = RowMatrix(rows_1)
mat2 = RowMatrix(rows_2)
我想做这样的事情:
mat = mat1 * mat2
我写了一个函数来处理矩阵乘法,但是恐怕要花很长的时间.这是我的功能:
I wrote a function to process the matrix multiplication but I'm afraid to have a long processing time. Here is my function:
def matrix_multiply(df1, df2):
nb_row = df1.count()
mat=[]
for i in range(0, nb_row):
row=list(df1.filter(df1['index']==i).take(1)[0])
row_out = []
for r in range(0, len(row)):
r_value = 0
col = df2.select(df2[list_col[r]]).collect()
col = [list(c)[0] for c in col]
for c in range(0, len(col)):
r_value += row[c] * col[c]
row_out.append(r_value)
mat.append(row_out)
return mat
我的功能产生了许多火花动作(采取,收集等).该功能会花费很多处理时间吗? 如果有人有其他想法,这对我会有所帮助.
My function make a lot of spark actions (take, collect, etc.). Does the function will take a lot of processing time? If someone have another idea it will be helpful for me.
推荐答案
您不能.由于RowMatrix
没有有意义的行索引,因此不能用于乘法.甚至忽略了唯一的分布式矩阵
You cannot. Since RowMatrix
has no meaningful row indices it cannot be used for multiplications. Even ignoring that the only distributed matrix which supports multiplication with another distributed structure is BlockMatrix
.
from pyspark.mllib.linalg.distributed import *
def as_block_matrix(rdd, rowsPerBlock=1024, colsPerBlock=1024):
return IndexedRowMatrix(
rdd.zipWithIndex().map(lambda xi: IndexedRow(xi[1], xi[0]))
).toBlockMatrix(rowsPerBlock, colsPerBlock)
as_block_matrix(rows_1).multiply(as_block_matrix(rows_2))
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