如何使用Python在Spark中添加两个稀疏向量 [英] How to add two Sparse Vectors in Spark using Python

问题描述

我到处搜索过，但找不到如何使用Python添加两个稀疏向量的方法. 我想添加两个这样的稀疏向量:-

I've searched everywhere but I couldn't find how to add two sparse vectors using Python. I want to add two sparse vectors like this:-

(1048576, {110522: 0.6931, 521365: 1.0986, 697409: 1.0986, 725041: 0.6931, 749730: 0.6931, 962395: 0.6931})

(1048576, {4471: 1.0986, 725041: 0.6931, 850325: 1.0986, 962395: 0.6931})

推荐答案

类似的方法应该起作用:

Something like this should work:

from pyspark.mllib.linalg import Vectors, SparseVector, DenseVector
import numpy as np

def add(v1, v2):
    """Add two sparse vectors
    >>> v1 = Vectors.sparse(3, {0: 1.0, 2: 1.0})
    >>> v2 = Vectors.sparse(3, {1: 1.0})
    >>> add(v1, v2)
    SparseVector(3, {0: 1.0, 1: 1.0, 2: 1.0})
    """
    assert isinstance(v1, SparseVector) and isinstance(v2, SparseVector)
    assert v1.size == v2.size 
    # Compute union of indices
    indices = set(v1.indices).union(set(v2.indices))
    # Not particularly efficient but we are limited by SPARK-10973
    # Create index: value dicts
    v1d = dict(zip(v1.indices, v1.values))
    v2d = dict(zip(v2.indices, v2.values))
    zero = np.float64(0)
    # Create dictionary index: (v1[index] + v2[index])
    values =  {i: v1d.get(i, zero) + v2d.get(i, zero)
       for i in indices
       if v1d.get(i, zero) + v2d.get(i, zero) != zero}

    return Vectors.sparse(v1.size, values)

如果您只喜欢单次通过而不关心引入的零，则可以这样修改上面的代码:

If you prefer only single pass and don't care about introduced zeros you can modify above code like this:

from collections import defaultdict

def add(v1, v2):
    assert isinstance(v1, SparseVector) and isinstance(v2, SparseVector)
    assert v1.size == v2.size
    values = defaultdict(float) # Dictionary with default value 0.0
    # Add values from v1
    for i in range(v1.indices.size):
        values[v1.indices[i]] += v1.values[i]
    # Add values from v2
    for i in range(v2.indices.size):
        values[v2.indices[i]] += v2.values[i]
    return Vectors.sparse(v1.size, dict(values))

如果需要，可以尝试使用猴子补丁SparseVector:

If you want you can try monkey patch SparseVector:

SparseVector.__add__ = add
v1 = Vectors.sparse(5, {0: 1.0, 2: 3.0})
v2 = Vectors.sparse(5, {0: -3.0, 2: -3.0, 4: 10})
v1 + v2
## SparseVector(5, {0: -2.0, 4: 10.0})

或者，您应该可以使用scipy.sparse.

Alternatively you should be able to use scipy.sparse.

from scipy.sparse import csc_matrix
from pyspark.mllib.regression import LabeledPoint

m1 = csc_matrix((
   v1.values,
   (v1.indices, [0] * v1.numNonzeros())),
   shape=(v1.size, 1))

m2 = csc_matrix((
   v2.values,
   (v2.indices, [0] * v2.numNonzeros())),
   shape=(v2.size, 1))

LabeledPoint(0, m1 + m2)

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