Question

似乎应该可以，但出现错误：

mu =  mean(df[input])
sigma = stddev(df[input])
dft = df.withColumn(output, (df[input]-mu)/sigma)

pyspark.sql.utils.AnalysisException: "grouping expressions sequence is
empty, and '`user`' is not an aggregate function. Wrap
'(((CAST(`sum(response)` AS DOUBLE) - avg(`sum(response)`)) /
stddev_samp(CAST(`sum(response)` AS DOUBLE))) AS `scaled`)' in
windowing function(s) or wrap '`user`' in first() (or first_value) if
you don't care which value you get.;;\nAggregate [user#0,
sum(response)#26L, ((cast(sum(response)#26L as double) -
avg(sum(response)#26L)) / stddev_samp(cast(sum(response)#26L as
double))) AS scaled#46]\n+- AnalysisBarrier\n      +- Aggregate
[user#0], [user#0, sum(cast(response#3 as bigint)) AS
sum(response)#26L]\n         +- Filter item_id#1 IN
(129,130,131,132,133,134,135,136,137,138)\n            +-
Relation[user#0,item_id#1,response_value#2,response#3,trait#4,response_timestamp#5] 
csv\n"

我不确定此错误消息是怎么回事。

Answer 1

一般来说，使用collect()并不是一个好的解决方案，并且您会发现它不会随着数据的增长而扩展。

如果您不想使用StandardScaler，更好的方法是使用Window计算平均值和标准偏差。

从StandardScaler in Spark not working as expected借用相同的示例：

from pyspark.sql.functions import col, mean, stddev
from pyspark.sql import Window

df = spark.createDataFrame(
    np.array(range(1,10,1)).reshape(3,3).tolist(),
    ["int1", "int2", "int3"]
)
df.show()
#+----+----+----+
#|int1|int2|int3|
#+----+----+----+
#|   1|   2|   3|
#|   4|   5|   6|
#|   7|   8|   9|
#+----+----+----+

假设您要标准化列int2：

input_col = "int2"
output_col = "int2_scaled"

w = Window.partitionBy()

mu = mean(input_col).over(w)
sigma = stddev(input_col).over(w)

df.withColumn(output_col, (col(input_col) - mu)/(sigma)).show()
#+----+----+----+-----------+
#|int1|int2|int3|int2_scaled|
#+----+----+----+-----------+
#|   1|   2|   3|       -1.0|
#|   7|   8|   9|        1.0|
#|   4|   5|   6|        0.0|
#+----+----+----+-----------+

如果要像其他示例一样使用总体标准差，请将pyspark.sql.functions.stddev替换为pyspark.sql.functions.stddev_pop()。

Answer 2

幸运的是，我能够找到有效的代码：

summary =  df.select([mean(input).alias('mu'), stddev(input).alias('sigma')])\
    .collect().pop()
dft = df.withColumn(output, (df[input]-summary.mu)/summary.sigma)

如何在不使用StandardScaler的情况下标准化PySpark中的列？

2 个答案: