上下文:我有一个有两列的DataFrame
:word和vector。 “vector”的列类型是VectorUDT
。
一个例子:
word | vector
assert | [435,323,324,212...]
我希望得到这个:
word | v1 | v2 | v3 | v4 | v5 | v6 ......
assert | 435 | 5435| 698| 356|....
题:
如何使用PySpark为每个维度拆分包含多列向量的列?
提前致谢
一种可能的方法是转换为RDD和从RDD转换:
from pyspark.ml.linalg import Vectors
df = sc.parallelize([
("assert", Vectors.dense([1, 2, 3])),
("require", Vectors.sparse(3, {1: 2}))
]).toDF(["word", "vector"])
def extract(row):
return (row.word, ) + tuple(row.vector.toArray().tolist())
df.rdd.map(extract).toDF(["word"]) # Vector values will be named _2, _3, ...
## +-------+---+---+---+
## | word| _2| _3| _4|
## +-------+---+---+---+
## | assert|1.0|2.0|3.0|
## |require|0.0|2.0|0.0|
## +-------+---+---+---+
另一种解决方案是创建UDF:
from pyspark.sql.functions import udf, col
from pyspark.sql.types import ArrayType, DoubleType
def to_array(col):
def to_array_(v):
return v.toArray().tolist()
return udf(to_array_, ArrayType(DoubleType()))(col)
(df
.withColumn("xs", to_array(col("vector")))
.select(["word"] + [col("xs")[i] for i in range(3)]))
## +-------+-----+-----+-----+
## | word|xs[0]|xs[1]|xs[2]|
## +-------+-----+-----+-----+
## | assert| 1.0| 2.0| 3.0|
## |require| 0.0| 2.0| 0.0|
## +-------+-----+-----+-----+
对于Scala等效,请参阅Spark Scala: How to convert Dataframe[vector] to DataFrame[f1:Double, ..., fn: Double)]。
def splitVecotr(df, new_features=['f1','f2']):
schema = df.schema
cols = df.columns
for col in new_features: # new_features should be the same length as vector column length
schema = schema.add(col,DoubleType(),True)
return spark.createDataFrame(df.rdd.map(lambda row: [row[i] for i in cols]+row.features.tolist()), schema)
该函数将特征向量列转换为单独的列