稀疏的矢量pyspark

Question

我想找到一种使用数据帧在PySpark中创建备用向量的有效方法.

让我们说给出交易输入:

df = spark.createDataFrame([
    (0, "a"),
    (1, "a"),
    (1, "b"),
    (1, "c"),
    (2, "a"),
    (2, "b"),
    (2, "b"),
    (2, "b"),
    (2, "c"),
    (0, "a"),
    (1, "b"),
    (1, "b"),
    (2, "cc"),
    (3, "a"),
    (4, "a"),
    (5, "c")
], ["id", "category"])

+---+--------+
| id|category|
+---+--------+
|  0|       a|
|  1|       a|
|  1|       b|
|  1|       c|
|  2|       a|
|  2|       b|
|  2|       b|
|  2|       b|
|  2|       c|
|  0|       a|
|  1|       b|
|  1|       b|
|  2|      cc|
|  3|       a|
|  4|       a|
|  5|       c|
+---+--------+

总结格式:

df.groupBy(df["id"],df["category"]).count().show()

+---+--------+-----+
| id|category|count|
+---+--------+-----+
|  1|       b|    3|
|  1|       a|    1|
|  1|       c|    1|
|  2|      cc|    1|
|  2|       c|    1|
|  2|       a|    1|
|  1|       a|    1|
|  0|       a|    2|
+---+--------+-----+

我的目标是通过id得到这个输出:

+---+-----------------------------------------------+
| id|                                       feature |
+---+-----------------------------------------------+
|  2|SparseVector({a: 1.0, b: 3.0, c: 1.0, cc: 1.0})|

你能指点我正确的方向吗？使用Java中的mapreduce对我来说似乎更容易.

Answer 1

这可以通过pivot和很容易地完成VectorAssembler.将聚合替换为pivot:

 pivoted = df.groupBy("id").pivot("category").count().na.fill(0)

和组装:

from pyspark.ml.feature import VectorAssembler

input_cols = [x for x in pivoted.columns if x != id]

result = (VectorAssembler(inputCols=input_cols, outputCol="features")
    .transform(pivoted)
    .select("id", "features"))

结果如下.这将根据稀疏性选择更有效的表示:

+---+---------------------+
|id |features             |
+---+---------------------+
|0  |(5,[1],[2.0])        |
|5  |(5,[0,3],[5.0,1.0])  |
|1  |[1.0,1.0,3.0,1.0,0.0]|
|3  |(5,[0,1],[3.0,1.0])  |
|2  |[2.0,1.0,3.0,1.0,1.0]|
|4  |(5,[0,1],[4.0,1.0])  |
+---+---------------------+

但当然你仍然可以将它转换为单一的表示形式:

from pyspark.ml.linalg import SparseVector, VectorUDT
import numpy as np

def to_sparse(c):
    def to_sparse_(v):
        if isinstance(v, SparseVector):
            return v
        vs = v.toArray()
        nonzero = np.nonzero(vs)[0]
        return SparseVector(v.size, nonzero, vs[nonzero])
    return udf(to_sparse_, VectorUDT())(c)

+---+-------------------------------------+
|id |features                             |
+---+-------------------------------------+
|0  |(5,[1],[2.0])                        |
|5  |(5,[0,3],[5.0,1.0])                  |
|1  |(5,[0,1,2,3],[1.0,1.0,3.0,1.0])      |
|3  |(5,[0,1],[3.0,1.0])                  |
|2  |(5,[0,1,2,3,4],[2.0,1.0,3.0,1.0,1.0])|
|4  |(5,[0,1],[4.0,1.0])                  |
+---+-------------------------------------+