如何在PySpark中构建稀疏矩阵？

Question

我是Spark的新手.我想使稀疏矩阵成为一个专门用于推荐引擎的user-id item-id矩阵.我知道如何在python中执行此操作.如何在PySpark中做到这一点？以下是我将如何在矩阵中完成它.该表现在看起来像这样.

Session ID| Item ID | Rating
     1          2       1
     1          3       5

    import numpy as np

    data=df[['session_id','item_id','rating']].values
    data

    rows, row_pos = np.unique(data[:, 0], return_inverse=True)
    cols, col_pos = np.unique(data[:, 1], return_inverse=True)

    pivot_table = np.zeros((len(rows), len(cols)), dtype=data.dtype)
    pivot_table[row_pos, col_pos] = data[:, 2]

Answer 1

像那样:

from pyspark.mllib.linalg.distributed import CoordinateMatrix, MatrixEntry

# Create an RDD of (row, col, value) triples
coordinates = sc.parallelize([(1, 2, 1), (1, 3, 5)])
matrix = CoordinateMatrix(coordinates.map(lambda coords: MatrixEntry(*coords)))